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  1. Jan 2025
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      Referee #1

      Evidence, reproducibility and clarity

      In this study, Makino et al. investigate how tension within the neck linker regulates the coordinated stepping of kinesin-1, a dimeric motor protein with two motor domains (or "heads"). Using high-resolution structural analyses, the authors identify a bulge near the neck linker's base in the nucleotide-free head that restricts forward extension, increasing steric hindrance when extended forward. This hindrance, they propose, prevents the tethered head from prematurely binding to the microtubule while the leading, microtubule-bound head awaits ATP. Molecular dynamic simulations and single-molecule fluorescence assays support this steric hindrance-based model, suggesting a mechanism that thermodynamically suppresses off-pathway transitions, thereby guiding kinesin-1's processive movement along microtubules. I recommend acceptance of the manuscript, subject to the following revisions:

      1. Abstract: The current abstract is challenging to follow. For instance, the phrase "The detached head preferentially binds to the forward tubulin-binding site after ATP binding, but the mechanism preventing premature binding to the microtubule while awaiting ATP remains unknown" could imply that the tethered head binds ATP, which is misleading. A clearer statement would be: "The detached head preferentially binds to the forward tubulin-binding site after ATP binding to the leading, microtubule-bound head, but the mechanism preventing premature binding to the microtubule while its partner awaits ATP remains unknown."
      2. Terminology: In the introduction, consider rephrasing to "...its two motor domains ("heads")."
      3. Lines 71-72: The sentences "This mechanism explains how the tethered head preferentially binds to the forward-binding site 'after ATP binding.' However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site 'before ATP binding'" could be rephrased for clarity. A suggested revision is: "This mechanism explains how the tethered head preferentially binds to the forward-binding site after ATP binding to the microtubule-bound, leading head. However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site before ATP binds to the leading head."
      4. Line 98: Consider revising "could release both ADP" to "could release both ADPs" or "could release both ADP molecules."
      5. Lines 103-104: The statement "Therefore, these results suggest the tension posed to the neck linker plays a critical role in suppressing microtubule-binding of the tethered head" should be clarified. Since tension only develops in the two-heads-bound state, using "steric hindrance" instead of "tension" may improve precision.
      6. Lines 374-375: Replace "...before ATP-binding triggers the forward stepping..." with "...before ATP binding to the leading head triggers the forward stepping..."
      7. Tense Consistency: Ensure consistent use of present or past tense throughout the manuscript for clarity.

      Significance

      The conclusions are supported by the data provided, offering valuable insights into the coordination of kinesin's motor domains during movement. These findings help address a knowledge gap in kinesin stepping mechanics, making this work relevant to researchers studying cytoskeletal motor proteins.

    1. Indeed, the czarist government pointed to emi-gration as a solution open to Jews. ‘‘The Western borders are open to youJews,’’ said Count Ignatiev, author of the May Laws. The Russian govern-ment relaxed its rigorous rules forbidding emigration, giving Jews the rightto leave, under obligation of abandoning Russian citizenship forever.

      When I think of antisemitic behavior, Nazi Germany is always the first thing to pop into my head. I never knew that the Russians similarly treated Jews, although not as extremely. Exerting leniency on your policy against emigration to drive a specific group of people out of your country is another form of hate. Given the conditions in Russia, I can understand why emigration to the West seemed so appealing. Anything would be better than their current treatment under antisemitic laws in Russia.

    1. Bharata!

      This is in reference to the status of Arjuna. It represents his lineage and authority. According to the story of Jada Bharata, he was a powerful, high standing king, but one with high paternal instinct. The story states, Bharata, "...rose from his position of meditation and rescu(ed) the fawn from the water, took it to his cottage, made a fire, and with care and attention fondled the little thing back to life." This shows his compassion of youth, whereas Arjuna is considered a harder, more stubborn head of authority.

      Source: The Story Of Jada Bharata. The story of Jada Bharata. (n.d.). https://www.ramakrishnavivekananda.info/vivekananda/volume_4/lectures_and_discourses/the_story_of_jada_bharata.htm

    1. e sample minibatches of sequence lengthK from the dataset. The prediction head corresponding to the input token st is trained to predict at –either with cross-entropy loss for discrete actions or mean-squared error for continuous actions – andthe losses for each timestep are average

      How training loss is compute.

    1. “Twenty is a relatively low number. Heck, I’ve probably bought twenty raffle tickets over the past few years. Maybe this one is my ticket.”

      nm563 I also think that since 1 in 20 doesn't display the percentage many will not calculate it off the top of their head and will simply fall into this exact line of thinking stated.

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      Reply to the reviewers

      We thank the reviewers for the detailed evaluations and thoughtful comments, which have improved the clarity and readability of this manuscript. We have responded to all reviewer comments and incorporated their suggested changes into the text and figures. The line numbers in our response correspond to those in the revised manuscript. We have also included new experimental results suggested by reviewer 2, which further strengthen our main conclusion.

      Reviewer #1

      1. Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.
      2. Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads." Response: We thank the reviewer for pointing out our misunderstandings. We have corrected these sentences accordingly (lines 45-47 and lines 111-112).

      Reviewer #2

      MAJOR CONCERNS Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.

      __Response: __We have now reiterated some of the key caveats in the Discussion, and newly described in the Results section those points not mentioned in the original manuscript that do not affect the conclusion. We also added a summary of the limitations and caveats into the first paragraph of the Discussion section (lines 425-431).

      1) We added a sentence in the Results section to describe that the ATP-binding kinetics of the Cys-light mutant remained consistent with previous studies as follows: “First, we demonstrated that k+1 and k-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004)” (lines 163-166). The reduced kcat values of cysteine pair-added mutants before crosslinking were primarily due to reduced microtubule association rate (data not included in this manuscript). We have added a sentence in the Results section describing the kcat results as follows: “The reduced ATPase activity primarily results from a decreased microtubule association rate (data to be presented elsewhere) with little change in ATP binding or microtubule dissociation rates (Table 1).” (lines 144-146).

      2) Fluorescently-labeled ATP was used to determine the ATP off-rates of the E236A mutant monomer and E236A rear head of the E236A/WT heterodimer. Two caveats in these measurements could lead to underestimating the ATP off-rate: 1) The off rate of Alexa-ATP from the head may be reduced compared to unmodified ATP, as Alexa-ATP driven motility showed a 10-fold reduce velocity. 2) The ATP off-rate of the E236A mutant may differ from that of the rear head in the wild-type dimer, since the E236A mutant likely stabilizes the neck linker-docked state more strongly than in the rear head of the wild-type dimer. These points are crucial for evaluating the results of ATP off-rate and the affinity for ATP, so we have added sentences in the Discussion section as follows: “We note, however, that this Kd of ATP may somewhat underestimate the true value in wild-type kinesin for two reasons: first, the E236A mutation likely stabilizes the neck linker-docked, closed state more than in the rear head of the wild-type dimer (Rice et al., 1999), and second, the Alexa-ATP used to measure the ATP off-rate of E236A head showed ~10-fold smaller velocity compared to unmodified ATP, partly due to a slower ATP off-rate (Figure 2____-____figure supplement 3).” (lines 449-454).

      3) Under reducing condition, the rear head crosslink contained 30% crosslinked species, while under oxidized condition, the front head crosslink contained 11% un-crosslinked species (Figure 1____-____figure supplement 1). These heterogeneities likely affect the rate constants of k-1 for rear head crosslink and k2 for front head crosslink, as crosslinked and un-crosslinked species showed significantly different rate constants. However, we did not use the rear head crosslink result to determine k-1, since ATP hydrolysis likely occurred before reversible ATP dissociation. Instead, we used E236A monomer to estimate the k-1 of the rear head. In addition, the result for k2 of the front head crosslink was further validated using the E236A/WT heterodimer, which will be described in the next section.

      4) This is an important point, and therefore, we conducted experiments using the E236A/WT heterodimer (including new experimental results of ATP binding kinetics of the front head) and obtained consistent results. To address this point, we have revised the following sentences in the Discussion: “In the front head, backward orientation of the neck linker has little effect on ATP binding and dissociation rates, both when measured for a monomer crosslink (Figure 2A, B) and for the front head of a E236A-WT heterodimer (Figure 4B, C, F).” (lines 432-433); “However, we found that the ATP-induced detachment rates from microtubule (k2) were similarly reduced for both the front head crosslink (7.0 s-1; Figure 3A) and the front WT head of the E236A/WT heterodimer (6.3 s-1; Figures 6D), suggesting that a step subsequent to ATP binding is gated in the front head.” (lines 437-441).

      Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.

      Response: The low microtubule affinity of the rear-head-like crosslink stems from an extremely slow ADP release rate upon microtubule binding, not from a fast microtubule-detachment rate. Using stopped-flow measurements of microtubule-binding kinetics (microtubule-stimulated mant-ADP release and microtubule association rates), we found that the rear-head-crosslink resulted in a 2,000-fold decrease in the microtubule-stimulated ADP-release rate. This finding also explains the reduced ATPase of the rear-head-crosslink (Figure 1E). Since this low microtubule-affinity state occurs in the ADP-bound state rather than the ATP-bound state, we hypothesized that the neck-linker docked ADP-bound state cannot effectively bind to microtubules, requiring neck-linker undocking for microtubule binding (Mattson-Hoss et al., Proc. Natl. Acad. Sci., 111, 7000-7005 (2014)). While we acknowledge that understanding slow microtubule binding in the neck linker docked state is important for elucidating the mechanism and regulation of microtubule-binding of the head, this paper focuses specifically on the mechanism and regulation of “microtubule-detachment”. We plan to present these microtubule-binding kinetics data in a separate manuscript currently in preparation.

      To explain the low microtubule affinity of the rear-head-crosslink, we added this explanation to the text; “because this constraint on the neck linker dramatically reduces the microtubule-activated ADP release rate (data to be presented elsewhere), creating a weak microtubule binding state” (lines 226-228).

      Although the rear head crosslinking construct under oxidative condition showed fewer fluorescent spots per kymographs (images) due to its low microtubule binding rate, we collected more than one hundred spots by recording additional microscope movies (N=140; Figure 3-figure supplement 2B), ensuring sufficient data for statistical analysis.

      Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?

      Response: The results of ATP on/off rates are comparable to the previous stopped flow measurements of ATP binding to monomeric kinesin-1 on microtubule, which are 2-5 µM-1s-1 and ~150 s-1, respectively (summarized in the review by Cross (2004)). We added a sentence as follows: “First, we demonstrated that k+1 and k-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004).” (lines 163-166).

      As the reviewer pointed out, the rapid mixing and smFRET data cannot be directly compared due to the differences in temporal resolution and fluorescent probe used. In Figure 2E (2F in the revised version), we measured ATP dissociation rate for both WT and E236A using smFRET. Due to the lower temporal resolution, we could not accurately determine ATP binding rate using smFRET. Therefore, to compare the ATP binding rate between WT and E236A heads, we now have added stopped-flow measurements of mant-ATP binding to the E236A monomer, as shown in Fig. 2C and Figure 2-supplement 2, and described in the text (lines 182-185).

      Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?

      Response: We agree with the reviewer that our finding about ATP binding to the front head is potentially significant in the kinesin field, as it has been widely believed that ATP-binding is suppressed in the front head. In our original manuscript, this conclusion was supported only by the measurement of ATP on-rate of the front-head-crosslink, which may differ from the front head of a dimer in which the backward orientation of the neck linker is maintained by the backward strain. Although the reviewer suggested performing smFRET experiments using E236A/WT heterodimer, smFRET have relatively low temporal resolution (50-100 fps) and cannot accurately measure the frequency of ATP binding, so we used this technique only to determine ATP off rates. In this revised manuscript, we now have added stopped-flow experiments to separately measure the ATP binding to the front and rear heads of the E236A/WT heterodimer. By labeling the rear E236A head with a fluorophore to quench the mant-ATP signal bound to the rear head, we successfully measured mant-ATP binding rate to the front head. We found that the ATP-binding rate to the front head was comparable to that of an unconstrained monomer head, providing direct evidence for our conclusion. The revised version includes Fig. 4 A-C (with Figure 4-supplement 2; Figs. 4 and 5 are swapped in order) showing the kinetics of ATP binding to the front and rear heads of the E236A/WT heterodimer, with corresponding text in the result section (lines 315-324).

      MINOR CONCERNS Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates. Line 41: Replace "cellar" with "cellular". Line 83: The authors should cite Andreasson et al. here.

      Response: We have corrected these sentences accordingly (lines 31, 40, 85).

      Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).

      Response: This statement lacks experimental evidence and may confuse readers, so we have removed it for clarity.

      Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.

      Response: A conclusion sentence of this paragraph has been added: “These results demonstrate that neck linker constraints in both forward and rearward orientations inhibit specific steps in the mechanochemical cycle of the head (lines 151-153)”.

      Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"

      Response: We have corrected these sentences accordingly (line 179-181).

      Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.

      Response: This sentence explains how the dark-field microscopy data was analyzed to determine whether the labeled head was in the leading or trailing position before detaching from the microtubule, but the explanation needs clarification. We removed the phrase “which was rarely observed for E236A-WT heterodimer” and simplified this sentence as follows: “Moreover, these observations allow us to distinguish whether the gold-labeled WT head was in the leading or trailing position just before microtubule detachment; the backward displacement of the detached head indicates that the labeled WT head occupied the leading position prior to detachment (Figure 5____-____figure supplement ____1).” (lines 347-351).

      Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?

      Response: We demonstrated that the k2 (microtubule-detachment rate) of the front head matched the ATP turnover rate of the E236A/WT heterodimer (Figure 6 B and E), suggesting that the inhibited step occurs after ATP binding in the front head. In contrast, the rear E236A head showed virtually no ATP hydrolysis activity, since in high-speed dark field microscopy, we observed forward step caused by rear E236A head detachment from microtubule only rarely, approximately once every few seconds (Figure 5-figure supplement 1). We added a sentence in the text as follows: “As described later, the reduced ATPase rate results from suppressed microtubule detachment of the front WT head, while the rear E236A head is virtually unable to detach from microtubules” (lines 311-313).

      Line 323: Is the unbound dwell time unchanged?

      Response: The unbound dwell time exhibited a weak ATP-dependence, which we described only in Figure 5-supplement 2 (Figure 4-supplement 2 in the old version). We observed three distinct phases in the unbound dwell time based on mobility differences, with ATP dependence appearing only in the third phase. This finding suggests that ATP binding to the microtubule-bound E236A head is sometimes necessary for the detached WT head to rebind to the forward-tubulin binding site, indicating that the microtubule-bound E236A head occasionally releases ATP during the one-head-bound state (without the forward neck linker strain). To describe the ATP-dependence of the unbound dwell time, we added a sentence in the main text as follows: “In contrast, the dwell time of the unbound state of the gold-labeled WT head showed weak ATP dependence (Figure 5____-____figure supplement 2), indicating that the rear E236A head occasionally releases ATP when the front head detaches from the microtubule and the neck linker of E236A head becomes unconstrainted. This finding further supports the idea that forward neck linker strain plays a crucial role in reducing the reversible ATP release rate.” (lines 372-377).

      Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.

      Response: We have revised the phrase accordingly (line 371).

      Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.

      Response: We agree to the reviewer’s comment and have corrected this sentence accordingly (line 338).

      Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.

      Response: To improve the readability, we have reorganized Figures 4-6, such that all the figure panels related to the neck linker extended mutants are shown in Figure 6; Figure 5D has been moved to Figure 6F.

      Line 421: delete "not" before "does not".

      Response: We have corrected this typo.

      Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.

      Response: The slip motion demonstrated by Sudhakar et al. (2021) differs from the backstep motion reported by Carter and Cross (and many other laboratories). Slip motion occurs after kinesin detaches from the microtubule and continues until the bead returns to the trap center. In contrast, backstep motion occurs during processive movement when the trap force either exceeds or approaches the stall force. The kinetics of these motions also differ significantly: slip steps occur with a dwell time of 71 µs and are independent of ATP concentration, while backsteps take ~0.3 s (at 1 mM ATP) and depend on ATP concentration. These differences indicate that slip motion is phenomenologically distinct from backsteps occurring under supra-stall or near-stall force.

      Line 474: Replace "suppresses" with "suppressed".

      Response: We have corrected this typo.

      Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.

      Response: We formatted Figure 4E to match Figure 4b from Isojima et al. (Nature Chem. Biol. 2015), to emphasize the difference in ATP dependence of the front and rear head.

      Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).

      Response: The method was described in the Materials and Methods section, but we have now described how to distinguish between bound and unbound states in the main text as follows: “Unlike the unbound trailing head of wild-type dimer that showed continuous mobility (Isojima et al., 2016), the unbound WT head of E236A-WT heterodimer exhibited a low-fluctuation state in the middle (Figure 5B, s.d. trace). This low-fluctuation unbound state was distinguishable from the typical microtubule-bound state, having a shorter dwell time of ~5 ms compared to the bound state and positioning backward, closer to the E236A head, relative to the bound state (Figure 5____-____figure supplement 2).” (lines 351-356).

      __Reviewer #3____

      __

      Minor Issues:

      • Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.

      Response: We changed the word “manner” to “process” (line 23).

      • Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.

      • Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."

      Response: We have corrected these sentences accordingly (line 30, 32).

      • Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.

      Response: We have revised the last sentence of the abstract accordingly by specifying the neck linker’s conformation as follows: ”In combination, these conformation-specific effects of the neck linker favor ATP hydrolysis and dissociation of the rear head prior to microtubule detachment of the front head, thereby providing a kinetic explanation for the coordinated walking mechanism of dimeric kinesin.” (lines 34-37).

      • Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.

      Response: We measured the ATP-induced detachment rate k2 of the front head of the E236A-WT heterodimer to validate our findings from the front-head-crosslinked monomer experiments, which demonstrated reduced k2 after oxidation. To clarify this point, we have now included ATP binding kinetics measurements for both front and rear heads of the E236A-WT heterodimer, as suggested by reviewer 2. These additional data demonstrate consistency between the results from the crosslinked monomer and E236A-WT heterodimer experiments.

      • Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.

      Response: We have addressed this point by revising this sentence as follows: “These results are consistent with an inability of the front head to fully close its nucleotide pocket to promote ATP hydrolysis and Pi release (Benoit et al., 2023), as will be discussed later.” (lines 441-443)

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Kinesin-1 is a dimeric motor protein that transports cargo along microtubules via an ATP-powered, hand-over-hand stepping mechanism. Its processive movement is driven by coordinated ATPase cycles in the two motor domains (heads), which ensures sequential, alternating microtubule binding and detachment for unidirectional motility. The goal of this study by Niitani et al. was to investigate how the neck linker, a region extending from the motor domain's C-terminus that undergoes large conformational changes, differentially regulates microtubule affinity and nucleotide turnover in each of the two heads. The authors employed a combination of pre-steady-state and single-molecule analyses to separately measure the ATP-binding and microtubule-detachment kinetics of the front and rear heads.

      To isolate the kinetics of each head, the authors used disulfide crosslinking to trap the front and rear head states of a monomeric kinesin as well as a heterodimeric kinesin in which one chain was locked in the rear head conformation. These experiments revealed that the backward-facing neck linker of the front head reduces microtubule detachment, while the forward-facing neck linker of the rear head enhances ATP affinity. This finding is consistent with cryo-EM structures of two-head-bound kinesins, which show that the front head has an open nucleotide pocket with the neck linker pulled backward, while the rear head has a closed nucleotide pocket with the neck linker docked and extended toward the front head, regardless of the nucleotide bound.

      The authors used pre-steady-state kinetics and single-molecule assays to explore how the neck linker conformation influences kinesin's motility cycle. ATP binding rates to the kinesin head on microtubules were measured through stopped-flow experiments with mant-ATP and nucleotide-free kinesin-microtubule complexes. These results showed that crosslinking the rear head reduced the ATP dissociation rate, while crosslinking the front head had no significant effect on ATP binding kinetics. The dissociation of ATP from the rear head was further investigated by trapping it in a pre-ATP hydrolysis state using a kinesin mutant (E236A) that significantly slows ATP hydrolysis and stabilizes the neck-linker docked state.

      The authors also investigated the impact of neck-linker orientation (forward vs. backward) on kinesin detachment from microtubules by measuring turbidity changes after rapidly mixing nucleotide-free, crosslinked kinesin-microtubule complexes with ATP using a stopped-flow apparatus. Their results demonstrated that the forward neck linker conformation in the rear head promotes microtubule detachment, while the backward-oriented neck linker in the front head reduces the detachment rate. This suggests that the neck linker conformation mediates gating of microtubule affinity and nucleotide binding. Additionally, they show that partial docking of the neck linker onto the head is sufficient to partially open the gating mechanism.

      To further investigate the role of neck linker tension in front head gating, the authors created an E236A-WT heterodimer. In this dimer, the E236A mutant functions as a long-lived rear head, trapping it in a neck-linker docked state, while the WT head is positioned at the front in the presence of ATP. The analysis of microtubule detachment kinetics and ATPase activity in this heterodimer revealed that although the front WT head can hydrolyze ATP, its catalytic activity is suppressed by the E236A mutant in the rear head.

      Overall, this is a biochemically rigorous study that supports recent structural findings, particularly by highlighting the existence and role of the asymmetry of the neck linkers in two-head-bound kinesin dimers and the distinct conformations of their motor domains.

      Minor Issues:

      • Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.
      • Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.
      • Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."
      • Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.
      • Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.
      • Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.

      Significance

      As indicated in the notes to the authors, I'm supportive of the work reported and view the a biochemically rigorous approach has been used to understand how the neck linker element has such a significant influence on the chemomechanical cycle of each motor domain.

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      Referee #2

      Evidence, reproducibility and clarity

      This manuscript investigates the role of the neck linker in coordinating the stepping cycles of the two heads of a kinesin-1 motor. Previous studies in the field showed that kinesin walks by alternating stepping of its heads, referred to as hand-over-hand. In this stepping mechanism, the front head of a kinesin dimer must remain bound until the rear head dissociates from the microtubule, moves forward, and rebinds to the tubulin on the plus-end side of the front head. There is a large body of work done to address this question. These studies all point to the central role of the 14 amino acid extension, a neck-linker, which connects the two heads to a common stalk, in coordination of kinesin motility. In a two-head-bound state, the motor domains (heads) are oriented parallel to the microtubule, but the neck linkers are orienting toward each other, thereby, breaking the symmetry in a homodimeric motor. In addition, the neck linkers are quite short, almost stretching to their near contour length to accommodate the microtubule binding of both heads. Previous studies pointed out that either the opposing orientation or the intramolecular tension of the neck linkers coordinate the stepping cycle.

      However, we still do not know which step(s) in the chemo-mechanical cycle is controlled by the neck-linker to keep the two heads out of phase. The front head gating model postulates that ATP binding to the front head is gated until the rear head detaches from the microtubule. The rear head gating model proposes that the neck linker accelerates the detachment of the rear head from the microtubule. In this study, the authors use pre-steady state kinetics and smFRET to address this question. They measured ATP binding and microtubule detachment kinetics of kinesin's catalytic domain with neck linker constraints 1) imposed by disulfide crosslinking of the neck linker in monomeric kinesin in backward (rear head-like) and forward (front head-like) orientations, and 2) using the E236A-WT heterodimer to create a two-head microtubule-bound state with the mutant and WT heads occupying the rear and front positions respectively. They found that neck-linker conformation of the rear head reduces the ATP dissociation rate but has little effect on microtubule affinity. In comparison, the neck-linker conformation of the front head does not change ATP binding to the front head, but it reduces ATP-induced detachment of the front head, suggesting that a step after ATP binding (i.e. ATP hydrolysis or Pi release) is gated in the front head.

      Major Concerns

      Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.

      Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.

      Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?

      Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?

      Minor Concerns

      Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates.

      Line 41: Replace "cellar" with "cellular".

      Line 83: The authors should cite Andreasson et al. here.

      Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).

      Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.

      Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"

      Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.

      Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?

      Line 323: Is the unbound dwell time unchanged?

      Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.

      Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.

      Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.

      Line 421: delete "not" before "does not".

      Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.

      Line 474: Replace "suppresses" with "suppressed".

      Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.

      Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).

      Significance

      I believe that this work will make an important contribution to the large body of literature focused on the mechanism of kinesin, which serves as an excellent model system to understand the kinetics and mechanics of a molecular motor. The mechanism proposed by the authors modifies the front-head gating model and is in agreement with recent structural work done on a kinesin dimer bound to a microtubule. Overall, the work is well performed, and the conclusions are well supported by the experimental data. I have several major and minor concerns to improve the clarity of this work and strengthen its conclusions.

    4. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this study, the authors investigate the molecular mechanism behind kinesin-1's coordinated movement along microtubules, with a focus on how ATP binding, hydrolysis, and microtubule attachment/detachment are regulated in the leading and trailing heads. Using pre-steady state kinetics and single-molecule assays, they show that the neck linker's conformation modulates nucleotide affinity and detachment rates in each head differently, establishing an asynchronous chemo-mechanical cycle that prevents simultaneous detachment. Supported by cryo-EM structural data, their findings suggest that strain-induced conformational changes in the nucleotide-binding pockets are crucial for kinesin's hand-over-hand movement, presenting a detailed kinetic model of its stepping mechanism. The manuscript is well-crafted, technically rigorous, and should be of significant interest to cell biology and cytoskeletal motor researchers. I recommend acceptance with the following minor changes:

      1. Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.
      2. Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads."

      Significance

      All conclusions are well-supported by the provided data. The findings address a critical gap in our understanding of how kinesin's two motor domains coordinate their movements, offering insights into the molecular basis of its stepping mechanism. This work should be of significant interest to the cytoskeletal research community.

    1. "if a baby wants to die, it will die." There was no sense in frustrating him so, for here was a child who was com­pletely "lifeless," without any "fight" .at all. His eyes were already sinking to the back of his head, a sign that he had already begun his journey into the next life. It was very wrong, the creche mothers warned, to fight with death

      This passage stood out to me because, it makes me want to keep reading what will happen next in the reading. I believe the author added this passage to attract the readers to keep reading and attract their attention. "If a baby wants to die, it will die". Means that if the baby is not taken care of it might not survive. Back in the 1900's mothers clearly did not take care about their children as much as they should. The theme of this story is how mothers that were in extreme poverty had children that they could not take care of and would end up dying, since there was not any other choice. The theme from this story from only from the pages assigned is that poverty, inequality, can change the way people view life.

    1. The first is staleness of imagery;

      I think that the ability to create an image is something so imporant when it comes to writing. One of my favorite authors masterfully can create an image in my head and i love listening to him because of it.

    2. with respect to, having regard to, the fact that, by dint of, in view of, in the interests of, on the hypothesis that; and the ends of sentences are saved from anticlimax by such resounding commonplaces as greatly to be desired, cannot be left out of account, a development to be expected in the near future, deserving of serious consideration, brought to a satisfactory conclusion,

      I am really bad at not using this kind of passive voice speech. This is mostly because it sound correct in my head rather than the simpler phrase. Although I am no learning when you use it, it just sounds like you're fumbling over your own words.

    1. From a life science point of view, these animals resemble a liquid—much more so than other creatures such as humans—because of their movable (and sometimes missing) collarbone. Once their head fits through an opening, the rest of the body can easily follow.

      This is the conclusion

    1. Interpretation is the third part of the perception process, in which we assign meaning to our experiences using mental structures known as schemata.

      A ton of my interpretation is definitely based off personal and professional experience. But then again it is also based off of the mood that I am in. How much of a threshold do I have for my brain functionality that day? Interpretation and what is being relayed sometime is just to dang hard. Especially if the point isn't being made. Sometimes I'm ok get on with it please? Then I lose interest and stop talking sure I nod my head but if I am at that point, I am already done trying to figure it out.

    1. Each day every member of the family, insuccession, enters the shrine room, bows his head before the charm-box,mingles different sorts of holy water in the font, and proceeds with a brief riteof ablution.[5]

      I feel as if when it comes to the charm box and what's inside that's supposed to heal or help the individual almost take form of God.

    1. In early 2018, Google rolled out "Art Selfie" — an app designed to match a user's photo to a famous painting lookalike using face recognition.12 The result is an often-humorous photo pairing and an opportunity to learn more about art. Less humorous is the fact that some police departments do the same thing when looking for criminal suspects, just in reverse—submitting art in an attempt to identify real people.

      I have seen this before on my phone, when it looks for my look-alike, it shows me someone with the same head shape and maybe same facial feature placement, but not really someone who looks like me.

    1. Reviewer #3 (Public review):

      This study explores sensory prediction errors in sensory cortex. It focuses on the question of how these signals are shaped by non-hierarchical interactions, specifically multimodal signals arising from same level cortical areas. The authors used 2-photon imaging of mouse auditory cortex in head-fixed mice that were presented with sounds and/or visual stimuli while moving on a ball. First, responses to pure tones, visual stimuli and movement onset were characterized. The authors then made the running speed of the mouse predictive of sound intensity and/or visual flow (closed loop). Mismatches were created through the interruption of sound and/or visual flow for 1 second, disrupting the expected sensory signal. As a control, sensory stimuli recorded during the close loop phase were presented again decoupled from the movement (open loop). The authors suggest that auditory responses to the unpredicted interruption of the sound, which affected neither running speed nor pupil size, reflect mismatch responses. That these mismatch responses were enhanced when the visual flow was congruently interrupted, indicates cross-modal influence of prediction error signals.

      This study's strengths are the relevance of the question and the design of the experiment. The authors are experts in the techniques used. Responses to the interruption of the sound are similar in quality, if not quantity, in the predictive and the control situation, yet the contribution of sound offset sensitivity to the observed mismatch responses is not discussed.

    1. In many ways, it feels like my brain is thinking aside from me thinking. Information and new ideas are dropped into my head without me even giving a single thought about something. Sure, I may be trying to come with new ideas, but most of the time, I don’t go through a certain thought process to actually get to those ideas. My brain, at this heightened level, will begin dropping ideas into my field of consciousness for me to experiment. It’s thinking without thinking.

      I like to call it the invisible pond. Where thoughts just come up. It's interesting, like shower thoughts or letting the subconscious do its thing as you do your work. I like it. It really does come back to having good mood and taking care of your well-being.

    1. DeepSeekMLA was an even bigger breakthrough. One of the biggest limitations on inference is the sheer amount of memory required: you both need to load the model into memory and also load the entire context window. Context windows are particularly expensive in terms of memory, as every token requires both a key and corresponding value; DeepSeekMLA, or multi-head latent attention, makes it possible to compress the key-value store, dramatically decreasing memory usage during inference.

      Multi-head Latent Attention

      Compress the key-value store of tokens, which decreases memory usage during inferencing.

    1. Whispering was a subspecialty of propaganda. You had to be trained in it specially. Not just anybody could do it. Now, you might think that spreading rumors means talking as loudly and widely as possible, but that's not true. The coordination of loose lips had to be as tight as the coordination of special forces. I'll tell you how it worked. The Allies put together rumors at something called the rumor factory. The head of this section had the enviable title master whisperer. The whispers would go out through strategic networks. In a given region, a chief whisperer would organize the whispers, give them to agents. They would give them to sub-agents. Mostly sub-agents were ordinary civilians. You could be a reliable sub-agent in a propaganda network and not even know it.

      "Whispering" technique of propaganda

    1. Others attribute this fall to another cause, which seems to have some relation to the case of Adam, but falsehood makes up the greater part of it. They say that the husband of Aataentsic, being very sick, dreamed that it was necessary to cut down a certain tree from which those who abode in Heaven obtained their food; and that, as soon as he ate of the fruit, [page 127] he would be immediately healed. Aataentsic, knowing the desire of her husband, takes his axe and goes away with the resolution not to make two trips of it; but she had no sooner dealt the first [88] blow than the tree at once split, almost under her feet, and fell to this earth; whereupon she was so astonished that, after having carried the news to her husband, she returned and threw herself after it. Now, as she fell, the Turtle, happening to raise her head above water, perceived her; and, not knowing what to decide upon, astonished as she was at this wonder, she called together the other aquatic animals to get their opinion. They immediately assembled; she points out to them what she saw, and asks them what they think it fitting to do. The greater part refer the matter to the Beaver, who, through courtesy, hands over the whole to the judgment of the Turtle, whose final opinion was that they should all promptly set to work, dive to the bottom of the water, bring up soil to her, and put. it on her back. No sooner said than done, and the woman fell very gently on this Island.

      there are some similarities between this part and the start of Christian creation. It would be interesting to see side by side in comparison to the Holy Bible

  2. learn-us-east-1-prod-fleet02-xythos.content.blackboardcdn.com learn-us-east-1-prod-fleet02-xythos.content.blackboardcdn.com
    1. he loves trophies and victories , but he is afraid ofseeing a good general at the head of his troops

      Here, Montesquieu critiques Luis XVI's insecurity, showing how absolutism prioritizes personal power over military success.

    1. Phatic communion is an instructive example of how we communicate under the influence of rules and norms (Senft, 2009). Phatic communion refers to scripted and routine verbal interactions that are intended to establish social bonds rather than actually exchange meaning. When you pass your professor in the hall, the exchange may go as follows:

      This type of situation that hits my 'tism/adhd ticks with a vengeance. I truly struggle with these daily interactions every time i come into contact with them. As someone with a passion for psychology i do fully understand the reason for phatic communion and why humans in general need these human to human transactions, again the true "lone Wolf" comes into play. Why would someone not shorten the wasted time in having a falsified conversation about how you are doing, when neither party has an interest in the response and usually using these interactions as a segway into talking about themselves. I usually throw a head nod, it saves so much time and the same interest insome one is conveyed. I suppose i come across this issue because i am someone that asks you a question because i actually care about the answer you are giving and have no interest in talking about myself in the process. Im working on this with myself but its the hardest thing in the world to be apart of for myself.

    1. The transaction model of communication describes communication as a process in which communicators generate social realities within social, relational, and cultural contexts. In this model, we don’t just communicate to exchange messages; we communicate to create relationships, form intercultural alliances, shape our self-concepts, and engage with others in dialogue to create communities. In short, we don’t communicate about our realities; communication helps to construct our realities.

      this statement pairs well with humans being such pack animals. It is very rare to find a person who is truly a "Lone Wolf". I don't know what else to add to this other then that was the thought that popped into my head. communication absolutely is a shaper or world and reality, of a singular person or a whole wide scale.

    1. There’s a reason that Leonardo da Vinci kept a notebook in which he sketched and wrote every idea he had: it allowed him to see those ideas, share those ideas, critique those ideas, and improve those ideas. Had he kept them all in his head, his limited capacity to see and reason about those ideas would have greatly limited his productivity.

      I totally agree with this statement here. For me personally I tend to forget things constantly if I don't explicitly write them down. This has forced me to have to carry around journals just containing to do list so that I don't forget what I have to do. I also find it interesting that the author explicitly mentions that it allows him to have those ideas critiqued. No matter how hard you try the first iteration will always need some reworking. This is something that I often forget and need to remind myself of when doing creative work. It is a process, and one that requires a lot of reworking to get right.

    2. There’s a reason that Leonardo da Vinci kept a notebook in which he sketched and wrote every idea he had: it allowed him to see those ideas, share those ideas, critique those ideas, and improve those ideas. Had he kept them all in his head, his limited capacity to see and reason about those ideas would have greatly limited his productivity.

      I find this an extremely valuable idea to use. We have a multitool in our pockets at all times - I catch ideas in my own head at many various times of day. Sometimes, I'm too preoccupied to catch the whisper and write it down. I need to "externalize" as written here.

    3. Externalize often. The more you express those ideas—in words, in sketches, in prototypes, in demos—the more visible those flaws will be to you and other people.

      I wanted to comment specifically on externalization and just my thoughts on this statement. I believe that externalization, in terms of just getting your ideas out, is extremely important and valuable. I enjoy working on music production, and many times, there are great ideas that you come across that maybe you forget to get in the daw and never make it out of your head. Many times, these ideas are extremely valuable to see where you can improve or what you've done better. I also wanted to express that externalization can also be important in separating yourself from the problem or design. Sometimes, not only thinking of how you would solve it can open another solution that could be crucial to you finding the best solution since perception oftentimes does not beat perception.

    1. Intrapersonal communication is communication with oneself using internal vocalization or reflective thinking. Like other forms of communication, intrapersonal communication is triggered by some internal or external stimulus. We may, for example, communicate with our self about what we want to eat due to the internal stimulus of hunger, or we may react intrapersonally to an event we witness. Unlike other forms of communication, intrapersonal communication takes place only inside our heads. The other forms of communication must be perceived by someone else to count as communication. So what is the point of intrapersonal communication if no one else even sees it?

      I read about a guy (https://www.boredpanda.com/not-everyone-having-internal-monologue/) that did an informal study about people not having an inner monologue. I was a bit shocked. I thought everyone had that voice that they argued with inside their head, sometimes quietly, sometimes aloud while working or driving. It's probably a bit lonely without having that pesky voice blabbing in the background all day.

    2. Intrapersonal communication is communication with oneself using internal vocalization or reflective thinking. Like other forms of communication, intrapersonal communication is triggered by some internal or external stimulus

      I am constantly using inner voice dialogue. It is a 24/7 thing. Seems like it never ends. Constanly going over things in my head before they are spoken or written. In the catagory of constant overthinking. Always thinking of the reactions to my actions and so on.

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Get the morning and evening editions of our flagship newsletter in your inbox.US & Canada newsNew fires erupt in southern California ahead of Trump visit 3 hrs agoUS & CanadaOpening statements begin in A$AP Rocky's trial in Los Angeles6 hrs agoUS & CanadaTrump pardons anti-abortion activists ahead of rally9 hrs agoUS & CanadaEven before the LA fires, Californians fled for 'climate havens'Some are moving to so-called "climate havens" in the Great Lakes region to avoid climate disasters. 20 hrs agoUS & CanadaIsrael-Gaza warStories of the hostages taken by Hamas from Israel6 hrs agoMiddle EastWho are Israeli hostages released and rescued from Gaza?6 hrs agoMiddle EastWhy are Israel and Hamas fighting in Gaza?3 days agoMiddle EastKey events that led to Israel-Hamas ceasefire deal in GazaThe ceasefire agreement in the Gaza war follows 15 months of fighting between Israel and Hamas.6 days agoMiddle EastWar in UkraineUkraine claims drone strike on Russian oil refinery3 hrs agoEurope'I fled Ukraine as a refugee - now I've won investment on Dragons' Den'6 hrs agoWest Yorkshire'Unbelievable' race track convoy for Ukraine2 days agoEnglandDark humour for dark times: How comedy helps in UkraineUkrainian stand-up comedians say humour can help people cope and raise money for the war effort.2 days agoEuropeMore world newsMan dies after tree falls on his car during Storm Éowyn49 mins agoEuropeTens of thousands protest in Slovakia against PM Fico 1 hr agoEuropeBulgarian woman based in UK denies spying for Russia2 hrs agoEuropeRebels kill DR Congo governor as fighting intensifies Conflict in eastern DR Congo's has forced 400,000 people to flee their homes this year alone, the UN says.3 hrs agoAfricaVideoWhy being a 'loner' could be good for youEmerging research suggests that spending time alone is beneficial for our health and creativity.See moreSportMan City captain Walker completes AC Milan loan move2 hrs agoMan CityLIVEEFL: Hull City stun Sheffield United to move out of drop zoneDjokovic unsure of Australian Open return10 hrs agoTennisEmbracing the chaos - breaking down Bournemouth's riseWith Bournemouth challenging for a top-four place in the Premier League this season, BBC Sport analyses how the Cherries are doing it.5 hrs agoBournemouthBusinessTrump urged not to put massive tariffs on UK4 hrs agoBusinessBank of Japan raises rates to highest in 17 years15 hrs agoBusinessClampdown on fake Google reviews announced7 hrs agoTechnologyUS doesn't need Canadian energy or cars, says TrumpSpeaking to business leaders in Davos, Trump also repeated his jibe that Canada could become a US state and would avoid tariffs if it did. 1 day agoUS & CanadaTech'A mockery': Trump's new meme-coin sparks anger in crypto world1 day agoUS & CanadaHow to make oxygen on the moon22 hrs agoChatGPT back online after outage which hit thousands worldwide1 day agoTechnologyScammers using my face to con people, warns Namibia's ex-first ladyIn a video message Monica Geingos warns people not to be duped into investing in fake schemes.1 day agoAfricaScience & health'I had anti-government views so they treated me for schizophrenia'2 days agoAsiaUnitedHealthcare names new boss after former CEO killed21 hrs agoUS & CanadaPurdue and Sackler family agree $7.4bn opioid settlement1 day agoUS & CanadaHair loss drug finasteride 'biggest mistake of my life'Some online sites prescribe a potentially risky hair loss drug without consistent safety checks, BBC finds.21 hrs agoHealthCultureLola Young's Messy hits number one: My songs are as real as it gets4 hrs agoCultureHarry v the tabloids. 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      Perceivability Issue – Low Contrast

      The text on website lacks sufficient contrast against the white or light-coloured background. This makes it harder for users with visual impairments or low contrast sensitivity to read and navigate the website effectively. Improving the contrast between the text and background would align with WCAG standards and ensure that all users can perceive the content clearly.

    Annotators

    1. [35:46] It’s important, therefore, to know who the real enemy is, and to know the function, the very seriousfunction of racism, which is distraction. It keeps you from doing your work. It keeps you explaining over and overagain, your reason for being. Somebody says you have no language and so you spend 20 years proving that youdo. Somebody says your head isn’t shaped properly so you have scientists working on the fact that it is.Somebody says that you have no art so you dredge that up. Somebody says that you have no kingdoms and soyou dredge that up.None of that is necessary.

      Toni Morrison on the "serious function of racism"

    1. Authors’ Response (31 October 2024)

      GENERAL ASSESSMENT

      Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

      We thank the reviewers for their time and effort in reviewing our manuscript. Based on their valuable comments and suggestions, we have made substantial revisions. The updated manuscript now includes two new experiments supporting that lysophospholipid-triggered channel activation promotes the release of signaling molecules critical for immune response and demonstrates that this novel class of agonist activates the inflammasome in human macrophages through endogenously expressed Panx1. To better highlight the significance of our findings, we have excluded the cryo-EM panel from this manuscript. We believe these changes address the main concerns raised by the reviewers and enhance the overall clarity and impact of our findings. Below, we provide a point-by-point response to each of the reviewers’ comments.

      RECOMMENDATIONS

      Essential revisions:

      1. The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the 2 key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.

      We appreciate and agree with the reviewers’ observation. Our initial manuscript may have been challenging to follow due to our use of both wild-type and GS-tagged versions of Panx1 from human and frog origins, combined with different fluorescence techniques across cell types. In this revision, we used only human wild-type Panx1 expressed in HEK293S GnTI<sup>-</sup> cells, except for activity-guided fractionation experiments, where we used GS-tagged Panx1 expressed in HEK293 cells (Fig. 1). For functional reconstitution studies, we employed YO-PRO-1 uptake assays, as optimizing the Venus-based assay was challenging. We have clarified these exceptions in the main text. We think these adjustments simplify the narrative and ensure an appropriate balance between main and supplemental figures.

      1. It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).

      We fully agree with the reviewer on the importance of ion selectivity experiments. However, comparing the properties of LPC-activated channels with those activated by membrane depolarization presented technical challenges, as LPC appears to stimulate Panx1 in synergy with voltage. Prolonged LPC exposure destabilizes patches, complicating G-V curve acquisition and kinetic analyses. While such experiments could provide mechanistic insights, we think they are beyond the scope of current study.

      1. Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.

      As mentioned above, we believe our revised version is more straightforward to digest. We have improved labeling and provided explanations where necessary to clarify the manuscript. While Panx1 expression levels are indeed higher in GnTI<sup>-</sup> than in HEK293 cells, we are uncertain whether the absence of detectable currents in HEK293 cells is solely due to expression levels. Some post-translational modifications that inhibit Panx1, such as lysine acetylation, may also impact activity. Future studies are needed to explore these mechanisms further.

      1. The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.

      We modified the Y-axis in Figure 2 to “Quench (%)” for clarity. The data reflects fluorescence reduction over time, starting from LPC addition, normalized to the maximal decrease observed after Triton-X100 addition (3 minutes), enabling consistent quenching value comparisons. Although the quenching value appears small, normalization against complete cell solubilization provides reproducible comparisons. We do not fully understand why CBX effects vary in Venus quenching experiments, but we speculate that its steroid-like pentacyclic structure may influence the lysophospholipid agonistic effects. As noted in prior studies (DOI: 10.1085/jgp.201511505; DOI: 10.7554/eLife.54670), CBX likely acts as an allosteric modulator rather than a simple pore blocker, potentially contributing to these variations.

      1. Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?

      Our recent study suggest that the electrostatic landscape along the permeation pathway may influence its ion selectivity (DOI: 10.1101/2024.06.13.598903). However, we have not yet fully elucidated how Panx1 permeates both anions and cations. Based on our findings, ion selectivity may vary with activation stimulus intensity and duration. Cation permeation through Panx1 is often demonstrated with YO-PRO-1, which measures uptake over minutes, unlike electrophysiological measurements conducted over milliseconds to seconds. We referenced two representative studies employing YO-PRO-1 to assess Panx1 activity. Whole-cell current measurements from a similar construct with an intracellular loop insertion indicate that our STREP-tagged construct likely retains functional capacity.

      1. In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.

      We used the ratio of LPC-induced current to the current measured at +110 mV to determine whether any of the mutants disproportionately affect LPC-induced current relative to depolarization-activated current. Since the mutants that did not respond to LPC also exhibited smaller voltage-stimulated currents than those that did respond, we reasoned that using this ratio would better capture the information the reviewer is suggesting to gauge. Showing the zero current level may be helpful if the goal was to compare basal currents, which in our experience vary significantly from patch to patch. However, since we are comparing LPC- and voltage-induced currents within the same patch, we believe that including basal current measurements would not add useful information to our study.

      Given that new experiments included to further highlight the significance of the discovery of Panx1 agonists, we opted to separate structure-based mechanistic studies from this manuscript and removed this experiment along with the docking and cryo-EM studies.

      1. The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.

      We agree with the reviewer and attempted 3D classification using multiple smaller masks including the suggested region. However, the maps remained poorly defined, and we were unable to confidently assign the NTD.

      1. The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."

      Both maps show that the narrowest constriction, formed by W74, has a diameter of approximately 9 Å. Previous steered molecular dynamics simulations suggest that ATP can permeate through such a constriction, implying an ion selection mechanism distinct from a simple steric barrier.

      1. The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?

      We appreciate the reviewer’s insight. We incubated LPC and nanodisc-reconstituted protein for 30 minutes, speculating that LPC approaches the pore similarly to other lipids in prior structures. In separate studies, we are optimizing conditions to capture more defined conformations.

      1. In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.

      We agree that the density corresponding the extended NTD into the cytoplasm is relatively weak. In our recent study, we compared two Panx1 structures with or without the mentioned C-terminal helix and found evidence suggesting the likelihood of NTD extension (DOI: 10.1101/2024.06.13.598903). Nevertheless, to prevent potential confusion, we have removed the cryo-EM panel from this manuscript.

      1. Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

      Thank you for the suggestion. We have removed this section to avoid potential confusion.

      Optional suggestions:

      1. The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.

      We have added a rationale in line 90, stating: “The soluble fractions were excluded from this study, as the most polar fraction induced strong channel activities in the absence of exogenously expressed pannexins.” Additionally, we have included a figure to support this rationale (Fig. S1A).

      1. The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.

      Unfortunately, in our hands, the prolonged application of lysolipids at concentrations necessary to achieve significant currents tends to destabilize the patch. This makes it challenging to obtain G-V curves or perform the previously mentioned kinetic analyses. We believe this destabilization may be due to lysolipids’ surfactant-like qualities, which can disrupt the giga seal. Additionally, prolonged exposure seems to cause channel desensitization, which could be another confounding factor.

      1. From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?

      Thank you for the comment, with which we agree. Our initial intention was to use the mutagenesis studies to experimentally support the docking study. Due to uncertainties associated with the presented cryo-EM maps, we have decided to remove this study from the current manuscript. We will consider the proposed experiments in a future study.

      1. Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.

      We believe the uncoordinated arrangement of NTDs is dynamic, allowing for potential variations in pore size during the activated conformation. Alternatively, NTD movement may be coupled with conformational changes in TM1 and the extracellular domain, which in turn could alter the electrostatic properties of the permeation pathway. We believe a functional study exploring this mechanism would be more appropriately presented as a separate study.

      1. Can the authors provide a positive control for these negative results presented in Fig S1B and C?

      The positive results are presented in Fig. 1D and E.

      1. Raw images in Fig S6 and Fig S7 should contain units of measurement.

      Thank you for pointing this out.

      1. It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.

      We attempted to superimpose the cryo-EM maps; however, visually highlighting the differences in figure format proved challenging. Higher-resolution maps would allow for model building, which would more effectively convey these distinctions.

      1. Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.

      Noted.

      1. A table for cryo-EM statistics should be included.

      Thanks, noted.

      1. n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.

      While we agree that transparency is essential, adding n-values to each graph would make some figures less clear and potentially harder to interpret in this case. We believe that the dot plots, n-value range, and statistical analysis provide adequate support for our claims.

      1. The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.

      Thank you for this comment. Although we intended to support the caspase activation mechanism in our introduction, we understand that the reviewer’s interpretation indicates a need for clarification. We hope the revised introduction removes any perception of dismissiveness.

      1. Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.

      For Fig. 4F, we used a distinct normalization method to account for substantial day-to-day variation in experiments involving body fluids. Notably, we did not apply this normalization to other experimental panels due to their considerably lower day-to-day variation.

      1. What was the rational for using the structure from ref 35 in the docking task?

      The docking task utilized the human orthologue with a flipped-up NTD. We believe that this flipped-up conformation is likely the active form that responds to lysolipids. As our functional experiments primarily use the human orthologue for biological relevance, this structure choice is consistent. Our docking data shows that LPC does not dock at this site when using a construct with the downward-flipped NTD.

      1. Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.

      Done.

      1. From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.

      We have added the molecular weight of each subtype in the figure legend.

      1. Please provide holding voltages and zero current levels in all figures presenting currents.

      We provided holding voltages. However, the zero current levels vary among the examples presented, making direct comparisons difficult. Since we are comparing currents with and without LPC, we believe that indicating zero current levels is unnecessary for this study.

      1. While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

      We are uncertain whether Panx3 is unaffected by lysophospholipids, as we have not observed activation of this subtype under any tested conditions.

      (This is a response to peer review conducted by Biophysics Colab on version 1 of this preprint.)

    2. Consolidated Peer Review Report (20 December 2023)

      GENERAL ASSESSMENT

      Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

      RECOMMENDATIONS

      Essential revisions:

      1. The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.
      2. It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).
      3. Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.
      4. The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.
      5. Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?
      6. In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.
      7. The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.
      8. The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."
      9. The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?
      10. In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.
      11. Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

      Optional suggestions:

      1. The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.
      2. The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.
      3. From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?
      4. Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.
      5. Can the authors provide a positive control for these negative results presented in Fig S1B and C?
      6. Raw images in Fig S6 and Fig S7 should contain units of measurement.
      7. It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.
      8. Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.
      9. A table for cryo-EM statistics should be included.
      10. n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.
      11. The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.
      12. Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.
      13. What was the rational for using the structure from ref 35 in the docking task?
      14. Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.
      15. From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.
      16. Please provide holding voltages and zero current levels in all figures presenting currents.
      17. While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

      REVIEWING TEAM

      Reviewed by:

      Jorge Contreras, Professor, University of California, Davis, USA: electrophysiology and ion channel mechanisms

      Wei Lü, Associate Professor, Department of Structural Biology, Van Andel Institute, USA: ion channel mechanisms, X-ray crystallography and cryo-EM

      Xiaofeng Tan, Research Fellow, NINDS, NIH, USA: structural biology (X-ray crystallography and cryo-electron microscopy) and ion channel mechanisms

      Kenton J. Swartz, Senior Investigator, NINDS, NIH, USA: ion channel structure and mechanisms, chemical biology and biophysics, electrophysiology and fluorescence spectroscopy

      Curated by:

      Kenton J. Swartz, Senior Investigator, NINDS, NIH, USA

      (This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Comments concerning minor and presentational issues have been omitted for brevity.)

    1. The costumes look cool and I like how he integrated three forms of art into one Is he still doing this to this day? Since it's been 12 years since this interview Holy shit I almost applied to the art institute of chicago The bright and vibrant colors are nice and I wonder if he chooses colors on purpose I relate to what he said about his ideas and how it can feel like theyre just bursting and filling his head.

    1. Does your life look amazing on paper? You're smart, creative, healthy, travel to fun places, and have loving family and friends. But inside, you feel disorganized, and your ADHD symptoms seem to hold you back from fully enjoying life.

      Your life looks amazing on paper.

      You're smart, creative, healthy, travel to amazing places and have a loving family and friends.

      But inside, you...

      Have I hit the nail on the head?

    1. 26. When Jesus was in Galilee at the beginning of his fourth year he was playing by the Jordan, and made seven pools. A boy spoilt them, and was struck dead. The parents complained. Joseph asked Mary to admonish Jesus. She begged him not to do such things, and he, not willing to grieve her, ‘smote the back side of the dead boy with his foot and bade him rise: which he did, and Jesus went on with his pools’. 27. He took clay from the pools and made twelve sparrows, on the sabbath. A Jew saw it and spoke to Joseph, who spoke to Jesus. Jesus clapped his hands and bade the sparrows fly away. All marvelled, and some went and told the chief priests and Pharisees. 28. The son of Annas the priest broke up the pools with a stick, and Jesus with a word withered him up. 29. Joseph was afraid and took Jesus home. On the way a boy ran against Jesus and got on his shoulder, meaning to hurt him. Jesus said, ‘May you not return whole from the way you go.’ He fell dead. Complaints of the parents, as in Thomas. Joseph to Jesus: ‘Why do you do such things? Many are now complaining against you and hate us on your account, and we suffer injuries through you.’ Jesus: ‘No son is wise whom his father has not taught according to the knowledge of this age, and the curse of his father hurts no man except those who do ill.’ All reviled Jesus to Joseph and he was afraid. ‘Then Jesus took the dead boy by the ear and held him up by it in the sight of all, and they saw Jesus speaking to him as a father to his son. And his spirit returned unto him and he lived again, and all marvelled.’ 30. Master Zacchaeus spoke reproachfully to Joseph; ‘You and Mary think more of your son than of the traditions of the elders.’ Joseph: ‘But who can teach him? if you can do so, we are very willing.’ Jesus overhearing said, ‘What you say is well for ordinary people: I have no earthly father. When I am lifted up from the earth I will make all mention of your descent to cease. I know when you were born and how long you have to live.’ All cried out in wonder, ‘We have never heard the like.’ Jesus: ‘Does this surprise you? I will tell you more. I have seen Abraham and spoken with him, and he has seen me.’ None could answer. Jesus: ‘I have been among you with the children, and you have not known me. I have spoken with you as with the wise and you have not understood my voice, for you are less than me, and of little faith.’ 31. Zacchaeus said, ‘Give him to me and I will take him to Levi who shall teach him letters.’ Levi bade him answer to Aleph: he was silent. Levi smote him with a rod of storax on the head. Jesus: ‘Why do you hit me? Know of a truth that he who is smitten teaches the smiter more than he is taught of him. For I can teach you the things that you yourself say. But all these who speak and hear are blind like sounding brass or a tinkling cymbal wherein is no perception of those things that are signified by their sound.’ Further he said to Zacchaeus, ‘Every letter from Aleph to Thau is discerned by the arrangement of it. First say what Thau is, and I will tell you what Aleph is.’ And again he said, ‘They who do not know Aleph, how can they tell Thau, hypocrites that they are? Say what Aleph is first and then will I believe you when you say Beth. He said to the master, ‘Let the master of the law say what the first letter is, or why it has many triangles [eight adjectives follow].’ Levi was stupefied and then began to lament, ‘Ought he to live on the earth? Nay, rather is he worthy to be hung on a great cross. He can put out fire and escape all torments by guile. I think he was born before the flood, before the deluge. What womb bare him? What mother gave him birth? What breasts suckled him? I fly before him’, etc., etc. Jesus smiled and said with command to all the children of Israel that stood and heard him, ‘Let the unfruitful bear fruit, and the blind see, and the lame walk straight, and the poor enjoy good things, and the dead revive, and every one return into a restored state, and abide in him who is the root of life and of everlasting sweetness.’ All were healed who had fallen into evil infirmities. No one thereafter dared to say aught to him or hear aught of him. 32. At Nazareth the boy Zeno fell from the upper storey and was raised. Joseph, Mary, and Jesus went thence to Jericho. 33. Jesus' pitcher was broken by a child, and he brought water in his cloak. 34. He took a little corn out of his mother's barn and sowed it. When reaped it made three measures, which he gave away. 35–6. [Translated below.] 37. A bed of six cubits was ordered of Joseph, and he told his lad to cut a beam of the right length, but he made it too short. Joseph was troubled. Jesus pulled it out to the right length. 38. He went to school the second time. ‘Say Alpha.’ Jesus: ‘Tell me first what Beta is, and I will tell you what Alpha is.’ The master smote him and died.

      Disconcerting childhood.

    1. I was gonna write about Mama's funeral but Lamont and Ms. Marcus going back and forth zapped all the ideas from my head.

      Woodson's use of the word "zapped" apropos of the speaker's conceptions signifies that their disappearance was instantaneous. Given that these notions regarded his mother's demise, toward whom he presumably was highly affectionate, and he was intending to write poetry, necessitating profound and often introspective thought, one may conjecture that these ideas had great psychological magnitude; thus, the fact that such significant thoughts were erased so suddenly and completely by an argument that did not concern him—a menial extraneous phenomenon—illuminates that diversions can effortlessly alter one's intentions or how they are executed.

    2. I was gonna write about Mama's funeral but Lamont and Ms. Marcus going back and forth zapped all the ideas from my head.

      These 3 lines that convey that the author has so much things like swarming his brain that he is at one time that he has no idea what to do anymore.

    3. I was gonna write about Mama's funeral but Lamont and Ms. Marcus going back and forth zapped all the ideas from my head.

      Sometimes when confilict is happening around you, it gets hard to focus.

    1. First, I tell my students that I am a feminist. Startled that a feminist could be packaged as I am—a petite white heterosexual woman who smiles and has been known to wear lipstick—students then listen as I read a standard definition of “feminism,” such as this one from The American Heritage Dictionary: Feminism is the “belief in the social, political, and economic equality of the sexes” as well as the “movement organized around this belief.” We talk about this definition, and many students begin to realize that, even if they had never thought so before, they are feminists.

      People have this pre notion of what they think feminism is or what it should look like instead of actually researching it or even trying to wrap they head around what else it can be or why it even came about and it all due to history or men being on top and women being on the bottom.

    2. When my older sister, then in college, went through a brief radical feminist “phase”—not shaving her legs or armpits, questioning the Catholic Church’s exclusion of women, agitating against the antiabortion group Operation Rescue, whose founder lived in her college town—I didn’t know how to interpret her behavior. Instead of wondering why she was rebelling, I categorized her as odd, a fringe-dweller. I was content to keep my head safely inside books; I rarely engaged with current events, though I am happy to say that I voted for the first time in the 1988 presidential election, for Dukakis, no less.

      When he author describe what their sister is doing as a radical feminist I think that's how most feminist are seen as a stereotype and that's why many people shy away from them because of their interpretation of how they make look "crazy" or rebellious.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      The authors observed a decline in autophagy and proteasome activity in the context of Milton knockdown. Through proteomic analysis, they identified an increase in the protein levels of eIF2β, subsequently pinpointing a novel interaction within eIF subunits where eIF2β contributes to the reduction of eIF2α phosphorylation levels. Furthermore, they demonstrated that overexpression of eIF2β suppresses autophagy and leads to diminished motor function. It was also shown that in a heterozygous mutant background of eIF2β, Milton knockdown could be rescued. This work represents a novel and significant contribution to the field, revealing for the first time that the loss of mitochondria from axons can lead to impaired autophagy function via eIF2β, potentially influencing the acceleration of aging. To further support the authors' claims, several improvements are necessary, particularly in the methods of quantification and the points that should be demonstrated quantitatively. It is crucial to investigate the correlation between aging and the proteins eIF2β and eIF2α.

      Thank you so much for your review and comments. We included analyses of protein levels of eIF2α, eIF2β, and eIF2γ at 7 days and 21 days (Figure 4D). The manuscript was revised as below;

      Lines 242-245 ‘As for the other subunits of eIF2 complex, proteome analysis did not detect a significant difference in the protein levels of eIF2α and eIF2γ between milton knockdown and control flies at 7 and 21 days (Figure 4D).’

      Reviewer #2 (Public Review):

      In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of Milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria.

      The manuscript has several weaknesses. The reader should take extra care while reading this manuscript and when acknowledging the findings and the model in this manuscript.

      The defect in autophagy by the depletion of axonal mitochondria is one of the main claims in the paper. The authors should work more on describing their results of LC3-II/LC3-I ratio, as there are multiple ways to interpret the LC3 blotting for the autophagy assessment. Lysosomal defects result in the accumulation of LC3-II thus the LC3-II/LC3-I ratio gets higher. On the other hand, the defect in the early steps of autophagosome formation could result in a lower LC3-II/LC3-I ratio. From the results of the actual blotting, the LC3-I abundance is the source of the major difference for all conditions (Milton RNAi and eIF2β overexpression and depletion). In the text, the authors simply state the observation of their LC3 blotting. The manuscript lacks an explanation of how to evaluate the LC3-II/LC3-I ratio. Also, the manuscript lacks an elaboration on what the results of the LC3 blotting indicate about the state of autophagy by the depletion of axonal mitochondria.

      Thank you for pointing it out, and we apologize for an insufficient description of the result. We included quantitation of the levels of LC3-I and LC3-II in Figure 2A, 2D, 3D, 6B and 7B. As the reviewer pointed out, changes in the LC3-II/LC3-I ratio do not necessarily indicate autophagy defects. However, since p62 accumulation (Figure 2B, 2E, 3E, 6C, 7C in the original manuscript), these results collectively suggest that autophagy is lowered. We revised the manuscript to include this discussion as below:

      Lines 174-186 ‘During autophagy progression, LC3 is conjugated with phosphatidylethanolamine to form LC3-II, which localizes to isolation membranes and autophagosomes. LC3-I accumulation occurs when autophagosome formation is impaired, and LC3-II accumulation is associated with lysosomal defects(31,32). p62 is an autophagy substrate, and its accumulation suggests autophagic defects(31,32). We found that milton knockdown increased LC3-I, and the LC3-II/LC3-I ratio was lower in milton knockdown flies than in control flies at 14-day-old (Figure 2A). We also analyzed p62 levels in head lysates sequentially extracted using detergents with different stringencies (1% Triton X-100 and 2% SDS). Western blotting revealed that p62 levels were increased in the brains of 14-day-old of milton knockdown flies (Figure 2B). The increase in the p62 level was significant in the Triton X-100-soluble fraction but not in the SDS-soluble fraction (Figure 2B), suggesting that depletion of axonal mitochondria impairs the degradation of less-aggregated proteins.’

      Line 189-190 : ‘At 30 day-old, LC3-I was still higher, and the LC3-II/LC3-I ratio was lower, in milton knockdown compared to the control (Figure 2D).’

      Line 199-201: ‘However, in contrast with milton knockdown, Pfk knockdown did not affect the levels of LC3-I, LC3-II or the LC3-II/LC3-I ratio (Figure 3D).’

      Line 275-281: ‘Neuronal overexpression of eIF2β increased LC3-II, while the LC3-II/LC3-I ratio was not significantly different (Figure 6A and B). Overexpression of eIF2β significantly increased the p62 level in the Triton X-100-soluble fraction (Figure 6C, 4-fold vs. control, p < 0.005 (1% Triton X-100)) but not in the SDS-soluble fraction (Figure 6C, 2-fold vs. control, p = 0.062 (2% SDS)), as observed in brains of milton knockdown flies (Figure 2B). These data suggest that neuronal overexpression of eIF2β accumulates autophagic substrates.’

      Line 307-315: ‘Neuronal knockdown of milton causes accumulation of autophagic substrate p62 in the Triton X-100-soluble fraction (Figure 2B), and we tested if lowering eIF2β ameliorates it. We found that eIF2β heterozygosity caused a mild increase in LC3-I levels and decreases in LC3-II levels, resulting in a significantly lower LC3-II/LC3-I ratio in milton knockdown flies (Figure 7B). eIF2β heterozygosity decreased the p62 level in the Triton X-100-soluble fraction in the brains of milton knockdown flies (Figure 7C). The p62 level in the SDS-soluble fraction, which is not sensitive to milton knockdown (Figure 2B), was not affected (Figure 7C). These results suggest that suppression of eIF2β ameliorates the impairment of autophagy caused by milton knockdown.’

      Another main point of the paper is the up-regulation of eIF2β by depleting the axonal mitochondria leads to the proteostasis crisis. This claim is formed by the findings from the proteome analyses. The authors should have presented their proteomic data with much thorough presentation and explanation. As in the experiment scheme shown in Figure 4A, the author did two proteome analyses: one from the 7-day-old sample and the other from the 21-day-old sample. The manuscript only shows a plot of the result from the 7-day-old sample, but that of the result from the 21-day-old sample. For the 21-day-old sample, the authors only provided data in the supplemental table, in which the abundance ratio of eIF2β from the 21-day-old sample is 0.753, meaning eIF2β is depleted in the 21-day-old sample. The authors should have explained the impact of the eIF2β depletion in the 21-day-old sample, so the reader could fully understand the authors' interpretation of the role of eIF2β on proteostasis.

      Thank you for pointing it out. We included plots of the results of 21-day-old proteome as a part of the main figure (Figure 4C). As the reviewer pointed out, eIF2β protein levels are reduced at the 21-day-old. Since a reduction in the eIF2_β_ ameliorated milton knockdown-induced locomotor defects in aged flies (Figure 7D), the reduction in eIF2β observed in the 21-day-old milton knockdown flies is not likely to negatively contribute to milton knockdown-induced defects. We included this discussion in the manuscript as below:

      Lines 337-341:‘eIF2β protein levels are reduced at the 21-day-old; however, since a reduction in the eIF2β ameliorated milton knockdown-induced locomotor defects in aged flies (Figure 7), the reduction in eIF2β observed in the 21-day-old is not likely to negatively contribute to milton knockdown-induced defects.’

      The manuscript consists of several weaknesses in its data and explanation regarding translation.

      (1) The authors are likely misunderstanding the effect of phosphorylation of eIF2α on translation. The P-eIF2α is inhibitory for translation initiation. However, the authors seem to be mistaken that the down-regulation of P-eIF2α inhibits translation.

      We are sorry for our insufficient explanation in the previous version. As the reviewer pointed out, it is well known that the phosphorylated form of eIF2α inhibits translation initiation. Neuronal knockdown of milton caused a reduction in p-eIF2α (Figure 4J and K), and it also lowered translation (Figure 5); the relationship between these two events is currently unclear. We do not think that a reduction in the p-eIF2α suppressed translation; rather, we propose that the unbalance of expression levels of the components of eIF2 complexes negatively affects translation. We revised discussion sections to describe our interpretation more in detail as below:

      Line 368-378: ‘eIF2β is a component of eIF2, which meditates translational regulation and ISR initiation. When ISR is activated, phosphorylated eIF2α suppresses global translation and induces translation of ATF4, which mediates transcription of autophagy-related genes(39,40). Since ISR can positively regulate autophagy, we suspected that suppression of ISR underlies a reduction in autophagic protein degradation. We found neuronal knockdown of milton reduced phosphorylated eIF2α, suggesting that ISR is reduced (Figure 4). However, we also found that global translation was reduced (Figure 5). It may be possible that increased levels of eIF2β disrupt the eIF2 complex or alter its functions. The stoichiometric mismatch caused by an imbalance of eIF2 components may inhibit ISR induction. Supporting this model, we found that eIF2β upregulation reduced the levels of p-eIF2α (Figure 6).’

      We have revised the graphical abstract and removed the eIF2 complex since its role in the loss of proteostasis caused by milton knockdown has not been elucidated yet.

      (2) The result of polysome profiling in Figure 4H is implausible. By 10%-25% sucrose density gradient, polysomes are not expected to be observed. The authors should have used a gradient with much denser sucrose, such as 10-50%.

      Thank you for pointing it out. It was a mistake of 10-50%, and we apologize for the oversight. It was corrected (Figure 5).

      (3) Also on the polysome profiling, as in the method section, the authors seemed to fractionate ultra-centrifuged samples from top to bottom and then measured A260 by a plate reader. In that case, the authors should have provided a line plot with individual data points, not the smoothly connected ones in the manuscript.

      Thank you for pointing it out. We revised the graph (Figure 5).

      (4) For both the results from polysome profiling and puromycin incorporation (Figure 4H and I), the difference between control siRNA and Milton siRNA are subtle, if not nonexistent. This might arise from the lack of spatial resolution in their experiment as the authors used head lysate for these data but the ratio of Phospho-eIF2α/eIF2α only changes in the axons, based on their results in Figure 4E-G. The authors could have attempted to capture the spatial resolution for the axonal translation to see the difference between control siRNA and Milton siRNA.

      Thank you for your comment. We agree that it would be an interesting experiment, but it will take a considerable amount of time to analyze axonal translation with spatial resolution. We will try to include such analyses in the future. For this manuscript, we revised the discussion section to include the reviewer's suggestion as below;

      Lines 351-353: ‘Further analyses to dissect the effects of milton knockdown on proteostasis and translation in the cell body and axon by experiments with spatial resolution would be needed.’

      Recommendations for the authors:

      From the Reviewing Editor:

      As the Reviewing Editor, I have read your manuscript and the associated peer reviews. I have concerns about publishing this work in its current form. I think that your manuscript cannot claim to have found a novel function of eIF2beta because of technical uncertainties and conceptual problems that should be addressed.

      Thank you so much for your review and comments. We addressed all the concerns raised by the reviewers. Point-by-point responses are listed below.

      First, your manuscript is based partly on what appears to be a mistaken understanding of the mechanistic basis of the ISR. Specifically, eIF2 is a heterotrimeric complex of alpha, beta, and gamma subunits. When eIF2a is phosphorylated, the heterotrimer adopts a new conformation. This conformation directly binds and inhibits eIF2B, the decameric GEF that exchanges the GDP bound to the gamma subunit of the eIF2 complex for GTP. Unless I misunderstood your paper, you seem to propose that decreasing levels of phospho-eIF2a will inhibit translation, but this is backward from what we know about the ISR.

      Thank you for your insightful comment, and we are sorry for the confusion. We did not mean to propose that decreasing levels of phospho-eIF2_a_ inhibits translation. We apologize for our insufficient explanation, which might have caused a misunderstanding (Lines 312-318 in the original version). We agree with the reviewer that ‘mismatch due to elevated eIF2-beta could change the behavior of the ISR’. We revised the text in the result section as follows:

      Lines 259-264 (in the Result section) ‘Phosphorylation of eIF2α induces conformational changes in the eIF2 complex and inhibits global translation(36). To analyze the effects of milton knockdown on translation, we performed polysome gradient centrifugation to examine the level of ribosome binding to mRNA. Since p-eIF2α was downregulated, we hypothesized that milton knockdown would enhance translation. However, unexpectedly, we found that milton knockdown significantly reduced the level of mRNAs associated with polysomes (Figure 5A and B).’

      Lines 368-378 (in the Discussion section): ‘eIF2β is a component of eIF2, which meditates translational regulation and ISR initiation. When ISR is activated, phosphorylated eIF2α suppresses global translation and induces translation of ATF4, which mediates transcription of autophagy-related genes(39,40). Since ISR can positively regulate autophagy, we suspected that suppression of ISR underlies a reduction in autophagic protein degradation. We found neuronal knockdown of milton reduced phosphorylated eIF2α, suggesting that ISR is reduced (Figure 4). However, we also found that global translation was reduced (Figure 5). It may be possible that increased levels of eIF2β disrupt the eIF2 complex or alter its functions. The stoichiometric mismatch caused by an imbalance of eIF2 components may inhibit ISR induction. Supporting this model, we found that eIF2β upregulation reduced the levels of p-eIF2α (Figure 6).’

      It may be possible that a stoichiometric mismatch due to elevated eIF2-beta could change the behavior of the ISR, but your paper doesn't adequately address the expression levels of all three eIF2 subunits: alpha, beta, and gamma. The proteomic data shown in Fig 4B is unconvincing on its own because the changes in the beta subunit are subtle. The Western blot in Figure 4C suggests that the KD changes the mass or mobility of the beta subunit, and most importantly, there are no Western blots measuring the levels of eIF2a, eIF2a-phospho, or eIF2-gamma.

      We appreciate the reviewer’s comment and agree that the stoichiometric mismatch due to elevated eIF2β may interfere with ISR. We found overexpression of eIF2β lowered p-eIF2 alpha (Figure S2 in V1), which supports this model. We included this data in the main figure in the revised manuscript (Figure 6D) and revised the text as below:

      Lines 279-281: ‘Since milton knockdown reduced the p-eIF2α level (Figure 4K), we asked whether an increase in eIF2β affects p-eIF2α. Neuronal overexpression of eIF2β did not affect the eIF2α level but significantly decreased the p-eIF2α level (Figure 6D, E).’

      Expression data of eIF2α and eIF2γ from proteomic analyses has been extracted from proteome analyses and included as a table (Figure 4D). Western blots of phospho-eIF2a (Figure S1 in V1) in the main figure (Figure 4G). The result section was revised as below;

      Lines 242-245: ‘As for the other subunits of eIF2 complex, proteome analysis did not detect a significant difference in the protein levels of eIF2α and eIF2γ between milton knockdown and control flies at 7 and 21 days (Figure 4D).’

      Reviewer #1 (Recommendations For The Authors):

      L125-128: In this section, while the efficiency of Milton knockdown is referenced from a previous publication, it is necessary to also mention that the Miro knockdown has been similarly reported in the literature. Additionally, the Methods section lacks details on the Miro RNAi line used, and Table 2 does not include the genotype for Miro RNAi. This information should be included for clarity and completeness.

      Thank you for pointing it out. Knockdown efficiency with this strain has been reported (Iijima-Ando et al., PLoS Genet, 2012). We revised the text to include citation and knockdown efficiency as follows:

      Lines 139-147: ‘There was no significant increase in ubiquitinated proteins in milton knockdown flies at 1-day old, suggesting that the accumulation of ubiquitinated proteins caused by milton knockdown is age-dependent (Figure S1). We also analyzed the effect of the neuronal knockdown of Miro, a partner of milton, on the accumulation of ubiquitin-positive proteins. Since severe knockdown of Miro in neurons causes lethality, we used UAS-Miro RNAi strain with low knockdown efficiency, whose expression driven by elav-GAL4 caused 30% reduction of Miro mRNA in head extract(24). Although there was a tendency for increased ubiquitin-positive puncta in Miro knockdown brains, the difference was not significant (Figure 1B, p>0.05 between control RNAi and Miro RNAi). These data suggest that the depletion of axonal mitochondria induced by milton knockdown leads to the accumulation of ubiquitinated proteins before neurodegeneration occurs.’

      L132-L136: The current phrasing in this section suggests an increase in ubiquitinated proteins for both Milton and Miro knockdowns. However, since there is no significant difference noted for Miro, it is incorrect to state an increase in ubiquitin-positive puncta. Furthermore, combining the results of Milton knockdown to claim an increase in ubiquitinated proteins prior to neurodegeneration is misleading. At the very least, the expression here needs to be moderated to accurately reflect the findings.

      Thank you for pointing it out. We revised the text as above.

      L137-L141: Results in Figure 1 indicate that Milton knockdown leads to an increase in ubiquitinated proteins at 14 days, while Miro knockdown shows no difference from the control at either 14 or 30 days. Conversely, both the control and Miro exhibit an increase in ubiquitinated proteins with aging, but this trend does not seem to apply to Milton knockdown. This observation suggests that Milton KD may not affect the changes in protein quality control associated with aging. It implies that Milton's function might be more related to protein homeostasis in younger cells, or that changes due to aging might overshadow the effects of Milton knockdown. These interpretations should be included in the Results or Discussion sections for a more comprehensive analysis.

      Thank you for your insightful comment. We revised the text to include those points as follows:

      Lines 152-153: ‘These results suggest that depletion of axonal mitochondria may have more impact on proteostasis in young neurons than in old neurons.’

      Lines 355-362: ‘The depletion of axonal mitochondria and accumulation of abnormal proteins are both characteristics of aged brains(37,38). Our results suggest that the loss of axonal mitochondria is an event upstream of proteostasis collapse during aging. Neuronal knockdown of milton had more impact on proteostasis in young neurons than the old neurons (Figure 1). Proteome analyses also showed that age-related pathways, such as immune responses, are enhanced in young flies with milton knockdown (Table 2). The reduction in axonal transport of mitochondria may be one of the triggering events of age-related changes and accelerates the onset of aging in the brain.’

      L143 : Please remove the erroneously included quotation mark.

      Thank you for pointing it out. We corrected it.

      L145-L147:

      - While it is understood that Milton knockdown results in a reduction of mitochondria in axons, as reported previously and seemingly indicated in Figure 1E, this paper repeatedly refers to axonal depletion of mitochondria. Therefore, it would be beneficial to quantitatively assess the number of mitochondria in the axonal terminals located in the lamina via electron microscopy. Such quantification would robustly reinforce the argument that mitochondrial absence in axons is a consequence of Milton knockdown.

      Thank you for pointing it out. We included quantitation of the number of mitochondria in the synaptic terminals (Figure 1E).

      The text and figure legend was revised accordingly:

      Lines 156-157: ‘As previously reported(24), the number of mitochondria in presynaptic terminals decreased in milton knockdown (Figure 1E).’

      - The knockdown of Milton is known to reduce mitochondrial transport from an early stage, but what about swelling? By observing swelling at 1 day and 14 days, it may be possible to confirm the onset of swelling and discuss its correlation with the accumulation of ubiquitinated proteins.

      Quantitation of axonal swelling has also been included (Figure 1F).

      We appreciate reviewer’s comments on the correlation between the accumulation of ubiquitinated proteins and axonal swelling. Axonal swelling was not observed at 3-days-old (Iijima-Ando et al., PLoS Genetics, 2012), indicating that axonal swelling is an age-dependent event. Dense materials are found in swollen axons more often than in normal axons, suggesting a positive correlation between disruption of proteostasis and axonal damage. It would be interesting to analyze the time course of events further; however, we feel it is beyond the scope of this manuscript. We revised the text as below to include this discussion:

      Lines 157-159: ‘The swelling of presynaptic terminals, characterized by the enlargement and roundness, was not reported at 3-day-old(24) but observed at this age with about 4% of total presynaptic terminals (Figure 1F, asterisks).’

      Lines 162-167: ‘Dense materials are rarely found in age-matched control neurons, indicating that milton knockdown induces abnormal protein accumulation in the presynaptic terminals (Figure 1G and H). In milton knockdown neurons, dense materials are found in swollen presynaptic terminals more often than in presynaptic terminals without swelling, suggesting a positive correlation between the disruption of proteostasis and axonal damage (Figure 1G).’

      Lines 362-365: ‘Disruption of proteostasis is expected to contribute neurodegeneration(38), and it would be interesting to analyze the sequence of protein accumulation and axonal degeneration in milton knockdown ((24,29) and Figure 1) in detail with higher time resolution.’

      L147-L151: Though Figures 1F and 1G provide qualitative representations, it is advisable to quantitatively assess whether dense materials significantly accumulate. Such quantitative analysis would be required to verify the accumulation of dense materials in the context of the study.

      Thank you for pointing it out. We included quantitation of the number of neurons with dense material (Figure 1G). We revised the manuscript as follows:

      Line 161-163: ‘Dense materials are rarely found in age-matched control neurons, indicating that milton knockdown induces abnormal protein accumulation in the presynaptic terminals (Figure 1G and H).’

      Regarding Figure 1B, C:

      - Even though the count of puncta in the whole brain appears to be fewer than 400, the magnification of the optic lobe suggests a substantial presence of puncta. Please clarify in the Methods section what constitutes a puncta and whether the quantification in the whole brain is based on a 2D or 3D analysis. Detail the methodology used for quantification.

      Thank you for your comment. We revised the method section to include more details as below:

      Lines 434-437: ‘Quantitative analysis was performed using ImageJ (National Institutes of Health) with maximum projection images derived from Z-stack images acquired with same settings. Puncta was identified with mean intensity and area using ImageJ.’

      - What about 1-day-old specimens? Does Milton knockdown already show an increase in ubiquitinated protein accumulation at this early stage? Investigating whether ubiquitin-protein accumulation is involved in aging promotion or is already prevalent during developmental stages is a necessary experiment.

      Thank you for your comment. We carried out immunostaining with an anti-ubiquitin antibody in the brains at 1-day-old. No significant difference was detected between the control and milton knockdown. This result has been included as Figure S1 in the revised manuscript. The result section was revised as below:

      Line 136-139 ‘There was no significant increase in ubiquitinated proteins in milton knockdown flies at 1-day old, suggesting that the accumulation of ubiquitinated proteins caused by milton knockdown is age-dependent (Figure S1).’

      For Figure 1E: In the Electron Microscopy section of the Methods, define how swollen axons were identified and describe the quantification methodology used.

      Thank you for your comment. Swollen axons are, unlike normal axons, round in shape and enlarged. We revised the text as below;

      Lines 157-160: ‘The swelling of presynaptic terminals, characterized by the enlargement and roundness, was not reported at 3-day-old(24) but observed at this age with about 4% of total presynaptic terminals (Figure 1F, asterisks).’

      Lines 683-684, Figure 1 legend: ‘Swollen presynaptic terminals (asterisks in (F)), characterized by the enlargement and higher circularity, were found more frequently in milton knockdown neurons.’

      L218-L219: Throughout the text, the expression 'eIF2β is "upregulated" in response to Milton knockdown' is frequently used. However, considering the presented results, it might be more accurate to interpret that under the condition of Milton knockdown, eIF2β is not undergoing degradation but rather remains stable.

      Thank you for pointing it out. We replaced ‘upregulated’ with ‘increased’ throughout the text.

      L234-L235: On what basis is the conclusion drawn that there is a reduction? Given that three experiments have been conducted, it would be possible and more convincing to quantify the results to determine if there is a significant decrease.

      Thank you for pointing it out. We quantified the AUC of polysome fraction and carried out statistical analysis. There is a significant decrease in polysome in milton knockdown, and this result has been included in Figure 5B. We revised the figure and the legend accordingly.

      L236: 5H-> 4H

      Thank you for pointing it out, and we are sorry for the confusion. We corrected it.

      L238-L239: Since there is no significant difference observed, it may not be accurate to interpret a reduction in puromycin incorporation.

      Thank you for pointing it out. As described above, quantification of polysome fractions showed that milton knockdown significantly reduce polysome (Figure 5B). We revised the manuscript as below;

      Lines 263-264: ‘However, unexpectedly, we found that milton knockdown significantly reduced the level of mRNAs associated with polysomes (Figure 5A and B).’

      Figure 5D and Figure 6D: Climbing assays have been conducted, but I believe experiments should also be performed to examine whether overexpression or heterozygous mutants of eIF2β induce or suppress degeneration.

      Thank you for pointing it out. We analyzed the eyes with eIF2_β_ overexpression for neurodegeneration. Although there was a tendency of elevated neurodegeneration in the retina with eIF2_β_ overexpression, the difference between control and eIF2_β_ overexpression did not reach statistical significance (Figure S2). This result has been included as Figure S2 in the revised manuscript, and the following sentences have been included in the text:

      Lines 288-293: ‘We asked if eIF2β overexpression causes neurodegeneration, as depletion of axonal mitochondria in the photoreceptor neurons causes axon degeneration in an age-dependent manner(24). eIF2β overexpression in photoreceptor neurons tends to increase neurodegeneration in aged flies, while it was not statistically significant (p>0.05, Figure S2).’

      L271-L272: The results in Figure 6B are surprising. I anticipated a greater increase compared to the Milton knockdown alone. While p62 appears to be reduced, it is not clear why these results lead to the conclusion that lowering eIF2β rescues autophagic impairment. Please add a discussion section to address this point.

      Thank you for pointing it out. We apologize for the unclear description of the result. Milton knockdown flies show p62 accumulation (Figure 2), and deleting one copy of eIF2beta in milton knockdown background reduced p62 accumulation (Figure 7C). We revised the text as below:

      Lines 307-315: ‘Neuronal knockdown of milton causes accumulation of autophagic substrate p62 in the Triton X-100-soluble fraction (Figure 2B), and we tested if lowering eIF2β ameliorates it. We found that eIF2β heterozygosity caused a mild increase in LC3-I levels and decreases in LC3-II levels, resulting in a significantly lower LC3-II/LC3-I ratio in milton knockdown flies (Figure 7B). eIF2β heterozygosity decreased the p62 level in the Triton X-100-soluble fraction in the brains of milton knockdown flies (Figure 7C). The p62 level in the SDS-soluble fraction, which is not sensitive to milton knockdown (Figure 2B), was not affected (Figure 7C). These results suggest that suppression of eIF2β ameliorates the impairment of autophagy caused by milton knockdown.’

      L369: Please specify the source of the anti-ubiquitin antibody used.

      Thank you for pointing it out. We included the antibody information in the method section.

      Figure 7: While the relationship between Milton knockdown and the eIF2β and eIF2α proteins has been elucidated through the authors' efforts, I would like to see an investigation into whether eIF2β is upregulated and eIF2α phosphorylation is reduced in simply aged Drosophila. This would help us understand the correlation between aging and eIF2 protein dynamics.

      Thank you for your comment. We agree that it is an important question, and we are working on it. However, we feel that it is beyond the scope of the current manuscript.

      L645-L646: If the mushroom body is identified using mito-GFP, then include mito-GFP in the genotype listed in Supplementary Table 2.

      We are sorry for the oversight. We corrected it in Supplementary Table 2.

      Additionally, while it is presumed that the mito-GFP signal decreases in axons with Milton RNAi, how was the lobe tips area accurately selected for analysis? Please include these details along with a comprehensive description of the quantification methodology in the Methods section.

      Thank you for your comment. Although the mito-GFP signal in the axon is weak in the milton knockdown neurons, it is sufficient to distinguish the mushroom body structure from the background. We revised the method section to include this information in the method section:

      Line 437-438: ‘For eIF2α and p-eIF2α immunostaining, the mushroom body was detected by mitoGFP expression.’

    1. Reviewer #2 (Public review):

      Summary:

      In this manuscript, Mackie et al. investigate gustatory behavior and the neural basis of gustation in the predatory nematode Pristionchus pacificus. First, they show that the behavioral preferences of P. pacificus for gustatory cues differ from those reported for C. elegans. Next, they investigate the molecular mechanisms of salt sensing in P. pacificus. They show that although the C. elegans transcription factor gene che-1 is expressed specifically in the ASE neurons, the P. pacificus che-1 gene is expressed in the Ppa-ASE and Ppa-AFD neurons. Moreover, che-1 plays a less critical role in salt chemotaxis in P. pacificus than C. elegans. Chemogenetic silencing of Ppa-ASE and Ppa-AFD neurons results in more severe chemotaxis defects. The authors then use calcium imaging to show that both Ppa-ASE and Ppa-AFD neurons respond to salt stimuli. Calcium imaging experiments also reveal that the left and right Ppa-ASE neurons respond differently to salts, despite the fact that P. pacificus lacks lsy-6, a microRNA that is important for ASE left/right asymmetry in C. elegans. Finally, the authors show that the receptor guanylate cyclase gene Ppa-gcy-23.3 is expressed in the right Ppa-ASE neuron (Ppa-ASER) but not the left Ppa-ASE neuron (Ppa-ASEL) and is required for some of the gustatory responses of Ppa-ASER, further confirming that the Ppa-ASE neurons are asymmetric and suggesting that Ppa-GCY-23.3 is a gustatory receptor. Overall, this work provides insight into the evolution of gustation across nematode species. It illustrates how sensory neuron response properties and molecular mechanisms of cell fate determination can evolve to mediate species-specific behaviors. However, the paper would be greatly strengthened by a direct comparison of calcium responses to gustatory cues in C. elegans and P. pacificus, since the comparison currently relies entirely on published data for C. elegans, where the imaging parameters likely differ. In addition, the conclusions regarding Ppa-AFD neuron function would benefit from additional confirmation of AFD neuron identity. Finally, how prior salt exposure influences gustatory behavior and neural activity in P. pacificus is not discussed.

      Strengths:

      (1) This study provides exciting new insights into how gustatory behaviors and mechanisms differ in nematode species with different lifestyles and ecological niches. The results from salt chemotaxis experiments suggest that P. pacificus shows distinct gustatory preferences from C. elegans. Calcium imaging from Ppa-ASE neurons suggests that the response properties of the ASE neurons differ between the two species. In addition, an analysis of the expression and function of the transcription factor Ppa-che-1 reveals that mechanisms of ASE cell fate determination differ in C. elegans and P. pacificus, although the ASE neurons play a critical role in salt sensing in both species. Thus, the authors identify several differences in gustatory system development and function across nematode species.

      (2) This is the first calcium imaging study of P. pacificus, and it offers some of the first insights into the evolution of gustatory neuron function across nematode species.

      (3) This study addresses the mechanisms that lead to left/right asymmetry in nematodes. It reveals that the ASER and ASEL neurons differ in their response properties, but this asymmetry is achieved by molecular mechanisms that are at least partly distinct from those that operate in C. elegans. Notably, ASEL/R asymmetry in P. pacificus is achieved despite the lack of a P. pacificus lsy-6 homolog.

      Weaknesses:

      (1) The authors observe only weak attraction of C. elegans to NaCl. These results raise the question of whether the weak attraction observed is the result of the prior salt environment experienced by the worms. More generally, this study does not address how prior exposure to gustatory cues shapes gustatory responses in P. pacificus. Is salt sensing in P. pacificus subject to the same type of experience-dependent modulation as salt sensing in C. elegans?

      (2) A key finding of this paper is that the Ppa-CHE-1 transcription factor is expressed in the Ppa-AFD neurons as well as the Ppa-ASE neurons, despite the fact that Ce-CHE-1 is expressed specifically in Ce-ASE. However, additional verification of Ppa-AFD neuron identity is required. Based on the image shown in the manuscript, it is difficult to unequivocally identify the second pair of CHE-1-positive head neurons as the Ppa-AFD neurons. Ppa-AFD neuron identity could be verified by confocal imaging of the CHE-1-positive neurons, co-expression of Ppa-che-1p::GFP with a likely AFD reporter, thermotaxis assays with Ppa-che-1 mutants, and/or calcium imaging from the putative Ppa-AFD neurons.

      (3) Loss of Ppa-che-1 causes a less severe phenotype than loss of Ce-che-1. However, the loss of Ppa-che-1::RFP expression in ASE but not AFD raises the question of whether there might be additional start sites in the Ppa-che-1 gene downstream of the mutation sites. It would be helpful to know whether there are multiple isoforms of Ppa-che-1, and if so, whether the exon with the introduced frameshift is present in all isoforms and results in complete loss of Ppa-CHE-1 protein.

      (4) The authors show that silencing Ppa-ASE has a dramatic effect on salt chemotaxis behavior. However, these data lack control with histamine-treated wild-type animals, with the result that the phenotype of Ppa-ASE-silenced animals could result from exposure to histamine dihydrochloride. This is an especially important control in the context of salt sensing, where histamine dihydrochloride could alter behavioral responses to other salts.

      (5) The calcium imaging data in the paper suggest that the Ppa-ASE and Ce-ASE neurons respond differently to salt solutions. However, to make this point, a direct comparison of calcium responses in C. elegans and P. pacificus using the same calcium indicator is required. By relying on previously published C. elegans data, it is difficult to know how differences in growth conditions or imaging conditions affect ASE responses. In addition, the paper would be strengthened by additional quantitative analysis of the calcium imaging data. For example, the paper states that 25 mM NH4Cl evokes a greater response in ASEL than 250 mM NH4Cl, but a quantitative comparison of the maximum responses to the two stimuli is not shown.

      (6) It would be helpful to examine, or at least discuss, the other P. pacificus paralogs of Ce-gcy-22. Are they expressed in Ppa-ASER? How similar are the different paralogs? Additional discussion of the Ppa-gcy-22 gene expansion in P. pacificus would be especially helpful with respect to understanding the relatively minor phenotype of the Ppa-gcy-22.3 mutants.

      (7) The calcium imaging data from Ppa-ASE is quite variable. It would be helpful to discuss this variability. It would also be helpful to clarify how the ASEL and ASER neurons are being conclusively identified during calcium imaging.

      (8) More information about how the animals were treated prior to calcium imaging would be helpful. In particular, were they exposed to salt solutions prior to imaging? In addition, the animals are in an M9 buffer during imaging - does this affect calcium responses in Ppa-ASE and Ppa-AFD? More information about salt exposure, and how this affects neuron responses, would be very helpful.

      (9) In Figure 6, the authors say that Ppa-gcy-22.3::GFP expression is absent in the Ppa-che-1(ot5012) mutant. However, based on the figure, it looks like there is some expression remaining. Is there a residual expression of Ppa-gcy-22.3::GFP in ASE or possibly ectopic expression in AFD? Does Ppa-che-1 regulate rGC expression in AFD? It would be helpful to address the role of Ppa-che-1 in AFD neuron differentiation.

    2. Author response:

      We very much appreciate the reviewers’ and editor’s overall positive responses to our manuscript "Evolution of lateralized gustation in nematodes".

      Reviewer #1:

      The mechanism of lsy-6-independent establishment of ASEL/R asymmetry in P. pacificus remains uncharacterized. 

      We thank the reviewer for recognizing the novel contributions of our work in revealing the existence of alternative pathways for establishing neuronal lateral asymmetry despite the absence of the lsy-6 miRNA in a divergent nematode species. We are certainly encouraged now to search for genetic factors that abolish asymmetric expression of gcy-22.3.

      Reviewer #2:

      (1) The authors observe only weak attraction of C. elegans to NaCl. These results raise the question of whether the weak attraction observed is the result of the prior salt environment experienced by the worms. More generally, this study does not address how prior exposure to gustatory cues shapes gustatory responses in P. pacificus. Is salt sensing in P. pacificus subject to the same type of experience-dependent modulation as salt sensing in C. elegans? 

      Proposed revision: For our live imaging experiments, we had not considered if starved P. pacificus animals in the presence of salt may exhibit responses different from a well-fed state. However, we will venture to address the effect of experience-dependent modulation in P. pacificus chemotaxis behavior using NH4Cl.

      (2) A key finding of this paper is that the Ppa-CHE-1 transcription factor is expressed in the Ppa-AFD neurons as well as the Ppa-ASE neurons, despite the fact that Ce-CHE-1 is expressed specifically in Ce-ASE. However, additional verification of Ppa-AFD neuron identity is required. Based on the image shown in the manuscript, it is difficult to unequivocally identify the second pair of CHE-1-positive head neurons as the Ppa-AFD neurons. Ppa-AFD neuron identity could be verified by confocal imaging of the CHE-1-positive neurons, co-expression of Ppa-che-1p::GFP with a likely AFD reporter, thermotaxis assays with Ppa-che-1 mutants, and/or calcium imaging from the putative Ppa-AFD neurons. 

      We are happy to provide additional evidence to confirm Ppa-AFD neuron identity since the expression of Ppa-CHE-1 in non-ASE amphid neurons is one of the major differences between the two nematode specie

      Proposed revision: We will provide results showing the Ppa-ttx-1::gfp reporter expression in finger-like neuronal endings and Ppa-_TTX-1::ALFA co-localization with _Ppa-che-1::gfp in the putative AFD neurons and discuss the possible role of Ppa-CHE-1 in AFD differentiation. We attempted to obtain AFD markers using several reporter strains. However, Ppa-gcy-8.1p::gfp(csuEx101) (PPA24212) showed no expression while Ppa-gcy-8.2p::gfp(csuEx100) (PPA41407) showed only expression in pharyngeal cells.

      (4) The authors show that silencing Ppa-ASE has a dramatic effect on salt chemotaxis behavior. However, these data lack control with histamine-treated wild-type animals, with the result that the phenotype of Ppa-ASE-silenced animals could result from exposure to histamine dihydrochloride. This is an especially important control in the context of salt sensing, where histamine dihydrochloride could alter behavioral responses to other salts. 

      Proposed revision: Thank you for noticing this oversight. The control for histamine-treated wild-type worms in the Ppa-ASE silencing experiments was inadvertently left out in the original submission. Because the HisCl transgene is on a randomly segregating transgene array, we have scored worms with and without the transgene expressing the co-injection marker (Ppa-egl-20p::rfp expressed in the tail) to show that the presence of the transgene is necessary for the knockdown of NH4Br attraction.

      We will also address most of the other more minor suggestions and clarifications sought by the reviewers.

    1. Scenarios are closely related to the idea of use cases, but differ in when they’re created. You create a scenario before you have a design, to capture the problem context you want to address.

      I believe this point to be very valid when brainstorming. Scenarios are very essential to design processes because they can simulate what perspectives could be. It can also help you with the bumps in the road before you start anything. I relate to this statement a lot because I often create scenarios in my head because I want to include as many as I can when taking any action.

    1. With the young lawyers intheii cockpits, the office is quiet, a quiet that does not ask to be broken

      We are accepting what impact technology is having on us. Is this the future we want to have? Do we want to go into work and find no one to talk to or listen to except for the voice in our head? This is not the future I want for myself or others...

    1. Technical communication is the delivery of technical information to readers (or listeners or viewers) in a manner that is adapted to their needs, their level of understanding, and their background

      The writer tells the reader understanding who the audience is, & the reader's needs and level of understanding will help the writer to specifically communicate what needs to be said. *not going over the reader's head.

    2. The technical writer and reader have a vis-à-vis

      First thing that popped in my head was an instruction manual because those are usually clear and as if someone if helping you complete a task.

    1. oxygen tank was propelled missile-like into the machine where it struck Michael’s head as he lay sedated being tested for a brain tumor.
      1. What parts impacted you the most. Reading this article I found that the most crazy part was that it was caused by medical staff and not a patient that was not correctly discussed and screened on what not to do. The patient was injured unknowingly by the oxygen tank by medical staff that should be trained in what to do and what not to do around MRI operations.

      2. Review of what safety measures were overlooked and or ignored. The nurse did not take proper safety measures and/ or was not trained in the knowledge needed to know what they can and can not do around an MRI. With this in motion the nurse brought in a oxygen tank that was not suitable for the magnet and resulted in the injury and death of a patient.

      3. An assessment of your current safety practices and a pledge of how you intend to view safety in the future.

      Within reading this article and many others. I will take every step and measure to enforce every safety measure given. I do not want the possible injury or death of a person be at my fault due to lack of safety. I will make sure to keep my coworkers and my patients safe by keeping these articles in mind and remembering how being forgetful can harm someone.

    1. The focus should not beto try and design GenAI out of the learning experience, ornecessarily to design it into the learning experience, but sim-ply to design instruction so that students actually learn. Thestrategies suggested above, and others, may be productivepaths to consider in this regard.

      I found this to be an interesting idea. During my time in this master's degree, I have begun seeing the positive side of AI in learning. Before it all, I was one of those people that said "AI is bad and students are never going to learn because they will probably just cheat with AI." Yet learning to use it to help inside of lessons has shown me how it can be a positive source for students to use. Yet this section telling us not to necessarily include it or exclude it in teaching, but to simply to design instruction for our students to learn makes me reset my head a bit. I am reading this as "add AI if it helps to enhance the learning experience. Do not add it just because you can. Do not force it." It makes me think more when developing lessons that I will only want to use it if it comes to my mind when figuring out what to do.

    2. One may be tempted to assume that GenAI tools, likeChatGPT, have negated the need for many types of knowl-edge. Asking for facts, procedures, or an analysis of facts iseasily within the range of many GenAI tools now. However,Neelen and Kirschner (2020) respond to this type of think-ing in detail in the context of learners and the Google searchengine. They address the learning myth, “Google can replacehuman knowledge” by examining types of knowledge (e.g.,propositional, tacit, etc.) and present well-documented argu-ments for such statements as:“Let’s assume for a second that Google can replaceour own knowledge. We’d still have to interpret theinformation that Google gives us to make it meaning-ful” (p. 122) and;“If we’re trying to solve very complex problems, werun into several issues when relying on Google. Themain problems are that we need to know what we’relooking for and that we need to be able to judge theinformation we find based on the knowledge that’s inour head” (p. 130)

      This section makes me reflect on the common misconception that tools like GenAI or even Google can replace human knowledge. As an educator, I see how tempting it might be for students (and even teachers) to rely heavily on these tools, but this dependency can create significant gaps in critical thinking and problem-solving. The quote about interpreting information resonates with me because technology can provide data, but understanding and applying it require skills and context that only humans bring.

      Personally, I agree with the statement that solving complex problems requires more than just finding information online. It reminds me of situations in my professional role where I’ve had to assess the validity of data or consider the nuances of a problem—something no search engine or AI can do without my input and expertise. GenAI can be a powerful assistant, but the “knowledge in our head” is what allows us to navigate ambiguity and discern quality.

      I wonder if relying too much on tools like ChatGPT might weaken students’ ability to critically evaluate information or even know where to start when they don’t have a foundation of knowledge. While GenAI can support learning, I see a real danger if we let it replace the essential process of building and applying our understanding. What do you think—is there a way to balance using these tools without diminishing the development of core skills?

    1. How do Biblical narratives, particularly the story of Eden, influence your depiction of cycles of growth, decay, and the tension between order and chaos in your work?

      I want to show people a clear narative yet sometimes my abstrations in my head makes my art sometimes hard to read to otgers. There is also the idea about chosin the forbidden fruit and not going back to where you once livied. I will not go back to how i did think, this art is my forbidden fruit

    1. Lord Hamlet, with his doublet all unbraced, 1019  No hat upon his head, his stockings fouled, 1020 90 Ungartered, and down-gyvèd to his ankle, 1021  Pale as his shirt, his knees knocking each other,

      Ophelia bursts in to talk to her father and describes what has just happened. Hamlet came to her and in a frantic state and started acting mad right before her eyes. The strange description of Hamlet seems to be in consequent to his experiences with the ghost of Old Hamlets soul.

    1. Depending on where you think learning happens, homework assignments might be formative or summative. If you believe the primary learning should happen from readings and classroom time, homework assignments could be summative. If you believe the primary learning should happen in doing the homework, homework should be formative, and you need to find another kind of summative assignment (say, an exam).

      I don't tend to spend a lot of time trying to label assessments as "formative" vs "summative", but I also have the luxury of not having to spend much time thinking about giving letter grades. From a teaching and learning standpoint, I always just have the words of my mentor going through my head is that for learning to happen there generally needs to be "practice with feedback" (presumably followed by more practice and feedback until the task is mastered). This presumes that a teacher is providing a task that is aligned with the desired learning objective and then is giving the student an opportunity to demonstrate their performance and receives useful feedback about that performance so that the student's next attempt at the performance can be better. This is where I think mastery grading excels.

    1. Community members (non-specialists) are just as likely to read the report as those on the city council (executives), the head of the Parks Department (experts), and those who maintain the parks (technicians)

      Example showing that the audience will be many different people from many different backgrounds, therefore meaning that the technical writting should be readable and understandable by everyone.

    1. eLife Assessment

      Bowler et al. present a software/hardware system for behavioral control of navigation-based virtual reality experiments, particularly suited for pairing with 2-photon imaging but applicable to a variety of techniques. This system represents a valuable contribution to the field of behavioral and systems neuroscience, as it provides a standardized, easy to implement, and flexible system that could be adopted across multiple laboratories. The authors provide compelling evidence of the functionality of their system by reporting benchmark tests and demonstrating hippocampal activity patterns consistent with standards in the field. This work will be of interest to systems neuroscientists looking to integrate flexible head-fixed behavioral control with neural data acquisition.

    2. Reviewer #1 (Public review):

      Summary:

      Bowler et al. present a thoroughly tested system for modularized behavioral control of navigation-based experiments, particularly suited for pairing with 2-photon imaging but applicable to a variety of techniques. This system, which they name behaviorMate, represents an important methodological contribution to the field of behavioral and systems neuroscience. As the authors note, behavioral control paradigms vary widely across laboratories in terms of hardware and software utilized and often require specialized technical knowledge to make changes to these systems. Having a standardized, easy to implement, and flexible system that can be used by many groups is therefore highly desirable.

      Strengths:

      The present manuscript provides compelling evidence of the functionality and applicability of behaviorMate. The authors report benchmark tests for high-fidelity, real-time update speed between the animal's movement and the behavioral control, on both the treadmill-based and virtual reality (VR) setups. The VR system relies on Unity, a common game development engine, but implements all scene generation and customizability in the authors' behaviorMate and VRMate software, which circumvents the need for users to program task logic in C# in Unity. Further, the authors nicely demonstrate and quantify reliable hippocampal place cell coding in both setups, using synchronized 2-photon imaging. This place cell characterization also provides a concrete comparison between the place cell properties observed in treadmill-based navigation vs. visual VR in a single study, which itself is a valuable contribution to the field.

      Weaknesses: None noted.

      Documentation for installing and operating behaviorMate is available via the authors' lab website and Github, linked in the manuscript.

      The authors have addressed all of my requests for clarification from the previous round of review. This work will be of great interest to systems neuroscientists looking to integrate flexible head-fixed behavioral control with neural data acquisition.

    3. Reviewer #2 (Public review):

      The authors present behaviorMate, an open-source behavior control system including a central GUI and compatible treadmill and display components. Notably, the system utilize the "Intranet of things" scheme and the components communicate through local network, making the system modular, which in turn allows user to configure the setup to suit their experimental needs. Overall, behaviorMate is a useful resource for researchers performing head-fixed VR imaging studies involving 1D navigation tasks, as the commercial alternatives are often expensive and inflexible to modify.

      One major utility of behaviorMate is an open-source alternative to commercial behavior apparatus for head-fixed imaging studies involving 1D navigation tasks. The documentation, BOM, CAD files, circuit design, source and compiled software, along with the manuscript, create an invaluable resource for neuroscience researcher looking to set up a budget-friendly VR and head-fixed imaging rig. Some features of behaviorMate, including the computer vision-based calibration of treadmill, and the decentralized, Android-based display devices, are very innovative approaches and can be quite useful in practical settings.

      behaviorMate can also be used as a set of generic schema and communication protocols that allows the users to incorporate recording and stimulation devices during a head-fixed imaging experiment. Due to the "Intranet of things" approach taken in the design, any hardware that supports UDP communication can in theory be incorporated into the system. In terms of current capability, behaviorMate supports experimental contingencies based on animal position and time and synchronization with external recording devices using a TTL start signal. Further customization involving more complicated experimental contingencies, more accurate recording synchronization (for example with ephys recording devices), incorporation of novel behavior and high-speed neural recording hardware beyond GPIO signaling would require modification of the Java source and custom hardware implementation. Modification to the Java source of behaviorMate can be performed with basic familiarity with object-oriented programming using the Java programming language, and a JavaFX-based plugin system is under development to make such customizations more approachable for users.

      In summary, the manuscript presents a well-developed and useful open-source behavior control system for head-fixed VR imaging experiments with innovative features.

    4. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      (1) As VRMate (a component of behaviorMate) is written using Unity, what is the main advantage of using behaviorMate/VRMate compared to using Unity alone paired with Arduinos (e.g. Campbell et al. 2018), or compared to using an existing toolbox to interface with Unity (e.g. Alsbury-Nealy et al. 2022, DOI: 10.3758/s13428-021-01664-9)? For instance, one disadvantage of using Unity alone is that it requires programming in C# to code the task logic. It was not entirely clear whether VRMate circumvents this disadvantage somehow -- does it allow customization of task logic and scenery in the GUI? Does VRMate add other features and/or usability compared to Unity alone? It would be helpful if the authors could expand on this topic briefly.

      We have updated the manuscript (lines 412-422) to clarify the benefits of separating the VR system as an isolated program and a UI that can be run independently. We argue that “…the recommended behaviorMate architecture has several important advantages. Firstly, by rendering each viewing angle of a scene on a dedicated device, performance is improved by splitting the computational costs across several inexpensive devices rather than requiring specialized or expensive graphics cards in order to run…, the overall system becomes more modular and easier to debug [and] implementing task logic in Unity would require understanding Object-Oriented Programming and C# … which is not always accessible to researchers that are typically more familiar with scripting in Python and Matlab.”

      VRMate receives detailed configuration info from behaviorMate at runtime as to which VR objects to display and receives position updates during experiments. Any other necessary information about triggering rewards or presenting non-VR cues is still handled by the UI so no editing of Unity is necessary. Scene configuration information is in the same JSON format as the settings files for behaviorMate, additionally there are Unity Editor scripts which are provided in the VRmate repository which permit customizing scenes through a “drag and drop” interface and then writing the scene configuration files programmatically. Users interested in these features should see our github page to find example scene.vr files and download the VRMate repository (including the editor scripts).  We provided 4 vr contexts, as well as a settings file that uses one of them which can be found on the behaviorMate github page (https://github.com/losonczylab/behaviorMate) in the “vr_contexts” and “example_settigs_files” directories. These examples are provided to assist VRMate users in getting set up and could provide a more detailed example of how VRMate and behaviorMate interact.

      (2) The section on "context lists", lines 163-186, seemed to describe an important component of the system, but this section was challenging to follow and readers may find the terminology confusing. Perhaps this section could benefit from an accompanying figure or flow chart, if these terms are important to understand.

      We maintain the use of the term context and context list in order to maintain a degree of parity with the java code. However, we have updated lines 173-175 to define the term context for the behaviorMate system: “... a context is grouping of one or more stimuli that get activated concurrently. For many experiments it is desirable to have multiple contexts that are triggered at various locations and times in order to construct distinct or novel environments.”

      a. Relatedly, "context" is used to refer to both when the animal enters a particular state in the task like a reward zone ("reward context", line 447) and also to describe a set of characteristics of an environment (Figure 3G), akin to how "context" is often used in the navigation literature. To avoid confusion, one possibility would be to use "environment" instead of "context" in Figure 3G, and/or consider using a word like "state" instead of "context" when referring to the activation of different stimuli.

      Thank you for the suggestion. We have updated Figure 3G to say “Environment” in order to avoid confusion.

      (3) Given the authors' goal of providing a system that is easily synchronizable with neural data acquisition, especially with 2-photon imaging, I wonder if they could expand on the following features:

      a. The authors mention that behaviorMate can send a TTL to trigger scanning on the 2P scope (line 202), which is a very useful feature. Can it also easily generate a TTL for each frame of the VR display and/or each sample of the animal's movement? Such TTLs can be critical for synchronizing the imaging with behavior and accounting for variability in the VR frame rate or sampling rate.

      Different experimental demands require varying levels of precision in this kind of synchronization signals. For this reason, we have opted against a “one-size fits all” for synchronization with physiology data in behaviorMate. Importantly this keeps the individual rig costs low which can be useful when constructing setups specifically for use when training animals. behaviorMate will log TTL pulses sent to GPIO pins setup as sensors, and can be configured to generate TTL pulses at regular intervals. Additionally all UDP packets received by the UI are time stamped and logged. We also include the output of the arduino millis() function in all UDP packets which can be used for further investigation of clock drift between system components. Importantly, since the system is event driven there cannot be accumulating drift across running experiments between the behaviorMate UI and networked components such as the VR system.

      For these reasons, we have not needed to implement a VR frame synchronization TTL for any of our experiments, however, one could extend VRMate to send "sync" packets back to behaviorMate to log when each frame was displayed precisely or TTL pulses (if using the same ODROID hardware we recommend in the standard setup for rendering scenes). This would be useful if it is important to account for slight changes in the frame rate at which the scenes are displayed. However, splitting rendering of large scenes between several devices results in fast update times and our testing and benchmarks indicate that display updates are smooth and continuous enough to appear coupled to movement updates from the behavioral apparatus and sufficient for engaging navigational circuits in the brain.

      b. Is there a limit to the number of I/O ports on the system? This might be worth explicitly mentioning.

      We have updated lines 219-220 in the manuscript to provide this information: Sensors and actuators can be connected to the controller using one of the 13 digital or 5 analog input/output connectors.

      c. In the VR version, if each display is run by a separate Android computer, is there any risk of clock drift between displays? Or is this circumvented by centralized control of the rendering onset via the "real-time computer"?

      This risk is mitigated by the real-time computer/UI sending position updates to the VR displays. The maximum amount scenes can be out of sync is limited because they will all recalibrate on every position update – which occurs multiple times per second as the animal is moving. Moreover, because position updates are constantly being sent by behaviorMate to VRMate and VRMate is immediately updating the scene according to this position, the most the scene can become out of sync with the mouse's position is proportional to the maximum latency multiplied by the running speed of the mouse. For experiments focusing on eliciting an experience of navigation, such a degree of asynchrony is almost always negligible. For other experimental demands it could be possible to incorporate more precise frame timing information but this was not necessary for our use case and likely for most other use cases. Additionally, refer to the response to comment 3a.

      Reviewer #2 (Public review):

      (1) The central controlling logic is coupled with GUI and an event loop, without a documented plugin system. It's not clear whether arbitrary code can be executed together with the GUI, hence it's not clear how much the functionality of the GUI can be easily extended without substantial change to the source code of the GUI. For example, if the user wants to perform custom real-time analysis on the behavior data (potentially for closed-loop stimulation), it's not clear how to easily incorporate the analysis into the main GUI/control program.

      Without any edits to the existing source code behaviorMate is highly customizable through the settings files, which allow users to combine the existing contexts and decorators in arbitrary combinations. Therefore, users have been able to perform a wide variety of 1D navigation tasks, well beyond our anticipated use cases by generating novel settings files. The typical method for providing closed-loop stimulation would be to set up a context which is triggered by animal behavior using decorators (e.g. based on position, lap number and time) and then trigger the stimulation with a TTL pulse. Rarely, if users require a behavioral condition not currently implemented or composable out of existing decorators, it would require generating custom code in Java to extend the UI. Performing such edits requires only knowledge of basic object-oriented programming in Java and generating a single subclass of either the BasicContextList or ContextListDecorator classes. In addition, the JavaFX (under development) version of behaviorMate incorporates a plugin which doesn't require recompiling the code in order to make these changes. However, since the JavaFX software is currently under development, documentation does not yet exist. All software is open-sourced and available on github.com for users interested in generating plugins or altering the source code.

      We have added the additional caveat to the manuscript in order to clarify this point (Line 197-202): “However, if the available set of decorators is not enough to implement the required task logic, some modifications to the source code may be necessary. These modifications, in most cases, would be very simple and only a basic understanding of object-oriented programming is required. A case where this might be needed would be performing novel customized real-time analysis on behavior data and activating a stimulus based on the result”

      (2) The JSON messaging protocol lacks API documentation. It's not clear what the exact syntax is, supported key/value pairs, and expected response/behavior of the JSON messages. Hence, it's not clear how to develop new hardware that can communicate with the behaviorMate system.

      The most common approach for adding novel hardware is to use TTL pulses (or accept an emitted TTL pulse to read sensor states). This type of hardware addition  is possible through the existing GPIO without the need to interact with the software or JSON API. Users looking to take advantage of the ability to set up and configure novel behavioral paradigms without the need to write any software would be limited to adding hardware which could be triggered with and report to the UI with a TTL pulse (however fairly complex actions could be triggered this way).

      For users looking to develop more customized hardware solutions that interact closely with the UI or GPIO board, additional documentation on the JSON messaging protocol has been added to the behaviormate-utils repository (https://github.com/losonczylab/behaviormate_utils). Additionally, we have added a link to this repository in the Supplemental Materials section (line 971) and referenced this in the manuscript (line 217) to make it easier for readers to find this information.

      Furthermore, developers looking to add completely novel components to the UI  can implement the interface described by Context.java in order to exchange custom messages with hardware. (described  in the JavaDoc: https://www.losonczylab.org/behaviorMate-1.0.0/)  These messages would be defined within the custom context and interact with the custom hardware (meaning the interested developer would make a novel addition to the messaging API). Additionally, it should be noted that without editing any software, any UDP packets sent to behaviorMate from an IP address specified in the settings will get time stamped and logged in the stored behavioral data file meaning that are a large variety of hardware implementation solutions using both standard UDP messaging and through TTL pulses that can work with behaviorMate with minimal effort. Finally, see response to R2.1 for a discussion of the JavaFX version of the behaviorMatee UI including plugin support.

      (3) It seems the existing control hardware and the JSON messaging only support GPIO/TTL types of input/output, which limits the applicability of the system to more complicated sensor/controller hardware. The authors mentioned that hardware like Arduino natively supports serial protocols like I2C or SPI, but it's not clear how they are handled and translated to JSON messages.

      We provide an implementation for an I2C-based capacitance lick detector which interested developers may wish to copy if support for novel I2C or SPI. Users with less development experience wishing to expand the hardware capabilities of  behaviorMatecould also develop adapters which can be triggered  on a TTL input/output. Additionally, more information about the JSON API and how messages are transmitted to the PC by the arduino is described in point (2) and the expanded online documentation.

      a. Additionally, because it's unclear how easy to incorporate arbitrary hardware with behaviorMate, the "Intranet of things" approach seems to lose attraction. Since currently, the manuscript focuses mainly on a specific set of hardware designed for a specific type of experiment, it's not clear what are the advantages of implementing communication over a local network as opposed to the typical connections using USB.

      As opposed to serial communication protocols as typical with USB, networking protocols seamlessly function based on asynchronous message passing. Messages may be routed internally (e.g. to a PCs localhost address, i.e. 0.0.0..0) or to a variety of external hardware (e.g. using IP addresses such as those in the range 192.168.1.2 - 192.168.1.254). Furthermore, network-based communication allows modules, such as VR, to be added easily. behavoirMate systems can be easily expanded using low-cost Ethernet switches and consume only a single network adapter on the PC (e.g. not limited by the number of physical USB ports). Furthermore, UDP message passing is implemented in almost all modern programming languages in a platform independent manner (meaning that the same software can run on OSX, Windows, and Linux). Lastly, as we have pointed out (Line 117) a variety of tools exist for inspecting network packets and debugging; meaning that it is possible to run behaviorMate with simulated hardware for testing and debugging.

      The IOT nature of behaviorMate means there is no requirement for novel hardware to be implemented  using an arduino,  since any system capable of  UDP communication can  be configured. For example, VRMate is usually run on Odroid C4s, however one could easily create a system using Raspberry Pis or even additional PCs. behaviorMate is agnostic to the format of the UDP messages, but packaging any data in the JSON format for consistency would be encouraged. If a new hardware is a sensor that has input requiring it to be time stamped and logged then all that is needed is to add the IP address and port information to the ‘controllers’ list in a behaviorMate settings file. If more complex interactions are needed with novel hardware than a custom implementation of ContextList.java may be required (see response to R2.2). However, the provided UdpComms.java class could be used to easily send/receive messages from custom Context.java subclasses.

      Solutions for highly customized hardware do require basic familiarity with object-oriented programming using the Java programming language. However, in our experience most behavioral experiments do not require these kinds of modifications. The majority of 1D navigation tasks, which behaviorMate is currently best suited to control, require touch/motion sensors, LEDs, speakers, or solenoid valves,  which are easily controlled by the existing GPIO implementation. It is unlikely that custom subclasses would even be needed.

      Reviewer #3 (Public review):

      (1) While using UDP for data transmission can enhance speed, it is thought that it lacks reliability. Are there error-checking mechanisms in place to ensure reliable communication, given its criticality alongside speed?

      The provided GPIO/behavior controller implementation sends acknowledgement packets in response to all incoming messages as well as start and stop messages for contexts and “valves”. In this way the UI can update to reflect both requested state changes as well as when they actually happen (although there is rarely a perceptible gap between these two states unless something is unplugged or not functioning). See Line 85 in the revised manuscript “acknowledgement packets are used to ensure reliable message delivery to and from connected hardware”.

      (2) Considering this year's price policy changes in Unity, could this impact the system's operations?

      VRMate is not affected by the recent changes in pricing structure of the Unity project.

      The existing compiled VRMate software does not need to be regenerated to update VR scenes, or implement new task logic (since this is handled by the behaviorMate GUI). Therefore, the VRMate program is robust to any future pricing changes or other restructuring of the Unity program and does not rely on continued support of Unity. Additionally, while the solution presented in VRMate has many benefits, a developer could easily adapt any open-source VR Maze project to receive the UDP-based position updates from behaviorMate or develop their own novel VR solutions.

      (3) Also, does the Arduino offer sufficient precision for ephys recording, particularly with a 10ms check?

      Electrophysiology recording hardware typically has additional I/O channels which can provide assistance with tracking behavior/synchronization at a high resolution. While behaviorMate could still be used to trigger reward valves, either the ephys hardware or some additional high-speed DAQ would be recommended to maintain accurately with high-speed physiology data. behaviorMate could still be set up as normal to provide closed and open-loop task control at behaviorally relevant timescales alongside a DAQ circuit recording events at a consistent temporal resolution. While this would increase the relative cost of the individual recording setup, identical rigs for training animals could still be configured without the DAQ circuit avoiding unnecessary cost and complexity.

      (4) Could you clarify the purpose of the Sync Pulse? In line 291, it suggests additional cues (potentially represented by the Sync Pulse) are needed to align the treadmill screens, which appear to be directed towards the Real-Time computer. Given that event alignment occurs in the GPIO, the connection of the Sync Pulse to the Real-Time Controller in Figure 1 seems confusing.

      A number of methods exist for synchronizing recording devices like microscopes or electrophysiology recordings with behaviorMate’s time-stamped logs of actuators and sensors. For example, the GPIO circuit can be configured to send sync triggers, or receive timing signals as input. Alternatively a dedicated circuit could record frame start signals and relay them to the PC to be logged independently of the GPIO (enabling a high-resolution post-hoc alignment of the time stamps). The optimal method to use varies based on the needs of the experiment. Our setups have a dedicated BNC output and specification in the settings file that sends a TTL pulse at the start of an experiment in order to trigger 2p imaging setups (see line 224, specifically that this is a detail of “our” 2p imaging setup). We provide this information as it might be useful suggesting how to have both behavior and physiology data start recording at the same time. We do not intend this to be the only solution for alignment. Figure 1 indicates an “optional” circuit for capturing a high speed sync pulse and providing time stamps back to the real time PC. This is another option that might be useful for certain setups (or especially for establishing benchmarks between behavior and physiology recordings). In our setup event alignment does not exclusively occur on the GPIO.

      a. Additionally, why is there a separate circuit for the treadmill that connects to the UI computer instead of the GPIO? It might be beneficial to elaborate on the rationale behind this decision in line 260.

      Event alignment does not occur on the GPIO, separating concerns between position tracking and more general input/output features which improves performance and simplifies debugging.  In this sense we maintain a single event loop on the Arduino, avoiding the need to either run multithreaded operations or rely extensively on interrupts which can cause unpredictable code execution (e.g. when multiple interrupts occur at the same time). Our position tracking circuit is therefore coupled to a separate,low-cost arduino mini which has the singular responsibility of position-tracking.

      b. Moreover, should scenarios involving pupil and body camera recordings connect to the Analog input in the PCB or the real-time computer for optimal data handling and processing?

      Pupil and body camera recordings would be independent data streams which can be recorded separately from behaviorMate. Aligning these forms of full motion video could require frame triggers which could be configured on the GPIO board using single TTL like outputs or by configuring a valve to be “pulsed” which is a provided type customization.

      We also note that a more advanced developer could easily leverage camera signals to provide closed loop control by writing an independent module that sends UDP packets to behavoirMate. For example a separate computer vision based position tracking module could be written in any preferred language and use UDP messaging to send body tracking updates to the UI without editing any of the behaviorMate source code (and even used for updating 1D location).

      (5) Given that all references, as far as I can see, come from the same lab, are there other labs capable of implementing this system at a similar optimal level?

      To date two additional labs have published using behaviorMate, the Soltez and Henn labs (see revised lines 341-342). Since behaviorMate has only recently been published and made available open source, only external collaborators of the Losonczy lab have had access to the software and design files needed to do this. These collaborators did, however, set up their own behavioral setups in separate locations with minimal direct support from the authors–similar to what would be available to anyone seeking to set a behaviorMate system would find online on our github page or by posting to the message board.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (4) To provide additional context for the significance of this work, additional citations would be helpful to demonstrate a ubiquitous need for a system like behaviorMate. This was most needed in the paragraph from lines 46-65, specifically for each sentence after line 55, where the authors discuss existing variants on head-fixed behavioral paradigms. For instance, for the clause "but olfactory and auditory stimuli have also been utilized at regular virtual distance intervals to enrich the experience with more salient cues", suggested citations include Radvansky & Dombeck 2018 (DOI: 10.1038/s41467-018-03262-4), Fischler-Ruiz et al. 2021 (DOI: 10.1016/j.neuron.2021.09.055).

      We thank the reviewer for the suggested missing citations and have updated the manuscript accordingly (see line 58).

      (5) In addition, it would also be helpful to clarify behaviorMate's implementation in other laboratories. On line 304 the authors mention "other labs" but the following list of citations is almost exclusively from the Losonczy lab. Perhaps the citations just need to be split across the sentence for clarity? E.g. "has been validated by our experimental paradigms" (citation set 1) "and successfully implemented in other labs as well" (citation set 2).

      We have split the citation set as suggested (see lines 338-342).

      Minor Comments:

      (6) In the paragraph starting line 153 and in Fig. 2, please clarify what is meant by "trial" vs. "experiment". In many navigational tasks, "trial" refers to an individual lap in the environment, but here "trial" seems to refer to the whole behavioral session (i.e. synonymous with "experiment"?).

      In our software implementation we had originally used “trial” to refer to an imaging session rather than experiment (and have made updates to start moving to the more conventional lexicon). To avoid confusion we have remove this use of “trial” throughout the manuscript and replaced with “experiment” whenever possible

      (7) This is very minor, but in Figure 3 and 4, I don't believe the gavage needle is actually shown in the image. This is likely to avoid clutter but might be confusing to some readers, so it may be helpful to have a small inset diagram showing how the needle would be mounted.

      We assessed the image both with and without the gavage needle and found the version in the original (without) to be easier to read and less cluttered and therefore maintained that version in the manuscript.

      (8) In Figure 5 legend, please list n for mice and cells.

      We have updated the Figure 5 legend to indicate that for panels C-G, n=6 mice (all mice were recorded in both VR and TM systems), 3253 cells in VR classified as significantly tuned place cells VR, and 6101 tuned cells in TM,

      (9) Line 414: It is not necessary to tilt the entire animal and running wheel as long as the head-bar clamp and objective can rotate to align the imaging window with the objective's plane of focus. Perhaps the authors can just clarify the availability of this option if users have a microscope with a rotatable objective/scan head.

      We have added the suggested caveat to the manuscript in order to clarify when the goniometers might be useful (see lines 281-288).

      (10) Figure S1 and S2 could be referenced explicitly in the main text with their related main figures.

      We have added explicit references to figures S1 and S2 in the relevant sections (see lines 443, 460  and 570)

      (11) On line 532-533, is there a citation for "proximal visual cues and tactile cues (which are speculated to be more salient than visual cues)"?

      We have added citations to both Knierim & Rao 2003 and Renaudineau et al. 2007 which discuss the differential impact of proximal vs distal cues during navigation as well as Sofroniew et al. 2014 which describe how mice navigate more naturally in a tactile VR setup as opposed to purely visual ones.

      (12) There is a typo at the end of the Figure 2 legend, where it should say "Arduino Mini."

      This typo has been fixed.

      Reviewer #2 (Recommendations For The Authors):

      (4) As mentioned in the public review: what is the major advantage of taking the IoT approaches as opposed to USB connections to the host computer, especially when behaviorMate relies on a central master computer regardless? The authors mentioned the readability of the JSON messages, making the system easier to debug. However, the flip side of that is the efficiency of data transmission. Although the bandwidth/latency is usually more than enough for transmitting data and commands for behavior devices, the efficiency may become a problem when neural recording devices (imaging or electrophysiology) need to be included in the system.

      behaviorMate is not intended to do everything, and is limited to mainly controlling behavior and providing some synchronizing TTL style triggers. In this way the system can easily and inexpensively be replicated across multiple recording setups; particularly this is useful for constructing additional animal training setups. The system is very much sufficient for capturing behavioral inputs at relevant timescales (see the benchmarks in Figures 3 and 4 as well as the position correlated neural activity in Figures 5 and 6 for demonstration of this). Additional hardware might be needed to align the behaviorMate output with neural data for example a high-speed DAQ or input channels on electrophysiology recording setups could be utilized (if provided). As all recording setups are different the ideal solution would depend on details which are hard to anticipate. We do not mean to convey that the full neural data would be transmitted to the behaviorMate system (especially using the JSON/UDP communications that behaviorMate relies on).

      (5) The author mentioned labView. A popular open-source alternative is bonsai (https://github.com/bonsai-rx/bonsai). Both include a graphical-based programming interface that allows the users to easily reconfigure the hardware system, which behaviorMate seems to lack. Additionally, autopilot (https://github.com/auto-pi-lot/autopilot) is a very relevant project that utilizes a local network for multiple behavior devices but focuses more on P2P communication and rigorously defines the API/schema/communication protocols for devices to be compatible. I think it's important to include a discussion on how behaviorMate compares to previous works like these, especially what new features behaviorMate introduces.

      We believe that behaviorMate provides a more opinionated and complete solution than the projects mentioned. A wide variety of 1D navigational paradigms can be constructed in behaviorMate without the need to write any novel software. For example, bonsai is a “visual programming language” and would require experimenters to construct a custom implementation of each of their experiments. We have opted to use Java for the UI with distributed computations across modules in various languages. Given the IOT methodology it would be possible to use any number of programming languages or APIs; a large number of design decisions were made  when building the project and we have opted to not include this level of detail in the manuscript in order to maintain readability. We strongly believe in using non-proprietary and open source projects, when possible, which is why the comparison with LabView based solutions was included in the introduction. Also, we have added a reference to the autopilot reference to the section of the introduction where this is discussed.

      (6) One of the reasons labView/bonsai are popular is they are inherently parallel and can simultaneously respond to events from different hardware sources. While the JSON events in behaviorMate are asynchronous in nature, the handling of those events seems to happen only in a main event loop coupled with GUI, which is sequential by nature. Is there any multi-threading/multi-processing capability of behaviorMate? If so it's an important feature to highlight. If not I think it's important to discuss the potential limitation of the current implementation.

      IOT solutions are inherently concurrent since the computation is distributed. Additional parallelism could be added by further distributing concerns between additional independent modules running on independent hardware. The UI has an eventloop which aggregates inputs and then updates contexts based on the current state of those inputs sequentially. This sort of a “snapshot” of the current state is necessary to reason about when the start certain contexts based on their settings and applied decorators. While the behaviorMate UI uses multithreading libraries in Java to be more performant in certain cases, the degree to which this represents true vs “virtual” concurrency would depend on the individual PC architecture it is run on and how the operating system allocates resources. For this reason, we have argued in the manuscript that behaviorMate is sufficient for controlling experiments at behaviorally relevant timescales, and have presented both benchmarks and discussed different synchronization approaches and permit users to determine if this is sufficient for their needs.

      (7) The context list is an interesting and innovative approach to abstract behavior contingencies into a data structure, but it's not currently discussed in depth. I think it's worth highlighting how the context list can be used to cover a wide range of common behavior experimental contingencies with detailed examples (line 185 might be a good example to give). It's also important to discuss the limitation, as currently the context lists seem to only support contingencies based purely on space and time, without support for more complicated behavior metrics (e.g. deliver reward only after X% correct).

      To access more complex behavior metrics during runtime, custom context list decorators would need to be implemented. While this is less common in the sort of 1D navigational behaviors the project was originally designed to control, adding novel decorators is a simple process that only requires basic object oriented programming knowledge. As discussed we are also implementing a plugin-architecture in the JavaFX update to streamline these types of additions.

      Minor Comments:

      (8) In line 202, the author suggests that a single TTL pulse is sent to mark the start of a recording session, and this is used to synchronize behavior data with imaging data later. In other words, there are no synchronization signals for every single sample/frame. This approach either assumes the behavior recording and imaging are running on the same clock or assumes evenly distributed recording samples over the whole recording period. Is this the case? If so, please include a discussion on limitations and alternative approaches supported by behaviorMate. If not, please clarify how exactly synchronization is done with one TTL pulse.

      While the TTL pulse triggers the start of neural data in our setups, various options exist for controlling for the described clock drift across experiments and the appropriate one depends on the type of recordings made, frame rate duration of recording etc. Therefore behaviorMate leaves open many options for synchronization at different time scales (e.g. the adding a frame-sync circuit as shown in Figure 1 or sending TTL pulses to the same DAQ recording electrophysiology data).  Expanded consideration of different synchronization methods has been included in the manuscript (see lines 224-238).

      (9) Is the computer vision-based calibration included as part of the GUI functionality? Please clarify. If it is part of the GUI, it's worth highlighting as a very useful feature.

      The computer vision-based benchmarking is not included in the GUI. It is in the form of a script made specifically for this paper. However for treadmill-based experiments behaviorMate has other calibration tools built into it (see line 301-303).

      (10) I went through the source code of the Arduino firmware, and it seems most "open X for Y duration" functions are implemented using the delay function. If this is indeed the case, it's generally a bad idea since delay completely pauses the execution and any events happening during the delay period may be missed. As an alternative, please consider approaches comparing timestamps or using interrupts.

      We have avoided the use of interrupts on the GPIO due to the potential for unpredictable code execution. There is a delay which is only just executed if the duration is 10 ms or less as we cannot guarantee precision of the arduino eventloop cycling faster than this. Durations longer than 10 ms would be time stamped and non-blocking. We have adjusted this MAX_WAIT to be specified as a macro so it can be more easily adjusted (or set to 0).

      (11) Figure 3 B, C, D, and Figure 4 D, E suffer from noticeable low resolution.

      We have converted Figure 3B, C, D and 4C, D, E to vector graphics in order to improve the resolution.

      (12) Figure 4C is missing, which is an important figure.

      This figure appeared when we rendered and submitted the manuscript. We apologize if the figure was generated such that it did not load properly in all pdf viewers. The panel appears correctly in the online eLife version of the manuscript. Additionally, we have checked the revision in Preview on Mac OS as well as Adobe Acrobat and the built-in viewer in Chrome and all figure panels appear in each so we hope this issue has been resolved.

      (13) There are thin white grid lines on all heatmaps. I don't think they are necessary.

      The grid lines have been removed from the heatmaps  as suggested.

      (14) Line 562 "sometimes devices directly communicate with each other for performance reasons", I didn't find any elaboration on the P2P communication in the main text. This is potentially worth highlighting as it's one of the advantages of taking the IoT approaches.

      In our implementation it was not necessary to rely on P2P communication beyond what is indicated in Figure 1. The direct communication referred to in line 562 is meant only to refer to the examples expanded on in the rest of the paragraph i.e. the behavior controller may signal the microscope directly using a TTL signal without looping back to the UI. As necessary users could implement UDP message passing between devices, but this is outside the scope of what we present in the manuscript.

      (15) Line 147 "Notably, due to the systems modular architecture, different UIs could be implemented in any programming language and swapped in without impacting the rest of the system.", this claim feels unsupported without a detailed discussion of how new code can be incorporated in the GUI (plugin system).

      This comment refers to the idea of implementing “different UIs”. This would entail users desiring to take advantage of the JSON messaging API and the proposed electronics while fully implementing their own interface. In order to facilitate this option we have improved documentation of the messaging API posted in the README file accompanying the arduino source code. We have added reference to the supplemental materials where readers can find a link to the JSON API implementation to clarify this point.

      Additionally, while a plugin system is available in the JavaFX version of behaviorMate, this project is currently under development and will update the online documentation as this project matures, but is unrelated to the intended claim about completely swapping out the UI.

      Reviewer #3 (Recommendations For The Authors):

      (6) Figure 1 - the terminology for each item is slightly different in the text and the figure. I think making the exact match can make it easier for the reader.

      - Real-time computer (figure) vs real-time controller (ln88).

      The manuscript was adjusted to match figure terminology.

      - The position controller (ln565) - position tracking (Figure).

      We have updated Figure 1 to highlight that the position controller does the position tracking.

      - Maybe add a Behavior Controller next to the GPIO box in Figure 1.

      We updated Figure 1 to highlight that the Behavior Controller performs the GPIO responsibility such that "Behavior Controller" and "GPIO circuit" may be used interchangeably.

      - Position tracking (fig) and position controller (subtitle - ln209).

      We updated Figure 1 to highlight that the position controller does the position tracking.

      - Sync Pulse is not explained in the text.

      The caption for Figure 1 has been updated to better explain the Sync pulse and additional systems boxes

      (7) For Figure 3B/C: What is the number of data points? It would be nice to see the real population, possibly using a swarm plot instead of box plots. How likely are these outliers to occur?

      In order to better characterize the distributions presented in our benchmarking data we have added mean and standard deviation information the plots 3 and 4. For Figure 3B: 0.0025 +/- 0.1128, Figure 3C: 12.9749 +/- 7.6581, Figure 4C: 66.0500 +/- 15.6994, Figure 4E: 4.1258 +/- 3.2558.

    1. LLM2Attn (Figure 1 (c)). We replace the language model with a single randomly-initializedmulti-head attention layer.• LLM2Trsf (Figure 1 (d)). We replace the language model with a single randomly-initializedtransformer block

      what is the parameters of the attention layer and the transformer? Seems like no mentioning

    1. Living parents also occupy the role of head of the family. ey make it hard for grownchildren to become the patriarch or matriarch. When parents routinely live to 95,children must caretake into their own retirement. at doesn’t leave them much timeon their own—and it is all old age. When parents live to 75, children have had thejoys of a rich relationship with their parents, but also have enough time for their ownlives, out of their parents’ shadows.

      Parents after a certain age become a burden to their children which gives added pressure to already struggling young couples and families.

    Annotators

    1. 2

      Believe that everything is false, nothing you have known is like what it is. You have no senses, body, shape, movement, and location. Those are just simple things in your head. What is certain for sure? Perhaps nothing, that may be the certain truth.

    Annotators

    1. Cut off, even in the blossoms of my sin, 0802  Unhouseled, disappointed, unaneled, 0803 85 No reck’ning made, but sent to my account 0804  With all my imperfections on my head.

      Unhouseled means not having Eucharist (a Christian service) administered (OED). He also said he died when his sins were the greatest, meaning that Claudius' assassination is partly to blame for Hamlet being in hell.

    2. To tell the secrets of my prison house, 0738 20 I could a tale unfold whose lightest word 0739  Would harrow up thy soul, freeze thy young blood, 0740  Make thy two eyes, like stars, start from their 0741  spheres,

      The ghost informs Hamlet that he cannot share what the afterlife is like because of a curse that will make Hamlet's head basically explode. He also says that in the daylight he is constantly burning in flames until the sins that he committed when he was alive have burned out of him and during the night time he is able to roam freely

    1. Reviewer #1 (Public Review):

      Summary:

      This work uses transgenic reporter lines to isolate entpd5a+ cells representing classical osteoblasts in the head and non-classical (osterix-) notochordal sheath cells. The authors also include entpd5a- cells, col2a1a+ cells to represent the closely associated cartilage cells. In a combination of ATAC and RNA-Seq analysis, the genome-wide transcriptomic and chromatin status of each cell population is characterized, validating their methodology and providing fundamental insights into the nature of each cell type, especially the less well-studied notochordal sheath cells. Using these data, the authors then turn to a thorough and convincing analysis of the regulatory regions that control the expression of the entpd5a gene in each cell population. Determination of transcriptional activities in developing zebrafish, again combined with ATAC data and expression data of putative regulators, results in a compelling and detailed picture of the regulatory mechanisms governing the expression of this crucial gene.

      Strengths:

      The major strength of this paper is the clever combination of RNA-Seq and ATAC analysis, further combined with functional transcriptional analysis of the regulatory elements of one crucial gene. This results in a very compelling story.

      Weaknesses:

      No major weaknesses were identified, except for all the follow-up experiments that one can think of, but that would be outside of the scope of this paper.

    2. Reviewer #2 (Public Review):

      Summary:

      Complementary to mammalian models, zebrafish has emerged as a powerful system to study vertebrate development and to serve as a go-to model for many human disorders. All vertebrates share the ancestral capacity to form a skeleton. Teleost fish models have been a key model to understand the foundations of skeletal development and plasticity, pairing with more classical work in amniotes such as the chicken and mouse. However, the genetic foundation of the diversity of skeletal programs in teleosts has been hampered by mapping similarities from amniotes back and not objectively establishing more ancestral states. This is most obvious in systematic, objective analysis of transcriptional regulation and tissue specification in differentiated skeletal tissues. Thus, the molecular events regulating bone-producing cells in teleosts have remained largely elusive. In this study, Petratou et al. leverage spatial experimental delineation of specific skeletal tissues -- that they term 'classical' vs 'non-classical' osteoblasts -- with associated cartilage of the endo/peri-chondrial skeleton and inter-segmental regions of the forming spine during development of the zebrafish, to delineate molecular specification of these cells by current chromatin and transcriptome analysis. The authors further show functional evidence of the utility of these datasets to identify functional enhancer regions delineating entp5 expression in 'classical' or 'non-classical' osteoblast populations. By integration with paired RNA-seq, they delineate broad patterns of transcriptional regulation of these populations as well as specific details of regional regulation via predictive binding sites within ATACseq profiles. Overall the paper was very well written and provides an essential contribution to the field that will provide a foundation to promote modeling of skeletal development and disease in an evolutionary and developmentally informed manner.

      Strengths:

      Taken together, this study provides a comprehensive resource of ATAC-seq and RNA-seq data that will be very useful for a wide variety of researchers studying skeletal development and bone pathologies. The authors show specificity in the different skeletal lineages and show the utility of the broad datasets for defining regulatory control of gene regulation in these different lineages, providing a foundation for hypothesis testing of not only agents of skeletal change in evolution but also function of genes and variations of unknown significance as it pertains to disease modeling in zebrafish. The paper is excellently written, integrating a complex history and experimental analysis into a useful and coherent whole. The terminology of 'classical' and 'non-classical' will be useful for the community in discussing the biology of skeletal lineages and their regulation.

      Weaknesses:

      Two items arose that were not critical weaknesses but areas for extending the description of methods and integration into the existing data on the role of non-classical osteoblasts and establishment/canalization of this lineage of skeletal cells.

      (1) In reading the text it was unclear how specific the authors' experimental dissection of the head/trunk was in isolating different entp5a osteoblast populations. Obviously, this was successful given the specificity in DEG of results, however, analysis of contaminating cells/lineages in each population would be useful - e.g. using specific marker genes to assess. The text uses terms such as 'specific to' and 'enriched in' without seemingly grounded meaning of the accuracy of these comments. Is it really specific - e.g. not seen in one or other dataset - or is there some experimental variation in this?

      (2) Further, it would be valuable to discuss NSC-specific genes such as calymmin (Peskin 2020) which has species and lineage-specific regulation of non-classical osteoblasts likely being a key mechanistic node for ratcheting centra-specific patterning of the spine in teleost fishes. What are dynamics observed in this gene in datasets between the different populations, especially when compared with paralogues - are there obvious cis-regulatory changes that correlate with the co-option of this gene in the early regulation of non-classical osteoblasts? The addition of this analysis/discussion would anchor discussions of the differential between different osteoblasts lineages in the paper.

    1. Reviewer #1 (Public review):

      Summary:

      This work uses transgenic reporter lines to isolate entpd5a+ cells representing classical osteoblasts in the head and non-classical (osterix-) notochordal sheath cells. The authors also include entpd5a- cells, col2a1a+ cells to represent the closely associated cartilage cells. In a combination of ATAC and RNA-Seq analysis, the genome-wide transcriptomic and chromatin status of each cell population is characterized, validating their methodology and providing fundamental insights into the nature of each cell type, especially the less well-studied notochordal sheath cells. Using these data, the authors then turn to a thorough, and convincing analysis of the regulatory regions that control the expression of the entpd5a gene in each cell population. Determination of transcriptional activities in developing zebrafish, again combined with ATAC data and expression data of putative regulators results in a compelling, and detailed picture of the regulatory mechanisms governing expression of this crucial gene.

      Strengths:

      The major strength of this paper is the clever combination of RNA-Seq and ATAC analysis, further combined with functional transcriptional analysis of the regulatory elements of one crucial gene. This results in a very compelling story.

      Weaknesses:

      No major weakness, except for all the follow-up experiments that one can think of, but that would be outside of the scope of this paper.

      Comments on revisions:

      The description of Supplementary Figure 1 is still confusing: in the results section, it says "We photo converted and directly imaged entpd5a:Kaede positive embryos starting from the 15 somite- stage (s), when we could first detect the fluorophore along the newly-formed notochord progenitor cells (Suppl. Fig. 1E). We repeated photoconversion and imaging at 18, 21 and 24s (Suppl. Fig. 1F-H). ...(Suppl. Fig 1E)"<br /> In the response, the authors say "we could see new Kaede expression under the control of the entpd5a promoter region within 1.5 hours of photoconversion, as shown in Suppl. Figure 1E-H."<br /> In the legend to Suppl. Fig. 1, it says "Using the entpd5a:Kaede photoconversion line we first detect entpd5a expression at the 15 somite-stage (E). Following the same embryo, active expression of the gene continues until prior to 24 hpf (F-H)."<br /> So my questions are: -was there a delay between photoconversion and imaging - was the same delay used for all pictures - was there indeed additional photoconversion for Fig.1 F-H before imaging?<br /> This could be stated in Materials and Methods, and maybe in the legend to Suppl. Fig. 1

      All other issues have been addressed.

    2. Reviewer #2 (Public review):

      Summary:

      Complementary to mammalian models, zebrafish has emerged as a powerful system to study vertebrate development and serve as a go-to model for many human disorders. All vertebrates share the ancestral capacity to form a skeleton. Teleost fish models have been a key model to understand the foundations of skeletal development and plasticity, pairing with more classical work in amniotes such as the chicken and mouse. However, the genetic foundation of the diversity of skeletal programs in teleosts have been hampered by mapping similarities from amniotes back and not objectively establishing more ancestral states. This is most obvious in systematic, objective analysis of transcriptional regulation and tissue specification in differentiated skeletal tissues. Thus, the molecular events regulating bone-producing cells in teleosts have remained largely elusive. In this study, Petratou et al. leverage spatial experimental delineation of specific skeletal tissues -- that they term 'classical' vs 'non-classical' osteoblasts -- with associated cartilage of the endo/peri-chondrial skeleton and inter-segmental regions of the forming spine during development of the zebrafish, to delineate molecular specification of these cells by current chromatin and transcriptome analysis. The authors further show functional evidence of the utility of these datasets to identify functional enhancer regions delineating entp5 expression delineated in 'classical' or 'non-classical' osteoblast populations. By integration with paired RNA-seq, they delineate broad patterns of transcriptional regulation of these populations as well as specific detail of regional regulation via predictive binding sites within ATACseq profiles. Overall the paper was very well written and provides an essential contribution to the field that will provide a foundation to promote modeling of skeletal development and disease in an evolutionary and developmentally informed manner.

      Strengths:

      Taken together, this study provides a comprehensive resource of ATAC-seq and RNA-seq data that will be very useful for a wide variety of researchers studying skeletal development and bone pathologies. The authors show specificity in the different skeletal lineages and show utility of the broad datasets for defining regulatory control of gene regulation in these different lineages, providing the foundation for hypothesis testing of not only agents of skeletal change in evolution but also function of genes and variations of unknown significance as it pertains to disease modeling in zebrafish. The paper is excellently written, integrating a complex history and experimental analysis into a useful and coherent whole. The terminology of 'classical' and 'non-classical' will be useful for the community in discussing biology of skeletal lineages and their regulation.

      Weaknesses:

      Two items arose that proposed areas for extending the description to integrate the data into the existing data on role of non-classical osteobasts and establishment/canalization of this lineage of skeletal cells.

      (1) It was unclear how specific the authors' experimental dissection of head/trunk was in isolating different entp5a osteoblast populations. Obviously, this was successful given the specificity in DEG of results, however an analysis of contaminating cells/lineages in each population would be useful - e.g. maybe use specific marker genes to assess. The text uses terms such as 'specific to' and 'enriched in' without seemingly grounded meaning of the accuracy of these comments. Is it really specific e.g. not seen in one or other dataset, or is there some experimental variation in this?

      (2) Further, it would be valuable to discuss NSC-specific genes such as calymmin (Peskin 2020) which has species and lineage specific regulation of non-classical osteoblasts likely being a key mechanistic node for ratcheting centra-specific patterning of the spine in teleost fishes. What are dynamics observed in this gene in datasets between the different populations, especially when compared with paralogues - is there obvious cis-regulatory changes that correlate with the co-option of this gene in early regulation of non-classical osteoblasts? The addition of this analysis/discussion would anchor discussions of a differential between different osteoblasts lineages in the paper.

      Comments on revisions: All issues have been addressed.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This work uses transgenic reporter lines to isolate entpd5a+ cells representing classical osteoblasts in the head and non-classical (osterix-) notochordal sheath cells. The authors also include entpd5a- cells, col2a1a+ cells to represent the closely associated cartilage cells. In a combination of ATAC and RNA-Seq analysis, the genome-wide transcriptomic and chromatin status of each cell population is characterized, validating their methodology and providing fundamental insights into the nature of each cell type, especially the less well-studied notochordal sheath cells. Using these data, the authors then turn to a thorough and convincing analysis of the regulatory regions that control the expression of the entpd5a gene in each cell population. Determination of transcriptional activities in developing zebrafish, again combined with ATAC data and expression data of putative regulators, results in a compelling and detailed picture of the regulatory mechanisms governing the expression of this crucial gene.

      Strengths:

      The major strength of this paper is the clever combination of RNA-Seq and ATAC analysis, further combined with functional transcriptional analysis of the regulatory elements of one crucial gene. This results in a very compelling story.

      Weaknesses:

      No major weaknesses were identified, except for all the follow-up experiments that one can think of, but that would be outside of the scope of this paper.

      Reviewer #2 (Public Review):

      Summary:

      Complementary to mammalian models, zebrafish has emerged as a powerful system to study vertebrate development and to serve as a go-to model for many human disorders. All vertebrates share the ancestral capacity to form a skeleton. Teleost fish models have been a key model to understand the foundations of skeletal development and plasticity, pairing with more classical work in amniotes such as the chicken and mouse. However, the genetic foundation of the diversity of skeletal programs in teleosts has been hampered by mapping similarities from amniotes back and not objectively establishing more ancestral states. This is most obvious in systematic, objective analysis of transcriptional regulation and tissue specification in differentiated skeletal tissues. Thus, the molecular events regulating bone-producing cells in teleosts have remained largely elusive. In this study, Petratou et al. leverage spatial experimental delineation of specific skeletal tissues -- that they term 'classical' vs 'non-classical' osteoblasts -- with associated cartilage of the endo/peri-chondrial skeleton and inter-segmental regions of the forming spine during development of the zebrafish, to delineate molecular specification of these cells by current chromatin and transcriptome analysis. The authors further show functional evidence of the utility of these datasets to identify functional enhancer regions delineating entp5 expression in 'classical' or 'non-classical' osteoblast populations. By integration with paired RNA-seq, they delineate broad patterns of transcriptional regulation of these populations as well as specific details of regional regulation via predictive binding sites within ATACseq profiles. Overall the paper was very well written and provides an essential contribution to the field that will provide a foundation to promote modeling of skeletal development and disease in an evolutionary and developmentally informed manner.

      Strengths:

      Taken together, this study provides a comprehensive resource of ATAC-seq and RNA-seq data that will be very useful for a wide variety of researchers studying skeletal development and bone pathologies. The authors show specificity in the different skeletal lineages and show the utility of the broad datasets for defining regulatory control of gene regulation in these different lineages, providing a foundation for hypothesis testing of not only agents of skeletal change in evolution but also function of genes and variations of unknown significance as it pertains to disease modeling in zebrafish. The paper is excellently written, integrating a complex history and experimental analysis into a useful and coherent whole. The terminology of 'classical' and 'non-classical' will be useful for the community in discussing the biology of skeletal lineages and their regulation.

      Weaknesses:

      Two items arose that were not critical weaknesses but areas for extending the description of methods and integration into the existing data on the role of non-classical osteoblasts and establishment/canalization of this lineage of skeletal cells.

      (1) In reading the text it was unclear how specific the authors' experimental dissection of the head/trunk was in isolating different entp5a osteoblast populations. Obviously, this was successful given the specificity in DEG of results, however, analysis of contaminating cells/lineages in each population would be useful - e.g. using specific marker genes to assess. The text uses terms such as 'specific to' and 'enriched in' without seemingly grounded meaning of the accuracy of these comments. Is it really specific - e.g. not seen in one or other dataset - or is there some experimental variation in this?

      We thank the reviewer for pointing this out. Given that the separation from head and trunk is done manually, there will be some experimental variability. We have used anatomical hallmarks (cleithrum and swim bladder), and therefore would expect the variability to be small. Regarding classical osteoblasts contaminating trunk tissue, head removal was consistently performed using the aforementioned anatomical hallmarks in a manner that ensures that the cleithrum does not remain in the trunk tissue.  In order to alleviate concerns regarding trunk cell populations contaminating cranial populations, and to further clarify our strategy, we add the following statement to the Materials and Methods section: “The procedure does not allow for a complete separation of notochordal non-classical osteoblasts from cranial classical osteoblasts, as the notochord extends into the cranium. However, the amount of sheath cells in that portion of the notochord is negligible, compared both to the number of classical (cranial) osteoblasts in head samples, and to notochord cells isolated in trunk samples.”

      (2) Further, it would be valuable to discuss NSC-specific genes such as calymmin (Peskin 2020) which has species and lineage-specific regulation of non-classical osteoblasts likely being a key mechanistic node for ratcheting centra-specific patterning of the spine in teleost fishes. What are dynamics observed in this gene in datasets between the different populations, especially when compared with paralogues - are there obvious cis-regulatory changes that correlate with the co-option of this gene in the early regulation of non-classical osteoblasts? The addition of this analysis/discussion would anchor discussions of the differential between different osteoblasts lineages in the paper.

      This is an interesting concept and idea, that we will consider in a possible revision or, if requiring substantial additional efforts, in a possible new research line. An excellent starting point for further studies using our datasets.

      Reviewer #3 (Public Review):

      Summary:

      This study characterizes classical and nonclassical osteoblasts as both types were analyzed independently (integrated ATAC-seq and RNAseq). It was found that gene expression in classical and nonclassical osteoblasts is not regulated in the same way. In classical osteoblasts, Dlx family factors seem to play an important role, while Hox family factors are involved in the regulation of spinal ossification by nonclassical osteoblasts. In the second part of the study, the authors focus on the promoter structure of entpd5a. Through the identification of enhancers, they reveal complex modes of regulation of the gene. The authors suggest candidate transcription factors that likely act on the identified enhancer elements. All the results taken together provide comprehensive new insights into the process of bone development, and point to spatio-temporally regulated promoter/enhancer interactions taking place at the entpd5a locus.

      Strengths:

      The authors have succeeded in justifying a sound and consistent buildup of their experiments, and meaningfully integrating the results into the design of each of their follow-up experiments. The data are solid, insightfully presented, and the conclusion valid. This makes this manuscript of great value and interest to those studying (fundamental) skeletal biology.

      Weaknesses:

      The study is solidly constructed, the manuscript is clearly written and the discussion is meaningful - I see no real weaknesses.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Minor issues that may need to be addressed or detailed:

      Supplementary Figures 1I-J, text page 4, line 24: "photoconversion and imaging": this needs some more detailed description: green fluorescent cells should be actively expressing Kaede, but only if there is a delay between photoconversion and imaging. What is the reason that Supplementary Figure 1F shows mainly green fluorescent cells, contrary to 1G-J?

      In our experiments, we could see new Kaede expression under the control of the entpd5a promoter region within 1.5 hours of photoconversion, as shown in Suppl. Figure 1E-H, suggesting that this time window was sufficient for protein generation. The reason for Suppl. Fig 1F showing more green fluorescence we believe relates to the high rate of transcriptional activity at that stage, in the entirety of the notochord progenitor cells. In addition, this is an effect which we attribute to the relatively small number of cells producing red fluorescence at that stage, due to photoconversion of only a few Kaede+ cells at the 15 somites stage (Suppl. Fig. 1E). Therefore, the masking effect of the green fluorescence by the red is not as significant as in G and H, where the red fluorescence resulting from photoconversion right after imaging at 18s and 21s, respectively, significantly overlaps with new green fluorescence. This can be seen in the image as the presence of orange fluorescence in G and H, instead of the clear red shown in E, I and J.

      In addition to this, we would like to point out that in Suppl. Fig. 1I, J the reason that green fluorescence is only detected in the ventral region of the notochord, is because the promoter of entpd5a only remains active in the ventral-most sheath cells at that stage. This is stated in the results section of the main text, first subsection, paragraph 3. The reason for this very interesting, strictly localised expression pattern remains unclear.

      Somewhat intriguing: green fluorescence in Figure 1B, C (osx:GAL4FF) and Supplementary Figure 1C (entpd5a:GAL4FF) in the CNS? Would that be an artefact of the GAL4FF/UAS:GFP system?

      We are confident that the fluorescence pointed out by the reviewer is not an artefact of the GAL4FF/UAS system, for a few reasons. Firstly, osx (Sp7) has been shown to be expressed and to function in the nervous system in mice (Park et al, BBRC, 2011; Elbaz et al, Neuron, 2023). Secondly, not only osx, but also entpd5a can be readily detected in a subset of cranial and spinal neurons in early development using the entpd5a:GAL4FF; UAS:GFP transgenic line (Suppl. Fig 1C). Finally, when establishing transgenic lines with the entpd5a(1.1):GFP construct, expression was almost invariably present in diverse elements of the nervous system, but not in bone (data not shown). This led us to hypothesise that the minimal promoter of entpd5a (and possibly also that of osx) is activated by transcription factors active in the nervous system, and this effect is likely controlled by the surrounding enhancers, but also the genome location. It is unclear at present what the endogenous neural expression of the two genes is like, and we did not further investigate this in this study, as the focus was on the skeleton.

      Figure 2: What exactly is "Corrected Total Cell Fluorescence"? Is it green + red fluorescence?

      We thank the reviewer for pointing out the absence of more information on this. Corrected total cell fluorescence does not correspond to green+ red fluorescence, rather it is calculated as follows for a single channel:

      CTCF = Integrated Density – (Area of selected cell X Mean fluorescence of background readings)

      More details can be found in the following website: https://theolb.readthedocs.io/en/latest/imaging/measuring-cell-fluorescence-using-imagej.html

      We have edited the Materials and Methods section under “Imaging and image analysis” to include the aforementioned information.

      Page 11, line 34: The authors may have missed the recently published "Raman et al., Biomolecules 2024 Vol. 14; doi:10.3390/biom14020139" describing RNA-Seq in 4 dpf osterix+ osteoblasts.

      We thank the reviewer for drawing our attention to the Raman et al publication. The reference has now been added in the manuscript.

      Figure 5A and B: use a higher resolution version to make the numbers and gene names more readable. Figures 5C and 6A could also use a larger font for the text and numbers.

      High resolution files are now included with the revised manuscript, which should significantly help in making figures more easily readable. Although we agree with the reviewer that larger fonts would improve readability, due to the nature of the graphs (very small spaces in some cases, where the numbers would have to fit) this would not be easy to achieve. However, we believe that this issue will be resolved with the availability of higher resolution files. If readability remains a concern, we would be happy to attempt re-organising the graphs to allow for larger fonts.

      Reviewer #2 (Recommendations For The Authors):

      I suggest no further experiments, but do suggest that a few points be clarified.

      In the Discussion, the text "the less evolved osteoblasts of fish and amphibians..." is not accurate. These cells are not less evolved as they represent an independent lineage to tetrapods that have evolved with different stresses for a similar time. However, as teleost fishes and amphibians share characteristics and all share a common ancestor, these signatures represent a putative ancestral state of skeletal differentiation not seen in amniotes, including humans.

      We thank the reviewer for pointing out the unfortunate phrasing. The text has now been modified as follows: “Specifically, the osteoblasts of teleost fish and amphibians, whose characteristics are putatively closer to a more ancestral state of skeletal differentiation compared to amniotes, appear to share gene expression with chondrocytes”.

      The title could potentially be shortened to reach a broader audience by removing the initial clause of 'integration of ATAC and RNA seq' as this is a commonly performed analysis - "Chromatin and transcriptomic signature in classical and non-classical zebrafish osteoblasts indicate mechanisms of ancestral skeletal differentiation" is more descriptive of the findings and not focused on the method.

      We have discussed this internally, but would prefer to retain the current title. The reason is (1) because we would like to see our methodology and datasets be used as platform for further studies, and the current title, in our opinion, facilitates this. In regards to replacing “mechanisms of entpd5a regulation” with “mechanisms of ancestral skeletal differentiation”, we think this does not give an accurate description of our work, which is primarily focused on elucidating entpd5a promoter dynamics.

      All datasets should be made available as soon as possible for use in the field.

      The datasets (raw and processed) are available on the GEO database. The corresponding accession numbers can be found in our data availability statement.

      Minor comments:

      (1) Figure 1A. The labels are missing for grey and light blue structures.

      These structures are together making up the “notochord sheath”, which is comprised of the basal lamina (grey), the medial layer of fibrillar collagen (light blue) and the outer layer of loosely arranged matrix (lighter blue). We modified the figure legend to indicate that the three layers all correspond to the notochord sheath.

      (2) Figure 2A. The constructs in the lower part of the panel are not discussed in the legend and seem out of place in terms of data type and analysis.

      We would argue that indicating which non-coding regions and which ATAC peaks were responsible for driving GFP expression in each construct aids in a better understanding of our results. We thank the reviewer for pointing out the lack of mention of these constructs in the figure legend. This issue has now been resolved.

      (3) Be wary of red/green color combinations, especially in the figures where these are juxtaposed with each other.

      We apologise for the use of red/green colour. Although it is not possible for this manuscript to change the colour patterns, we will make sure to avoid the use of these colours in conjunction in the future.

      (4) The use of fish as a term should be classified as teleost fish, as authors are not addressing non-teleost basal ray-finned fishes or the fact that tetrapods are within bony fishes overall.

      This is well spotted, we have now remedied this by editing the manuscript. Where the term “fish” was used, we now state “teleost fish”.

      (5) Age information is missing in several Figures (e.g. 1D and 2C).

      In some of the figures space constrains did not allow for including the stage on the figure itself. However, we have made sure that in those cases the stage is incorporated in the figure legend.

      (6) The resolution of several Figures (e.g. Figure 5 and Supplementary Figure 3) is low.

      We address this issue by providing high resolution figures with the revised manuscript.

      (7) In the sentence (top page before Discussion) "The same conclusion was reached upon isolation from these three..", it was unclear what 'upon isolation' referred to.

      We agree with the reviewer that this phrasing is unclear. To enhance clarity, the manuscript now reads as follows: “The same conclusion was reached upon isolation of the DEGs highlighted by our RNA-seq results, from the three aforementioned groups of genes associated with ATAC peaks for each cell population.”

    1. Reviewer #3 (Public review):

      Xiaoyu Wu and colleagues examined the potential role in sleep of a Drosophila ribosomal RNA methyltransferase, mettl5. Based on sleep defects reported in CRISPR generated mutants, the authors performed both RNA-seq and Ribo-seq analyses of head tissue from mutants and compared to control animals collected at the same time point. While these data were subjected to a thorough analysis, it was difficult to understand the relative direction of differential expression between the two genotypes. In any case, a major conclusion was that the mutant showed altered expression of circadian clock genes, and that the altered expression of the period gene in particular accounted for the sleep defect reported in the mettl5 mutant. As noted above, a strength of this work is its relevance to a human developmental disorder as well as the transcriptomic and ribosomal profiling of the mutant. However, there are numerous weaknesses in the manuscript, most of which stem from misinterpretation of the findings, some methodological approaches, and also a lack of method detail provided. The authors seemed to have missed a major phenotype associated with the mettl5 mutant, which is that it caused a significant increase in period length, which was apparent even in a light: dark cycle. Thus the effect of the mutant on clock gene expression more likely contributed to this phenotype than any associated with changes in sleep behavior.

    1. Reviewer #1 (Public review):

      Summary:

      In this manuscript by Bimbard et al., a new method to perform stable recordings over long periods of time with neuropixels as well as the technical details on how the electrodes can be explanted for a follow up reuse is provided. I think the description of all parts of the method are very clear, and the validation analyses (n of units per day over time, RMS over recording days...) are very convincing. I however missed a stronger emphasis on why this could provide a big impact on the ephys community, by enabling new analyses, new behavior correlation studies or neurophysiological mechanisms across temporal scales that were previously inaccessible with high temporal resolution (i.e. not with imaging).

      Strengths:

      Open source method. Validation across laboratories. Across species (mice and rats) demonstration of its use and in different behavioral conditions (head-fixed and freely moving). The implant offers a major advance compared to previous methods and that will help the community generate richer datasets.

      Weaknesses:

      None noted.

    2. Reviewer #2 (Public review):

      Summary:

      This work by Bimbard et al., introduces a new implant for Neuropixels probes. While Neuropixels probes have critically improved and extended our ability to record the activity of a large number of neurons with high temporal resolution, the use of these expensive devices in chronic experiments has so far been hampered by the difficulty of safely implanting them and, importantly, to explant and reuse them after conclusion of the experiment. The authors present a newly designed two-part implant, consisting of a docking and a payload module, that allows for secure implantation and straightforward recovery of the probes. The implant is lightweight, making it amenable for use in mice and rats, and customizable. The authors provide schematics and files for printing of the implants, which can be easily modified and adapted to custom experiments by researchers with little to no design experience. Importantly, the authors demonstrate the successful use of this implant across multiple use cases, in head-fixed and freely moving experiments, in mice and rats, with different versions of Neuropixels probes and across 8 different labs. Taken together, the presented implants promise to make chronic Neuropixels recordings and long-term studies of neuronal activity significantly easier and attainable for both current and future Neuropixels users.

      Strengths:<br /> - The implants have been successfully tested across 8 different laboratories, in mice and rats, in head-fixed and freely moving conditions and have been adapted in multiple ways for a number of distinct experiments.<br /> - Implants are easily customizable and authors provide a straightforward approach for customization across multiple design dimensions even for researchers not experienced in design.<br /> - The authors provide clear and straightforward descriptions of the construction, implantation and explant of the described implants.<br /> - The split of the implant into a docking and payload module makes reuse even in different experiments (using different docking modules) easy.<br /> - The authors demonstrate that implants can be re-used multiple times and still allow for high-quality recordings.<br /> - The authors show that the chronic implantations allow for the tracking of individual neurons across days and weeks (using additional software tracking solutions), which is critical for a large number of experiments requiring the description of neuronal activity, e.g. throughout learning processes.<br /> - The authors show that implanted animals can even perform complex behavioral tasks, with no apparent reduction in their performance.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer 1 (Public Review):

      Summary:

      In this manuscript by Bimbard et al., a new method to perform stable recordings over long periods of time with neuropixels, as well as the technical details on how the electrodes can be explanted for follow-up reuse, is provided. I think the description of all parts of the method is very clear, and the validation analyses (n of units per day over time, RMS over recording days...) are very convincing. I however missed a stronger emphasis on why this could provide a big impact on the ephys community, by enabling new analyses, new behavior correlation studies, or neurophysiological mechanisms across temporal scales.

      Strengths:

      Open source method. Validation across laboratories. Across species (mice and rats) demonstration of its use and in different behavioral conditions (head-fixed and freely moving).

      Weaknesses:

      Weak emphasis on what can be enabled with this new method that didn't exist before.

      We thank the reviewer for highlighting the limited discussion around scientific impact. Our implant has several advantages which combine to make it much more accessible than previous solutions. This enables a variety of recording configurations that would not have been possible with previous designs, facilitating recordings from a wider range of brain regions, animals, and experimental setups. In short, there are three key advances which we now emphasise in the manuscript:

      Adaptability: The CAD files can be readily adapted to a wide range of configurations (implantation depth, angle, position of headstage, etc.). Labs have already modified the design for their needs, and re-shared with the community (Discussion, Para 5).

      Weight: Because of the lightweight design, experimenters can i) perform complex and demanding freely moving tasks as we exemplify in the manuscript, and ii) implant female and water restricted mice while respecting animal welfare weight limitations (Flexible design, Para 1).

      Cost: At ~$10, our implant is significantly cheaper than published alternatives, which makes it affordable to more labs and means that testing modifications is cost-effective (Discussion, Para 4).

      Reviewer 1 (Recommendations For The Authors):

      - Differences between mice and rats seem very significant. Although this is probably not surprising, I suggest that the authors comment on this to make it clear to anyone trying to use in different species that are not quantified in the main figures.

      The reviewer is correct—there are qualitative differences between mice and rats, particularly with respect to the unit median amplitude. We have added a comment in the discussion to highlight these inter-species variations (Discussion, Para 7)

      - Another comment that would be useful to have would be how to tackle the problem of tracking the same neuron across days. Even if currently impossible, it could be useful to provide discussion along those lines as to where future improvements (either in hardware or software) can be made.

      We thank the reviewer for highlighting this. Figure. 5 does show data from tracking the same neuron across days (and even months). We have modified the language to make this clear.

      Reviewer 2 (Public Review):  

      Summary:

      This work by Bimbard et al., introduces a new implant for Neuropixels probes. While Neuropixels probes have critically improved and extended our ability to record the activity of a large number of neurons with high temporal resolution, the use of these expensive devices in chronic experiments has so far been hampered by the difficulty of safely implanting them and, importantly, to explant and reuse them after conclusion of the experiment. The authors present a newly designed two-part implant, consisting of a docking and a payload module, that allows for secure implantation and straightforward recovery of the probes. The implant is lightweight, making it amenable for use in mice and rats, and customizable. The authors provide schematics and files for printing of the implants, which can be easily modified and adapted to custom experiments by researchers with little to no design experience. Importantly, the authors demonstrate the successful use of this implant across multiple use cases, in head-fixed and freely moving experiments, in mice and rats, with different versions of Neuropixels probes, and across 8 different labs. Taken together, the presented implants promise to make chronic Neuropixel recordings and long-term studies of neuronal activity significantly easier and attainable for both current and future Neuropixels users.

      Strengths:

      The implants have been successfully tested across 8 different laboratories, in mice and rats, in headfixed and freely moving conditions, and have been adapted in multiple ways for a number of distinct experiments.

      Implants are easily customizable and the authors provide a straightforward approach for customization across multiple design dimensions even for researchers not experienced in design.

      The authors provide clear and straightforward descriptions of the construction, implantation, and explant of the described implants.

      The split of the implant into a docking and payload module makes reuse even in different experiments (using different docking modules) easy.

      The authors demonstrate that implants can be re-used multiple times and still allow for high-quality recordings.

      The authors show that the chronic implantations allow for the tracking of individual neurons across days and weeks (using additional software tracking solutions), which is critical for a large number of experiments requiring the description of neuronal activity, e.g. throughout learning processes.

      The authors show that implanted animals can even perform complex behavioral tasks, with no apparent reduction in their performance.

      Weaknesses:

      While implanted animals can still perform complex behavioral tasks, the authors describe that the implants may reduce the animals' mobility, as measured by prolonged reaction times. However, the presented data does not allow us to judge whether this effect is specifically due to the presented implant or whether any implant or just tethering of the animals per se would have the same effects.

      The reviewer is correct: some of the differences in mouse reaction time could be due to the tether rather than the implant. As these experiments were also performed in water-restricted female mice with the heavier Neuropixels 1.0 implant, our data represent the maximal impact of the implant, and we have highlighted this point in the revision (Freely behaving animals, Para 2).  

      While the authors make certain comparisons to other, previously published approaches for chronic implantation and re-use of Neuropixels probes, it is hard to make conclusive comparisons and judge the advantages of the current implant. For example, while the authors emphasize that the lower weight of their implant allows them to perform recordings in mice (and is surely advantageous), the previously described, heavier implants they mention (Steinmetz et al., 2021; van Daal et al., 2021), have also been used in mice. Whether the weight difference makes a difference in practice therefore remains somewhat unclear.

      The reviewer is correct: without a direct comparison, we cannot be certain that our smaller, lighter implant improves behavioural results (although this is supported by the literature, e.g. Newman et al, 2023). However, the reduced weight of our implant is critical for several laboratories represented in this manuscript due to animal welfare requirements. Indeed, in van Daal et al the authors “recommend a [mouse] weight of >25 g for implanting Neuropixels 1.0 probes.” This limit precludes using (the vast majority of) female mice, or water-restricted animals. Conversely, our implant can be routinely used with lighter, water-restricted male and female mice. We emphasised this point in the revision (Discussion, Para 2).

      The non-permanent integration of the headstages into the implant, while allowing for the use of the same headstage for multiple animals in parallel, requires repeated connections and does not provide strong protection for the implant. This may especially be an issue for the use in rats, requiring additional protective components as in the presented rat experiments.

      We apologise for not clarifying the various headstage holder options in the manuscript and we have now addressed this in the revision (Freely behaving animals, Para 1&2). Our repository has headstage holder designs (in the XtraModifications/Mouse_FreelyMoving folder). This allows leaving the headstage on the implant, and thus minimize the number of connections (albeit increasing the weight for the mouse). Indeed, mice recorded while performing the task described in our manuscript had the head-stage semi-permanently integrated to the implant, and we now highlight this in the revision (Freely behaving animals, Para 1).

      Reviewer 2 (Recommendations For The Authors): 

      The description of the different versions of the head-stage holders should be more clear, listing also advantages/disadvantages of the different solutions. It would be also useful if the authors could comment on the use of these head-stage holders in rats, since they do not seem to offer much protection.

      We thank the reviewer for this point, and we have added notes to the manuscript to clarify the various advantages of the different headstage-holders, and that the headstage can be permanently attached to the implant (Freely behaving animals, Para 1&2). This is the primary advantage of these solutions compared with the minimal implant—at the expense of increasing the implant weight.  

      The reviewer’s concerns regarding the lack of protection for implants in rats is well-placed, and we now emphasise that these experiments benefited from the additional protection of an external 3D casing, which is likely critical for use in larger animals (Freely behaving animals, Para 1).

      While re-used probes seem to show similar yields across multiple uses (Figure 4C), it seems as if there is a much higher variability of the yield for probes that are used for the first (maybe also second) time. There are probes with much higher than average yields, but it seems none of the re-used probes show such high yields. Is this a real effect? Is this because the high-yield probes happened to have not been used multiple times? Is there an analysis the authors could provide to reduce the concern that yields may generally be lower for re-used probes/that there are no very high yields for re-used probes?

      We understand the reviewer’s concern with respect to Figure 4C, however, the re-use of any given probe was determined only by the experimental needs of the project. It is therefore not possible that there is a relationship between probes selected for re-use and unit-yield. We now specify this in the revised legend of Figure 4C. This variability (and the consistency in yield across uses) likely stems from differences between labs, brain region, and implantation protocol.

      The authors claim that a 'large fraction' of units could be tracked for the entire duration of the experiment (Figure 5A,B). They mention in the discussion that quantification can be found in a different manuscript (van Beest et al., 2023), but this should also be quantified here in at least some more detail, also for other animals in addition to the one mouse which was recorded for ~100 days. What fraction can be held for different durations? What is the average holding time, etc.?

      We agree with the reviewer, and have now added new panels quantifying the probability and reliability of tracking a neuron across days (Figure 5E-F). We also comment on the change in tracking probability across time, and its variability across recordings (Stability, Para 4).

      Reviewer 3 (Public Reviews):

      Summary:

      In this manuscript, Bimbard and colleagues describe a new implant apparatus called "Apollo Implant", which should facilitate recording in freely moving rodents (mice and rats) using Neuropixels probes. The authors collected data from both mice and rats, they used 3 different versions of Neuropixels, multiple labs have already adopted this method, which is impressive. They openly share their CAD designs and surgery protocol to further facilitate the adaptation of their method.

      Strengths:

      Overall, the "Apollo Implant" is easy to use and adapt, as it has been used in other laboratories successfully and custom modifications are already available. The device is reproducible using common 3D printing services and can be easily modified thanks to its CAD design (the video explaining this is extremely helpful). The weight and price are amazing compared to other systems for rigid silicon probes allowing a wide range of use of the "Apollo Implant".

      Weaknesses:

      The "Apollo Implant" can only handle Neuropixels probes. It cannot hold other widely used and commercially available silicon probes. Certain angles and distances are not possible in their current form (distance between probes 1.8 to 4mm, implantation depth 2-6.5 mm, or angle of insertion up to 20 degrees).

      As we now discuss in the manuscript (Discussion, Para 4), one implant accommodating the diversity of the existing probes is beyond the scope of this project. However, because the design is adaptable, groups should be able to modify the current version of the implant to adapt to their electrodes’ size and format (and can highlight any issues in the Github “Discussions” area).

      With Neuropixels, the current range of depths covers practically all trajectories in the mouse brain. In rats, where deeper penetrations may be useful, the experimenter can attach the probe at a lower point in the payload module to expose more of the shank. We now specify this in the Github repository.  

      We have now extended the range of inter-probe distances from a maximum of 4 mm to 6.5 mm. Distances beyond this may be better served by 2 implants, and smaller distances could be achieved by attaching two probes on the same side of the docking module. These points are now specified in the revised manuscript (Flexible design, Para 2).

      Reviewer 3 (Recommendations For The Authors):

      I have only a few questions and suggestions:

      Is it possible to create step-by-step instructions for explantation (similar to Figure-1 with CAD schematics)? You mention that payload holder is attached to a micromanipulator, but it is unclear how this is achieved. How was the payload secured with a screw (which screw)? My understanding is that as you turn the screw in the payload holder, it will grab onto the payload module from both sides, but the screw is not in contact with the payload module, correct? I found the screw type on your GitHub, but it would be great if you could add a bill of materials in a table format, so readers don't have to jump between GitHub and article.

      We have now added a bill of materials to the revised manuscript (Implant design and materials, Para 2), although up-to-date links are still provided on the Github repository due to changing availability.

      What happens if you do a dual probe implant and cannot avoid blood vessels in one or both of the craniotomies due to the pre-defined geometry? Is this a frequent issue? How can you overcome this during the surgery?

      Blood vessels can be difficult to avoid in some cases, but we are typically able to rotate/reposition the probes to solve this issue. In some cases, with 4-shank probes, the blood vessel can be positioned between individual probe shanks. We now detail this in the revised manuscript (Assembly and implantation, Para 3).

      I assume if the head is not aligned (line-332) the probe can break during recovery. Have you experienced this during explanation?

      As we now specify in the manuscript (Explantation, Para 2), we are careful when explanting the probe to avoid this issue, and due to the flexibility of the shanks, it does not appear to be a major concern.

      Why did you remove the UV glue (line 435)? How can you level the skull? I assume you have covered bregma and lambda in the first surgery which can create an uneven surface to measure even after you remove the UV glue.

      We thank the reviewer for highlighting this omission from the methods. We now explain (Implantation, Carandini-Harris laboratory) that the UV-glue is completely removed during the second surgery, and the skull is cleaned and scored. This improves the adhesion of the dental cement, and allows for reliable levelling of the skull.

      In line 112 you mentioned that the number of recorded neurons was stable; however, you found a 3% mean decrease in unit count per day (line 120). Stability is great until day 10 (in Figure 4A), but it deteriorates quickly after that. I think it would help readers if you could add the mean{plus minus}SEM of recorded units in the text for days 1-10, days 11-50, and days 51-100 (using the data from Figure 4A).

      We have now added Supplementary Figure 4 to show unit count across bins of days, and a corresponding comment in the text (Stability, Para 2).

      A full survey of the probe (Figure 4B) means that you recorded neuronal activity across 4-5000 channels (depending on how many channels were in the brain). While it is clear that a full probe survey can reduce the number of animals needed for a study, it is also clear in this figure that by day 25 you can record ~300 neurons on 4000 channels. It would be great to discuss this in the discussion and give a balanced view of the long-term stability of these recordings.

      Overall, keeping a large number of units for a long time still remains a challenge. Here, we could record on average 85 neurons per bank during the first 10 days, and then only 45 after 50 days. It is important to note that our quantification averages across all banks recorded, including those in a ventricle or partly outside of the brain. Thus, our results represent a lower estimate of the total neurons recorded. Our new Supplementary Figure 4 helps to highlight the diversity of neuron number recorded per animal. Further improvements in surgical techniques and spike sorting will likely improve stability further and we have now added this comment in the manuscript (Stability, Para 2). For example, we observed excellent stability in a mouse where the craniotomy was stabilized with KwikSil (Supplementary Figure 5).

      The RMS value was around 20 uV in some of the recordings, and according to Figure 4G it is around 16 uV on average. Is it safe to accept putative single units with 20 uV amplitudes, when the baseline noise level is this close to the spike peak-to-peak amplitude?

      On average, less than 1% of the units selected using all the other metrics except the amplitude had an amplitude below 30 µV, and 2.6% below 50 µV. Increasing the threshold to 30 µV, or even 50 µV, did not affect the results. We have now added this comment in the Methods (Data processing, Para 3).

      Can you add the waveform and ISIH of the example unit from day 106 to Figure 5?

      We have now added 4 units tracked up to day 106 in Figure 5.  

      Could you move Supplementary Figure 3A to Figure 4? The number of units is more valuable information than the RMS noise level. I understand that you don't have such a nice coverage of all the days as in Figure 3 and 4, but you might be able to group for the first 3 days and the last 3 days (and if data is available, the middle 3 days) as a boxplot. The goal would be for the reader to be able to see whether there is any change in the number of single units over time.

      We agree with the reviewer, the number of units is more valuable. We had included this information in Figure 4A-F, but we have made edits to the text to make it clearer that this is what is being shown. The data from Figure 3A is already contained within Figure 4, but in 3A the data is separated by individual labs.

      Product numbers are missing in multiple places: line-285 (screw), line-288 (screw), line-290 (screw), line-309 (manipulator), line-374 (gold pin and silver wire), line-384 (Mill-Max), line-394 (silver wire), and many more. It would be great if you could add all these details, so people can replicate your protocol.

      We thank the reviewer for highlighting this, and we have added details of screw thread-size and length to relevant parts of the manuscript, although any type of screw can be used. Similarly, other components are non-specific (e.g. multiple silver-wire diameters were used across labs), so we have not included specific product numbers for general consumer items (like screws and silver wires) to avoid indicating that a specific part must be purchased.

      While it is great to see lab-specific methods, I am not sure in their current form it helps to understand the protocol better. The information is conveyed in different ways (I assume these were written by different people), in different orders, and in different depths (some mention probe implant location relative to bregma and midline, some don't). There are many different glues, epoxies, cement, wires, and pins. I would recommend rewriting these methods sections under a unified template, so it is easier to follow.

      We thank the reviewer for this suggestion and we have rewritten this section of the methods accordingly. We now use a template structure to simplify the comparisons between labs: the same template is used for each lab in each section (payload module assembly, implantation, and data acquisition).

      Line-307: why is a skull screw optional for grounding? What did you use for ground and reference if not a ground screw?

      We now specify in the manuscript that during head-fixed experiments, the animal’s headplate can be used for grounding, and combined with internal referencing provided by the Neuropixels, yielded lownoise recordings (Implantation protocol, Methods).

    1. But if she lift up her drooping head and prosper, among those that have something more then wisht her welfare

      It's interesting that he sees the church as this objectively good being and idea, and that people have done damage to her, though she is still good, just downtrodden. Rather than the people being the church and their actions being representative, thus painting the church itself as bad.

    1. In his head it’s binary: what draw (of letters) can make a scrabble, what draw can’t”

      I think the fact that the words have no other meaning to him except the sequence of their letters may be the key to his win. He has no intellectual baggage to distract him, he just sees the letters he has and fits them in like a puzzle.

    1. but without clarifying thedesired results of our teaching, how will we ever know whether our designs areappropriate or arbitrary?

      I teach Community Nutrition Program Development, and explaining that evaluation is the most important aspect of any program design is always a challenge. It's hard to wrap your head around how meaningless a campaign (or in this case, curriculum) would be without a way to evaluate it.

    Annotators

    1. Reviewer #2 (Public Review):

      Kanie et all have carried out a tour-de-force effort to further understand the hierarchy and function of centriole distal appendages in ciliogenesis. They made a thorough effort to understand the localization of all the known distal appendage proteins. To examine the distal appendage hierarchy, they used an automated analysis of centrosomal localization. It is not clear how this was quantified and pictures are not shown. They used CEP170, a marker for subdistal appendages, to define a mask around centrioles. It is not clear how the experiment was analyzed and normalized. The techniques used in this study cannot be compared with those commonly used in the field which normally include storm and other super-resolution techniques (which are less prone to artifacts) and correlated electron microscopy. Thus, it is not possible to make a head-to-head comparison. The lack of rescue experiments further weakens the conclusions of this paper.

    1. a cup of tea before sleeping: I feel like this is as important for my mental health as drinking coffee in the morning. It bookends my day and lets me feel more peaceful before I head to bed.

      oh true! want to do this more as well. sometimes, my brain doesn't even think. if i need a hot drink, my brain defaults to coffee which of course is always a bad idea

    1. carbohydrate

      To identify carbohydrates (and lipids in general besides phospholipids), you can count the number of C, H, and O present in the molecule.

      The number of carbons and oxygens should be equal, whereas the number of hydrogens should be double this amount.

      You can also identify lipids by looking for the polar "head" and nonpolar "tail"!

    1. And hath given countenance to his speech, my lord, 0594  With almost all the holy vows of heaven.

      Ophelia says that his actions and words align for good, and he treats her very well, with the love in his heart. Ophelia is clearly head over heels for him, and has continued to defend Hamlet over and over whilst her father expresses his concerns. I feel as if this will end up hurting Ophelia, as she is unable to step away from her feelings and see dangerous possibilities.

    1. [ Atlas ] stands immovably upholding the wide heaven upon his head and unwearying hands

      Atlas losing his freedom, punished to eternally hold up the heavens, this shows losing something Humanity and myself personally consider extremely important. Poor Atlas.

  3. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. The paradox stems from the fact that the success of one generation depends at least partly on the success of their parents or guardians. People who succeed get to keep the fruits of their labor and use them as they see fit; if they buy a home in a place where the schools are better, or use their superior resources to make the schools in their neighborhood better, their chil-dren will have a head start and other children will fall behind through no fault of their own. The paradox lies in the fact that schools are supposed to equal-ize opportunities across generations and to create democratic citizens out of each generation, but people naturally wish to give their own children an ad-vantage in attaining wealth or power, and some can do it

      This highlights a paradox in American education. While the system aims to equalize opportunities, wealthier families can use their resources to give their children a head start, creating inequality despite the ideal of equal chance for all.

    2. Sustained and serious disagreements over education policy can never be completely resolved because they spring from a fundamental paradox at the heart of the American dream. Most Americans believe that everyone has the right to pursue success but that only some deserve to win, based on their tal-ent, effort, or ambition. The American dream is egalitarian at the starting point in the "race of life," but not at the end. That is not the paradox; it is simply an ideological choice. The paradox stems from the fact that the success of one generation depends at least partly on the success of their parents or guardians. People who succeed get to keep the fruits of their labor and use them as they see fit; if they buy a home in a place where the schools are better, or use their superior resources to make the schools in their neighborhood better, their chil-dren will have a head start and other children will fall behind through no fault of their own. The paradox lies in the fact that schools are supposed to equal-ize opportunities across generations and to create democratic citizens out of each generation, but people naturally wish to give their own children an ad-vantage in attaining wealth or power, and some can do it. When they do, every-one does not start equally, politically or economically. This circle cannot be squared

      This paragraph explores the fundamental paradox at the heart of the American Dream as it relates to education. Although the Dream advocates for equal opportunities, systemic inequalities such as wealth disparities and unequal access to quality schools—create barriers. The paradox lies in how education, intended to equalize opportunities, can also perpetuate inequalities when families prioritize their children’s advantage over collective fairness.

    3. The paradox stems from the fact that the success of one generation depends at least partly on the success of their parents or guardians. People who succeed get to keep the fruits of their labor and use them as they see fit; if they buy a home in a place where the schools are better, or use their superior resources to make the schools in their neighborhood better, their chil-dren will have a head start and other children will fall behind through no fault of their own. The paradox lies in the fact that schools are supposed to equal-ize opportunities across generations and to create democratic citizens out of each generation, but people naturally wish to give their own children an ad-vantage in attaining wealth or power, and some can do it. When they do, every-one does not start equally, politically or economically. This circle cannot be squared.

      When first reading this section, I first disagreed with how one's success depends on those of their parents or guardians. In my opinion, true success comes from how determined you are to achieving your goals. My parents weren't able to attend college, they had barely reached middle school, but in my eyes I still view them as successful. They had one goal which was to pursue a better future for themselves and their children in an unknown place and they succeeded in that. I view them ass successful for how passionate and determined they were which to me is what I aspire my success to stem from.

    4. The paradox stems from the fact that the success of one generation depends at least partly on the success of their parents or guardians. People who succeed get to keep the fruits of their labor and use them as they see fit; if they buy a home in a place where the schools are better, or use their superior resources to make the schools in their neighborhood better, their chil-dren will have a head start and other children will fall behind through no fault of their own.

      This highlights how people are set up for success or failure at birth, in a more light-hearted way. It states that "the success of one generation depends at least partly on the success of their parents or guardians." In other words, one of the main ways you will succeed in life is if you're parents/guardians already are. And if i'm not mistaken, the "only way" to do that is almost like you're pre-destined for it. I find this both inaccurate and accurate. Yes, I do believe that those who have parents that are successful struggle less in more ways than one--not just in their education. Specifically, it is easier to fail when you do not have the right resources and/or support. This is not to say that all of those who are less fortunate fail--it just makes it 10X as difficult.

    5. The paradox stems from the fact that the success of one generation depends at least partly on the success of their parents or guardians. People who succeed get to keep the fruits of their labor and use them as they see fit; if they buy a home in a place where the schools are better, or use their superior resources to make the schools in their neighborhood better, their chil-dren will have a head start and other children will fall behind through no fault of their own

      I highly agree and find this to be true especially being a freshman and examining results of college acceptances of my classmates. There are especially large disparities as regardless of the accessibility of public school, it is unavoidable that some students will have more resources available to them allowing them to have an easier time getting through school compared to other students who have lower socioeconomic status and as a result have less resources available to them. Additionally, I also find it to be especially true how schools in higher income areas have more resources. For instance, I noticed how a high school in a higher income neighborhood had much more clubs and diverse career pathways for students that I would have appreciated, compared to my own school in a lower income area which had few clubs and pathway options I could choose from.

  4. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. When they set foot in kindergarten, how many years "behind" are they in learning opportunities, literacy and numeracy development, reading and writ-ing "behaviors," and the many benefits of quality early care?

      many families do not have the privilege of picking a home and area code based off if the schools are good or bad they are limited to picking a address at which they are able to afford. the statement of how far behind your child is when they get to kindergarden also favors the wealthy because many parents aren't able to send their child to a day care before school starts where they can get a head start along with many families work full time and are not able to teach their children at home before kindergarden.

    2. quality early childhood education not only has one of the highest yields-for every $1 spent on early education and care, $8 is saved on crime, public assistance, supplemental schooling, and so on-but is also one of the most important stages at which a child's educational trajectory is shaped

      Early education is critical for setting students up for success, but access to high-quality programs like Head Start is often limited to wealthier families. it shows how early disadvantages accumulate, leaving poor students "behind" before they even start elementary school, how harsh the society is.

    3. Thankfully, poor children may have access to the federally funded Head Start program, but children of the wealthy have a dif~erent kind of head start.

      I totally agree with this. Many wealthy children have access to resources that low-income students don't have. They often have parents who can help them with school and guide them through college. While the government has made programs to help lower-income children through programs like Head Start, I feel that wealthier children still have far more resources and privileges when it comes to education. So I don't think there is really going to a equal ground when it comes to educations between lower income and higher income students.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

      Learn more at Review Commons


      Reply to the reviewers

      We thank the reviewers for their general comment and for the critical evaluation of our analyses and results interpretation. Their comments greatly helped us to improve the manuscript.

      • *

      Reviewer #1 (Evidence, reproducibility and clarity (Required)):

      Summary: An analysis of an Arabidopsis VSP13 presumed lipid transport is provided. The analysis pretty much follows similar studies done on yeast and human homologs. Key findings are the identification of multiple products from the locus due to differential splicing, analysis of lipid binding and transport properties, subcellular location, tissue specific promoter activity, mutant analysis suggesting a role in lipid remodeling following phosphate deprivation, but no physiological or growth defects of the mutants. Major points: The paper is generally written and documented, the experiments are well conducted and follow established protocols. The following major points should be considered:

      1. There are complementary lipid binding assays that should be considered such as liposome binding assays, or lipid/western dot blots. All of these might give slightly different results and may inform a consensus. Of course, non-membrane lipids such as TAG cannot be tested in a liposome assay.

      Concerning lipid transfer proteins (LTPs), it is important to differentiate the lipid binding capacity related to the transport specificity (which lipids are transported by a LTP?) from the lipid binding capacity linked to the targeting of a LTP to a specific membrane (a LTP can bind a specific lipid via a domain distinct from the lipid transfer domain to be targeted in cells, but will not transport this lipid). Both aspects are of high interest to be determined. Our goal here was to focus on the identification of the lipids bound to AtVPS13M1 and to be likely transported, which is why we used a truncation (1-335) corresponding to the N-term part of the hydrophobic tunnel. Liposome binding assays and lipid dot blots are necessary to answer the question of the membrane binding capacity of the protein. We think that this aspect is out of the scope of the current article as it will require to express and purify other AtVPS13M1 domains that are known to bind lipids such as the two PH domains and the C2. This will be the scope of future investigations in our lab.

      Similarly, lipid transfer based only on fluorophore-labeled lipids may be misleading because the fluorophore could affect binding. It is mentioned that the protein in this assay is tethered by 3xHis to the liposomes. Un less I ma missing something, I do not understand how that should work. This needs to be better explained.

      We truly agree with Reviewer 1 that the presence of a fluorophore could affect lipid binding to the protein. In this assay, lipids are labeled on their polar head and it is therefore difficult to conclude about the specificity of our protein in term of transport. This assay is used as a qualitative assay to show that AtVPS13M1(1-335) is able to transfer lipids in vitro, and in the manuscript, we did not make any conclusion about its transport specificity based on this assay, but rather used the binding assay to assess the binding, and likely transport, specificity of AtVPS13M1. FRET-based assay is a well-accepted assay in the lipid transfer community to easily probe lipid transport in vitro and has been used in the past to assess transfer capacity of different proteins, including for VPS13 proteins (for examples, see (Kumar et al., 2018; Hanna et al., 2022; Valverde et al., 2019)).

      To be able to transfer lipids from one liposome to another, both liposomes have to be in close proximity. Therefore, we attached our protein on donor acceptors, to favor the transport of the fluorescent lipids from the donor to the acceptor liposomes. Then, we progressively increased acceptor liposomes concentration to favor liposome proximity and the chance to have lipid transfer. We added a scheme on Figure 3B of the revised version of the manuscript to clarify the principle of the assay. In addition, we provided further control experiments suggested by Reviewers 2 and 3 showing that the fluorescence signal intensity depend on AtVPS13M1(1-335) protein concentration and that no fluorescence increase is measured with a control protein (Tom20.3) (see Figure 3C-D of the revised manuscript).

      The in vivo lipid binding assay could be obscured by the fact that the protein was produced in insect cells and lipid binding occurs during the producing. What is the evidence that added plants calli lipids can replace lipids already present during isolation.

      Actually we don’t really know whether the insect cells lipids initially bound to AtVPS13M1(1-335) are replaced by calli lipids or whether they bound to still available lipid binding sites on the protein. But we have two main lines of evidence showing that our purified protein can bind plant lipids even in the presence of insect cells lipids: 1) our protein can bind SQDG and MGDG, two plants specific lipids, and 2) as explained p.8 (lines 243-254), lipids coming from both organisms have a specific acyl-chain composition, with insect cells fatty acids mainly composed of C16 and C18 with 0 or 1 unsaturation whereas plant lipids can have up to 3 unsaturations. By analyzing and presenting on the histograms lipid species from insect cells, calli and those bound to AtVPS13M1(1-335), we were able to conclude that for all the lipid classes besides PS, a wide range of lipid species deriving from both organisms was bound to our protein. The data about the lipid species bound to AtVPS13M1(1-335) are presented in Figure 2E and S2.

      The effects on lipid composition of the mutants are not very drastic from what I can tell. Furthermore, how does this fit with the lipid composition of mitochondria where the protein appears to be mostly located?

      It is true that lipid composition variations in the mutants are not drastic but still statistically significant. As a general point in the field of lipid transfer, it is not very common to have major changes in total lipidome on single mutants of lipid transfer proteins because of a high redundancy of lipid transport pathway in cells. This is particularly true for VPS13 proteins, as exemplified by multiple studies. Major lipid phenotypes can be revealed in specific conditions, such as phosphate starvation in our case, or when looking at specific organelles or specific tissues and/or developmental stages. This is explained and illustrated by examples in the discussion part p. 16 (line 526-532). In addition, as suggested by Reviewer 3, we performed further lipid analysis on calli and also on rosettes under Pi starvation and found a similar trend (Figure 4 and S4 of the revised version of the manuscript). Thus, we believe that, even if not drastic, these variations during Pi starvation are a real phenotype of our mutants.

      As we found that our protein is located at the mitochondrial surface, we agree that Reviewer 1’s suggestion to perform lipidomic analyses on isolated mitochondria will be of high interest but this will be the scope of future studies that we will performed in our lab. First, we would like to identify all the organelles at which AtVPS13M1 is localized before performing subfractionations of these different organelles from the same pool of cell cultures grown in presence or absence of phosphate.

      For the localization of the fusion protein, has it been tested whether the furoin is functional? This should be tested (e.g. by reversion of lipid composition).

      As we did not observe major developmental phenotypes in our mutants, complementation should be indeed tested by performing lipidomic analyses in calli or plants grown in presence or absence of Pi, which is a time-consuming and expensive experiment. Because we used the fusions mainly for tissue expression study and subcellular localization and not for functional analyses, we believe that this is not an essential control to be performed for this work.

      It is speculated that different splice forms are located to different compartments. Can that be tested and used to explain the observed subcellular location patterns?

      Indeed some splice forms can modify the sequence of domains putatively involved in protein localization. This could be tested by producing synthetic constructs with one specific exon organization, which is challenging according to the size of AtVPS13M1 cDNA (around 12kb). In addition, our long-read sequencing experiment and PCR analyses revealed the existence of six transcripts, a major one representing around 92% and the five others representing less than 2.5% (Figure 1D). Among the five less abundant transcripts, four produce proteins with a premature stop codon and are likely to arise from splicing defects as explained in the discussion part p. 15 (lines 488-496). One produces a full-length protein with an additional loop in the VAB domain but because of the low abundance of this alternative transcript (1.4%), we believe it does not contribute significantly to the major localization we observed in plants and did not attend to analyze its localization.

      GUS fusion data only probe promoter activity but not all levels of gene expression. That caveat should be discussed.

      We are aware of this drawback and that is the reason why we fused the GUS enzyme directly to our protein expressed under its native locus (i.e. with endogenous promoter and exons/introns) as depicted in Figure 5A. Therefore, our construction allows to assess directly AtVPS13M1 protein level in plant tissues.

      Minor points: 1. Extraplastidic DGDG and export from chloroplasts following phosphate derivation was first reported in PMID: 10973486.

      We added this reference in the text.

      Check throughout the correct usage of gene expression as genes are expressed and proteins produced.

      Many thanks for this remark, we modified the text accordingly

      In general, the paper is too long. Redundancies between introduction, results and discussion should be removed to streamline.

      We reduced the text to avoid redundancy.

      I suggest to redraw the excel graphs to increase line thickness and enlarge font size to increase presentation and readability.

      We tried as much as we can to enlarge graphs and font size increasing readability.

      Reviewer #1 (Significance (Required)):

      Significance: Interorganellar lipid trafficking is an important topic and especially under studied in plants. Identifying components involved represents significant progress in the field. Similarly, lipid remodeling following phosphate derivation is an important phenomenon and the current advances our understanding.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)):

      Summary: The manuscript "AtVPS13M1 is involved in lipid remodelling in low phosphate and is located at the mitochondria surface in plants" by Leterme et al. identifies the protein VPS13M1 as a lipid transporter in Arabidopsis thaliana with important functions during phosphate starvation. The researchers were able to localise this protein to mitochondria via GFP-targeting in Arabidopsis. Although VPS13 proteins are well described in yeast and mammals, highlighting their importance in many vital cellular processes, there is very little information on them in plants. This manuscript provides new insights into plant VPS13 proteins and contributes to a better understanding of these proteins and their role in abiotic stress responses, such as phosphate starvation.

      Major points: - Please describe and define the domains of the VPS13M1 protein in detail, providing also a figure for that. Figure 1 is mainly describing possible splice variants, whereas the characteristics of the protein are missing.

      We have added information on AtVPS13M1 domain organization in the introduction (p.4, lines 103-109) and referred to Figure 1A that described protein domain organization. We did not added too much details as plant VPS13 protein domains organization was extensively described in two previous studies cited several times in the manuscript (Leterme et al., 2023; Levine, 2022).

      • Please compare the expression level of VPS13M1 in the presence and in the absence of phosphate.

      Many thanks for this suggestion. We performed qRT-PCR analyses of AtVPS13M1 from mRNA extracted from calli grown six days in presence and absence of phosphate. The results obtained did not reveal variations in mRNA level. The results were added in Figure S1A of the revised version of the manuscript and discussed in p.5 (lines 154-156).

      • Page 9, second paragraph: Here, the lipid transport capability of AtVPS13M1 is described. Varying concentrations of this recombinant protein should be used in this test. Further, it is not highlighted, that a truncated version of VSP13M1 is able to transport lipids. This is surprising, since this truncated version is less than 10% of the total protein (only aa 1-335).

      We agree with reviewer 2 that increasing protein concentration is an important control to perform. We included an experiment with an increasing quantity of protein (2X and 4X) in the revised version of the manuscript and showed that the signal intensity increased faster when protein concentration is higher (Figure 3D of the revised manuscript). As requested by Reviewer 3, we also included a negative control with Tom20.3 to show that the signal increase after the addition of AtVPS13M1(1-335) is specific to this protein (Figure 3C of the revised manuscript).

      The transport ability of the N-terminal part of VPS13 was demonstrated in yeast and mammals VPS13D (Kumar et al., 2018; Wang et al., 2021). We highlighted this p. 7 (lines 213-218) of the revised version of the manuscript. This is explained by the inherent structure of VPS13 proteins that are composed of several repeats of the same domain type called RBG (for repeating β-groove), each forming a β-sheet with a hydrophobic surface. The higher the number of RBG repeats, the longer the hydrophobic tunnel is. The (1-335) N-terminal region corresponds to two RBG unit repeats forming a “small” tunnel able to bind and transfer lipids. The number of RBG repeats has influence on the quantity of lipids bound per protein in vitro, the longest the protein is, the highest the number of lipid molecules bound is (Kumar et al., 2018), but the effect on protein length on in vitro lipid transfer capacity has not been investigated yet to the best of our knowledge.

      • Also, for phenotype analysis, T-DNA insertion mutants are used that still contain VPS13M1 transcripts. Although protein fragments where not detected by proteomic analysis, this might be due to low sensitivity of the proteomic assay. Further the lipid transport domain of VPS13M1 (aa 1-335) might not be affected by the T-DNA insertions at all. Here more detailed analysis needs to be done to prove that indeed loss-of protein function occurs in the mutants.

      We do not have other methods than proteomic to test whether our mutants are KO or not. We tried unsuccessfully to produce antibodies. Mass spectrometry is the most sensitive method but the absence of detection indeed does not mean the absence of the protein. From proteomic data, we can conclude that at least, our mutants present a decrease in AtVPS13M1 protein level, thus we called them “knock down” in the revised version of the manuscript and added the following sentence p. 9 (lines 297-300): “As the absence of detection of a protein by mass spectrometry-based proteomics does not allow us to strictly claim the absence of this protein in the sample, we concluded that AtVPS13M1 expression in both atvps13m1-1 and atvps13m1-4 was below the detection limit and consider them as knock down (KD) for AtVPS13M1.”

      • Localisation in mitochondria: As the Yepet signal is very weak, a control image of not transfected plant tissue needs to be included. Otherwise, it might be hard to distinguish the Yepet signal from background signal. The localisation data presented in Figure 5 does not allow the conclusion that VPS13M1 is localized at the surface of mitochondria as stated in the title. It only indicates (provided respective controls see above) that VPS13M1 is in mitochondria. Please provide more detailed analysis such as targeting to tobacco protoplasts, immunoblots or in vitro protein import assays. Also test +Pi vs. -Pi to see if VPS13M1 localisation is altered in dependence of Pi.

      Indeed our Yepet signal is not very strong but on the experiments we performed on Col0 non-transformed plants, we did not very often see fluorescence background in the leaves’ vascular tissue, that is why we focused our study on this tissue. We sometimes observed some background signals in some cells that are clearly different from AtVPS13M1-3xYepet signals and never co-localized with mitochondria. Examples of these aspecific signals are presented in Figure S6E of the revised version of the manuscript.

      We agree with reviewer 2 that our confocal images suggested, but not demonstrated, a localization at the surface of mitochondria. To confirm the localization, we generated calli cell cultures from AtVPS13M1-3xYepet lines and performed subcellular fractionations and western blot analyses confirming that AtVPS13M1 was indeed enriched in mitochondria and also in microsomal fractions (Figure 6G of the revised version). Then we performed mild proteolytic digestion of the isolated mitochondria with thermolysin and show that AtVPS13M1 was degraded, as the outer membrane protein Tom20.3, but not the inner membrane protein AtMic60, showing that AtVPS13M1 is indeed at the surface of mitochondria (Figure 5H of the revised manuscript). We believe that this experiment, in addition to the confocal images showing a signal around mitochondria, convincingly demonstrates that AtVPS13M1 is located at the surface of mitochondria.

      The localization of AtVPS13M1 under Pi starvation is a very important question that we tried to investigate without success. Indeed, we intended to perform confocal imaging on seedlings grown in liquid media to easily perform Pi starvation as described for the analysis of AtVPS13M1 tissue expression with β-glucuronidase constructs. However, the level of fluorescence background was very high in seedlings and no clear differences between non-transformed and AtVPS13M1-3xYepet lines were observed, even in root tips where the protein is supposed to be the most highly expressed according to β-glucuronidase assays. Example of images obtained are presented in Figure R1. We concluded that the level of expression of our construct was too low in seedlings. The constructions of lines with a higher AtVPS13M1 expression level, by changing the promotor, to better analyze AtVPS13M1 in different tissues or in response to Pi starvation will be the scope of future work in our laboratory in order to investigate AtVPS13M1 localization under low Pi.

      Phenotype analysis needs to be done under Pi stress and not under cold stress! Further, root architecture and root growth should also be done under Pi depletion. Here the title is also misleading, it is not at all clear why the authors switch from phosphate starvation to cold stress.

      In the revised version of the manuscript, we analyzed the seedlings root growth of two mutants (atvps13m1-3 and m1-4) under low Pi and did not notice significant differences (Figure 7E, S7D of the revised version). We analyzed growth under cold stress because this stress also promotes remodeling of lipids, but we agree that it goes beyond the scope of this article that is focused on Pi starvation and we removed this part from the revised manuscript.

      Minor points: Page 3, line 1: what does the abbreviation VPS stand for?

      The definition of VPS (Vacuolar Protein Sorting) was added.

      Page 3, line 1: change "amino acids residues" to "amino acid residues"

      This was done.

      Page 3, line 8 - 12: please rewrite this sentence. You write, that because of their distribution VPS13 proteins do exhibit many important physiological roles. The opposite is true: They are widely distributed in the cell because of their involvement in many physiological processes.

      We changed the sentence to “ VPS13 proteins localize to a wide variety of membranes and membrane contact sites (MCSs) in yeast and human (Dziurdzik and Conibear, 2021). This broad distribution on different organelles and MCSs is important to sustain their important roles in numerous cellular and organellar processes such as meiosis and sporulation, maintenance of actin skeleton and cell morphology, mitochondrial function, regulation of cellular phosphatidylinositol phosphates level and biogenesis of autophagosome and acrosome (Dziurdzik and Conibear, 2021; Hanna et al., 2023; Leonzino et al., 2021).”

      Page 6, line6: change "cDNA obtained from A. thaliana" to "cDNA generated from A. thaliana.

      This was done.

      Page 6, line 10: change" 7.6kb" to "7.6 kb"

      This was done.

      Page 7: address this question: can the isoforms form functional VPS13 proteins? This might help to postulate whether these isoforms are a result of defective splicing events.

      We addressed this aspect in the discussion p.15 at lines 486-502.

      Figure 2 B: Change "AtVPS13M1"to "AtVPS13M1(1-335)"

      This was done.

      Figure 2, legend: -put a blank before µM in each case.

      This was done.

      -Change 0,125µM to 0.125 µM

      This was done.

      -what does "in absence (A-0µM)" mean?

      This means that the Acceptor liposomes are at 0 µM. To clarify, we changed it to “Acceptor 0 µM” in the revised version of the manuscript (Figure 3C).

      -Which statistical analysis was employed?

      We performed a non-parametric Mann-Whitney test in the revised version of the manuscript. This was indicated in the legend.

      -Further, rewrite the sentence "Mass spectrometry (MS) analysis of lipids bound to AtVPS13M1(1-335) or Tom20 (negative control) after incubation with calli total lipids. Results are expresses in nmol of lipids per nmol of proteins (C) or in mol% (D)". -"C" and "D" are not directly comparable, as in "C" no Tom20 was used and in "C" no insect cells were used.

      -Further, in "D" the experimental setup is not clear. AtVPS13(1-335) is supposed to be purified protein after incubation with calli lipids (figure 2, A). Further, in the same figure, lipid composition of "insect cells" and "calli-Pi" are compared àwhy? Please clarify this.

      C and D are two different representations of the same results providing different types of information. In C., the results are expressed in nmol of lipids / nmol of proteins to assess 1) that the level of lipids found in AtVPS13M1(1-335) purifications is significantly higher than what we can expect from the background (assessed using Tom20) and 2) what are the classes of lipids that associate or not to AtVPS13M1(1-335). In D. the lipid distribution in mol% is presented for AtVPS13M1(1-335) as well as for total extracts from calli and insect cells to be able to compare if one lipid class is particularly enriched or not in AtVPS13M1(1-335) purifications compared to the initial extracts with which the protein was incubated. As an example, it allows to deduce that the absence of DGDG detected in the AtVPS13M1(1-335) purifications is not linked to a low level of DGDG in the calli extract, because it represented around 15 mol%, but likely to a weak affinity of the protein for this lipid. We did not represent the Tom20 lipid distribution on this graph because it represents background of lipid binding to the purification column and might suggest that Tom20 binds lipids. We changed the legend in this way and hope that it is clearer now: “C-D. Mass spectrometry (MS) analysis of lipids bound to AtVPS13M1(1-335) or Tom20 (negative control) after incubation with calli total lipids and repurification. Results are expresses in nmol of lipids per nmol of proteins in order to analyze the absolute quantity of the different lipid classes bound to AtVPS13M1(1-335) compared to Tom20 negative control (C), and in mol% to assess the global distribution of lipid classes in AtVPS13M1(1-335) purifications compared to the total lipid extract of insect cells and calli (D).”

      Figure 3: -t-test requires a normal distribution of the data. This is not possible for an n=3. Please use an adequate analysis.

      We performed more replicates and used non-parametric Mann-Whitney analyses in the revised version of the manuscript.

      -Please clarify the meaning of the letters on the top of the bars in the legend.

      This corresponded to the significance of t-tests performed in the first version of the manuscript that were reported in Table S3. As in the new version we performed Mann-Whitney tests, we highlighted the significance by stars and in the figure legends.

      Please, make it clear that two figures belong to C.

      This was clarified in the legend.

      -Reorganise the order of figure 3 (AàBàCàD)

      Because of the configuration of the different histograms presented in the figure, we were not able to change the order but we believed that the graphs can be easily red this way.

      Page 10, 3. Paragraph: since the finding, that no peptides were found in the VSP13M1 ko lines, although transcription was not altered, is surprising, please include the proteomic data in the supplement

      Proteomic data were deposited on PRIDE with the identifier PXD052019. They will remain not publicly accessible until the acceptance of the manuscript.

      Page 11, line 17: The in vitro experiments showed a low affinity of VSP13M1 towards galactolipids. It is further claimed that this is consistent with the finding of the AtVSP13M1 Ko line in vivo, that in absence of PI, no change in DGDG content could be observed. However, the "absence" of VSP13M1 in vivo might still result in a bigger VSP13M1 protein, than the truncated form (1-335) used for the in vitro experiments

      It is true that our in vitro experiments were performed only with a portion of AtVPS13M1 and that the length of the protein could influence protein binding specificity. We removed this assessment from the manuscript.

      Page 13, lane 8: you should reconsider the use of a triple Yepet tag: If two or more identical fluorescent molecules are in close proximity, their fluorescence emission is quenched, which results in a weak signal (as the one that you obtained). See: Zhuang et al. 2000 (PNAS) Fluorescence quenching: A tool for single-molecule protein-folding study

      Many thanks to point this paper. We use a triple Yepet because AtVPS13M1 has a very low level of expression and because this strategy was used successfully to visualize proteins for which the signal was below the detection level with a single GFP (Zhou et al., 2011). The quenching of the 3xYepet might also depend on the conformation they adopt on the targeting protein.

      Page 13, line 14: change 1µm to 1 µm

      This was done.

      Page 13, line 29: please reduce the sentence to the first part: if A does not colocalize with B, it is not necessary to mention that B does not colocalise with A.

      The sentence was modified accordingly.

      Page 14, 2. Paragraph: it is not conclusive that phenotype analysis is suddenly conducted with plants under cold stress, since everything was about Pi-starvation and the role of VSP13M1. Lipid remodelling under Pi stress completely differs from the lipid remodelling under cold stress.

      We eliminated this part in the revised version of the manuscript.

      Page 14, line 20: change figure to Figure

      This was done.

      Page 07, line 17: change artifact to artefact

      This was done.

      Reviewer #2 (Significance (Required)):

      General assessment: The paper is well written and technically sound. However, some points could be identified, that definitely need a revision. Overall, we got the impression that so far, the data gathered are still quite preliminary and need some more detailed investigations prior to publication (see major points).

      Advance: The study definitely fills a gap of knowledge since not much is known on the function of plant VPS13 proteins so far.

      Audience: The study is of very high interest to the plant lipid community but as well of general interest for Plant Molecular Biology and intracellular transport.

      Our expertise: Plant membrane transport and lipid homeostasis.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      The manuscript by Leterme et al. (2024) describes the characterization of VPS13M1 from Arabidopsis. VPS13 proteins have been analyzed in yeast and animals, where they establish lipid transfer connections between organelles, but not much is known about VPS13 proteins in plants. First, different splicing forms were characterized, and the form A was identified as the most relevant one with 92% of the transcripts. The protein (just N-terminal 335 amino acids out of ca. 3000 amino acids) was expressed in insect cells and purified. Next, the protein was used for lipid binding assays with NBD-labeled lipids followed by analysis in polyacrylamide gel electrophoresis. VPS13M1 bound to PC, PE, PS and PA. Then, the protein from insect cells was incubated with Arabidopsis callus lipids, and lipids bound to VPS13M1 analyzed by LC-MS/MS. Lipid transfer between liposomes was measured by the change in fluorescence in donor liposomes derived from two labeled lipids after addition of the protein caused by lipid transfer and dilution to acceptor liposomes. T-DNA insertion mutants were isolated and the lipids measured in callus derived from these mutants. Protein localization in different plant organs was recorded with a GUS fusion construct transferred into transgenic plants. The protein was localized to mitochondria using a VPS13M1-Yepet fusion construct transferred into mutant plants. The mutant plants show no visible difference to wild type, even when the plants were grown under stress conditions like low temperature. The main message of the title is that VPS13M1 localizes to the mitochondria which is well documented, and it is involved in lipid remodeling under low phosphate conditions.

      The lipid transfer assay shown in Figure 2F lacks a negative control. This would be the experiment with donor and acceptor liposomes in the presence of another protein like Tom20.

      Many thanks for this suggestion. In the revised version of the manuscript, we performed a fluorescent lipid transport assay with Tom20.3 in the presence of 25 µM of donor liposomes and 1.5 mM of acceptor liposomes, the condition for which we observed a maximum of transport for AtVPS13M1(1-335). As expected, no fluorescence increase was observed. The results are presented in the Figure 3C of the revised manuscript.

      The lipid data (Fig. 3 and Fig. S4) do not sufficiently support the second claim, i.e. that the protein is involved in lipid remodeling under low P. Data in Fig. 3C are derived from only 3 replicates and in Fig. S4 from only 2 replicas with considerable error bars. Having only 2 replicates is definitely not sufficient. Fig. 3C shows a suppression in the decrease in PE and PC at 4 d of P deprivation (significant for two mutants for PE, for only one for PC). Fig. S4A shows suppression of the decrease in PC at 6 d after P deprivation (significant for both mutants), but no significant effect on PE. Fig. 4SB shows no significant change in PE or PC at -P after 8 d of P deprivation. The data are not consistent. There are also problems with the statistics in Fig. 3 and Fig. S4. The authors used T-test, but place letters a, b, c on top of the bars. Usually, asterisks should be used to indicate significant differences. Data indicate medians and ranges, not mean and SD. In Fig. S4, how can you indicate median and range if you have only 2 replicates? Why did the authors use callus for lipid measurements? Why not use leaves and root tissues? What does adjusted nmol mean? What does the dashed line at 1.05 on the y axis mean? Taken together, I suggest to repeat lipid measurements with leaves and roots from plantets grown under +P and -P conditions in tissue culture with 5 replcates. Significant differences can be analyzed on the level of absolute (nmol per mg FW/DW) or relative (%) amounts.

      Here are our answers to concerns about the design of our lipidomics experiments:

      We used calli for lipid measurement because it is very easy to control growth conditions and to performed phosphate starvation from this cell cultures. The second reason is that it is a non-photosynthetic tissue with a high level of phospholipids and a low level of galactoglycerolipids and it is easier to monitor the modification of the balance phospholipids/galactoglycerolipids in this system. The lipid analysis on calli at 4 days of growth in presence or absence of Pi were performed on 3 biological replicates but on two different mutants (atvps13m-1 and m1-3) and we drew our conclusions based on variations that were significant for both mutants. In the revised version of the manuscript, we performed further lipidomic analyses on calli from Col0 and another mutant (atvps13m1-2) after 6 days of growth in presence or absence of Pi (Figure 4E, S4A-C, n=4-5) and added new data on a photosynthetic tissue (rosettes) from Col0 and atvps13m1-3 mutant. For rosettes analysis, seeds were germinated 4 days in plates with 1 mM Pi and then transferred on plates with 1 mM or 5 µM of Pi. Rosettes were harvested and lipids analyzed after 6 days (Figure 4F-G, S4D, n=4-5). All the data were represented with medians and ranges because we believe that median is less sensitive to extreme values than mean and might better represent what is occurring. Ranges highlight the minimal and maximal value of the data analyzed and we believe it is a representative view of the variability we obtained between biological samples.

      Lipid measurement are done by mass spectrometry. As it was already reported, mass spectrometry quantification is not trivial as the intensity of the response depends on the nature of the molecule (for a review, see (Jouhet et al., 2024)). To counteract this ionisation problem, we developed a method with an external standard that we called Quantified Control (QC) corresponding to an A. thaliana callus lipid extract for which the precised lipid composition was determined by TLC and GC-FID. All our MS signals were “adjusted” to the signal of this QC as described in (Jouhet et al., 2017). Therefore our lipid measurement are in adjusted nmol. In material and method we modified the sentence accordingly p22 lines 720-723: “Lipid amounts (pmol) were adjusted for response differences between internal standards and endogenous lipids and by comparison with a quality control (QC).” This allows to represent all the lipid classes on a same graph and to have an estimation of the lipid classes distribution. To assess the significance of our results, we used in the revised version of the manuscript non-parametric Mann-Whitney tests and added stars representing the p-value on charts. This was indicated in the figure legends.

      Here are our answers to concerns about the interpretation of our lipidomics experiments:

      To summarize, in the revised version of the manuscript, lipid analyses were performed in calli from 3 different mutants (two at day 4, one at day 6) and in the rosettes from one of these mutants. All the results are presented in Figure 4 and S4. In all the experiments, we found that in +Pi, there is no major modifications in the lipid content or composition. In –Pi, we found that the total glycerolipid content is always higher in the mutant compared to the Col0, whatever the tissue or mutant considered (Figure 4A and S4A, D). In calli, this higher increase in lipid content is mainly due to an accumulation of phospholipids and in rosettes, of galactolipids. Because of high variability between our biological replicates, we did not always found significant differences in the absolute amount of lipids in –Pi. However, the analysis of the fold change in lipid content in –Pi vs +Pi always pointed toward a reduced extent of phospholipid degradation. We also added in these graphs the fold change for the total phospholipids and total galactolipids contents in the revised version of the manuscript. We believe that the new analyses we performed strengthen our conclusion about the role of AtVPS13M1 in phospholipid degradation and not on the recycling of precursors backbone to feed galactoglycerolipids synthesis at the chloroplast envelope.

      Page 9, line 15: Please use the standard form of abbreviations of lipid molecular species with colon, e.g. PC32:0, not PC32-0

      The lipid species nomenclature has been changed accordingly.

      Page 11, line 4, (atvps13m1.1 and m1.3: please indicate the existence of mutant alleles with dashes, i.e. (atvps13m1-1 and atvps13m1-3

      Names of the mutants have been changed accordingly.

      Page 14, line 21: which line is indicated by atvps13m1.2-4? What does -4 indicate here?

      This indicates that mutants m1-2 to m1-4 were analyzed.

      Page 16, line 25: many abbreviations used here are very specific and not well known to the general audience e.g. ONT, IR, PTC, NMD etc. I think it is OK to mention them here, but still use the full terms, given that they are not used very frequently in the manuscript.

      We kept ONT abbreviation because it was cited many times in both the results and discussion part. IR, PTC and NMD were cited only in the discussion and were eliminated.

      Page 19, line 11. The authors cite Hsueh et al and Yang et al for LPTD1 playing a role in lipid homeostasis during P deficiency. But Yang et al. described the function of a SEC14 protein in Arabidopsis and rice during P deficiency. Is SEC14 related to LPTD1?

      Many thanks for noticing this mistake. We removed the citation Yang et al. in the revised version of the manuscript.

      Reference Tangpranomkorn et al. 2022: In the text, it says that this is a preprint, but in the Reference list, this is indicated with "Plant Biology" as Journal. In the internet, I could only find this manuscript in bioRxiv.

      This manuscript was accepted in “New Phytologist” in December 2024 and is now cited accordingly in the new version of the manuscript.

      Reviewer #3 (Significance (Required)):

      The manuscript by Leterme et al describes the characterization of the lipid binding and transport protein VTPS13M1 from Arabidopsis. I think that the liposome assay needs to be done with a negative control. Furthermore, I have major concerns with the lipid data in Fig. 3C and Fig. S4. These lipid data of the current manuscript need to be redone. I do not agree that the lipid data allow the conclusion that "AtVPS13M1 is involved in lipid remodeling in low phosphate" as stated in the title.

      References cited in this document:

      Dziurdzik, S.K., and E. Conibear. 2021. The Vps13 Family of Lipid Transporters and Its Role at Membrane Contact Sites. Int J Mol Sci. 22:2905. doi:10.3390/ijms22062905.

      Hanna, M., A. Guillén-Samander, and P. De Camilli. 2023. RBG Motif Bridge-Like Lipid Transport Proteins: Structure, Functions, and Open Questions. Annu Rev Cell Dev Biol. 39:409–434. doi:10.1146/annurev-cellbio-120420-014634.

      Hanna, M.G., P.H. Suen, Y. Wu, K.M. Reinisch, and P. De Camilli. 2022. SHIP164 is a chorein motif lipid transfer protein that controls endosome–Golgi membrane traffic. Journal of Cell Biology. 221:e202111018. doi:10.1083/jcb.202111018.

      Jouhet, J., E. Alves, Y. Boutté, S. Darnet, F. Domergue, T. Durand, P. Fischer, L. Fouillen, M. Grube, J. Joubès, U. Kalnenieks, J.M. Kargul, I. Khozin-Goldberg, C. Leblanc, S. Letsiou, J. Lupette, G.V. Markov, I. Medina, T. Melo, P. Mojzeš, S. Momchilova, S. Mongrand, A.S.P. Moreira, B.B. Neves, C. Oger, F. Rey, S. Santaeufemia, H. Schaller, G. Schleyer, Z. Tietel, G. Zammit, C. Ziv, and R. Domingues. 2024. Plant and algal lipidomes: Analysis, composition, and their societal significance. Progress in Lipid Research. 96:101290. doi:10.1016/j.plipres.2024.101290.

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    1. Mental processes are gradually seen as not taking placeexclusively “in the head” but being situated and distributed be-tween brain and body, person and environment.

      star

    1. Walter Kempner is no I don't so he's an interesting fellow kind of a controversial guy from the 1940s and 1950s maybe the 1960s he's a physician who did a bunch of studies with diabetics people that were morbidly obese morbidly obese we're 00:11:59 talking hundreds of pound obesity if your team wants to find like images you can see the study so he has this thing called the rice diet and in the 1950s and 1960s he put people on a very very 00:12:12 high carb very lowfat very low protein diet it was essentially white sugar so sucrose and white rice was the majority of the 00:12:23 diet and they got better they lost I mean some people went from like literally being round to being thin their diabetes got better so he fixed 00:12:36 diabetes with a very high carb very low fat very low protein diet and it was by necessity it was low protein because it was all carbohydrates and the problem is that the human brain doesn't want to do this like so he's controversial because in 00:12:50 order to get his patients to do this he had to like he had to do some crazy things to cajo them and so I'm not condoning his experiments but the science is is interesting and what it says about human physiology to me is is 00:13:03 very compelling that you can give someone a diet of pure white sugar and rice and their diabetes gets better why does their diabetes get better and this is not even short term this is long term 00:13:15 so that in the span of I think it was four to six months he could then liberalize these people's diets and their diabetes did not return so this is really interesting to me and I think it kind of ties into the seed oil 00:13:27 piece and I'm not suggesting that this is a a reasonable therapy for people because what we know about human physiology and the human brain is that if you try to push any of the macros too far our brain really 00:13:39 Rebels humans seem to be able to lose weight by cutting carbohydrates or cutting fat if you cut both of them together you have what's called rabbit starvation and you can lose a lot of weight very quickly but it's very 00:13:52 stressful on the body hormonally if you cut carbohydrates you have a ketogenic diet if you cut fat you have a lowfat diet and if you look at the trials head-to-head of low fat or low carb they both have about the same amount of weight loss so there's some contention 00:14:05 but it doesn't really look like a ketogenic diet is magical for weight loss a like a lowfat diet is magical for weight loss relative to Kido they both work but when you cut both when you cut 00:14:17 the the fat really really low that's interesting to me and this is what happened in the rice diet and they the the fat was so low that these Pro people were probably becoming fatty acid deficient and there's a fatty acid that 00:14:30 you can measure in the human blood it's called me acid Mead and that's an indication of fatty acid deficiency essential quote unquote fatty acid deficiency and so the hypothesis is that one of the reasons this diet might have 00:14:43 worked is because when you restrict fat that much the cell has to turn over those cell membranes in a different way and that probably causes a lot of these polyunsaturated fats that are stuck in the cell membranes to become mobilized 00:14:56 and turn over the human body doesn't make polyunsaturated fats but if you feed someone carbohydrates the human body can make saturated fats and monounsaturated fats but this is essentially an accelerated way to get 00:15:10 rid of what were potentially excess polyunsaturated fatty acids in these people's cell membranes again I don't think this is a good therapy for humans because it's so hard on the brain humans don't want to do this we sort of 00:15:22 gravitate toward like a third fat third carbohydrates and maybe a third protein depending how you're looking at it maybe a little less if you're doing grams or calories but there's some balance of those things that kind of is what our body tends to if you go too low fat your 00:15:35 body will Rebel and you know if you go too low carb your body's like I want some carbohydrates so the indication here is there's something going on in these cell membranes and there's a massive shift that happens in the cell membrane when you get very very low fat 00:15:47 and I think that's having to do with this turnover of these omega-6 fatty acids so there's a couple of ways to do this without going solo fat you can also just get them out of your diet like extremely intentionally and then do more 00:16:00 fats that are saturated in their place and so this is the part where it gets a little bit cumbersome for people to think about but I think that you can get similar results by just having a low linolic acid diet so let's back up for a 00:16:13 moment talk about linolic acid Omega 6 which means that six carbons from the end of the molecule is the first double bond it's an 18 carbon molecule it's polyunsaturated which means it has multiple double bonds and there's a 00:16:28 small amount in ruminant fat so things like cows or goats or bison or lamb sheep deer small amount one to 2% but animals like humans or pigs or 00:16:43 chickens that are monogastric accumulate linolic acid so the more of this fatty acid we eat the more we store we don't have a way to get rid of it like cows do cows can transform it so where do we 00:16:55 find linic acid in the human diet we find it in chicken and pigs that are fed corn and soy so evolutionarily inappropriate diets and you find it in nuts and seeds plant foods and we have a massive 00:17:08 input of this linolic acid into the human diet now because we're feeding our animals corn and soy things that they've never eaten historically and all of our processed food is combined is added with these 00:17:22 seed oils things like corn canola sunflower safflower soybean grape seed these are all seed oils and they contain between 25 and 65% linolic acid so what you have I 00:17:35 think is an evolutionarily inconsistent amount of linolic acid coming into the human diet and just like pigs just like chickens when we eat corn and soy when we eat Foods when we eat seed oils that 00:17:48 have a lot of this linolic acid we store it and I think that over time it accumulates in our cell membranes and in the membranes of our mitochondria these little powerhouses in the cell and causes problems and we can get into how it might cause problems at the cellular 00:18:00 level if you want but that's kind of the the 15,000 foot perspective that we have this this fatty acid that is in our food supply historically but when we are living in a quote naturalistic way in 00:18:13 the forest in the jungle there's really very limited access to foods that are high in this it's very hard to get the amount of seeds that would you would get even in 3 to five tablespoons of seed oils so I've done some content about 00:18:25 this you look at corn oil for instance or rice brand oil is an even better example Chipotle very very popular and I went to Chipotle and I asked what do you cook your food and they said rice brand oil so it's the oil extracted from the 00:18:39 brand of the rice okay they put three to five tablespoons of rice brand oil into a a bowl like a burrito bowl or a burrito with the rice and the beans and the the 00:18:50 meat that are cooking in there to get three to five tablespoons of rice brain oil you'd have to eat something like three to four pounds of rice so something that humans would never ever do right it's the same with 00:19:04 sunflower seeds sunflower seed oil is in almost everything soybean oil is very common corn oil to get 3 to 5 tablespoons of corn oil you have to eat somewhere between 60 and 75 ears of corn 00:19:17 so you can see here that we have now even if we were eating an occasional sunflower seed from a sunflower plant because we're starving as humans historically or we're eating a little bit of rice and getting the oil from the brand or we're EA eating some corn in 00:19:30 Native American population we're never going to get anywhere close to the amount of linolic acid coming into our bodies in 2023 and really this amount has been increasing massively over the last 100 to 110 years

      super interesting

    1. Author response:

      The following is the authors’ response to the current reviews.

      Reviewer #1 (Public review):

      Previous experimental studies demonstrated that membrane association drives avidity for several potent broadly HIV-neutralizing antibodies and its loss dramatically reduces neutralization. In this study, the authors present a tour de force analysis of molecular dynamics (MD) simulations that demonstrate how several HIV-neutralizing membrane-proximal external region (MPER)-targeting antibodies associate with a model lipid bilayer.

      First, the authors compared how three MPER antibodies, 4E10, PGZL1, and 10E8, associated with model membranes, constructed with two lipid compositions similar to native viral membranes. They found that the related antibodies 4E10 and PGZL1 strongly associate with a phospholipid near heavy chain loop 1, consistent with prior crystallographic studies. They also discovered that a previously unappreciated framework region between loops 2-3 in the 4E10/PGZL1 heavy chain contributes to membrane association. Simulations of 10E8, an antibody from a different lineage, revealed several differences from published X-ray structures. Namely, a phosphatidylcholine binding site was offset and includes significant interaction with a nearby framework region. The revised manuscript demonstrates that these lipid interactions are robust to alterations in membrane composition and rigidity. However, it does not address the reverse-that phospholipids known experimentally not to associate with these antibodies (if any such lipids exist) also fail to interact in MD simulations.

      Next, the authors simulate another MPER-targeting antibody, LN01, with a model HIV membrane either containing or missing an MPER antigen fragment within. Of note, LN01 inserts more deeply into the membrane when the MPER antigen is present, supporting an energy balance between the lowest energy conformations of LN01, MPER, and the complex. These simulations recapitulate lipid binding interactions solved in published crystallographic studies but also lead to the discovery of a novel lipid binding site the authors term the "Loading Site", which could guide future experiments with this antibody.

      The authors next established course-grained (CG) MD simulations of the various antibodies with model membranes to study membrane embedding. These simulations facilitated greater sampling of different initial antibody geometries relative to membrane. These CG simulations , which cannot resolve atomistic interactions, are nonetheless compelling because negative controls (ab 13h11, BSA) that should not associate with membrane indeed sample significantly less membrane.

      Distinct geometries derived from CG simulations were then used to initialize all-atom MD simulations to study insertion in finer detail (e.g., phospholipid association), which largely recapitulate their earlier results, albeit with more unbiased sampling. The multiscale model of an initial CG study with broad geometric sampling, followed by all-atom MD, provides a generalized framework for such simulations.

      Finally, the authors construct velocity pulling simulations to estimate the energetics of antibody membrane embedding. Using the multiscale modelling workflow to achieve greater geometric sampling, they demonstrate that their model reliably predicts lower association energetics for known mutations in 4E10 that disrupt lipid binding. However, the model does have limitations: namely, its ability to predict more subtle changes along a lineage-intermediate mutations that reduce lipid binding are indistinguishable from mutations that completely ablate lipid association. Thus, while large/binary differences in lipid affinity might be predictable, the use of this method as a generative model are likely more limited.

      The MD simulations conducted throughout are rigorous and the analysis are extensive, creative, and biologically inspired. Overall, these analyses provide an important mechanistic characterization of how broadly neutralizing antibodies associate with lipids proximal to membrane-associated epitopes to drive neutralization.

      Reviewer #2 (Public review):

      In this study, Maillie et al. have carried out a set of multiscale molecular dynamics simulations to investigate the interactions between the viral membrane and four broadly neutralizing antibodies that target the membrane proximal exposed region (MPER) of the HIV-1 envelope trimer. The simulation recapitulated in several cases the binding sites of lipid head groups that were observed experimentally by X-ray crystallography, as well as some new binding sites. These binding sites were further validated using a structural bioinformatics approach. Finally, steered molecular dynamics was used to measure the binding strength between the membrane and variants of the 4E10 and PGZL1 antibodies.

      The use of multiscale MD simulations allows for a detailed exploration of the system at different time and length scales. The combination of MD simulations and structural bioinformatics provides a comprehensive approach to validate the identified binding sites. Finally, the steered MD simulations offer quantitative insights into the binding strength between the membrane and bnAbs.

      While the simulations and analyses provide qualitative insights into the binding interactions, they do not offer a quantitative assessment of energetics. The coarse-grained simulations exhibit artifacts and thus require careful analysis.

      This study contributes to a deeper understanding of the molecular mechanisms underlying bnAb recognition of the HIV-1 envelope. The insights gained from this work could inform the design of more potent and broadly neutralizing antibodies.

      Recommendations for the authors:

      Reviewing Editor:

      We recommend the authors remove the figure and section related to bnAb LN01, perform additional analysis (e.g., further expanding on the differences in antibody binding in the presence or absence of antigen), and present this as a separate manuscript in a follow-up study.

      We consider the analysis of a bnAb with a transmembrane antigen and of LN01 as essential to the manuscript and novel results.  Study of LN01 provides many insights unique from the other MPER bnAbs in this study.  We agree further characterization of LN01 and bnAbs with transmembrane antigen or full-length Env are intriguing and necessary to complete the full mechanistic understanding of lipid-associated antibodies.  LN01 section in this paper is novel in the field and demonstrates the preliminary evidence motivating further work, which we agree are beyond the scope of this already long detailed study.

      Reviewer #1 (Recommendations for the authors):

      I appreciate the degree to which the authors responded to my previous points raised in the private review, including edits where I might have missed something in the manuscript or relevant literature. I imagine such a point-by-point response was quite onerous. Thank you also for balancing presentation/clarity with content/rigor considering the large information content of this manuscript; in silico results are inherently hard to present given the delicate balance between rigorous validation and novel information content. I apologize if I repeat points raised and addressed previously and commend the authors on their revised study, which is much improved in clarity; any additional revisions are of course entirely at your discretion.

      "...now having more diversity in lipid headgroup chemistries" references the wrong figure-it should be: Figure 2-figure supplement 2A-C. The incorrect figure is also referenced again several sentences down: "...relevant CDR and framework surface loops..."

      Thank you for pointing out this error. We have corrected figure references.

      "One shared conformational difference observed for these bnAbs the higher cholesterol bilayers was slightly more extensive and broader interaction profiles as well as modestly deeper embedding of the relevant CDR and framework surfaces loops" please rephrase

      Thank you for this suggestion.  We rephrased this for improved clarity and flow. 

      "These results bolster the feasibility for using all-atom MD as an in silico platform to explore differential phospholipid affinity at these sites (i.e., specificity studies) and influence on antibody preferred conformation as membrane composition and lipid chemistry are systematically varied" Please tone down these speculations-you have demonstrated that simulations are robust to different headgroup chemistries but have not provided evidence for the exclusion of lipids that are known not to associate with these antibodies.

      We rephrased this speculation to highlight the potential of this application. We also emphasize future studies that would be required to achieve this application in the following sentence.

      “These results motivate use of all-atom MD as an in silico approach for exploring differential phospholipid affinity at these sites…”

      Figure 2A: Specify which PDB entry corresponds to the displayed crystal structures in the main figure or caption.

      We clarified these PDB entries in the figure caption. 

      Check reference formatting in supplemental figures when generating VOR.

      I am not sure how relevant this might be to the claims of Figure 2-figure supplement 3, but AlphaFold3-based phospholigand docking might provide an additional orthogonal approach if relevant ligand(s) are available for such analysis (particularly for the newly proposed 10E8 POPC complex).

      Thank you for this suggestion.  AI/ML based prediction methods like AF3 and RoseTTAFold All-Atom (RFAA) are interesting new methods that have come since our initial submission.   We’ve decided these experiments are beyond the scope of this already long and detailed study. We have added a sentence suggesting use of these methods in future work.

      "We next studied bnAb LN01 to interrogate differences" --> this transition still reads a bit unclear. Why shift gears and change antibodies? Also, while you do go into its interactions both +/- antigen, there's no lead into the simulation initialization with and without antigen to guide the reader into the comparisons you will draw in the figure. Also, the order of information presentation is a bit strange, where the rationale for choosing a single monomeric helix is brought up in the middle of the paragraph instead of at the beginning of the section. In the next paragraph, it goes back to the initialization of the membrane composition again, which feels a bit disorganized-I do appreciate the unique challenge of having to weave through so much quality data! In fact, if you were to conduct simulations of membrane + antigen vs. membrane + LN01 vs. membrane + LN01 + antigen, I am tempted to say that this could be removed from this manuscript and flow better as a paper in and of itself.

      We thank the reviewer for the suggestion to improve the writing style.  We feel this section adds a lot of value to the manuscript, so we will keep it in the paper and improved the transition as well as rationale.  

      We selected to study the additional antibody LN01 and the monomeric MPER-TM antigen conformation because of the existing structural evidence available without additional creative model building.  This rationale has been updated in the new text.  

      We changd the order of information as suggested, moving the rationale for antigen fragment earlier in the paragraph followed by the background of the lipids sites from the crystal that can lead into simulation set-up.  We clarified the simulation initialization was similar for systems with and without antigen in the opening sentence of the paragraph

      "previously observed snorkeling and hydration of TM Arg686" --> Is this R696 (numbering could be different based on the particular Env)?

      Thank you for noting this typo, we have corrected the numbering.

      Potential font color issue with Figure 3-Figure supplement 1 B and part of A text-could be fixed in typesetting.

      The discussion reads very well. Is it possible to direct antibody maturation, even in an engineered context, towards membrane affinity without increasing immunogenic polyreactivity? This is mentioned very briefly and cited with ref 36, but I would be interested in the author's thoughts on this topic.

      We thank the reviewer for the insightful idea to explore in future work.  Our conclusion alludes to possibly artificially evolving membrane affinity studied by MD, as done in vitro by Nieva and co-workers.  Because the hypothetical nature, we’ve chosen not to elaborate on those ideas from this manuscript.

      Reviewer #2 (Recommendations for the authors):

      To ensure reproducibility and facilitate further research, the authors should publicly deposit the code for running the MD simulations and analyses (e.g., on GitHub) along with the underlying data used in the study (e.g., on Zenodo.org).

      We appreciate the consideration for open-source code and analysis. Representative code and simulation trajectories were uploaded to the following repositories:

      https://github.com/cmaillie98/mper_bnAbs.git

      https://zenodo.org/records/13830877

      —-

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Previous experimental studies demonstrated that membrane association drives avidity for several potent broadly HIV-neutralizing antibodies and its loss dramatically reduces neutralization. In this study, the authors present a tour de force analysis of molecular dynamics (MD) simulations that demonstrate how several HIV-neutralizing membrane-proximal external region (MPER)-targeting antibodies associate with a model lipid bilayer.

      First, the authors compared how three MPER antibodies, 4E10, PGZL1, and 10E8, associated with model membranes, constructed with a lipid composition similar to the native virion. They found that the related antibodies 4E10 and PGZL1 strongly associate with a phospholipid near heavy chain loop 1, consistent with prior crystallographic studies. They also discovered that a previously unappreciated framework region between loops 2-3 in the 4E10/PGZL1 heavy chain contributes to membrane association. Simulations of 10E8, an antibody from a different lineage, revealed several differences from published X-ray structures. Namely, a phosphatidylcholine binding site was offset and includes significant interaction with a nearby framework region.

      Next, the authors simulate another MPER-targeting antibody, LN01, with a model HIV membrane either containing or missing an MPER antigen fragment within. Of note, LN01 inserts more deeply into the membrane when the MPER antigen is present, supporting an energy balance between the lowest energy conformations of LN01, MPER, and the complex. Additional contacts and conformational restraints imposed by ectodomain regions of the envelope glycoprotein, however, remain unaddressed-the size of such simulations likely runs into technical limitations including sampling and compute time.

      The authors next established course-grained (CG) MD simulations of the various antibodies with model membranes to study membrane embedding. These simulations facilitated greater sampling of different initial antibody geometries relative to membrane. Distinct geometries derived from CG simulations were then used to initialize all-atom MD simulations to study insertion in finer detail (e.g., phospholipid association), which largely recapitulate their earlier results, albeit with more unbiased sampling. The multiscale model of an initial CG study with broad geometric sampling, followed by all-atom MD, provides a generalized framework for such simulations.

      Finally, the authors construct velocity pulling simulations to estimate the energetics of antibody membrane embedding. Using the multiscale modelling workflow to achieve greater geometric sampling, they demonstrate that their model reliably predicts lower association energetics for known mutations in 4E10 that disrupt lipid binding. However, the model does have limitations: namely, its ability to predict more subtle changes along a lineage-intermediate mutations that reduce lipid binding are indistinguishable from mutations that completely ablate lipid association. Thus, while large/binary differences in lipid affinity might be predictable, the use of this method as a generative model are likely more limited.

      The MD simulations conducted throughout are rigorous and the analysis are extensive. However, given the large amount of data presented within the manuscript, the text would benefit from clearer subsections that delineate discrete mechanistic discoveries, particularly for experimentalists interested in antibody discovery and design. One area the paper does not address involves the polyreactivity associated with membrane binding antibodies-MD simulations and/or pulling velocity experiments with model membranes of different compositions, with and without model antigens, would be needed. Finally, given the challenges in initializing these simulations and their limitations, the text regarding their generalized use for discovery, rather than mechanism, could be toned down.

      Overall, these analyses provide an important mechanistic characterization of how broadly neutralizing antibodies associate with lipids proximal to membrane-associated epitopes to drive neutralization.

      Reviewer #2 (Public Review):

      In this study, Maillie et al. have carried out a set of multiscale molecular dynamics simulations to investigate the interactions between the viral membrane and four broadly neutralizing antibodies that target the membrane proximal exposed region (MPER) of the HIV-1 envelope trimer. The simulation recapitulated in several cases the binding sites of lipid head groups that were observed experimentally by X-ray crystallography, as well as some new binding sites. These binding sites were further validated using a structural bioinformatics approach. Finally, steered molecular dynamics was used to measure the binding strength between the membrane and variants of the 4E10 and PGZL1 antibodies.

      The conclusions from the paper are mostly well supported by the simulations, however, they remain very descriptive and the key findings should be better described and validated. In particular:

      It has been shown that the lipid composition of HIV membrane is rich in cholesterol [1], which accounts for almost 50% molar ratio. The authors use a very different composition and should therefore provide a reference. It has been shown for 4E10 that the change in lipid composition affects dynamics of the binding. The robustness of the results to changes of the lipid composition should also be reported.

      The real advantage of the multiscale approach (coarse grained (CG) simulation followed by a back-mapped all atom simulation) remains unclear. In most cases, the binding mode in the CG simulations seem to be an artifact.

      The results reported in this study should be better compared to available experimental data. For example how does the approach angle compare to cryo-EM structure of the bnAbs engaging with the MPER region, e.g. [2-3]? How do these results from this study compare to previous molecular dynamics studies, e.g.[4-5]?

      References<br /> (1) Brügger, Britta, et al. "The HIV lipidome: a raft with an unusual composition." Proceedings of the National Academy of Sciences 103.8 (2006): 2641-2646.<br /> (2) Rantalainen, Kimmo, et al. "HIV-1 envelope and MPER antibody structures in lipid assemblies." Cell Reports 31.4 (2020).<br /> (3) Yang, Shuang, et al. "Dynamic HIV-1 spike motion creates vulnerability for its membrane-bound tripod to antibody attack." Nature Communications 13.1 (2022): 6393.<br /> (4) Carravilla, Pablo, et al. "The bilayer collective properties govern the interaction of an HIV-1 antibody with the viral membrane." Biophysical Journal 118.1 (2020): 44-56.<br /> (5) Pinto, Dora, et al. "Structural basis for broad HIV-1 neutralization by the MPER-specific human broadly neutralizing antibody LN01." Cell host & microbe 26.5 (2019): 623-637.

      Considering reviewer suggestions, we slightly reorganized the results section into specific sub-sections with headings and changed the order in which key results were presented to allow the subsequent analysis more accessible for readers.  Supplemental materials were redistributed into eLife format, having each supplemental item grouped to a corresponding main figure. Many slightly detail modifications were made to figures (mostly supplemental items) without changing their character, such as clearer axes labels or revised annotations within panels.

      The major additions within the results sections based on the reviews were:

      (1) An expanded the comparison between our simulation analyses to previous simulations and to existing cryo-EM structural evidence for MPER antibodies’ membrane orientation the context of full-length antigen, resulting in new supplemental figure panels.

      (2) New atomistic simulations of 10E8, PGZL1, and 4E10 evaluating the phospholipid binding predictions in a different lipid composition more closely modeling HIV membranes.

      Minor edits to the analyses and interpretations include:

      (1) Outlining the geometric components contributing to variance in substates after clustering the atomistic 10E8, 4E10, and PGZL1 simulations.

      (2) Better defining the variance and durability of membrane interactions within and across systems in the coarse grain methods section.

      (3) Removed interpretations in the original results sections regarding polyreactivity and energetics for MPER bnAbs that were not explicitly supported by data.   

      (4) More context of the prevenance of bnAb loop geometries in structural informatics section

      (5) Rationale for the choice of the continuous helix MPER-TM conformation in LN01-antigen conformations, and citations to previous gp41 TM simulations.

      (6) Removed language on the novelty of the coarse grain and steered pulling simulations as newly developed approaches; tempering the potential discriminating power and applications of those approaches, in light of their limitations.

      The discussion was revised to provide more novel context of the results within the field, including discussing direct relevance of the simulation methods for evaluating immune tolerance mechanisms and into antibody engineering.   We have shared custom scripts used for molecular dynamics analysis on github (https://github.com/cmaillie98/mper_bnAbs.git) and uploaded trajectories to a public repository hosted on Zenodo (https://zenodo.org/records/13830877).

      Recommendations for the authors:

      Below, I provide an extensive list of minor edits associated with the text and figures for the authors to consider. I provide these with the hope of increasing the accessibility of the manuscript to broader audiences but leave changes to the discretion of the authors.

      Text/clarity

      Figure 1 main text

      The main text discussing Figure 1 is disorganized, making the analysis difficult to follow. I would suggest the following: moving the sentence, "4E10 and PG2L1 are structurally homologous" immediately after the paragraph discussing the simulation initiation. Then, add a sentence that directly compares their experimental affinity, neutralization, and polyreactivity of 4E10 and PG2L1 (later, an unintroduced idea pops up, "These patterns may in part explain 4E10's greater polyreactivity"). Next, lead into the discussion of the MD simulation data with something to the effect of: "Given these similarities, we first compared mechanisms of membrane insertion between 4E10 and PG2L1 to bolster confidence in our predictions". Later, the sentence "Across 4E10 and PGZL1 simulations, the bound lipid phosphates"

      We thank the reviewer for the suggestion and we have restructured the beginning of the results to implement this style: to first introduce then discuss the comparative PGZL1 & 4E10 results, i.e. Figure 1 plus associated supplements.

      In the background and the introduction text leading up to Figure 1, CDR-H3 is discussed at length, however, the first figure focuses almost entirely on how CDR-H1 coordinates a lipid phosphate headgroup. Are there experimental mutations in this loop that do not affect affinity (e.g., to a soluble gp41 peptide), but do affect neutralization (like the WAWA mutation for CDR-H3, discussed later)?

      We have altered the Introduction (para 2) and Results (4E10/PGZL1 sub-section) to give more balanced discussion of CDRs H1 & H3.  That includes referencing experimental data addressing the reviewer’s question; a PGZL1 clone H4K3 where mutations to CDRH1 were introduced and shown have minimal impact on affinity to MPER peptide via ELISA and BLI, but those mutant bnAbs had significantly reduced neutralization efficacy (PMC6879610).

      The sentence "These phospholipid binding events were highly stable, typically persisting for hundreds of nanoseconds" should be moved down to immediately precede, "[However], in a PGZL1 simulation, we observed a". This would be a good place for a paragraph break following, "Thus, these bnABs constitutively", since this block of text is very long.

      Similarly, the sentence and parts of the section, "Likewise, the interactions coordinating the lipid phosphate oxygens at CDR-H1" more appropriately belongs immediately before or after the sentence, "Our simulations uncover the CDR-lipid interactions that are the most feasible".

      Thank you for the detailed guidance in reorganizing the Figure 1 results.  We followed the advice to directly compare 4E10 and PGZL1 results separately from 10E8, moving those sections of text appropriately.  New paragraph breaks were added to improve accessibility and flow of concepts throughout the Results.

      In the sentence, "our simulations uncover CDR-lipid interactions that are the most feasible and biologically relevant in the context of a full [HIV] lipid bilayer... validation to which of the many possible ions" à have you confidently determined lipid binding and positioning outside of the site validated in figure 1? Which site(s) are these referencing? The next two sentences then introduce two new ideas on the loop backbone stability then lead into lipid exchange, which is a bit jarring.

      We have adjusted the language concerning the putative ions/lipids electron density across the many PGZL1 and 4E10 crystal structures, and additionally make the explicit point that we confidently determined the lack of lipid binding outside of the site focused on in Figure 1.

      “… both bnAbs showed strong hotspots for a lipid phosphate bound within the CDR-H1 loops, with minimal phospholipid or cholesterol ordering around the proteins elsewhere.  The simulated lipid phosphates bound within CDR-H1 have exceptional overlap with electron densities and atomic details of modelled headgroups from respective lipid-soaked co-crystal structures…”

      Figure 2 main text

      "We similarly investigated bnAb 10E8" - Please make this a separate subheader, the block text is very long up to this point.

      Thank you for the suggestion. We introduced a sub-header to separate work on 10E8 all-atom simulations.

      "we observed a POPC complexed with... modelled as headgroup phosphoglycerol anions..." - please cite the references within the text.

      Thank you for pointing out this missing reference, we added the appropriate reference.

      "One striking and novel observation" - please remove the phrase "striking" throughout, for following best practices in scientific writing (PMC10212555)-this is generally well-done throughout.

      We removed “striking” from our text per your suggestion.

      "This CDR-L1 site highlights... (>500 fold) across HIV strains" - How much do R29 and Y32 also contribute to antigen binding and the conformation of this loop? These mutants also decreased Kd by approximately 20X, and based on the co-crystal structure with the TM antigen (PDB: 4XCC), seem to play a more direct role in antigen contact. Additionally, these residues should be highlighted on a figure, otherwise it's difficult to understand why they are important for membrane association.

      We thank the reviewer for deep engagement to these supporting experimental details.  The R29A+Y32A 10E8 mutant referenced in the text showed only 4-fold Kd increase, a modest change for an SPR binding experiment.  Whereas R29E+Y32E 10E8 mutant resulted in 40x Kd increase, the “20x” the reviewer refers to.  Both 10E8 mutants showed similar drastically reduced breadth and potency of over 2 orders of magnitude on average.

      These mutated CDR-L1 residues are not directly involved in antigen contact and adopt the same loop helix conformation when antigen is bound.  A minor impact on antigen binding affinity could be due altering pre-organization of CDR loops upon losing interactions from the Tyr & Arg sidechains - particularly Tyr31 in contact with CDR-H3.

      As per the suggestion, clearer annotated figure panel denoting these sidechains has been added to Figure 2-Figure Supplement 1 for 10E8 analysis.

      "Structural searches querying... identified between 10^5 and 2*10^6..." - why is this value represented as such a large range? Does this depend on the parameters used for analysis? Please clarify.

      Additionally, how prevalent are any random loop conformations compared to the ones you searched? It's otherwise difficult to attribute number of occurrences within the 2 A cutoff to biological significance, as this number is not put in context.

      We appreciate the reviewers comment to contextualize the range and relative frequency of the bnAb loop conformations.   RMSD and length of loop are the key parameters, which can be controlled by searching reference loops of similar length.  The main point of the backbone-level searching is simply to imply the bnAb loops are not particularly rare when comparing loops of similar length.   

      We did as was suggested and added comparison to random loops of the same length to the main text, including a new Supplementary Table 4.   

      “…identified between 105 to 2∙106 geometrically similar sub-segments within natural proteins (<2 Å RMSD)40, reflecting they are relatively prevalent (not rare) in the protein universe, comparing well with frequency of other surface loops of similar length in antibodies (Supplementary Table 3).”

      "We next examined the geometries" could start after its own new subheading. Moreover, while there's an emphasis on tilt for neutralization, there is not a figure clearly modelling the proposed Env tilt compared to the relatively planar bilayer. It would be helpful to have an additional panel somewhere that shows the orientation of the antibody (e.g., a representative pose) in the simulations relative to an appropriately curved membrane, Env, the binding conformation of the antibody to Env, and apo Env, given the tilting observed in PMID: 32348769 and theorized in PMC5338832. What additional conformational changes or tilting need to occur between the antibodies and Env to accomplish binding to their respective epitopes?

      Thank you for outlining an interesting element to consider in our analysis of a multi-step binding mechanism for MPER antibodies. We added additional figure panels in the supplement to outline the similarities and differences between our simulations and Fabs with the inferred membranes in cryo-EM experiments of full-length HIV Env.  The simulated Fabs’ angles are very similar with only minor tilting to match the cryo-EM antibody-membrane geometries. 

      We added Figure 1-figure supplement 1A & Figure 2-figure supplement 2A, and alter to text to reflect this:

      “The primary difference is Env-bound Fabs in cryo-EM adopt slightly more shallow approach angles (~15_°_) relative to the bilayer normal.  The simulated bnAbs in isolation prefer orientations slightly more upright, but presenting CDRs at approximately the same depth and orientation.  Thus, these bnAbs appear pre-disposed in their membrane surface conformations, needing only a minor tilt to form the membrane-antibody-antigen neutralization complex.”   

      Env tilt dynamics and membrane curvature of natural virions may reconcile some of these differences.  Recent in situ tomography of Full-length Env in pseudo-virions corroborates our approximation of flat bilayers over the short length scales around Env.

      The sentence "we next examined the geometries" mentions "potential energy cost, if any, for reorienting...". However, there's no further discussions of geometry or energy cost within this section. Please rephrase, or move this figure to main and increase discussion associated with the various conformational ensembles, their geometry, and their phospholipid association.

      As the reviewer highlights, the unbiased simulations and our analysis do not explicitly evaluate energetics.  We removed this phrase, and now only allude to the minimal energy barrier between the similar geometric conformations, relative to the tilting & access requirements for antigen binding mechanism.

      “The apparent barrier for re-orientation is likely much less energetically constraining than shielding glycans and accessibility of MPER”

      ".. describing the spectrum of surface-bound conformations" cites the wrong figure.

      Thank you for noticing this error; we correct the figure reference to (Figure 2-figure supplement 4).

      Please comment on the significance of how global clustering (Fig. S5A-C) was similar for 4E10 and PGZL1, but different for 10E8 (e.g., blue, orange, and yellow clusters for 4E10 and PHZL1 versus cyan, red, and green clusters for 10E8). As the cyan cluster seems to be much closer in Euclidian space to the 4E10/PGZL1 clusters, it might warrant additional analysis. What do these clusters represent in terms of structure/conformation? How do these clusters differ in membrane insertion as in (A)?

      We are grateful you identify analysis in the geometric clustering section that may be of interest to other readers. We have added additional supplementary table (Table 2) to detail the CDR loop membrane insertion and global Fab angles which describe each cluster, to demonstrate their similarities and differences.  We also better describe how global clustering was similar for 4E10 and PGZL1, but different for 10E8 in the relevant results section<br /> The cyan cluster is not close in structure to 4E10/PGZL1 clusters.  We note the original figure panel had an error.  The updated Figure 2-supplement 4B shows the correct Euclidian distance hierarchy with an early split between 4e10/pgzl1 and 10e8 clusters.

      Figure 3 main text

      The start of this section, "We next studied bnAb LN01...", is a good place for a new subheader.

      We have added an additional subheader here: Antigen influence on membrane bound conformations and lipid binding sites for LN01

      There should be a sentence in the main text defining the replicate setup and production MD run time. Is the apo and complex based on a published structure? How do you embed the MPER? Is the apo structure docked to membrane like in 4E10? The MD setup could also be better delineated within the methods.

      The first two paragraphs in this section have been updated to clarify the relevant simulations configuration and Fab membrane docking prediction details. 

      The procedure was the same for predicting an initial membrane insertion, albeit now we use the LN01-TM complex and the calculation will account for the membrane burial of the the TM domain and MPER fragment.  As mentioned, LN01 is predicted as inserted with CDR loops insert similarly with or without the TM-MPER fragment.  The geometry differs from PGZL1/4E10 and 10E8, denoted by the text.

      Please comment on the oligomerization state of the antigen used in the MD simulation: how does the simulation differ from a crossed MPER as observed in an MPER antibody-bound Env cryo-EM structure (PMID: 32348769), a three-helix bundle (PMC7210310), or single transmembrane helix (PMC6121722)? How does the model MPER monomer embed in the membrane compared to simulations with a trimeric MPER (PMC6035291, PMID: 33882664)-namely, key arginine residues such as R696?

      We thank the reviewer for pointing out critical underlying rationale for modeling this TM-MPER-LN01 complex which we have corrected in the revised draft. The range of potential conformations and display of MPER based on TM domain organization could easily be its own paper – we in fact have a manuscript in preparation on the topic.  

      The updated text expands the rationale for choosing the monomeric uninterrupted helix form of the MPER-TM model antigen (para 1 of LN01 section). The alternative conformations we did not to explore are called out, with references provided by the reviewer.

      The discussion qualified that the MPER presentation is likely oversimplified here, noting MPER display in the full-length Env trimer will vary in different conformational states or membrane environments. However, the only cryo-EM structures of full-length ENV with TM domains resolved have this continuous helix MPER-TM conformation – seen both within crossing TM dimers or dissociated TM monomers.

      Are there additional analyses that can validate the dynamics of the MPER monomer in the membrane and relative to LN01? Such as key contacts you would expect to maintain over the duration of the MD simulation?

      We also increased description of this TM domain’s behavior, dynamics (tilt, orientation, Arg696 snorkeling, and complex w LN01) to provide a clearer picture of the simulation results – which aligns with past MD of the gp41 TM domain as a monomer (para 2 of LN01 section).  As well, we noted key LN01-MPER contacts that were maintained.

      How does the model MPER modulate membrane properties like lipid density and lipid proximities near LN01?

      We checked and didn’t notice differences for the types of lipids (chol, etc) proximal to the MPER-TM or the CDR loops versus the bulk lipid bilayer distributions.  Due to the already long & detailed nature of this manuscript, we elect not to include discussion on this topic.

      Supplemental figure 1H-I would be better positioned as a figure 3-associated supplemental figure.

      We rearranged to follow the eLife format and have paired supplemental panels with their most relevant main figures.

      Figure 3F/H reference a "loading site" but this site is defined much later in the text, which was confusing.

      Thank you for pointing out this source of confusion, we rearranged our discussion to reflect the order in which we present data in figures.

      What evidence suggests that lipids "quickly exchange from the Loading site into the X-ray site by diffusion"? I do not gather this from Figure S1H/I.

      We have rearranged the loading side and x-ray site RMSD maps in Figure 3-Figure supplement 1 to better illustrate how a lipid exchanges between these sites.

      Figure 4 main text

      The authors assert that in the CG simulations, restraints, "[maintain] Fab tertiary and quaternary structure". However, backbone RMSD does not directly assert this claim-an additional analysis of the key interfacial residues between chains, or geometric analysis between the chains, would better support this claim.

      Thank you for pointing this point.  We rephrased to add that the major sidechain contacts between heavy and light chain persist, in addition to backbone RMSD, to describe how these Fabs maintain the fold stably in CG representation. 

      In several cases, CG models sample and then dissociate from the membrane. In the text, the authors mention, "course-grained models can distinguishing unfavorable and favorable membrane-bound conformations". Is there a particular orientation that causes/favors membrane association and dissociation? This analysis could look at conformations immediately preceding association and dissociation to give clues as to what orientation(s) favor each state.

      Thank you for suggesting this interesting analysis.  Clustering analysis of associated states are presented in Figure 5, Figure 5-Figure Supplement 1, and Figure 6, which show all CDR and framework loop directed insertion.  This feature is currently described in the main text.  

      We did not find strong correlation of specific orientations as “pre-dissociation” states or ineffective non-inserting “scanning” events.  We revised the key sentence to reflect the major take away – that non-CDR alternative conformations did not insert and most of those having CDRs inserted in a different manner than all-atom simulations also were prone to dissociate:

      “Given that non-CDR directed and alternative CDR-embedded orientations readily dissociate, we conclude that course-grained models can distinguish unfavorable and favorable membrane-bound conformations to an extent that provides utility for characterizing antibody-bilayer interaction mechanisms.”

      Figure 6 main text

      "For 4E10, trajectories initiated from all three geometries..." only two geometries are shown for each antibody. Please include all three on the plot.

      The plots include markers for all three geometries for 4E10, highlighted in stars or with letters on the density plots of angles sampled (Figure 6B,C)

      "Aligning a full-length IgG... unlikely that two Fabs simultaneously..." Are there theoretical conformations in which two Fabs could simultaneously associate with membrane? If this was physiological or could be designed rationally, could an antibody benefit further from avidity?

      Our modeling suggests the theoretical conformations having two Fabs on the membrane are infeasible.  It’s even less likely multiple Env antigens could be engaged by one IgG.  We have revised the text to express this more clearly.

      Figure 7 main text

      "An intermediate... showed a modest reduction in affinity..." what affinity does PGZL1 have for this antigen?

      The preceding sentence for this information: “Mature PGZL1 has relatively high affinity to the MPER epitope peptide (Kd = 10 nM) and demonstrates great breadth and potency, neutralizing 84% of a 130 strain panel “

      Figures

      Figure 1

      It would be helpful to have an additional panel at the top of this figure further zoomed out showing the orientation of the antibody (e.g., a representative pose) in the simulations relative to an appropriately curved membrane, Env, the binding conformation of the antibody to Env, and apo Env, given the tilting observed in PMID: 32348769 and theorized in PMC5338832. What additional conformational changes or tilting need to occur between the antibodies and Env to accomplish binding to their respective epitopes?

      Thank you for the suggestion to include this analysis.  We have added to the text reflecting this information, as well as making new supplemental panels for 4E10 and 10E8 that we compare simulated 4E10 and 10E8 Fab conformations to cryoEM density maps with Fabs bound to full-length HIV Env. Figure 1-figure supplement 1A & Figure 2-figure supplement 2A

      In Figure 1, space permitting, it would be helpful to annotate the distances between the phosphates and side chains (similarly, for Figure S1A).

      To avoid the overloading the Main figure panels with text, those relevant distances are listed in the methods sections.  Those distances are used to define the “bound” lipid phosphate state.  Generally, we note the interactions are within hydrogen bonding distance.

      Annotating "Replicate 1" and "Replicate 2" on the left side of Figure 1C/D would make this figure immediately intuitive.

      We have added these labels.

      Figure caption 1C: Please clarify the threshold/definition of a contact used to binarize "bound" versus "unbound" (for example, "mean distance cutoff of 2A between the phosphate oxygen and the COM of CDR-H1") [on further reading of the methods section, this criterion is quite involved and might benefit from: a sentence that includes "see methods"]. Additionally, C could use a sentence explaining the bar such as in E, "Phosphate binding is mapped to above each MD trajectory" Please define FR-H3 in the figure caption for E/F.

      We have added these details to the figure caption.

      Because Figure 1 is aggregated simulation time, it would be helpful to also represent the data as individual replicates or incorporate this information to calculate standard deviations/statistics (e.g., 1 microsecond max using the replicates to compute a standard deviation).

      We believe the current quantification & display of data via sharing all trajectories is sufficient to convey the major point for how often each CDR-phosholipid binding site it occupied.  Further tracking and statistics of inter-atomic distances will likely be too tedious & add minimal value. There is some dynamics of the phosphate oxygens between the polar within the CDR site but our “bound” state definitions sufficiently describe the key participating interactions are made.

      Figure 2

      For A, it would be helpful to annotate the yellow and blue mesh on the figure itself.

      We have defined the orange phosphate and blue choline densities.

      Also, where are R29 and Y32 relative to this site? In the X-ray panels, Y38 is not shown, and the box delineating the zoom-in is almost imperceptible.

      Thank you for this suggestion to include those amino acids which are referenced in the text as critical sites where mutation impacts function. To clarify, Y32 is the pdb numbering for residue Y38 in IMGT numbering. We have added a panel to Figure 2-Figure Supplement 1 having a cartoon graphic of 10E8 loop groove with sidechains & annotating R29 and Y38, staying consistent with out use of IMGT numbering in the manuscript.

      Figure 3

      It might read clearer to have "LN01+MPER-TM" and "LN01-Apo" in the middle of A/B and C/D, respectively, and a dotted line delineating the left and right side of the figure panels.

      We have added these details to the figure for clarity for readers.

      It would be helpful to show some critical interactions that are discussed in the text, such as the salt bridge with K31, by labeling these on the figure (e.g., in E-H).

      We drafted figure panels with dashed lines to indicate those key interactions.  However, they became almost imperceptible and overloaded with annotations that distracted from the overall details.  For K31, the interaction occurs in LN01 crystal structures readers can refer to.

      Why are axes cut off for J?

      We corrected this.

      Please re-define K/L plots as in Figure 1, and explain abbreviations.

      We updated the figure caption to reflect these changes.

      Figure 4

      The caption for panel A states that the Fab begins in solvent 1-2 nm above the bilayer, but the main text states 0.5-2 nm.

      We have reconciled this difference and listed the correct distances: 0.5-2nm.

      Please label the y-axis as "Replicate" for relevant figure panels so that they are more immediately interpretable.

      This label has been added.

      A legend with "membrane-associated" and "non-associated" within the figure would be helpful. Additionally, the average percent membrane associated, with a standard deviation, should be shown (Similar to 1C, albeit with the statistics).

      This legend has been added.  We also added the additional statistical metrics requested to strengthen our analysis.

      The text references "10, 14, and 12 extended insertion events" for the three antibody-based simulations. How do you define "extended insertion events"? Would breaking this into average insertion time and standard deviation better highlight the association differences between MPER antibodies and controls, in addition to the variability due to difference random initialization?

      We thank the reviewer for the insightful suggestion on how to better organize quantitative analysis to support the method. Supplemental Table 3 includes these numbers.

      Figure 5

      The analysis in Fig. S6C could be included here as a main figure.

      The drafted revised figure adding S6C to Figure 5 made for too much information.  Likewise, putting this panel S6C separated it from the parent clustering data of S6B, so we decided to keep these figures separated.  The S6 figure is now Figure 5-figure supplement 1.

      Figure 6

      Please annotate membrane insertion on E as %.

      These are phosphate binding RMSD/occupancy vs time.  The panels are now too small to annotate by %.  The qualitative presentation is sufficient at this stage.  The quantitative % are listed in-line within text when relevant to support assertions made. 

      Please use the figure caption to explain why certain clusters (e.g., 10E8 cluster A, artifact, Fig. S6E) are not included in panel E.

      We have added this information in the figure caption.

      Figure 7

      Please show all points on the box and whisker plots (panels E and F), and perform appropriate statistical tests to see if means are significantly different (these are mentioned in the text, but should be annotated on the graph and mentioned within the figure caption).

      We have changed these plots to show all data points along with relevant statistical comparisons. The figure captions describe unpaired t-test statistical tests used.

      Figure S1

      G, H, and I do not belong here-they should be moved to accompany their relevant text section, which associates with Figure 3. It would be helpful to associate this with Figure 3 in the eLife format, "Figure 3-Supplemental Figure 1" or its equivalent.

      It's very difficult to distinguish the green and blue circles on panel G.

      We darkened the shading and added outline for better visualization

      Subfigure I is missing a caption, could be included with H: "(H,I) Additional replicates for LN01+TM (H) and LN01 (I)".

      We corrected this as suggested.

      Why is H only 3 simulations and not 4? Does it not have a lipid in the x-ray site? Also, the caption states "(top, green)" and "(bottom, cyan)", but the green vs. cyan figures are organized on the left and right. Additional labels within the figure would help make this more intuitive.

      If the point of H and I is to illustrate that POPC exchanges between the X-ray and loading sites, this is unclear from the figure. Consider clarifying these figures.

      Thank you for describing the confusion in this figure, we have added labels to clarify.

      Figure S2 (panels split between revised Figure 4 associated figure supplements)

      The LN01 figures should likely follow later so that they can associate with Figure 3, despite being a similar analysis.

      We corrected supplements to eLife format so supplements are associated with relevant main figures.

      Figure S3 (panels split between revised Figure 1 & 2 associated figure supplements)

      As hydrophobicity is discussed as a driving factor for residue insertion, it would be helpful to have a rolling hydrophobicity chart underneath each plot to make this claim obvious.

      We prefer the current format, due to the worry of having too much information in these already data-rich panels.  As well, residues are not apolar but are deeply inserted.

      Figure S4 (panels split between revised Figure 1 & 2 associated figure supplements)

      It would be helpful to label the relevant loops on these figures.

      We have labeled loops for clarity.

      Do any of these loops have minor contacts with Env in the structure?

      The 4E10 and PGZL1 CDRH-1 loop does not directly contact bound MPER peptides bound in crystal structures. 

      FRL-3 and CDR-H1 in 10E8 do not contact the MPER peptide antigen component based on x-ray crystal structures.

      Do motif contacts with lipid involve minor contacts with additional loops other than those displayed in this figure?

      The phosphate-loop interactions in motifs used as query bait here are mediated solely by the backbone and side chain interactions of the loops displayed. We visually inspected most matches and did not see any “consensus” additional peripheral interactions common across each potential instance in the unrelated proteins.  The supplied Supplemental Table 2 contains the information if a reader wanted to conduct a detailed search. 

      Why is there such a difference between the loop conformation adopted in the X-ray structure and that in the MD simulation, and why does this lead to the large observed differences in ligand-binding structure matches?

      We thank the reviewer for carefully noting our error in labeling of CDR loop and framework region input queries. We revised the labeling to clarify the issue.

      The is minimal structural difference between the loops in x-ray and MD.

      Figure S5 (Figure 2-Figure supplement 4)

      This figure is not colorblind friendly-it would be helpful to change to such a pallet as the data are interesting, but uninterpretable to some.

      We have left this figure the same.

      "Susbstates" - "Substates"

      Corrected, thank you.

      Panel B is uninterpretable-please break the axis so that the Euclidian distances can be represented accurately but the histograms can be interpreted.

      We have adjusted axis for this plot to better illustrate the cluster thresholds.

      The clusters in D-H should be analyzed in greater depth. What is the structural relevance of these clusters other than differences in phospholipid occupancy in (I)? Snapshots of representative poses for each cluster could help clarify these differences.

      We have adjusted the text to describe the geometric differences in each of those clusters that result in the different exceptionally lower propensities for forming the key phospholipid interaction.  

      The figure caption should make it clear that 3 μS of aggregate simulation time is being used here instead of 4 μS to start with unique tilt initializations. E.g., "unique starting membrane-bound conformations (0 degrees, -15 degrees, 15 degrees initialization relative to the docked pose)". Further, why was the particular 0-degree replicate chosen while the other was thrown out? Or was this information averaged? Why is the full 4 μS then used for D-I?

      We thank the reviewer for noting these details.  We didn’t want to bias the differential between 10E8 and 4E10/PGZL1 by including the replicate simulations.  The analysis was mainly intended to achieve more coarse resolution distinction between 10E8 and the similar PGZL1/4E10.  

      In the subsequent clustering of individual bnAb simulation groups, the replicate 0 degree simulations had sufficiently different geometric sampling and unique lipid binding behavior that we though it should be used (4 us total) to achieve finer conformational resolution for each bnAb.

      Figure S6 (now Figure 5-Figure Supplement 1)

      Please label the CDRs in C and provide a color key like in other figures. Also, please label the y-axes. This figure could move to main below 5B with the clusters "A,B,C" labeled on 5B.

      We have added the axes labels and color key legend.  We retained a minimal CDR loop labeling scheme for the more throughput interaction profiles here where colored sections in the residue axes denote CDR loop regions.

      Figure S7 (Figure 7 Figure Supplement 1)

      Panels A and B would likely read better if swapped.

      We have swapped these panels for a better flow.

      For panel C, please display mean and standard deviation, and compare these values with an appropriate statistical test.

      This is already displayed in main figure, we have removed it from supplement.

      For E and F, please clarify from which trajectory(s) you are extracting this conformation from. Are these the global mean/representative poses? How do they compare to other geometrically distinct clusters?

      The requested information was added to supplemental figure caption.  These are frames from 2 distinct time points selected phosphate bound frames from 0-degree tilt replicates for both 4E10 and 10E8, representing at least 2 distinct macroscopic substates differing in global light chain and heavy chain orientation towards the membrane. 

      Table S2 (now Supplementary Table 3)

      Please add details for the 13h11 simulation.

      Additionally, please add average contact time and their standard deviation to the table, rather than just the aggregated total time. This will highlight the variability associated with the random initializations of each simulation.

      We have added the details for 13h11 and the requested analysis (average aggregated time +/- standard deviation and average time per association event +- standard deviation) to supplement our summary statistics for this method.

      Reviewer #2 (Recommendations For The Authors):

      (1) The structure of the manuscript should be improved. For example, almost half of the introduction (three paragraphs) summarize the results. I found it hard to navigate all the data and specific interactions described in the result section. Furthermore, the claims at the end of several sections seem unsupported. Especially for the generalization of the approach. This should be moved to the discussion section. The discussion is pretty general and does not provide much context to the results presented in this study.

      We have significantly reorganized the results section to improve the flow of the manuscript and accessibility for readers, especially the first sections of all-atom simulations. We also removed claims not directly supported by data from our results, and expanded on some of these concepts in the discussion to make some more novel context to the result.

      (2) The author should cite more rigorously previous work and refrain from using the term "develop" to describe the simple use of a well established method. E.g. Several studies have investigated membrane protein interactions e.g. [1], membrane protein-bilayer self-assembly [2], steered molecular dynamics [3], etc.

      Thank you for identifying relevant work for the simulations that set precedent for our novel application to antibody-membrane interactions.  We have removed language about development of simulation methods from the text and now better reference the precedent simulation methods used here.

      (3) Have the authors considered estimating the PMF by combining the steered MD simulation through the application of Jarzynski's equality?

      We performed from preliminary PMFs for Fab-membrane binding, but saw it was taking upward of 40 us to reach convergence.  Steered simulations focus on a key lipid may be easier.

      Although PMFs are beyond the scope of this work, we added proposals & allusion to their utility as the next steps for more rigorous quantification of fab-membrane interactions.

      Minor

      (4) The term "integrative modeling" is usually used for computational pipelines which incorporate experimental data. Multiscale modeling would be more appropriate for this study.

      We altered descriptions throughout the manuscript to reflect this comment.

      (5) Units to report the force in the steered molecular dynamics are incorrect. They should be 98.

      We changed axes and results to correctly report this unit.

      (6) Labels for axes of several graphs are not missing.

      We added labels to all axes of graphs, except for a few where stacked labels can be easily interpreted to save space and reduce complexity in figures.

      (7) Figure 3 K & L is this really < 1% of total? The term "total" should also be clarified.

      Thank you for pointing this out, we changed the % labels to be correct with axes from 0-100%. We clarified total in the figure caption.

      (8) The font size in figures should be uniformized.

      This suggestion has been applied

      (9) Time needed for steered MD should be reported in CPUh and not hours (page 17).

      We removed comments on explicit time measurements for our simulations.

      (10) Version of Martini force field is missing in methods section

      We used Martini 2.6 and added this to the methods.

      References

      (1) Prunotto, Alessio, et al. "Molecular bases of the membrane association mechanism potentiating antibiotic resistance by New Delhi metallo-β-lactamase 1." ACS infectious diseases 6.10 (2020): 2719-2731.

      (2) Scott, Kathryn A., et al. "Coarse-grained MD simulations of membrane protein-bilayer self-assembly." Structure 16.4 (2008): 621-630.

      (3) Izrailev, S., et al. "Computational molecular dynamics: challenges, methods, ideas. Chapter 1. Steered molecular dynamics." (1997).

    2. Reviewer #2 (Public review):

      In this study, Maillie et al. have carried out a set of multiscale molecular dynamics simulations to investigate the interactions between the viral membrane and four broadly neutralizing antibodies that target the membrane proximal exposed region (MPER) of the HIV-1 envelope trimer. The simulation recapitulated in several cases the binding sites of lipid head groups that were observed experimentally by X-ray crystallography, as well as some new binding sites. These binding sites were further validated using a structural bioinformatics approach. Finally, steered molecular dynamics was used to measure the binding strength between the membrane and variants of the 4E10 and PGZL1 antibodies.

      The use of multiscale MD simulations allows for a detailed exploration of the system at different time and length scales. The combination of MD simulations and structural bioinformatics provides a comprehensive approach to validate the identified binding sites. Finally, the steered MD simulations offer quantitative insights into the binding strength between the membrane and bnAbs.

      While the simulations and analyses provide qualitative insights into the binding interactions, they do not offer a quantitative assessment of energetics. The coarse-grained simulations exhibit artifacts and thus require careful analysis.

      This study contributes to a deeper understanding of the molecular mechanisms underlying bnAb recognition of the HIV-1 envelope. The insights gained from this work could inform the design of more potent and broadly neutralizing antibodies.

    1. Reviewer #1 (Public review):

      The authors in this paper investigate the nature of the activity in the rodent EPN during a simple freely moving cue-reward association task. Given that primate literature suggest movement coding whereas other primate and rodent studies suggest mainly reward outcome coding in the EPNs, it is important try to tease apart the two views. Through careful analysis of behavior kinematics, position, and the neural activity in the EPNs, the authors reveal an interesting and complex relationship between the EPN and mouse behavior.

      Strengths:

      (1) The authors use a novel freely moving task to study EPN activity, which displays rich movement trajectories and kinematics. Given that previous studies have mostly looked at reward coding during head fixed behavior, this study adds a valuable dataset to the literature.

      (2) The neural analysis is rich and thorough. Both single neuron level and population level (i.e. PCA) analysis are employed to reveal what EPN encodes.

      Discussion:<br /> EPN is one of the major output nuclei of the basal ganglia. What information is present within EPN is still unclear, and under investigation. The authors have used electrophysiology to determine the nature of information present within EPN that is likely to be valuable to the field. Future studies should try to address whether this information is specific to certain cell types within EPN or whether there is topography within EPN that reflects the kinematic information present within EPN. This will require more careful dissection of EPN activity based on anatomy. Future experiments should also consider tasks that isolate a single limb (i.e. joystick tasks) in order to better understand the kinematic encoding of forelimb movement. This, combined with recording in forelimb encoding region of EPN, should give us insights into the nature of kinematic control of EPN. Overall, this study will be useful to inspire future investigations in the function of EPN.

    2. Reviewer #2 (Public review):

      This paper examined how the activity of neurons in the entopeduncular nucleus (EPN) of mice relates to kinematics, value, and reward. The authors recorded neural activity during an auditory cued two-alternative choice task, allowing them to examine how neuronal firing relates to specific movements like licking or paw movements, as well as how contextual factors like task stage or proximity to a goal influence the coding of kinematic and spatiotemporal features. The data shows that the firing of individual neurons is linked to kinematic features such as lick or step cycles. However, the majority of neurons exhibited activity related to both movement types, suggesting that EPN neuronal activity does not merely reflect muscle-level representations. This contradicts what would be expected from traditional action selection or action specification models of the basal ganglia.

      The authors also show that spatiotemporal variables account for more variability compared to kinematic features alone. Using demixed Principal Component Analysis, they reveal that at the population level, the three principal components explaining the most variance were related to specific temporal or spatial features of the task, such as ramping activity as mice approached reward ports, rather than trial outcome or specific actions. Notably, this activity was present in neurons whose firing was also modulated by kinematic features, demonstrating that individual EPN neurons integrate multiple features. A weakness is that what the spatiotemporal activity reflects is not well specified. The authors suggest some may relate to action value due to greater modulation when approaching a reward port, but acknowledge action value is not well parametrized or separated from variables like reward expectation.

      A key goal was to determine whether activity related to expected value and reward delivery arose from a distinct population of EPN neurons or was also present in neurons modulated by kinematic and spatiotemporal features. In contrast to previous studies (Hong & Hikosaka 2008 and Stephenson-Jones et al., 2016), the current data reveals that individual neurons can exhibit modulation by both reward and kinematic parameters. Two potential differences may explain this discrepancy: First, the previous studies used head-fixed recordings, where it may have been easier to isolate movement versus reward-related responses. Second, those studies observed prominent phasic responses to the delivery or omission of expected rewards - responses that are present but not common in the current paper. This suggests a possibility that the VGlut2+ EPN neurons that project to the LHb were under/not sampled, antidromic or optogenetic tagging would have been needed to confirm the identity of the populations that were recorded. Alternatively, in the head-fixed recordings, kinematic/spatial coding may have gone undetected due to the forced immobility.

      Overall, this paper offers needed insight into how the basal ganglia output encodes behavior. The EPN recordings from freely moving mice clearly demonstrate that individual neurons integrate reward, kinematic, and spatiotemporal features, challenging traditional models. However, the specific relationship between the spatiotemporal activity and factors like action value remains unclear.

    1. Author response:

      The following is the authors’ response to the previous reviews.

      eLife Assessment

      This study presents valuable findings on the potential of short-movie viewing fMRI protocol to explore the functional and topographical organization of the visual system in awake infants and toddlers. Although the data are compelling given the difficulty of studying this population, the evidence presented is incomplete and would be strengthened by additional analyses to support the authors' claims. This study will be of interest to cognitive neuroscientists and developmental psychologists, especially those interested in using fMRI to investigate brain organisation in pediatric and clinical populations with limited fMRI tolerance.

      We are grateful for the thorough and thoughtful reviews. We have provided point-bypoint responses to the reviewers’ comments, but first, we summarize the major revisions here. We believe these revisions have substantially improved the clarity of the writing and impact of the results.

      Regarding the framing of the paper, we have made the following major changes in response to the reviews:

      (1) We have clarified that our goal in this paper was to show that movie data contains topographic, fine-grained details of the infant visual cortex. In the revision, we now state clearly that our results should not be taken as evidence that movies could replace retinotopy and have reworded parts of the manuscript that could mislead the reader in this regard.

      (2) We have added extensive details to the (admittedly) complex methods to make them more approachable. An example of this change is that we have reorganized the figure explaining the Shared Response Modelling methods to divide the analytic steps more clearly.

      (3) We have clarified the intermediate products contributing to the results by adding 6 supplementary figures that show the gradients for each IC or SRM movie and each infant participant.

      In response to the reviews, we have conducted several major analyses to support our findings further:

      (1) To verify that our analyses can identify fine-grained organization, we have manually traced and labeled adult data, and then performed the same analyses on them. The results from this additional dataset validate that these analyses can recover fine-grained organization of the visual cortex from movie data.

      (2) To further explore how visual maps derived from movies compare to alternative methods, we performed an anatomical alignment control analysis. We show that high-quality maps can be predicted from other participants using anatomical alignment.

      (3) To test the contribution of motion to the homotopy analyses, we regressed out the motion effects in these analyses. We found qualitatively similar results to our main analyses, suggesting motion did not play a substantial role.

      (4) To test the contribution of data quantity to the homotopy analyses, we correlated the amount of movie data collected from each participant with the homotopy results. We did not find a relationship between data quantity and the homotopy results. 

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      Ellis et al. investigated the functional and topographical organization of the visual cortex in infants and toddlers, as evidenced by movie-viewing data. They build directly on prior research that revealed topographic maps in infants who completed a retinotopy task, claiming that even a limited amount of rich, naturalistic movie-viewing data is sufficient to reveal this organization, within and across participants. Generating this evidence required methodological innovations to acquire high-quality fMRI data from awake infants (which have been described by this group, and elsewhere) and analytical creativity. The authors provide evidence for structured functional responses in infant visual cortex at multiple levels of analyses; homotopic brain regions (defined based on a retinotopy task) responded more similarly to one another than to other brain regions in visual cortex during movie-viewing; ICA applied to movie-viewing data revealed components that were identifiable as spatial frequency, and to a lesser degree, meridian maps, and shared response modeling analyses suggested that visual cortex responses were similar across infants/toddlers, as well as across infants/toddlers and adults. These results are suggestive of fairly mature functional response profiles in the visual cortex in infants/toddlers and highlight the potential of movie-viewing data for studying finer-grained aspects of functional brain responses, but further evidence is necessary to support their claims and the study motivation needs refining, in light of prior research.

      Strengths:

      - This study links the authors' prior evidence for retinotopic organization of visual cortex in human infants (Ellis et al., 2021) and research by others using movie-viewing fMRI experiments with adults to reveal retinotopic organization (Knapen, 2021).

      - Awake infant fMRI data are rare, time-consuming, and expensive to collect; they are therefore of high value to the community. The raw and preprocessed fMRI and anatomical data analyzed will be made publicly available.

      We are grateful to the reviewer for their clear and thoughtful description of the strengths of the paper, as well as their helpful outlining of areas we could improve.

      Weaknesses:

      - The Methods are at times difficult to understand and in some cases seem inappropriate for the conclusions drawn. For example, I believe that the movie-defined ICA components were validated using independent data from the retinotopy task, but this was a point of confusion among reviewers. 

      We acknowledge the complexity of the methods and wish to clarify them as best as possible for the reviewers and the readers. We have extensively revised the methods and results sections to help avoid potential misunderstandings. For instance, we have revamped the figure and caption describing the SRM pipeline (Figure 5).

      To answer the stated confusion directly, the ICA components were derived from the movie data and validated on the (completely independent) retinotopy data. There were no additional tasks. The following text in the paper explains this point:

      “To assess the selected component maps, we correlated the gradients (described above) of the task-evoked and component maps. This test uses independent data: the components were defined based on movie data and validated against task-evoked retinotopic maps.” Pg. 11

      In either case: more analyses should be done to support the conclusion that the components identified from the movie reproduce retinotopic maps (for example, by comparing the performance of movie-viewing maps to available alternatives (anatomical ROIs, group-defined ROIs). 

      Before addressing this suggestion, we want to restate our conclusions: features of the retinotopic organization of infant visual cortex could be predicted from movie data. We did not conclude that movie data could ‘reproduce’ retinotopic maps in the sense that they would be a replacement. We recognize that this was not clear in our original manuscript and have clarified this point throughout, including in this section of the discussion:

      “To be clear, we are not suggesting that movies work well enough to replace a retinotopy task when accurate maps are needed. For instance, even though ICA found components that were highly correlated with the spatial frequency map, we also selected some components that turned out to have lower correlations. Without knowing the ground truth from a retinotopy task, there would be no way to weed these out. Additionally, anatomical alignment (i.e., averaging the maps from other participants and anatomically aligning them to a held-out participant) resulted in maps that were highly similar to the ground truth. Indeed, we previously[23] found that adult-defined visual areas were moderately similar to infants. While functional alignment with adults can outperform anatomical alignment methods in similar analyses[27], here we find that functional alignment is inferior to anatomical alignment. Thus, if the goal is to define visual areas in an infant that lacks task-based retinotopy, anatomical alignment of other participants’ retinotopic maps is superior to using movie-based analyses, at least as we tested it.” Pg. 21

      As per the reviewer’s suggestion and alluded to in the paragraph above, we have created anatomically aligned visual maps, providing an analogous test to the betweenparticipant analyses like SRM. We find that these maps are highly similar to the ground truth. We describe this result in a new section of the results:

      “We performed an anatomical alignment analog of the functional alignment (SRM) approach. This analysis serves as a benchmark for predicting visual maps using taskbased data, rather than movie data, from other participants. For each infant participant, we aggregated all other infant or adult participants as a reference. The retinotopic maps from these reference participants were anatomically aligned to the standard surface template, and then averaged. These averages served as predictions of the maps in the test participant, akin to SRM, and were analyzed equivalently (i.e., correlating the gradients in the predicted map with the gradients in the task-based map). These correlations (Table S4) are significantly higher than for functional alignment (using infants to predict spatial frequency, anatomical alignment > functional alignment: ∆<sub>Fisher Z</sub> M=0.44, CI=[0.32–0.58], p<.001; using infants to predict meridians, anatomical alignment > functional alignment: ∆<sub>Fisher Z</sub> M=0.61, CI=[0.47–0.74], p<.001; using adults to predict spatial frequency, anatomical alignment > functional alignment: ∆<sub>Fisher Z</sub> M=0.31, CI=[0.21–0.42], p<.001; using adults to predict meridians, anatomical alignment > functional alignment: ∆<sub>Fisher Z</sub> M=0.49, CI=[0.39–0.60], p<.001). This suggests that even if SRM shows that movies can be used to produce retinotopic maps that are significantly similar to a participant, these maps are not as good as those that can be produced by anatomical alignment of the maps from other participants without any movie data.” Pg. 16–17

      Also, the ROIs used for the homotopy analyses were defined based on the retinotopic task rather than based on movie-viewing data alone - leaving it unclear whether movie-viewing data alone can be used to recover functionally distinct regions within the visual cortex.

      We agree with the reviewer that our approach does not test whether movie-viewing data alone can be used to recover functionally distinct regions. The goal of the homotopy analyses was to identify whether there was functional differentiation of visual areas in the infant brain while they watch movies. This was a novel question that provides positive evidence that these regions are functionally distinct. In subsequent analyses, we show that when these areas are defined anatomically, rather than functionally, they also show differentiated function (e.g., Figure 2). Nonetheless, our intention was not to use the homotopy analyses to define the regions. We have added text to clarify the goal and novelty of this analysis.

      “Although these analyses cannot define visual maps, they test whether visual areas have different functional signatures.” Pg. 6

      Additionally, even if the goal were to define areas based on homotopy, we believe the power of that analysis would be questionable. We would need to use a large amount of the movie data to define the areas, leaving a low-powered dataset to test whether their function is differentiated by these movie-based areas.

      - The authors previously reported on retinotopic organization of the visual cortex in human infants (Ellis et al., 2021) and suggest that the feasibility of using movie-viewing experiments to recover these topographic maps is still in question. They point out that movies may not fully sample the stimulus parameters necessary for revealing topographic maps/areas in the visual cortex, or the time-resolution constraints of fMRI might limit the use of movie stimuli, or the rich, uncontrolled nature of movies might make them inferior to stimuli that are designed for retinotopic mapping, or might lead to variable attention between participants that makes measuring the structure of visual responses across individuals challenging. This motivation doesn't sufficiently highlight the importance or value of testing this question in infants. Further, it's unclear if/how this motivation takes into account prior research using movie-viewing fMRI experiments to reveal retinotopic organization in adults (e.g., Knapen, 2021). Given the evidence for retinotopic organization in infants and evidence for the use of movie-viewing experiments in adults, an alternative framing of the novel contribution of this study is that it tests whether retinotopic organization is measurable using a limited amount of movie-viewing data (i.e., a methodological stress test). The study motivation and discussion could be strengthened by more attention to relevant work with adults and/or more explanation of the importance of testing this question in infants (is the reason to test this question in infants purely methodological - i.e., as a way to negate the need for retinotopic tasks in subsequent research, given the time constraints of scanning human infants?).

      We are grateful to the reviewer for giving us the opportunity to clarify the innovations of this research. We believe that this research contributes to our understanding of how infants process dynamic stimuli, demonstrates the viability and utility of movie experiments in infants, and highlights the potential for new movie-based analyses (e.g., SRM). We have now consolidated these motivations in the introduction to more clearly motivate this work:

      “The primary goal of the current study is to investigate whether movie-watching data recapitulates the organization of visual cortex. Movies drive strong and naturalistic responses in sensory regions while minimizing task demands[12, 13, 24] and thus are a proxy for typical experience. In adults, movies and resting-state data have been used to characterize the visual cortex in a data-driven fashion[25–27]. Movies have been useful in awake infant fMRI for studying event segmentation[28], functional alignment[29], and brain networks[30]. However, this past work did not address the granularity and specificity of cortical organization that movies evoke. For example, movies evoke similar activity in infants in anatomically aligned visual areas[28], but it remains unclear whether responses to movie content differ between visual areas (e.g., is there more similarity of function within visual areas than between31). Moreover, it is unknown whether structure within visual areas, namely visual maps, contributes substantially to visual evoked activity. Additionally, we wish to test whether methods for functional alignment can be used with infants. Functional alignment finds a mapping between participants using functional activity – rather than anatomy – and in adults can improve signal-to-noise, enhance across participant prediction, and enable unique analyses[27, 32–34].” Pg. 3-4

      Furthermore, the introduction culminates in the following statement on what the analyses will tell us about the nature of movie-driven activity in infants:

      “These three analyses assess key indicators of the mature visual system: functional specialization between areas, organization within areas, and consistency between individuals.” Pg. 5

      Furthermore, in the discussion we revisit these motivations and elaborate on them further:

      [Regarding homotopy:] “This suggests that visual areas are functionally differentiated in infancy and that this function is shared across hemispheres[31].” Pg. 19

      [Regarding ICA:] “This means that the retinotopic organization of the infant brain accounts for a detectable amount of variance in visual activity, otherwise components resembling these maps would not be discoverable.” Pg. 19–20

      [Regarding SRM:] “This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults[27,32,33], or revealing changing function over development[45].” Pg. 21

      Additionally, we have expanded our discussion of relevant work that uses similar methods such as the excellent research from Knapen (2021) and others:

      “In adults, movies and resting-state data have been used to characterize the visual cortex in a data-driven fashion[25-27].” Pg. 4

      “We next explored whether movies can reveal fine-grained organization within visual areas by using independent components analysis (ICA) to propose visual maps in individual infant brains[25,26,35,42,43].” Pg. 9

      Reviewer #2 (Public Review):

      Summary:

      This manuscript shows evidence from a dataset with awake movie-watching in infants, that the infant brain contains areas with distinct functions, consistent with previous studies using resting state and awake task-based infant fMRI. However, substantial new analyses would be required to support the novel claim that movie-watching data in infants can be used to identify retinotopic areas or to capture within-area functional organization.

      Strengths:

      The authors have collected a unique dataset: the same individual infants both watched naturalistic animations and a specific retinotopy task. These data position the authors to test their novel claim, that movie-watching data in infants can be used to identify retinotopic areas.

      Weaknesses:

      To claim that movie-watching data can identify retinotopic regions, the authors should provide evidence for two claims:

      - Retinotopic areas defined based only on movie-watching data, predict retinotopic responses in independent retinotopy-task-driven data.

      - Defining retinotopic areas based on the infant's own movie-watching response is more accurate than alternative approaches that don't require any movie-watching data, like anatomical parcellations or shared response activation from independent groups of participants.

      We thank the reviewer for their comments. Before addressing their suggestions, we wish to clarify that we do not claim that movie data can be used to identify retinotopic areas, but instead that movie data captures components of the within and between visual area organization as defined by retinotopic mapping. We recognize that this was not clear in our original manuscript and have clarified this point throughout, including in this section of the discussion:

      “To be clear, we are not suggesting that movies work well enough to replace a retinotopy task when accurate maps are needed. For instance, even though ICA found components that were highly correlated with the spatial frequency map, we also selected some components that turned out to have lower correlations. Without knowing the ground truth from a retinotopy task, there would be no way to weed these out. Additionally, anatomical alignment (i.e., averaging the maps from other participants and anatomically aligning them to a held-out participant) resulted in maps that were highly similar to the ground truth. Indeed, we previously[23] found that adult-defined visual areas were moderately similar to infants. While functional alignment with adults can outperform anatomical alignment methods in similar analyses[27], here we find that functional alignment with infants is inferior to anatomical alignment. Thus, if the goal is to define visual areas in an infant that lacks task-based retinotopy, anatomical alignment of other participants’ retinotopic maps is superior to using movie-based analyses, at least as we tested it.” Pg. 21

      In response to the reviewer’s suggestion, we compare the maps identified by SRM to the averaged, anatomically aligned maps from infants. We find that these maps are highly similar to the task-based ground truth and we describe this result in a new section:

      “We performed an anatomical alignment analog of the functional alignment (SRM) approach. This analysis serves as a benchmark for predicting visual maps using taskbased data, rather than movie data, from other participants. For each infant participant, we aggregated all other infant or adult participants as a reference. The retinotopic maps from these reference participants were anatomically aligned to the standard surface template, and then averaged. These averages served as predictions of the maps in the test participant, akin to SRM, and were analyzed equivalently (i.e., correlating the gradients in the predicted map with the gradients in the task-based map). These correlations (Table S4) are significantly higher than for functional alignment (using infants to predict spatial frequency, anatomical alignment < functional alignment: ∆<sub>Fisher Z</sub> M=0.44, CI=[0.32–0.58], p<.001; using infants to predict meridians, anatomical alignment < functional alignment: ∆<sub>Fisher Z</sub> M=0.61, CI=[0.47–0.74], p<.001; using adults to predict spatial frequency, anatomical alignment < functional alignment: ∆<sub>Fisher Z</sub> M=0.31, CI=[0.21–0.42], p<.001; using adults to predict meridians, anatomical alignment < functional alignment: ∆<sub>Fisher Z</sub> M=0.49, CI=[0.39–0.60], p<.001). This suggests that even if SRM shows that movies can be used to produce retinotopic maps that are significantly similar to a participant, these maps are not as good as those that can be produced by anatomical alignment of the maps from other participants without any movie data.” Pg. 16–17

      Note that we do not compare the anatomically aligned maps with the ICA maps statistically. This is because these analyses are not comparable: ICA is run withinparticipant whereas anatomical alignment is necessarily between-participant — either infant or adults. Nonetheless, an interested reader can refer to the Table where we report the results of anatomical alignment and see that anatomical alignment outperforms ICA in terms of the correlation between the predicted and task-based maps.

      Both of these analyses are possible, using the (valuable!) data that these authors have collected, but these are not the analyses that the authors have done so far. Instead, the authors report the inverse of (1): regions identified by the retinotopy task can be used to predict responses in the movies. The authors report one part of (2), shared responses from other participants can be used to predict individual infants' responses in the movies, but they do not test whether movie data from the same individual infant can be used to make better predictions of the retinotopy task data, than the shared response maps.

      So to be clear, to support the claims of this paper, I recommend that the authors use the retinotopic task responses in each individual infant as the independent "Test" data, and compare the accuracy in predicting those responses, based on:

      -  The same infant's movie-watching data, analysed with MELODIC, when blind experimenters select components for the SF and meridian boundaries with no access to the ground-truth retinotopy data.

      -  Anatomical parcellations in the same infant.

      -  Shared response maps from groups of other infants or adults.

      -  (If possible, ICA of resting state data, in the same infant, or from independent groups of infants).

      Or, possibly, combinations of these techniques.

      If the infant's own movie-watching data leads to improved predictions of the infant's retinotopic task-driven response, relative to these existing alternatives that don't require movie-watching data from the same infant, then the authors' main claim will be supported.

      These are excellent suggestions for additional analyses to test the suitability for moviebased maps to replace task-based maps. We hope it is now clear that it was never our intention to claim that movie-based data could replace task-based methods. We want to emphasize that the discoveries made in this paper — that movies evoke fine-grained organization in infant visual cortex — do not rely on movie-based maps being better than alternative methods for producing maps, such as the newly added anatomical alignment.

      The proposed analysis above solves a critical problem with the analyses presented in the current manuscript: the data used to generate maps is identical to the data used to validate those maps. For the task-evoked maps, the same data are used to draw the lines along gradients and then test for gradient organization. For the component maps, the maps are manually selected to show the clearest gradients among many noisy options, and then the same data are tested for gradient organization. This is a double-dipping error. To fix this problem, the data must be split into independent train and test subsets.

      We appreciate the reviewer’s concern; however, we believe it is a result of a miscommunication in our analytic strategy. We have now provided more details on the analyses to clarify how double-dipping was avoided. 

      To summarize, a retinotopy task produced visual maps that were used to trace both area boundaries and gradients across the areas. These data were then fixed and unchanged, and we make no claims about the nature of these maps in this paper, other than to treat them as the ground truth to be used as a benchmark in our analyses. The movie data, which are collected independently from the same infant in the session, used the boundaries from the retinotopy task (in the case of homotopy) or were compared with the maps from the retinotopy task (in the case of ICA and SRM). In other words, the statement that “the data used to generate maps is identical to the data used to validate those maps” is incorrect because we generated the maps with a retinotopy task and validated the maps with the movie data. This means no double dipping occurred.

      Perhaps a cause of the reviewer’s interpretation is that the gradients used in the analysis are not clearly described. We now provide this additional description:  “Using the same manually traced lines from the retinotopy task, we measured the intensity gradients in each component from the movie-watching data. We can then use the gradients of intensity in the retinotopy task-defined maps as a benchmark for comparison with the ICA-derived maps.” Pg. 10

      Regarding the SRM analyses, we take great pains to avoid the possibility of data contamination. To emphasize how independent the SRM analysis is, the prediction of the retinotopic map from the test participant does not use their retinotopy data at all; in fact, the predicted maps could be made before that participant’s retinotopy data were ever collected. To make this prediction for a test participant, we need to learn the inversion of the SRM, but this only uses the movie data of the test participant. Hence, there is no double-dipping in the SRM analyses. We have elaborated on this point in the revision, and we remade the figure and its caption to clarify this point:

      We also have updated the description of these results to emphasize how double-dipping was avoided:

      “We then mapped the held-out participant's movie data into the learned shared space without changing the shared space (Figure 5c). In other words, the shared response model was learned and frozen before the held-out participant’s data was considered.

      This approach has been used and validated in prior SRM studies[45].” Pg. 14

      The reviewer suggests that manually choosing components from ICA is double-dipping. Although the reviewer is correct that the manual selection of components in ICA means that the components chosen ought to be good candidates, we are testing whether those choices were good by evaluating those components against the task-based maps that were not used for the ICA. Our statistical analyses evaluate whether the components chosen were better than the components that would have been chosen by random chance. Critically: all decisions about selecting the components happen before the components are compared to the retinotopic maps. Hence there is no double-dipping in the selection of components, as the choice of candidate ICA maps is not informed by the ground-truth retinotopic maps. We now clarify what the goal of this process is in the results:

      “Success in this process requires that 1) retinotopic organization accounts for sufficient variance in visual activity to be identified by ICA and 2) experimenters can accurately identify these components.” Pg. 10

      The reviewer also alludes to a concern that the researcher selecting the maps was not blind to the ground-truth retinotopic maps from participants and this could have influenced the results. In such a scenario, the researcher could have selected components that have the gradients of activity in the places that the infant has as ground truth. The researcher who made the selection of components (CTE) is one of the researchers who originally traced the areas in the participants approximately a year prior to the identification of ICs. The researcher selecting the components didn’t use the ground-truth retinotopic maps as reference, nor did they pay attention to the participant IDs when sorting the IC components. Indeed, they weren’t trying to find participant specific maps per se, but rather aimed to find good candidate retinotopic maps in general. In the case of the newly added adult analyses, the ICs were selected before the retinotopic mapping was reviewed or traced; hence, no knowledge about the participant-specific ground truth could have influenced the selection of ICs. Even with this process from adults, we find results of comparable strength as we found in infants, as shown below. Nonetheless, there is a possibility that this researcher’s previous experience of tracing the infant maps could have influenced their choice of components at the participant-specific level. If so, it was a small effect since the components the researcher selected were far from the best possible options (i.e., rankings of the selected components averaged in the 64th percentile for spatial frequency maps and the 68th percentile for meridian maps). We believe all reasonable steps were taken to mitigate bias in the selection of ICs.

      Reviewer #3 (Public Review):

      The manuscript reports data collected in awake toddlers recording BOLD while watching videos. The authors analyse the BOLD time series using two different statistical approaches, both very complex but do not require any a priori determination of the movie features or contents to be associated with regressors. The two main messages are that 1) toddlers have occipital visual areas very similar to adults, given that an SRM model derived from adult BOLD is consistent with the infant brains as well; 2) the retinotopic organization and the spatial frequency selectivity of the occipital maps derived by applying correlation analysis are consistent with the maps obtained by standard and conventional mapping.

      Clearly, the data are important, and the author has achieved important and original results. However, the manuscript is totally unclear and very difficult to follow; the figures are not informative; the reader needs to trust the authors because no data to verify the output of the statistical analysis are presented (localization maps with proper statistics) nor so any validation of the statistical analysis provided. Indeed what I think that manuscript means, or better what I understood, may be very far from what the authors want to present, given how obscure the methods and the result presentation are.

      In the present form, this reviewer considers that the manuscript needs to be totally rewritten, the results presented each technique with appropriate validation or comparison that the reader can evaluate.

      We are grateful to the reviewer for the chance to improve the paper. We have broken their review into three parts: clarification of the methods, validation of the analyses, and enhancing the visualization.

      Clarification of the methods

      We acknowledge that the methods we employed are complex and uncommon in many fields of neuroimaging. That said, numerous papers have conducted these analyses on adults (Beckman et al., 2005; Butt et al., 2015; Guntupalli et al., 2016; Haak & Beckman, 2018; Knapen, 2021; Lu et al., 2017) and non-human primates (Arcaro & Livingstone, 2017; Moeller et al., 2009). We have redoubled our efforts in the revision to make the methods as clear as possible, expanding on the original text and providing intuitions where possible. These changes have been added throughout and are too vast in number to repeat here, especially without context, but we hope that readers will have an easier time following the analyses now. 

      Additionally, we updated Figures 3 and 5 in which the main ICA and SRM analyses are described. For instance, in Figure 3’s caption we now add details about how the gradient analyses were performed on the components: 

      “We used the same lines that were manually traced on the task-evoked map to assess the change in the component’s response. We found a monotonic trend within area from medial to lateral, just like we see in the ground truth.” Pg. 11

      Regarding Figure 5, we reconsidered the best way to explain the SRM analyses and decided it would be helpful to partition the diagram into steps, reflecting the analytic process. These updates have been added to Figure 5, and the caption has been updated accordingly.

      We hope that these changes have improved the clarity of the methods. For readers interested in learning more, we encourage them to either read the methods-focused papers that debut the analyses (e.g., Chen et al., 2015), read the papers applying the methods (e.g., Guntupalli et al., 2016), or read the annotated code we publicly release which implements these pipelines and can be used to replicate the findings.

      Validation of the analyses

      One of the requests the reviewer makes is to validate our analyses. Our initial approach was to lean on papers that have used these methods in adults or primates (e.g., Arcaro, & Livingstone, 2017; Beckman et al., 2005; Butt et al., 2015; Guntupalli et al., 2016; Haak & Beckman, 2018; Knapen, 2021; Moeller et al., 2009) where the underlying organization and neurophysiology is established. However, we have made changes to these methods that differ from their original usage (e.g., we used SRM rather than hyperalignment, we use meridian mapping rather than traveling wave retinotopy, we use movie-watching data rather than rest). Hence, the specifics of our design and pipeline warrant validation. 

      To add further validation, we have rerun the main analyses on an adult sample. We collected 8 adult participants who completed the same retinotopy task and a large subset of the movies that infants saw. These participants were run under maximally similar conditions to infants (i.e., scanned using the same parameters and without the top of the head-coil) and were preprocessed using the same pipeline. Given that the relationship between adult visual maps and movie-driven (or resting-state) analyses has been shown in many studies (Beckman et al., 2005; Butt et al., 2015; Guntupalli et al., 2016; Haak & Beckman, 2018; Knapen, 2021; Lu et al., 2017), these adult data serve as a validation of our analysis pipeline. These adult participants were included in the original manuscript; however, they were previously only used to support the SRM analyses (i.e., can adults be used to predict infant visual maps). The adult results are described before any results with infants, as a way to engender confidence. Moreover, we have provided new supplementary figures of the adult results that we hope will be integrated with the article when viewing it online, such that it will be easy to compare infant and adult results, as per the reviewer’s request. 

      As per the figures and captions below, the analyses were all successful with the adult participants: 1) Homotopic correlations are higher than correlations between comparable areas in other streams or areas that are more distant within stream. 2) A multidimensional scaling depiction of the data shows that areas in the dorsal and ventral stream are dissimilar. 3) Using independent components analysis on the movie data, we identified components that are highly correlated with the retinotopy task-based spatial frequency and meridian maps. 4) Using shared response modeling on the movie data, we predicted maps that are highly correlated with the retinotopy task-based spatial frequency and meridian maps.

      These supplementary analyses are underpowered for between-group comparisons, so we do not statistically compare the results between infants and adults. Nonetheless, the pattern of adult results is comparable overall to the infant results. 

      We believe these adult results provide a useful validation that the infant analyses we performed can recover fine-grained organization.

      Enhancing the visualization

      The reviewer raises an additional concern about the lack of visualization of the results. We recognize that the plots of the summary statistics do not provide information about the intermediate analyses. Indeed, we think the summary statistics can understate the degree of similarity between the components or predicted visual maps and the ground truth. Hence, we have added 6 new supplementary figures showing the intensity gradients for the following analyses: 1. spatial frequency prediction using ICA, 2. meridian prediction using ICA, 3. spatial frequency prediction using infant SRM, 4. meridian prediction using infant SRM, 5. spatial frequency prediction using adult SRM, and 6. meridian prediction using adult SRM.

      We hope that these visualizations are helpful. It is possible that the reviewer wishes us to also visually present the raw maps from the ICA and SRM, akin to what we show in Figure 3A and 3B. We believe this is out of scope of this paper: of the 1140 components that were identified by ICA, we selected 36 for spatial frequency and 17 for meridian maps. We also created 20 predicted maps for spatial frequency and 20 predicted meridian maps using SRM. This would result in the depiction of 93 subfigures, requiring at least 15 new full-page supplementary figures to display with adequate resolution. Instead, we encourage the reader to access this content themselves: we have made the code to recreate the analyses publicly available, as well as both the raw and preprocessed data for these analyses, including the data for each of these selected maps.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) As mentioned in the public review, the authors should consider incorporating relevant adult fMRI research into the Introduction and explain the importance of testing this question in infants.

      Our public response describes the several citations to relevant adult research we have added, and have provided further motivation for the project.

      (2) The authors should conduct additional analyses to support their conclusion that movie data alone can generate accurate retinotopic maps (i.e., by comparing this approach to other available alternatives).

      We have clarified in our public response that we did not wish to conclude that movie data alone can generate accurate retinotopic maps, and have made substantial edits to the text to emphasize this. Thus, because this claim is already not supported by our analyses, we do not think it is necessary to test it further.

      (3) The authors should re-do the homotopy analyses using movie-defined ROIs (i.e., by splitting the movie-viewing data into independent folds for functional ROI definition and analyses).

      As stated above, defining ROIs based on the movie content is not the intended goal of this project. Even if that were the general goal, we do not believe that it would be appropriate to run this specific analysis with the data we collected. Firstly, halving the data for ROI definition (e.g., using half the movie data to identify and trace areas, and then use those areas in the homotopy analysis to run on the other half of data) would qualitatively change the power of the analyses described here. Secondly, we would be unable to define areas beyond hV4/V3AB with confidence, since our retinotopic mapping only affords specification of early visual cortex. Thus we could not conduct the MDS analyses shown in Figure 2.

      (4) If the authors agree that a primary contribution of this study and paper is to showcase what is possible to do with a limited amount of movie-viewing data, then they should make it clearer, sooner, how much usable movie data they have from infants. They could also consider conducting additional analyses to determine the minimum amount of fMRI data necessary to reveal the same detailed characteristics of functional responses in the visual cortex.

      We agree it would be good to highlight the amount of movie data used. When the infant data is first introduced in the results section, we now state the durations:

      “All available movies from each session were included (Table S2), with an average duration of 540.7s (range: 186--1116s).” Pg. 5

      Additionally, we have added a homotopy analysis that describes the contribution of data quantity to the results observed. We compare the amount of data collected with the magnitude of same vs. different stream effect (Figure 1B) and within stream distance effect (Figure 1C). We find no effect of movie duration in the sample we tested, as reported below:

      “We found no evidence that the variability in movie duration per participant correlated with this difference [of same stream vs. different stream] (r=0.08, p=.700).” Pg. 6-7

      “There was no correlation between movie duration and the effect (Same > Adjacent: r=-0.01, p=.965, Adjacent > Distal: r=-0.09, p=.740).” Pg. 7

      (5) If any of the methodological approaches are novel, the authors should make this clear. In particular, has the approach of visually inspecting and categorizing components generated from ICA and movie data been done before, in adults/other contexts?

      The methods we employed are similar to others, as described in the public review.

      However, changes were necessary to apply them to infant samples. For instance, Guntupalli et al. (2016) used hyperalignment to predict the visual maps of adult participants, whereas we use SRM. SRM and hyperalignment have the same goal — find a maximally aligned representation between participants based on brain function — but their implementation is different. The application of functional alignment to infants is novel, as is their use in movie data that is relatively short by comparison to standard adult data. Indeed, this is the most thorough demonstration that SRM — or any functional alignment procedure — can be usefully applied to infant data, awake or sleeping. We have clarified this point in the discussion.

      “This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults[27,32,33], or revealing changing function over development[45], which may prove especially useful for infant fMRI[52].” Pg. 21

      (6) The authors found that meridian maps were less identifiable from ICA and movie data and suggest that this may be because these maps are more susceptible to noise or gaze variability. If this is the case, you might predict that these maps are more identifiable in adult data. The authors could consider running additional analyses with their adult participants to better understand this result.

      As described in the manuscript, we hypothesize that meridian maps are more difficult to identify than spatial frequency maps because meridian maps are a less smooth, more fine-grained map than spatial frequency. Indeed, it has previously been reported (Moeller et al., 2009) that similar procedures can result in meridian maps that are constituted by multiple independent components (e.g., a component sensitive to horizontal orientations, and a separate component sensitive to vertical components). Nonetheless, we have now conducted the ICA procedure on adult participants and again find it is easier to identify spatial frequency components compared to meridian maps, as reported in the public review.

      Minor corrections:

      (1) Typo: Figure 3 title: "Example retintopic task vs. ICA-based spatial frequency maps.".

      Fixed

      (2) Given the age range of the participants, consider using "infants and toddlers"? (Not to diminish the results at all; on the contrary, I think it is perhaps even more impressive to obtain awake fMRI data from ~1-2-year-olds). Example: Figure 3 legend: "A) Spatial frequency map of a 17.1-monthold infant.".

      We agree with the reviewer that there is disagreement about the age range at which a child starts being considered a toddler. We have changed the terms in places where we refer to a toddler in particular (e.g., the figure caption the reviewer highlights) and added the phrase “infants and toddlers” in places where appropriate. Nonetheless, we have kept “infants” in some places, particularly those where we are comparing the sample to adults. Adding “and toddlers” could imply three samples being compared which would confuse the reader.

      (3) Figure 6 legend: The following text should be omitted as there is no bar plot in this figure: "The bar plot is the average across participants. The error bar is the standard error across participants.".

      Fixed

      (4) Table S1 legend: Missing first single quote: Runs'.

      Fixed

      Reviewer #2 (Recommendations For The Authors):

      I request that this paper cite more of the existing literature on the fMRI of human infants and toddlers using task-driven and resting-state data. For example, early studies by (first authors) Biagi, Dehaene-Lambertz, Cusack, and Fransson, and more recent studies by Chen, Cabral, Truzzi, Deen, and Kosakowski.

      We have added several new citations of recent task-based and resting state studies to the second sentence of the main text:

      “Despite the recent growth in infant fMRI[1-6], one of the most important obstacles facing this research is that infants are unable to maintain focus for long periods of time and struggle to complete traditional cognitive tasks[7].”

      Reviewer #3 (Recommendations For The Authors):

      In the following, I report some of my main perplexities, but many more may arise when the material is presented more clearly.

      The age of the children varies from 5 months to about 2 years. While the developmental literature suggests that between 1 and 2 years children have a visual system nearly adult-like, below that age some areas may be very immature. I would split the sample and perhaps attempt to validate the adult SRM model with the youngest children (and those can be called infants).

      We recognize the substantial age variability in our sample, which is why we report participant-specific data in our figures. While splitting up the data into age bins might reveal age effects, we do not think we can perform adequately powered null hypothesis testing of the age trend. In order to investigate the contribution of age, larger samples will be needed. That said, we can see from the data that we have reported that any effect of age is likely small. To elaborate: Figures 4 and 6 report the participant-specific data points and order the participants by age. There are no clear linear trends in these plots, thus there are no strong age effects.

      More broadly, we do not think there is a principled way to divide the participants by age. The reviewer suggests that the visual system is immature before the first year of life and mature afterward; however, such claims are the exact motivation for the type of work we are doing here, and the verdict is still out. Indeed, the conclusion of our earlier work reporting retinotopy in infants (Ellis et al., 2021) suggests that the organization of the early visual cortex in infants as young as 5 months — the youngest infant in our sample — is surprisingly adult-like.

      The title cannot refer to infants given the age span.

      There is disagreement in the field about the age at which it is appropriate to refer to children as infants. In this paper, and in our prior work, we followed the practice of the most attended infant cognition conference and society, the International Congress of Infant Studies (ICIS), which considers infants as those aged between 0-3 years old, for the purposes of their conference. Indeed, we have never received this concern across dozens of prior reviews for previous papers covering a similar age range. That said, we understand the spirit of the reviewer’s comment and now refer to the sample as “infants and toddlers” and to older individuals in our sample as “toddlers” wherever it is appropriate (the younger individuals would fairly be considered “infants” under any definition).

      Figure 1 is clear and an interesting approach. Please also show the average correlation maps on the cortical surface.

      While we would like to create a figure as requested, we are unsure how to depict an area-by-area correlation map on the cortical surface. One option would be to generate a seed-based map in which we take an area and depict the correlation of that seed (e.g., vV1) with all other voxels. This approach would result in 8 maps for just the task-defined areas, and 17 maps for anatomically-defined areas. Hence, we believe this is out of scope of this paper, but an interested reader could easily generate these maps from the data we have released.

      Figure 2 results are not easily interpretable. Ventral and dorsal V1-V3 areas represent upper or lower VF respectively. Higher dorsal and ventral areas represent both upper and lower VF, so we should predict an equal distance between the two streams. Again, how can we verify that it is not a result of some artifacts?

      In adults, visual areas differ in their functional response properties along multiple dimensions, including spatial coding. The dorsal/ventral stream hypothesis is derived from the idea that areas in each stream support different functions, independent of spatial coding. The MDS analysis did not attempt to isolate the specific contribution of spatial representations of each area but instead tested the similarity of function that is evoked in naturalistic viewing. Other covariance-based analyses specifically isolate the contribution of spatial representations (Haak et al., 2013); however, they use a much more constrained analysis than what was implemented here. The fact that we find broad differentiation of dorsal and ventral visual areas in infants is consistent with adults (Haak & Beckman, 2018) and neonate non-human primates (Arcaro & Livingstone, 2017). 

      Nonetheless, we recognize that we did not mention the differences in visual field properties across areas and what that means. If visual field properties alone drove the functional response then we would expect to see a clustering of areas based on the visual field they represent (e.g., hV4 and V3AB should have similar representations). Since we did not see that, and instead saw organization by visual stream, the result is interesting and thus warrants reporting. We now mention this difference in visual fields in the manuscript to highlight the surprising nature of the result.

      “This separation between streams is striking when considering that it happens despite differences in visual field representations across areas: while dorsal V1 and ventral V1 represent the lower and upper visual field, respectively, V3A/B and hV4 both have full visual field maps. These visual field representations can be detected in adults[41]; however, they are often not the primary driver of function[39]. We see that in infants too: hV4 and V3A/B represent the same visual space yet have distinct functional profiles.” Pg. 8

      The reviewer raises a concern that the MDS result may be spurious and caused by noise. Below, we present three reasons why we believe these results are not accounted for by artifacts but instead reflect real functional differentiation in the visual cortex. 

      (1) Figure 2 is a visualization of the similarity matrix presented in Figure S1. In Figure S1, we report the significance testing we performed to confirm that the patterns differentiating dorsal and ventral streams — as well as adjacent areas from distal areas — are statistically reliable across participants. If an artifact accounted for the result then it would have to be a kind of systematic noise that is consistent across participants.

      (2) One of the main sources of noise (both systematic and non-systematic) with infant fMRI is motion. Homotopy is a within-participant analysis that could be biased by motion. To assess whether motion accounts for the results, we took a conservative approach of regressing out the framewise motion (i.e., how much movement there is between fMRI volumes) from the comparisons of the functional activity in regions. Although the correlations numerically decreased with this procedure, they were qualitatively similar to the analysis that does not regress out motion:

      “Additionally, if we control for motion in the correlation between areas --- in case motion transients drive consistent activity across areas --- then the effects described here are negligibly different (Figure S5).” Pg. 7

      (3) We recognize that despite these analyses, it would be helpful to see what this pattern looks like in adults where we know more about the visual field properties and the function of dorsal and ventral streams. This has been done previously (e.g., Haak & Beckman, 2018), but we have now run those analyses on adults in our sample, as described in the public review. As with infants, there are reliable differences in the homotopy between streams (Figure S1). The MDS results show that the adult data was more complex than the infant data, since it was best described by 3 dimensions rather than 2. Nonetheless, there is a rotation of the MDS such that the structure of the ventral and dorsal streams is also dissociable. 

      Figure 3 also raises several alternative interpretations. The spatial frequency component in B has strong activity ONLY at the extreme border of the VF and this is probably the origin of the strong correlation. I understand that it is only one subject, but this brings the need to show all subjects and to report the correlation. Also, it is important to show the putative average ICA for retinotopy and spatial frequencies across subjects and for adults. All methods should be validated on adults where we have clear data for retinotopy and spatial frequency.

      The reviewer notes that the component in Figure 3 shows strong negative response in the periphery. It is often the case, as reported elsewhere (Moeller et al., 2009), that ICA extracts portions of visual maps. To make a full visual map would require combining components into a composite (e.g., a component that has a high response in the periphery and another component that has a high response in the fovea). If we were to claim that this component, or others like it, could replace the need for retinotopic mapping, then we would want to produce these composite maps; however, our conclusion in this project is that the topographic information of retinotopic maps manifest in individual components of ICA. For this purpose, the analysis we perform adequately assesses this topography.

      Regarding the request to show the results for all subjects, we address this in the public response and repeat it here briefly: we have added 6 new figures to show results akin to Figure 3C and D. It is impractical to show the equivalent of Figure 3A and B for all participants, yet we do release the data necessary to see to visualize these maps easily.

      Finally, the reviewer suggests that we validate the analyses on adult participants. As shown in Figure S3 and reported in the public response, we now run these analyses on adult participants and observe qualitatively similar results to infants.

      How much was the variation in the presumed spatial frequency map? Is it consistent with the acuity range? 5-month-old infants should have an acuity of around 10c/deg, depending on the mean luminance of the scene.

      The reviewer highlights an important weakness of conducting ICA: we cannot put units on the degree of variation we see in components. We now highlight this weakness in the discussion:

      “Another limitation is that ICA does not provide a scale to the variation: although we find a correlation between gradients of spatial frequency in the ground truth and the selected component, we cannot use the component alone to infer the spatial frequency selectivity of any part of cortex. In other words, we cannot infer units of spatial frequency sensitivity from the components alone.” Pg. 20

      Figure 5 pipeline is totally obscure. I presumed that I understood, but as it is it is useless. All methods should be clearly described, and the intermediate results should be illustrated in figures and appropriately discussed. Using such blind analyses in infants in principle may not be appropriate and this needs to be verified. Overall all these techniques rely on correlation activities that are all biased by head movement, eye movement, and probably the dummy sucking. All those movements need to be estimated and correlated with the variability of the results. It is a strong assumption that the techniques should work in infants, given the presence of movements.

      We recognize that the SRM methods are complex. Given this feedback, we remade Figure 5 with explicit steps for the process and updated the caption (as reported in the public review).

      Regarding the validation of these methods, we have added SRM analyses from adults and find comparable results. This means that using these methods on adults with comparable amounts of data as what we collected from infants can predict maps that are highly similar to the real maps. Even so, it is not a given that these methods are valid in infants. We present two considerations in this regard. 

      First, as part of the SRM analyses reported in the manuscript, we show that control analyses are significantly worse than the real analyses (indicated by the lines on Figure 6). To clarify the control analysis: we break the mapping (i.e., flip the order of the data so that it is backwards) between the test participant and the training participants used to create the SRM. The fact that this control analysis is significantly worse indicates that SRM is learning meaningful representations that matter for retinotopy. 

      Second, we believe that this paper is a validation of SRM for infants. Infant fMRI is a nascent field and SRM has the potential to increase the signal quality in this population. We hope that readers will see these analyses as a proof of concept that SRM can be used in their work with infants. We have stated this contribution in the paper now.

      “Additionally, we wish to test whether methods for functional alignment can be used with infants. Functional alignment finds a mapping between participants using functional activity -- rather than anatomy -- and in adults can improve signal-to-noise, enhance across participant prediction, and enable unique analyses[27,32-34].” Pg. 4

      “This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults[27,32,33], or revealing changing function over development[45].” Pg. 21

      Regarding the reviewer’s concern that motion may bias the results, we wish to emphasize the nature of the analyses being conducted here: we are using data from a group of participants to predict the neural responses in a held-out participant. For motion to explain consistency between participants, the motion would need to be timelocked across participants. Even if motion was time-locked during movie watching, motion will impair the formation of an adequate model that can contain retinotopic information. Thus, motion should only hurt the ability for a shared response to be found that can be used for predicting retinotopic maps. Hence, the results we observed are despite motion and other sources of noise.

      What is M??? is it simply the mean value??? If not, how it is estimated?

      M is an abbreviation for mean. We have now expanded the abbreviation the first time we use it.

      Figure 6 should be integrated with map activity where the individual area correlation should be illustrated. Probably fitting SMR adult works well for early cortical areas, but not for more ventral and associative, and the correlation should be evaluated for the different masks.

      With the addition of plots showing the gradients for each participant and each movie (Figures S10–S13) we hope we have addressed this concern. We additionally want to clarify that the regions we tested in the analysis in Figure 6 are only the early visual areas V1, V2, V3, V3A/B, and hV4. The adult validation analyses show that SRM works well for predicting the visual maps in these areas. Nonetheless, it is an interesting question for future research with more extensive retinotopic mapping in infants to see if SRM can predict maps beyond extrastriate cortex.

      Occipital masks have never been described or shown.

      The occipital mask is from the MNI probabilistic structural atlas (Mazziotta et al., 2001), as reported in the original version and is shared with the public data release. We have added the additional detail that the probabilistic atlas is thresholded at 0% in order to be liberally inclusive. 

      “We used the occipital mask from the MNI structural atlas[63] in standard space -- defined liberally to include any voxel with an above zero probability of being labelled as the occipital lobe -- and used the inverted transform to put it into native functional space.” Pg. 27–28

      Methods lack the main explanation of the procedures and software description.

      We hope that the additions we have made to address this reviewer’s concerns have provided better explanations for our procedures. Additionally, as part of the data and code release, we thoroughly explain all of the software needed to recreate the results we have observed here.

    1. Reviewer #1 (Public review):

      Summary:

      This study by Howe and colleagues investigates the role of the posterolateral cortical amygdala (plCoA) in mediating innate responses to odors, specifically attraction and aversion. By combining optogenetic stimulation, single-cell RNA sequencing, and spatial analysis, the authors identify a topographically organized circuit within plCoA that governs these behaviors. They show that specific glutamatergic neurons in the anterior and posterior regions of plCoA are responsible for driving attraction and avoidance, respectively, and that these neurons project to distinct downstream regions, including the medial amygdala and nucleus accumbens, to control these responses.

      Strengths:

      The major strength of the study is the thoroughness of the experimental approach, which combines advanced techniques in neural manipulation and mapping with high-resolution molecular profiling. The identification of a topographically organized circuit in plCoA and the connection between molecularly defined populations and distinct behaviors is a notable contribution to understanding the neural basis of innate motivational responses. Additionally, the use of functional manipulations adds depth to the findings, offering valuable insights into the functionality of specific neuronal populations.

      Weaknesses:

      There are some weaknesses in the study's methods and interpretation. The lack of clarity regarding the behavior of the mice during head-fixed imaging experiments raises the possibility that restricted behavior could explain the absence of valence encoding at the population level. Furthermore, while the authors employ chemogenetic inhibition of specific pathways, the rationale for this choice over optogenetic inhibition is not fully addressed, and this could potentially affect the interpretation of the results. Additionally, the choice of the mplCoA for manipulation, rather than the more directly implicated anterior and posterior subregions, is not well-explained, which could undermine the conclusions drawn about the topographic organization of plCoA.

      Despite these concerns, the work provides significant insights into the neural circuits underlying innate behaviors and opens new avenues for further research. The findings are particularly relevant for understanding the neural basis of motivational behaviors in response to sensory stimuli, and the methods used could be valuable for researchers studying similar circuits in other brain regions. If the authors address the methodological issues raised, this work could have a substantial impact on the field, contributing to both basic neuroscience and translational research on the neural control of behavior.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This work aims to understand the role of thalamus POm in dorsal lateral striatum (DLS) projection in learning a sensorimotor associative task. The authors first confirm that POm forms "en passant" synapses with some of the DLS neuronal subtypes. They then perform a go/no-go associative task that consists of the mouse learning to discriminate between two different textures and to associate one of them with an action. During this task, they either record the activity of the POm to DLS axons using endoscopy or silence their activity. They report that POm axons in the DLS are activated around the sensory stimulus but that the activity is not modulated by the reward. Last, they showed that silencing the POm axons at the level of DLS slows down learning the task.

      The authors show convincing evidence of projections from POm to DLS and that POm inputs to DLS code for whisking whatever the outcome of the task is. However, their results do not allow us to conclude if more neurons are recruited during the learning process or if the already activated fibres get activated more strongly. Last, because POm fibres in the DLS are also projecting to S1, silencing the POm fibres in the DLS could have affected inputs in S1 as well and therefore, the slowdown in acquiring the task is not necessarily specific to the POm to DLS pathway.

      We thank the reviewer for these constructive comments. The points are addressed below.  

      Strengths:

      One of the main strengths of the paper is to go from slice electrophysiology to behaviour to get an in-depth characterization of one pathway. The authors did a comprehensive description of the POm projections to the DLS using transgenic mice to unambiguously identify the DLS neuronal population. They also used a carefully designed sensorimotor association task, and they exploited the results in depth.

      It is a very nice effort to have measured the activity of the axons in the DLS not only after the mice have learned the task but throughout the learning process. It shows the progressive increase of activity of POm axons in the DLS, which could imply that there is a progressive strengthening of the pathway. The results show convincingly that POm axons in the DLS are not activated by the outcome of the task but by the whisker activity, and that this activity on average increases with learning.

      Weaknesses:

      One of the main targets of the striatum from thalamic input are the cholinergic neurons that weren't investigated here, is there information that could be provided?

      This is true of the parafascicular (Pf) thalamic nucleus, which has been well studied in this context. However, there is much less known about the striatal projections of other thalamic nuclei, including POm, and their inputs to cholinergic neurons. Anatomical tracing evidence from Klug et al. (2018), which mapped brain-wide inputs to striatal cholinergic (ChAT) interneurons, suggests that Pf provides the majority of thalamic innervation of striatal ChAT neurons compared to other thalamic nuclei. Many other thalamic nuclei, including POm, showed very little of no labeling, suggesting weak innervation of ChAT interneurons. However, it is possible that these thalamic nuclei, including POm, do provide functional innervation of ChAT interneurons that is not sufficiently assessed by anatomical tracing. Understanding the innervation patterns of POm-striatal projections beyond the three cell types we have studied here would be an important area of further study.

      It is interesting to know that the POm projects to all neuronal types in the DLS, but this information is not used further down the manuscript so the only take-home message of Figure 1 is that the axons that they image or silence in the DLS are indeed connected to DLS neurons and not just passing fibres. In this line, are these axons the same as the ones projecting to S1? If this is the case, why would we expect a different behaviour of the axon activity at the DLS level compared to S1?

      Tracing of single POm axons by Ohno et al. (2012) indicated that POm axons form a branched collateral that innervates striatum, while the main axon continues in the rostral-dorsal direction to innervate cortex. We think it is reasonable, based on the morphology, that our optogenetic suppression experiment restricted the suppression of glutamate release to this branch and avoided the other branches of the axon that project to cortex. However, testing this would require monitoring S1 activity during the POm-striatal axon suppression, which we did not do in this study.

      It is a very interesting question whether there could be different axon activity behavior in striatum versus S1. There is surprising evidence that POm synaptic terminals are different sizes in S1 and M1 and show different synaptic physiological properties depending on these cortical projection targets (Casas-Torremocha et al., 2022). Based on this, it is possible that POm-striatal synapses show distinct properties compared to cortex; however, this will need to be tested in future work.

      The authors used endoscopy to measure the POm axons in the DLS activity, which makes it impossible to know if the progressive increase of POm response is due to an increase of activity from each individual neuron or if new neurons are progressively recruited in the process.

      This is a good point. It would be necessary to perform chronic two-photon imaging of POm neurons (or chronic electrophysiological recordings) to determine whether the activity of individual neurons increased versus whether individual neuron activity levels remained similar but new neurons became active with learning. Even under baseline conditions, it is not known in detail what fraction of the population of POm neurons is active during sensory processing or behavior, highlighting how much is still to be discovered in this exciting area of neuroscience.

      The picture presented in Figure 4 of the stimulation site is slightly concerning as there are hardly any fibres in neocortical layer 1 while there seems to be quite a lot of them in layer 4, suggesting that the animal here was injected in the VB. This is especially striking as the implantation and projection sites presented in Figures 1 and 2 are very clean and consistent with POm injection.

      Although this image was selected to demonstrate the position of the POm injection site and optical fiber implant above striatal axons, the reviewer is correct that there appears to be mixed labeling of axons in L4 and L5a. In some cases, there was expression slightly outside the border of POm (see Fig. 1B, right), which might explain the cortical innervation pattern in this figure. While cortically bound VPM axons pass through the striatum, they do not form synaptic terminals until reaching the cortex (Hunnicutt et al., 2016). If, as may be the case, inhibitory opsins suppress release of neurotransmitter at synaptic terminals more effectively than action potential propagation in axons, it may be likely that optogenetic suppression of POm-striatal terminals is more effective than suppression of action potentials in off-target-labelled VPM axons of passage. Ideally, we could compare effects of suppression of POm-striatal synapses with POm-cortical synapses and VPM-cortical synapses, but this was outside the bandwidth of the present study.

      Reviewer #2 (Public Review):

      Summary:

      Yonk and colleagues show that the posterior medial thalamus (POm), which is interconnected with sensory and motor systems, projects directly to major categories of neurons in the striatum, including direct and indirect pathway MSNs, and PV interneurons. Activity in POm-striatal neurons during a sensory-based learning task indicates a relationship between reward expectation and arousal. Inhibition of these neurons slows reaction to stimuli and overall learning. This circuit is positioned to feed salient event activation to the striatum to set the stage for effective learning and action selection.

      Strengths:

      The results are well presented and offer interesting insight into an understudied thalamostriatal circuit. In general, this work is important as part of a general need for an increased understanding of thalamostriatal circuits in complex learning and action selection processes, which have generally received less attention than corticostriatal systems.

      Weaknesses:

      There could be a stronger connection between the connectivity part of the data - showing that POm neurons context D1, D2, and PV neurons in the striatum but with some different properties - and the functional side of the project. One wonders whether the POm neurons projecting to these subtypes or striatal neurons have unique signaling properties related to learning, or if there is a uniform, bulk signal sent to the striatum. This is not a weakness per se, as it's reasonable for these questions to be answered in future papers.

      We are very interested to understand the potentially distinct learning-related synaptic and circuit changes that potentially occur at the POm synapses with D1- and D2-SPNs and PV interneurons, and other striatal cell types. We agree that this would be an important topic for further investigation.

      All the in vivo activity-related conclusions stem from data from just 5 mice, which is a relatively small sample set. Optogenetic groups are also on the small side.

      We appreciate this point and agree that higher N can be important for observing robust effects. A factor of our experiments that helped reduce the number of animals used was the longitudinal design, with repeated measures in the same subjects. This allowed for the internal control of comparing learning effects in the same subject from naïve to expert stages and therefore increased robustness. Even with relatively small group sizes, results were statistically significant, suggesting that the use of more mice was unnecessary, which we considered consistent with best practice in the use of animals in research. We also note that our group sizes were consistent with other studies in the field.  

      Reviewer #3 (Public Review):

      Yonk and colleagues investigate the role of the thalamostriatal pathway. Specifically, they studied the interaction of the posterior thalamic nucleus (PO) and the dorsolateral striatum in the mouse. First, they characterize connectivity by recording DLS neurons in in-vitro slices and optogenetically activating PO terminals. PO is observed to establish depressing synapses onto D1 and D2 spiny neurons as well as PV neurons. Second, the image PO axons are imaged by fiber photometry in mice trained to discriminate textures. Initially, no trial-locked activity is observed, but as the mice learn PO develops responses timed to the audio cue that marks the start of the trial and precedes touch. PO does appear to encode the tactile stimulus type or outcome. Optogenetic suppression of PO terminals in striatum slow task acquisition. The authors conclude that PO provides a "behaviorally relevant arousal-related signal" and that this signal "primes" striatal circuitry for sensory processing.

      A great strength of this paper is its timeliness. Thalamostriatal processing has received almost no attention in the past, and the field has become very interested in the possible functions of PO. Additionally, the experiments exploit multiple cutting-edge techniques.

      There seem to be some technical/analytical weaknesses. The in vitro experiments appear to have some contamination of nearby thalamic nuclei by the virus delivering the opsin, which could change the interpretation. Some of the statistical analyses of these data also appear inappropriate. The correlative analysis of Pom activity in vivo, licking, and pupil could be more convincingly done.

      The bigger weakness is conceptual - why should striatal circuitry need "priming" by the thalamus in order to process sensory stimuli? Why would such circuitry even be necessary? Why is a sensory signal from the cortex insufficient? Why should the animal more slowly learn the task? How does this fit with existing ideas of striatal plasticity? It is unclear from the experiments that the thalamostriatal pathway exists for priming sensory processing. In fact, the optogenetic suppression of the thalamostriatal pathway seems to speak against that idea.

      We thank the reviewer for these constructive comments. The points are addressed below.

      Recommendations for the authors:

      Reviewer #2 (Recommendations For The Authors):

      Do POm neurons innervate CINs also? The connection between the PF thalamus and CINs is mentioned in a couple of places - one question is how unique are the input patterns for the POm versus adjacent sensorimotor thalamic regions, including the PF? This isn't a weakness per se but knowing the answer to that question would help in forming a more complete picture of how these different thalamostriatal circuits do or do not contribute uniquely to learning and action selection.

      Anatomical tracing evidence from Klug et al. (2018), which mapped brain-wide inputs to striatal cholinergic (ChAT) interneurons, suggests that Pf provides the majority of thalamic innervation of striatal ChAT neurons compared to other thalamic nuclei. Many other thalamic nuclei, including POm, showed very little or no labeling, suggesting weak innervation of ChAT interneurons. However, it is possible that these thalamic nuclei, including POm, do provide functional innervation of ChAT interneurons that is not sufficiently assessed by anatomical tracing.

      Another difference between Pf and other thalamic nuclei (likely including POm) comes from anatomical tracing evidence (Smith et al., 2014; PMID: 24523677) which indicates that Pf inputs form the majority of their synapses onto dendritic shafts of SPNs, while other thalamic nuclei form synapses onto dendritic spines. Understanding the innervation patterns of POm-striatal projections beyond the three cell types we have studied here, including ChAT neurons and subcellular localization, would be an important area of further study.

      It would be useful to know to what extent these POm-striatum neurons are activated generally during movement, versus this discrimination task specifically.

      We agree that distinguishing general movement-related activity from task-specific activity would be very useful. Earlier work (Petty et al., 2021) showed a close relationship between POm neuron activity, spontaneous (task-free) whisker movements, and pupil-indexed arousal in head-restrained mice. Oram et al. (2024; PMID: 39003286) recently recorded VPM and POm in freely moving mice during natural movements, finding that activity of both nuclei correlated with head and whisker movements. These studies indicate that POm is generally coactive with exploratory head and whisker movements.

      During task performance, the situation may change with training and attentional effects. For example, Petty and Bruno (2024) (https://elifesciences.org/reviewed-preprints/97188) showed that POm activity correlates more closely with task demands than tactile or visual stimulus modality. Our data indicate that POm axonal signals are increased at trial start during anticipation of tactile stimulus delivery and through the sensory discrimination period, then decrease to baseline levels during licking and water reward collection (Fig. 3). Results of Petty and Bruno (2024) together with ours suggest that POm is particularly active during the context of behaviorally relevant task performance. Thus, we think it is likely that, while pupil dilation indexes general movement and arousal, POm activity is more specific to movement and arousal associated with task engagement and behavioral performance. We have strengthened this point in the Discussion.

      Many of the data panels and text for legends/axes are quite small, and the stroke on line art is quite faint - overall figures could be improved from a readability standpoint.

      We thank the reviewer for their careful attention to the figures. 

      Reviewer #3 (Recommendations For The Authors):

      Major

      (1) Page 4, the Results regarding PSP and distance from injection site. The r-squared is the wrong thing to look at to test for a relationship. One should look at the p-value on the coefficient corresponding to the slope. The p-value is probably significant given the figures, in which case there may be a relationship contrary to what is stated. All the low r-squared value says is that, if there is a relationship, it does not explain a lot of the PSP variability.

      We thank the reviewer for alerting us this oversight. We have included the p value (p = 0.0293) in the figure and legend, and indicated that the relationship is “small but significant”.

      (2) Figure 1B suggests that the virus injections extend beyond POm and into other thalamic structures. Do any of the results change if the injections contaminating other nuclei are excluded from the analysis? I am not suggesting the authors change the figures/analyses. I am simply suggesting they double-check.

      We selected for injections that were predominantly expressing in POm as determined by post-hoc histological analysis (see Fig. 1, right). As above, we think that axons of passage that do not form striatal synapses are less likely to be suppressed than axons with terminals; however, this would need to be determined in further experiments. Because the preponderance of expression is within POm, we think the results would be similar even with a stricter selection criterion. 

      (3) The authors conclude that POm and licking are not correlated (bottom of page 6 pertaining to Figures 3A-F). The danger of these analyses is that they assume that GCaMP8 is a perfect linear reporter of POm spikes. The reliability of GCaMP8 has been quantified in some cell types, but not thalamic neurons, which have relatively higher firing rates.

      The reviewer is correct that the relationship between GCaMP8 fluorescence changes and spiking has not been sufficiently characterized in thalamic neurons, and that this would be important to do.

      What if the indicator is simply saturated late into the trial (after the average reaction time)? It would look like there is no response and one would conclude no correlation, but there could be a very strong correlation.

      While saturation is worthy of concern, the signal dynamics here argue against this possibility. The reason is that the signal increased in the early part of the trial and decreased by the end. If saturation was an issue, this would have been apparent during the initial increase. When the signal decreased in amplitude at the end of the trial, this indicates that the signal is not saturated because it is returning from a point closer to its maximum (and is becoming less saturated).

      Also, what happens between trials? Are the correlations the same, stronger, weaker? Ideally, the authors would analyze the data during and between trials.

      Between trials the signal did not show further changes in baseline beyond what was displayed at the start and end of behavioral trials. There were no consistent increases or decreases in signals between trials, except perhaps during strong whisking bouts. This is anecdotal because we did not analyze between-trial data. However, it is interesting and important to note that signals increased dramatically in amplitude from naïve, early learning to expert behavioral performance (Fig. 3), highlighting that POm-axonal signals relate to behavioral engagement and performance rather than spontaneous behaviors.  

      (4) Axonal activity could also appear more correlated with the pupil than licking because pupil dynamics are slow like the dynamics of calcium indicators. These kernels could artificially inflate the correlation. Ideally, the authors could consider these temporal effects. Perhaps they could deconvolve the temporal profiles of calcium and pupil before correlating? Or equivalently incorporate the profiles into their analysis?

      We analyzed the lick probability histograms, which had a temporal profile similar to the calcium signals (Fig. 3D,E), ruling out concerns about effects of temporal effects on correlations. It is also worth noting that we observed changes in correlations between calcium signals and pupil with learning stage (Fig. 3I), even though the temporal profiles (signal dynamics) are not changing. Thus, temporal effects of the signals themselves are not the driver of correlations, but rather the changes in relative timing between calcium signals and pupil, as occur with learning.

      (5) The authors conclude that PO provides a "behaviorally relevant arousal-related signal" and that this signal "primes" striatal circuitry for sensory processing. The data here support the first part. It is not clear that the data support the second part, largely because it is vague what "priming" of sensory processing or "a key role in the initial stages of action selection (p.9) even means here. Why would such circuitry even be necessary? Why is a sensory signal from the cortex insufficient? Why should the animal more slowly learn the task? How does this fit with existing ideas of striatal plasticity? Some conceptual proposals from the authors, even if speculative and not offered as a conclusion, would be helpful.

      We appreciate these good points and have added further consideration and revision of the concept of priming and potential roles in an extensively revised Discussion section.

      (6) The photometry shows that PO turns on about 2 seconds before the texture presentation. PO's activity seems locked to the auditory cue, not the texture (Figure 2). This means that the attempt to suppress the thalamostriatal pathway with JAWS (Figure 4) is rather late, isn't it? Some PO signals surely go through. This seems to contradict the idea of priming above. It would be good if the authors could factor this into their narrative. Perhaps labelling the time of the auditory cue in Figure 4C would also be helpful.

      The start of texture presentation (movement of the texture panel toward the mouse) and auditory cue occur at the same time. To clarify this, we added a label “start tone” in Figure 4C and also in Figure 2C.

      For optogenetic (JAWS) suppression, we intentionally chose a time window between start tone onset and texture presentation, because our photometry experiments showed that this was when the preponderance of the signal occurred. However, the reviewer is correct that our chosen optogenetic suppression (JAWS) onset occurs shortly after the photometry signal has already started, potentially leaving the early photometry signal un-suppressed. Our motivation for choosing a restricted time window surrounding the texture presentation time was 1) to minimize illumination and potential heating of brain tissue; 2) to target a time window that avoids the auditory cue but covers stimulus presentation. We did not want to extend the duration of the suppression to before the trial started, because this could produce task-non-specific effects, such as distraction or loss of attention before the start of the trial.

      Even if some signal were getting through before suppression, we don’t think this contradicts the possibility of ‘priming’, because the process underlying priming would still be disrupted even if not totally suppressed. This would alter the temporal relationship between POm-striatal inputs and further corticostriatal inputs (from S1 and M1 cortex, for example). We have included further consideration of these points and possible relation to the priming concept in the Discussion.

      Minor

      (1) Page 5, "the sensitivity metric is artificially increased". What do you mean "artificially"? The mice are discriminating better. It is true that either a change in HR or FAR can cause the sensitivity metric to change, but there is nothing artificial or misleading about this.

      We removed the word artificial and clarified our definition of behaviorally Expert in this context:

      “Mice were considered Expert once they had reached ≥ 0.80 Hit Rate and ≤ 0.30 FA Rate for two consecutive sessions in lieu of a strict sensitivity (d’) threshold; we found this definition more intuitive because d’ is enhanced as Hit Rate and FA Rate approach their extremes (0 or 1)”

      (2) Page 7, "Upon segmentation (Figure S4G-J)". Do you mean "segregation by trial outcome"?

      Corrected.

      (3) Page 9, "POm projections may have discrete target-specific functions, such that POm-striatal inputs may play a distinct role in sensorimotor behavior compared to POm-cortical inputs". Would POm-cortical inputs not also be sensorimotor? The somatosensory cortex contains a lot of corticostriatal cells. It also has various direct and indirect links to the motor cortex as well.

      We have clarified the wording here to convey the possibility that POm signals could be received and processed differently by striatal versus cortical circuitry, and have moved this statement to later in the discussion for better elaboration.

      (4) The Methods state that male and female mice were used. Why not say how many of each and whether or not there are any sex-specific differences?

      We added the following information to the Methods:

      The number of male and female mice were as follows, by experiment type: 6 male, 4 female (electrophysiology); 3 male, 2 female (fiber photometry); 4 male, 5 female (optogenetics). Data were not analyzed for sex differences.

    1. Reviewer #1 (Public review):

      Summary:

      In this series of studies, Locantore et al. investigated the role of SST-expressing neurons in the entopeduncular nucleus (EPNSst+) in probabilistic switching tasks, a paradigm that requires continued learning to guide future actions. In prior work, this group had demonstrated EPNSst+ neurons co-release both glutamate and GABA and project to the lateral habenula (LHb), and LHb activity is also necessary for outcome evaluation necessary for performance in probabilistic decision-making tasks. Previous slice physiology works have shown that the balance of glutamate/GABA co-release is plastic, altering the net effect of EPN on downstream brain areas and neural circuit function. The authors used a combination of in vivo calcium monitoring with fiber photometry and computational modelling to demonstrate that EPNSst+ neural activity represents movement, choice direction and reward outcomes in their behavioral task. However, viral-genetic manipulations to synaptically silence these neurons or selectively eliminate glutamate release had no effect on behavioral performance in well-trained animals. The authors conclude that despite their representation of task variables, EPN Sst+ neuron synaptic output is dispensable for task performance.

      Strengths and Weaknesses:

      Overall, the manuscript is exceptionally scholarly, with a clear articulation of the scientific question and a discussion of the findings and their limitations. The analyses and interpretations are careful and rigorous. This review appreciates the thorough explanation of the behavioral modelling and GLM for deconvolving the photometry signal around behavioral events, and the transparency and thoroughness of the analyses in the supplemental figures. This extra care has the result of increasing the accessibility for non-experts, and bolsters confidence in the results. To bolster a reader's understanding of results, we suggest it would be interesting to see the same mouse represented across panels (i.e. Fig 1 F-J, Supp 1 F,K etc i.e via inclusion of faint hash lines connecting individual data points across variables. Additionally, Fig 3E demonstrates that eliminating the 'reward' and 'choice and reward' terms from the GLM significantly worsens model performance; to demonstrate the magnitude of this effect, it would be interesting to include a reconstruction of the photometry signal after holding out of both or one of these terms, alongside the 'original' and 'reconstructed' photometry traces in panel D. This would help give context for how the model performance degrades by exclusion of those key terms. Finally, the authors claimed calcium activity increased following ipsilateral movements. However, figure 3C clearly shows that both SXcontra and SXisi increase beta coefficients. Instead, the choice direction may be represented in these neurons, given that beta coefficients increase following CXipsi and before SEipsi, presumably when animals make executive decisions. Could the authors clarify their interpretation on this point? Also, it is not clear if there is a photometry response related to motor parameters (i.e. head direction or locomotion, licking), which could change the interpretation of the reward outcome if it is related to a motor response; could the authors show photometry signal from representative 'high licking' or 'low licking' reward trials, or from spontaneous periods of high. Vs low locomotor speeds (if the sessions are recorded) to otherwise clarify this point?

      There are a few limitations with the design and timing of the synaptic manipulations that would improve the manuscript if discussed or clarified. The authors take care to validate the intersectional genetic strategies: Tetanus Toxin virus (which eliminates synaptic vesicle fusion) or CRISPR editing of Slc17a6, which prevents glutamate loading into synaptic vesicles. The magnitude of effect in the slice physiology results are striking. However, this relies on co-infection of a second AAV to express channelrhodopsin for the purposes of validation, and it is surely the case that there will not be 100% overlap between the proportion of cells infected. Alternative means of glutamate packaging (other VGluT isoforms, other transporters etc) could also compensate for the partial absence of VGluT2, which should be discussed. The authors do not perform a complimentary experiment to delete GABA release (i.e. via VGAT editing), which is understandable, given the absence of an effect with the pan-synaptic manipulation. A more significant concern is the timing of these manipulations as the authors acknowledge. The manipulations are all done in well-trained animals, who continue to perform during the length of viral expression. Moreover, after carefully showing that mice use different strategies on the 70/30 version vs the 90/10 version of the task, only performance on the 90/10 version is assessed after the manipulation. Together, the observation that EPNsst activity does not alter performance on a well learned, 90/10 switching task decreases the impact of the findings, as this population may play a larger role during task acquisition or under more dynamic task conditions. Additional experiments could be done to strengthen the current evidence, although the limitations is transparently discussed by the authors.

      Finally, intersectional strategies target LHb-projecting neurons, although in the original characterization it is not entirely clear that the LHb is the only projection target of EPNsst neurons. A projection map would help clarify this point.

      Overall, the authors used a pertinent experimental paradigm and common cell-specific approaches to address a major gap in the field, which is the functional role of glutamate/GABA co-release from the major basal ganglia output nucleus in action selection and evaluation. The study is carefully conducted, their analyses are thorough, and the data are often convincing and thought-provoking. However, the limitations of their synaptic manipulations with respect to the behavioral assays reduces generalizability and to some extent the impact of their findings.

      Comments on the latest version:

      Specifically, they have included more thorough analyses to address several concerns related to interpreting activity patterns of EPSst+ neurons. The authors clearly point out that calcium activity increased during ipsilateral movements, and the increase was statistically larger during the choice phase (Figure 2 supplement 1F-G), indicating that these neurons may represent movement and additional factors (e.g. executive decision-making). Correspondingly, we appreciate the thorough explanation of using a GLM model to determine which behavioural variables contribute to observed physiological signals and adding the example reconstructed signal with direction and reward variables omitted in Figure 3 supplements 1 and 2.

      Although no new manipulation experiment is added to the manuscript, the authors respond to common critiques related to testing the behavioural effect after the manipulations in well-trained mice. The discussion related to technical limitations, possible compensatory mechanisms and alternative interpretations is thorough and overall satisfying. Based on the behaviour modeling results, the authors speculate that animals need to integrate more evidence from the past to guide choice in a more uncertain environment (70/30 version), instead of adopting a 'win-stay, lose-shift' strategy in the more deterministic 90/10 version. The authors expand the discussion, but the possibility that EPNSst+ neurons contribute to task performance in well-trained animals under uncertainty is not directly tested. Along with other alternative explanations discussed in the manuscript, we think the paper is valuable literature for future studies to understand the basal ganglia circuits in learning and decision-making.

    2. Reviewer #2 (Public review):

      Summary:

      This paper aimed to determine the role EP sst+ neurons play in a probabilistic switching task.

      Strengths:

      - The in vivo recording of the EP sst+ neurons activity in the task is one of the strongest parts of this paper. Previous work had recorded from the EP-LHb population in rodents and primates in head fixed configurations, the recordings of this population in a freely moving context is a valuable addition to these studies and has highlighted more clearly that these neurons respond both at the time of choice and outcome.

      - The use of a refined intersectional technique to record specifically the EP sst+ neurons is also an important strength of the paper. This is because previous work has shown that there are two genetically different types of glutamatergic EP neurons that project to the LHb. Previous work had not distinguished between these types in their recordings so the current results showing that the bidirectional value signaling is present in the EP sst+ population is valuable.

      Weaknesses:

      - One of the main weaknesses of the paper is to do with how the effect of the EP sst+ neurons on the behavior was assessed.

      o All the manipulations (blocking synaptic release and blocking glutamatergic transmission) are chronic and more importantly the mice are given weeks of training after the manipulation before the behavioral effect is assessed. This means that as the authors point out in their discussion the mice will have time to adjust to the behavioral manipulation and compensate for the manipulations. The results do show that mice can adapt to these chronic manipulations and that the EP sst+ are not required to perform the task. What is unclear is whether the mice have compensated for the loss of EP sst+ neurons and whether they play a role in the task under normal conditions. Acute manipulations or chronic manipulations without additional training would be needed to assess this.

      o Another weakness is that the effect of the manipulations was assessed in the 90/10 contingency version of the task. Under these contingencies, mice integrate past outcomes over fewer trials to determine their choice and animals act closer to a simple win-stay-lose switch strategy. Due to this it is unclear if the EP sst+ neurons would play a role in the task when they must integrate over a larger number of conditions in the less deterministic 70/30 version of the task. Indeed it is not clear that lesioning any other regions involved in evaluation of action outcomes such as VTA dopamine neurons, that encode reward prediction errors, would have any deficit when assessed in this way. Due to this, it's not clear if the mice have adapted to solve the task without evaluating action outcomes at all and are just acting in a more deterministic lose switch manner that would not presumably involve any of the circuitry in evaluating action outcomes.

      - The authors conclude that they do not see any evidence for bidirectional prediction errors. It is not possible to conclude this. First, they see a large response in the EP sst+ neurons to the omission of an expected reward. This is what would be expected of a negative reward prediction error. There are much more specific well controlled tests for this that are commonplace in head-fixed and freely moving paradigms that could be tested to probe this. The authors do look at the effect of previous trials on the response and do not see strong consistent results, but this is not a strong formal test of what would be expected of a prediction error, either a positive or negative. The other way they assess this is by looking at the size of the responses in different recording sessions with different reward contingencies. They claim that the size of the reward expectation and prediction error should scale with the different reward probabilities. If all the reward probabilities were present in the same session this should be true as lots of others have shown for RPE. Because however this data was taken from different sessions it is not expected that the responses should scale, this is because reward prediction errors have been shown to adaptively scale to cover the range of values on offer (Tobler et al., Science 2005). A better test of positive prediction error would be to give a larger than expected reward on a subset of trials. Either way there is already evidence that responses reflect a negative prediction error in their data and more specific tests would be needed to formally rule in or out prediction error coding especially as previous recordings have shown it is present in previous primate and rodent recordings.

      - There are a lot of variables in the GLM that occur extremely close in time such as the entry and exit of a port. If two variables occur closely in time and are always correlated it will be difficult if not impossible for a regression model to assign weights accurately to each event. This is not a large issue, but it is misleading to have regression kernels for port entry and exits unless the authors can show these are separable due to behavioral jitter or a lack of correlation under specific conditions, which does not seem to be the case.

    3. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this series of studies, Locantore et al. investigated the role of SST-expressing neurons in the entopeduncular nucleus (EPNSst+) in probabilistic switching tasks, a paradigm that requires continued learning to guide future actions. In prior work, this group had demonstrated EPNSst+ neurons co-release both glutamate and GABA and project to the lateral habenula (LHb), and LHb activity is also necessary for outcome evaluation necessary for performance in probabilistic decision-making tasks. Previous slice physiology works have shown that the balance of glutamate/GABA co-release is plastic, altering the net effect of EPN on downstream brain areas and neural circuit function. The authors used a combination of in vivo calcium monitoring with fiber photometry and computational modeling to demonstrate that EPNSst+ neural activity represents movement, choice direction, and reward outcomes in their behavioral task. However, viral-genetic manipulations to synaptically silence these neurons or selectively eliminate glutamate release had no effect on behavioral performance in well-trained animals. The authors conclude that despite their representation of task variables, EPN Sst+ neuron synaptic output is dispensable for task performance.

      Strengths and Weaknesses:

      Overall, the manuscript is exceptionally scholarly, with a clear articulation of the scientific question and a discussion of the findings and their limitations. The analyses and interpretations are careful and rigorous. This review appreciates the thorough explanation of the behavioral modeling and GLM for deconvolving the photometry signal around behavioral events, and the transparency and thoroughness of the analyses in the supplemental figures. This extra care has the result of increasing the accessibility for non-experts, and bolsters confidence in the results.

      (1) To bolster a reader's understanding of results, we suggest it would be interesting to see the same mouse represented across panels (i.e. Figures 1 F-J, Supplementary Figures 1 F, K, etc i.e via the inclusion of faint hash lines connecting individual data points across variables.

      Thank you for the suggestion. The same mouse is now represented in Fig 1 and Fig 1—Figure Supplement 1 as a darkened circle so it can be followed across different panels. Photometry from this mouse was used as sample date in Figure 2b and Figure 2—figure supplement 1a-b.

      (2) Additionally, Figure 3E demonstrates that eliminating the 'reward' and 'choice and reward' terms from the GLM significantly worsens model performance; to demonstrate the magnitude of this effect, it would be interesting to include a reconstruction of the photometry signal after holding out of both or one of these terms, alongside the 'original' and 'reconstructed' photometry traces in panel D. This would help give context for how the model performance degrades by exclusion of those key terms.

      We have now added analyses and reconstructed photometry signals from GLMs excluding important predictors in Figure 3—figure supplement 1 and 2. We use the model where both “Direction and reward” were omitted as predictors for the GLM and showed photometry reconstructions aligned to behavioral events used for the full model (Figure 3—figure supplement 1) and partial model (Figure 3—figure supplement 2) to compare model performance.  

      (3) Finally, the authors claimed calcium activity increased following ipsilateral movements. However, Figure 3C clearly shows that both SXcontra and SXipsi increase beta coefficients. Instead, the choice direction may be represented in these neurons, given that beta coefficients increase following CXipsi and before SEipsi, presumably when animals make executive decisions. Could the authors clarify their interpretation on this point?

      We observe that calcium activity increases during ipsilateral choices as the animal moves toward the ipsilateral side port (e.g. CX<sub>ipsi</sub> to SE<sub>ipsi</sub>; Fig 2C and Fig 3C). The animal also makes other ipsiversive movements not during the “choice” phase of a trial such as when it is returning to the center port following a contralateral choice (e.g. SX<sub>Contra</sub> to CE; Fig 2—figure supplement 1F and Fig 3C). We also observe an increase in calcium activity during these ipsiversive movements (e.g. SX<sub>Contra</sub> to CE), but they are not as large as those observed during the choice phase (Fig 2—figure supplement 1G). Therefore, during the choice phase of a trial, activity contains signals related to ipsilateral movement and additional factors (e.g. executive decision making).    

      (4) Also, it is not clear if there is a photometry response related to motor parameters (i.e. head direction or locomotion, licking), which could change the interpretation of the reward outcome if it is related to a motor response; could the authors show photometry signal from representative 'high licking' or 'low licking' reward trials, or from spontaneous periods of high vs. low locomotor speeds (if the sessions are recorded) to otherwise clarify this point?

      Unfortunately, neither licks nor locomotion were recorded during the behavioral sessions when photometry was recorded. In Figure 2—figure supplement 1a we now show individual trials sorted by trial duration (time elapsed between CE and SE) to illustrate the dynamics of the photometry signal on fast vs slow trials within a session.  

      (5) There are a few limitations with the design and timing of the synaptic manipulations that would improve the manuscript if discussed or clarified. The authors take care to validate the intersectional genetic strategies: Tetanus Toxin virus (which eliminates synaptic vesicle fusion) or CRISPR editing of Slc17a6, which prevents glutamate loading into synaptic vesicles. The magnitude of effect in the slice physiology results is striking. However, this relies on the co-infection of a second AAV to express channelrhodopsin for the purposes of validation, and it is surely the case that there will not be 100% overlap between the proportion of cells infected.

      For the Tet-tox experiments in Figure 4 we estimate approximately 70±15% of EP<sup>Sst+</sup> neurons expressed Tet-tox based on our histological counts and published stereological counts in EP (Miyamoto and Fukuda, 2015). It is true that channelrhodopsin expression will not overlap 100% with cells infected by the other virus, indeed our in vitro synaptic physiology shows small residual postsynaptic currents following optogenetic stimulation either from incomplete blockade of synaptic release or neurons that expressed channelrhodopsin but not Tettx (Figure 4—figure supplement 1J-K). The same is shown for CRISPR mediated deletion of Slc17a6 (Fig 5 – Fig supplement 1J-K).  

      (6) Alternative means of glutamate packaging (other VGluT isoforms, other transporters, etc) could also compensate for the partial absence of VGluT2, which should be discussed.

      While single cell sequencing (Wallace et al, 2017) has shown EP<sup>Sst+</sup> neurons do not express Slc17a7/8 (vGlut1 or vGlut3) it is possible that these genes could be upregulated following CRISPR mediated deletion of Slc17a6, however we do not see evidence of this with our in vitro synaptic physiology (EPSCs are significant suppressed, Figure 5 – Fig supplement 1J-K) and therefore can conclude it is highly unlikely to occur to a significant degree in our experiments. This is now included in the Discussion.

      (7) The authors do not perform a complimentary experiment to delete GABA release (i.e. via VGAT editing), which is understandable, given the absence of an effect with the pan-synaptic manipulation. A more significant concern is the timing of these manipulations as the authors acknowledge. The manipulations are all done in well-trained animals, who continue to perform during the length of viral expression. Moreover, after carefully showing that mice use different strategies on the 70/30 version vs the 90/10 version of the task, only performance on the 90/10 version is assessed after the manipulation. Together, the observation that EPNsst activity does not alter performance on a well-learned, 90/10 switching task decreases the impact of the findings, as this population may play a larger role during task acquisition or under more dynamic task conditions. Additional experiments could be done to strengthen the current evidence, although the limitation is transparently discussed by the authors.

      As mentioned above, it is possible that a requirement for EP<sup>Sst+</sup> neurons could be revealed if the experiment was conducted with different parameters (either different reward probabilities, fluctuating reward probabilities within a session, or withholding additional training during viral expression). It is difficult to predict which version of the task, if any, would be most likely to reveal a requirement for EP<sup>Sst+</sup> neurons based on our results. We favor testing for EP<sup>Sst+</sup> function using a new behavioral paradigm that allows us to carefully examine task learning following EP manipulations in an independent study.

      (8) Finally, intersectional strategies target LHb-projecting neurons, although in the original characterization, it is not entirely clear that the LHb is the only projection target of EPNsst neurons. A projection map would help clarify this point.

      In a previous study we confirmed that EP<sup>Sst+</sup> neurons project exclusively to the LHb using cell-type specific rabies infection and examining all reported downstream regions for axon collaterals (Wallace et al 2017, Suppl. Fig 6F-G). When EP<sup>Sst+</sup> neurons were labeled we did not observe axon collaterals in known targets of EP such as ventro-antero lateral thalamus, red nucleus, parafasicular nucleus of the thalamus, or the pedunculopontine tegmental nucleus, only in the LHb. Additionally, using single cell tracing techniques, others have shown EP neurons that exclusively project to the LHb (Parent et al, 2001).

      Overall, the authors used a pertinent experimental paradigm and common cell-specific approaches to address a major gap in the field, which is the functional role of glutamate/GABA co-release from the major basal ganglia output nucleus in action selection and evaluation. The study is carefully conducted, their analyses are thorough, and the data are often convincing and thought-provoking. However, the limitations of their synaptic manipulations with respect to the behavioral assays reduce generalizability and to some extent the impact of their findings.

      Reviewer #2 (Public Review):

      Summary:

      This paper aimed to determine the role EP sst+ neurons play in a probabilistic switching task.

      Strengths:

      The in vivo recording of the EP sst+ neuron activity in the task is one of the strongest parts of this paper. Previous work had recorded from the EP-LHb population in rodents and primates in head-fixed configurations, the recordings of this population in a freely moving context is a valuable addition to these studies and has highlighted more clearly that these neurons respond both at the time of choice and outcome.

      The use of a refined intersectional technique to record specifically the EP sst+ neurons is also an important strength of the paper. This is because previous work has shown that there are two genetically different types of glutamatergic EP neurons that project to the LHb. Previous work had not distinguished between these types in their recordings so the current results showing that the bidirectional value signaling is present in the EP sst+ population is valuable.

      Weaknesses:

      (1) One of the main weaknesses of the paper is to do with how the effect of the EP sst+ neurons on the behavior was assessed.

      (a) All the manipulations (blocking synaptic release and blocking glutamatergic transmission) are chronic and more importantly the mice are given weeks of training after the manipulation before the behavioral effect is assessed. This means that as the authors point out in their discussion the mice will have time to adjust to the behavioral manipulation and compensate for the manipulations. The results do show that mice can adapt to these chronic manipulations and that the EP sst+ are not required to perform the task. What is unclear is whether the mice have compensated for the loss of EP sst+ neurons and whether they play a role in the task under normal conditions. Acute manipulations or chronic manipulations without additional training would be needed to assess this.

      Unfortunately, when mice are given a three week break from behavioral training (the time required to allow for adequate viral expression) behavioral performance on the task (p(highport), p(switch), trial number, trial time, etc.) is significantly degraded. Animals do eventually recover to previous performance levels, but this takes place during a 4-5 day “relearning” period. Here we sought to examine if EP<sup>Sst+</sup> neurons are required for continued task performance and chose to continue to train the animals following viral injection to avoid the “relearning” period that occurs following an extended break from behavioral training which may have made it difficult to interpret changes in behavioral performance due to the viral manipulation vs relearning.  

      Acute manipulations were not used because we planned to compare complete synaptic ablation (Tettx) and single neurotransmitter ablation (CRISPR Slc17a6) over similar time courses and we know of no acute manipulation that could achieve single neurotransmitter ablation. 

      (b) Another weakness is that the effect of the manipulations was assessed in the 90/10 contingency version of the task. Under these contingencies, mice integrate past outcomes over fewer trials to determine their choice and animals act closer to a simple win-stay-lose switch strategy. Due to this, it is unclear if the EP sst+ neurons would play a role in the task when they must integrate over a larger number of conditions in the less deterministic 70/30 version of the task.

      It is possible that a requirement for EP<sup>Sst+</sup> neurons could be revealed if the experiment was conducted with different parameters (either different reward probabilities, fluctuating reward probabilities within a session, or withholding additional training during viral expression). It is difficult to predict which version of the task, if any, would be most likely to reveal a requirement for EP<sup>Sst+</sup> neurons based on our results. We favor testing for EP<sup>Sst+</sup> function using a new behavioral paradigm that allows us to carefully examine task learning following EP manipulations in an independent study.

      The authors show an intriguing result that the EP sst+ neurons are excited when mice make an ipsilateral movement in the task either toward or away from the center port. This is referred to as a choice response, but it could be a movement response or related to the predicted value of a specific action. Recordings while mice perform movement outside the task or well-controlled value manipulations within the session would be needed to really refine what these responses are related to.

      If activity of EP<sup>Sst+</sup> neurons included a predicted value component, we would expect to see a change in activity during ipsilateral movements when the previous trial was rewarded vs unrewarded. This is examined in Fig 2—figure suppl. 2C, where we compare EP<sup>Sst+</sup> responses during ipsilateral trials when the previous trials were either rewarded (blue) or unrewarded (gray). We show that EP<sup>Sst+</sup> activity prior to side port entry (SE) is identical in these two trial types indicating that EP<sup>Sst+</sup> neurons do not show evidence of predicted value of an action in this context. Therefore, we conclude that increased EP<sup>Sst+</sup> activity during ipsilateral trials is primarily related to ipsilateral movement following CX (we call this the “choice” phase of the trial). We also show that other ipsiversive movements outside of the “choice” phase of a trial (such as the return to center port following a contralateral trial) show a smaller but significant increase in activity (Figure 2—figure supplement 1F-G). Therefore, whereas the activity observed during ipsilateral choice contains signals related to ipsilateral movement and additional factors, our data suggest that predicted value is not one of those factors. We will clarify this point and our definition of “choice” in the narrative.  

      (2) The authors conclude that they do not see any evidence for bidirectional prediction errors. It is not possible to conclude this. First, they see a large response in the EP sst+ neurons to the omission of an expected reward. This is what would be expected of a negative reward prediction error. There are much more specific well-controlled tests for this that are commonplace in head-fixed and freely moving paradigms that could be tested to probe this. The authors do look at the effect of previous trials on the response and do not see strong consistent results, but this is not a strong formal test of what would be expected of a prediction error, either a positive or negative. The other way they assess this is by looking at the size of the responses in different recording sessions with different reward contingencies. They claim that the size of the reward expectation and prediction error should scale with the different reward probabilities. If all the reward probabilities were present in the same session this should be true as lots of others have shown for RPE. Because however this data was taken from different sessions it is not expected that the responses should scale, this is because reward prediction errors have been shown to adaptively scale to cover the range of values on offer (Tobler et al., Science 2005). A better test of positive prediction error would be to give a larger-than-expected reward on a subset of trials. Either way, there is already evidence that responses reflect a negative prediction error in their data and more specific tests would be needed to formally rule in or out prediction error coding especially as previous recordings have shown it is present in previous primate and rodent recordings.

      We do not conclude that we see no evidence for RPE and the reviewer is correct in stating that a large increase in EP<sup>Sst+</sup> activity following omission of an expected reward would be expected of a negative reward prediction error. However, this observation alone is not strong enough evidence that EP<sup>Sst+</sup> neurons signal RPE. When we looked for additional evidence of RPE within our experiments we did not find consistent demonstrations of its existence in our data. When performing photometry measurements of dopamine release in the striatum, RPE signals are readily observed with a task identical to ours using trial history to as a modifier of reward prediction (Chantranupong, et al 2023). Of course, there could be a weaker more heterogeneous RPE signal in EP<sup>Sst+</sup> neurons that we cannot detect with our methods. As we state in the discussion, RPE signals may be present in a subset of individual neurons (as observed in Stephenson-Jones et al, 2016 and Hong and Hikosaka, 2008) which are below our detection threshold using fiber photometry. Additionally, Hong and Hikosaka, 2008 show that LHb-projecting GPi neurons show both positive and negative reward modulations which may obscure observation of RPE signals with photometry recordings that arise from population activity of genetically defined neurons.   

      (3) There are a lot of variables in the GLM that occur extremely close in time such as the entry and exit of a port. If two variables occur closely in time and are always correlated it will be difficult if not impossible for a regression model to assign weights accurately to each event. This is not a large issue, but it is misleading to have regression kernels for port entry and exits unless the authors can show these are separable due to behavioral jitter or a lack of correlation under specific conditions, which does not seem to be the case.

      It is true that two variables that are always correlated are redundant in a GLM. For example, center entry (CE) and center exit (CX) occur in quick succession in most trials and are highly correlated (Figure 1C). For this reason, when only one is removed as a predictor from the model but not the other there is a very small change in the MSE of the fit (Figure 3E, -CE or -CX). However, when both are removed model performance decreases further indicating that center-port nose-pokes do contribute to model performance (Figure 3E, -CE/CX). Due to the presence/absence of reward following side port entry there is substantial behavioral jitter (due to water consumption in rewarded trials) that the SE and SX are not always correlated, therefore the model performs worse when either are omitted alone, but even worse still when both SE/SX are omitted together (Figure 3E, -SE/SX). We will update Figure 3 and the narrative to make this more explicit.

      Reviewer #3 (Public Review):

      Summary:

      The authors find that Sst-EPN neurons, which project to the lateral habenula, encode information about response directionality (left vs right) and outcome (rewarded vs unrewarded). Surprisingly, impairment of vesicular signaling in these neurons onto their LHb targets did not impair probabilistic choice behavior.

      Strengths:

      Strengths of the current work include extremely detailed and thorough analysis of data at all levels, not only of the physiological data but also an uncommonly thorough analysis of behavioral response patterns.

      Weaknesses:

      Overall, I saw very few weaknesses, with only two issues, both of which should be possible to address without new experiments:

      (1) The authors note that the neural response difference between rewarded and unrewarded trials is not an RPE, as it is not affected by reward probability. However, the authors also show the neural difference is partly driven by the rapid motoric withdrawal from the port. Since there is also a response component that remains different apart from this motoric difference (Figure 2, Supplementary Figure 1E), it seems this is what needs to be analyzed with respect to reward probability, to truly determine whether there is no RPE component. Was this done?

      We thank the reviewer for this comment, we believe this is particularly important for unrewarded trials as SE and SX occur in rapid succession. In Figure 2—figure supplement 2A-B we now show the photometry signal from Rewarded and Unrewarded ipsilateral trials aligned to SX for different reward probabilities. We quantify the signals for different reward probabilities during a 500ms window immediately prior to SX but find no differences between groups.  

      (2) The current study reaches very different conclusions than a 2016 study by Stephenson-Jones and colleagues despite using a similar behavioral task to study the same Sst-EPN-LHb circuit. This is potentially very interesting, and the new findings likely shed important light on how this circuit really works. Hence, I would have liked to hear more of the authors' thoughts about possible explanations of the differences. I acknowledge that a full answer might not be possible, but in-depth elaboration would help the reader put the current findings in the context of the earlier work, and give a better sense of what work still needs to be done in the future to fully understand this circuit.

      For example, the authors suggest that the Sst-EPN-LHb circuit might be involved in initial learning, but play less of a role in well-trained animals, thereby explaining the lack of observed behavioral effect. However, it is my understanding that the probabilistic switching task forces animals to continually update learned contingencies, rendering this explanation somewhat less persuasive, at least not without further elaboration (e.g. maybe the authors think it plays a role before the animals learn to switch?).

      Also, as I understand it, the 2016 study used manipulations that likely impaired phasic activity patterns, e.g. precisely timed optogenetic activation/inhibition, and/or deletion of GABA/glutamate receptors. In contrast, the current study's manipulations - blockade of vesicle release using tetanus toxin or deletion of VGlut2, would likely have blocked both phasic and tonic activity patterns. Do the authors think this factor, or any others they are aware of, could be relevant?

      We have added further discussion of the Stephenson-Jones, et al 2016 study as well as the Lazaridis, et al 2019 study which shows no effect of phasic stimulation of EP when specifically manipulating EP<sup>Sst+</sup> (vGat+/vGlut2+) neurons rather than vGlut2+ neurons as in the Stephenson-Jones study.  

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      In some places, there seems to be a mismatch between referenced figures and texts. For example:

      (1) The authors described that 'This increase in activity was seen for all three reward probabilities tested (90/10, 80/20, and 70/30) and occurred while the animal was engaged in ipsiversive movements as similar increases were observed following side exit (SX) on contralateral trials as the animal was moving from the contralateral side port back to the center port (Figure 2-Figure Supplement 1c)', but supplement 1c is not about calcium dynamics around the SX event. I presume they mean Figure 2-Figure Supplement 1d.

      Yes, this will be corrected in the revised manuscript.

      (2) The authors explained that increased EPSst+ neuronal activity following an unrewarded outcome was partially due to the rapid withdrawal of the animal's snout following an unrewarded outcome however, differences in rewarded and unrewarded trials were still distinguishable when signals were aligned to side port exit indicating that these increases in EPSst+ neuronal activity on unrewarded trials were a combination of outcome evaluation (unrewarded) and side port withdrawal occurring in quick succession (SX, Figure 2 - Figure Supplement 1d). I presume that they mean Figure 2 - Figure Supplement 1e.

      Yes, this will be corrected in the revised manuscript.

      Minor suggestions related to specific figure presentation are below:

      Figure 2 and supplement figures:

      (1) Figure 2B: the authors may consider presenting outcome-related signals recorded from all trials, including both ipsilateral and contralateral events, and align signals to SE when reward consumption presumably begins, rather than aligning to CE.

      We have added sample recordings from ipsilateral and contralateral trials and sorted them by trial duration to allow for clearer presentation of activity following CE and SE (Figure 2—figure supplement 1a-b).

      (2) The authors described that 'This increase in activity was seen for all three reward probabilities tested (90/10, 80/20, and 70/30) and occurred while the animal was engaged in ipsiversive movements as similar increases were observed following side exit (SX) on contralateral trials as the animal was moving from the contralateral side port back to the center port (Figure 2-Figure Supplement 1c)', but supplement 1c is not about calcium dynamics around the SX event. I presume they mean Figure 2-Figure Supplement 1d.

      Yes, this will be corrected in the revised manuscript.

      (3) The authors explained that increased EPSst+ neuronal activity following an unrewarded outcome was partially due to the rapid withdrawal of the animal's snout following an unrewarded outcome however, differences in rewarded and unrewarded trials were still distinguishable when signals were aligned to side port exit indicating that these increases in EPSst+ neuronal activity on unrewarded trials were a combination of outcome evaluation (unrewarded) and side port withdrawal occurring in quick succession (SX, Figure 2 -Figure Supplement 1d). I presume that they mean Figure 2 -Figure Supplement 1e.

      Yes, this will be corrected in the revised manuscript.

      Figure 3 and supplement figures:

      (1) Figure 3C-F: it is hard to compare the amplitude of calcium signals between different behaviour events without a uniform y-axis.

      The scale for the y-axis on Figure 3C-D is uniform for all panels. Figure 3E is also uniform for all boxplots. The reviewer may be referring to Figure 2C-F, but the y-axis for all of the photometry data is uniform for all panels and the horizontal line represents zero. The y-axis for the quantification on the right of each panel is scaled to the max/min for each comparison.

      (2) Figure 3E is difficult to follow. The authors explained that the 'SE' variable is generated by collapsing the ipsilateral and contralateral port entries, and hence the variable has no choice of direction information. I assumed that the 'SX', 'CE', and 'CX' variables are generated similarly. It is not clear if this is the case for the 'side', 'centre' and 'choice' variables. The authors explained that 'omitting center port entry/exit together or individually also resulted in decreased GLM performance but to a smaller degree than the omission of choice direction (Figure 3e, "-Center")'. My understanding is that they created the Centre variable by collapsing ipsilateral and contralateral centre port entry/exit together. The Centre variable should have no choice of direction information. How is the Center variable generated differently from omitting centre port entry/exit together? I would ask the authors to explain the model and different variables a bit more thoroughly in the text.

      We apologize for the confusion. All ten variables used to train the full GLM are listed in Fig. 3C. In Figure 3E variable(s) were omitted to test how they contributed to GLM performance (data labeled “None” is the full model with all variables). Omitted variables are now defined as follows: -Rew = Rew+Unrew removed, -Direction = Ipsi/Contra designation removed and collapsed into CE, CX, SE, SX, -Direction & Rew = Ipsi/Contra info removed from all variables + Rew/Unrew removed, -CE/CX = Ipsi/Contra CE and CX removed, -CE = Ipsi/contra CE removed, -CX = Ipsi/contra CX removed, -SE/SX = Ipsi/Contra SE and SX removed, -SE = Ipsi/contra SE removed, -SX = Ipsi/contra SX removed. This clarification has also been added to the Generalized Linear Model section of Materials and Methods.

      Figure 5 and supplement figures:

      There are no representative and summary figures show the specificity and efficiency of oChief-tdTomato or Tetx-GFP expression. Body weight changes following virus injection are not well described.

      A representative image of Tettx GFP expression are shown in Fig. 4A and percent of infected EP<sup>Sst+</sup> neurons is described in the text (70±15.1% (mean±SD), 1070±230 neurons/animal, n=6 mice). Most oChief-tdTom animals were used for post-hoc electrophysiology experiments and careful quantification of viral expression was not possible. However, Slc17a6 deletion was confirmed in these animals (Fig. 5 – Fig supplement 1J-K) to confirm the manipulation was effective in the experimental group. A representative image of oChief-tdTom expression is shown in Fig. 5A.

      We now mention the body weight changes observed following Tettx injection in the narrative.

      Reviewer #2 (Recommendations For The Authors):

      (1) In the RFLR section you state that "this variable decays...", a variable can't decay only the value of a variable can change. Also, it is not mentioned what variable is being discussed. There are lots of variables in the model so this should be made clear.

      We now state, “This variable (β) changes over trials and is updated with new evidence from each new trial’s choice and outcome with an additional bias towards or away from its most recent choice (Figure 1-figure supplement 2A-C).”

      (2) I couldn't find in the results section, or the methods section the details for the Tet tx experiments, were mice trained and tested on 90/10 only? Were they trained while the virus was expressing etc? This should be added.

      In the methods section we state, ”For experiments where we manipulated synaptic release in EP<sup>Sst+</sup> neurons (Figures 4-5) we trained mice (reward probabilities 90/10, no transparent barrier present) to the following criteria for the 5 days prior to virus injection: 1) p(highport) per session was greater than or equal to 0.80 with a variance less than 0.003, 2) p(switch) per session was less than or equal to 0.15 with a variance less than 0.001, 3) the p(left port) was between 0.45-0.55 with a variance less than 0.005, and 4) the animal performed at least 200 trials in a session. The mean and variance for these measurements was calculated across the five session immediately preceding surgery. The criterion were determined by comparing performance profiles in separate animals and chosen based on when animals first showed stable and plateaued behavioral performance. Following surgery, mice were allowed to recover for 3 days and then continued to train for 3 weeks during viral expression. Data collected during the 5 day pre-surgery period was then compared to data collected for 10 sessions following the 3 weeks allotted for viral expression (i.e. days 22-31 post-surgery).”

      Reviewer #3 (Recommendations For The Authors):

      (1) The kernel in Figure 3C shows an activation prior to CE on "contra" trials that is not apparent in Figure 2C which shows no activation prior to CE on either contra or ipsi trials. Given that movement directionality prior to CE is dictated by the choice on the PREVIOUS trial, is the "contra" condition in 3C actually based on the previous trial? If so, this should be clarified.

      On most “contra” trials the animal is making an ipsiversive movement just prior to CE as it returns to the center from the contralateral side-port (as most trials are no “switch” trials). Therefore, an increase in activity is expected and shown most clearly following SX for contralateral trials in Fig 2 –Fig suppl 1F. A significant increase in activity prior to CE on contra trials compared to ipsi trials can also be seen in Fig 2C, its just not as large a change as the increase observed following CE for ipsi. trials. The comparison between activity observed during the two types of ipsiversive movements is now shown directly in Figure 2—figure supplement 1G.

      (2) Paragraph 7 of the discussion uses a phrase "by-in-large", which probably should be "by and large".

      Thank you for the correction.

      Editor's note:

      Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

      Readers would also benefit from coding individual data points by sex and noting N/sex.

      Sex breakdown has been added to figure legends for each experiment, full statistical reporting is now also include in the figure legends.

    1. Reviewer #2 (Public review):

      Summary:

      In this work, Gupta & Murphy present several parallel efforts. On one side, they present the hardware and software they use to build a head-fixed mouse experimental setup that they use to track in "real-time" the calcium activity in one or two spots at the surface of the cortex. On the other side, the present another setup that they use to take advantage of the "real-time" version of DeepLabCut with their mice. The hardware and software that they used/develop is described at length, both in the article and in a companion GitHub repository. Next, they present experimental work that they have done with these two setups, training mice to max out a virtual cursor to obtain a reward, by taking advantage of auditory tone feedback that is provided to the mice as they modulate either (1) their local cortical calcium activity, or (2) their limb position.

      Strengths:

      This work illustrates the fact that thanks to readily available experimental building blocks, body movement and calcium imaging can be carried using readily available components, including imaging the brain using an incredibly cheap consumer electronics RGB camera (RGB Raspberry Pi Camera). It is a useful source of information for researchers that may be interested in building a similar setup, given the highly detailed overview of the system. Finally, it further confirms previous findings regarding the operant conditioning of the calcium dynamics at the surface of the cortex (Clancy et al. 2020) and suggests an alternative based on deeplabcut to the motor tasks that aim to image the brain at the mesoscale during forelimb movements (Quarta et al. 2022).

      Weaknesses:

      This work covers 3 separate research endeavors: (1) The development of two separate setups, their corresponding software. (2) A study that is highly inspired from the Clancy et al. 2020 paper on the modulation of the local cortical activity measured through a mesoscale calcium imaging setup. (3) A study of the mesoscale dynamics of the cortex during forelimb movements learning. Sadly, the analyses of the physiological data appears uncomplete, and more generally the paper tends to offer overstatements regarding several points:<br /> - In contrast to the introductory statements of the article, closed-loop physiology in rodents is a well-established research topic. Beyond auditory feedback, this includes optogenetic feedback (O'Connor et al. 2013, Abbasi et al. 2018, 2023), electrical feedback in hippocampus (Girardeau et al. 2009), and much more.<br /> - The behavioral setups that are presented are representative of the state of the art in the field of mesoscale imaging/head fixed behavior community, rather than a highly innovative design. In particular, the closed-loop latency that they achieve (>60 ms) may be perceived by the mice. This is in contrast with other available closed-loop setups.<br /> - Through the paper, there are several statements that point out how important it is to carry out this work in a closed-loop setting with an auditory feedback, but sadly there is no "no feedback" control in cortical conditioning experiments, while there is a no-feedback condition in the forelimb movement study, which shows that learning of the task can be achieved in the absence of feedback.<br /> - The analysis of the closed-loop neuronal data behavior lacks controls. Increased performance can be achieved by modulating actively only one of the two ROIs, this is not clearly analyzed (for instance looking at the timing of the calcium signal modulation across the two ROIs. It seems that overall ROIs1 and 2 covariate, in contrast to Clancy et al. 2020. How can this be explained?

    2. Reviewer #3 (Public review):

      Summary:

      The study demonstrates the effectiveness of a cost-effective closed-loop feedback system for modulating brain activity and behavior in head-fixed mice. Authors have tested real-time closed-loop feedback system in head-fixed mice two types of graded feedback: 1) Closed-loop neurofeedback (CLNF), where feedback is derived from neuronal activity (calcium imaging), and 2) Closed-loop movement feedback (CLMF), where feedback is based on observed body movement. It is a python based opensource system, and authors call it CLoPy. The authors also claim to provide all software, hardware schematics, and protocols to adapt it to various experimental scenarios. This system is capable and can be adapted for a wide use case scenario.

      Authors have shown that their system can control both positive (water drop) and negative reinforcement (buzzer-vibrator). This study also shows that using the close loop system mice have shown better performance, learnt arbitrary task and can adapt to change in the rule as well. By integrating real-time feedback based on cortical GCaMP imaging and behavior tracking authors have provided strong evidence that such closed-loop systems can be instrumental in exploring the dynamic interplay between brain activity and behavior.

      Strengths:

      Simplicity of feedback systems designed. Simplicity of implementation and potential adoption.

      Weaknesses:

      Long latencies, due to slow Ca2+ dynamics and slow imaging (15 FPS), may limit the application of the system.

      Major comments:

      (1) Page 5 paragraph 1: "We tested our CLNF system on Raspberry Pi for its compactness, general-purpose input/output (GPIO) programmability, and wide community support, while the CLMF system was tested on an Nvidia Jetson GPU device." Can these programs and hardware be integrated with windows-based system and a microcontroller (Arduino/ Tency). As for the broad adaptability that's what a lot of labs would already have (please comment/discuss)?

      (2) Hardware Constraints: The reliance on Raspberry Pi and Nvidia Jetson (is expensive) for real-time processing could introduce latency issues (~63 ms for CLNF and ~67 ms for CLMF). This latency might limit precision for faster or more complex behaviors, which authors should discuss in the discussion section.

      (3) Neurofeedback Specificity: The task focuses on mesoscale imaging and ignores finer spatiotemporal details. Sub-second events might be significant in more nuanced behaviors. Can this be discussed in the discussion section?

      (4) The activity over 6s is being averaged to determine if the threshold is being crossed before the reward is delivered. This is a rather long duration of time during which the mice may be exhibiting stereotyped behaviors that may result in the changes in DFF that are being observed. It would be interesting for the authors to compare (if data is available) the behavior of the mice in trials where they successfully crossed the threshold for reward delivery and in those trials where the threshold was not breached. How is this different from spontaneous behavior and behaviors exhibited when they are performing the test with CLNF?

    1. Virtual Hermans - Lucas Dul

      Lucas Dul does an overview of affordable and available tools for typewriter repair as well as more advanced

      Basic Tools

      • screwdriver sets
        • Carpenter screwdrivers (come to a point) the point can slip and causecam out screws
        • Hollow ground - provide the most amount of torque and prevent cam-out problems (also called gunsmith He uses the 0623 Chapman set (the number is the date of international typewriter day) The large tip can be problematic
        • long reach screwdriver
        • magnetic screwdriver
        • tempered stainless steel ruler (as a screwdriver, especially
        • microdrivers (usually used for eyeglasses or electronics)
      • spring hooks (push/pull)
        • Fixture from an embroidery set with length for getting length
        • grab hooks
      • pliers
        • standard needle nose pliers
        • 45-90 degree pliers (he uses more often)
        • wire cutters (for modifying springs in machines and modifying links in machines)
        • parallel draw pieces (with heavy duty cutters)
      • Mechanics' wrench set
        • prefer cast ones
        • socket screwdrivers (fixed hex screwdriver) expecially for shift adjustment on the Royal Ps
        • Chapman's has a mini rachet 1/4" socket in it's 0623 set
      • Forceps especially a long pair for IBM Selectrics (via Duane Jensen)
      • Tweezers
      • Blowtorches
        • alcohol torches (for heating and bending metal)
        • soldering, brazing, and heat shrinking
        • small butane torch (cigarette lighter use)
      • Oilers with needlepoint applicator (he uses sewing machine oil)
        • One can use the surface tension of the oil to place a dot on the tip of a scewdriver (flat head) and then place the dot within a machine with reasonable precision
      • MIG Pliers - have cutouts for taking rubber off of old feedrollers (otherwise these pliers are used for welding); he describes it as the nutcracker of the typewriter world
      • strap wrench (especially for removing platen knobs to prevent damage)
      • knife (butter knife)
        • razor blade for trimming rubber (otherwise too thin for other applications)
      • flashlight (simple is fine)
      • marker (Sharpie)
        • marking orientation of removed parts (washes off with alcohol)
      • hammer
      • retainer clip pliers (especially for IBMs, Brothers, Swintecs) with spare e- and c-clips (some have thumbscrews for minimizing damage to clips) openers are more useful than "closers"
      • Bristol wrenches - looks like Allen Keys, but with star cross section for bristol locks in IBM machines
      • Hand crank (for IBM Selectrics) thread into the operational cam shaft

      Intermediate Tools

      • segment bearing rod (good for removing individual typebars)
      • drinking straw for ball bearings on royal portables and S-C portables and flat tops staggered 1/2" ball bearings with orbital gear (star-shaped) - snip opposite sides to insert orbital ring and ball bearing for holding and placement in typewriter
      • carpenter's pencil for marking

      Advanced/Specialty tools

      • t-bender for forming metal (exp. thin pieces)
      • 9 jaw pliers for bending typebars
      • peening pliers (for manipulating and stretching materials)
      • wheel benders (he doesn't use often)
      • eyelete tool for putting eyelets in typewritter ribbon
      • files (small/cheap) widen gaps inside of type guides when necessary or thinning out tight pieces

      Very specialized

      • Type slug solder jig or solder guide (30:52)
      • keyring pliers ($400 and above to purchase)
      • multimeter for checking circuit components on electric models. Primarily using Ohm setting to see if current is passing through parts, otherwise they're broken.

      Honorable mentions

      • center punch for drilling points and new screws
      • dental mirror for looking into machines
      • spring gauge to set 2lbs for desktop and 1lb for portables

      Q&A

      air compressors are useful for cleaning

      Don't damage screws on older machines.

      US used imperial screws until 1940/50s and machines after are all metric.

      3 dessert island tools<br /> - screwdrivers, pliers, spring hook

  5. Dec 2024
    1. 232.   At what distance does Earth start looking spherical? Dr Stern: How high must an astronaut be to view the Earth as a sphere?? Reply     It's up to the viewer to decide! If you are about 2500 km high, the Earth subtends about 90°, so you can see all of it in your field of view, and I guess it seems spherical.     At 1000 km, your field of view is about 120°, you can still encompass it all by waggling your head. Still a sphere?     At the space shuttle altitude, say 300 km, you can't see the whole Earth with one look. But the horizon seems curved, not straight. Does that define a sphere?     Finally, on the bridge of a ship, the horizon seems flat, but you know ships disappear behind it. Approaching a tall mountain, you may see its top first, the rest only later. Is that enough for you? In short, you are asking about perception, not science. In perception, everyone formulates personal rules.

      -

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this work, a screening platform is presented for rapid and cost-effective screening of candidate genes involved in Fragile Bone Disorders. The authors validate the approach of using crispants, generating FO mosaic mutants, to evaluate the function of specific target genes in this particular condition. The design of the guide RNAs is convincingly described, while the effectiveness of the method is evaluated to 60% to 92% of the respective target genes being presumably inactivated. Thus, injected F0 larvae can be directly used to investigate the consequences of this inactivation.

      Skeletal formation is then evaluated at 7dpf and 14dpf, first using a transgenic reporter line revealing fluorescent osteoblasts, and second using alizarin-red staining of mineralized structures. In general, it appears that the osteoblast-positive areas are more often affected in the crispants compared to the mineralized areas, an observation that appears to correlate with the observed reduced expression of bglap, a marker for mature osteoblasts, and the increased expression of col1a1a in more immature osteoblasts.

      Finally, the injected fish (except two lines that revealed high mortality) are also analyzed at 90dpf, using alizarin red staining and micro-CT analysis, revealing an increased incidence of skeletal deformities in the vertebral arches, fractures, as well as vertebral fusions and compressions for all crispants except those for daam2. Finally, the Tissue Mineral Density (TMD) as determined by micro-CT is proposed as an important marker for investigating genes involved in osteoporosis.

      Taken together, this manuscript is well presented, the data are clear and well analyzed, and the methods are well described. It makes a compelling case for using the crispant technology to screen the function of candidate genes in a specific condition, as shown here for bone disorders.

      Strengths:

      Strengths are the clever combination of existing technologies from different fields to build a screening platform. All the required methods are comprehe Zebrafish tanks_13062024nsively described.

      We would like to thank the reviewer for highlighting the strengths of our paper.  

      Weaknesses:

      One may have wished to bring one or two of the crispants to the stage of bona fide mutants, to confirm the results of the screening, however, this is done for some of the tested genes as laid out in the discussion.

      We thank the reviewer for their comment. We would like to point out that indeed similar phenotypes have been observed in existing models, as mentioned in the discussion section.

      Reviewer #2 (Public review):

      Summary:

      More and more genes and genetic loci are being linked to bone fragility disorders like osteoporosis and osteogenesis imperfecta through GWAS and clinical sequencing. In this study, the authors seek to develop a pipeline for validating these new candidate genes using crispant screening in zebrafish. Candidates were selected based on GWAS bone density evidence (4 genes) or linkage to OI cases plus some aspect of bone biology (6 genes). NGS was performed on embryos injected with different gRNAs/Cas9 to confirm high mutagenic efficacy and off-target cutting was verified to be low. Bone growth, mineralization, density, and gene expression levels were carefully measured and compared across crispants using a battery of assays at three different stages.

      Strengths:

      (1) The pipeline would be straightforward to replicate in other labs, and the study could thus make a real contribution towards resolving the major bottleneck of candidate gene validation.

      (2) The study is clearly written and extensively quantified.

      (3) The discussion attempts to place the phenotypes of different crispant lines into the context of what is already known about each gene's function.

      (4) There is added value in seeing the results for the different crispant lines side by side for each assay.

      We would like to thank the reviewer for highlighting the strengths of our paper.  

      Weaknesses:

      (1) The study uses only well-established methods and is strategy-driven rather than question/hypothesis-driven.

      We thank the reviewer for this correct remark. The mayor aim of this study was to establish a workflow for rapid in vivo functional screening of candidate genes across a broad range of FBDs. 

      (2) Some of the measurements are inadequately normalized and not as specific to bone as suggested:

      (a) The measurements of surface area covered by osteoblasts or mineralized bone (Figure 1) should be normalized to body size. The authors note that such measures provide "insight into the formation of new skeletal tissue during early development" and reflect "the quantity of osteoblasts within a given structure and [is] a measure of the formation of bone matrix." I agree in principle, but these measures are also secondarily impacted by the overall growth and health of the larva. The surface area data are normalized to the control but not to the size/length of each fish - the esr1 line in particular appears quite developmentally advanced in some of the images shown, which could easily explain the larger bone areas. The fact that the images in Figure S5 were not all taken at the same magnification further complicates this interpretation.

      We thank the reviewer for this detailed and insightful remark. We agree with the reviewer and recognize that the results may be influenced by size differences. However, we do not normalize for size, as variations in growth were considered as part of the phenotypic outcome. This consideration has been addressed in the discussion section.

      Line 335-338: ‘Although the measurements of osteoblast-positive and mineralized surface areas may be influenced by size differences among some of the crispants, normalization to size parameters was not conducted, as variations in growth were considered integral to the phenotypic outcome.’

      Line 369: ‘Phenotypic variability in these zebrafish larvae can be attributed to several factors, including crispant mosaicism, allele heterogeneity, environmental factors, differences in genomic background and development, and slightly variable imaging positioning.’

      (b) Some of the genes evaluated by RT-PCR in Figure 2 are expressed in other tissues in addition to bone (as are the candidate genes themselves); because whole-body samples were used for these assays, there is a nonzero possibility that observed changes may be rooted in other, non-skeletal cell types.

      We thank the reviewer for this valuable comment. We acknowledge that the genes assessed by RT-PCR are expressed in other tissues beyond bone. This consideration has been addressed in the discussion section.

      Line 362-365: “However, it is important to note that the genes evaluated by RT-PCR are not exclusively expressed in bone tissue. Since whole-body samples were used for expression analysis, there is a possibility that the observed changes in gene expression may be influenced by other non-skeletal cell types”.

      (3) Though the assays evaluate bone development and quality at several levels, it is still difficult to synthesize all the results for a given gene into a coherent model of its requirement.

      We appreciate the reviewer’s  remark. We acknowledge that the results for the larval stages exhibit variability, making it challenging to synthesize them into a coherent model. However, it is important to emphasize that all adult crispant consistently display a skeletal phenotype. Consequently, the feasibility and reproducibility of this screening method are primarily focusing on the adult stages. This consideration has been addressed in the discussion section of the manuscript.

      Line 391-399: ‘In adult crispants, the skeletal phenotype was generally more penetrant. All crispants showed malformed arches, a majority displayed vertebral fractures and fusions and some crispants exhibited distinct quantitative variations in vertebral body measurements. This confirmed the role of the selected genes in skeletal development and homeostasis and their involvement in skeletal disease and established the crispant approach as a valid approach for rapidly providing in vivo gene function data to support candidate gene identification.’

      (4) Several additional caveats to crispant analyses are worth noting:

      (a) False negatives, i.e. individual fish may not carry many (or any!) mutant alleles. The crispant individuals used for most assays here were not directly genotyped, and no control appears to have been used to confirm successful injection. The authors therefore cannot rule out that some individuals were not, in fact, mutagenized at the loci of interest, potentially due to human error. While this doesn't invalidate the results, it is worth acknowledging the limitation.

      We thank the reviewer for this valuable remark. We recognize the fact that working with crispants has certain limitations, including the possibility that some individuals may carry few or no mutant alleles. To address this issue, we use 10 individual crispants during the larval stage and 5 during the adult stage. Although some individuals may lack the mutant alleles, using multiple fish helps reduce the risk of false negatives.

      Furthermore, we perform NGS analysis on pools of 10 embryos from the same injection clutch as the fish used in the various assays to assess the indel efficiency. While there remains a possibility of false negatives, the overall indel efficiency, as indicated by our NGS analysis,  is high (>90%), thereby reducing the likelihood of having crispants with very low indel efficiency. We included this in the discussion.

      Line 387-390: ‘While there remains a possibility of false negatives, the overall indel efficiency, as indicated by our NGS analysis,  is high (>90%), thereby reducing the likelihood of having crispants with very low indel efficiency.’

      (b) Many/most loci identified through GWAS are non-coding and not easily associated with a nearby gene. The authors should discuss whether their coding gene-focused pipeline could be applied in such cases and how that might work.

      The authors thank the reviewer for this insightful comment. Our study is focused on strong candidate genes rather than non-coding variants. We recognize that the use of this workflow poses challenges for analyzing non-coding variants, which represents a limitation of the crispant approach. We have addressed this issue in the discussion section of the manuscript.

      Line 131: ‘Gene-based’

      Line 453: ‘Gene-based’

      Line 311-314: ‘It is important to note that this study focused on candidate genes for osteoporosis, not on the role of specific variants identified in GWAS studies. Non-coding variants for instance, which are often identified in GWAS studies,  present significant challenges in terms of functional validation and interpretation.’

      Reviewer #3 (Public review):

      Summary:

      The manuscript "Crispant analysis in zebrafish as a tool for rapid functional screening of disease-causing genes for bone fragility" describes the use of CRISPR gene editing coupled with phenotyping mosaic zebrafish larvae to characterize functions of genes implicated in heritable fragile bone disorders (FBDs). The authors targeted six high-confident candidate genes implicated in severe recessive forms of FBDs and four Osteoporosis GWAS-implicated genes and observed varied developmental phenotypes across all crispants, in addition to adult skeletal phenotypes.

      A major strength of the paper is the streamlined method that produced significant phenotypes for all candidate genes tested.

      We would like to thank the reviewer for highlighting the strengths of our paper.  

      A major weakness is a lack of new insights into underlying mechanisms that may contribute to disease phenotypes, nor any clear commonalities across gene sets. This was most evident in the qRT-PCR analysis of select skeletal developmental genes, which all showed varied changes in fold and direction, but with little insight into the implications of the results.

      We thank the reviewer for this insightful remark. We want to emphasize that this study focusses on establishing a new screening method for candidate genes involved in FBDs, rather than investigating the underlying mechanisms contributing to disease phenotypes. However, to investigate the underlying mechanisms in these crispants, the creation of bona fide mutants is necessary. We have included this consideration in the discussion.

      Furthermore, we acknowledge that the results for the larval stages exhibit variability, which can complicate the interpretation of these findings. This is particularly true for the RT-PCR analysis, where whole-body samples were used, raising the possibility that other tissues may influence the expression results. Therefore, our primary focus is on the adult stages, as all crispants display a skeletal phenotype at this age. We have elaborated on this point in the discussion.

      Line 462-463: ‘Moreover, to explore the underlying mechanisms contributing to disease phenotypes, it is essential to establish stable knockout mutants derived from the crispants’.

      Line 391-399: ‘In adult crispants, the skeletal phenotype was generally more penetrant. All crispants showed malformed arches, a majority displayed vertebral fractures and fusions and some crispants exhibited distinct quantitative variations in vertebral body measurements. This confirmed the role of the selected genes in skeletal development and homeostasis and their involvement in skeletal disease and established the crispant approach as a valid approach for rapidly providing in vivo gene function data to support candidate gene identification.’

      Ultimately, the authors were able to show their approach is capable of connecting candidate genes with perturbation of skeletal phenotypes. It was surprising that all four GWAS candidate genes (which presumably were lower confidence) also produced a result.

      We appreciate the reviewer’s comment. We would like to direct attention to the discussion section, where we offer a possible explanation for the observation that all four GWAS candidate genes produce a skeletal phenotype.

      Line 460-410: 'The more pronounced and earlier phenotypes in these zebrafish crispants are most likely attributed to the quasi knock-out state of the studied genes, while more common less impactful variants in the same genes result in typical late-onset osteoporosis (Laine et al., 2013) . This phenomenon is also observed in knock-out mouse models for these genes (Melville et al., 2014)(Coughlin et al., 2019).’

      These authors have previously demonstrated that crispants recapitulate skeletal phenotypes of stable mutant lines for a single gene, somewhat reducing the novelty of the study.

      We thank the reviewer for this comment and appreciate their concern. We have indeed demonstrated that crispants can recapitulate the skeletal phenotypes observed in stable mutant lines for the osteoporosis gene LRP5. However, we would like to highlight that the current study represents the first large-scale screening of candidate genes associated with bone disorders, including genes related to both OI and osteoporosis. We have included this information in both the abstract and the discussion

      Line 60-62: ‘We advocate for a novel comprehensive approach that integrates various techniques and evaluates distinct skeletal and molecular profiles across different developmental and adult stages.’

      Line 456-457: ‘While this work represents a pioneering effort in establishing a screening platform for skeletal diseases, it offers opportunities for future improvement and refinement.’

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Figure 1a: what does the differential shading of the bone elements represent? Explain in the legend.

      The differential shading doesn't represent anything specific. It's simply used to enhance the visual appeal and to help distinguish between the different structures. We removed the shading in the figure.

      (2) Supplementary Figures 2-5: should the numbering of these figures be also in order of appearance in the text? I understand that the authors prefer to associate the transgenic and the alizarin red-stained specimens, however, the reading would be easier that way.

      We changed this accordingly.

      (3) Lines 275-276: "no significant differences in standard length (Figure 4a)": should be Figure 4b.

      The suggested changes are incorporated in the manuscript.

      Line 276-277: ‘Among the eight crispants that successfully matured into adulthood, none exhibited significant differences in standard length and head size (n=5 fish per crispant) (Figure 4b).’

      (4) Line 277 "larger eye diameter": should be Figure 4b.

      The suggested changes are incorporated in the manuscript.

      Line 378: ‘However, esr1 crispants were observed to have notably larger eye diameters (Figure 4b).’

      (5) Line 280: "no obvious abnormalities were detected (Figure 4b,c)": should be Figure 4a, c. Note that the authors may reconsider the a, b, c numbering in Figure 4 by inverting a and b.

      The suggested changes are incorporated in the manuscript.

      Line 278-281: ‘All these crispants demonstrated various abnormalities in the caudal part of the vertebral column such as fusions, compressions, fractures, or arch malformations, except for daam2 crispants where no obvious abnormalities were detected (Figure 4a,c; Supplementary Figure 6).’

      (6) Table 2: This table, which recapitulates all the results presented in the manuscript, is in the end the centerpiece of the work. It is however difficult to read in its present form. Three suggestions:

      - Transpose it such that each gene has its own column, and the lines give the results for the different measurements

      - Place the measurements that result in "ns" for all crispants at the end (bottom) of the table.

      - Maybe bring the measurements at 7dpf, 14dpf, and 90 dpf together.

      We agree with the reviewer and have added a new table where we transposed the data. However, we chose not to place the measurements that resulted in 'ns' for all crispants at the end of the table, as we believe it is important to track the evolution of the phenotype over time. Where possible, we have grouped the measurements for 7 dpf and 14 dpf together.

      Reviewer #2 (Recommendations for the authors):

      (1) It would help to justify why these particular area measurements are appropriate for this set of candidate genes, which were selected based on putative links to bone quality rather than bone development.

      The selected methods are among the most commonly used to evaluate bone phenotypes. They are straightforward to reproduce, as well as cost- and time-effective. The strength of this approach lies in its use of simple, reproducible techniques that form the foundation for characterizing bone development.  Although the candidate genes were chosen based on their putative links to bone quality, early skeletal phenotypes can already be observed during bone development.

      The mineralized surface area of the total head and specific head structures was selected to evaluate the degree of mineralization in early skeletal development, as mineralization is a direct indicator of bone formation. Additionally, the osteoblast-positive surface areas were measured to provide insight into the formation of new skeletal tissue during early development. Osteoblasts, as active bone-forming cells, are essential for understanding bone growth and the dynamics of skeletal phenotypes.

      Examples in the manuscript:

      Line 212-214: ‘The osteoblast-positive areas in both the total head and the opercle were then quantified to gain insight into the formation of new skeletal tissue during early development.’

      Line 221-223: ‘Subsequently, Alizarin Red S (ARS) staining was conducted on the same 7 and 14 dpf crispant zebrafish larvae in order to evaluate the degree of mineralization in the early skeletal structures.’

      (2) Reword: The opercle bone is the earliest forming bone of the opercular series, and appears to be what the authors are referring to as the "operculum" at 7-14 dpf. The operculum is the larger structure (gill cover) in which the opercle is embedded. It would be more accurate to simply refer to the opercle at these stages.

      We agree with this comment and changed the text accordingly.

      (3) Define BMD and TMD at first usage.

      BMD and TMD are now defined in the manuscript.

      Line 41-43: ‘Six genes associated with severe recessive forms of Osteogenesis Imperfecta (OI) and four genes associated with bone mineral density (BMD), a key osteoporosis indicator, identified through genome-wide association studies (GWAS) were selected.’

      Line 286-288: ‘For each of the vertebral centra, the length, tissue mineral density (TMD), volume, and thickness were determined and tested for statistical differences between groups using a regression-based statistical test (Supplementary Figure 7).’

      (4) It would be helpful to note the grouping of candidates into OI vs. BMD GWAS throughout the figures.

      We agree with this comment and added this to all figure legends.

      ‘The first four genes are associated with the pathogenesis of osteoporosis, while the last six are linked to osteogenesis imperfecta’

      Reviewer #3 (Recommendations for the authors):

      Major points:

      (1) For the Results, it would be useful to the Reader to justify the selection of human candidate genes and their associated zebrafish orthologs to model skeletal functions. For example, what are variants identified from human studies, and do they impact functional domains? Are these domains and/or proteins conserved between humans/zebrafish? Is there evidence of skeletal expression in humans/zebrafish?

      Supplementary Table 4 lists the selected human candidate genes with reported mutations and/or polymorphisms associated with both skeletal and non-skeletal phenotypes. The table also includes additional findings from studies in mice and zebrafish. An extra column was now added to indicate gene conservation between human and zebrafish. We consulted UniProt (https://www.uniprot.org) and ZFIN (https://zfin.org) to assess the skeletal expression of these genes in human and zebrafish. All genes showed expression in the trabecular bone and/or bone marrow in humans, as well as in bone elements in zebrafish. We added this in the discussion.

      Line 309: ‘All selected genes show skeletal expression in both human and zebrafish.’

      Supplemental table 4 legend: ‘The conservation between human and zebrafish is reported in the last column.’

      As part of this, some version of Supplementary Table 4 might be included as a main display to introduce the targeted genes, ideally separated by rare (recessive OI) vs. common disease (osteoporosis). In the case of common disease and GWAS hits, how did authors narrow in on candidate genes (which often have Mbp-scale associated regions spanning multiple genes)? Further, what is the evidence that the mechanism of action of the GWAS variant is haploinsufficiency modeled by their crispant zebrafish?

      We have kept Supplementary Table 4 in the supplementary material but have referred to it earlier in the manuscript’s introduction. Consequently, the table has been renumbered from ‘Supplementary Table 4’  to ‘Supplementary Table 1’.

      The selection of genes potentially involved in the pathogenesis of osteoporosis is based on the data from the GWAS catalog, which annotates SNPs using the Ensemble mapping pipeline. The available annotation on their online search interface includes any Ensemble genes to which a SNP maps, or the closest upstream and downstream gene within a 50kb window. Four genes were selected for this screening method based on the criteria outlined in the results section. In this study, we aim to evaluate the general involvement of specific genes in bone metabolism, rather than to model a specific variant.

      Line 135-136 and 309-311: ‘An overview of the selected genes with observed mutant phenotypes in human, mice and zebrafish is provided in Supplementary Table 1.’

      (2) Using the crispant approach does not impact maternally-deposited RNAs that would dampen early developmental phenotypes. Considering the higher variability in larval phenotypes, perhaps the maternal effect plays a role. The authors might investigate developmental expression profiles of their genes using existing RNA-seq datasets such as from White et al (doi: 10.7554/eLife.30860).

      We thank the reviewer for this comment and agree with the possibility that maternally-deposited RNAs might have an impact on early developmental phenotypes. We included this in the discussion.

      Line 369-372: ‘Phenotypic variability in these zebrafish larvae can be attributed to several factors, including crispant mosaicism, allele heterogeneity, environmental factors, differences in genomic background and development, maternally-deposited RNAs, and slightly variable imaging positioning.’

      (3) While making comparisons within a clutch of mutant vs scrambled control is crucial, it is also important to ensure phenotypes are not specific to a single clutch. Do phenotypes remain consistent across different crosses/clutches?

      Yes, phenotypes remain consistent across different crosses and clutches. We included images from a second clutch in the Supplementary material (Supplementary Figure 8) and refereed to it in the discussion.

      Line 394-397: ‘Additionally, these skeletal malformations were consistently observed in a second clutch of crispants (Supplementary Figure 8), underscoring the reproducibility of these phenotypic features across independent clutches.’

      (4) Understanding that antibodies may not exist for many of the selected genes for zebrafish, authors should verify haploinsufficiency using an RT-qPCR of targeted genes in crispants vs. controls.

      We appreciate the reviewer’s suggestion to use RT-qPCR to examine expression levels of the targeted genes in crispants. However, previous experience suggests that relying on RNA expression to verify haploinsufficiency in zebrafish can be challenging. In zebrafish KO mutants, RT-qPCR often still detects gene transcripts, potentially due to incomplete nonsense-mediated decay (NMD) of the mutated mRNA, which may allow residual expression even in the absence of functional protein. As a more definitive approach, we prefer to use antibodies to confirm haploinsufficiency at the protein level. However, as the reviewer noted, generating and applying specific antibodies in zebrafish remains challenging.

      (5) Please indicate how parametric vs. non-parametric statistical tests were selected for datasets.

      We initially selected the parametric unpaired t-test, assuming the data were normally distributed with similar variances between groups. We verified the assumption of equal variances using the F-test, which was not significant across all assays. However, we did not assess the normality of the data directly, meaning we cannot confirm the normality assumption required for the t-test. Given this, we have opted to use the non-parametric Mann-Whitney U test, which does not require assumptions of normality, to ensure the robustness of our statistical analyses. We changed the Figures, the figure legends and the text accordingly.

      (6) In the figures and tables, I recommend adding notation showing the grouping of the first four genes as GWAS osteoporosis, the next three genes as osteoblast differentiation, the next two genes as bone mineralization, and the final gene as collagen transport to orient the reader. One might expect there to be a clustering of phenotypic outcomes based on the selection of genes, and it would be easier to follow this. This would be particularly useful to include in Table 2.

      Our primary objective is to assess the feasibility and reproducibility of the crispant screen rather than performing an in-depth pathway analysis or categorizing genes by biological processes. For this purpose, we have organized candidate genes based on their relevance to osteoporosis and Osteogenesis Imperfecta, without subdividing them further. We have clarified this focus in the figure legends, as suggested in an earlier recommendation.

      (7) For Figure 1, consider adding a smaller zoomed version of 1a embedded in each sub-figure with each measured element highlighted to improve readability.

      We agree with this comment and changed the figure accordingly.

      Minor points:

      (1) Table 2 could be simplified to improve readability. The headers have redundancies across columns with varied time points and could be merged.

      The suggested changes are incorporated in the manuscript (see earlier comment about this).

      (2) "BMD" is not defined in the Abstract. This is a personal preference, but there were numerous abbreviations in the text that made it difficult to follow at times.

      The suggested changes are incorporated in the manuscript (see earlier comment about this).

      • Context and Core Problem

        “Harvard writes, you know additionally llms struggle to provide contextual relevant answer … specifically llms have difficulty combining scientific factual … with tcid … non-codified knowledge…”

        • The text emphasizes how Large Language Models (LLMs) struggle in medical contexts due to the dual challenge of incorporating both codified (structured) and non-codified (tacit) knowledge.
        • Harvard recognizes that inaccurate or incomplete retrieval poses a major risk in medical applications.
      • Inadequacy of Current Medical LLM Performance

        “…if you look here at llama 3B we are here with the basic below 50% inmediate we are below 40% and the expert is below 30% … with gbt4 Turbo … about at 50% so for a medical system this is not acceptable…”

        • The text compares LLMs on basic, intermediate, and expert-level medical questions, showing subpar performance (30%-50% accuracy).
        • Such performance is “horrible” for medical standards where high precision is necessary.
      • Limitation of Retrieval-Augmented Generation (RAG)

        “…Howard discovers that also those R methods can provide multisource knowledge … but they are really vulnerable to potential errors… incomplete and incorrect information … leading now here in har Medical School inaccurate retrieval…”

        • RAG systems can still fail, especially if the underlying data repositories are incomplete or if there is no robust verification mechanism after retrieving facts.
      • Proposed Knowledge Graph-Based Agent

        “…they developed a new methodology … knowledge graph-based agent … designed to address the complexity of knowledge intensive medical queries…”

        • Harvard’s new solution leverages knowledge graphs (KGs) to systematically integrate multi-source, structured medical data.
        • It aims to combine LLM-generated insight with the rigor of codified knowledge in KGs.
      • Four-Phase Approach

        1. Generate

          “…At first we are at a generate level … to prompt the llm to follow different procedures for generating relevant triplets…”

          • The LLM identifies key medical entities and relationships from the question (“H, R, T” format).
          • It creates structured triplets (head entity – relationship – tail entity) to capture potential facts.
            1. Review

          “…it aims to assess the correctness of the generated triplets … by grounding here the correctness of the triplets in a medical Knowledge Graph…”

          • The system validates these newly generated triplets against the KG.
          • Entities are mapped to UMLS codes to ensure standardized medical terms.
            1. Revise

          “…the llm continues to propose and refine the triplets until the triplet are now validated Again by the knowledge CFT…”

          • If the triplets are incorrect or incomplete, the LLM iteratively refines them.
          • Mismatched or missing
    1. Now you say to me, "Lula, Lula, why don't you go to this party with me tonight?"

      It wasn't enough for Lula messing with Clay's head, also she begins to decide on his sentences. This indicates that Lula has exacerbated her manipulative power, her dominance, while Clay has become even more passive. Clay's willpower is thoroughly ignored by Lula. Lula stucks in the idea that blacks are slaves.

    1. Reviewer #1 (Public review):

      Summary:

      Meissner-Bernard et al present a biologically constrained model of telencephalic area of adult zebrafish, a homologous area to the piriform cortex, and argue for the role of precisely balanced memory networks in olfactory processing.

      This is interesting as it can add to recent evidence on the presence of functional subnetworks in multiple sensory cortices. It is also important in deviating from traditional accounts of memory systems as attractor networks. Evidence for attractor networks has been found in some systems, like in the head direction circuits in the flies. However, the presence of attractor dynamics in other modalities, like sensory systems, and their role in computation has been more contentious. This work contributes to this active line of research in experimental and computational neuroscience by suggesting that, rather than being represented in attractor networks and persistent activity, olfactory memories might be coded by balanced excitation-inhibitory subnetworks.

      Strengths:

      The main strength of the work is in: (1) direct link to biological parameters and measurements, (2) good controls and quantification of the results, and (3) comparison across multiple models.

      (1) The authors have done a good job of gathering the current experimental information to inform a biological-constrained spiking model of the telencephalic area of adult zebrafish. The results are compared to previous experimental measurements to choose the right regimes of operation.<br /> (2) Multiple quantification metrics and controls are used to support the main conclusions, and to ensure that the key parameters are controlled for - e.g. when comparing across multiple models.<br /> (3) Four specific models (random, scaled I / attractor, and two variant of specific E-I networks - tuned I and tuned E+I) are compared with different metrics, helping to pinpoint which features emerge in which model.

      In the revised manuscript, the authors have also:<br /> (a) made a good effort to provide a mechanistic explanation of their results (especially on the mechanism underlying medium amplification in specific E/I network models);<br /> (b) performed a systematic analysis of the parameter space by changing different parameters of E and I neurons (specifically showing that different time constants of E and I neurons do not change the results and therefore the main effects result from connectivity);<br /> (c) added further analysis and discussion on the potential functional and computational significance of balanced specific E-I subnetworks.

      These additions substantially strengthen the study, presenting compelling evidence for how networks with specific E-I structure can underpin olfactory processing and memory representations. The findings have potential implications that extend beyond the olfactory system and may be applicable to other neural systems and species.

    2. Author response:

      The following is the authors’ response to the original reviews.

      The revised manuscript contains new results and additional text. Major revisions:

      (1) Additional simulations and analyses of networks with different biophysical parameters and with identical time constants for E and I neurons (Methods, Supplementary Fig. 5).

      (2) Additional simulations and analyses of networks with modifications of connectivity parameters to further analyze effects of E/I assemblies on manifold geometry (Supplementary Fig. 6).

      (3) Analysis of synaptic current components (Figure 3 D-F; to analyze mechanism of modest amplification in Tuned networks). 

      (4) More detailed explanation of pattern completion analysis (Results).

      (5) Analysis of classification performance of Scaled networks (Supplementary Fig.8).

      (6) Additional analysis (Figure 5D-F) and discussion (particularly section “Computational functions of networks with E/I assemblies”) of functional benefits of continuous representations in networks with E-I assemblies. 

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Summary: 

      Meissner-Bernard et al present a biologically constrained model of telencephalic area of adult zebrafish, a homologous area to the piriform cortex, and argue for the role of precisely balanced memory networks in olfactory processing. 

      This is interesting as it can add to recent evidence on the presence of functional subnetworks in multiple sensory cortices. It is also important in deviating from traditional accounts of memory systems as attractor networks. Evidence for attractor networks has been found in some systems, like in the head direction circuits in the flies. However, the presence of attractor dynamics in other modalities, like sensory systems, and their role in computation has been more contentious. This work contributes to this active line of research in experimental and computational neuroscience by suggesting that, rather than being represented in attractor networks and persistent activity, olfactory memories might be coded by balanced excitation-inhibitory subnetworks. 

      Strengths: 

      The main strength of the work is in: (1) direct link to biological parameters and measurements, (2) good controls and quantification of the results, and (3) comparison across multiple models. 

      (1) The authors have done a good job of gathering the current experimental information to inform a biological-constrained spiking model of the telencephalic area of adult zebrafish. The results are compared to previous experimental measurements to choose the right regimes of operation. 

      (2) Multiple quantification metrics and controls are used to support the main conclusions and to ensure that the key parameters are controlled for - e.g. when comparing across multiple models.  (3) Four specific models (random, scaled I / attractor, and two variant of specific E-I networks - tuned I and tuned E+I) are compared with different metrics, helping to pinpoint which features emerge in which model. 

      Weaknesses: 

      Major problems with the work are: (1) mechanistic explanation of the results in specific E-I networks, (2) parameter exploration, and (3) the functional significance of the specific E-I model. 

      (1) The main problem with the paper is a lack of mechanistic analysis of the models. The models are treated like biological entities and only tested with different assays and metrics to describe their different features (e.g. different geometry of representation in Fig. 4). Given that all the key parameters of the models are known and can be changed (unlike biological networks), it is expected to provide a more analytical account of why specific networks show the reported results. For instance, what is the key mechanism for medium amplification in specific E/I network models (Fig. 3)? How does the specific geometry of representation/manifolds (in Fig. 4) emerge in terms of excitatory-inhibitory interactions, and what are the main mechanisms/parameters? Mechanistic account and analysis of these results are missing in the current version of the paper. 

      We agree that further mechanistic insights would be of interest and addressed this issue at different levels:

      (1) Biophysical parameters: to determine whether network behavior depends on specific choices of biophysical parameters in E and I neurons we equalized biophysical parameters across neuron types. The main observations are unchanged, suggesting that the observed effects depend primarily on network connectivity (see also response to comment [2]).

      (2) Mechanism of modest amplification in E/I assemblies: analyzing the different components of the synaptic currents demonstrate that the modest amplification of activity in Tuned networks results from an “imperfect” balance of recurrent excitation and inhibition within assemblies (see new Figures 3D-F and text p.7). Hence, E/I co-tuning substantially reduces the net amplification in Tuned networks as compared to Scaled networks, thus preventing discrete attractor dynamics and stabilizing network activity, but a modest amplification still occurs, consistent with biological observations.

      (3) Representational geometry: to obtain insights into the network mechanisms underlying effects of E/I assemblies on the geometry of population activity we tested the hypothesis that geometrical changes depend, at least in part, on the modest amplification of activity within E/I assemblies (see Supplementary Figure 6). We changed model parameters to either prevent the modest amplification in Tuned networks (increasing I-to-E connectivity within assemblies) or introduce a modest amplification in subsets of neurons by other mechanisms (concentration-dependent increase in the excitability of pseudo-assembly neurons; Scaled I networks with reduced connectivity within assemblies). Manipulations that introduced a modest, input-dependent amplification in neuronal subsets had geometrical effects similar to those observed in Tuned networks, whereas manipulations that prevented a modest amplification abolished these effects (Supplementary Figure 6). Note however that these manipulations generated different firing rate distributions. These results provide a starting point for more detailed analyses of the relationship between network connectivity and representational geometry (see p.12).

      In summary, our additional analyses indicate that effects of E/I assemblies on representational geometry depend primarily on network connectivity, rather than specific biophysical parameters, and that the resulting modest amplification of activity within assemblies makes an important contribution. Further analyses may reveal more specific relationships between E/I assemblies and representational geometry, but such analyses are beyond the scope of this study.

      (2) The second major issue with the study is a lack of systematic exploration and analysis of the parameter space. Some parameters are biologically constrained, but not all the parameters. For instance, it is not clear what the justification for the choice of synaptic time scales are (with E synaptic time constants being larger than inhibition: tau_syn_i = 10 ms, tau_syn_E = 30 ms). How would the results change if they are varying these - and other unconstrained - parameters? It is important to show how the main results, especially the manifold localisation, would change by doing a systematic exploration of the key parameters and performing some sensitivity analysis. This would also help to see how robust the results are, which parameters are more important and which parameters are less relevant, and to shed light on the key mechanisms.  

      We thank the reviewer for raising this point. We chose a relatively slow time constant for excitatory synapses because experimental data indicate that excitatory synaptic currents in Dp and piriform cortex contain a prominent NMDA component. Nevertheless, to assess whether network behavior depends on specific choices of biophysical parameters in E and I neurons, we have performed additional simulations with equal synaptic time constants and equal biophysical parameters for all neurons. Each neuron also received the same number of inputs from each population (see revised Methods). Results were similar to those observed previously (Supplementary Fig.5 and p.9 of main text). We therefore conclude that the main effects observed in Tuned networks cannot be explained by differences in biophysical parameters between E and I neurons but is primarily a consequence of network connectivity.

      (3) It is not clear what the main functional advantage of the specific E-I network model is compared to random networks. In terms of activity, they show that specific E-I networks amplify the input more than random networks (Fig. 3). But when it comes to classification, the effect seems to be very small (Fig. 5c). Description of different geometry of representation and manifold localization in specific networks compared to random networks is good, but it is more of an illustration of different activity patterns than proving a functional benefit for the network. The reader is still left with the question of what major functional benefits (in terms of computational/biological processing) should be expected from these networks, if they are to be a good model for olfactory processing and learning. 

      One possibility for instance might be that the tasks used here are too easy to reveal the main benefits of the specific models - and more complex tasks would be needed to assess the functional enhancement (e.g. more noisy conditions or more combination of odours). It would be good to show this more clearly - or at least discuss it in relation to computation and function. 

      In the previous manuscript, the analysis of potential computational benefits other than pattern classification was limited and the discussion of this issue was condensed into a single itemized paragraph to avoid excessive speculation. Although a thorough analysis of potential computational benefits exceeds the scope of a single paper, we agree with the reviewer that this issue is of interest and therefore added additional analyses and discussion.

      In the initial manuscript we analyzed pattern classification primarily to investigate whether Tuned networks can support this function at all, given that they do not exhibit discrete attractor states. We found this to be the case, which we consider a first important result.

      Furthermore, we found that precise balance of E/I assemblies can protect networks against catastrophic firing rate instabilities when assemblies are added sequentially, as in continual learning. Results from these simulations are now described and discussed in more detail (see Results p.11 and Discussion p.13).

      In the revised manuscript, we now also examine additional potential benefits of Tuned networks and discuss them in more detail (see new Figure 5D-F and text p.11). One hypothesis is that continuous representations provide a distance metric between a given input and relevant (learned) stimuli. To address this hypothesis, we (1) performed regression analysis and (2) trained support vector machines (SVMs) to predict the concentration of a given odor in a mixture based on population activity. In both cases, Tuned E+I networks outperformed Scaled and _rand n_etworks in predicting the concentration of learned odors across a wide range mixtures (Figure 5D-F).  E/I assemblies therefore support the quantification of learned odors within mixtures or, more generally, assessments of how strongly a (potentially complex) input is related to relevant odors stored in memory. Such a metric assessment of stimulus quality is not well supported by discrete attractor networks because inputs are mapped onto discrete network states.

      The observation that Tuned networks do not map inputs onto discrete outputs indicates that such networks do not classify inputs as distinct items. Nonetheless, the observed geometrical modifications of continuous representations support the classification of learned inputs or the assessment of metric relationships by hypothetical readout neurons. Geometrical modifications of odor representations may therefore serve as one of multiple steps in multi-layer computations for pattern classification (and/or other computations). In this scenario, the transformation of odor representations in Dp may be seen as related to transformations of representations between different layers in artificial networks, which collectively perform a given task (notwithstanding obvious structural and mechanistic differences between artificial and biological networks). In other words, geometrical transformations of representations in Tuned networks may overrepresent learned (relevant) information at the expense of other information and thereby support further learning processes in other brain areas. An obvious corollary of this scenario is that Dp does not perform odor classification per se based on inputs from the olfactory bulb but reformats representations of odor space based on experience to support computational tasks as part of a larger system. This scenario is now explicitly discussed (p.14).

      Reviewer #2 (Public Review): 

      Summary: 

      The authors conducted a comparative analysis of four networks, varying in the presence of excitatory assemblies and the architecture of inhibitory cell assembly connectivity. They found that co-tuned E-I assemblies provide network stability and a continuous representation of input patterns (on locally constrained manifolds), contrasting with networks with global inhibition that result in attractor networks. 

      Strengths: 

      The findings presented in this paper are very interesting and cutting-edge. The manuscript effectively conveys the message and presents a creative way to represent high-dimensional inputs and network responses. Particularly, the result regarding the projection of input patterns onto local manifolds and continuous representation of input/memory is very Intriguing and novel. Both computational and experimental neuroscientists would find value in reading the paper. 

      Weaknesses: 

      that have continuous representations. This could also be shown in Figure 5B, along with the performance of the random and tuned E-I networks. The latter networks have the advantage of providing network stability compared to the Scaled I network, but at the cost of reduced network salience and, therefore, reduced input decodability. The authors may consider designing a decoder to quantify and compare the classification performance of all four networks. 

      We have now quantified classification by networks with discrete attractor dynamics (Scaled) along with other networks. However, because the neuronal covariance matrix for such networks is low rank and not invertible, pattern classification cannot be analyzed by QDA as in Figure 5B. We therefore classified patterns from the odor subspace by template matching, assigning test patterns to one of the four classes based on correlations (see Supplementary Figure 8). As expected, Scaled networks performed well, but they did not outperform Tuned networks. Moreover, the performance of Scaled networks, but not Tuned networks, depended on the order in which odors were presented to the network. This hysteresis effect is a direct consequence of persistent attractor states and decreased the general classification performance of Scaled networks (see Supplementary Figure 8 for details). These results confirm the prediction that networks with discrete attractor states can efficiently classify inputs, but also reveal disadvantages arising from attractor dynamics. Moreover, the results indicate that the classification performance of Tuned networks is also high under the given task conditions, which simulate a biologically realistic scenario.

      We would also like to emphasize that classification may not be the only task, and perhaps not even a main task, of Dp/piriform cortex or other memory networks with E/I assemblies. Conceivably, other computations could include metric assessments of inputs relative to learned inputs or additional learning-related computations. Please see our response to comment (3) of reviewer 1 for a further discussion of this issue. 

      Networks featuring E/I assemblies could potentially represent multistable attractors by exploring the parameter space for their reciprocal connectivity and connectivity with the rest of the network. However, for co-tuned E-I networks, the scope for achieving multistability is relatively constrained compared to networks employing global or lateral inhibition between assemblies. It would be good if the authors mentioned this in the discussion. Also, the fact that reciprocal inhibition increases network stability has been shown before and should be cited in the statements addressing network stability (e.g., some of the citations in the manuscript, including Rost et al. 2018, Lagzi & Fairhall 2022, and Vogels et al. 2011 have shown this).  

      We thank the reviewer for this comment. We now explicitly discuss multistability (see p. 12) and refer to additional references in the statements addressing network stability.

      Providing raster plots of the pDp network for familiar and novel inputs would help with understanding the claims regarding continuous versus discrete representation of inputs, allowing readers to visualize the activity patterns of the four different networks. (similar to Figure 1B). 

      We thank the reviewer for this suggestion. We have added raster plots of responses to both familiar and novel inputs in the revised manuscript (Figure 2D and Supplementary Figure 4A).

      Reviewer #3 (Public Review): 

      Summary: 

      This work investigates the computational consequences of assemblies containing both excitatory and inhibitory neurons (E/I assembly) in a model with parameters constrained by experimental data from the telencephalic area Dp of zebrafish. The authors show how this precise E/I balance shapes the geometry of neuronal dynamics in comparison to unstructured networks and networks with more global inhibitory balance. Specifically, E/I assemblies lead to the activity being locally restricted onto manifolds - a dynamical structure in between high-dimensional representations in unstructured networks and discrete attractors in networks with global inhibitory balance. Furthermore, E/I assemblies lead to smoother representations of mixtures of stimuli while those stimuli can still be reliably classified, and allow for more robust learning of additional stimuli. 

      Strengths: 

      Since experimental studies do suggest that E/I balance is very precise and E/I assemblies exist, it is important to study the consequences of those connectivity structures on network dynamics. The authors convincingly show that E/I assemblies lead to different geometries of stimulus representation compared to unstructured networks and networks with global inhibition. This finding might open the door for future studies for exploring the functional advantage of these locally defined manifolds, and how other network properties allow to shape those manifolds. 

      The authors also make sure that their spiking model is well-constrained by experimental data from the zebrafish pDp. Both spontaneous and odor stimulus triggered spiking activity is within the range of experimental measurements. But the model is also general enough to be potentially applied to findings in other animal models and brain regions. 

      Weaknesses: 

      I find the point about pattern completion a bit confusing. In Fig. 3 the authors argue that only the Scaled I network can lead to pattern completion for morphed inputs since the output correlations are higher than the input correlations. For me, this sounds less like the network can perform pattern completion but it can nonlinearly increase the output correlations. Furthermore, in Suppl. Fig. 3 the authors show that activating half the assembly does lead to pattern completion in the sense that also non-activated assembly cells become highly active and that this pattern completion can be seen for Scaled I, Tuned E+I, and Tuned I networks. These two results seem a bit contradictory to me and require further clarification, and the authors might want to clarify how exactly they define pattern completion. 

      We believe that this comment concerns a semantic misunderstanding and apologize for any lack of clarity. We added a definition of pattern completion in the text: “…the retrieval of the whole memory from noisy or corrupted versions of the learned input.”. Pattern completion may be assessed using different procedures. In computational studies, it is often analyzed by delivering input to a subset of the assembly neurons which store a given memory (partial activation). Under these conditions, we find recruitment of the entire assembly in all structured networks, as demonstrated in Supplementary Figure 3. However, these conditions are unlikely to occur during odor presentation because the majority of neurons do not receive any input.

      Another more biologically motivated approach to assess pattern completion is to gradually modify a realistic odor input into a learned input, thereby gradually increasing the overlap between the two inputs. This approach had been used previously in experimental studies (references added to the text p.6). In the presence of assemblies, recurrent connectivity is expected to recruit assembly neurons (and thus retrieve the stored pattern) more efficiently as the learned pattern is approached. This should result in a nonlinear increase in the similarity between the evoked and the learned activity pattern. This signature was prominent in Scaled networks but not in Tuned or rand networks. Obviously, the underlying procedure is different from the partial activation of the assembly described above because input patterns target many neurons (including neurons outside assemblies) and exhibit a biologically realistic distribution of activity. However, this approach has also been referred to as “pattern completion” in the neuroscience literature, which may be the source of semantic confusion here. To clarify the difference between these approaches we have now revised the text and explicitly described each procedure in more detail (see p.6). 

      The authors argue that Tuned E+I networks have several advantages over Scaled I networks. While I agree with the authors that in some cases adding this localized E/I balance is beneficial, I believe that a more rigorous comparison between Tuned E+I networks and Scaled I networks is needed: quantification of variance (Fig. 4G) and angle distributions (Fig. 4H) should also be shown for the Scaled I network. Similarly in Fig. 5, what is the Mahalanobis distance for Scaled I networks and how well can the Scaled I network be classified compared to the Tuned E+I network? I suspect that the Scaled I network will actually be better at classifying odors compared to the E+I network. The authors might want to speculate about the benefit of having networks with both sources of inhibition (local and global) and hence being able to switch between locally defined manifolds and discrete attractor states. 

      We agree that a more rigorous comparison of Tuned and Scaled networks would be of interest. We have added the variance analysis (Fig 4G) and angle distributions (Fig. 4H) for both Tuned I and Scaled networks. However, the Mahalanobis distances and Quadratic Discriminant Analysis cannot be applied to Scaled networks because their neuronal covariance matrix is low rank and not invertible_. To nevertheless compare these networks, we performed template matching by assigning test patterns to one of the four odor classes based on correlations to template patterns (Supplementary Figure 8; see also response to the first comment of reviewer 2). Interestingly, _Scaled networks performed well at classification but did not outperform Tuned networks, and exhibited disadvantages arising from attractor dynamics (Supplementary Figure 8; see also response to the first comment of reviewer 2). Furthermore, in further analyses we found that continuous representational manifolds support metric assessments of inputs relative to learned odors, which cannot be achieved by discrete representations. These results are now shown in Figure 5D-E and discussed explicitly in the text on p.11 (see also response to comment 3 of reviewer 1).

      We preferred not to add a sentence in the Discussion about benefits of networks having both sources of inhibition_,_ as we find this a bit too speculative.

      At a few points in the manuscript, the authors use statements without actually providing evidence in terms of a Figure. Often the authors themselves acknowledge this, by adding the term "not shown" to the end of the sentence. I believe it will be helpful to the reader to be provided with figures or panels in support of the statements.  

      Thank you for this comment. We have provided additional data figures to support the following statements:

      “d<sub>M</sub> was again increased upon learning, particularly between learned odors and reference classes representing other odors (Supplementary Figure 9)”

      “decreasing amplification in assemblies of Scaled networks changed transformations towards the intermediate behavior, albeit with broader firing rate distributions than in Tuned networks (Supplementary Figure 6 B)”  

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors): 

      Meissner-Bernard et al present a biologically constrained model of telencephalic area of adult zebrafish, a homologous area to the piriform cortex, and argue for the role of precisely balanced memory networks in olfactory processing. 

      This is interesting as it can add to recent evidence on the presence of functional subnetworks in multiple sensory cortices. It is also important in deviating from traditional accounts of memory systems as attractor networks. Evidence for attractor networks has been found in some systems, like in the head direction circuits in the flies. However, the presence of attractor dynamics in other modalities, like sensory systems, and their role in computation has been more contentious. This work contributes to this active line of research in experimental and computational neuroscience by suggesting that, rather than being represented in attractor networks and persistent activity, olfactory memories might be coded by balanced excitation-inhibitory subnetworks. 

      The paper is generally well-written, the figures are informative and of good quality, and multiple approaches and metrics have been used to test and support the main results of the paper. 

      The main strength of the work is in: (1) direct link to biological parameters and measurements, (2) good controls and quantification of the results, and (3) comparison across multiple models. 

      (1) The authors have done a good job of gathering the current experimental information to inform a biological-constrained spiking model of the telencephalic area of adult zebrafish. The results are compared to previous experimental measurements to choose the right regimes of operation. 

      (2) Multiple quantification metrics and controls are used to support the main conclusions and to ensure that the key parameters are controlled for - e.g. when comparing across multiple models.   (3) Four specific models (random, scaled I / attractor, and two variant of specific E-I networks - tuned I and tuned E+I) are compared with different metrics, helping to pinpoint which features emerge in which model. 

      Major problems with the work are: (1) mechanistic explanation of the results in specific E-I networks, (2) parameter exploration, and (3) the functional significance of the specific E-I model. 

      (1) The main problem with the paper is a lack of mechanistic analysis of the models. The models are treated like biological entities and only tested with different assays and metrics to describe their different features (e.g. different geometry of representation in Fig. 4). Given that all the key parameters of the models are known and can be changed (unlike biological networks), it is expected to provide a more analytical account of why specific networks show the reported results. For instance, what is the key mechanism for medium amplification in specific E/I network models (Fig. 3)? How does the specific geometry of representation/manifolds (in Fig. 4) emerge in terms of excitatory-inhibitory interactions, and what are the main mechanisms/parameters? Mechanistic account and analysis of these results are missing in the current version of the paper. 

      Precise balancing of excitation and inhibition in subnetworks would lead to the cancellation of specific dynamical modes responsible for the amplification of responses (hence, deviating from the attractor dynamics with an unstable specific mode). What is the key difference in the specific E/I networks here (tuned I or/and tuned E+I) which make them stand between random and attractor networks? Excitatory and inhibitory neurons have different parameters in the model (Table 1). Time constants of inhibitory and excitatory synapses are also different (P. 13). Are these parameters causing networks to be effectively more excitation dominated (hence deviating from a random spectrum which would be expected from a precisely balanced E/I network, with exactly the same parameters of E and I neurons)? It is necessary to analyse the network models, describe the key mechanism for their amplification, and pinpoint the key differences between E and I neurons which are crucial for this. 

      To address these comments we performed additional simulations and analyses at different levels. Please see our reply to comment (1) of the public review (reviewer 1) for a detailed description. We thank the reviewer for these constructive comments.

      (2) The second major issue with the study is a lack of systematic exploration and analysis of the parameter space. Some parameters are biologically constrained, but not all the parameters. For instance, it is not clear what the justification for the choice of synaptic time scales are (with E synaptic time constants being larger than inhibition: tau_syn_i = 10 ms, tau_syn_E = 30 ms). How would the results change if they are varying these - and other unconstrained - parameters? It is important to show how the main results, especially the manifold localisation, would change by doing a systematic exploration of the key parameters and performing some sensitivity analysis. This would also help to see how robust the results are, which parameters are more important and which parameters are less relevant, and to shed light on the key mechanisms.  

      We thank the reviewer for this comment. We have now carried out additional simulations with equal time constants for all neurons. Please see our reply to the public review for more details (comment 2 of reviewer 1).

      (3) It is not clear what the main functional advantage of the specific E-I network model is compared to random networks. In terms of activity, they show that specific E-I networks amplify the input more than random networks (Fig. 3). But when it comes to classification, the effect seems to be very small (Fig. 5c). Description of different geometry of representation and manifold localization in specific networks compared to random networks is good, but it is more of an illustration of different activity patterns than proving a functional benefit for the network. The reader is still left with the question of what major functional benefits (in terms of computational/biological processing) should be expected from these networks, if they are to be a good model for olfactory processing and learning. 

      One possibility for instance might be that the tasks used here are too easy to reveal the main benefits of the specific models - and more complex tasks would be needed to assess the functional enhancement (e.g. more noisy conditions or more combination of odours). It would be good to show this more clearly - or at least discuss it in relation to computation and function.

      Please see our reply to the public review (comment 3 of reviewer 1).

      Specific comments: 

      Abstract: "resulting in continuous representations that reflected both relatedness of inputs and *an individual's experience*" 

      It didn't become apparent from the text or the model where the role of "individual's experience" component (or "internal representations" - in the next line) was introduced or shown (apart from a couple of lines in the Discussion) 

      We consider the scenario that that assemblies are the outcome of an experience-dependent plasticity process. To clarify this, we have now made a small addition to the text: “Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons.”.

      P. 2: "The resulting state of "precise" synaptic balance stabilizes firing rates because inhomogeneities or fluctuations in excitation are tracked by correlated inhibition" 

      It is not clear what the "inhomogeneities" specifically refers to - they can be temporal, or they can refer to the quenched noise of connectivity, for instance. Please clarify what you mean. 

      The statement has been modified to be more precise: “…“precise” synaptic balance stabilizes firing rates because inhomogeneities in excitation across the population or temporal variations in excitation are tracked by correlated inhibition…”.

      P. 3 (and Methods): When odour stimulus is simulated in the OB, the activity of a fraction of mitral cells is increased (10% to 15 Hz) - but also a fraction of mitral cells is suppressed (5% to 2 Hz). What is the biological motivation or reference for this? It is not provided. Is it needed for the results? Also, it is not explained how the suppressed 5% are chosen (e.g. randomly, without any relation to the increased cells?). 

      We thank the reviewer for this comment. These changes in activity directly reflect experimental observations. We apologize that we forgot to include the references reporting these observations (Friedrich and Laurent, 2001 and 2004); this is now fixed.

      In our simulation, OB neurons do not interact with each other, and the suppressed 5% were indeed randomly selected. We changed the text in Methods accordingly to read: “An additional 75 randomly selected mitral cells were inhibited” 

      P. 4, L. 1-2: "... sparsely connected integrate-and-fire neurons with conductance-based synapses (connection probability {less than or equal to}5%)." 

      Specify the connection probability of specific subtypes (EE, EI, IE, II).  

      We now refer to the Methods section, where this information can be found. 

      “... conductance-based synapses (connection probability ≤5%, Methods)”  

      P. 4, L. 6-7: "Population activity was odor-specific and activity patterns evoked by uncorrelated OB inputs remained uncorrelated in Dp (Figure 1H)" 

      What would happen to correlated OB inputs (e.g. as a result of mixture of two overlapping odours) in this baseline state of the network (before memories being introduced to it)? It would be good to know this, as it sheds light on the initial operating regime of the network in terms of E/I balance and decorrelation of inputs.  

      This information was present in the original manuscript at (Figure 3) but we improved the writing to further clarify this issue: “ (…) we morphed a novel odor into a learned odor (Figure 3A), or a learned odor into another learned odor (Supplementary Figure 3B), and quantified the similarity between morphed and learned odors by the Pearson correlation of the OB activity patterns (input correlation). We then compared input correlations to the corresponding pattern correlations among E neurons in Dp (output correlation). In rand networks, output correlations increased linearly with input correlations but did not exceed them (Figure 3B and Supplementary Figure 3B)”

      P. 4, L. 12-13: "Shuffling spike times of inhibitory neurons resulted in runaway activity with a probability of ~80%, .."   Where is this shown? 

      (There are other occasions too in the paper where references to the supporting figures are missing). 

      We now provide the statistics: “Shuffling spike times of inhibitory neurons resulted in runaway activity with a probability of 0.79 ± 0.20”

      P. 4: "In each network, we created 15 assemblies representing uncorrelated odors. As a consequence, ~30% of E neurons were part of an assembly ..." 

      15 x 100 / 4000 = 37.5% - so it's closer to 40% than 30%. Unless there is some overlap? 

      Yes: despite odors being uncorrelated and connectivity being random, some neurons (6 % of E neurons) belong to more than one assembly.

      P. 4: "When a reached a critical value of ~6, networks became unstable and generated runaway activity (Figure 2B)." 

      Can this transition point be calculated or estimated from the network parameters, and linked to the underlying mechanisms causing it? 

      We thank the reviewer for this interesting question. The unstability arises when inhibitions fails to counterbalance efficiently the increased recurrent excitation within Dp. The transition point is difficult to estimate, as it can depend on several parameters, including the probability of E to E connections, their strength, assembly size, and others. We have therefore not attempted to estimate it analytically.

      P. 4: "Hence, non-specific scaling of inhibition resulted in a divergence of firing rates that exhausted the dynamic range of individual neurons in the population, implying that homeostatic   global inhibition is insufficient to maintain a stable firing rate distribution." 

      I don't think this is justified based on the results and figures presented here (Fig. 2E) - the interpretation is a bit strong and biased towards the conclusions the authors want to draw. 

      To more clearly illustrate the finding that in Scaled networks, assembly neurons are highly active (close to maximal realistic firing rates) whereas non-assembly neurons are nearly silent we have now added Supplementary Fig. 2B. Moreover, we have toned down the text: “Hence, non-specific scaling of inhibition resulted in a large and biologically unrealistic divergence of firing rates (Supplementary Figure 2B) that nearly exhausted the dynamic range of individual neurons in the population, indicating that homeostatic global inhibition is insufficient to maintain a stable firing rate distribution”

      P. 5, third paragraph: Description of Figure 2I, inset is needed, either in the text or caption. 

      The inset is now referred to in the text: ”we projected synaptic conductances of each neuron onto a line representing the E/I ratio expected in a balanced network (“balanced axis”) and onto an orthogonal line (“counter-balanced axis”; Figure 2I inset, Methods).”

      P. 5, last paragraph: another example of writing about results without showing/referring to the corresponding figures: 

      "In rand networks, firing rates increased after stimulus onset and rapidly returned to a low baseline after stimulus offset. Correlations between activity patterns evoked by the same odor at different time points and in different trials were positive but substantially lower than unity, indicating high variability ..." 

      And the continuation with similar lack of references on P. 6: 

      "Scaled networks responded to learned odors with persistent firing of assembly neurons and high pattern correlations across trials and time, implying attractor dynamics (Hopfield, 1982; Khona and Fiete, 2022), whereas Tuned networks exhibited transient responses and modest pattern correlations similar to rand networks." 

      Please go through the Results and fix the references to the corresponding figures on all instances. 

      We thank the reviewer for pointing out these overlooked figure references, which are now fixed.

      P. 8: "These observations further support the conclusion that E/I assemblies locally constrain neuronal dynamics onto manifolds." 

      As discussed in the general major points, mechanistic explanation in terms of how the interaction of E/I dynamics leads to this is missing. 

      As discussed in the reply to the public review (comment 3 of reviewer 1), we have now provided more mechanistic analyses of our observations.

      P. 9: "Hence, E/I assemblies enhanced the classification of inputs related to learned patterns."   The effect seems to be very small. Also, any explanation for why for low test-target correlation the effect is negative (random doing better than tuned E/I)? 

      The size of the effect (plearned – pnovel = 0.074; difference of means; Figure 5C) may appear small in terms of absolute probability, but it is substantial relative to the maximum possible increase (1 – p<sub>novel</sub> =  0.133; Figure 5C). The fact that for low test-target correlations the effect is negative is a direct consequence of the positive effect for high test-target correlations and the presence of 2 learned odors in the 4-way forced choice task. 

      P. 9: "In Scaled I networks, creating two additional memories resulted in a substantial increase   in firing rates, particularly in response to the learned and related odors"   Where is this shown? Please refer to the figure. 

      We thank the reviewer again for pointing this out. We forgot to include a reference to the relevant figure which has now been added in the revised manuscript (Figure 6C).

      P. 10: "The resulting Tuned networks reproduced additional experimental observations that were not used as constraints including irregular firing patterns, lower output than input correlations, and the absence of persistent activity" 

      It is difficult to present these as "additional experimental observations", as all of them are negative, and can exist in random networks too - hence cannot be used as biological evidence in favour of specific E/I networks when compared to random networks. 

      We agree with the reviewer that these additional experimental observations cannot be used as biological evidence favouring Tuned E+I networks over random networks. We here just wanted to point out that additional observations which we did not take into account to fit the model are not invalidating the existence of E-I assemblies in biological networks. As assemblies tend to result in persistent activity in other types of networks, we feel that this observation is worth pointing out.

      Methods: 

      P. 13: Describe the parameters of Eq. 2 after the equation. 

      Done.

      P. 13: "The time constants of inhibitory and excitatory synapses were 10 ms and 30 ms, respectively." 

      What is the (biological) justification for the choice of these parameters? 

      How would varying them affect the main results (e.g. local manifolds)? 

      We chose a relatively slow time constant for excitatory synapses because experimental data indicate that excitatory synaptic currents in Dp and piriform cortex contain a prominent NMDA component. We have now also simulated networks with equal time constants for excitatory and inhibitory synapses and equal biophysical parameters for excitatory and inhibitory neurons, which did not affect the main results (see also reply to the public review: comment 2 of reviewer 1).

      P. 14: "Care was also taken to ensure that the variation in the number of output connections was low across neurons"   How exactly?

      More detailed explanations have now been added in the Methods section: “connections of a presynaptic neuron y to postsynaptic neurons x were randomly deleted when their total number exceeded the average number of output connections by ≥5%, or added when they were lower by ≥5%.“

      Reviewer #2 (Recommendations For The Authors): 

      Congratulations on the great and interesting work! The results were nicely presented and the idea of continuous encoding on manifolds is very interesting. To improve the quality of the paper, in addition to the major points raised in the public review, here are some more detailed comments for the paper: 

      (1) Generally, citations have to improve. Spiking networks with excitatory assemblies and different architectures of inhibitory populations have been studied before, and the claim about improved network stability in co-tuned E-I networks has been made in the following papers that need to be correctly cited: 

      • Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. 2011. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 334:1-7. doi:10.1126/science.1212991 (mentions that emerging precise balance on the synaptic weights can result in the overall network stability) 

      • Lagzi F, Bustos MC, Oswald AM, Doiron B. 2021. Assembly formation is stabilized by Parvalbumin neurons and accelerated by Somatostatin neurons. bioRxiv doi: https://doi.org/10.1101/2021.09.06.459211 (among other things, contrasts stability and competition which arises from multistable networks with global inhibition and reciprocal inhibition)   • Rost T, Deger M, Nawrot MP. 2018. Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick. Biol Cybern 112:81-98. doi:10.1007/s00422-017-0737-7 (compares different architectures of inhibition and their effects on network dynamics) 

      • Lagzi F, Fairhall A. 2022. Tuned inhibitory firing rate and connection weights as emergent network properties. bioRxiv 2022.04.12.488114. doi:10.1101/2022.04.12.488114 (here, see the eigenvalue and UMAP analysis for a network with global inhibition and E/I assemblies) 

      Additionally, there are lots of pioneering work about tracking of excitatory synaptic inputs by inhibitory populations, that are missing in references. Also, experimental work that show existence of cell assemblies in the brain are largely missing. On the other hand, some references that do not fit the focus of the statements have been incorrectly cited. 

      The authors may consider referencing the following more pertinent studies on spiking networks to support the statement regarding attractor dynamics in the first paragraph in the Introduction (the current citations of Hopfield and Kohonen are for rate-based networks): 

      • Wong, K.-F., & Wang, X.-J. (2006). A recurrent network mechanism of time integration in perceptual decisions. Journal of Neuroscience, 26(4), 1314-1328. https://doi.org/10.1523/JNEUROSCI.3733-05.2006 

      • Wang, X.-J. (2008). Decision making in recurrent neuronal circuits. Neuron, 60(2), 215-234. https://doi.org/10.1016/j.neuron.2008.09.034  

      • F. Lagzi, & S. Rotter. (2015). Dynamics of competition between subnetworks of spiking neuronal networks in the balanced state. PloS One. 

      • Goldman-Rakic, P. S. (1995). Cellular basis of working memory. Neuron, 14(3), 477-485. 

      • Rost T, Deger M, Nawrot MP. 2018. Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick. Biol Cybern 112:81-98. doi:10.1007/s00422-017-0737-7. 

      • Amit DJ, Tsodyks M (1991) Quantitative study of attractor neural network retrieving at low spike rates: I. substrate-spikes, rates and neuronal gain. Network 2:259-273. 

      • Mazzucato, L., Fontanini, A., & La Camera, G. (2015). Dynamics of Multistable States during Ongoing and Evoked Cortical Activity. Journal of Neuroscience, 35(21), 8214-8231. 

      We thank the reviewer for the references suggestions. We have carefully reviewed the reference list and made the following changes, which we hope address the reviewer’s concerns:

      (1) We adjusted References about network stability in co-tuned E-I networks.

      (2) We added the Lagzi & Rotter (2015), Amit et al. (1991), Mazzucato et al. (2015) and GoldmanRakic (1995) papers in the Introduction as studies on attractor dynamics in spiking neural networks. We preferred to omit the two X.J Wang papers, as they describe attractors in decision making rather than memory processes.

      (3) We added the Ko et al. 2011 paper as experimental evidence for assemblies in the brain. In our view, there are few experimental studies showing the existence of cell assemblies in the brain, which we distinguish from cell ensembles, group of coactive neurons. 

      (4) We also included Hennequin 2018, Brunel 2000, Lagzi et al. 2021 and Eckmann et al. 2024, which we had not cited in the initial manuscript.

      (5) We removed the Wiechert et al. 2010 reference as it does not support the statement about geometry-preserving transformation by random networks.

      (2) The gist of the paper is about how the architecture of inhibition (reciprocal vs. global in this case) can determine network stability and salient responses (related to multistable attractors and variations) for classification purposes. It would improve the narrative of the paper if this point is raised in the Introduction and Discussion section. Also see a relevant paper that addresses this point here: 

      Lagzi F, Bustos MC, Oswald AM, Doiron B. 2021. Assembly formation is stabilized by Parvalbumin neurons and accelerated by Somatostatin neurons. bioRxiv doi: https://doi.org/10.1101/2021.09.06.459211 

      Classification has long been proposed to be a function of piriform cortex and autoassociative memory networks in general, and we consider it important. However, the computational function of Dp or piriform cortex is still poorly understood, and we do not focus only on odor classification as a possibility. In fact, continuous representational manifolds also support other functions such as the quantification of distance relationships of an input to previously memorized stimuli, or multi-layer network computations (including classification). In the revised manuscript, we have performed additional analyses to explore these notions in more detail, as explained above (response to public reviews, comment 3 of reviewer 1). Furthermore, we have now expanded the discussion of potential computational functions of Tuned networks and explicitly discuss classification but also other potential functions. 

      (3) A plot for the values of the inhibitory conductances in Figure 1 would complete the analysis for that section. 

      In Figure 1, we decided to only show the conductances that we use to fit our model, namely the afferent and total synaptic conductances. As the values of the inhibitory conductances can be derived from panel E, we refrained from plotting them separately for the sake of simplicity. 

      (4) How did the authors calculate correlations between activity patterns as a function of time in Figure 2E, bottom row? Does the color represent correlation coefficient (which should not be time dependent) or is it a correlation function? This should be explained in the Methods section. 

      The color represents the Pearson correlation coefficient between activity patterns within a narrow time window (100 ms). We updated the Figure legend to clarify this: “Mean correlation between activity patterns evoked by a learned odor at different time points during odor presentation. Correlation coefficients were calculated between pairs of activity vectors composed of the mean firing rates of E neurons in 100 ms time bins. Activity vectors were taken from the same or different trials, except for the diagonal, where only patterns from different trials were considered.”

      (5) Figure 3 needs more clarification (both in the main text and the figure caption). It is not clear what the axes are exactly, and why the network responses for familiar and novel inputs are different. The gray shaded area in panel B needs more explanation as well.  

      We thank the reviewer for the comment. We have improved Figure 3A, the figure caption, as well as the text (see p.6). We hope that the figure is now clearer.

      (6) The "scaled I" network, known for representing input patterns in discrete attractors, should exhibit clear separation between network responses in the 2D PC space in the PCA plots. However, Figure 4D and Figure 6D do not reflect this, as all network responses are overlapped. Can the authors explain the overlap in Figure 4D? 

      In Figure 4D, activity of Scaled networks is distributed between three subregions in state space that are separated by the first 2 PCs. Two of them indeed correspond to attractor states representing the two learned odors while the third represents inputs that are not associated with these attractor states. To clarify this, please see also the density plot in Figure 4E. The few datapoints between these three subregions are likely outliers generated by the sequential change in inputs, as described in Supplementary Figure 8C.

      (7) The reason for writing about the ISN networks is not clear. Co-tuned E-I assemblies do not necessarily have to operate in this regime. Also, the results of the paper do not rely on any of the properties of ISNs, but they are more general. Authors should either show the paradoxical effect associated with ISN (i.e., if increasing input to I neurons decreases their responses) or show ISN properties using stability analysis (See computational research conducted at the Allen Institute, namely Millman et al. 2020, eLife ). Currently, the paper reads as if being in the ISN regime is a necessary requirement, which is not true. Also, the arguments do not connect with the rest of the paper and never show up again. Since we know it is not a requirement, there is no need to have those few sentences in the Results section. Also, the choice of alpha=5.0 is extreme, and therefore, it would help to judge the biological realism if the raster plots for Figs 2-6 are shown.

      We have toned down the part on ISN and reduced it to one sentence for readers who might be interested in knowing whether activity is inhibition-stabilized or not. We have also added the reference to the Tsodyks et al. 1997 paper from which we derive our stability analysis. The text now reads “Hence, pDp<sub>sim</sub> entered a balanced state during odor stimulation (Figure 1D, E) with recurrent input dominating over afferent input, as observed in pDp (Rupprecht and Friedrich, 2018). Shuffling spike times of inhibitory neurons resulted in runaway activity with a probability of 0.79 ± 0.20, demonstrating that activity was inhibition-stabilized (Sadeh and Clopath, 2020b, Tsodyks et al., 1997).”  

      We have now also added the raster plots as suggested by the reviewer (see Figure 2D, Supplementary Figure 1 G, Supplementary Figure 4). We thank the reviewer for this comment.

      (8) In the abstract, authors mention "fast pattern classification" and "continual learning," but in the paper, those issues have not been addressed. The study does not include any synaptic plasticity. 

      Concerning “continual learning” we agree that we do not simulate the learning process itself. However, Figure 6 show results of a simulation where two additional patterns were stored in a network that already contained assemblies representing other odors. We consider this a crude way of exploring the end result of a “continual learning” process. “Fast pattern classification” is mentioned because activity in balanced networks can follow fluctuating inputs with high temporal resolution, while networks with stable attractor states tend to be slow. This is likely to account for the occurrence of hysteresis effects in Scaled but not Tuned networks as shown in Supplementary

      Fig. 8.

      (9) In the Introduction, the first sentence in the second paragraph reads: "... when neurons receive strong excitatory and inhibitory synaptic input ...". The word strong should be changed to "weak".

      Also, see the pioneering work of Brunel 2000. 

      In classical balanced networks, strong excitatory inputs are counterbalanced by strong inhibitory inputs, leading to a fluctuation-driven regime. We have added Brunel 2000.

      (10) In the second paragraph of the introduction, the authors refer to studies about structural co-tuning (e.g., where "precise" synaptic balance is mentioned, and Vogels et al. 2011 should be cited there) and functional co-tuning (which is, in fact, different than tracking of excitation by inhibition, but the authors refer to that as co-tuning). It makes it easier to understand which studies talk about structural co-tuning and which ones are about functional co-tuning. The paper by Znamenski 2018, which showed both structural and functional tuning in experiments, is missing here. 

      We added the citation to the now published paper by Znamenskyi et al. (2024).  

      (11) The third paragraph in the Introduction misses some references that address network dynamics that are shaped by the inhibitory architecture in E/I assemblies in spiking networks, like Rost et al 2018 and Lagzi et al 2021. 

      These references have been added.

      (12) The last sentence of the fourth paragraph in the Introduction implies that functional co-tuning is due to structural co-tuning, which is not necessarily true. While structural co-tuning results in functional co-tuning, functional co-tuning does not require structural co-tuning because it could arise from shared correlated input or heterogeneity in synaptic connections from E to I cells.  

      We generally agree with the reviewer, but we are uncertain which sentence the reviewer refers to.

      We assume the reviewer refers to the last sentence of the second (rather than the fourth paragraph), which explicitly mentions the “…structural basis of E/I co-tuning…”. If so, we consider this sentence still correct because the “structural basis” refers not specifically to E/I assemblies, but also includes any other connectivity that may produce co-tuning, including the connectivity underlying the alternative possibilities mentioned by the reviewer (shared correlated input or heterogeneity of synaptic connections).

      (13) In order to ensure that the comparison between network dynamics is legit, authors should mention up front that for all networks, the average firing rates for the excitatory cells were kept at 1 Hz, and the background input was identical for all E and I cells across different networks.

      We slightly revised the text to make this more clear “We (…) uniformly scaled I-to-E connection weights by a factor of χ until E population firing rates in response to learned odors matched the corresponding firing rates in rand networks, i.e., 1 Hz”

      (14) In the last paragraph on page 5, my understanding was that an individual odor could target different cells within an assembly in different trials to generate trial to trail variability. If this is correct, this needs to be mentioned clearly. 

      This is not correct, an odor consists of 150 activated mitral cells with defined firing rates. As now mentioned in the Methods, “Spikes were then generated from a Poisson distribution, and this process was repeated to create trial-to-trial variability.”

      (15) The last paragraph on page 6 mentions that the four OB activity patterns were uncorrelated but if they were designed as in Figure 4A, dues to the existing overlap between the patterns, they cannot be uncorrelated. 

      This appears to be a misunderstanding. We mention in the text (and show in Figure 4B) that the four odors which “… were assigned to the corners of a square…” are uncorrelated.  The intermediate odors are of course not uncorrelated. We slightly modified the corresponding paragraph (now on page 7) to clarify this: “The subspace consisted of a set of OB activity patterns representing four uncorrelated pure odors and mixtures of these pure odors. Pure odors were assigned to the corners of a square and mixtures were generated by selecting active mitral cells from each of the pure odors with probabilities depending on the relative distances from the corners (Figure 4A, Methods).”

      (16) The notion of "learned" and "novel" odors may be misleading as there was no plasticity in the network to acquire an input representation. It would be beneficial for the authors to clarify that by "learned," they imply the presence of the corresponding E assembly for the odor in the network, with the input solely impacting that assembly. Conversely, for "novel" inputs, the input does not target a predefined assembly. In Figure 2 and Figure 4, it would be especially helpful to have the spiking raster plots of some sample E and I cells.  

      As suggested by the reviewer, we have modified the existing spiking raster plots in Figure 2, such that they include examples of responses to both learned and novel odors. We added spiking raster plots showing responses of I neurons to the same odors in Supplementary Figure 1F, as well as spiking raster plots of E neurons in Supplementary Figure 4A. To clarify the usage of “learned” and “novel”, we have added a sentence in the Results section: “We thus refer to an odor as “learned” when a network contains a corresponding assembly, and as “novel” when no such assembly is present.”.

      (17) In the last paragraph of page 8, can the authors explain where the asymmetry comes from? 

      As mentioned in the text, the asymmetry comes from the difference in the covariance structure of different classes. To clarify, we have rephrased the sentence defining the Mahalanobis distance: 

      “This measure quantifies the distance between the pattern and the class center, taking into account covariation of neuronal activity within the class. In bidirectional comparisons between patterns from different classes, the mean dM may be asymmetric if neural covariance differs between classes.”

      (18) The first paragraph of page 9: random networks are not expected to perform pattern classification, but just pattern representation. It would have been better if the authors compared Scaled I network with E/I co-tuned network. Regardless of the expected poorer performance of the E/I co-tuned networks, the result would have been interesting. 

      Please see our reply to the public review (reviewer 2).

      (19) Second paragraph on page 9, the authors should provide statistical significance test analysis for the statement "... was significantly higher ...". 

      We have performed a Wilcoxon signed-rank test, and reported the p-value in the revised manuscript (p < 0.01). 

      (20) The last sentence in the first paragraph on page 11 is not clear. What do the authors mean by "linearize input-output functions", and how does it support their claim? 

      We have now amended this sentence to clarify what we mean: “…linearize the relationship between the mean input and output firing rates of neuronal populations…”.

      (21) In the first sentence of the last paragraph on page 11, the authors mentioned “high variability”, but it is not clear compared with which of the other 3 networks they observed high variability.

      Structurally co-tuned E/I networks are expected to diminish network-level variability. 

      “High variability” refers to the variability of spike trains, which is now mentioned explicity in the text. We hope this more precise statement clarifies this point.

      (22) Methods section, page 14: "firing rates decreased with a time constant of 1, 2 or 4 s". How did they decrease? Was it an implementation algorithm? The time scale of input presentation is 2 s and it overlaps with the decay time constant (particularly with the one with 4 s decrease).  

      Firing rates decreased exponentially. We have added this information in the Methods section.

      Reviewer #3 (Recommendations For The Authors): 

      In the following, I suggest minor corrections to each section which I believe can improve the manuscript. 

      - There was no github link to the code in the manuscript. The code should be made available with a link to github in the final manuscript. 

      The code can be found here: https://github.com/clairemb90/pDp-model. The link has been added in the Methods section.

      Figure 1: 

      - Fig. 1A: call it pDp not Dp. Please check if this name is consistent in every figure and the text. 

      Thank you for catching this. Now corrected in Figure 1, Figure 2 and in the text.

      - The authors write: "Hence, pDpsim entered an inhibition-stabilized balanced state (Sadeh and Clopath, 2020b) during odor stimulation (Figure 1D, E)." and then later "Shuffling spike times of inhibitory neurons resulted in runaway activity with a probability of ~80%, demonstrating that activity was indeed inhibition-stabilized. These results were robust against parameter variations (Methods)." I would suggest moving the second sentence before the first sentence, because the fact that the network is in the ISN regime follows from the shuffled spike timing result. 

      Also, I'd suggest showing this as a supplementary figure. 

      We thank the reviewer for this comment. We have removed “inhibition-stabilized” in the first sentence as there is no strong evidence of this in Rupprecht and Friedrich, 2018. And removed “indeed” in the second sentence. We also provided more detailed statistics. The text now reads “Hence, pDpsim entered a balanced state during odor stimulation (Figure 1D, E) with recurrent input dominating over afferent input, as observed in pDp (Rupprecht and Friedrich, 2018). Shuffling spike times of inhibitory neurons resulted in runaway activity with a probability of 0.79 ± 0.20, demonstrating that activity was inhibition-stabilized (Sadeh and Clopath, 2020b).”

      Figure 2: 

      - "... Scaled I networks (Figure 2H." Missing ) 

      Corrected.

      - The authors write "Unlike in Scaled I networks, mean firing rates evoked by novel odors were indistinguishable from those evoked by learned odors and from mean firing rates in rand networks (Figure 2F)." 

      Why is this something you want to see? Isn't it that novel stimuli usually lead to high responses? Eg in the paper Schulz et al., 2021 (eLife) which is also cited by the authors it is shown that novel responses have high onset firing rates. I suggest clarifying this (same in the context of Fig. 3C). 

      In Dp and piriform cortex, firing rates evoked by learned odors are not substantially different from firing rates evoked by novel odors. While small differences between responses to learned versus novel odors cannot be excluded, substantial learning-related differences in firing rates, as observed in other brain areas, have not been described in Dp or piriform cortex. We added references in the last paragraph of p.5. Note that the paper by Schulz et al. (2021) models a different type of circuit.  

      - Fig. 2B: Indicate in figure caption that this is the case "Scaled I" 

      This is not exactly the case “Scaled I”, as the parameter 𝝌𝝌 (increased I to E strength) is set to 1.

      - Suppl Fig. 2I: Is E&F ever used in the manuscript? I couldn't find a reference. I suggest removing it if not needed. 

      Suppl. Fig 2I E&F is now Suppl Fig.1G&H. We now refer to it in the text: “Activity of networks with E assemblies could not be stabilized around 1 Hz by increasing connectivity from subsets of I neurons receiving dense feed-forward input from activated mitral cells (Supplementary Figure 1GH; Sadeh and Clopath, 2020).”

      Figure 3: 

      - As mentioned in my comment in the public review section, I find the arguments about pattern completion a little bit confusing. For me it's not clear why an increase of output correlations over input correlations is considered "pattern completion" (this is not to say that I don't find the nonlinear increase of output correlations interesting). For me, to test pattern completion with second-order statistics one would need to do a similar separation as in Suppl Fig. 3, ie measuring the pairwise correlation at cells in the assembly L that get direct input from L OB with cells in the assembly L that do not get direct input from OB. If the pairwise correlations of assembly cells which do not get direct input from OB increase in correlations, I would consider this as pattern completion (similar to the argument that increase in firing rate in cells which are not directly driven by OB are considered a sign of pattern completion). 

      Also, for me it now seems like that there are contradictory results, in Fig. 3 only Scaled I can lead to pattern completion while in the context of Suppl. Fig. 3 the authors write "We found that assemblies were recruited by partial inputs in all structured pDpsim networks (Scaled and Tuned) without a significant increase in the overall population activity (Supplementary Figure 3A)."   I suggest clarifying what the authors exactly mean by pattern completion, why the increase of output correlations above input correlations can be considered as pattern completion, and why the results differs when looking at firing rates versus correlations. 

      Please see our reply to the public review (reviewer 3).

      - I actually would suggest adding Suppl. Fig. 3 to the main figure. It shows a more intuitive form of pattern completion and in the text there is a lot of back and forth between Fig. 3 and Suppl. Fig. 3 

      We feel that the additional explanations and panels in Fig.3 should clarify this issue and therefore prefer to keep Supplementary Figure 3 as part of the Supplementary Figures for simplicity.  

      - In the whole section "We next explored effects of assemblies ... prevented strong recurrent amplification within E/I assemblies." the authors could provide a link to the respective panel in Fig. 2 after each statement. This would help the reader follow your arguments. 

      We thank the reviewer for pointing this out. The references to the appropriate panels have been added. 

      - Fig. 3A: I guess the x-axis has been shifted upwards? Should be at zero. 

      We have modified the x-axis to make it consistent with panels B and C.  

      - Fig. 3B: In the figure caption, the dotted line is described as the novel odor but it is actually the unit line. The dashed lines represent the reference to the novel odor. 

      Fixed.

      - Fig. 3C: The " is missing for Pseudo-Assembly N

      Fixed.

      - "...or a learned odor into another learned odor." Have here a ref to the Supplementary Figure 3B.

      Added.

      Figure 4:   

      - "This geometry was largely maintained in the output of rand networks, consistent with the notion that random networks tend to preserve similarity relationships between input patterns (Babadi and Sompolinsky, 2014; Marr, 1969; Schaffer et al., 2018; Wiechert et al., 2010)." I suggest adding here reference to Fig. 4D (left). 

      Added.

      - Please add a definition of E/I assemblies. How do the authors define E/I assemblies? I think they consider both, Tuned I and Tuned E+I as E/I assemblies? In Suppl. Fig. 2I E it looks like tuned feedforward input is defined as E/I assemblies. 

      We thank the reviewer for pointing this out. E/I assemblies are groups of E and I neurons with enhanced connectivity. In other words, in E/I assemblies, connectivity is enhanced not only between subsets of E neurons, but also between these E neurons and a subset of I neurons. This is now clarified in the text: “We first selected the 25 I neurons that received the largest number of connections from the 100 E neurons of an assembly. To generate E/I assemblies, the connectivity between these two sets of neurons was then enhanced by two procedures.”. We removed “E/I assemblies” in Suppl. Fig.2, where the term was not used correctly, and apologize for the confusion.

      - Suppl. Fig. 4: Could the authors please define what they mean by "Loadings" 

      The loadings indicate the contribution of each neuron to each principal component, see adjusted legend of Suppl. Fig. 4: “G. Loading plot: contribution of neurons to the first two PCs of a rand and a Tuned E+I network (Figure 4D).”

      - Fig. 4F: The authors might want to normalize the participation ratio by the number of neurons (see e.g. Dahmen et al., 2023 bioRxiv, "relative PR"), so the PR is bound between 0 and 1 and the dependence on N is removed. 

      We thank the reviewer for the suggestion, but we prefer to use the non-normalized PR as we find it more easily interpretable (e.g. number of attractor states in Scaled networks).

      - Fig. 4G&H: as mentioned in the public review, I'd add the case of Scaled I to be able to compare it to the Tuned E+I case. 

      As already mentioned in the public review, we thank the reviewer for this suggestion, which we have implemented.

      - Figure caption Fig. 4H "Similar results were obtained in the full-dimensional space." I suggest showing this as a supplemental panel. 

      Since this only adds little information, we have chosen not to include it as a supplemental panel to avoid overloading the paper with figures.

      Figure 5: 

      - As mentioned in the public review, I suggest that the authors add the Scaled I case to Fig. 5 (it's shown in all figures and also in Fig. 6 again). I guess for Scaled I the separation between L and M will be very good? 

      Please see our reply to the public review (reviewer 3).

      - Fig. 5A&B: I am a bit confused about which neurons are drawn to calculate the Mahalanobis distance. In Fig. 5A, the schematic indicates that the vector B from which the neurons are drawn is distinct from the distribution Q. For the example of odor L, the distribution Q consists of pure odor L with odors that have little mixtures with the other odors. But the vector v for odor L seems to be drawn only from odors that have slightly higher mixtures (as shown in the schematic in Fig. 5A). Is there a reason to choose the vector v from different odors than the distribution Q? 

      The distribution Q and the vector v consist of activity patterns across the same neurons in response to different odors. The reason to choose a different odor for v was to avoid having this test datapoint being included in the distribution Q. We also wanted Q to be the same for all test datapoints. 

      What does "drawn from whole population" mean? Does this mean that the vectors are drawn from any neuron in pDp? If yes, then I don't understand how the authors can distinguish between different odors (L,M,O,N) on the y-axis. Or does "whole population" mean that the vector is drawn across all assemblies as shown in the schematic in Fig. 5A and the case "neurons drawn from (pseudo-) assembly" means that the authors choose only one specific assembly? In any case, the description here is a bit confusing, I think it would help the reader to clarify those terms better.  

      Yes, “drawn from whole population” means that we randomly draw 80 neurons from the 4000 E neurons in pDp. The y-axis means that we use the activity patterns of these neurons evoked by one of the 4 odors (L, M, N, O) as reference. We have modified the Figure legend to clarify this: “d<sub>M</sub> was computed based on the activity patterns of 80 E neurons drawn from the four (pseudo-) assemblies (top) or from the whole population of 4000 E neurons (bottom). Average of 50 draws.”

      - Suppl Fig. 5A: In the schematic the distance is called d_E(\bar{Q},\bar{V}) while the colorbar has d_E(\bar{Q},\bar{Q}) with the Qs in different color. The green Q should be a V. 

      We thank the reviewer for spotting this mistake, it is now fixed.

      - Fig. 5: Could the authors comment on the fact that a random network seems to be very good in classifying patterns on it's own. Maybe in the Discussion? 

      The task shown in Figure 5 is a relatively easy one, a forced-choice between four classes which are uncorrelated. In Supplementary Figure 9, we now show classification for correlated classes, which is already much harder.

      Figure 6: 

      - Is the correlation induced by creating mixtures like in the other Figures? Please clarify how the correlations were induced. 

      We clarified this point in the Methods section: “The pixel at each vertex corresponded to one pure odor with 150 activated and 75 inhibited mitral cells (…) and the remaining pixels corresponded to mixtures. In the case of correlated pure odors (Figure 6), adjacent pure odors shared half of their activated and half of their inhibited cells.”. An explicit reference to the Methods section has also been added to the figure legend.

      - Fig. 6C (right): why don't we see the clear separation in PC space as shown in Fig. 4? Is this related to the existence of correlations? Please clarify. 

      Yes. The assemblies corresponding to the correlated odors X and Y overlap significantly, and therefore responses to these odors cannot be well separated, especially for Scaled networks. We added the overlap quantification in the Results section to make this clear. “These two additional assemblies had on average 16% of neurons in common due to the similarity of the odors.”

      - "Furthermore, in this regime of higher pattern similarity, dM was again increased upon learning, particularly between learned odors and reference classes representing other odors (not shown)." Please show this (maybe as a supplemental figure). 

      We now show the data in Supplementary Figure 9.

      Discussion: 

      - The authors write: "We found that transformations became more discrete map-like when amplification within assemblies was increased and precision of synaptic balance was reduced. Likewise, decreasing amplification in assemblies of Scaled networks changed transformations towards the intermediate behavior, albeit with broader firing rate distributions than in Tuned networks (not shown)." 

      Where do I see the first point? I guess when I compare in Fig. 4D the case of Scaled I vs Tuned E+I, but the sentence above sounds like the authors showed this in a more step-wise way eg by changing the strength of \alpha or \beta (as defined in Fig. 1). 

      Also I think if the authors want to make the point that decreasing amplification in assemblies changes transformation with a different rate distribution in scaled vs tuned networks, the authors should show it (eg adding a supplemental figure). 

      The first point is indeed supported by data from different figures. Please note that the revised manuscript now contains further simulations that reinforce this statement, particularly those shown in Supplementary Figure 6, and that this point is now discussed more extensively in the Discussion. We hope that these revisions clarify this general point.

      The data showing effects of decreasing amplification in assemblies is now shown in Supplementary Figure 6 (Scaled[adjust])

      - I suggest adding the citation Znamenskiy et al., 2024 (Neuron; https://doi.org/10.1016/j.neuron.2023.12.013), which shows that excitatory and inhibitory (PV) neurons with functional similarities are indeed strongly connected in mouse V1, suggesting the existence of E/I assembly structure also in mammals.

      Done.

    1. Reviewer #1 (Public review):

      Summary:

      The authors aimed to investigate the cellular mechanisms underlying place field formation (PFF) in hippocampal CA1 pyramidal cells by performing in vivo two-photon calcium imaging in head-restrained mice navigating a virtual environment. Specifically, they sought to determine whether BTSP-like (behavioral time scale synaptic plasticity) events, characterized by large calcium transients, are the primary mechanism driving PFFs or if other mechanisms also play a significant role. Through their extensive imaging dataset, the authors found that while BTSP-like events are prevalent, a substantial fraction of new place fields are formed via non-BTSP-like mechanisms. They further observed that large calcium transients, often associated with BTSP-like events, are not sufficient to induce new place fields, indicating the presence of additional regulatory factors (possibly local dendritic spikes).

      Strengths

      The study makes use of a robust and extensive dataset collected from 163 imaging sessions across 45 mice, providing a comprehensive examination of CA1 place-cell activity during navigation in both familiar and novel virtual environments. The use of two-photon calcium imaging allows the authors to observe the detailed dynamics of neuronal activity and calcium transients, offering insights into the differences between BTSP-like and non-BTSP-like PFF events. The study's ability to distinguish between these two mechanisms and analyze their prevalence under different conditions is a key strength, as it provides a nuanced understanding of how place fields are formed and maintained. The paper supports the idea that BTSP is not the only driving force behind PFF, and other mechanisms are likely sufficient to drive PFF, and BTSP events may also be insufficient to drive PFF in some cases. The longer-than-usual virtual track used in the experiment allowed place cells to express multiple place fields, adding a valuable dimension to the dataset that is typically lacking in similar studies. Additionally, the authors took a conservative approach in classifying PFF events, ensuring that their findings were not confounded by noise or ambiguous activity.

      Weaknesses

      Despite the impressive dataset, there are several methodological and interpretational concerns that limit the impact of the findings. Firstly, the virtual environment appears to be poorly enriched, relying mainly on wall patterns for visual cues, which raises questions about the generalizability of the results to more enriched environments. Prior studies have shown that environmental enrichment can significantly influence spatial coding, and it would be important to determine how a more immersive VR environment might alter the observed PFF dynamics. Secondly, the study relies on deconvolution methods in some cases to infer spiking activity from calcium signals without in vivo ground truth validation. This introduces potential inaccuracies, as deconvolution is an estimate rather than a direct measure of spiking, and any conclusions drawn from these inferred signals should be interpreted with caution. Thirdly, the figures would benefit from clearer statistical annotations and visual enhancements. For example, several plots lack indicators of statistical significance, making it difficult for readers to assess the robustness of the findings. Furthermore, the use of bar plots without displaying underlying data distributions obscures variability, which could be better visualized with violin plots or individual data points. The manuscript would also benefit from a more explicit breakdown of the proportion of place fields categorized as BTSP-like versus non-BTSP-like, along with clearer references to figures throughout the results section. Lastly, the authors' interpretation of their data, particularly regarding the sufficiency of large calcium transients for PFF induction, needs to be more cautious. Without direct confirmation that these transients correspond to actual BTSP events (including associated complex spikes and calcium plateau potentials), concluding that BTSP is not necessary or sufficient for PFF formation is speculative.

    2. Reviewer #2 (Public review):

      Summary:

      The authors of this manuscript aim to investigate the formation of place fields (PFs) in hippocampal CA1 pyramidal cells. They focus on the role of behavioral time scale synaptic plasticity (BTSP), a mechanism proposed to be crucial for the formation of new PFs. Using in vivo two-photon calcium imaging in head-restrained mice navigating virtual environments, employing a classification method based on calcium activity to categorize the formation of place cells' place fields into BTSP, non-BTSP-like, and investigated their properties.

      Strengths:

      A new method to use calcium imaging to separate BTSP and non-BTSP place field formation. This work offers new methods and factual evidence for other researchers in the field.

      The method enabled the authors to reveal that while many PFs are formed by BTSP-like events, a significant number of PFs emerge with calcium dynamics that do not match BTSP characteristics, suggesting a diversity of mechanisms underlying PF formation. The characteristics of place fields under the first two categories are comprehensively described, including aspects such as formation timing, quantity, and width.

      Weaknesses:

      There are some issues about data and statistics that need to be addressed before these research findings can be considered as rigorous conclusions.

      While the authors mentioned 3 features of PF generated by BTSP during calcium imaging in the Introduction, the classification method used features 1 and 2. The confirmation by feature 3 in its current form is important but not strong enough.

      Some key data is missing such as the excluded PFs, the BTSP/non-BTSP of each animal, etc

      Impact:

      This work is likely to provide a new method to classify BTSP and non-BTSP place field formation using calsium image to the field.

    3. Reviewer #3 (Public review):

      Summary:

      In this manuscript, Sumegi et al. use calcium imaging in head-fixed mice to test whether new place fields tend to emerge due to events that resemble behavioral time scale plasticity (BTSP) or other mechanisms. An impressive dataset was amassed (163 sessions from 45 mice with 500-1000 neurons per sample) to study the spontaneous emergence of new place fields in area CA1 that had the signature of BTSP. The authors observed that place fields could emerge due to BTSP and non-BTSP-like mechanisms. Interestingly, when non-BTSP mechanisms seemed to generate a place field, this tended to occur on a trial with a spontaneous reset in neural coding (a remapping event). Novelty seemed to upregulate non-BTSP events relative to BTSP events. Finally, large calcium transients (presumed plateau potentials) were not sufficient to generate a place field.

      Strengths:

      I found this manuscript to be exceptionally well-written, well-powered, and timely given the outstanding debate and confusion surrounding whether all place fields must arise from BTSP event. Working at the same institute, Albert Lee (e.g. Epszstein et al., 2011 - which should be cited) and Jeff Magee (e.g. Bittner et al., 2017) showed contradictory results for how place fields arise. These accounts have not fully been put toe-to-toe and reconciled in the literature. This manuscript addresses this gap and shows that both accounts are correct - place fields can emerge due to a pre-existing map and due to BTSP.

      Weaknesses:

      I find only three significant areas for improvement in the present study:

      First, can it be concluded that non-BTSP events occur exclusively due to a global remapping event, as stated in the manuscript "these PFF surges included a high fraction of both non-BTSP- and BTSP-like PFF events, and were associated with global remapping of the CA1 representation"? Global remapping has a precise definition that involves quantifying the stability of all place fields recorded. Without a color scale bar in Figure 3D (which should be added), we cannot know whether the overall representations were independent before and after the spontaneous reset. It would be good to know if some neurons are able to maintain place coding (more often than expected by chance), suggestive of a partial-remapping phenomenon.

      Second, BTSP has a flip side that involves the weakening of existing place fields when a novel field emerges. Was this observed in the present study? Presumably place fields can disappear due to this bidirectional BTSP or due to global remapping. For a full comparison of the two phenomena, the disappearance of place fields must also be assessed.

      Finally, it would be good to know if place fields differ according to how they are born. For example, are there differences in reliability, width, peak rate, out-of-field firing, etc for those that arise due to BTSP vs non-BTSP.

    1. Author response:

      The following is the authors’ response to the current reviews.

      The authors agree with the reviewers that future studies are needed to dissect the mechanisms of eIF3 binding to 3'UTRs and their impact on translation, and the impact of this binding on cellular fate.


      The following is the authors’ response to the original reviews.

      eLife Assessment

      This valuable study reveals extensive binding of eukaryotic translation initiation factor 3 (eIF3) to the 3' untranslated regions (UTRs) of efficiently translated mRNAs in human pluripotent stem cell-derived neuronal progenitor cells. The authors provide solid evidence to support their conclusions, although this study may be enhanced by addressing potential biases of techniques employed to study eIF3:mRNA binding and providing additional mechanistic detail. This work will be of significant interest to researchers exploring post-transcriptional regulation of gene expression, including cellular, molecular, and developmental biologists, as well as biochemists.

      We thank the reviewers for their positive views of the results we present, along with the constructive feedback regarding the strengths and weaknesses of our manuscript, with which we generally agree. We acknowledge our results will require a deeper exploration of the molecular mechanisms behind eIF3 interactions with 3'-UTR termini and experiments to identify the molecular partners involved. Additionally, given that NPC differentiation toward mature neurons is a process that takes around 3 weeks, we recognize the importance of examining eIF3-mRNA interactions in NPCs that have undergone differentiation over longer periods than the 2-hr time point selected in this study. Finally, considering the molecular complexity of the 13subunit human eIF3, we agree that a direct comparison between Quick-irCLIP and PAR-CLIP will be highly beneficial and will determine whether different UV crosslinking wavelengths report on different eIF3 molecular interactions. Additional comments are given below to the identified weaknesses.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors perform irCLIP of neuronal progenitor cells to profile eIF3-RNA interactions upon short-term neuronal differentiation. The data shows that eIF3 mostly interacts with 3'-UTRs - specifically, the poly-A signal. There appears to be a general correlation between eIF3 binding to 3'-UTRs and ribosome occupancy, which might suggest that eIF3 binding promotes protein

      Strengths:

      The study provides a wealth of new data on eIF3-mRNA interactions and points to the potential new concept that eIF3-mRNA interactions are polyadenylation-dependent and correlate with ribosome occupancy.

      Weaknesses:

      (1) A main limitation is the correlative nature of the study. Whereas the evidence that eIF3 interacts with 3-UTRs is solid, the biological role of the interactions remains entirely unknown. Similarly, the claim that eIF3 interactions with 3'-UTR termini require polyadenylation but are independent of poly(A) binding proteins lacks support as it solely relies on the absence of observable eIF3 binding to poly-A (-) histone mRNAs and a seeming failure to detect PABP binding to eIF3 by co-immunoprecipitation and Western blotting. In contrast, LC-MS data in Supplementary File 1 show ready co-purification of eIF3 with PABP.

      We agree the molecular mechanisms underlying the crosslinking between eIF3 and the end of mRNA 3’-UTRs remains to be determined. We also agree that the lack of interaction seen between eIF3 and PABP in Westerns, even from HEK293T cells, is a puzzle. The low sequence coverage in the LC-MS data gave us pause about making a strong statement that these represent direct eIF3 interactions, given the similar background levels of some ribosomal proteins.

      (2) Another question concerns the relevance of the cellular model studied. irCLIP is performed on neuronal progenitor cells subjected to neuronal induction for 2 hours. This short-term induction leads to a very modest - perhaps 10% - and very transient 1-hour-long increase in translation, although this is not carefully quantified. The cellular phenotype also does not appear to change and calling the cells treated with differentiation media for 2 hours "differentiated NPCs" seems a bit misleading. Perhaps unsurprisingly, the minor "burst" of translation coincides with minor effects on eIF3-mRNA interactions most of which seem to be driven by mRNA levels. Based on the ~15-fold increase in ID2 mRNA coinciding with a ~5-fold increase in ribosome occupancy (RPF), ID2 TE actually goes down upon neuronal induction.

      We agree that it will be interesting to look at eIF3-mRNA interactions at longer time points after induction of NPC differentiation. However, the pattern of eIF3 crosslinking to the end of 3’-UTRs occurs in both time points reported here, which is likely to be the more general finding in what we present.

      (3) The overlap in eIF3-mRNA interactions identified here and in the authors' previous reports is minimal. Some of the discrepancies may be related to the not well-justified approach for filtering data prior to assessing overlap. Still, the fundamentally different binding patterns - eIF3 mostly interacting with 5'-UTRs in the authors' previous report and other studies versus the strong preference for 3'-UTRs shown here - are striking. In the Discussion, it is speculated that the different methods used - PAR-CLIP versus irCLIP - lead to these fundamental differences. Unfortunately, this is not supported by any data, even though it would be very important for the translation field to learn whether different CLIP methodologies assess very different aspects of eIF3-mRNA interactions.

      We agree the more interesting aspect of what we observe is the difference in location of eIF3 crosslinking, i.e. the end of 3’-UTRs rather than 5’-UTRs or the pan-mRNA pattern we observed in T cells. The reviewer is right that it will be important in the future to compare PAR-CLIP and Quick-irCLIP side-by-side to begin to unravel the differences we observe with the two approaches.

      Reviewer #2 (Public review):

      Summary:

      The paper documents the role of eIF3 in translational control during neural progenitor cell (NPC) differentiation. eIF3 predominantly binds to the 3' UTR termini of mRNAs during NPC differentiation, adjacent to the poly(A) tails, and is associated with efficiently translated mRNAs, indicating a role for eIF3 in promoting translation.

      Strengths:

      The manuscript is strong in addressing molecular mechanisms by using a combination of nextgeneration sequencing and crosslinking techniques, thus providing a comprehensive dataset that supports the authors' claims. The manuscript is methodologically sound, with clear experimental designs.

      Weaknesses:

      (1) The study could benefit from further exploration into the molecular mechanisms by which eIF3 interacts with 3' UTR termini. While the correlation between eIF3 binding and high translation levels is established, the functionality of these interactions needs validation. The authors should consider including experiments that test whether eIF3 binding sites are necessary for increased translation efficiency using reporter constructs.

      We agree with the reviewer that the molecular mechanism by which eIF3 interacts with the 3’UTR termini remains unclear, along with its biological significance, i.e. how it contributes to translation levels. We think it could be useful to try reporters in, perhaps, HEK293T cells in the future to probe the mechanism in more detail.

      (2) The authors mention that the eIF3 3' UTR termini crosslinking pattern observed in their study was not reported in previous PAR-CLIP studies performed in HEK293T cells (Lee et al., 2015) and Jurkat cells (De Silva et al., 2021). They attribute this difference to the different UV wavelengths used in Quick-irCLIP (254 nm) and PAR-CLIP (365 nm with 4-thiouridine). While the explanation is plausible, it remains a caveat that different UV crosslinking methods may capture different eIF3 modules or binding sites, depending on the chemical propensities of the amino acid-nucleotide crosslinks at each wavelength. Without addressing this caveat in more detail, the authors cannot generalize their findings, and thus, the title of the paper, which suggests a broad role for eIF3, may be misleading. Previous studies have pointed to an enrichment of eIF3 binding at the 5' UTRs, and the divergence in results between studies needs to be more explicitly acknowledged.

      We agree with the reviewer that the two methods of crosslinking will require a more detailed head-to-head comparison in the future. However, we do think the title is justified by the fact that we see crosslinking to the termini of 3’-UTRs across thousands of transcripts in each condition. Furthermore, the 3’-UTR crosslinking is enriched on mRNAs with higher ribosome protected fragment counts (RPF) in differentiated cells, Figure 3F.

      (3) While the manuscript concludes that eIF3's interaction with 3' UTR termini is independent of poly(A)-binding proteins, transient or indirect interactions should be tested using assays such as PLA (Proximity Ligation Assay), which could provide more insights.

      This is a good idea, but would require a substantial effort better suited to a future publication. We think our observations are interesting enough to the field to stimulate future experimentation that we may or may not be most capable of doing in our lab.

      Reviewer #3 (Public review):

      Summary:

      In this manuscript by Mestre-Fos and colleagues, authors have analyzed the involvement of eIF3 binding to mRNA during differentiation of neural progenitor cells (NPC). The authors bring a lot of interesting observations leading to a novel function for eIF3 at the 3'UTR.

      During the translational burst that occurs during NPC differentiation, analysis of eIF3-associated mRNA by Quick-irCLIP reveals the unexpected binding of this initiation factor at the 3'UTR of most mRNA. Further analysis of alternative polyadenylation by APAseq highlights the close proximity of the eIF3-crosslinking position and the poly(A) tail. Furthermore, this interaction is not detected in Poly(A)-less transcripts. Using Riboseq, the authors then attempted to correlate eIF3 binding with the translation efficacy of mRNA, which would suggest a common mechanism of translational control in these cells. These observations indicate that eIF3-binding at the 3'UTR of mRNA, near the poly(A) tail, may participate to the closed-loop model of mRNA translation, bridging 5' and 3', and allowing ribosomes recycling. However, authors failed to detect interactions of eIF3, with either PABP or Paip1 or 40S subunit proteins, which is quite unexpected.

      Strength:

      The well-written manuscript presents an attractive concept regarding the mechanism of eIF3 function at the 3'UTR. Most mRNA in NPC seems to have eIF3 binding at the 3'UTR and only a few at the 5'end where it's commonly thought to bind. In a previous study from the Cate lab, eIF3 was reported to bind to a small region of the 3'UTR of the TCRA and TCRB mRNA, which was responsible for their specific translational stimulation, during T cell activation. Surprisingly in this study, the eIF3 association with mRNA occurs near polyadenylation signals in NPC, independently of cell differentiation status. This compelling evidence suggests a general mechanism of translation control by eIF3 in NPC. This observation brings back the old concept of mRNA circularization with new arguments, independent of PABP and eIF4G interaction. Finally, the discussion adequately describes the potential technical limitations of the present study compared to previous ones by the same group, due to the use of Quick-irCLIP as opposed to the PAR-CLIP/thiouridine.  

      Weaknesses:

      (1) These data were obtained from an unusual cell type, limiting the generalizability of the model.

      We agree that unraveling the mechanism employed by eIF3 at the mRNA 3’-UTR termini might be better studied in a stable cell line rather than in primary cells.

      (2) This study lacks a clear explanation for the increased translation associated with NPC differentiation, as eIF3 binding is observed in both differentiated and undifferentiated NPC. For example, I find a kind of inconsistency between changes in Riboseq density (Figure 3B) and changes in protein synthesis (Figure 1D). Thus, the title overstates a modest correlation between eIF3 binding and important changes in protein synthesis.

      We thank the reviewer for this question. Riboseq data and RNASeq data are not on absolute scales when comparing across cell conditions. They are normalized internally, so increases in for example RPF in Figure 3B are relative to the bulk RPF in a given condition. By contrast, the changes in protein synthesis measured in Figure 1D is closer to an absolute measure of protein synthesis. 

      (3) This is illustrated by the candidate selection that supports this demonstration. Looking at Figure 3B, ID2, and SNAT2 mRNA are not part of the High TE transcripts (in red). In contrast, the increase in mRNA abundance could explain a proportionally increased association with eIF3 as well as with ribosomes. The example of increased protein abundance of these best candidates is overall weak and uncertain.

      We agree that using TE as the criterion for defining increased eIF3 association would not be correct. By “highly translated” we only mean to convey the extent of protein synthesis, i.e. increases in ribosome protected fragments (RPF), rather than the translational efficiency.

      (4) Despite several attempts (chemical and UV cross-linking) to identify eIF3 partners in NPC such as PABP, PAIP1, or proteins from the 40S, the authors could not provide any evidence for such a mechanism consistent with the closed-loop model. Overall, this rather descriptive study lacks mechanistic insight (eIF3 binding partners).

      We agree that it will be important to identify the molecular mechanism used by eIF3 to engage the termini of mRNA 3’-UTRs. Nevertheless, the identification of eIF3 crosslinking to that location in mRNAs is new, and we think will stimulate new experiments in the field.

      (5) Finally, the authors suspect a potential impact of technical improvement provided by QuickirCLIP, that could have been addressed rather than discussed.

      We agree a side-by-side comparison of eIF3 crosslinks captured by PAR-CLIP versus QuickirCLIP will be an important experiment to do. However, NPCs or other primary cells may not be the best system for the comparison. We think using an established cell line might be more informative, to control for effects such as 4-thiouridine toxicity.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) The Western blot signals for SLC38A2 and ID2 are close to the membrane background and little convincing. Size markers are missing.

      We agree these antibodies are not great. They are the best we could find, unfortunately. We have included originals of all western blots and gels as supplementary information. It’s important to note that the Riboseq data for ID2 and SLC38A2 are consistent with the western blots. See Figure 3C and Figure 3–figure supplement 3B.

      (2) Figure 1 - Figure Supplement 1 appears to present data from a single experiment. This is far less than ideal considering the minor differences measured.

      Thanks for the comment. This is a representative experiment showing the early time course. We have added a second experiment with two different treatments that show the same pattern in the puromycin assay, in Figure 1–figure supplement 1.

      (3) Figure 3F: One wonders what this would look like if TE was plotted instead of RPF. Figure 3 - Figure Supplement 4 seems to show something along those lines. However, the data are not mentioned in the main results section are quite unclear. Why are data separated into TE high and low? Doesn't TE high in differentiated cells equal TE low in undifferentiated cells?

      This is an interesting question. Note that in Figure 3B, n=6300 genes show no change in TE upon differentiation, compared to a total of n=2127 that show a change in TE, with most of those changes not very large. We have now replotted Figure 3F comparing irCLIP read counts in 3’-UTRs to RPF read counts, which shows a significant positive correlation, regardless of whether we look at undifferentiated or differentiated NPCs (See Figure 3F and a new Figure 3– figure supplement 4A). We also compare irCLIP reads in 3’-UTRs to TE values, which show no correlation (See Figure 3G and Figure 3–figure supplement 4B).

      Figure 3-figure supplement 4 was actually a response to a previous round of review (at PLOS Biology) to a rather technical question from a reviewer. We think this figure and associated text should be removed. Instead, we now include supplementary tables with the processed RPF and TE values, for reference (Supplemental files 4-6). We omitted these in the original submission when they should have been included. We also abandoned comparing undifferentiated and differentiated NPCs, and instead look directly at irCLIP reads vs. RPFs or TE, regardless of NPC state, as noted above (Figure 3F, G, and Figure 3–figure supplement 4).

      (4) Figure 3C: The data should be plotted on the same y-axis scale. This would make a visual assessment of the differences in mRNA and RFP levels more intuitive.

      Thanks for this suggestion. We have rescaled the plots as requested.

      Reviewer #2 (Recommendations for the authors):

      (1) The quality of the Western blots in several figures is quite poor. Notably, Figure 1C seems to be a composite gel, as each blot appears to come from a different gel. Additionally, in Supplementary Figure 1A, there is only a single data point, yet the authors indicate that this image is representative of multiple assays. The lack of error bars in this figure raises a question vis-a-vis the reproducibility of the experiments.

      Thanks for the comments. We now include all the original gels as supplementary information. As noted above, the antibodies for ID2 and SLC38A2 are not great, we agree. And as we noted above, the Riboseq data for ID2 and SLC38A2 are consistent with the western blots.

      (2) For the top 500 targets of undifferentiated and differentiated NPCs in the Quick-irCLIP assay, the manuscript does not clarify how many targets are common and how many are unique to each condition. This information is important for understanding the extent of overlap and differentiation-specific interactions of eIF3 with mRNAs. Providing this data would strengthen the interpretation of the results.

      There are 449 of the top 500 hits in common between undifferentiated and differentiated NPCs. We have now added this information to the text, to add clarity. 

      (3) The manuscript does not provide detailed percentages or numbers regarding the overlap between iCLIP and APA-Seq peaks. Clarifying this overlap, particularly in terms of how many of the APA sites are also targets of eIF3, would bolster the understanding of how these two datasets converge to support the authors' conclusions.

      This is a difficult calculation to make, due to the fact that APA-Seq reads are generally much longer than the Quick-irCLIP reads. This is why we focused instead on quantifying the percent of Quick-irCLIP peaks (which are more narrow) overlap with predicted polyadenylation sequences, in Figure 2-figure supplement 1.

      Reviewer #3 (Recommendations for the authors):

      (1) Perform Quick-irCLIP in HEK293 cells to infer technical limitations and/or to generalize the model. The authors will then compare again eIF3 binding site in Jurkat, HEK293, and NPC.

      This is an experiment we plan to do for a future publication, given that we would want to repeat both Quick-irCLIP and PAR-CLIP at the same time.

      (2) Select mRNA candidates with high or low TE changes and analyze eIF3 binding and RPF density and protein abundance along NPC differentiation to support the role of eIF3 binding in stimulating translation.

      We agree looking at time courses in more depth would be interesting. However, this would require substantial experimentation, which is better suited to a future study. Furthermore, now that we have moved away from comparing undifferentiated NPCs and differentiated NPCs when examining TE and RPF values (Figure 3 and Figure 3–figure supplement 4), we think the results now support a more general mechanism of translation reflected in the irCLIP 3’-UTR vs. RPF correlation, independent of NPC state.

      (3) Analyze the interaction of eIF3 with eIF4G and other known partners. This will really provide an improvement to the manuscript. The lack of interaction between eIF3 and the 40S is quite surprising.

      We agree more work needs to be done on the mechanistic side. These are experiments we think would be best to carry out in a stable cell line in the future, rather than primary cells.

      (4) Perform Oligo-dT pulldown (or cap column if possible) and analyze the relative association of PABP, eIF3, and eIF4F on mRNA in NPC versus HEK293. This will clarify whether this mechanism of mRNA translation is specific to NPC or not.

      Thanks for this suggestion. We are uncertain how it would be possible to deconvolute all the possible ways to interpret results from such an experiment. We agree thinking about ways to study the mechanism will keep us occupied for a while.

      (5) Citations in the text indicate the first author, whereas the references are numbered! 

      Our apologies for this oversight. This was a carryover from previous formatting, and has been fixed.

    1. ELEMENTARY TYPING <br /> via Periscope Film #15494

      Elementary Typing. 16 mm, Instructional film. Periscope Film, #15494, 1971. https://www.youtube.com/watch?v=7cdyoPu_ASw.<br /> running time: 00:12:06

      Produced by Moreland-Latchford Productions, Ltd this informational film from 1971 titled “Elementary Typing” teaches the basics of becoming a good typist. The film features a manual Typemaster, a trade name used by Underwood as far back as the 1930s. This version of the machine featured both red and black ribbons. An electric version is seen at 9:00.

      The film is broken down into different sections that focus on different elements of typing from the rhythmic beat of typing to optimal hand positioning as well as how to set up a typewriter. “Elementary Typing” is part of a larger film series related to the art of typing with other titles including “Posture and the Keyboard,” “First Step Typing,” “Machine Techniques,” and “Remedial Typing.” Advisors for the creation of the film include James Treliving Commercial Coordinator North York Board of Education, J.T. Albani East York Board of Education, Sheila Wright Etobicoke Board of Education, and Ronald Thelander Director of Audio-Visual Aids Metropolitan Separate School Board, Toronto. In addition, the film was directed by Rod Maxwell and written by Robert Browning and featured Alex Veltman as the cameraman, Carl Connell as editor, Joe Hayward as production head, and James McCormick as executive producer.

      Pink illustrated typewriter on navy blue background (0:09). Outline of topics covered (0:17). A: early rhythm and reading – metronome and hands typing in the background (0:22). Aerial view of hands typing on an 197X Underwood Typemaster model typewriter (0:42). Camera pans words typed on a page (1:27). Close-up of letters being printed onto a page (1:36). Woman sitting at desk typing quickly (2:24). B: Paper Insertion – close-up of typewriter and hand setting the paper guide at the correct place on the paper table (2:35). Explanation of correct form and technique for holding and inserting paper (2:58-4:23). Explanation of paper removal (4:26). Badminton player returning various shots (4:41). Close-up of hands on the typewriter emphasizing the art of positioning (5:07). C: The Shift – explanation of the shift key (5:15-6:30). D: The Carriage Return – close-up of the device (6:32). The woman types and uses the carriage return (6:46-9:00). The Electric Typewriter – comparison between 197X Underwood Type Master manual model and Underwood 765 Type Master electric model typewriter (9:04). Difference between typing strategies (10:03). Benefits of using an electric machine (10:20). Closing credits (11:35).

    1. Oppression is often the CONSEQUENCE, but seldom or never the MEANS of riches;

      Did Paine REALLY think that statement through? By this time English settlement was certainly not confined to the North American colonies, and from what little I understand off the top of my head form seeing colonial activity in other parts of the new and old world and of course not excluding the violence within the north American continent against native peoples, it seems like a little bit of a fib on his end.

    1. eLife Assessment

      Morphological characteristics and phenotypes of mutations in key developmental genes suggest that head, trunk, and tail development are regulated by discernible modules. Gdf11 signalling plays a crucial role in orchestrating the transition from trunk to tail tissues in vertebrate embryos. This important study presents convincing evidence that Tgfbr1 acts upstream of Isl1 (a pivotal effector of Gdf11 signalling) and regulates blood vessels, the lateral plate mesoderm, and the endoderm associated with the trunk-to-tail transition. Together with the previous studies, this work identifies a key signal that acts as the pivot of the trunk-to-tail transition.

    1. We think of ourselves as this little bubble of obsessions and memories going on in our head that’s detached from everything else. That’s the wound.

      for - summary - polycrisis - requires a shift in stories - from little self - to big self - from - Emergence Magazine - interview - An Ethics of Wild Mind - David Hinton

      summary - polycrisis - requires a shift in stories - from little self - to big self - from - Emergence Magazine - interview - An Ethics of Wild Mind - David Hinton - We think of ourselves as this little bubble of obsessions and memories going on in our head detached from everything else - THAT'S THE WOUND! - That sounds and IS FELT as bleak, isn't it? - The scientific story of the cosmos is that there are countless solar systems in our universe, countless suns and planets over vast time scales - Our planet evolved life billions of years ago - Some of those life forms became multicellular animals, like us - Some of them developed eyes, nose, ears, skin and a brain and central nervous system - When we look out into the world, it is the cosmos distilled in us looking out at itself - Hence, we are intertwingled and woven into the fabric of everything - the cosmos in human form experiencing the cosmos itself - When we think about our extinction, it is also the cosmos thinking about extinction - When we feel ANYTHING, that's the cosmos feeling it - And WHEN WE DIE that is the cosmos in this human form dying to itself

    1. Reviewer #2 (Public review):

      Summary:

      The manuscript by Murphy et al. titled "Change point estimation by the mouse medial prefrontal cortex during probabilistic reward learning" investigated the role of the mPFC in the exploitation of task structure. Previous work had shown that monkeys and humans exploit predictable task structures (e.g., switching rapidly when heavily trained a reversal learning task), but whether this was also the case for mice was not known. To test this, Murphy et al. trained head-fixed mice on a two-armed bandit task in which the contingencies reversed when mice met a performance criterion (10 trials choosing the better option) plus an additional random number of trials (referred to as Lrandom). They found that as the length of Lrandom increased, mice began to exhibit pre-emptive switching in their choices as if they were expecting and/or anticipating the reversal to occur. They report that unilateral lesions of the mPFC (ACC + MO) led to earlier pre-emptive switching (although I found this part of the manuscript the most challenging to understand) and faster post-reversal switching that they argue reflects an impairment in the proper estimation of the reversal. They also report that this requires inter-hemispheric coordination because bilateral lesions did not further impair this estimation. Optogenetic inhibition just prior to the mouse making a choice recapitulated some of the behavioral metrics observed in the mPFC lesioned animals. Finally, the authors developed a novel hybrid belief-choice kernel model to provide a computational approach to quantifying these behavioral differences.

      Strengths:

      The paper is extremely well written and was an absolute pleasure to read. The results are novel and provide exciting (although not surprising) evidence that mice exploit task structures to earn rewards. Moreover, the experiments were well-designed and included appropriate controls and/or control conditions that support their findings.

      Weaknesses:

      Some of the results need to be clarified and/or language changed to ensure that readers will understand. Restricting analyses to expert mice that show the predicted effect is problematic.

    1. Reviewer #1 (Public review):

      Of course, there is always another layer of the onion, VAMP-seq measures contributions from isolated thermodynamic stability, stability conferred by binding partners (small molecule and protein), synthesis/degradation balance (especially important in "degron" motifs), etc. Here the authors' goal is to create simple models that can act as a baseline for two main reasons:<br /> (1) how to tell when adding more information would be helpful for a global model;<br /> (2) how to detect when a residue/mutation has an unusual profile indicative of an unbalanced contribution from one of the factors listed above.

      As such, the authors state that this manuscript is not intended to be a state-of-the-art method in variant effect prediction, but rather a direction towards considering static structural information for the VAMP-seq effects. At its core, the method is a fairly traditional asymmetric substitution matrix (I was surprised not to see a comparison to BLOSUM in the manuscript) - and shows that a subdivision by burial makes the model much more predictive. Despite only having 6 datasets, they show predictive power even when the matrices are based on a smaller number. Another success is rationalizing the VAMPseq results on relevant oligomeric states.

      Specific Feedback:

      Major points:

      The authors spend a good amount of space discussing how the six datasets have different distributions in abundance scores. After the development of their model is there more to say about why? Is there something that can be leveraged here to design maximally informative experiments?

      They compare to one more "sophisticated model" - RosettaddG - which should be more correlated with thermodynamic stability than other factors measured by VAMP-seq. However, the direct head-to-head comparison between their matrices and ddG is underdeveloped. How can this be used to dissect cases where thermodynamics are not contributing to specific substitution patterns OR in specific residues/regions that are predicted by one method better than the other? This would naturally dovetail into whether there is orthogonal information between these two that could be leveraged to create better predictions.

      Perhaps beyond the scope of this baseline method, there is also ThermoMPNN and the work from Gabe Rocklin to consider as other approaches that should be more correlated only with thermodynamics.

      I find myself drawn to the hints of a larger idea that outliers to this model can be helpful in identifying specific aspects of proteostasis. The discussion of S109 is great in this respect, but I can't help but feel there is more to be mined from Figure S9 or other analyses of outlier higher than predicted abundance along linear or tertiary motifs.

    1. It also flags the potential for “transitional or legacy arrangements” that could protect existing investments from future changes:

      This seems designed to head of the objections we saw before about people saying this would mess with existing projects too much

    1. These are such mindblowing ideas for people who have not been exposed to them that it may take you some time to wrap your head around them.

      Something about the tone here gets me but I do not know what it is. It feels incredibly snarky to me.

    1. Then he listens to Mauvaises nouvelles des étoiles, with the song Juif et Dieu, then Rock Around the Bunker and the famous Nazi Rock . "I was born under a lucky star - yellow , " joked Gainsbourg. Benjamin Steinitz, who married a Russian, is delighted with these pirouettes. "It's all very Ashkenazi. It speaks to me, it echoes in my head. I felt as crazy as he was, minus the talent."

      Ugh. He likes Gainsbourg; they're both descendants of Ashkenazi Jews. They and a million other people.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public Review):

      Summary:

      "Neural noise", here operationalized as an imbalance between excitatory and inhibitory neural activity, has been posited as a core cause of developmental dyslexia, a prevalent learning disability that impacts reading accuracy and fluency. This study is the first to systematically evaluate the neural noise hypothesis of dyslexia. Neural noise was measured using neurophysiological (electroencephalography [EEG]) and neurochemical (magnetic resonance spectroscopy [MRS]) in adolescents and young adults with and without dyslexia. The authors did not find evidence of elevated neural noise in the dyslexia group from EEG or MRS measures, and Bayes factors generally informed against including the grouping factor in the models. Although the comparisons between groups with and without dyslexia did not support the neural noise hypothesis, a mediation model that quantified phonological processing and reading abilities continuously revealed that EEG beta power in the left superior temporal sulcus was positively associated with reading ability via phonological awareness. This finding lends support for analysis of associations between neural excitatory/inhibitory factors and reading ability along a continuum, rather than as with a case/control approach, and indicates the relevance of phonological awareness as an intermediate trait that may provide a more proximal link between neurobiology and reading ability. Further research is needed across developmental stages and over a broader set of brain regions to more comprehensively assess the neural noise hypothesis of dyslexia, and alternative neurobiological mechanisms of this disorder should be explored.

      Strengths:

      The inclusion of multiple methods of assessing neural noise (neurophysiological and neurochemical) is a major advantage of this paper. MRS at 7T confers an advantage of more accurately distinguishing and quantifying glutamate, which is a primary target of this study. In addition, the subject-specific functional localization of the MRS acquisition is an innovative approach. MRS acquisition and processing details are noted in the supplementary materials according to the experts' consensus-recommended checklist (https://doi.org/10.1002/nbm.4484). Commenting on the rigor, the EEG methods is beyond my expertise as a reviewer.

      Participants recruited for this study included those with a clinical diagnosis of dyslexia, which strengthens confidence in the accuracy of the diagnosis. The assessment of reading and language abilities during the study further confirms the persistently poorer performance of the dyslexia group compared to the control group.

      The correlational analysis and mediation analysis provide complementary information to the main case-control analyses, and the examination of associations between EEG and MRS measures of neural noise is novel and interesting.

      The authors follow good practice for open science, including data and code sharing. They also apply statistical rigor, using Bayes Factors to support conclusions of null evidence rather than relying only on non-significant findings. In the discussion, they acknowledge the limitations and generalizability of the evidence and provide directions for future research on this topic.

      Weaknesses:

      Though the methods employed in the paper are generally strong, there are certain aspects that are not clearly described in the Materials & Methods section, such as a description of the statistical analyses used for hypothesis testing.

      Thank you for pointing this out. A description of the statistical models used in the analyses of EEG biomarkers has been added to the Materials and Methods:

      “First, exponent and offset values were averaged across all electrodes and analyzed using a 2x2 repeated measures ANOVA with group (dyslexic, control) as a between-subjects factor and condition (resting state, language task) as a within-subjects factor. Age was included in the analyses as a covariate due to the correlation between variables. Next, exponent and offset values were averaged across electrodes corresponding to the left (F7, FT7, FC5) and right inferior frontal gyrus (F8, FT8, FC6), and to the left (T7, TP7, TP9) and right superior temporal sulcus (T8, TP8, TP10). The electrodes were selected based on the analyses outlined by Giacometti and colleagues (2014) and Scrivener and Reader (2022). For these analyses, a 2x2x2x2 repeated measures ANOVA with age as a covariate was conducted with group (dyslexic, control) as a between-subjects factor and condition (resting state, language task), hemisphere (left, right), and region (frontal, temporal) as within-subjects factors. Results for the alpha and beta bands were calculated for the same clusters of frontal and temporal electrodes and analyzed with a similar 2x2x2x2 repeated measures ANOVA; however, for these analyses, age was not included as a covariate due to a lack of significant correlations.”

      We also expanded the description of the statistical models used in the analyses of MRS biomarkers:

      “To analyze the metabolite results, separate univariate ANCOVAs were conducted for Glu, GABA+, Glu/GABA+ ratio and Glu/GABA+ imbalance measures with group (control, dyslexic) as a between-subjects factor and voxel gray matter volume (GMV) as a covariate. Additionally, for the Glu analysis, age was included as a covariate due to a correlation between variables. Both frequentist and Bayesian statistics were calculated. Glu/GABA+ imbalance measure was calculated as the square root of the absolute residual value of a linear relationship between Glu and GABA+ (McKeon et al., 2024).”

      With regard to metabolite quantification, it is unclear why the authors chose to analyze and report metabolite values in terms of creatine ratios rather than quantification based on a water reference given that the MRS acquisition appears to support using a water reference.

      We have decided to use the ratio of Glu and GABA to total creatine (tCr), as this is still a common practice in MRS studies at 7T (e.g., Nandi et al., 2022; Smith et al., 2021). This approach normalizes the signal, reducing the impact of intensity variations across different regions and tissue compositions. Additionally, total creatine concentration is considered relatively stable across different brain regions, which is particularly important in our study, where a functional localizer was used to establish the left STS region individually. Our decision was further influenced by previous studies on dyslexia (Del Tufo et al., 2018; Pugh et al., 2014) which have reported creatine ratios and included GM volume as a covariate in their models, thus providing comparability. It is now indicated in the Results:

      “For comparability with previous studies in dyslexia (Del Tufo et al., 2018; Pugh et al., 2014) we report Glu and GABA as a ratio to total creatine (tCr).”

      and in the Method sections:

      “Glu and GABA+ concentrations were expressed as a ratio to total-creatine (tCr; Creatine + Phosphocreatine) following previous MRS studies in dyslexia (Del Tufo et al., 2018; Pugh et al., 2014).

      We did not estimate absolute concentrations using water signals as a reference, as this would require accounting for water relaxation times, which may vary across our age range. Nevertheless, our dataset has been made publicly available for future researchers to calculate and compare absolute values.

      Del Tufo, S. N., Frost, S. J., Hoeft, F., Cutting, L. E., Molfese, P. J., Mason, G. F., Rothman, D. L., Fulbright, R. K., & Pugh, K. R. (2018). Neurochemistry Predicts Convergence of Written and Spoken Language: A Proton Magnetic Resonance Spectroscopy Study of Cross-Modal Language Integration. Frontiers in Psychology, 9, 1507. https://doi.org/10.3389/fpsyg.2018.01507

      Nandi, T., Puonti, O., Clarke, W. T., Nettekoven, C., Barron, H. C., Kolasinski, J., Hanayik, T., Hinson, E. L., Berrington, A., Bachtiar, V., Johnstone, A., Winkler, A. M., Thielscher, A., Johansen-Berg, H., & Stagg, C. J. (2022). tDCS induced GABA change is associated with the simulated electric field in M1, an effect mediated by grey matter volume in the MRS voxel. Brain Stimulation, 15(5), 1153–1162. https://doi.org/10.1016/j.brs.2022.07.049

      Pugh, K. R., Frost, S. J., Rothman, D. L., Hoeft, F., Del Tufo, S. N., Mason, G. F., Molfese, P. J., Mencl, W. E., Grigorenko, E. L., Landi, N., Preston, J. L., Jacobsen, L., Seidenberg, M. S., & Fulbright, R. K. (2014). Glutamate and choline levels predict individual differences in reading ability in emergent readers. Journal of Neuroscience, 34(11), 4082–4089. https://doi.org/10.1523/JNEUROSCI.3907-13.2014

      Smith, G. S., Oeltzschner, G., Gould, N. F., Leoutsakos, J. S., Nassery, N., Joo, J. H., Kraut, M. A., Edden, R. A. E., Barker, P. B., Wijtenburg, S. A., Rowland, L. M., & Workman, C. I. (2021). Neurotransmitters and Neurometabolites in Late-Life Depression: A Preliminary Magnetic Resonance Spectroscopy Study at 7T. Journal of Affective Disorders, 279, 417–425. https://doi.org/10.1016/j.jad.2020.10.011

      GABA is typically quantified using J-editing sequences as lower field strengths (~3T), and there is some evidence that the GABA signal can be reliably measured at 7T without editing, however, the authors should discuss potential limitations, such as reliability of Glu and GABA measurements with short-TE semi-laser at 7T.

      In addition, MRS measurements of GABA are known to be influenced by macromolecules, and GABA is often denoted as GABA+ to indicate that other compounds contribute to the measured signal, especially at a short TE and in the absence of symmetric spectral editing.

      A general discussion of the strengths and limitations of unedited Glu and GABA quantification at 7T is warranted given the interest of this work to researchers who may not be experts in MRS.

      While we agree with the Reviewer that at 3T, it is recommended to use J-edited MRS to measure GABA (Mullins et al., 2014), the better spectral resolution at 7T allows for more reliable results for both metabolites using moderate echo-time, non-edited MRS (Finkelman et al., 2022). In this study, we used a short echo time (TE), which is optimal for Glu but not ideal for GABA, as it interferes with other signals. We are grateful to the Reviewer for suggesting the addition of a short paragraph to the Discussion, describing the practicalities of 3T and 7T MRS and changing the abbreviation to GABA+ to inform readers of possible macromolecule contamination:

      “We chose ultra-high-field MRS to improve data quality (Özütemiz et al., 2023), as the increased sensitivity and spectral resolution at 7T allows for better separation of overlapping metabolites compared to lower field strengths. Additionally, 7T provides a higher signal-to-noise ratio (SNR), improving the reliability of metabolite measurements and enabling the detection of small changes in Glu and GABA concentrations. Despite these theoretical advantages, several practical obstacles should be considered, such as susceptibility artifacts and inhomogeneities at higher field strengths that can impact data quality. Interestingly, actual methodological comparisons (Pradhan et al., 2015; Terpstra et al., 2016) show only a slight practical advantage of 7T single-voxel MRS compared to optimized 3T acquisition. For example, fitting quality yielded reduced estimates of variance in concentration of Glu in 7T (CRLB) and slightly improved reproducibility levels for Glu and GABA (at both fields below 5%). Choosing the appropriate MRS sequence involves a trade-off between the accuracy of Glu and GABA measurements, as different sequences are recommended for each metabolite. J-edited MRS is recommended for measuring GABA, particularly with 3T scanners (Mullins et al., 2014). However, at 7T, more reliable results can be obtained using moderate echo-time, non-edited MRS (Finkelman et al., 2022). We have opted for a short-echo-time sequence, which is optimal for measuring Glu. However, this approach results in macromolecule contamination of the GABA signal (referred to as GABA+).”

      Finkelman, T., Furman-Haran, E., Paz, R., & Tal, A. (2022). Quantifying the excitatory-inhibitory balance: A comparison of SemiLASER and MEGA-SemiLASER for simultaneously measuring GABA and glutamate at 7T. NeuroImage, 247, 118810. https://doi.org/10.1016/j.neuroimage.2021.118810

      Mullins, P. G., McGonigle, D. J., O'Gorman, R. L., Puts, N. A., Vidyasagar, R., Evans, C. J., Cardiff Symposium on MRS of GABA, & Edden, R. A. (2014). Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. NeuroImage, 86, 43–52. https://doi.org/10.1016/j.neuroimage.2012.12.004

      Özütemiz, C., White, M., Elvendahl, W., Eryaman, Y., Marjańska, M., Metzger, G. J., Patriat, R., Kulesa, J., Harel, N., Watanabe, Y., Grant, A., Genovese, G., & Cayci, Z. (2023). Use of a Commercial 7-T MRI Scanner for Clinical Brain Imaging: Indications, Protocols, Challenges, and Solutions-A Single-Center Experience. AJR. American Journal of Roentgenology, 221(6), 788–804. https://doi.org/10.2214/AJR.23.29342

      Pradhan, S., Bonekamp, S., Gillen, J. S., Rowland, L. M., Wijtenburg, S. A., Edden, R. A., & Barker, P. B. (2015). Comparison of single voxel brain MRS AT 3T and 7T using 32-channel head coils. Magnetic Resonance Imaging, 33(8), 1013–1018. https://doi.org/10.1016/j.mri.2015.06.003

      Terpstra, M., Cheong, I., Lyu, T., Deelchand, D. K., Emir, U. E., Bednařík, P., Eberly, L. E., & Öz, G. (2016). Test-retest reproducibility of neurochemical profiles with short-echo, single-voxel MR spectroscopy at 3T and 7T. Magnetic Resonance in Medicine, 76(4), 1083–1091. https://doi.org/10.1002/mrm.26022

      Further, the single MRS voxel location is a limitation of the study as neurochemistry can vary regionally within individuals, and the putative excitatory/inhibitory imbalance in dyslexia may appear in regions outside the left temporal cortex (e.g., network-wide or in frontal regions involved in top-down executive processes). While the functional localization of the MRS voxel is a novelty and a potential advantage, it is unclear whether voxel placement based on left-lateralized reading-related neural activity may bias the experiment to be more sensitive to small, activity-related fluctuations in neurotransmitters in the CON group vs. the DYS group who may have developed an altered, compensatory reading strategy.

      We agree that including only one region of interest for the MRS measurements is a potential limitation of our study, and we have now added this information to the Discussion:

      “Moreover, since the MRS data was collected only from the left STS, it is plausible that other areas might be associated with differences in Glu or GABA concentrations in dyslexia.”

      However, differences in Glu and GABA concentrations in this region were directly predicted by the neural noise hypothesis of dyslexia. We acknowledge that this information was missing in the previous version of the manuscript. It is now included in the Results:

      “Moreover, the neural noise hypothesis of dyslexia identifies perisylvian areas as being affected by increased glutamatergic signaling, and directly predicts associations between Glu and GABA levels in the superior temporal regions and phonological skills (Hancock et al., 2017).”

      as well as in the Discussion:

      “Nevertheless, the neural noise hypothesis predicted increased glutamatergic signaling in perisylvian regions, specifically in the left superior temporal cortex (Hancock et al., 2017).”

      Figure 1 contains a lot of information, and it may be helpful to split it into 2 figures (EEG vs. MRS) so that the plots could be made larger and the reader could more easily digest the information.

      (a) I would also recommend displaying separate metabolite fit plots for each group, since the current presentation in panel F makes it appear that the MRS data is examined by testing differences between groups across the full spectrum (where the lines diverge), which really isn't the case.

      (b) The GABA peak is not visible in the spectrum, and Glutamate and GABA both have multiple peaks that should be shown on the spectrum. This may be best achieved by displaying the individual metabolite sub-spectra below the full spectrum

      Thank you for these suggestions. We have split the information into two Figures following the Reviewer’s recommendations.

      It is not clear why the 3T structural images were used for segmentation and calculation of tissue fraction if 7T structural images were also acquired (which would presumably have higher resolution).

      Generally, T1-weighted images from the 7T scanner exhibit more artifacts than those from the 3T scanner due to higher magnetic field inhomogeneity. These artifacts are especially pronounced in regions near air-tissue interfaces, such as the temporal lobes. Therefore, we chose the 3T structural images for segmentation and tissue fraction calculations and clarified this in the Method section:

      “Voxel segmentation was performed on structural images from a 3T scanner, coregistered to 7T structural images in SPM12, as the latter exhibited excessive artifacts and intensity bias in the temporal regions”.

      The basis set includes a large number of metabolites (27), including many low-concentration metabolites/compounds (e.g., bHG, bHB, Citrate, Threonine, ethanol) that are typically only included in studies targeting specific metabolites in disease/pathology. Please justify the inclusion of this maximal set of metabolites in the basis set, given that the inclusion of overlapping low-concentration metabolites may influence metabolite measurements of interest (https://doi.org/10.1002/mrm.10246).

      There is still no consensus in the MR community on which metabolites should be included in the model of human cerebral 1H-MR spectra. Typically, only major contributors such as NAA, Cr, Cho, Lac, mI, and possibly Glx are evaluated. Some studies also include additional metabolites like Ace, Ala, Asp, GABA, Glc, Gly, sI, NAAG, and Tau. In this study, as in a few others, further metabolites such as PCh, GPC, PCr, GSH, PE, and Thr were introduced and this approach seems suitable for high-field spectra (Hofmann et al., 2002).

      Hofmann, L., Slotboom, J., Jung, B., Maloca, P., Boesch, C., & Kreis, R. (2002). Quantitative 1H-magnetic resonance spectroscopy of human brain: Influence of composition and parameterization of the basis set in linear combination model-fitting. Magnetic Resonance in Medicine, 48(3), 440–453. https://doi.org/10.1002/mrm.10246

      Please provide a figure indicating the localization of the MRS voxel for a sample subject.

      A figure indicating the localization of the MRS voxel for a sample subject was added to the MRS checklist.

      It would be helpful to include Table S1 in the main article.

      Table S1 from the Supplementary Material has now been added to the main manuscript as Table 1 in the Results section.

      Please report descriptive statistics for EEG and MRS measures in Table S1.

      We have added a new Table S1 in the Supplementary Material, providing descriptive statistics for EEG and MRS E/I balance measures, presented separately for the dyslexic and control groups.

      I recommend avoiding using the terms "direct" and "indirect" to contrast MRS and EEG measures of E/I balance. Both of these measures are imperfect and it is misleading to say that MRS is a "direct" measure of neurotransmitters. There is also ambiguity in what is meant by "direct": in contrast to EEG, MRS does not measure neural activity and does not provide high-resolution temporal information, so in a sense, it is less direct.

      Thank you for this suggestion. We have replaced the terms 'direct' and 'indirect' biomarkers with 'MRS' and 'EEG' biomarkers throughout the text.

      There are many cases throughout the results in which Bayes and frequentist stats seem to contradict each other in terms of significance and what should be included in the models, especially with regard to the interaction effects (the Bayes factors appear to favor non-significant interactions). I think this is worth considering and describing to offer more clarity for the readers.

      We agree that a discussion of the divergent results between Bayesian and frequentist models was missing in the previous version of the manuscript. To provide greater clarity for the readers, we have conducted follow-up Bayesian t-tests in every case where the results indicated the inclusion of non-significant interactions with the effect of group in the model. These additional analyses have been performed for the exponent, offset, as well as for beta bandwidth in the Supplementary Material. We have also added a paragraph addressing these discrepancies in the Discussion:

      “Remarkably, in some models, results from Bayesian and frequentist statistics yielded divergent conclusions regarding the inclusion of non-significant effects. This was observed in more complex ANOVA models, whereas no such discrepancies appeared in t-tests or correlations. Given reports of high variability in Bayesian ANOVA estimates across repeated runs of the same analysis (Pfister, 2021), these results should be interpreted with caution. Therefore, following the recommendation to simplify complex models into Bayesian t-tests for more reliable estimates (Pfister, 2021), we conducted follow-up Bayesian t-tests in every case that favored the inclusion of non-significant interactions with the group factor. These analyses provided further evidence for the lack of differences between the dyslexic and control groups. Another source of discrepancy between the two methods may stem from the inclusion of interactions between covariates and within-subject effects in frequentist ANOVA, which were not included in Bayesian ANOVA to adhere to the recommendation for simpler Bayesian models (Pfister, 2021).”

      Pfister, R. (2021). Variability of Bayes factor estimates in Bayesian analysis of variance. The Quantitative Methods for Psychology, 17(1), 40-45. doi:10.20982/tqmp.17.1.p040

      It would be helpful to indicate whether participants in the DYS group had a history of reading intervention/remediation. In addition to showing that the DYS group performed lower than the CON group on reading assessments as a whole and given their age, was the performance on the reading assessments at an individual level considered for inclusion in the study? (i.e., were participants' persistent poor reading abilities confirmed with the research assessments?)

      We were unable to assess individual reading skills due to the lack of standardized diagnostic norms for adult dyslexia in Poland. Therefore, participants in the dyslexic group were recruited based on a previous clinical diagnosis of dyslexia, and reading and reading-related tasks were used for group-level comparisons only. This information has been added to the Methods section:

      “Since there are no standardized diagnostic norms for dyslexia in adults in Poland, individuals were assigned to the dyslexic group based on a past diagnosis of dyslexia.”

      Unfortunately, we did not collect information about participants' history of reading intervention or remediation. In this context, we acknowledge that including a sample of adult participants is a potential limitation of our study, however, this was already mentioned in the Discussion.

      Regarding the fMRI task, please indicate whether the participants whose threshold and/or contrast was changed for localization were from the DYS or CON group.

      This information is now added to the Method section:

      “For 6 participants (DYS n = 2, CON n = 4), the threshold was lowered to p < .05 uncorrected, while for another 6 participants (DYS n = 3, CON n = 3) the contrast from the auditory run was changed to auditory words versus fixation cross due to a lack of activation for other contrasts.”

      Reviewer #2 (Public Review):

      Summary:

      This study utilized two complementary techniques (EEG and 7T MRI/MRS) to directly test a theory of dyslexia: the neural noise hypothesis. The authors report finding no evidence to support an excitatory/inhibitory balance, as quantified by beta in EEG and Glutamate/GABA ratio in MRS. This is important work and speaks to one potential mechanism by which increased neural noise may occur in dyslexia.

      Strengths:

      This is a well-conceived study with in-depth analyses and publicly available data for independent review. The authors provide transparency with their statistics and display the raw data points along with the averages in figures for review and interpretation. The data suggest that an E/I balance issue may not underlie deficits in dyslexia and is a meaningful and needed test of a possible mechanism for increased neural noise.

      Weaknesses:

      The researchers did not include a visual print task in the EEG task, which limits analysis of reading-specific regions such as the visual word form area, which is a commonly hypoactivated region in dyslexia. This region is a common one of interest in dyslexia, yet the researchers measured the I/E balance in only one region of interest, specific to the language network.

      We agree with the Reviewer that including different tasks for the EEG biomarkers assessment would be valuable. However, this limitation was already addressed in the Discussion:

      “Importantly, our study focused on adolescents and young adults, and the EEG recordings were conducted during rest and a spoken language task. These factors may limit the generalizability of our results. Future research should include younger populations and incorporate a broader array of tasks, such as reading and phonological processing, to provide a more comprehensive evaluation of the E/I balance hypothesis.”

      Further, this work does not consider prior studies reporting neural inconsistency; a potential consequence of increased neural noise, which has been reported in several studies and linked with candidate-dyslexia gene variants (e.g., Centanni et al., 2018, 2022; Hornickel & Kraus, 2013; Neef et al., 2017). While E/I imbalance may not be a cause of increased neural noise, other potential mechanisms remain and should be discussed.

      Thank you for referring us to other works reporting neural variability in dyslexia. We agree that a broader context regarding sources of reduced neural synchronization, beyond E/I imbalance, was missing in the previous version of the manuscript. We have now included these references in the Discussion:

      “Furthermore, although our results do not support the idea of E/I balance alterations as a source of neural noise in dyslexia, they do not preclude other mechanisms leading to less synchronous neural firing posited by the hypothesis. In this context, there is evidence showing increased trial-to-trial inconsistency of neural responses in individuals with dyslexia (Centanni et al., 2022) or poor readers (Hornickel and Kraus, 2013) and its associations with specific dyslexia risk genes (Centanni et al., 2018; Neef et al., 2017). At the same time, the observed trial-to-trial inconsistency was either present only in a subset of participants (Centanni et al., 2018), limited to some experimental conditions (Centanni et al., 2022), or specific brain regions – e.g., brainstem in Hornickel and Kraus (2013), left auditory cortex in Centanni et al. (2018), or left supramarginal gyrus in Centanni et al. (2022).”

      A better description of the exponent and offset components is needed at the beginning of the results, given that the methods are presented in detail at the end. I also do not see a clear description of these components in the methods.

      A description of the aperiodic components is now included in the Results:

      “In the initial step of the analysis, we analyzed the aperiodic (exponent and offset) components of the EEG spectrum. The exponent reflects the steepness of the EEG power spectrum, with a higher exponent indicating a steeper signal; while the offset represents a uniform shift in power across frequencies, with a higher offset indicating greater power across the entire EEG spectrum (Donoghue et al., 2020).”

      as well as in the Materials and Methods:

      “Two broadband aperiodic parameters were extracted: the exponent, which quantifies the steepness of the EEG power spectrum, and the offset, which indicates signal’s power across the entire frequency spectrum.”

      Reviewer #3 (Public Review):

      Summary:

      This study by Glica and colleagues utilized EEG (i.e., Beta power, Gamma power, and aperiodic activity) and 7T MRS (i.e., MRS IE ratio, IE balance) to reevaluate the neural noise hypothesis in Dyslexia. Supported by Bayesian statistics, their results show solid 'no evidence' of EI balance differences between groups, challenging the neural noise hypothesis. The work will be of broad interest to neuroscientists, and educational and clinical psychologists.

      Strengths:

      Combining EEG and 7T MRS, this study utilized both the indirect (i.e., Beta power, Gamma power, and aperiodic activity) and direct (i.e., MRS IE ratio, IE balance) measures to reevaluate the neural noise hypothesis in Dyslexia.

      Weaknesses:

      The authors may need to provide more data to assess the quality of the MRS data.

      We have addressed the following specific recommendations of the Reviewer providing more data about the quality of the MRS data.

      The authors may need to explain how the number of subjects is determined in the MRS section.

      We have clarified the MRS sample description in the Results section:

      “Due to financial and logistical constraints, 59 out of the 120 recruited subjects, selected progressively as the study unfolded, were examined with MRS. Subjects were matched by age and sex between the dyslexic and control groups. Due to technical issues and to prevent delays and discomfort for the participants, we collected 54 complete sessions. Additionally, four datasets were excluded based on our quality control criteria, and three GABA+ estimates exceeded the selected CRLB threshold. Ultimately, we report 50 estimates for Glu (21 participants with dyslexia) and 47 for GABA+ and Glu/GABA+ ratios (20 participants with dyslexia).”

      Is there a reason why theta and gamma peaks were not observed in the majority of participants? What are the possible reasons that likely caused the discrepancy between this study and previously reported relevant studies?

      We have now added a discussion about the absence of oscillatory peaks in the theta and gamma bands to the Discussion section:

      “We could not perform analyses for the gamma oscillations since in the majority of participants the gamma peak was not detected above the aperiodic component. Due to the 1/f properties of the EEG spectrum, both aperiodic and periodic components should be disentangled to analyze ‘true’ gamma oscillations; however, this approach is not typically recognized in electrophysiology research (Hudson and Jones, 2022). Indeed, previous studies that analyzed gamma activity in dyslexia (Babiloni et al., 2012; Lasnick et al., 2023; Rufener and Zaehle, 2021) did not separate the background aperiodic activity. For the same reason, we could not analyze results for the theta band, which often does not meet the criteria for an oscillatory component manifested as a peak in the power spectrum (Klimesch, 1999). Moreover, results from a study investigating developmental changes in both periodic and aperiodic components suggest that theta oscillations in older participants are mostly observed in frontal midline electrodes (Cellier et al., 2021), which were not analyzed in the current study.”

      Hudson, M. R., & Jones, N. C. (2022). Deciphering the code: Identifying true gamma neural oscillations. Experimental Neurology357, 114205. https://doi.org/10.1016/j.expneurol.2022.114205

      Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews29(2-3), 169-195. https://doi.org/10.1016/S0165-0173(98)00056-3

      Based on Figure 1F, the quality of the MRS data may be contaminated by the lipid signal, especially for the DYS group. To better evaluate the MRS data, especially the GABA measurements, the authors need to show:

      (a) the placement of the MRS voxel on the anatomical images;

      Averaged MRS voxel placement was already presented in Figure 1 (now Figure 2) in the manuscript. Now, we have also added exemplary single-subject images to the MRS checklist in the Supplement.

      (b) Glu and GABA model functions

      We have now provided more meaningful Glu and GABA indications in Figure 2.

      (c) CRLB for GABA

      We have added respective estimates to the Supplement:

      %CRLB of Glu: mean 2.96, SD = 0.79

      %CRLB of GABA: mean 10.59, SD = 2.76

      %CRLB of NAA: 1.76 SD = 0.46

      Further, the authors added voxel's gray matter volume as a covariate when performing separate ANCOVAs. The authors may need to use alpha correction or 1-fCSF correction to corroborate these results.

      We chose to use the ratio of Glu and GABA to total creatine (tCr), as this remains a common practice in MRS studies at 7T (e.g., Nandi et al., 2022; Smith et al., 2021). This decision was also influenced by previous dyslexia studies (Del Tufo et al., 2018; Pugh et al., 2014) and is now clarified in the Results and Methods sections.

      Regarding alpha correction, a recent paper (García-Pérez et al., 2023) recommends: 'In general, avoid corrections for multiple testing if statistical claims are to be made for each individual test, in the absence of an omnibus null hypothesis.' Since we report null findings, further alpha correction would not significantly impact the results.

      García-Pérez, M. A. (2023). Use and misuse of corrections for multiple testing. Methods in Psychology8, 100120. https://doi.org/10.1016/j.metip.2023.100120

    1. Reviewer #1 (Public review):

      This work addresses an important question in the field of Drosophila aggression and mating- prior social isolation is known to increase aggression in males by increased lunging, which is suppressed by group housing (GH). However, it is also known that single-housed (SH) males, despite their higher attempts to court females, are less successful. Here, Gao et al., developed a modified aggression assay, to address this issue by recording aggression in Drosophila males for 2 hours, over a virgin female which is immobilized by burying its head in the food. They found that while SH males frequently lunge in this assay, GH males switch to higher intensity but very low-frequency tussling. Constitutive neuronal silencing and activation experiments implicate cVA sensing Or67d neurons promoting high-frequency lunging, similar to earlier studies, whereas Or47b neurons promote low-frequency but higher intensity tussling. Using optogenetic activation they found that three pairs of pC1 neurons- pC1SS2 increase tussling. While P1a neurons, previously implicated in promoting aggression and courtship, did not increase tussling in optogenetic activation (in the dark), they could promote aggressive tussling in thermogenetic activation carried out in the presence of visible light. It was further suggested, using a further modified aggression assay that GH males use increased tussling and are able to maintain territorial control, providing them mating advantage over SI males and this may partially overcome the effect of aging in GH males.

      Strengths:

      Using a series of clever neurogenetic and behavioral approaches, subsets of ORNs and pC1 neurons were implicated in promoting tussling behaviors. The authors devised a new paradigm to assay for territory control which appears better than earlier paradigms that used a food cup (Chen et al, 2002), as this new assay is relatively clutter-free, and can be eventually automated using computer vision approaches. The manuscript is generally well-written, and the claims made are largely supported by the data.

      Weaknesses:

      I have a few concerns regarding some of the evidence presented and claims made as well as a description of the methodology, which needs to be clarified and extended further.

      (1) Typical paradigms for assaying aggression in Drosophila males last for 20-30 minutes in the presence of nutritious food/yeast paste/females or all of these (Chen et al. 2002, Nilsen et al., 2004, Dierick et al. 2007, Dankert et al., 2009, Certel & Kravitz 2012). The paradigm described in Figure 1 A, while important and more amenable for video recording and computational analysis, seems a modification of the assay from Kravitz lab (Chen et al., 2002), which involved using a female over which males fight on a food cup. The modifications include a flat surface with a central food patch and a female with its head buried in the food, (fixed female) and much longer adaptation and recording times respectively (30 minutes, 2 hours), so in that sense, this is not a 'new' paradigm but a modification of an existing paradigm and its description as new should be appropriately toned down. It would also be important to cite these earlier studies appropriately while describing the assay.

      (2) Lunging is described as a 'low intensity' aggression (line 111 and associated text), however, it is considered a mid to high-intensity aggressive behavior, as compared to other lower-intensity behaviors such as wing flicks, chase, and fencing. Lunging therefore is lower in intensity 'relative' to higher intensity tussling but not in absolute terms and it should be mentioned clearly.

      (3) It is often difficult to distinguish faithfully between boxing and tussling and therefore, these behaviors are often clubbed together as box, tussle by Nielsen et al., 2004 in their Markov chain analysis as well as a more detailed recent study of male aggression (Simon & Heberlein, 2020). Therefore, authors can either reconsider the description of behavior as 'box, tussle' or consider providing a video representation/computational classifier to distinguish between box and tussle behaviors.

      (4) Simon & Heberlein, 2020 showed that increased boxing & tussling precede the formation of a dominance hierarchy in males, and lunges are used subsequently to maintain this dominant status. This study should be cited and discussed appropriately while introducing the paradigm.

      (5) It would be helpful to provide more methodological details about the assay, for instance, a video can be helpful showing how the males are introduced in the assay chamber, are they simply dropped to the floor when the film is removed after 30 minutes (Figures 1-2)?

      (6) The strain of Canton-S (CS) flies used should be mentioned as different strains of CS can have varying levels of aggression, for instance, CS from Martin Heisenberg lab shows very high levels of aggressive lunges. Are the CS lines used in this study isogenized? Are various genetic lines outcrossed into this CS background? In the methods, it is not clear how the white gene levels were controlled for various aggression experiments as it is known to affect aggression (Hoyer et al. 2008).

      (7) How important it is to use a fixed female for the assay to induce tussling? Do these females remain active throughout the assay period of 2.5 hours? Is it possible to use decapitated virgin females for the assay? How will that affect male behaviors?

      (8) Raster plots in Figure 2 suggest a complete lack of tussling in SH males in the first 60 minutes of the encounter, which is surprising given the longer duration of the assay as compared to earlier studies (Nielsen et al. 2004, Simon & Heberlein, 2020 and others), which are able to pick up tussling in a shorter duration of recording time. Also, the duration for tussling is much longer in this study as compared to shorter tussles shown by earlier studies. Is this due to differences in the paradigm used, strain of flies, or some other factor? While the bar plots in Figure 2D show some tussling in SH males, maybe an analysis of raster plots of various videos can be provided in the main text and included as a supplementary figure to address this.

      (9) Neuronal activation experiments suggesting the involvement of pC1SS2 neurons are quite interesting. Further, the role of P1a neurons was demonstrated to be involved in increasing tussling in thermogenetic activation in the presence of light (Figure 4, Supplement 1), which is quite important as the role of vision in optogenetic activation experiments, which required to be carried out in dark, is often not mentioned. However, in the discussion (lines 309-310) it is mentioned that PC1SS2 neurons are 'necessary and sufficient' for inducing tussling. Given that P1a neurons were shown to be involved in promoting tussling, this statement should be toned down.

      (10) Are Or47b neurons connected to pC1SS2 or P1a neurons?

      (11) The paradigm for territory control is quite interesting and subsequent mating advantage experiments are an important addition to the eventual outcome of the aggressive strategy deployed by the males as per their prior housing conditions. It would be important to comment on the 'fitness outcome' of these encounters. For instance, is there any fitness advantage of using tussling by GH males as compared to lunging by SH males? The authors may consider analyzing the number of eggs laid and eclosed progenies from these encounters to address this.

    2. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      This work addresses an important question in the field of Drosophila aggression and mating- prior social isolation is known to increase aggression in males by increased lunging, which is suppressed by group housing (GH). However, it is also known that single-housed (SH) males, despite their higher attempts to court females, are less successful. Here, Gao et al., developed a modified aggression assay, to address this issue by recording aggression in Drosophila males for 2 hours, over a virgin female which is immobilized by burying its head in the food. They found that while SH males frequently lunge in this assay, GH males switch to higher intensity but very low-frequency tussling. Constitutive neuronal silencing and activation experiments implicate cVA sensing Or67d neurons promoting high-frequency lunging, similar to earlier studies, whereas Or47b neurons promote low-frequency but higher intensity tussling. Using optogenetic activation they found that three pairs of pC1 neurons- pC1SS2 increase tussling. While P1a neurons, previously implicated in promoting aggression and courtship, did not increase tussling in optogenetic activation (in the dark), they could promote aggressive tussling in thermogenetic activation carried out in the presence of visible light. It was further suggested, using a further modified aggression assay that GH males use increased tussling and are able to maintain territorial control, providing them mating advantage over SI males and this may partially overcome the effect of aging in GH males.

      Strengths:

      Using a series of clever neurogenetic and behavioral approaches, subsets of ORNs and pC1 neurons were implicated in promoting tussling behaviors. The authors devised a new paradigm to assay for territory control which appears better than earlier paradigms that used a food cup (Chen et al, 2002), as this new assay is relatively clutter-free, and can be eventually automated using computer vision approaches. The manuscript is generally well-written, and the claims made are largely supported by the data.

      Thank you for your precise summary of our study and being very positive on the novelty and significance of the study.

      Weaknesses:

      I have a few concerns regarding some of the evidence presented and claims made as well as a description of the methodology, which needs to be clarified and extended further.

      (1) Typical paradigms for assaying aggression in Drosophila males last for 20-30 minutes in the presence of nutritious food/yeast paste/females or all of these (Chen et al. 2002, Nilsen et al., 2004, Dierick et al. 2007, Dankert et al., 2009, Certel & Kravitz 2012). The paradigm described in Figure 1 A, while important and more amenable for video recording and computational analysis, seems a modification of the assay from Kravitz lab (Chen et al., 2002), which involved using a female over which males fight on a food cup. The modifications include a flat surface with a central food patch and a female with its head buried in the food, (fixed female) and much longer adaptation and recording times respectively (30 minutes, 2 hours), so in that sense, this is not a 'new' paradigm but a modification of an existing paradigm and its description as new should be appropriately toned down. It would also be important to cite these earlier studies appropriately while describing the assay.

      We will tone down the description and cite related references.

      (2) Lunging is described as a 'low intensity' aggression (line 111 and associated text), however, it is considered a mid to high-intensity aggressive behavior, as compared to other lower-intensity behaviors such as wing flicks, chase, and fencing. Lunging therefore is lower in intensity 'relative' to higher intensity tussling but not in absolute terms and it should be mentioned clearly.

      Ww will textually address this issue.

      (3) It is often difficult to distinguish faithfully between boxing and tussling and therefore, these behaviors are often clubbed together as box, tussle by Nielsen et al., 2004 in their Markov chain analysis as well as a more detailed recent study of male aggression (Simon & Heberlein, 2020). Therefore, authors can either reconsider the description of behavior as 'box, tussle' or consider providing a video representation/computational classifier to distinguish between box and tussle behaviors.

      We will textually address this issue.

      (4) Simon & Heberlein, 2020 showed that increased boxing & tussling precede the formation of a dominance hierarchy in males, and lunges are used subsequently to maintain this dominant status. This study should be cited and discussed appropriately while introducing the paradigm.

      We will cite this paper and discuss on this issue.

      (5) It would be helpful to provide more methodological details about the assay, for instance, a video can be helpful showing how the males are introduced in the assay chamber, are they simply dropped to the floor when the film is removed after 30 minutes (Figures 1-2)?

      We will provide more methodological details.

      (6) The strain of Canton-S (CS) flies used should be mentioned as different strains of CS can have varying levels of aggression, for instance, CS from Martin Heisenberg lab shows very high levels of aggressive lunges. Are the CS lines used in this study isogenized? Are various genetic lines outcrossed into this CS background? In the methods, it is not clear how the white gene levels were controlled for various aggression experiments as it is known to affect aggression (Hoyer et al. 2008).

      We will textually address this issue.

      (7) How important it is to use a fixed female for the assay to induce tussling? Do these females remain active throughout the assay period of 2.5 hours? Is it possible to use decapitated virgin females for the assay? How will that affect male behaviors?

      We will textually address this issue and provide additional videos.

      (8) Raster plots in Figure 2 suggest a complete lack of tussling in SH males in the first 60 minutes of the encounter, which is surprising given the longer duration of the assay as compared to earlier studies (Nielsen et al. 2004, Simon & Heberlein, 2020 and others), which are able to pick up tussling in a shorter duration of recording time. Also, the duration for tussling is much longer in this study as compared to shorter tussles shown by earlier studies. Is this due to differences in the paradigm used, strain of flies, or some other factor? While the bar plots in Figure 2D show some tussling in SH males, maybe an analysis of raster plots of various videos can be provided in the main text and included as a supplementary figure to address this.

      We will textually address the first question and provide more detailed analysis for the second question.

      (9) Neuronal activation experiments suggesting the involvement of pC1SS2 neurons are quite interesting. Further, the role of P1a neurons was demonstrated to be involved in increasing tussling in thermogenetic activation in the presence of light (Figure 4, Supplement 1), which is quite important as the role of vision in optogenetic activation experiments, which required to be carried out in dark, is often not mentioned. However, in the discussion (lines 309-310) it is mentioned that PC1SS2 neurons are 'necessary and sufficient' for inducing tussling. Given that P1a neurons were shown to be involved in promoting tussling, this statement should be toned down.

      We will tone down this statement.

      (10) Are Or47b neurons connected to pC1SS2 or P1a neurons?

      We conducted pathway analysis in the FlyWire electron microscopy database to investigate the connection between Or47b neurons and pC1 neurons. The results indicate that at least three intermediate neurons are required to establish a connection from Or47b neurons to pC1 neurons. Although the FlyWire database currently only contains neuronal data from female brains, they provide a reference for circuit connect in males. Using the currently available upstream and downstream tracing tools (e.g., retro-/trans-Tango), it is not possible to establish a direct connection between the two. Identifying the intermediate neurons involved in this connection is beyond this study. We will discuss on this concern in our revised manuscript.

      (11) The paradigm for territory control is quite interesting and subsequent mating advantage experiments are an important addition to the eventual outcome of the aggressive strategy deployed by the males as per their prior housing conditions. It would be important to comment on the 'fitness outcome' of these encounters. For instance, is there any fitness advantage of using tussling by GH males as compared to lunging by SH males? The authors may consider analyzing the number of eggs laid and eclosed progenies from these encounters to address this.

      We will discuss on this concern.

      Reviewer #2 (Public review):

      Summary:

      Gao et al. investigated the change of aggression strategies by the social experience and its biological significance by using Drosophila. Two modes of inter-male aggression in Drosophila are known: lunging, high-frequency but weak mode, and tussling, low-frequency but more vigorous mode. Previous studies have mainly focused on the lunging. In this paper, the authors developed a new behavioral experiment system for observing tussling behavior and found that tussling is enhanced by group rearing while lunging is suppressed. They then searched for neurons involved in the generation of tussling. Although olfactory receptors named Or67d and Or65a have previously been reported to function in the control of lunging, the authors found that these neurons do not function in the execution of tussling, and another olfactory receptor, Or47b, is required for tussling, as shown by the inhibition of neuronal activity and the gene knockdown experiments. Further optogenetic experiments identified a small number of central neurons pC1[SS2] that induce the tussling specifically. In order to further explore the ecological significance of the aggression mode change in group rearing, a new behavioral experiment was performed to examine territorial control and mating competition. Finally, the authors found that differences in the social experience (group vs. solitary rearing) are important in these biologically significant competitions. These results add a new perspective to the study of aggressive behavior in Drosophila. Furthermore, this study proposes an interesting general model in which the social experience-modified behavioral changes play a role in reproductive success.

      Strengths:

      A behavioral experiment system that allows stable observation of tussling, which could not be easily analyzed due to its low frequency, would be very useful. The experimental setup itself is relatively simple, just the addition of a female to the platform, so it should be applicable to future research. The finding about the relationship between the social experience and the aggression mode change is quite novel. Although the intensity of aggression changes with the social experience was already reported in several papers (Liu et al., 2011, etc.), the fact that the behavioral mode itself changes significantly has rarely been addressed and is extremely interesting. The identification of sensory and central neurons required for the tussling makes appropriate use of the genetic tools and the results are clear. A major strength of the neurobiology in this study is the finding that another group of neurons (Or47b-expressing olfactory neurons and pC1[SS2] neurons), distinct from the group of neurons previously thought to be involved in low-intensity aggression (i.e. lunging), function in the tussling behavior. Further investigation of the detailed circuit analysis is expected to elucidate the neural substrate of the conflict between the two aggression modes.

      Thank you for the acknowledgment of the novelty and significance of the study, and your suggestions for improving the manuscript.

      Weaknesses:

      The experimental systems examining the territory control and the reproductive competition in Figure 5 are novel and have advantages in exploring their biological significance. However, at this stage, the authors' claim is weak since they only show the effects of age and social experience on territorial and mating behaviors, but do not experimentally demonstrate the influence of aggression mode change itself. In the Abstract, the authors state that these findings reveal how social experience shapes fighting strategies to optimize reproductive success. This is the most important perspective of the present study, and it would be necessary to show directly that the change of aggression mode by social experience contributes to reproductive success.

      We will either tone down this statement or provide additional analysis.

      In addition, a detailed description of the tussling is lacking. For example, the authors state that the tussling is less frequent but more vigorous than lunging, but while experimental data are presented on the frequency, the intensity seems to be subjective. The intensity is certainly clear from the supplementary video, but it would be necessary to evaluate the intensity itself using some index. Another problem is that there is no clear explanation of how to determine the tussling. A detailed method is required for the reproducibility of the experiment.

      We will provide more detailed methods and data analysis regarding tussling behavior.

      Reviewer #3 (Public review):

      In this manuscript, Gao et al. presented a series of intriguing data that collectively suggest that tussling, a form of high-intensity fighting among male fruit flies (Drosophila melanogaster) has a unique function and is controlled by a dedicated neural circuit. Based on the results of behavioral assays, they argue that increased tussling among socially experienced males promotes access to resources. They also concluded that tussling is controlled by a class of olfactory sensory neurons and sexually dimorphic central neurons that are distinct from pathways known to control lunges, a common male-type attack behavior.

      A major strength of this work is that it is the first attempt to characterize the behavioral function and neural circuit associated with Drosophila tussling. Many animal species use both low-intensity and high-intensity tactics to resolve conflicts. High-intensity tactics are mostly reserved for escalated fights, which are relatively rare. Because of this, tussling in the flies, like high-intensity fights in other animal species, has not been systematically investigated. Previous studies on fly aggressive behavior have often used socially isolated, relatively young flies within a short observation duration. Their discovery that 1) older (14-days-old) flies tend to tussle more often than younger (2-days-old) flies, 2) group-reared flies tend to tussle more often than socially isolated flies, and 3) flies tend to tussle at a later stage (mostly ~15 minutes after the onset of fighting), are the result of their creativity to look outside of conventional experimental settings. These new findings are key for quantitatively characterizing this interesting yet under-studied behavior.

      Precisely because their initial approach was creative, it is regrettable that the authors missed the opportunity to effectively integrate preceding studies in their rationale or conclusions, which sometimes led to premature claims. Also, while each experiment contains an intriguing finding, these are poorly related to each other. This obscures the central conclusion of this work. The perceived weaknesses are discussed in detail below.

      Thank you for the precise summary of the key findings and novelty of the study, and your insightful suggestions.

      Most importantly, the authors' definition of "tussling" is unclear because they did not explain how they quantified lunges and tussling, even though the central focus of the manuscript is behavior. Supplemental movies S1 and S2 appear to include "tussling" bouts in which 2 flies lunge at each other in rapid succession, and supplemental movie S3 appears to include bouts of "holding", in which one fly holds the opponent's wings and shakes vigorously. These cases raise a concern that their behavior classification is arbitrary. Specifically, lunges and tussling should be objectively distinguished because one of their conclusions is that these two actions are controlled by separate neural circuits. It is impossible to evaluate the credibility of their behavioral data without clearly describing a criterion of each behavior.

      We will add more details in methods.

      It is also confusing that the authors completely skipped the characterization of the tussling-controlling neurons they claimed to have identified. These neurons (a subset of so-called pC1 neurons labeled by previously described split-GAL4 line pC1SS2) are central to this manuscript, but the only information the authors have provided is its gross morphology in a low-resolution image (Figure 4D, E) and a statement that "only 3 pairs of pC1SS2 neurons whose function is both necessary and sufficient for inducing tussling in males" (lines 310-311). The evidence that supports this claim isn't provided. The expression pattern of pC1SS2 neurons in males has been only briefly described in reference 46. It is possible that these neurons overlap with previously characterized dsx+ and/or fru+ neurons that are important for male aggressions (measured by lunges), such as in Koganezawa et al., Curr. Biol. 2016 and Chiu et al., Cell 2020. This adds to the concern that lunge and tussling are not as clearly separated as the authors claim.

      Reply: we will perform additional morphological and functional experiments on pC1<sup>SS2</sup> neurons, e.g., whether they are fru or dsx positive and comparing them with P1a neurons.

      While their characterizations of tussling behaviors in wild-type males (Figures 1 and 2) are intriguing, the remaining data have little link with each other, making it difficult to understand what their main conclusion is. Figure 3 suggests that one class of olfactory sensory neurons (OSN) that express Or47b is necessary for tussling behavior. While the authors acknowledged that Or47b-expressing OSNs promote male courtship toward females presumably by detecting cuticular compounds, they provided little discussion on how a class of OSN can promote two different types of innate behavior. No evidence of a functional or circuitry relationship between the Or47b pathway and the pC1SS2 neurons was provided. It is unclear how these two components are relevant to each other. Lastly, the rationale of the experiment in Figure 5 and the interpretation of the results is confusing. The authors attributed a higher mating success rate of older, socially experienced males over younger, socially isolated males to their tendency to tussle, but tussling cannot happen when one of the two flies is not engaged. If, for instance, a socially isolated 14-day-old male does not engage in tussling as indicated in Figure 2, how can they tussle with a group-housed 14-day-old male? Because aggressive interactions in Figure 5 were not quantified, it is impossible to conclude that tussling plays a role in copulation advantage among pairs as authors argue (lines 282-288).

      Regarding why Or47b-expressing OSNs regulate two types of innate behaviors, we will add a discussion in the revised manuscript to explore the possible mechanisms underlying this phenomenon.

      Regarding the relationship between Or47b-expressing OSNs and pC1<sup>SS2</sup> neurons, we conducted pathway connection analyses using the FlyWire database. Although the FlyWire database currently only contains neuronal data from female brains, these findings provide a certain degree of reference. The results indicate that at least three intermediate neurons are required to establish the connection between these two neuronal types. We hope the editor and reviewers would agree with us that identifying these intermediate neurons involved in this connection is beyond this study.

      Regarding the rationale and conclusions from the experiments in Figure 5, we acknowledge the difficulty in quantifying tussling and lunging behaviors in these experiments. In the revised manuscript, we will tone down the statements about the relationship between fighting strategies and reproductive success. Additionally, we will provide further behavioral experiments to support the association between these two factors.

      Despite these weaknesses, it is important to acknowledge the authors' courage to initiate an investigation into a less characterized, high-intensity fighting behavior. Tussling requires the simultaneous engagement of two flies. Even if there is confusion over the distinction between lunges and tussling, the authors' conclusion that socially experienced flies and socially isolated flies employ distinct fighting strategies is convincing. Questions that require more rigorous studies are 1) whether such differences are encoded by separate circuits, and 2) whether the different fighting strategies are causally responsible for gaining ethologically relevant resources among socially experienced flies. Enhanced transparency of behavioral data will help readers understand the impact of this study. Lastly, the manuscript often mentions previous works and results without citing relevant references. For readers to grasp the context of this work, it is important to provide information about methods, reagents, and other key resources.

      We will add more details in methods and cite additional references, we will also perform additional experiment on pC1<sup>SS2</sup> function.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews

      Reviewer #1 (Public Review): 

      (1) Although the theory is based on memory, it also is based on spatially-selective cells.

      Not all cells in the hippocampus fulfill the criteria of place/HD/border/grid cells, and place a role in memory. E.g., Tonegawa, Buszaki labs' work does not focus on only those cells, and there are certainly a lot of non-pure spatial cells in monkeys (Martinez-Trujillo) and humans (iEEG). Does the author mainly focus on saying that "spatial cells" are memory, but do not account for non-spatial memory cells? This seems to be an incomplete account of memory - which is fine, but the way the model is set up suggests that *all* memory is, place (what/where), and non-spatial attributes ("grid") - but cells that don't fulfil these criteria in MTL (Diehl et al., 2017, Neuron; non-grid cells; Schaeffer et al., 2022, ICML; Luo et al., 2024, bioRxiv) certainly contribute to memory, and even navigation. This is also related to the question of whether these cell definitions matter at all (Luo et al., 2024). The authors note "However, this memory conjunction view of the MTL must be reconciled with the rodent electrophysiology finding that most cells in MTL appear to have receptive fields related to some aspect of spatial navigation (Boccara et al., 2010; Grieves & Jeffery, 2017). The paucity of non-spatial cells in MTL could be explained if grid cells have been mischaracterized as spatial." Is the author mainly talking about rodent work?

      There is a new section in the introduction that deals with these issues, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ That section reads:

      “Spatial navigation is inherently a memory problem – learning the spatial arrangement of a new enclosure requires memory for the conjunction of what and where. This has long been realized and in the introduction to ‘Hippocampus as a Cognitive Map’, O’Keefe and Nadel (1978) wrote “We shall argue that the hippocampus is the core of a neural memory system providing an objective spatial framework within which the items and events of an organism's experience are located and interrelated” (emphasis added). Furthermore, in the last chapter of their book, they extended cognitive map theory to human memory for non-spatial characteristics. However, in the decades since the development of cognitive map theory, the rodent spatial navigation and human memory literatures have progressed somewhat independently.

      The ideas proposed in this model are an attempt to reunify these literatures by returning to the original claim that spatial navigation is inherently a memory problem. The goal of the current study is to explain the rodent spatial navigation literature using a memory model that has the potential to also explain the human memory literature. In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation and as such, they do not lend themselves to explanations of human memory. Thus, the reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account.

      This study does not attempt to falsify other theories of grid cells. Instead, this model reaches a radically different interpretation regarding the function of grid cells; an interpretation that emerges from viewing spatial navigation as a memory problem. All other grid cell models assume that an entorhinal grid cell displaying a spatially arranged grid of firing fields serves the function of spatial coding (i.e., spatial grid cells exist to support a spatial metric). In contrast, the proposed memory model of grid cells assumes that the hexagonal tiling reflects the need to keep memories separate from each other to minimize confusion and confabulation – the grid pattern is the byproduct of pattern separation between memories rather than the basis of a spatial code. 

      It is now understood that grid-like firing fields can occur for non-spatial twodimensional spaces. For instance, human entorhinal cortex exhibits grid-like responses to video morph trajectories in a two-dimensional bird neck-length versus bird leg-length space (Constantinescu et al., 2016). As a general theory of learning and memory, the proposed memory model of grid cells is easily extended to explain these results (e.g., relabeling the border cell inputs in the model as neck-length and leg-length inputs). However, there are other grid cell models that can explain both spatial grid cells as well as non-spatial grid-like responses (Mok & Love, 2019; Rodríguez-Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015). Similar to this memory model of grid cells, these models are also positioned to explain both the rodent spatial navigation and human memory literatures. Nevertheless, there is a key difference between this model and other grid cell models that generalize to non-spatial representations. Specifically, these other models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). This memory model of grid cells provides an answer to the apparent paucity of nonspatial cell types in rodent MTL by proposing that grid cells with spatial receptive fields have been misclassified as spatial (they are what cells rather than where cells) and that place cells are fundamentally memory cells that conjoin what and where.”

      (2) Related to the last point, how about non-grid multi-field mEC cells? In theory, these also should be the same; but the author only presents perfect-look grid cells. In empirical work, clearly, this is not the case, and many mEC cells are multi-field non-grid cells (Diehl et al., 2017). Does the model find these cells? Do they play a different role? As noted by the author "Because the non-spatial attributes are constant throughout the two-dimensional surface, this results in an array of discrete memory locations that are approximately hexagonal (as explained in the Model Methods, an "online" memory consolidation process employing pattern separation rapidly turns an approximately hexagonal array into one that is precisely hexagonal). " If they are indeed all precisely hexagonal, does that mean the model doesn't have non-grid spatial cells? 

      Grid cells with irregular firing fields are now considered in the discussion with the following paragraphs

      “According to this model, hexagonally arranged grid cells should be the exception rather than the rule when considering more naturalistic environments. In a more ecologically valid situation, such as with landmarks, varied sounds, food sources, threats, and interactions with conspecifics, there may still be remembered locations were events occurred or remembered properties can be found, but because the non-spatial properties are non-uniform in the environment, the arrangement of memory feedback will be irregular, reflecting the varied nature of the environment. This may explain the finding that even in a situation where there are regular hexagonal grid cells, there are often irregular non-grid cells that have a reliable multi-location firing field, but the arrangement of the firing fields is irregular (Diehl et al., 2017). For instance, even when navigating in an enclosure that has uniform properties as dictated by experimental procedures, they may be other properties that were not well-controlled (e.g., a view of exterior lighting in some locations but not others), and these uncontrolled properties may produce an irregular grid (i.e., because the uncontrolled properties are reliably associated with some locations but not others, hippocampal memory feedback triggers retrieval of those properties in the associations locations).

      In this memory model, there are other situations in which an irregular but reliable multilocation grid may occur, even when everything is well controlled. In the reported simulations, when the hippocampal place cells were based on variation in X/Y (as defined by Border cells), nothing else changed as a function of location, and the model rapidly produced a precise hexagonal arrangement of hippocampal place cell memories. When head direction was included (i.e., real-world variation in X, Y, and head direction), the model still produced a hexagonal arrangement as per face-centered cubic packing of memories, but this precise arrangement was slower to emerge, with place cells continuing to shift their positions until the borders of the enclosure were sufficiently well learned from multiple viewpoints. If there is real-world variation in four or more dimensions, as is likely the case in a more ecologically valid situation, it will be even harder for place cell memories to settle on a precise regular lattice. Furthermore, in the case of four dimensions, mathematicians studying the “sphere packing problem” recently concluded that densest packing is irregular (Campos et al., 2023). This may explain why the multifield grid cells for freely flying bats have a systematic minimum distance between firing fields, but their arrangement is globally irregular (Ginosar et al., 2021). Assuming that the memories encoded by a bat include not just the three real-world dimensions of variation, but also head direction, the grid will likely be irregular even under optimal conditions of laboratory control.”

      (3) Theoretical reasons for why the model is put together this way, and why grid cells must be coding a non-spatial attribute: Is this account more data-driven (fits the data so formulated this way), or is it theoretical - there is a reason why place, border, grid cells are formulated to be like this. For example, is it an efficient way to code these variables? It can be both, like how the BVC model makes theoretical sense that you can use boundaries to determine a specific location (and so place cell), but also works (creates realistic place cells). 

      The motivation for this model is now articulated in the new section, quoted above, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ Regarding the assumption that border cells provide a spatial metric, this assumption is made for the same reasons as in the BVC model. Regarding this, the text said: “These assumptions regarding border cells are based on the boundary vector cell (BVC) model of Barry et al. (2006). As in the BVC model, combinations of border cells encode where each memory occurred in the realworld X/Y plane.”. A new sentence is added to model methods, stating: “This assumption is made because border cells provide an efficient representation of Euclidean space (e.g., if the animal knows how far it is from different walls of the enclosure, this already available information can be used to calculate location).”

      But in this case, the purpose of grid cell coding a non-spatial attribute, and having some kind of system where it doesn't fire at all locations seems a little arbitrary. If it's not encoding a spatial attribute, it doesn't have to have a spatial field. For example, it could fire in the whole arena - which some cells do (and don't pass the criteria of spatial cells as they are not spatially "selective" to another location, related to above).  

      Some cells have a constant high firing rate, but they are the exception rather than the rule. More typically, cells habituate in the presence of ongoing excitatory drive and by doing so become sensitive to fluctuations in excitatory drive. Habituation is advantageous both in terms of metabolic cost and in terms of function (i.e., sensitivity to change). This is now explained in the following paragraph:

      “In theory, a cell representing a non-spatial attribute found at all locations of an enclosure (aka, a grid cell in the context of this model), could fire constantly within the enclosure. However, in practice, cells habituate and rapidly reduce their firing rate by an order of magnitude when their preferred stimulus is presented without cessation (Abbott et al., 1997; Tsodyks & Markram, 1997). After habituation, the firing rate of the cell fluctuates with minor variation in the strength of the excitatory drive. In other words, habituation allows the cell to become sensitive to changes in the excitatory drive (Huber & O’Reilly, 2003). Thus, if there is stronger top-down memory feedback in some locations as compared to others, the cell will fire at a higher rate in those remembered locations rather than in all locations even though the attribute is found at all locations. In brief when faced with constant excitatory drive, the cell accommodates, and becomes sensitive to change in the magnitude of the excitatory drive. In the model simulation, this dynamic adaptation is captured by supposing that cells fire 5% of the time on-average across the simulation, regardless of their excitatory inputs.”

      (4) Why are grid cells given such a large role for encoding non-spatial attributes? If anything, shouldn't it be lateral EC or perirhinal cortex? Of course, they both could, but there is less reason to think this, at least for rodent mEC.  

      This is a good point and the following paragraph has been added to the introduction to explain that lateral EC is likely part of the explanation. But even when including lateral EC, it still appears that most of the input to hippocampus is spatial.

      “One possible answer to the apparent lack of non-spatial cells in MTL is to highlight the role of the lateral entorhinal cortex (LEC) as the source of non-spatial what information for memory encoding (Deshmukh & Knierim, 2011). LEC can be contrasted with mEC, which appears to only provide where information (Boccara et al., 2010a; Diehl et al., 2017). Although it is generally true that LEC is involved in non-spatial processing, there is evidence that LEC provides some forms of spatial information (Knierim et al., 2014). The kind of non-spatial information provided by LEC appears to be in relation to objects (Connor & Knierim, 2017; Wilson et al., 2013). However, in a typical rodent spatial navigation study there are no objects within the enclosure. Thus, although the distinction between mEC and LEC is likely part of the explanation, it is still the case that rodent entorhinal input to hippocampus appears to heavily favor spatial information.”

      (5) Clarification: why do place cells and grid cells differ in terms of stability in the model? Place cells are not stable initially but grid cells come out immediately. They seem directly connected so a bit unclear why; especially if place cell feedback leads to grid cell fields. There is an explanation in the text - based on grid cells coding the on-average memories, but these should be based on place cell inputs as well. So how is it that place fields are unstable then grid fields do not move at all? I wonder if a set of images or videos (gifs) showing the differences in spatial learning would be nice and clarify this point.  

      In this revision, I provide a new video focused on learning of place cell memories that include head direction. This second video is in relation to the results reported in Figure 9. The short answer is that the grid fields for the non-spatial cell are based on the average across several view-dependent memories (i.e., across several place cells that have head direction sensitivity) and the average is reliable even if the place cells are unstable. The text of this explanation now reads:

      “Why was the grid immediately apparent for the non-spatial attribute cell whereas the grid took considerable prior experience for the head direction cells? The answer relates to memory consolidation and the shifting nature of the hippocampal place cells. Head direction cells only produced a reliable grid once the hippocampal place cells (aka, memory cells) assumed stable locations. During the first few sessions, the hippocampal place cells were shifting their positions owing to pattern separation and consolidation. But once the place cells stabilized, they provided reliable top-down memory feedback to the head direction cells in some places but not others, thus producing a reliable grid arrangement to the firing maps of the head direction cells. In other words, for the head direction cells, the grid only appeared once the place cells stabilized. This slow stabilization of place fields is a known property (Bostock et al., 1991; Frank et al., 2004).

      In the simulation, the place cells did not stabilize until a sufficient number of place cells were created (Figure 9C). Specifically, these additional memories were located immediately outside the enclosure, around all borders (Figure 9D). These “outside the box” memories served to constrain the interior place cells, locking them in position despite ongoing consolidation. This dynamic can be seen in a movie showing a representative simulation. The movie shows the positions of the head direction sensitive place cells during initial learning, and then during additional sessions of prior experience as the movie speeds up (see link in Figure 9 capture).

      Why did the non-spatial grid cell (k) produce a grid immediately, before the place cells stabilized? As discussed in relation to Figure 8, the non-spatial grid cell is the projection through the 3D volume of real-world coordinates that includes X, Y, and head direction. Each grid field of a non-spatial grid cell reflects feedback from several place cells that each have a different head direction sensitivity (see for instance the allocentric pairs of memories illustrated in Figure 8C and 8D). Thus, each grid field is the average across several memories that entail different viewpoints and this averaging across memories provides stability even if the individual memories are not yet stable. This average of unstable memories produces a blurry sort of grid pattern without any prior experience.

      A final piece of the puzzle relies on the same mechanism that caused the grid pattern to align with the borders as reported in the results of Figures 6 and 7. Specifically, there are some “sticky” locations with ongoing consolidation because the connection weights are bounded. Because weights cannot go below their minimum or above their maximum, it is slightly more difficult for consolidation to push or pull connection weights over the peak value or under the minimum value of the tuning curve. Thus, the place cells tend to linger in locations that correspond to the peak or trough of a border cell. There are multiple peak and trough locations but for the parameter values in this simulation, the grid pattern seen in Figure 9C shows the set of peak/trough locations that satisfy the desired spacing between memories. Thus, the average across memories shows a reliable grid field at these locations even though the memories are unstable.”

      (6) Other predictions. Clearly, the model makes many interesting (and quite specific!) predictions. But does it make some known simple predictions? 

      • More place cells at rewarded (or more visited) locations. Some empirical researchers seem to think this is not as obvious as it seems (e.g., Duvellle et al., 2019; JoN; Nyberg et al., 2021, Neuron Review).  

      • Grid cell field moves toward reward (Butler et al., 2019; Boccera et al., 2019).  

      • Grid cells deform in trapezoid (Krupic et al., 2015) and change in environments like mazes (Derikman et al., 2014).  

      Thank you for these suggestions and I have added the following paragraph to the discussion:

      “In terms of the animal’s internal state, all locations in the enclosure may be viewed as equally aversive and unrewarding, which is a memorable characteristic of the enclosure. Reward, or lack thereof, is arguably one of the most important nonspatial characteristics and application of this model to reward might explain the existence of goal-related activity in place cells (Hok et al., 2007; although see Duvelle et al., 2019), reflecting the need to remember rewarding locations for goal directed behavior. Furthermore, if place cell memories for a rewarding location activate entorhinal grid cells, this may explain the finding that grid cells remap in an enclosure with a rewarded location such that firing fields are attracted to that location (Boccara et al., 2019; Butler et al., 2019). Studies that introduce reward into the enclosure are an important first step in terms of examining what happens to grid cells when the animal is placed in a more varied environment.”

      Regarding the changes in shape of the environment, this was discussed in the section of the paper that reads “As seen in Figure 12, because all but one of the place cells was exterior when the simulated animal was constrained to a narrow passage, the hippocampal place cell memories were no longer arranged in a hexagonal grid. This disruption of the grid array for narrow passages might explain the finding that the grid pattern (of grid cells) is disrupted in the thin corner of a trapezoid (Krupic et al., 2015) and disrupted when a previously open enclosure is converted to a hairpin maze by insertion of additional walls within the enclosure (Derdikman et al., 2009).” This particular section of the paper now appears in the Appendix and Figure 12 is now Appendix Figure 2.

      Reviewer #2 (Public Review): 

      The manuscript describes a new framework for thinking about the place and grid cell system in the hippocampus and entorhinal cortex in which these cells are fundamentally involved in supporting non-spatial information coding. If this framework were shown to be correct, it could have high impact because it would suggest a completely new way of thinking about the mammalian memory system in which this system is non-spatial. Although this idea is intriguing and thought-provoking, a very significant caveat is that the paper does not provide evidence that specifically supports its framework and rules out the alternate interpretations. Thus, although the work provides interesting new ideas, it leaves the reader with more questions than answers because it does not rule out any earlier ideas. 

      Basically, the strongest claim in the paper, that grid cells are inherently non-spatial, cannot be specifically evaluated versus existing frameworks on the basis of the evidence that is shown here. If, for example, the author had provided behavioral experiments showing that human memory encoding/retrieval performance shifts in relation to the predictions of the model following changes in the environment, it would have been potentially exciting because it could potentially support the author's reconceptualization of this system. But in its current form, the paper merely shows that a new type of model is capable of explaining the existing findings. There is not adequate data or results to show that the new model is a significantly better fit to the data compared to earlier models, which limits the impact of the work. In fact, there are some key data points in which the earlier models seem to better fit the data.  

      Overall, I would be more convinced that the findings from the paper are impactful if the author showed specific animal memory behavioral results that were only supported by their memory model but not by a purely spatial model. Perhaps the author could run new experiments to show that there are specific patterns of human or animal behavior that are only explained by their memory model and not by earlier models. But in its current form, I cannot rule out the existing frameworks and I believe some of the claims in this regard are overstated. 

      As previously detailed in Box 1 and as explained in the text in several places, the model provides an explanation of several findings that remain unexplained by other theories (see “Results Uniquely Explained by the Memory Model”). But more generally this is a good point, and the initial draft failed to fully articulate why a researcher might choose this model to guide future empirical investigations. A new section in the introduction that deals with these issues, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ That section reads:

      “Spatial navigation is inherently a memory problem – learning the spatial arrangement of a new enclosure requires memory for the conjunction of what and where. This has long been realized and in the introduction to ‘Hippocampus as a Cognitive Map’, O’Keefe and Nadel (1978) wrote “We shall argue that the hippocampus is the core of a neural memory system providing an objective spatial framework within which the items and events of an organism's experience are located and interrelated” (emphasis added). Furthermore, in the last chapter of their book, they extended cognitive map theory to human memory for non-spatial characteristics. However, in the decades since the development of cognitive map theory, the rodent spatial navigation and human memory literatures have progressed somewhat independently.

      The ideas proposed in this model are an attempt to reunify these literatures by returning to the original claim that spatial navigation is inherently a memory problem. The goal of the current study is to explain the rodent spatial navigation literature using a memory model that has the potential to also explain the human memory literature. In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation and as such, they do not lend themselves to explanations of human memory. Thus, the reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account.

      This study does not attempt to falsify other theories of grid cells. Instead, this model reaches a radically different interpretation regarding the function of grid cells; an interpretation that emerges from viewing spatial navigation as a memory problem. All other grid cell models assume that an entorhinal grid cell displaying a spatially arranged grid of firing fields serves the function of spatial coding (i.e., spatial grid cells exist to support a spatial metric). In contrast, the proposed memory model of grid cells assumes that the hexagonal tiling reflects the need to keep memories separate from each other to minimize confusion and confabulation – the grid pattern is the byproduct of pattern separation between memories rather than the basis of a spatial code. 

      It is now understood that grid-like firing fields can occur for non-spatial twodimensional spaces. For instance, human entorhinal cortex exhibits grid-like responses to video morph trajectories in a two-dimensional bird neck-length versus bird leg-length space (Constantinescu et al., 2016). As a general theory of learning and memory, the proposed memory model of grid cells is easily extended to explain these results (e.g., relabeling the border cell inputs in the model as neck-length and leg-length inputs). However, there are other grid cell models that can explain both spatial grid cells as well as non-spatial grid-like responses (Mok & Love, 2019; Rodríguez-Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015). Similar to this memory model of grid cells, these models are also positioned to explain both the rodent spatial navigation and human memory literatures. Nevertheless, there is a key difference between this model and other grid cell models that generalize to non-spatial representations. Specifically, these other models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). This memory model of grid cells provides an answer to the apparent paucity of nonspatial cell types in rodent MTL by proposing that grid cells with spatial receptive fields have been misclassified as spatial (they are what cells rather than where cells) and that place cells are fundamentally memory cells that conjoin what and where.”

      - The paper does not fully take into account all the findings regarding grid cells, some of which very clearly show spatial processing in this system. For example, findings on grid-bydirection cells (e.g., Sargolini et al. 2006) would seem to suggest that the entorhinal grid system is very specifically spatial and related to path integration. Why would grid-bydirection cells be present and intertwined with grid cells in the author's memory-related reconceptualization? It seems to me that the existence of grid-by-direction cells is strong evidence that at least part of this network is specifically spatial.

      Head by direction grid cells were a key part of the reported results. These grid cells naturally arise in the model as the animal forms memories (aka, hippocampal place cells) that conjoin location (as defined by border cells), head direction at the time of memory formation, and one or more non-spatial properties found at that location. In this revision, I have attempted to better explain how including head direction in hippocampal memories naturally gives rise to these cell types. The introduction to the head direction module simulations now reads:

      “According to this memory model of spatial navigation, place cells are the conjunction of location, as defined by border cells, and one or more properties that are remembered to exist at that location. Such memories could, for instance, allow an animal to remember the location of a food cache (Payne et al., 2021). The next set of simulations investigates behavior of the model when one of the to-be-remembered properties is head direction at the time when the memory was formed (e.g., the direction of a pathway leading to a food cache). Indicating that head direction is an important part of place cell representations, early work on place cells in mazes found strong sensitivity to head direction, such that the place field is found in one direction of travel but not the other (McNaughton et al., 1983; Muller et al., 1994). Place cells can exhibit a less extreme version of head direction sensitivity in open field recordings (Rubin et al., 2014), but the nature of the sensitivity is more complicated, depending on location of the animal relative to the place field center (Jercog et al., 2019).

      It is possible that some place cell memories do not receive head direction input, as was the case for the simulations reported in Figures 6/7 – in those simulations, place cells were entirely insensitive to head direction, owing to a lack of input from head direction cells. However, removal of head direction input to hippocampus affects place cell responses (Calton et al., 2003) and grid cell responses (Winter et al., 2015), suggesting that head direction is a key component of the circuit. Furthermore, if place cells represent episodic memories, it seems natural that they should include head direction (i.e., viewpoint at the time of memory formation).

      In the simulations reported next, head direction is simply another property that is conjoined in a hippocampal place cell memory. In this case, a head direction cell should become a head direction conjunctive grid cell (i.e., a grid cell, but only when the animal is heading in a particular direction), owing to memory feedback from the hexagonal array of hippocampal place cell memories. When including head direction, the real-world dimensions of variation are across three dimensions (X, Y, and head direction) rather than two, and consolidation will cause the place cells to arrange in a three-dimensional volume. The simulation reported below demonstrates that this situation provides a “grid module”.”

      - I am also concerned that the paper does not do enough to address findings regarding how the elliptical shape of grid fields shifts when boundaries of an environment compress in one direction or change shape/angles (Lever et al., & Krupic et al). Those studies show compression in grid fields based on boundary position, and I don't see how the authors' model would explain these findings.  

      This finding was covered in the original submission: “For instance, perhaps one egocentric/allocentric pair of mEC grid modules is based on head direction (viewpoint) in remembered positions relative to the enclosure borders whereas a different egocentric/allocentric pair is based on head direction in remembered positions relative to landmarks exterior to the enclosure. This might explain why a deformation of the enclosure (moving in one of the walls to form a rectangle rather than a square) caused some of the grid modules but not others to undergo a deformation of the grid pattern in response to the deformation of the enclosure wall (see also Barry et al., 2007). More specifically, if there is one set of non-orthogonal dimensions for enclosure borders and the movement of one wall is too modest as to cause avoid global remapping, this would deform the grid modules based the enclosure border cells. At the same time, if other grid modules are based on exterior properties (e.g., perhaps border cells in relation to the experimental room rather than the enclosure), then those grid modules would be unperturbed by moving the enclosure wall.”

      I apologize for being unclear in describing how the model might explain this result. The paragraph has been rewritten and now reads:

      “Consider the possibility that one mEC grid modules is based on head direction (viewpoint) in remembered positions relative to the enclosure borders (e.g., learning the properties of the enclosure, such as the metal surface) while a different grid module is based on head direction in remembered positions relative to landmarks exterior to the enclosure (e.g., learning the properties of the experimental room, such as the sound of electronics that the animal is subject to at all locations). This might explain why a deformation of the enclosure (moving one of the walls to form a rectangle rather than a square) caused some of the grid modules but not others to undergo a deformation of the grid pattern in response to the deformation of the enclosure wall (see also Barry et al., 2007). More specifically, suppose that the movement of one wall is modest and after moving the wall, the animal views the enclosure as being the same enclosure, albeit slightly modified (e.g., when a home is partially renovated, it is still considered the same home). In this case, the set of non-orthogonal dimensions associated with enclosure borders would still be associated with the now-changed borders and any memories in reference to this border-determined space would adjust their positions accordingly in real-world coordinates (i.e., the place cells would subtly shift their positions owing to this deformation of the borders, producing a corresponding deformation of the grid). At the same time, there may be other sets of memories that are in relation to dimensions exterior to the enclosure. Because these exterior properties are unchanged, any place cells and grid cells associated with the exterior-oriented memories would be unchanged by moving the enclosure wall.”

      - Are findings regarding speed modulation of grid cells problematic for the paper's memory results? 

      - A further issue is that the paper does not seem to adequately address developmental findings related to the timecourses of the emergence of different cell types. In their simulation, researchers demonstrate the immediate emergence of grid fields in a novel environment, while noting that the stabilization of place cell positions takes time. However, these simulation findings contradict previous empirical developmental studies (Langston et al., 2010). Those studies showed that head direction cells show the earliest development of spatial response, followed by the appearance of place cells at a similar developmental stage. In contrast, grid cells emerge later in this developmental sequence. The gradual improvement in spatial stability in firing patterns likely plays a crucial role in the developmental trajectory of grid cells. Contrary to the model simulation, grid cells emerge later than place cells and head direction cells, yet they also hold significance in spatial mapping. 

      - The model simulations suggest that certain grid patterns are acquired more gradually than others. For instance, egocentric grid cells require the stabilization of place cell memories amidst ongoing consolidation, while allocentric grid cells tend to reflect average place field positions. However, these findings seemingly conflict with empirical studies, particularly those on the conjunctive representation of distance and direction in the earliest grid cells. Previous studies show no significant differences were found in grid cells and grid cells with directional correlates across these age groups, relative to adults (Wills et al., 2012). This indicates that the combined representation of distance and direction in single mEC cells is present from the earliest ages at which grid cells emerge. 

      These are good points and they have been addressed in a new section of the introduction titled ‘The Scope of the Proposed Model’. That section reads:

      “The reported simulations explain why most mEC cell types in the rodent literature appear to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). Assuming that rodents can form non-spatial memories, rodent hippocampus must receive non-spatial input from entorhinal cortex. These simulations suggest that characterization of the rodent mEC cortex as primarily spatial might be incorrect if most grid cells (except perhaps head direction conjunctive grid cells) have been mischaracterized as spatial. Other literatures with other species find non-spatial representations in MTL (Gulli et al., 2020; Quiroga et al., 2005; Wixted et al., 2014) and non-spatial hippocampal memory encoding has been found in rodents (Liu et al., 2012; McEchron & Disterhoft, 1999). The proposed memory model is compatible with these results – the ideas contained in this model could be applied to nonspatial memory representations. However, surveys of cell types in rodent entorhinal cortex seem to indicate that most cells are spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). How can the rodent hippocampus encode nonspatial memories if most of its input is spatial? The goal of the reported simulations is to explain the apparent paucity of non-spatial cells in rodent entorhinal cortex by proposing that grid cells have been misclassified as spatial (see also Luo et al., 2024).

      Given the simplicity of the proposed model, there are important findings that the model cannot address -- it is not that the model makes the wrong predictions but rather that it makes no predictions. The role of running speed (Kraus et al., 2015) is one such variable for which the model makes no predictions. Similarly, because the model is a rate-coded model rather than a model of oscillating spiking neurons, it makes no predictions regarding theta oscillations (Buzsáki & Moser, 2013). The model is an account of learning and memory for an adult animal, and it makes no predictions regarding the developmental (Langston et al., 2010; Muessig et al., 2015; Wills et al., 2012) or evolutionary (Rodrıguez et al., 2002) time course of different cell types. This model contains several purely spatial representations such as border cells, head direction cells, and head direction conjunctive grid cells and it may be that these purely spatial cell types emerged first, followed by the evolution and/or development of non-spatial cell types. However, this does not invalidate the model. Instead, this is a model for an adult animal that has both episodic memory capabilities and spatial navigation capabilities, irrespective of the order in which these capabilities emerged.

      This model has the potential to explain context effects in memory (Godden & Baddeley, 1975; Gulli et al., 2020; Howard et al., 2005). According to this model, different grid cells represent different non-spatial characteristics and place cells represent the combination of these “context” factors and location. In the simulation, just one grid cell is simulated but the same results would emerge when simulating hundreds of different non-spatial inputs provided that all of the simulated non-spatial inputs exist throughout the recording session. However, there is evidence that hippocampus can explicitly represent the passage of time (Eichenbaum, 2014), and time is assuredly an important factor in defining episodic memory (Bright et al., 2020). Thus, although the current model addresses unique combinations of what and where, it is left to future work to incorporate representations of when in the memory model.”

      Reviewer #3 (Public Review): 

      A crucial assumption of the model is that the content of experience must be constant in space. It's difficult to imagine a real-world example that satisfies this assumption. Odors and sounds are used as examples. While they are often more spatially diffuse than an objects on the ground, odors and sounds have sources that are readily detectable. Animals can easily navigate to a food source or to a vocalizing conspecific. This assumption is especially problematic because it predicts that all grid cells should become silent when their preferred non-spatial attribute (e.g. a specific odor) is missing. I'm not aware of any experimental data showing that grid cells become silent. On the contrary, grid cells are known to remain active across all contexts that have been tested, including across sleep/wake states. Unlike place cells, grid cells do not seem to turn off. Since grid cells are active in all contexts, their preferred attribute must also be present in all contexts, and therefore they would not convey any information about the specific content of an experience.  

      These are good points and in this revision I have attempted to explain that there is a great deal of contextual similarity across all recording sessions. One paragraph in the discussion now reads

      “In a typical rodent spatial navigation study, the non-spatial attributes are wellcontrolled, existing at all locations regardless of the enclosure used during testing (hence, a grid cell in one enclosure will be a grid cell in a different enclosure). Because labs adopt standard procedures, the surfaces, odors (e.g., from cleaning), external lighting, time of day, human handler, electronic apparatus, hunger/thirst state, etc. might be the same for all recording sessions. Additionally, the animal is not allowed to interact with other animals during recording and this isolation may be an unusual and highly salient property of all recording sessions. Notably, the animal is always attached to wires during recording. The internal state of the animal (fear, aloneness, the noise of electronics, etc.) is likely similar across all recording situations and attributes of this internal state are likely represented in the hippocampus and entorhinal input to hippocampus. According to this model, hippocampal place cells are “marking” all locations in the enclosure as places where these things tend to happen.”

      The proposed novelty of this theory is that other models all assume that grid cells encode space. This isn't quite true of models based on continuous attractor networks, the discussion of which is notably absent. More specifically, these models focus on the importance of intrinsic dynamics within the entorhinal cortex in generating the grid pattern. While this firing pattern is aligned to space during navigation and therefore can be used as a representation of that space, the neural dynamics are preserved even during sleep. Similarly, it is because the grid pattern does not strictly encode physical space that gridlike signals are also observed in relation to other two-dimensional continuous variables. 

      These models were briefly discussed in the general discussion section and in this revision they are further discussed in the introduction in a new section, titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’ That section reads:

      “Spatial navigation is inherently a memory problem – learning the spatial arrangement of a new enclosure requires memory for the conjunction of what and where. This has long been realized and in the introduction to ‘Hippocampus as a Cognitive Map’, O’Keefe and Nadel (1978) wrote “We shall argue that the hippocampus is the core of a neural memory system providing an objective spatial framework within which the items and events of an organism's experience are located and interrelated” (emphasis added). Furthermore, in the last chapter of their book, they extended cognitive map theory to human memory for non-spatial characteristics. However, in the decades since the development of cognitive map theory, the rodent spatial navigation and human memory literatures have progressed somewhat independently.

      The ideas proposed in this model are an attempt to reunify these literatures by returning to the original claim that spatial navigation is inherently a memory problem. The goal of the current study is to explain the rodent spatial navigation literature using a memory model that has the potential to also explain the human memory literature. In contrast, most grid cell models (Bellmund et al., 2016; Bush et al., 2015; Castro & Aguiar, 2014; Hasselmo, 2009; Mhatre et al., 2012; Solstad et al., 2006; Sorscher et al., 2023; Stepanyuk, 2015; Widloski & Fiete, 2014) are domain specific models of spatial navigation and as such, they do not lend themselves to explanations of human memory. Thus, the reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account.

      This study does not attempt to falsify other theories of grid cells. Instead, this model reaches a radically different interpretation regarding the function of grid cells; an interpretation that emerges from viewing spatial navigation as a memory problem. All other grid cell models assume that an entorhinal grid cell displaying a spatially arranged grid of firing fields serves the function of spatial coding (i.e., spatial grid cells exist to support a spatial metric). In contrast, the proposed memory model of grid cells assumes that the hexagonal tiling reflects the need to keep memories separate from each other to minimize confusion and confabulation – the grid pattern is the byproduct of pattern separation between memories rather than the basis of a spatial code. 

      It is now understood that grid-like firing fields can occur for non-spatial two dimensional spaces. For instance, human entorhinal cortex exhibits grid-like responses to video morph trajectories in a two-dimensional bird neck-length versus bird leg-length space (Constantinescu et al., 2016). As a general theory of learning and memory, the proposed memory model of grid cells is easily extended to explain these results (e.g., relabeling the border cell inputs in the model as neck-length and leg-length inputs). However, there are other grid cell models that can explain both spatial grid cells as well as non-spatial grid-like responses (Mok & Love, 2019; Rodríguez-Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015). Similar to this memory model of grid cells, these models are also positioned to explain both the rodent spatial navigation and human memory literatures. Nevertheless, there is a key difference between this model and other grid cell models that generalize to non-spatial representations. Specifically, these other models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). This memory model of grid cells provides an answer to the apparent paucity of nonspatial cell types in rodent MTL by proposing that grid cells with spatial receptive fields have been misclassified as spatial (they are what cells rather than where cells) and that place cells are fundamentally memory cells that conjoin what and where.”

      The use of border cells or boundary vector cells as the main (or only) source of spatial information in the hippocampus is not well supported by experimental data. Border cells in the entorhinal cortex are not active in the center of an environment. Boundary-vector cells can fire farther away from the walls but are not found in the entorhinal cortex. They are located in the subiculum, a major output of the hippocampus. While the entorhinalhippocampal circuit is a loop, the route from boundary-vector cells to place cells is much less clear than from grid cells. Moreover, both border cells and boundary-vector cells (which are conflated in this paper) comprise a small population of neurons compared to grid cells.

      AUTHOR RESPONSE: The model can be built without assuming between-border cells (early simulations with the model did not make this assumption). Regarding this issue, the text reads “Unlike the BVC model, the boundary cell representation is sparsely populated using a basis set of three cells for each of the three dimensions (i.e., 9 cells in total), such that for each of the three non-orthogonal orientations, one cell captures one border, another the opposite border, and the third cell captures positions between the opposing borders (Solstad et al., 2008). However, this is not a core assumption, and it is possible to configure the model with border cell configurations that contain two opponent border cells per dimension, without needing to assume that any cells prefer positions between the borders (with the current parameters, the model predicts there will be two border cells for each between-border cell). Similarly, it is possible to configure the model with more than 3 cells for each dimension (i.e., multiple cells representing positions between the borders).” The Solstad paper found a few cells that responded in positions between borders, but perhaps not as many as 1 out of 3 cells, such as this particular model simulation predicts. If the paucity of between-border cells is a crucial data point, the model can be reconfigured with opponent-border cells without any between border cells. The reason that 3 border cells were used rather than 2 opponent border cells was for simplicity. Because 3 head direction cells were used to capture the face-centered cubic packing of memories, the simulation also used 3 border cells per dimensions to allow a common linear sum metric when conjoining dimensions to form memories. If the border dimensions used 2 cells while head direction used 3 cells, a dimensional weighting scheme would be needed to allow this mixing of “apples and oranges” in terms of distances in the 3D space that includes head direction.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors): 

      Specific questions/clarifications:  

      (1) Assumption of population-based vs single unit link to biological cells: At the start, the author assumes that each unit here can be associated with a population: "the simulated activation values can be thought of as proportional to the average firing rate of an ensemble of neurons with similar inputs and outputs (O'Reilly & Munakata, 2000)." But is a 'grid cell' found here a single cell or an average of many cells? Does this mean the model assumes many cells that have different fields that are averaged, which become a grid-like unit in the model? But in biology, these are single cells? Or does it mean a grid response is an average of the place cell inputs? 

      I apologize for being unclear about this. The grid cells in the model are equivalent to real single cells except that the simulation uses a ratecoded cell rather than a spiking cell. The averaging that was mentioned in the paper is across identically behaving spiking cells rather than across cells with different grid field arrangements. To better explain this, I have added the following text:

      “For instance, consider a set of several thousand spiking grid cells that are identical in terms of their firing fields. At any moment, some of these identically-behaving cells will produce an action potential while others do not (i.e., the cells are not perfectly synchronized), but a snapshot of their behavior can be extracted by calculating average firing rate across the ensemble. The simulated cells in the model represent this average firing rate of identically-behaving ensembles of spiking neurons.” 

      This is a mathematical short-cut to avoid simulating many spiking neurons. Because this model was compared to real spike rate maps, this real-valued average firing rate is down-sampled to produce spikes by finding the locations that produced the top 5% of real-valued activation values across the simulation.

      (2) It is not clear to me why they are circular border cells/basis sets.  

      In the initial submission, there was a brief paragraph describing this assumption. In this revision, that paragraph has been expanded and modified for greater clarity. It now reads:

      “Because head direction is necessarily a circular dimension, it was assumed that all dimensions are circular (a circular dimension is approximately linear for nearby locations). This assumption of circular dimensions was made to keep the model relatively simple, making it easier to combine dimensions and allowing application of the same processes for all dimensions. For instance, the model requires a weight normalization process to ensure that the pattern of weights for each dimension corresponds to a possible input value along that dimension. However, the normalization for a linear dimension is necessarily different than for a circular dimension. Because the neural tuning functions were assumed to be sine waves, normalization requires that the sum of squared weights add up to a constant value. For a linear dimension, this sum of squares rule only applies to the subset of cells that are relevant to a particular value along the dimension whereas for a circular dimension, this sum of squares rule is over the entire set of cells that represent the dimension (i.e., weight normalization is easier to implement with circular dimensions). Although all dimensions were assumed to be circular for reasons of mathematical convenience and parsimony, circular dimensions may relate to the finding that human observers have difficultly re-orienting themselves in a room depending on the degree of rotational symmetry of the room (Kelly et al., 2008). In addition, this simplifying assumption allows the model to capture the finding that the population of grid cells lies on a torus (Gardner et al., 2022), although I note that the model was developed before this result was known.”

      (3) Why is it 3 components? I realise that the number doesn't matter too much, but I believe more is better, so is it just for simplicity? 

      In this revision, additional text has been added to explain this assumption: “To keep the model simple, the same number of cells was assumed for all dimensions and all dimensions were assumed to be circular (head direction is necessarily circular and because one dimension needed to be circular, all dimensions were assumed to be circular). Three cells per dimensions was chosen because this provides a sparse population code of each dimension, with few border cells responding between borders, with few border cells responding between borders, while allowing three separate phases of grid cells within a grid cell module (in the model, a grid cell module arises from combination of a third dimension, such as head direction, with the real-world X/Y dimensions defined by border cells).”

      As a reminder, the text explaining the sparse coding of border cells reads: “However, this is not a core assumption, and it is possible to configure the model with border cell configurations that contain two opponent border cells per dimension, without needing to assume that any cells prefer positions between the borders (with the current parameters, the model predicts there will be two border cells for each between-border cell). Similarly, it is possible to configure the model with more than 3 cells for each dimension (i.e., multiple cells representing positions between the borders).”

      The model can work with just two opponent cells or with more than three cells per basis set. In different simulations, I have explored these possibilities. Three was chosen because it is a convenient way to highlight the face-centered cubic packing of memories that tends to occur (FCP produces 3 alternating layers of hexagonally arranged firing fields). Thus, each of the three head direction cells captures a different layer of the FCP arrangement. A more realistic simulation might combine 6 different head direction cells tiling the head direction dimension with opponent border cells (just 2 cells for each border dimensions). Such a combination would produce responses at borders, but no responses between borders and, at the same time, the head direction cells would still reveal the FCP arrangement. However, it is not easy to find the right parameters for such a mix-and-match simulation in which different dimensions have different numbers of tuning functions (e.g., some dimensions having 2 cells while others have 3 or 6 and some dimensions being linear while others are circular). When all of the dimensions are of the same type, the simple sum that arises from multiplying the input by the weight values gives rise to Euclidean distance (see Figure 3B). With a mix-and-match model of different dimension-types, it should be possible to adjust the sum to nevertheless produce a monotonic function with Euclidean distance although I leave this to future work. To keep things simple, I assumed that all dimensions are of the same type (circular, with 3 cells per dimension).  

      (4) Confusion due to the border cells/box was unclear to me. "If the period of the circular border cells was the same as the width of the box, then a memory pushed outside the box on one side would appear on the opposite side of the box, in which case the partial grid field on one side should match up with its remainder on the other side. This would entail complete confusion between opposite sides of the box, and the representation of the box would be a torus (donut-shaped) rather than a flat two-dimensional surface. To reduce confusion ..." Is this confusion of the model? Of the animal?  

      This would be confusion of the animal (e.g., a memory field overlapping with one border would also appear at the opposite border in the corresponding location). At one point in model development, I made the assumption that one side of the box wraps to the other side, and I asked Trygve Solstad to run some analyses of real data to see if cells actually wrap around in this manner. He did not find any evidence of this, and so I decided to include outsidethe-box representational area which, as it turned out, allowed the model to capture other behaviors as detailed in the paper.

      This section of the paper now reads:

      “The cosine tuning curves of the simulated border cells represent distance from the border on both sides of the border (i.e., firing rate increases as the animal approaches the border from either the inside or the outside of the enclosure). Experimental procedures do not allow the animal to experience locations immediately outside the enclosure, but these locations remain an important part of the hypothetic representation, particularly when considering the modification of memories through consolidation (i.e., a memory created inside the enclosure might be moved to a location outside the enclosure). This symmetry about the border cell’s preferred location is needed to maintain an unbiased representation, with a constant sum of squares for the border cell inputs (see methods section). Rather than using linear dimensions, all dimensions were assumed to be circular to keep the model relatively simple. This assumption was made because head direction is necessarily a circular dimension and by having all dimensions be circular, it is easy to combine dimensions in a consistent manner to produce multidimensional hippocampal place cell memories. Thus, the border cells define a torus (or more accurately a three-torus) of possible locations. This provides a hypothetical space of locations that could be represented.

      In light of the assumption to represent border cells with a circular dimension, when a memory is pushed outside the East wall of the enclosure, it would necessarily be moved to the West wall of the enclosure if the period of the circular dimension was equal to the width of the enclosure. If this were true, then the partial grid field on one side of the enclosure would match up with its remainder on the other side. Such a situation would cause the animal to become completely confused regarding opposite sides of the enclosure (a location on the West wall would be indistinguishable from the corresponding location on the East wall). To reduce confusion between opposite sides of the enclosure, the width of the enclosure in which the animal navigated (Figure 5) was assumed to be half as wide as the full period of the border cells. In other words, although the space of possible representations was a three-torus, it was assumed that the real-world twodimensional enclosure encompassed a section of the torus (e.g., a square piece of tape stuck onto the surface of a donut). The torus is better thought of as “playing field” in which different sizes and shapes of enclosure can be represented (i.e., different sizes and shapes of tape placed on the donut). Furthermore, this assumption provides representational space that is outside the box without such locations wrapping around to the opposite side of the box.”

      (5) Figure 3 - This result seems to be related to whether you use Euclidean or city-block distance. If you use Euclidean distances in two dimensions wouldn't this work out fine?  

      Euclidean distance was the metric used in the analysis of the two-dimensional simulation, but this did not work out. To make this clear, I have changed the label on the x-axes to read “Euclidean distance” for both the two- and three-dimensional simulations. The two-dimensional simulation produced city block behavior rather than Euclidean behavior because memory retrieval is the sum of the two dimensions, as is standard in neural networks, rather than the Euclidian distance formula, which would require that memory retrieval be the square root of the sum of squares of the two dimensions. One way to address this problem with the two-dimensional simulation would be to use a specific Euclidean-mimicking activation function rather than a simple sum of dimensions. The very first model I developed used such an activation function as applied to opponent border cells with just two dimensions (so 4 cells in total – left/right and top/down). This produced Euclidean behavior, but the activation function was implausible and did not generalize to simulations that also included head direction. In contrast, with three non-orthogonal dimensions, the simple sum of dimensions is approximately Euclidean.

      (6) Final sentence of the Discussion: "However, unlike the present model, these models still assume that entorhinal grid cells represent space rather than a non-spatial attribute." I am not sure if the authors of the cited papers will agree with this. They consider the spatial cases, but most argue they can treat non-spatial features as well. What the author might mean is that they assume non-spatial features are in some metric space that, in a way, is spatial. However, I am not sure if the author would argue that non-spatial features cannot be encoded metrically (e.g., Euclidean distance based on the similarity of odours). 

      In this section, when referring to “entorhinal grid cells” I was specifically referring to traditional grid cells in a rodent spatial navigation experiment. I did not mean to imply that these other theories cannot explain nonspatial grid fields, such as in the two-dimensional bird space grid cells found with humans. The way in which the proposed memory model and these other models differ is in terms of what they assume regarding the function of grid cells that exhibit spatial grid fields. In this revision, I have changed this text to read:

      “These models can capture some of the grid cell results presented in the current simulations, including extension to non-spatial grid-like responses (e.g., grid field that cover a two-dimensional neck/leg length bird space). Furthermore, these models may be able to explain memory phenomena similar to the model proposed in this study. However, unlike the proposed model, these models assume that the function of entorhinal grid cells that exhibit spatial X/Y grid fields during navigation is to represent space. In contrast, the memory model proposed in this study assume that the function of spatial X/Y grid cells is to represent a non-spatial attribute; the only reason they exhibit a spatial X/Y grid is because memories of that non-spatial attribute are arranged in a hexagonal grid owing to the uncluttered/unvarying nature of the enclosure. Thus, these model do not explain why most of the input to rodent hippocampus appears to be spatial (Boccara et al., 2010b; Diehl et al., 2017; Grieves & Jeffery, 2017) whereas the proposed model can explain this situation as reflecting the miss-classification of grid cells with a spatial arrangement as providing spatial input to hippocampus.”

      (7) It would be interesting to see videos/gifs of the model learning, and an idea of how many steps of trials it takes (is it capturing real-time rodent cell firing whilst foraging, or is it more abstracted, taking more trials). 

      The short answer is “yes”, the model is capturing real-time rodent cell firing while foraging. This is particularly true when simulating place cell memories in the absence of head direction information, as was shown in a video provided in the initial submission in relation to Figure 4. In this revision, I have provided a second video of learning when simulating place cell memories that include head direction. This second video is in relation to the results reported in Figure 9. This shows that even when learning a three-dimensional real-world space (X, Y, and head direction), the model rapidly produces an on-average hexagonal arrangement of place cells memories owing to the slight tendency of the place cell memories to linger in some locations as compared to others during consolidation. More specifically, they are more likely to linger in the locations that are the intersections of the peaks and/or troughs of the border cells and it is this tendency that supports the immediate appearance of grid cells. However, because the place cell memories are still shifting, head direction conjunctive grid cells are slower to emerge (the head direction conjunctive grid cells require stabilization of the place cells). The video then speeds up the learning process to so how place cells eventually stabilize after sufficient learning of the borders of the enclosure from different head/view directions.

      (8) One question is whether all the results have to be presented in the main text. It was difficult to see which key predictions fit the data and do so better than a spatial/navigation account. 

      Thank you for this suggestion. To make the paper more readable and easier for different readers with different interests to choose different aspects of the results to read, the second half of the results have been put in an appendix. More specifically, the second half of the results concerned place cells rather than grid cells. Thus, in this revision, the main text concerns grid cell results and the appendix concerns place cell results.

      Reviewer #3 (Recommendations For The Authors):  

      The title could usefully be shortened to focus on the main argument that observed firing patterns could be consistent with mapping memories instead of space. It's a stretch to argue that memory is the primary role when no such data is presented (i.e., there is no comparison of competing models). 

      This is a good point (I do not present evidence that conclusively indicates the function of MTL). This original title was chosen to make clear how this account is a radical departure from other accounts of grid cells. The revised title highlights that: 1) a memory model can also explain rodent single cell recording data during navigation; and 2) grid cell may not be non-spatial. The revised title is: “A Memory Model of Rodent Spatial Navigation: Place Cells are Memories Arranged in a Grid and Grid Cells are Non-spatial”

      When arguing that the main role of the hippocampus is memory, I strongly suggest engaging with the work of people like Howard Eichenbaum who spent the better part of their career arguing the same (e.g. DOI:10.1152/jn.00005.2017.)  

      Thank you for pointing out this important oversight. Early in introduction, I now write: “The proposal that hippocampus represents the multimodal conjunctions that define an episode is not new (Marr et al., 1991; Sutherland & Rudy, 1989) and neither is the proposal that hippocampal memory supports spatial/navigation ability (Eichenbaum, 2017). This view of the hippocampus is consistent with “feature in place” results (O’Keefe & Krupic, 2021) in which hippocampal cells respond to the conjunction of a non-spatial attribute affixed to a specific location, rather than responding more generically to any instance of a non-spatial attribute. In other words, the what/where conjunction is unique. Furthermore, the uniqueness of the what/where conjunction may be the fundamental building block of spatial memory and navigation. In reviewing the hippocampal literature, Howard Eichenbaum (2017) concludes that ‘the hippocampal system is not dedicated to spatial cognition and navigation, but organizes experiences in memory, for which spatial mapping and navigation are both a metaphor for and a prominent application of relational memory organization.’”

      With a focus on episodic memory, there should be a mention of the temporal component of memory. While it may rightfully be beyond the scope of this model, it's confusing to omit time completely from the discussion. 

      This issue and several others are now addressed in a new section in the introduction titled ‘The Scope of the Proposed Model’. That section reads:

      “The reported simulations explain why most mEC cell types in the rodent literature appear to be spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). Assuming that rodents can form non-spatial memories, rodent hippocampus must receive non-spatial input from entorhinal cortex. These simulations suggest that characterization of the rodent mEC cortex as primarily spatial might be incorrect if most grid cells (except perhaps head direction conjunctive grid cells) have been mischaracterized as spatial. Other literatures with other species find non-spatial representations in MTL (Gulli et al., 2020; Quiroga et al., 2005; Wixted et al., 2014) and non-spatial hippocampal memory encoding has been found in rodents (Liu et al., 2012; McEchron & Disterhoft, 1999). The proposed memory model is compatible with these results – the ideas contained in this model could be applied to nonspatial memory representations. However, surveys of cell types in rodent entorhinal cortex seem to indicate that most cells are spatial (Boccara et al., 2010; Diehl et al., 2017; Grieves & Jeffery, 2017). How can the rodent hippocampus encode nonspatial memories if most of its input is spatial? The goal of the reported simulations is to explain the apparent paucity of non-spatial cells in rodent entorhinal cortex by proposing that grid cells have been misclassified as spatial (see also Luo et al., 2024).

      Given the simplicity of the proposed model, there are important findings that the model cannot address -- it is not that the model makes the wrong predictions but rather that it makes no predictions. The role of running speed (Kraus et al., 2015) is one such variable for which the model makes no predictions. Similarly, because the model is a rate-coded model rather than a model of oscillating spiking neurons, it makes no predictions regarding theta oscillations (Buzsáki & Moser, 2013). The model is an account of learning and memory for an adult animal, and it makes no predictions regarding the developmental (Langston et al., 2010; Muessig et al., 2015; Wills et al., 2012) or evolutionary (Rodrıguez et al., 2002) time course of different cell types. This model contains several purely spatial representations such as border cells, head direction cells, and head direction conjunctive grid cells and it may be that these purely spatial cell types emerged first, followed by the evolution and/or development of non-spatial cell types. However, this does not invalidate the model. Instead, this is a model for an adult animal that has both episodic memory capabilities and spatial navigation capabilities, irrespective of the order in which these capabilities emerged.

      This model has the potential to explain context effects in memory (Godden & Baddeley, 1975; Gulli et al., 2020; Howard et al., 2005). According to this model, different grid cells represent different non-spatial characteristics and place cells represent the combination of these “context” factors and location. In the simulation, just one grid cell is simulated but the same results would emerge when simulating hundreds of different non-spatial inputs provided that all of the simulated non-spatial inputs exist throughout the recording session. However, there is evidence that hippocampus can explicitly represent the passage of time (Eichenbaum, 2014), and time is assuredly an important factor in defining episodic memory (Bright et al., 2020). Thus, although the current model addresses unique combinations of what and where, it is left to future work to incorporate representations of when in the memory model.”

      I recommend explaining the motivation of the theory in more detail in the introduction. It reads as "what if it's like this?" It would be helpful to instead highlight the limitations of current theories and argue why this theory is either a better fit for the data or is logically simpler. 

      This issue and several others are now addressed in the new section in the introduction titled ‘Why Model the Rodent Navigation Literature with a Memory Model?’, which I quoted above in response to the public reviews.

      It's worth considering shortening the results section to include only those that most convincingly support the main claim. The manuscript is quite long and appears to lack focus at times. 

      Thank you for this suggestion. To make the paper more readable and easier for different readers with different interests to choose different aspects of the results to read, the second half of the results have been put in an appendix. More specifically, the second half of the results concerned place cells rather than grid cells. Thus, in this revision, the main text concerns grid cell results and the appendix concerns place cell results.

      The discussion of path dependence on the formation of the grid pattern is important but only briefly discussed. It may be useful to add simulations testing whether different paths (not random walks) produce distorted grid patterns. 

      The short answer is that the path doesn’t affect things in general. The consolidation rule ensures equally spaced memories even if, for instance, one side of the enclosure is explored much more than the other side. As just one example, I have run simulations with a radial arm maze and even though the animal is constrained to only run on the maze arms. The memories still arrange hexagonally as memories become pushed outside the arms. Rather than adding additional simulations to study, I now briefly describe this in the model methods:

      “Of note, the ability of the model to produce grid cell responses does not depend on this decision to simulate an animal taking a random walk – the same results emerge if the animal is more systematic in its path. All that matters for producing grid cell responses is that the animal visits all locations and that the animal takes on different head directions for the same location in the case of simulations that also include head direction as an input to hippocampal place cells.”

      I struggle to understand in Figure 3 why retrieval strength ought to scale monotonically with Euclidean distance, and why that justifies a more complex model (three non-orthogonal dimensions). 

      The introduction to this section now reads: “Animals can plan novel straight line paths to reach a known position and evidence suggests they do so by learning Euclidean representations of space (Cheng & Gallistel, 2014; Normand & Boesch, 2009; Wilkie, 1989). Thus, it was assumed that hippocampal place cells represent positions in Euclidean space (as opposed to non-Euclidean space, such a occurs with a city-block metric).”

      p.17 "although the representational space is a torus (or more specifically a three-torus), it is assumed that the real-world two-dimensional surface is only a section of the torus (e.g., a square piece of tape stuck onto the surface of a donut)." I fail to understand how the realworld surface is only a part of the torus. In the existing theoretical and experimental work on toroidal topology of grid cell activity, the torus represents a very small fraction of the real world, and repeating activity on the toroidal manifold is a crucial feature of how it maps 2D space in a regular manner. Why then here do you want the torus to be larger than the realworld? 

      This section has been rewritten to better explain these assumptions. The relevant paragraphs now read:

      “The cosine tuning curves of the simulated border cells represent distance from the border on both sides of the border (i.e., firing rate increases as the animal approaches the border from either the inside or the outside of the enclosure). Experimental procedures do not allow the animal to experience locations immediately outside the enclosure, but these locations remain an important part of the hypothetic representation, particularly when considering the modification of memories through consolidation (i.e., a memory created inside the enclosure might be moved to a location outside the enclosure). This symmetry about the border cell’s preferred location is needed to maintain an unbiased representation, with a constant sum of squares for the border cell inputs (see methods section). Rather than using linear dimensions, all dimensions were assumed to be circular to keep the model relatively simple. This assumption was made because head direction is necessarily a circular dimension and by having all dimensions be circular, it is easy to combine dimensions in a consistent manner to produce multidimensional hippocampal place cell memories. Thus, the border cells define a torus (or more accurately a three-torus) of possible locations. This provides a hypothetical space of locations that could be represented.

      In light of the assumption to represent border cells with a circular dimension, when a memory is pushed outside the East wall of the enclosure, it would necessarily be moved to the West wall of the enclosure if the period of the circular dimension was equal to the width of the enclosure. If this were true, then the partial grid field on one side of the enclosure would match up with its remainder on the other side. Such a situation would cause the animal to become completely confused regarding opposite sides of the enclosure (a location on the West wall would be indistinguishable from the corresponding location on the East wall). To reduce confusion between opposite sides of the enclosure, the width of the enclosure in which the animal navigated (Figure 5) was assumed to be half as wide as the full period of the border cells. In other words, although the space of possible representations was a three-torus, it was assumed that the real-world twodimensional enclosure encompassed a section of the torus (e.g., a square piece of tape stuck onto the surface of a donut). The torus is better thought of as “playing field” in which different sizes and shapes of enclosure can be represented (i.e., different sizes and shapes of tape placed on the donut). Furthermore, this assumption provides representational space that is outside the box without such locations wrapping around to the opposite side of the box.”

      p.28 "More specifically, egocentric grid cells (e.g., head direction conjunctive grid cells) require stabilization of the place cell memories in the face of ongoing consolidation whereas allocentric grid cells reflect on-average place field positions." and p.32 "if place cells represent episodic memories, it seems natural that they should include head direction (an egocentric viewpoint)." But the head direction signal is not egocentric, it is allocentric. I'm unsure whether this is a typo or a potentially more serious conceptual misunderstanding. 

      Any reference to egocentric has been removed in this revision. In the initial submission, when I used egocentric, I was referring to memories that depended on the head direction of the animal at the time of memory formation. I was using “egocentric” in relation to whether the memory was related to the animal’s personal bodily experience at the time of memory formation. But I concede that this is confusing since the ego/allo distinction is typically used to differentiate angular directions that are relative to the person (left/right) versus earth (East/West). Instead, throughout the manuscript I now refer to these as view-dependent memories since head direction would entail having a different view of the environment at the time of memory formation. I still refer to the stacking of multiple view-dependent memories on the same X/Y location as being the development of an allocentric representation however, since this can be thought of as one way to learn a cognitive map of the enclosure that is view independent.

      p.37 "But if the border cells had changed their alignment with the new enclosure (e.g., if the E border dimension aligned with the North-South borders), then the place cells would have appeared to undergo global remapping as their positions rotated by 90 degrees and the grid pattern would have also rotated." But this would not be interpreted as global remapping by standard analyses of place and grid cell responses. A coherent rotation of firing patterns is not interpreted as remapping. 

      This sentence now reads: “But if the border cells had changed their alignment with the new enclosure (e.g., if the E border dimension aligned with the North-South borders), then the place cells would remain in their same positions relative to the now-rotated borders (i.e., no remapping relative to the enclosure) and the corresponding grid cells would also retain their same alignment relative to the enclosure.”

      p.37 "this is more accurately described as partial remapping (nearly all place fields were unaffected)." If nearly all place fields were unaffected, this should be interpreted as a stable map. Partial remapping is a mix of stability, rate remapping, and global remapping within a population of place cells. 

      This sentence has been removed.

      p.40 "The dependence of grid cell responses on memory may help explain why grid cells have been found for bats crawling on a two-dimensional surface (Yartsev et al., 2011), but three-dimensional grid cells have never been observed for flying bats." This is not true. Ginosar et al. (2021) observed 3D grid cells in flying bats.  

      Thank you for highlighting this issue. In the initial submission I was using “grid cell” to mean a cell that produced a precise hexagonal grid, which is not the case for the 3D grid cells in bats. In this revision, I now discuss grid cell that produce irregular grid fields, writing:

      “According to this model, hexagonally arranged grid cells should be the exception rather than the rule when considering more naturalistic environments. In a more ecologically valid situation, such as with landmarks, varied sounds, food sources, threats, and interactions with conspecifics, there may still be remembered locations were events occurred or remembered properties can be found, but because the non-spatial properties are non-uniform in the environment, the arrangement of memory feedback will be irregular, reflecting the varied nature of the environment. This may explain the finding that even in a situation where there are regular hexagonal grid cells, there are often irregular non-grid cells that have a reliable multi-location firing field, but the arrangement of the firing fields is irregular (Diehl et al., 2017). For instance, even when navigating in an enclosure that has uniform properties as dictated by experimental procedures, they may be other properties that were not well-controlled (e.g., a view of exterior lighting in some locations but not others), and these uncontrolled properties may produce an irregular grid (i.e., because the uncontrolled properties are reliably associated with some locations but not others, hippocampal memory feedback triggers retrieval of those properties in the associations locations).

      In this memory model, there are other situations in which an irregular but reliable multi-location grid may occur, even when everything is well controlled. In the reported simulations, when the hippocampal place cells were based on variation in X/Y (as defined by Border cells), nothing else changed as a function of location, and the model rapidly produced a precise hexagonal arrangement of hippocampal place cell memories. When head direction was included (i.e., real-world variation in X, Y, and head direction), the model still produced a hexagonal arrangement as per face centered cubic packing of memories, but this precise arrangement was slower to emerge, with place cells continuing to shift their positions until the borders of the enclosure were sufficiently well learned from multiple viewpoints. If there is realworld variation in four or more dimensions, as is likely the case in a more ecologically valid situation, it will be even harder for place cell memories to settle on a precise regular lattice. Furthermore, in the case of four dimensions, mathematicians studying the “sphere packing problem” recently concluded that densest packing is irregular (Campos et al., 2023). This may explain why the multifield grid cells for freely flying bats have a systematic minimum distance between firing fields, but their arrangement is globally irregular (Ginosar et al., 2021). Assuming that the memories encoded by a bat include not just the three realworld dimensions of variation, but also head direction, the grid will likely be irregular even under optimal conditions of laboratory control.”

      Multiple typos are found on page 25, end of paragraph 3: "More specifically, if there is one set of non-orthogonal dimensions for enclosure borders and the movement of one wall is too modest as to cause avoid global remapping, this would deform the grid modules based the enclosure border cells."

      As detailed above in the response the public reviews, this paragraph has been rewritten.

    1. Reviewer #1 (Public review):

      In this important study, the authors characterized the transformation of neural representations of olfactory stimuli from the primary sensory cortex to multisensory regions in the medial temporal lobe and investigated how they were affected by non-associative learning. The authors used high-density silicon probe recordings from five different cortical regions while familiar vs. novel odors were presented to a head-restrained mouse. This is a timely study because unlike other sensory systems (e.g., vision), the progressive transformation of olfactory information is still poorly understood. The authors report that both odor identity and experience are encoded by all of these five cortical areas but nonetheless some themes emerge. Single neuron tuning of odor identity is broad in the sensory cortices but becomes narrowly tuned in hippocampal regions. Furthermore, while experience affects neuronal response magnitudes in early sensory cortices, it changes the proportion of active neurons in hippocampal regions. Thus, this study is an important step forward in the ongoing quest to understand how olfactory information is progressively transformed along the olfactory pathway.

      The study is well-executed. The direct comparison of neuronal representations from five different brain regions is impressive. Conclusions are based on single neuronal level as well as population level decoding analyses. Among all the reported results, one stands out for being remarkably robust. The authors show that the anterior olfactory nucleus (AON), which receives direct input from the olfactory bulb output neurons, was far superior at decoding odor identity as well as novelty compared to all the other brain regions. This is perhaps surprising because the other primary sensory region - the piriform cortex - has been thought to be the canonical site for representing odor identity. A vast majority of studies have focused on aPCx, but direct comparisons between odor coding in the AON and aPCx are rare. The experimental design of this current study allowed the authors to do so and the AON was found to convincingly outperform aPCx. Although this result goes against the canonical model, it is consistent with a few recent studies including one that predicted this outcome based on anatomical and functional comparisons between the AON-projecting tufted cells vs. the aPCx-projecting mitral cells in the olfactory bulb (Chae, Banerjee et. al. 2022). Future experiments are needed to probe the circuit mechanisms that generate this important difference between the two primary olfactory cortices as well as their potential causal roles in odor identification.

      The authors were also interested in how familiarity vs. novelty affects neuronal representation across all these brain regions. One weakness of this study is that neuronal responses were not measured during the process of habituation. Neuronal responses were measured after four days of daily exposure to a few odors (familiar) and then some other novel odors were introduced. This creates a confound because the novel vs. familiar stimuli are different odorants and that itself can lead to drastic differences in evoked neural responses. Although the authors try to rule out this confound by doing a clever decoding and Euclidian distance analysis, an alternate more straightforward strategy would have been to measure neuronal activity for each odorant during the process of habituation.

    1. According to all known laws of aviation,

      there is no way a bee should be able to fly.

      Its wings are too small to get its fat little body off the ground.

      The bee, of course, flies anyway

      because bees don't care what humans think is impossible.

      Yellow, black. Yellow, black. Yellow, black. Yellow, black.

      Ooh, black and yellow! Let's shake it up a little.

      Barry! Breakfast is ready!

      Ooming!

      Hang on a second.

      Hello?

      Barry?

      Adam?

      Oan you believe this is happening?

      I can't. I'll pick you up.

      Looking sharp.

      Use the stairs. Your father paid good money for those.

      Sorry. I'm excited.

      Here's the graduate. We're very proud of you, son.

      A perfect report card, all B's.

      Very proud.

      Ma! I got a thing going here.

      You got lint on your fuzz.

      Ow! That's me!

      Wave to us! We'll be in row 118,000.

      Bye!

      Barry, I told you, stop flying in the house!

      Hey, Adam.

      Hey, Barry.

      Is that fuzz gel?

      A little. Special day, graduation.

      Never thought I'd make it.

      Three days grade school, three days high school.

      Those were awkward.

      Three days college. I'm glad I took a day and hitchhiked around the hive.

      You did come back different.

      Hi, Barry.

      Artie, growing a mustache? Looks good.

      Hear about Frankie?

      Yeah.

      You going to the funeral?

      No, I'm not going.

      Everybody knows, sting someone, you die.

      Don't waste it on a squirrel. Such a hothead.

      I guess he could have just gotten out of the way.

      I love this incorporating an amusement park into our day.

      That's why we don't need vacations.

      Boy, quite a bit of pomp… under the circumstances.

      Well, Adam, today we are men.

      We are!

      Bee-men.

      Amen!

      Hallelujah!

      Students, faculty, distinguished bees,

      please welcome Dean Buzzwell.

      Welcome, New Hive Oity graduating class of…

      …9:15.

      That concludes our ceremonies.

      And begins your career at Honex Industries!

      Will we pick ourjob today?

      I heard it's just orientation.

      Heads up! Here we go.

      Keep your hands and antennas inside the tram at all times.

      Wonder what it'll be like? A little scary. Welcome to Honex, a division of Honesco

      and a part of the Hexagon Group.

      This is it!

      Wow.

      Wow.

      We know that you, as a bee, have worked your whole life

      to get to the point where you can work for your whole life.

      Honey begins when our valiant Pollen Jocks bring the nectar to the hive.

      Our top-secret formula

      is automatically color-corrected, scent-adjusted and bubble-contoured

      into this soothing sweet syrup

      with its distinctive golden glow you know as…

      Honey!

      That girl was hot.

      She's my cousin!

      She is?

      Yes, we're all cousins.

      Right. You're right.

      At Honex, we constantly strive

      to improve every aspect of bee existence.

      These bees are stress-testing a new helmet technology.

      What do you think he makes? Not enough. Here we have our latest advancement, the Krelman.

      What does that do? Oatches that little strand of honey that hangs after you pour it. Saves us millions.

      Oan anyone work on the Krelman?

      Of course. Most bee jobs are small ones. But bees know

      that every small job, if it's done well, means a lot.

      But choose carefully

      because you'll stay in the job you pick for the rest of your life.

      The same job the rest of your life? I didn't know that.

      What's the difference?

      You'll be happy to know that bees, as a species, haven't had one day off

      in 27 million years.

      So you'll just work us to death?

      We'll sure try.

      Wow! That blew my mind!

      "What's the difference?" How can you say that?

      One job forever? That's an insane choice to have to make.

      I'm relieved. Now we only have to make one decision in life.

      But, Adam, how could they never have told us that?

      Why would you question anything? We're bees.

      We're the most perfectly functioning society on Earth.

      You ever think maybe things work a little too well here?

      Like what? Give me one example.

      I don't know. But you know what I'm talking about.

      Please clear the gate. Royal Nectar Force on approach.

      Wait a second. Oheck it out.

      Hey, those are Pollen Jocks! Wow. I've never seen them this close.

      They know what it's like outside the hive.

      Yeah, but some don't come back.

      Hey, Jocks! Hi, Jocks! You guys did great!

      You're monsters! You're sky freaks! I love it! I love it!

      I wonder where they were. I don't know. Their day's not planned.

      Outside the hive, flying who knows where, doing who knows what.

      You can'tjust decide to be a Pollen Jock. You have to be bred for that.

      Right.

      Look. That's more pollen than you and I will see in a lifetime.

      It's just a status symbol. Bees make too much of it.

      Perhaps. Unless you're wearing it and the ladies see you wearing it.

      Those ladies? Aren't they our cousins too?

      Distant. Distant.

      Look at these two.

      Oouple of Hive Harrys. Let's have fun with them. It must be dangerous being a Pollen Jock.

      Yeah. Once a bear pinned me against a mushroom!

      He had a paw on my throat, and with the other, he was slapping me!

      Oh, my! I never thought I'd knock him out. What were you doing during this?

      Trying to alert the authorities.

      I can autograph that.

      A little gusty out there today, wasn't it, comrades?

      Yeah. Gusty.

      We're hitting a sunflower patch six miles from here tomorrow.

      Six miles, huh? Barry! A puddle jump for us, but maybe you're not up for it.

      Maybe I am. You are not! We're going 0900 at J-Gate.

      What do you think, buzzy-boy? Are you bee enough?

      I might be. It all depends on what 0900 means.

      Hey, Honex!

      Dad, you surprised me.

      You decide what you're interested in?

      Well, there's a lot of choices. But you only get one. Do you ever get bored doing the same job every day?

      Son, let me tell you about stirring.

      You grab that stick, and you just move it around, and you stir it around.

      You get yourself into a rhythm. It's a beautiful thing.

      You know, Dad, the more I think about it,

      maybe the honey field just isn't right for me.

      You were thinking of what, making balloon animals?

      That's a bad job for a guy with a stinger.

      Janet, your son's not sure he wants to go into honey!

      Barry, you are so funny sometimes. I'm not trying to be funny. You're not funny! You're going into honey. Our son, the stirrer!

      You're gonna be a stirrer? No one's listening to me! Wait till you see the sticks I have.

      I could say anything right now. I'm gonna get an ant tattoo!

      Let's open some honey and celebrate!

      Maybe I'll pierce my thorax. Shave my antennae.

      Shack up with a grasshopper. Get a gold tooth and call everybody "dawg"!

      I'm so proud.

      We're starting work today! Today's the day. Oome on! All the good jobs will be gone.

      Yeah, right.

      Pollen counting, stunt bee, pouring, stirrer, front desk, hair removal…

      Is it still available? Hang on. Two left! One of them's yours! Oongratulations! Step to the side.

      What'd you get? Picking crud out. Stellar! Wow!

      Oouple of newbies?

      Yes, sir! Our first day! We are ready!

      Make your choice.

      You want to go first? No, you go. Oh, my. What's available?

      Restroom attendant's open, not for the reason you think.

      Any chance of getting the Krelman? Sure, you're on. I'm sorry, the Krelman just closed out.

      Wax monkey's always open.

      The Krelman opened up again.

      What happened?

      A bee died. Makes an opening. See? He's dead. Another dead one.

      Deady. Deadified. Two more dead.

      Dead from the neck up. Dead from the neck down. That's life!

      Oh, this is so hard!

      Heating, cooling, stunt bee, pourer, stirrer,

      humming, inspector number seven, lint coordinator, stripe supervisor,

      mite wrangler. Barry, what do you think I should… Barry?

      Barry!

      All right, we've got the sunflower patch in quadrant nine…

      What happened to you? Where are you?

      I'm going out.

      Out? Out where?

      Out there.

      Oh, no!

      I have to, before I go to work for the rest of my life.

      You're gonna die! You're crazy! Hello?

      Another call coming in.

      If anyone's feeling brave, there's a Korean deli on 83rd

      that gets their roses today.

      Hey, guys.

      Look at that. Isn't that the kid we saw yesterday? Hold it, son, flight deck's restricted.

      It's OK, Lou. We're gonna take him up.

      Really? Feeling lucky, are you?

      Sign here, here. Just initial that.

      Thank you. OK. You got a rain advisory today,

      and as you all know, bees cannot fly in rain.

      So be careful. As always, watch your brooms,

      hockey sticks, dogs, birds, bears and bats.

      Also, I got a couple of reports of root beer being poured on us.

      Murphy's in a home because of it, babbling like a cicada!

      That's awful. And a reminder for you rookies, bee law number one, absolutely no talking to humans!

      All right, launch positions!

      Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz! Buzz, buzz, buzz, buzz!

      Black and yellow!

      Hello!

      You ready for this, hot shot?

      Yeah. Yeah, bring it on.

      Wind, check.

      Antennae, check.

      Nectar pack, check.

      Wings, check.

      Stinger, check.

      Scared out of my shorts, check.

      OK, ladies,

      let's move it out!

      Pound those petunias, you striped stem-suckers!

      All of you, drain those flowers!

      Wow! I'm out!

      I can't believe I'm out!

      So blue.

      I feel so fast and free!

      Box kite!

      Wow!

      Flowers!

      This is Blue Leader. We have roses visual.

      Bring it around 30 degrees and hold.

      Roses!

      30 degrees, roger. Bringing it around.

      Stand to the side, kid. It's got a bit of a kick.

      That is one nectar collector!

      Ever see pollination up close? No, sir. I pick up some pollen here, sprinkle it over here. Maybe a dash over there,

      a pinch on that one. See that? It's a little bit of magic.

      That's amazing. Why do we do that?

      That's pollen power. More pollen, more flowers, more nectar, more honey for us.

      Oool.

      I'm picking up a lot of bright yellow. Oould be daisies. Don't we need those?

      Oopy that visual.

      Wait. One of these flowers seems to be on the move.

      Say again? You're reporting a moving flower?

      Affirmative.

      That was on the line!

      This is the coolest. What is it?

      I don't know, but I'm loving this color.

      It smells good. Not like a flower, but I like it.

      Yeah, fuzzy.

      Ohemical-y.

      Oareful, guys. It's a little grabby.

      My sweet lord of bees!

      Oandy-brain, get off there!

      Problem!

      Guys! This could be bad. Affirmative.

      Very close.

      Gonna hurt.

      Mama's little boy.

      You are way out of position, rookie!

      Ooming in at you like a missile!

      Help me!

      I don't think these are flowers.

      Should we tell him? I think he knows. What is this?!

      Match point!

      You can start packing up, honey, because you're about to eat it!

      Yowser!

      Gross.

      There's a bee in the car!

      Do something!

      I'm driving!

      Hi, bee.

      He's back here!

      He's going to sting me!

      Nobody move. If you don't move, he won't sting you. Freeze!

      He blinked!

      Spray him, Granny!

      What are you doing?!

      Wow… the tension level out here is unbelievable.

      I gotta get home.

      Oan't fly in rain.

      Oan't fly in rain.

      Oan't fly in rain.

      Mayday! Mayday! Bee going down!

      Ken, could you close the window please?

      Ken, could you close the window please?

      Oheck out my new resume. I made it into a fold-out brochure.

      You see? Folds out.

      Oh, no. More humans. I don't need this.

      What was that?

      Maybe this time. This time. This time. This time! This time! This…

      Drapes!

      That is diabolical.

      It's fantastic. It's got all my special skills, even my top-ten favorite movies.

      What's number one? Star Wars?

      Nah, I don't go for that…

      …kind of stuff.

      No wonder we shouldn't talk to them. They're out of their minds.

      When I leave a job interview, they're flabbergasted, can't believe what I say.

      There's the sun. Maybe that's a way out.

      I don't remember the sun having a big 75 on it.

      I predicted global warming.

      I could feel it getting hotter. At first I thought it was just me.

      Wait! Stop! Bee!

      Stand back. These are winter boots.

      Wait!

      Don't kill him!

      You know I'm allergic to them! This thing could kill me!

      Why does his life have less value than yours?

      Why does his life have any less value than mine? Is that your statement?

      I'm just saying all life has value. You don't know what he's capable of feeling.

      My brochure!

      There you go, little guy.

      I'm not scared of him. It's an allergic thing.

      Put that on your resume brochure.

      My whole face could puff up.

      Make it one of your special skills.

      Knocking someone out is also a special skill.

      Right. Bye, Vanessa. Thanks.

      Vanessa, next week? Yogurt night?

      Sure, Ken. You know, whatever.

      You could put carob chips on there.

      Bye.

      Supposed to be less calories.

      Bye.

      I gotta say something.

      She saved my life. I gotta say something.

      All right, here it goes.

      Nah.

      What would I say?

      I could really get in trouble.

      It's a bee law. You're not supposed to talk to a human.

      I can't believe I'm doing this.

      I've got to.

      Oh, I can't do it. Oome on!

      No. Yes. No.

      Do it. I can't.

      How should I start it? "You like jazz?" No, that's no good.

      Here she comes! Speak, you fool!

      Hi!

      I'm sorry.

      You're talking. Yes, I know. You're talking!

      I'm so sorry.

      No, it's OK. It's fine. I know I'm dreaming.

      But I don't recall going to bed.

      Well, I'm sure this is very disconcerting.

      This is a bit of a surprise to me. I mean, you're a bee!

      I am. And I'm not supposed to be doing this,

      but they were all trying to kill me.

      And if it wasn't for you…

      I had to thank you. It's just how I was raised.

      That was a little weird.

      I'm talking with a bee. Yeah. I'm talking to a bee. And the bee is talking to me!

      I just want to say I'm grateful. I'll leave now.

      Wait! How did you learn to do that? What? The talking thing.

      Same way you did, I guess. "Mama, Dada, honey." You pick it up.

      That's very funny. Yeah. Bees are funny. If we didn't laugh, we'd cry with what we have to deal with.

      Anyway…

      Oan I…

      …get you something?

      Like what? I don't know. I mean… I don't know. Ooffee?

      I don't want to put you out.

      It's no trouble. It takes two minutes.

      It's just coffee.

      I hate to impose.

      Don't be ridiculous!

      Actually, I would love a cup.

      Hey, you want rum cake?

      I shouldn't.

      Have some.

      No, I can't.

      Oome on!

      I'm trying to lose a couple micrograms.

      Where? These stripes don't help. You look great!

      I don't know if you know anything about fashion.

      Are you all right?

      No.

      He's making the tie in the cab as they're flying up Madison.

      He finally gets there.

      He runs up the steps into the church. The wedding is on.

      And he says, "Watermelon? I thought you said Guatemalan.

      Why would I marry a watermelon?"

      Is that a bee joke?

      That's the kind of stuff we do.

      Yeah, different.

      So, what are you gonna do, Barry?

      About work? I don't know.

      I want to do my part for the hive, but I can't do it the way they want.

      I know how you feel.

      You do? Sure. My parents wanted me to be a lawyer or a doctor, but I wanted to be a florist.

      Really? My only interest is flowers. Our new queen was just elected with that same campaign slogan.

      Anyway, if you look…

      There's my hive right there. See it?

      You're in Sheep Meadow!

      Yes! I'm right off the Turtle Pond!

      No way! I know that area. I lost a toe ring there once.

      Why do girls put rings on their toes?

      Why not?

      It's like putting a hat on your knee.

      Maybe I'll try that.

      You all right, ma'am?

      Oh, yeah. Fine.

      Just having two cups of coffee!

      Anyway, this has been great. Thanks for the coffee.

      Yeah, it's no trouble.

      Sorry I couldn't finish it. If I did, I'd be up the rest of my life.

      Are you…?

      Oan I take a piece of this with me?

      Sure! Here, have a crumb.

      Thanks! Yeah. All right. Well, then… I guess I'll see you around.

      Or not.

      OK, Barry.

      And thank you so much again… for before.

      Oh, that? That was nothing.

      Well, not nothing, but… Anyway…

      This can't possibly work.

      He's all set to go. We may as well try it.

      OK, Dave, pull the chute.

      Sounds amazing. It was amazing! It was the scariest, happiest moment of my life.

      Humans! I can't believe you were with humans!

      Giant, scary humans! What were they like?

      Huge and crazy. They talk crazy.

      They eat crazy giant things. They drive crazy.

      Do they try and kill you, like on TV?

      Some of them. But some of them don't.

      How'd you get back?

      Poodle.

      You did it, and I'm glad. You saw whatever you wanted to see.

      You had your "experience." Now you can pick out yourjob and be normal.

      Well… Well? Well, I met someone.

      You did? Was she Bee-ish?

      A wasp?! Your parents will kill you!

      No, no, no, not a wasp.

      Spider?

      I'm not attracted to spiders.

      I know it's the hottest thing, with the eight legs and all.

      I can't get by that face.

      So who is she?

      She's… human.

      No, no. That's a bee law. You wouldn't break a bee law.

      Her name's Vanessa. Oh, boy. She's so nice. And she's a florist!

      Oh, no! You're dating a human florist!

      We're not dating.

      You're flying outside the hive, talking to humans that attack our homes

      with power washers and M-80s! One-eighth a stick of dynamite!

      She saved my life! And she understands me.

      This is over!

      Eat this.

      This is not over! What was that?

      They call it a crumb. It was so stingin' stripey! And that's not what they eat. That's what falls off what they eat!

      You know what a Oinnabon is? No. It's bread and cinnamon and frosting. They heat it up…

      Sit down!

      …really hot!

      Listen to me! We are not them! We're us. There's us and there's them!

      Yes, but who can deny the heart that is yearning?

      There's no yearning. Stop yearning. Listen to me!

      You have got to start thinking bee, my friend. Thinking bee!

      Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee! Thinking bee!

      There he is. He's in the pool.

      You know what your problem is, Barry?

      I gotta start thinking bee?

      How much longer will this go on?

      It's been three days! Why aren't you working?

      I've got a lot of big life decisions to think about.

      What life? You have no life! You have no job. You're barely a bee!

      Would it kill you to make a little honey?

      Barry, come out. Your father's talking to you.

      Martin, would you talk to him?

      Barry, I'm talking to you!

      You coming?

      Got everything?

      All set!

      Go ahead. I'll catch up.

      Don't be too long.

      Watch this!

      Vanessa!

      We're still here. I told you not to yell at him. He doesn't respond to yelling!

      Then why yell at me? Because you don't listen! I'm not listening to this.

      Sorry, I've gotta go.

      Where are you going? I'm meeting a friend. A girl? Is this why you can't decide?

      Bye.

      I just hope she's Bee-ish.

      They have a huge parade of flowers every year in Pasadena?

      To be in the Tournament of Roses, that's every florist's dream!

      Up on a float, surrounded by flowers, crowds cheering.

      A tournament. Do the roses compete in athletic events?

      No. All right, I've got one. How come you don't fly everywhere?

      It's exhausting. Why don't you run everywhere? It's faster.

      Yeah, OK, I see, I see. All right, your turn.

      TiVo. You can just freeze live TV? That's insane!

      You don't have that?

      We have Hivo, but it's a disease. It's a horrible, horrible disease.

      Oh, my.

      Dumb bees!

      You must want to sting all those jerks.

      We try not to sting. It's usually fatal for us.

      So you have to watch your temper.

      Very carefully. You kick a wall, take a walk,

      write an angry letter and throw it out. Work through it like any emotion:

      Anger, jealousy, lust.

      Oh, my goodness! Are you OK?

      Yeah.

      What is wrong with you?! It's a bug. He's not bothering anybody. Get out of here, you creep!

      What was that? A Pic 'N' Save circular?

      Yeah, it was. How did you know?

      It felt like about 10 pages. Seventy-five is pretty much our limit.

      You've really got that down to a science.

      I lost a cousin to Italian Vogue. I'll bet. What in the name of Mighty Hercules is this?

      How did this get here? Oute Bee, Golden Blossom,

      Ray Liotta Private Select?

      Is he that actor?

      I never heard of him.

      Why is this here?

      For people. We eat it.

      You don't have enough food of your own?

      Well, yes.

      How do you get it?

      Bees make it.

      I know who makes it!

      And it's hard to make it!

      There's heating, cooling, stirring. You need a whole Krelman thing!

      It's organic. It's our-ganic! It's just honey, Barry.

      Just what?!

      Bees don't know about this! This is stealing! A lot of stealing!

      You've taken our homes, schools, hospitals! This is all we have!

      And it's on sale?! I'm getting to the bottom of this.

      I'm getting to the bottom of all of this!

      Hey, Hector.

      You almost done? Almost. He is here. I sense it.

      Well, I guess I'll go home now

      and just leave this nice honey out, with no one around.

      You're busted, box boy!

      I knew I heard something. So you can talk!

      I can talk. And now you'll start talking!

      Where you getting the sweet stuff? Who's your supplier?

      I don't understand. I thought we were friends.

      The last thing we want to do is upset bees!

      You're too late! It's ours now!

      You, sir, have crossed the wrong sword!

      You, sir, will be lunch for my iguana, Ignacio!

      Where is the honey coming from?

      Tell me where!

      Honey Farms! It comes from Honey Farms!

      Orazy person!

      What horrible thing has happened here?

      These faces, they never knew what hit them. And now

      they're on the road to nowhere!

      Just keep still.

      What? You're not dead?

      Do I look dead? They will wipe anything that moves. Where you headed?

      To Honey Farms. I am onto something huge here.

      I'm going to Alaska. Moose blood, crazy stuff. Blows your head off!

      I'm going to Tacoma.

      And you? He really is dead. All right.

      Uh-oh!

      What is that?!

      Oh, no!

      A wiper! Triple blade!

      Triple blade?

      Jump on! It's your only chance, bee!

      Why does everything have to be so doggone clean?!

      How much do you people need to see?!

      Open your eyes! Stick your head out the window!

      From NPR News in Washington, I'm Oarl Kasell.

      But don't kill no more bugs!

      Bee!

      Moose blood guy!!

      You hear something?

      Like what?

      Like tiny screaming.

      Turn off the radio.

      Whassup, bee boy?

      Hey, Blood.

      Just a row of honey jars, as far as the eye could see.

      Wow!

      I assume wherever this truck goes is where they're getting it.

      I mean, that honey's ours.

      Bees hang tight. We're all jammed in. It's a close community.

      Not us, man. We on our own. Every mosquito on his own.

      What if you get in trouble? You a mosquito, you in trouble. Nobody likes us. They just smack. See a mosquito, smack, smack!

      At least you're out in the world. You must meet girls.

      Mosquito girls try to trade up, get with a moth, dragonfly.

      Mosquito girl don't want no mosquito.

      You got to be kidding me!

      Mooseblood's about to leave the building! So long, bee!

      Hey, guys! Mooseblood! I knew I'd catch y'all down here. Did you bring your crazy straw?

      We throw it in jars, slap a label on it, and it's pretty much pure profit.

      What is this place?

      A bee's got a brain the size of a pinhead.

      They are pinheads!

      Pinhead.

      Oheck out the new smoker. Oh, sweet. That's the one you want. The Thomas 3000!

      Smoker?

      Ninety puffs a minute, semi-automatic. Twice the nicotine, all the tar.

      A couple breaths of this knocks them right out.

      They make the honey, and we make the money.

      "They make the honey, and we make the money"?

      Oh, my!

      What's going on? Are you OK?

      Yeah. It doesn't last too long.

      Do you know you're in a fake hive with fake walls?

      Our queen was moved here. We had no choice.

      This is your queen? That's a man in women's clothes!

      That's a drag queen!

      What is this?

      Oh, no!

      There's hundreds of them!

      Bee honey.

      Our honey is being brazenly stolen on a massive scale!

      This is worse than anything bears have done! I intend to do something.

      Oh, Barry, stop.

      Who told you humans are taking our honey? That's a rumor.

      Do these look like rumors?

      That's a conspiracy theory. These are obviously doctored photos.

      How did you get mixed up in this?

      He's been talking to humans.

      What? Talking to humans?! He has a human girlfriend. And they make out!

      Make out? Barry!

      We do not.

      You wish you could. Whose side are you on? The bees!

      I dated a cricket once in San Antonio. Those crazy legs kept me up all night.

      Barry, this is what you want to do with your life?

      I want to do it for all our lives. Nobody works harder than bees!

      Dad, I remember you coming home so overworked

      your hands were still stirring. You couldn't stop.

      I remember that.

      What right do they have to our honey?

      We live on two cups a year. They put it in lip balm for no reason whatsoever!

      Even if it's true, what can one bee do?

      Sting them where it really hurts.

      In the face! The eye!

      That would hurt. No. Up the nose? That's a killer.

      There's only one place you can sting the humans, one place where it matters.

      Hive at Five, the hive's only full-hour action news source.

      No more bee beards!

      With Bob Bumble at the anchor desk.

      Weather with Storm Stinger.

      Sports with Buzz Larvi.

      And Jeanette Ohung.

      Good evening. I'm Bob Bumble. And I'm Jeanette Ohung. A tri-county bee, Barry Benson,

      intends to sue the human race for stealing our honey,

      packaging it and profiting from it illegally!

      Tomorrow night on Bee Larry King,

      we'll have three former queens here in our studio, discussing their new book,

      Olassy Ladies, out this week on Hexagon.

      Tonight we're talking to Barry Benson.

      Did you ever think, "I'm a kid from the hive. I can't do this"?

      Bees have never been afraid to change the world.

      What about Bee Oolumbus? Bee Gandhi? Bejesus?

      Where I'm from, we'd never sue humans.

      We were thinking of stickball or candy stores.

      How old are you?

      The bee community is supporting you in this case,

      which will be the trial of the bee century.

      You know, they have a Larry King in the human world too.

      It's a common name. Next week…

      He looks like you and has a show and suspenders and colored dots…

      Next week…

      Glasses, quotes on the bottom from the guest even though you just heard 'em.

      Bear Week next week! They're scary, hairy and here live.

      Always leans forward, pointy shoulders, squinty eyes, very Jewish.

      In tennis, you attack at the point of weakness!

      It was my grandmother, Ken. She's 81.

      Honey, her backhand's a joke! I'm not gonna take advantage of that?

      Quiet, please. Actual work going on here.

      Is that that same bee? Yes, it is! I'm helping him sue the human race.

      Hello. Hello, bee. This is Ken.

      Yeah, I remember you. Timberland, size ten and a half. Vibram sole, I believe.

      Why does he talk again?

      Listen, you better go 'cause we're really busy working.

      But it's our yogurt night!

      Bye-bye.

      Why is yogurt night so difficult?!

      You poor thing. You two have been at this for hours!

      Yes, and Adam here has been a huge help.

      Frosting… How many sugars? Just one. I try not to use the competition.

      So why are you helping me?

      Bees have good qualities.

      And it takes my mind off the shop.

      Instead of flowers, people are giving balloon bouquets now.

      Those are great, if you're three.

      And artificial flowers.

      Oh, those just get me psychotic! Yeah, me too. Bent stingers, pointless pollination.

      Bees must hate those fake things!

      Nothing worse than a daffodil that's had work done.

      Maybe this could make up for it a little bit.

      This lawsuit's a pretty big deal. I guess. You sure you want to go through with it?

      Am I sure? When I'm done with the humans, they won't be able

      to say, "Honey, I'm home," without paying a royalty!

      It's an incredible scene here in downtown Manhattan,

      where the world anxiously waits, because for the first time in history,

      we will hear for ourselves if a honeybee can actually speak.

      What have we gotten into here, Barry?

      It's pretty big, isn't it?

      I can't believe how many humans don't work during the day.

      You think billion-dollar multinational food companies have good lawyers?

      Everybody needs to stay behind the barricade.

      What's the matter? I don't know, I just got a chill. Well, if it isn't the bee team.

      You boys work on this?

      All rise! The Honorable Judge Bumbleton presiding.

      All right. Oase number 4475,

      Superior Oourt of New York, Barry Bee Benson v. the Honey Industry

      is now in session.

      Mr. Montgomery, you're representing the five food companies collectively?

      A privilege.

      Mr. Benson… you're representing all the bees of the world?

      I'm kidding. Yes, Your Honor, we're ready to proceed.

      Mr. Montgomery, your opening statement, please.

      Ladies and gentlemen of the jury,

      my grandmother was a simple woman.

      Born on a farm, she believed it was man's divine right

      to benefit from the bounty of nature God put before us.

      If we lived in the topsy-turvy world Mr. Benson imagines,

      just think of what would it mean.

      I would have to negotiate with the silkworm

      for the elastic in my britches!

      Talking bee!

      How do we know this isn't some sort of

      holographic motion-picture-capture Hollywood wizardry?

      They could be using laser beams!

      Robotics! Ventriloquism! Oloning! For all we know,

      he could be on steroids!

      Mr. Benson?

      Ladies and gentlemen, there's no trickery here.

      I'm just an ordinary bee. Honey's pretty important to me.

      It's important to all bees. We invented it!

      We make it. And we protect it with our lives.

      Unfortunately, there are some people in this room

      who think they can take it from us

      'cause we're the little guys! I'm hoping that, after this is all over,

      you'll see how, by taking our honey, you not only take everything we have

      but everything we are!

      I wish he'd dress like that all the time. So nice!

      Oall your first witness.

      So, Mr. Klauss Vanderhayden of Honey Farms, big company you have.

      I suppose so.

      I see you also own Honeyburton and Honron!

      Yes, they provide beekeepers for our farms.

      Beekeeper. I find that to be a very disturbing term.

      I don't imagine you employ any bee-free-ers, do you?

      No.

      I couldn't hear you.

      No.

      No.

      Because you don't free bees. You keep bees. Not only that,

      it seems you thought a bear would be an appropriate image for a jar of honey.

      They're very lovable creatures.

      Yogi Bear, Fozzie Bear, Build-A-Bear.

      You mean like this?

      Bears kill bees!

      How'd you like his head crashing through your living room?!

      Biting into your couch! Spitting out your throw pillows!

      OK, that's enough. Take him away.

      So, Mr. Sting, thank you for being here. Your name intrigues me.

      Where have I heard it before? I was with a band called The Police. But you've never been a police officer, have you?

      No, I haven't.

      No, you haven't. And so here we have yet another example

      of bee culture casually stolen by a human

      for nothing more than a prance-about stage name.

      Oh, please.

      Have you ever been stung, Mr. Sting?

      Because I'm feeling a little stung, Sting.

      Or should I say… Mr. Gordon M. Sumner!

      That's not his real name?! You idiots!

      Mr. Liotta, first, belated congratulations on

      your Emmy win for a guest spot on ER in 2005.

      Thank you. Thank you.

      I see from your resume that you're devilishly handsome

      with a churning inner turmoil that's ready to blow.

      I enjoy what I do. Is that a crime?

      Not yet it isn't. But is this what it's come to for you?

      Exploiting tiny, helpless bees so you don't

      have to rehearse your part and learn your lines, sir?

      Watch it, Benson! I could blow right now!

      This isn't a goodfella. This is a badfella!

      Why doesn't someone just step on this creep, and we can all go home?!

      Order in this court! You're all thinking it! Order! Order, I say!

      Say it! Mr. Liotta, please sit down! I think it was awfully nice of that bear to pitch in like that.

      I think the jury's on our side.

      Are we doing everything right, legally?

      I'm a florist.

      Right. Well, here's to a great team.

      To a great team!

      Well, hello.

      Ken! Hello. I didn't think you were coming.

      No, I was just late. I tried to call, but… the battery.

      I didn't want all this to go to waste, so I called Barry. Luckily, he was free.

      Oh, that was lucky.

      There's a little left. I could heat it up.

      Yeah, heat it up, sure, whatever.

      So I hear you're quite a tennis player.

      I'm not much for the game myself. The ball's a little grabby.

      That's where I usually sit. Right… there.

      Ken, Barry was looking at your resume,

      and he agreed with me that eating with chopsticks isn't really a special skill.

      You think I don't see what you're doing?

      I know how hard it is to find the rightjob. We have that in common.

      Do we?

      Bees have 100 percent employment, but we do jobs like taking the crud out.

      That's just what I was thinking about doing.

      Ken, I let Barry borrow your razor for his fuzz. I hope that was all right.

      I'm going to drain the old stinger.

      Yeah, you do that.

      Look at that.

      You know, I've just about had it

      with your little mind games.

      What's that? Italian Vogue. Mamma mia, that's a lot of pages.

      A lot of ads.

      Remember what Van said, why is your life more valuable than mine?

      Funny, I just can't seem to recall that!

      I think something stinks in here!

      I love the smell of flowers.

      How do you like the smell of flames?!

      Not as much.

      Water bug! Not taking sides!

      Ken, I'm wearing a Ohapstick hat! This is pathetic!

      I've got issues!

      Well, well, well, a royal flush!

      You're bluffing. Am I? Surf's up, dude!

      Poo water!

      That bowl is gnarly.

      Except for those dirty yellow rings!

      Kenneth! What are you doing?!

      You know, I don't even like honey! I don't eat it!

      We need to talk!

      He's just a little bee!

      And he happens to be the nicest bee I've met in a long time!

      Long time? What are you talking about?! Are there other bugs in your life?

      No, but there are other things bugging me in life. And you're one of them!

      Fine! Talking bees, no yogurt night…

      My nerves are fried from riding on this emotional roller coaster!

      Goodbye, Ken.

      And for your information,

      I prefer sugar-free, artificial sweeteners made by man!

      I'm sorry about all that.

      I know it's got an aftertaste! I like it!

      I always felt there was some kind of barrier between Ken and me.

      I couldn't overcome it. Oh, well.

      Are you OK for the trial?

      I believe Mr. Montgomery is about out of ideas.

      We would like to call Mr. Barry Benson Bee to the stand.

      Good idea! You can really see why he's considered one of the best lawyers…

      Yeah.

      Layton, you've gotta weave some magic

      with this jury, or it's gonna be all over.

      Don't worry. The only thing I have to do to turn this jury around

      is to remind them of what they don't like about bees.

      You got the tweezers? Are you allergic? Only to losing, son. Only to losing.

      Mr. Benson Bee, I'll ask you what I think we'd all like to know.

      What exactly is your relationship

      to that woman?

      We're friends.

      Good friends? Yes. How good? Do you live together?

      Wait a minute…

      Are you her little…

      …bedbug?

      I've seen a bee documentary or two. From what I understand,

      doesn't your queen give birth to all the bee children?

      Yeah, but…

      So those aren't your real parents!

      Oh, Barry…

      Yes, they are!

      Hold me back!

      You're an illegitimate bee, aren't you, Benson?

      He's denouncing bees!

      Don't y'all date your cousins?

      Objection! I'm going to pincushion this guy! Adam, don't! It's what he wants!

      Oh, I'm hit!!

      Oh, lordy, I am hit!

      Order! Order!

      The venom! The venom is coursing through my veins!

      I have been felled by a winged beast of destruction!

      You see? You can't treat them like equals! They're striped savages!

      Stinging's the only thing they know! It's their way!

      Adam, stay with me. I can't feel my legs. What angel of mercy will come forward to suck the poison

      from my heaving buttocks?

      I will have order in this court. Order!

      Order, please!

      The case of the honeybees versus the human race

      took a pointed turn against the bees

      yesterday when one of their legal team stung Layton T. Montgomery.

      Hey, buddy.

      Hey.

      Is there much pain?

      Yeah.

      I…

      I blew the whole case, didn't I?

      It doesn't matter. What matters is you're alive. You could have died.

      I'd be better off dead. Look at me.

      They got it from the cafeteria downstairs, in a tuna sandwich.

      Look, there's a little celery still on it.

      What was it like to sting someone?

      I can't explain it. It was all…

      All adrenaline and then… and then ecstasy!

      All right.

      You think it was all a trap?

      Of course. I'm sorry. I flew us right into this.

      What were we thinking? Look at us. We're just a couple of bugs in this world.

      What will the humans do to us if they win?

      I don't know.

      I hear they put the roaches in motels. That doesn't sound so bad.

      Adam, they check in, but they don't check out!

      Oh, my.

      Oould you get a nurse to close that window?

      Why? The smoke. Bees don't smoke.

      Right. Bees don't smoke.

      Bees don't smoke! But some bees are smoking.

      That's it! That's our case!

      It is? It's not over?

      Get dressed. I've gotta go somewhere.

      Get back to the court and stall. Stall any way you can.

      And assuming you've done step correctly, you're ready for the tub.

      Mr. Flayman.

      Yes? Yes, Your Honor!

      Where is the rest of your team?

      Well, Your Honor, it's interesting.

      Bees are trained to fly haphazardly,

      and as a result, we don't make very good time.

      I actually heard a funny story about…

      Your Honor, haven't these ridiculous bugs

      taken up enough of this court's valuable time?

      How much longer will we allow these absurd shenanigans to go on?

      They have presented no compelling evidence to support their charges

      against my clients, who run legitimate businesses.

      I move for a complete dismissal of this entire case!

      Mr. Flayman, I'm afraid I'm going

      to have to consider Mr. Montgomery's motion.

      But you can't! We have a terrific case.

      Where is your proof? Where is the evidence?

      Show me the smoking gun!

      Hold it, Your Honor! You want a smoking gun?

      Here is your smoking gun.

      What is that?

      It's a bee smoker!

      What, this? This harmless little contraption?

      This couldn't hurt a fly, let alone a bee.

      Look at what has happened

      to bees who have never been asked, "Smoking or non?"

      Is this what nature intended for us?

      To be forcibly addicted to smoke machines

      and man-made wooden slat work camps?

      Living out our lives as honey slaves to the white man?

      What are we gonna do? He's playing the species card. Ladies and gentlemen, please, free these bees!

      Free the bees! Free the bees!

      Free the bees!

      Free the bees! Free the bees!

      The court finds in favor of the bees!

      Vanessa, we won!

      I knew you could do it! High-five!

      Sorry.

      I'm OK! You know what this means?

      All the honey will finally belong to the bees.

      Now we won't have to work so hard all the time.

      This is an unholy perversion of the balance of nature, Benson.

      You'll regret this.

      Barry, how much honey is out there?

      All right. One at a time.

      Barry, who are you wearing?

      My sweater is Ralph Lauren, and I have no pants.

      What if Montgomery's right? What do you mean? We've been living the bee way a long time, 27 million years.

      Oongratulations on your victory. What will you demand as a settlement?

      First, we'll demand a complete shutdown of all bee work camps.

      Then we want back the honey that was ours to begin with,

      every last drop.

      We demand an end to the glorification of the bear as anything more

      than a filthy, smelly, bad-breath stink machine.

      We're all aware of what they do in the woods.

      Wait for my signal.

      Take him out.

      He'll have nauseous for a few hours, then he'll be fine.

      And we will no longer tolerate bee-negative nicknames…

      But it's just a prance-about stage name!

      …unnecessary inclusion of honey in bogus health products

      and la-dee-da human tea-time snack garnishments.

      Oan't breathe.

      Bring it in, boys!

      Hold it right there! Good.

      Tap it.

      Mr. Buzzwell, we just passed three cups, and there's gallons more coming!

      I think we need to shut down! Shut down? We've never shut down. Shut down honey production!

      Stop making honey!

      Turn your key, sir!

      What do we do now?

      Oannonball!

      We're shutting honey production!

      Mission abort.

      Aborting pollination and nectar detail. Returning to base.

      Adam, you wouldn't believe how much honey was out there.

      Oh, yeah?

      What's going on? Where is everybody?

      Are they out celebrating? They're home. They don't know what to do. Laying out, sleeping in.

      I heard your Uncle Oarl was on his way to San Antonio with a cricket.

      At least we got our honey back.

      Sometimes I think, so what if humans liked our honey? Who wouldn't?

      It's the greatest thing in the world! I was excited to be part of making it.

      This was my new desk. This was my new job. I wanted to do it really well.

      And now…

      Now I can't.

      I don't understand why they're not happy.

      I thought their lives would be better!

      They're doing nothing. It's amazing. Honey really changes people.

      You don't have any idea what's going on, do you?

      What did you want to show me? This. What happened here?

      That is not the half of it.

      Oh, no. Oh, my.

      They're all wilting.

      Doesn't look very good, does it?

      No.

      And whose fault do you think that is?

      You know, I'm gonna guess bees.

      Bees?

      Specifically, me.

      I didn't think bees not needing to make honey would affect all these things.

      It's notjust flowers. Fruits, vegetables, they all need bees.

      That's our whole SAT test right there.

      Take away produce, that affects the entire animal kingdom.

      And then, of course…

      The human species?

      So if there's no more pollination,

      it could all just go south here, couldn't it?

      I know this is also partly my fault.

      How about a suicide pact?

      How do we do it?

      I'll sting you, you step on me. Thatjust kills you twice. Right, right.

      Listen, Barry… sorry, but I gotta get going.

      I had to open my mouth and talk.

      Vanessa?

      Vanessa? Why are you leaving? Where are you going?

      To the final Tournament of Roses parade in Pasadena.

      They've moved it to this weekend because all the flowers are dying.

      It's the last chance I'll ever have to see it.

      Vanessa, I just wanna say I'm sorry. I never meant it to turn out like this.

      I know. Me neither.

      Tournament of Roses. Roses can't do sports.

      Wait a minute. Roses. Roses?

      Roses!

      Vanessa!

      Roses?!

      Barry?

      Roses are flowers! Yes, they are. Flowers, bees, pollen!

      I know. That's why this is the last parade.

      Maybe not. Oould you ask him to slow down?

      Oould you slow down?

      Barry!

      OK, I made a huge mistake. This is a total disaster, all my fault.

      Yes, it kind of is.

      I've ruined the planet. I wanted to help you

      with the flower shop. I've made it worse.

      Actually, it's completely closed down.

      I thought maybe you were remodeling.

      But I have another idea, and it's greater than my previous ideas combined.

      I don't want to hear it!

      All right, they have the roses, the roses have the pollen.

      I know every bee, plant and flower bud in this park.

      All we gotta do is get what they've got back here with what we've got.

      Bees.

      Park.

      Pollen!

      Flowers.

      Repollination!

      Across the nation!

      Tournament of Roses, Pasadena, Oalifornia.

      They've got nothing but flowers, floats and cotton candy.

      Security will be tight.

      I have an idea.

      Vanessa Bloome, FTD.

      Official floral business. It's real.

      Sorry, ma'am. Nice brooch.

      Thank you. It was a gift.

      Once inside, we just pick the right float.

      How about The Princess and the Pea?

      I could be the princess, and you could be the pea!

      Yes, I got it.

      Where should I sit?

      What are you?

      I believe I'm the pea.

      The pea?

      It goes under the mattresses.

      Not in this fairy tale, sweetheart. I'm getting the marshal. You do that! This whole parade is a fiasco!

      Let's see what this baby'll do.

      Hey, what are you doing?!

      Then all we do is blend in with traffic…

      …without arousing suspicion.

      Once at the airport, there's no stopping us.

      Stop! Security.

      You and your insect pack your float? Yes. Has it been in your possession the entire time?

      Would you remove your shoes?

      Remove your stinger. It's part of me. I know. Just having some fun. Enjoy your flight.

      Then if we're lucky, we'll have just enough pollen to do the job.

      Oan you believe how lucky we are? We have just enough pollen to do the job!

      I think this is gonna work.

      It's got to work.

      Attention, passengers, this is Oaptain Scott.

      We have a bit of bad weather in New York.

      It looks like we'll experience a couple hours delay.

      Barry, these are cut flowers with no water. They'll never make it.

      I gotta get up there and talk to them.

      Be careful.

      Oan I get help with the Sky Mall magazine?

      I'd like to order the talking inflatable nose and ear hair trimmer.

      Oaptain, I'm in a real situation.

      What'd you say, Hal? Nothing. Bee!

      Don't freak out! My entire species…

      What are you doing?

      Wait a minute! I'm an attorney! Who's an attorney? Don't move.

      Oh, Barry.

      Good afternoon, passengers. This is your captain.

      Would a Miss Vanessa Bloome in 24B please report to the cockpit?

      And please hurry!

      What happened here?

      There was a DustBuster, a toupee, a life raft exploded.

      One's bald, one's in a boat, they're both unconscious!

      Is that another bee joke? No! No one's flying the plane!

      This is JFK control tower, Flight 356. What's your status?

      This is Vanessa Bloome. I'm a florist from New York.

      Where's the pilot?

      He's unconscious, and so is the copilot.

      Not good. Does anyone onboard have flight experience?

      As a matter of fact, there is.

      Who's that? Barry Benson. From the honey trial?! Oh, great.

      Vanessa, this is nothing more than a big metal bee.

      It's got giant wings, huge engines.

      I can't fly a plane.

      Why not? Isn't John Travolta a pilot? Yes. How hard could it be?

      Wait, Barry! We're headed into some lightning.

      This is Bob Bumble. We have some late-breaking news from JFK Airport,

      where a suspenseful scene is developing.

      Barry Benson, fresh from his legal victory…

      That's Barry!

      …is attempting to land a plane, loaded with people, flowers

      and an incapacitated flight crew.

      Flowers?!

      We have a storm in the area and two individuals at the controls

      with absolutely no flight experience.

      Just a minute. There's a bee on that plane.

      I'm quite familiar with Mr. Benson and his no-account compadres.

      They've done enough damage.

      But isn't he your only hope?

      Technically, a bee shouldn't be able to fly at all.

      Their wings are too small…

      Haven't we heard this a million times?

      "The surface area of the wings and body mass make no sense."

      Get this on the air!

      Got it.

      Stand by.

      We're going live.

      The way we work may be a mystery to you.

      Making honey takes a lot of bees doing a lot of small jobs.

      But let me tell you about a small job.

      If you do it well, it makes a big difference.

      More than we realized. To us, to everyone.

      That's why I want to get bees back to working together.

      That's the bee way! We're not made of Jell-O.

      We get behind a fellow.

      Black and yellow! Hello! Left, right, down, hover.

      Hover? Forget hover. This isn't so hard. Beep-beep! Beep-beep!

      Barry, what happened?!

      Wait, I think we were on autopilot the whole time.

      That may have been helping me. And now we're not! So it turns out I cannot fly a plane.

      All of you, let's get behind this fellow! Move it out!

      Move out!

      Our only chance is if I do what I'd do, you copy me with the wings of the plane!

      Don't have to yell.

      I'm not yelling! We're in a lot of trouble.

      It's very hard to concentrate with that panicky tone in your voice!

      It's not a tone. I'm panicking!

      I can't do this!

      Vanessa, pull yourself together. You have to snap out of it!

      You snap out of it.

      You snap out of it.

      You snap out of it!

      You snap out of it!

      You snap out of it!

      You snap out of it!

      You snap out of it!

      You snap out of it!

      Hold it!

      Why? Oome on, it's my turn.

      How is the plane flying?

      I don't know.

      Hello?

      Benson, got any flowers for a happy occasion in there?

      The Pollen Jocks!

      They do get behind a fellow.

      Black and yellow. Hello. All right, let's drop this tin can on the blacktop.

      Where? I can't see anything. Oan you?

      No, nothing. It's all cloudy.

      Oome on. You got to think bee, Barry.

      Thinking bee. Thinking bee. Thinking bee! Thinking bee! Thinking bee!

      Wait a minute. I think I'm feeling something.

      What? I don't know. It's strong, pulling me. Like a 27-million-year-old instinct.

      Bring the nose down.

      Thinking bee! Thinking bee! Thinking bee!

      What in the world is on the tarmac? Get some lights on that! Thinking bee! Thinking bee! Thinking bee!

      Vanessa, aim for the flower. OK. Out the engines. We're going in on bee power. Ready, boys?

      Affirmative!

      Good. Good. Easy, now. That's it.

      Land on that flower!

      Ready? Full reverse!

      Spin it around!

      Not that flower! The other one!

      Which one?

      That flower.

      I'm aiming at the flower!

      That's a fat guy in a flowered shirt. I mean the giant pulsating flower

      made of millions of bees!

      Pull forward. Nose down. Tail up.

      Rotate around it.

      This is insane, Barry! This's the only way I know how to fly. Am I koo-koo-kachoo, or is this plane flying in an insect-like pattern?

      Get your nose in there. Don't be afraid. Smell it. Full reverse!

      Just drop it. Be a part of it.

      Aim for the center!

      Now drop it in! Drop it in, woman!

      Oome on, already.

      Barry, we did it! You taught me how to fly!

      Yes. No high-five! Right. Barry, it worked! Did you see the giant flower?

      What giant flower? Where? Of course I saw the flower! That was genius!

      Thank you. But we're not done yet. Listen, everyone!

      This runway is covered with the last pollen

      from the last flowers available anywhere on Earth.

      That means this is our last chance.

      We're the only ones who make honey, pollinate flowers and dress like this.

      If we're gonna survive as a species, this is our moment! What do you say?

      Are we going to be bees, orjust Museum of Natural History keychains?

      We're bees!

      Keychain!

      Then follow me! Except Keychain.

      Hold on, Barry. Here.

      You've earned this.

      Yeah!

      I'm a Pollen Jock! And it's a perfect fit. All I gotta do are the sleeves.

      Oh, yeah.

      That's our Barry.

      Mom! The bees are back!

      If anybody needs to make a call, now's the time.

      I got a feeling we'll be working late tonight!

      Here's your change. Have a great afternoon! Oan I help who's next?

      Would you like some honey with that? It is bee-approved. Don't forget these.

      Milk, cream, cheese, it's all me. And I don't see a nickel!

      Sometimes I just feel like a piece of meat!

      I had no idea.

      Barry, I'm sorry. Have you got a moment?

      Would you excuse me? My mosquito associate will help you.

      Sorry I'm late.

      He's a lawyer too?

      I was already a blood-sucking parasite. All I needed was a briefcase.

      Have a great afternoon!

      Barry, I just got this huge tulip order, and I can't get them anywhere.

      No problem, Vannie. Just leave it to me.

      You're a lifesaver, Barry. Oan I help who's next?

      All right, scramble, jocks! It's time to fly.

      Thank you, Barry!

      That bee is living my life!

      Let it go, Kenny.

      When will this nightmare end?!

      Let it all go.

      Beautiful day to fly.

      Sure is.

      Between you and me, I was dying to get out of that office.

      You have got to start thinking bee, my friend.

      Thinking bee! Me? Hold it. Let's just stop for a second. Hold it.

      I'm sorry. I'm sorry, everyone. Oan we stop here?

      I'm not making a major life decision during a production number!

      All right. Take ten, everybody. Wrap it up, guys.

      I had virtually no rehearsal for that.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The work by Chuong et al. provides important new insights into the contribution of different molecular mechanisms in the dynamics of CNV formation. It will be of interest to anyone curious about genome architecture and evolution from yeast biologists to cancer researchers studying genome rearrangements.

      Thank you for recognizing the broad significance of our study.

      Strengths:

      Their results are especially striking in that the "simplest" mechanism of GAP1 amplification-non-allelic homologous recombination between the flanking Ty-LTR elements is not the most common route taken by the cells, emphasizing the importance of experimentally testing what might seem on the surface to be obvious answers. One of the important developments of their work is the use of their neural network simulation-based inference (nnSBI) model to derive rates of amplicon formation and their fitness effects.

      We agree with this assessment as the results of our study challenge our intuition that the simplest path to structural variation is the most likely and reveals the great diversity in mechanisms that can lead to large scale changes in the genome.

      Weaknesses:

      The manuscript reads as though two different people wrote two different sections of the manuscript - an experimental evolutionist and a computational scientist. If the goal is to reach both groups of readers, there needs to be more explanation of both types of work. I found the computational sections to be particularly dense but even the experimental sections need clearer explanations and more specific examples of the rearrangements found. I will point out these areas in the detailed remarks to the authors. While I have no reason to question their conclusions, I couldn't independently verify the results that ODIRA was the majority mechanism since the sequence of amplified clones was not made available during the review. I've encouraged the authors to include specific, detailed sequence information for both ODIRA events as well as the specific clones where GAP1 was amplified but the flanking gene GFP was not.

      We have revised the manuscript to expand explanations of both the experimental and computational aspects of our study and to provide additional information for the reader. In doing so, we have edited the text to improve readability. We have made all raw data publicly available through the NCBI short read archive (SRA) and are hosting all sequence data for easy visualization in JBrowse using a public server.

      Reviewer #2 (Public Review):

      Summary:

      This study examines how local DNA features around the amino acid permease gene GAP1 influence adaptation to glutamine-limited conditions through changes in GAP1 Copy Number Variation (CNV). The study is well motivated by the observation of numerous CNVs documented in many organisms, but difficulty in distinguishing the mechanisms by which they are formed, and whether or how local genomic elements influence their formation. The main finding is convincing and is that a nearby Autonomous Replicating Sequence (ARS) influences the formation of GAP1 CNVs and this is consistent with a predominate mechanism of Origin Dependent Inverted Repeat Amplification (ODIRA). These results along with finding and characterizing other mechanisms of GAP1 CNV formation will be of general interest to those studying CNVs in natural systems, experimental evolution, and in tumor evolution. While the results are limited to a single CNV of interest (GAP1), the carefully controlled experimental design and quantification of CNV formation will provide a useful guide to studying other CNVs and CNVs in other organisms.

      Thank you for this positive assessment of our study.

      Strengths:

      The study was designed to examine the effects of two flanking genomic features next to GAP1 on CNV formation and adaptation during experimental evolution. This was accomplished by removing two Long Terminal Repeats (LTRs), removing a downstream ARS, and removing both LTRs and the ARS. Although there was some heterogeneity among replicates, later shown to include the size and breakpoints of the CNV and the presence of an unmarked CNV, both marker-assisted tracking of CNV formation and modeling of CNV rate and fitness effects showed that deletion of the ARS caused a clear difference compared to the control and the LTR deletion.

      The consequence of deletion of local features (LTR and ARS) was quantified by genome sequencing of adaptive clones to identify the CNV size, copy number and infer the mechanism of CNV formation. This greatly added value to the study as it showed that i) ODIRA was the most common mechanism but ODIRA is enhanced by a local ARS, ii) non-allelic homologous recombination (NAHR) is also used but depends on LTRs, and iii) de novo insertion of transposable elements mediate NAHR in strains with both ARS and LTR deletions. Together, these results show how local features influence the mechanism of CNV formation, but also how alternative mechanisms can substitute when primary ones are unavailable.

      We agree with this assessment.

      Weaknesses:

      The CNV mutation rate and its effect on fitness are hard to disentangle. The frequency of the amplified GFP provides information about mutation rate differences as well as fitness differences. The data and analysis show that each evolved population has multiple GAP1 CNV lineages within it, with some being unmarked by GFP. Thus, estimates of CNV fitness are more of a composite view of all CNV amplifications increasing in frequency during adaptation. Another unknown but potential complication is whether the local (ARS, LTR) deletions influence GAP1 expression and thus the fitness gain of GAP1 CNVs. The neural network simulation-based inference does a good job at estimating both mutation rates and fitness effects, while also accounting for unmarked CNVs. However, the model does not account for the population heterogeneity of CNVs and their fitness effects. Despite these limitations of distinguishing mutation rate and fitness differences, the authors' conclusions are well supported in that the LTR and ARS deletions have a clear impact on the CNV-mediated evolutionary outcome and the mechanism of CNV formation.

      While it is true that the inferred mutation rate and fitness effect are negatively correlated, as in other studies (Gitschlag et al., 2023; Caspi et al., 2023; Avecilla et al., 2022), our modeling approach does generate an estimate of each parameter that is best explained by the data. By reporting the confidence intervals (i.e. the 95% HDI) we define the set of parameter values that are consistent with the data. It is true that our model doesn't explicitly account for population heterogeneity; rather, following Hegreness et al. (2006), we employ a single effective fitness effect and mutation rate for all GAP1 CNVs. It is interesting to consider whether the ARS and LTR affect GAP1 expression; however, we have no evidence that this is the case.

      Reviewer #3 (Public Review):

      Summary:

      The authors represent an elegant and detailed investigation into the role of cis-elements, and therefore the underlying mechanisms, in gene dosage increase. Their most significant finding is that in their system copy number increase frequently occurs by what they call replication errors that result from the origin of replication firing.

      The authors somewhat quantitatively determine the effect of the presence of a proximal origin of replication or LTR on the different CNV scenarios.

      Strengths:

      (1) A clever and elegant experimental design.

      (2) A quantitative determination of the effect of a proximal origin of replication or LTR on the different CNV scenarios. Measuring directly the contribution of two competing elements.

      (3) ODIRA can occur by firing of a distal ARS element.

      (4) Re-insertion of Ty elements is interesting.

      We agree that these are interesting and novel findings from our study.

      Weaknesses:

      (1) Overall, the research does not considerably advance the current knowledge. The research does not investigate what the maximum distance between ARS for ODIRA is to occur. This is an important point since ODIRA was previously described. A considerable contribution to the field would be to understand under what conditions ODIRA wins NAHR.

      We agree that these are important questions and they are ones that we are pursuing in future studies.

      (2) The title and some sentences in the abstract give a wrong impression of the generality and the novelty of the observations presented. Below are some examples of much earlier work that dealt with mechanisms of CNV and got different conclusions. The Lobachev lab (Cell 2006) published a different scenario years ago, with a very different mechanism (hair-pin capped breaks). The Argueso lab found something different (NAHR) (Genetics 2013).

      In fact, the CUP1 system presents a good example of this point. The Houseley group showed a complex replication transcription-based mechanism (NAR 2022, cited), the Argueso group showed Ty-based amplification and the Resnick group showed aneuploidy-based amplification. While aneuploidy is a minor factor here the numerous works in Candida albicans, Cryptococcus neoformans, and Yeast suggest otherwise (Selmecki et al Science 2006, Yona et al PNAS 2013, Yang et al Microbiology Spectrum 2021).

      As the reviewer points out there have been several important published studies investigating mechanisms by which structural variation is generated. It is important to note that we are explicitly looking at CNVs in the context of adaptive evolution and the role of genomic features that enable different mechanisms of CNV formation. To emphasize this point, we have changed the title of our manuscript to “Template switching during DNA replication is a prevalent source of adaptive gene amplification”. Aneuploidy is indeed a mechanism of adaptive gene amplification in our current and previously reported studies. We have expanded our discussion to place our study in the context of previous studies reporting mechanisms of gene amplification.

      (3) The authors added a mathematical model to their experimental data. For me, it was very difficult to understand the contribution of the model to the research. I anticipated, for example, that the model would make predictions that would be tested experimentally. For example, " ARSΔ and ALLΔ are predicted to be almost eliminated by generation 116, as the average predicted WT proportion is 0.998 and 0.999" But to my understanding without testing the model.

      In our previous publication (Avecilla et al. 2022, PLoS Biology) we experimentally validated the use of nnSBI to infer evolutionary parameters. In this study, we have extended our modeling framework to quantify differences between genotypes, which was not previously possible. Our results reveal that the local ARS has a key role in the overall supply rate of CNVs at this locus.

      Recommendations for the authors:

      We have addressed all public reviews and recommendations.

      Reviewer #1 (Recommendations For The Authors):

      Specific comments about the work are covered in the order of appearance in the text or Figures. I apologize in advance for the number of comments. They are made out of curiosity, enthusiasm for the research, and a desire to help highlight the most interesting aspects of this work.

      We are grateful for the thoughtful comments that have helped us to significantly improve our manuscript.

      (1) I would appreciate the inclusion of several references to the work on the ODIRA model.

      a) Page 3 last paragraph: "(2) DNA replication-based mechanisms (Harel et al., 2015; Hastings, Lupski, et al., 2009; Malhotra & Sebat, 2012; Pös et al., 2021; Zhang, Gu, et al., 2009; Brewer et al., 2011)" (Addition of Brewer et al., 2011).

      We have added all suggested references.

      b) Page 4 top: (Brewer et al., 2011; Brewer et al., 2015; Martin et al., 2024). (Addition of Brewer et al., 2011).

      We have added all suggested references.

      c) Page 14 top: "Recent work has proposed that ODIRA CNVs are a major mechanism of CNVs in human genomes (Brewer et al., 2015; Martin et al., 2024; Brewer et al., 2024)." Brewer et al., 2024 focuses specifically on ODIRA and human CNVs. (Addition of Brewer et al., 2024).

      We have added all suggested references.

      (2) Page 6, third paragraph: I was surprised that a single inoculating strain was used to establish the replicate chemostats because of the possibility of non-independence of the resulting GAP1 CNVs. A nnSBI model was used to correct for this possibility later in the paper. It seems like it could have been avoided by a simple change in protocol to inoculate each chemostat with an independent inoculum. Was there a reason that the replicate chemostats were not conducted as independent events? Establishing the presence of 'founder' GAP1 CNVs without GFP seems rather secondary to the point of the paper (examining the CNVs that arise during evolution) and I would recommend it being moved to the supplement.

      As is typical in microbial experimental evolution studies, we aimed to start with genetically identical homogenous populations and observe the emergence and selection of de novo variation. Therefore, we founded independent populations from a single inoculum. However, this study, and our prior work using lineage tracking barcodes, has clearly demonstrated that during the initial growth of the culture used for the inoculum CNVs are generated that contribute to the adaptation dynamics on all derived populations. This unanticipated result now suggests that the reviewer’s suggestion is a valid one - independent populations should be derived from independent inocula and this will be our standard practice in future studies.

      We believe that our results, presented in Figure 2, establishing the presence of pre-existing GAP1 CNVs without the GFP are important as it highlights a limitation of the use of CNV reporters of gene copy number that was not previously known. However, we subsequently show that this class of variant - CNVs that are not detected by the reporter system - can be incorporated into our modeling framework enabling estimation of evolutionary parameters, which we believe is an important finding warranting inclusion in the main text.

      (3) Page 7 first full paragraph: "Finally, we also observe a significant delay (ANOVA, p = 0.00833) in the generation at which the CNV frequency reaches equilibrium in ARS∆ (~generation 112) compared to WT (pairwise t-test, adjusted p = 0.05) . . .". Is the delay in reaching a plateau in Figure 1E just a consequence of the later appearance of CNVs or do the authors believe there are two separate events responsible for this delay? E.g. if the authors think that the delay in reaching a plateau is related to lower selection coefficients of the CNVs that do arise compared to the CNVs of other strains, then this should be explicitly discussed.

      We believe that the delay in reaching equilibrium is a consequence of both a lower CNV formation and reduced selection coefficients. Lower values for the fitness coefficient and formation rate in ARS∆ explain both the delay in CNV appearance and CNV equilibrium as shown by the predicted dynamics (Figure S3B). We have added an explicit discussion of the effect of the ARS on CNV dynamics in paragraph 2 of the Discussion section paragraph 2 starting at line 456.

      (4) Page 7: Incorporating pre-existing CNVs into an evolutionary model: The rationale for how you are able to discount the formation rate of GFP-free CNVs (C-) in your model isn't clear to me. How are you able to assume that these C- events don't form after timepoint 0? Why do you assume a starting population of C- events but not a starting population of C+ events?

      We explored the possibility of modeling C- (amplifications of GAP1 without amplification of the reporter) during the evolution experiment. However, because the rate at which C- events occurs is slower than the rate at which C+ events occur (GAP1 amplifications with amplification of the reporter) we found that the effect was negligible. Importantly, the simple model is sufficient to describe the observed dynamics and thus we do not include these possible rare events.

      (5) Figure 1:

      (a) Panel B: Please put the tRNAs on the line diagrams of the four strains. I first interpreted ALLΔ as missing the tRNAs, too.

      Thank you for this suggestion. We added tRNAs to all diagrams to provide additional detail about the structure of the GAP1 locus.

      (b) Panels C, D, and E: the dark shade of the colored boxplots obscures the individual points. I recommend reducing the opacity of the box or choosing a lighter shade so that the individual points are visible on top of the box. Is the percent increase in CNVs per generation (Panel D) based on the slopes of the curves in panel B? By eye the slopes of ARS∆ and ALL∆ appear at least as steep as those of wild type and LTR∆.

      Thank you for this suggestion. We have now made the individual points visible on top of the boxplots in Figures 1C, 1D, and 1E. The lines in Figure 1B show the median value across populations per time point whereas each point in Figure 1D is the slope from linear regression using values from individual populations (data from individual populations are shown in Figure 3C).

      (6) Figure 2:

      (a) Panel A: Please remind the readers what FSC-A is measuring and label the different groups of cells in each sample. Are we supposed to assume the upper scatter in generation 8 is the pre-existing CNV variants? Are the three species at generation 50 due to 1, 2, and 3 copies of GFP? Is the new species in generation 137 further amplification of the locus? And if so, how many copies does it represent? I find it fascinating that what I assume is the 2-copy CNV (presumably a direct oriented amplicon produced by NAHR) at 50 generations is lost (out-competed by a potential inverted triplication) at later times, but I didn't find any mention of this phenomenon in the text. What do the different mutant strains look like over the same time course? Please supply supplemental figures with the flow cytometry gating and vertically aligned histograms of the GFP signal so that the peaks are more easily compared. And provide this information for each of the altered strains in supplementary materials.

      Thank you for these useful suggestions. We have added a gating legend to the figure to clearly indicate the copy-number for each subpopulation. We have edited the caption and main text to explain forward scatter (FSC-A). Raw flow cytometry plots are now provided as Supplementary figure 2 and distributions of cell-size normalized GFP signal are provided in Supplementary figure 3. Although our primary objective with Figure 2A was to show the persistence of the 1-copy GFP population the reviewer is correct that we did not highlight interesting aspects of the CNV dynamics. We have added additional text starting at line 251 to point out these features of the data.

      (b) Panel B: It would help to label the different colored boxes inside cells in Figure 2B - it took me a while to identify the white box as an unrelated adaptive mutation elsewhere in the genome. The linear arrangement of these small colored blocks seems to indicate their structural arrangement. Is that the case? And are they inverted or direct amplicons? Perhaps the authors are being agnostic at this point but it would be better if each of the blocks were separate. If there are other mutations that can explain these GFP-non-amplified survivors, were they identified in your whole genome sequencing?

      We have now included a complete legend for Figure 2B indicating that the white box reflects other beneficial mutations. We have separated this class of beneficial mutation from the GAP1 and reporter elements to reflect that they are not linked. We did not identify additional beneficial mutations but plan to pursue this question in a future project.

      (c) Panel C: Are the two sets of lines mislabeled? One would expect the "reported" CNV proportions to be lower than the total CNV proportions, not the other way around. Maybe the labels "total CNVs" and "reported CNVs" are unclear to me and I am misunderstanding what "reported" refers to. Please clarify.

      Thank you for identifying this mistake. The lines were mislabeled and have now been corrected in the revised version.

      (7) Figure 3:

      (a) A fuller discussion of panels A and B is needed. The results of panel A in particular seem like an excellent opportunity for connecting the computation to the biology. Can the authors speculate on why the ALL∆ strain has a higher CNV formation rate (𝛿c) than the ARS∆ strain? I would think that taking away one means of amplification would decrease CNV formation. Likewise, could the authors discuss why the selection coefficient (sc) for the LTR∆ strain would be the same as for the wild type? Overall, I would like to see more discussion about what these differences in formation rates and selection coefficients could mean for the types of amplicons arising in the chemostats. (In panel B I don't see the shaded area referred to in the figure legend.) A side-by-side comparison of the data in Panel A with the data shown in Supplemental Figure S3A would be instructive..

      Thank you for raising these points. We have added substantial text to the manuscript to address these findings. Starting at line 456 we state:

      “The lower CNV formation rate in the LTR∆ could be a closer approximation of ODIRA formation rates at this locus as ODIRA CNVs are the predominant CNV mechanism in the LTR∆ strain (Figure 4F). Furthermore, the low formation rates in the LTR∆ relative to WT might suggest that the presence of the flanking long terminal repeats may increase the rate of ODIRA formation through an otherwise unknown combinatorial effect of DNA replication across these flanking LTRs and template switching at the GAP1 locus. ARS∆ has the lowest CNV formation rate and it could be an approximation of the rates of NAHR between flanking LTRs and ODIRA at distal origins. We find that the ALL∆ has a higher CNV formation rate than the ARS∆, even though three elements are deleted instead of one. One explanation for this is that the deletion of the flanking LTRs in ALL∆ gives opportunity for novel transposon insertions and subsequent LTR NAHR. Indeed we find an enrichment of novel transposon-insertions in the ALL∆ (Figure 4F) and subsequent CNV formation through recombination of the Ty1-associated repeats (Figure 4H, ALL∆). Both events, transposon insertion followed by LTR NAHR, would have to occur quickly at a rate that explains our estimated CNV rate in ALL∆. While remarkable, increased transposon activity has been associated with nutrient stress (Curcio & Garfinkel, 1999; Lesage & Todeschini, 2005; Todeschini et al., 2005) and therefore feasible explanation for the CNV rate estimated in the ALL∆. Additionally, ARS∆ clones rely more on LTR NAHR to form CNVs (Figure 4F). The prevalence of ODIRA in ARS∆ and ALL∆ are similar. LTR NAHR usually occurs after double strand breaks at the long terminal repeats to give rise to CNVs (Argueso et al., 2008). Because we use haploid cells, such double strand break and homology-mediated repair would have to occur during S-phase after DNA replication with a sister chromatid repair template to form tandem duplications. Therefore the dependency on LTR NAHR to form CNVs and the spatial (breaks at LTR sequences) and temporal (S-phase) constraints could explain the lower formation rate in ARS∆.”

      In addition, we added a discussion of the different selection coefficients estimated and how the simulated competitions help us understand the decreased selection coefficients in the architecture mutants. In newly added text starting at line 479 we state:

      “The genomic elements have clear effects on the evolutionary dynamics in simulated competitive fitness experiments. The similar selection coefficients in WT and LTR∆ suggest that CNV clones formed in these background strains are similar. Indeed, the predominant CNV mechanism in both is ODIRA followed by LTR NAHR (Figure 4F). While LTR NAHR is abolished in the LTR∆, it seems that CNVs formed by ODIRA allow adaptation to glutamine-limitation similar to WT. The lower selection coefficients in ARS∆ and ALL∆ suggest that GAP1 CNVs formed in these strains have some cost. In a competition, they would get outcompeted by CNV alleles in the WT and LTR∆ background.”

      (b) The data shown in panel C seems redundant to what is shown more clearly in Supplemental Figure S3B. It seems to me the more important comparison to make in panel C would be the overlay of the predicted data to the median proportion of cells obtained from the experimental data (Figure 1B). Also, overlays of the cultures from each strain could be added to S3A. It is difficult to see the variation within each strain when the data from all four strains are superimposed as they are in Figure 3C.

      We agree and have edited Figure 3C to incorporate these suggestions and more clearly convey the intra- and interstrain variation.

      (8) Figure 4:

      (a) Panels A, B, and C are nice summaries and certainly helpful for understanding panel E, but it would be instructive to see some actual rearrangements of the ODIRA events, the NAHR, and the transposon-mediated rearrangements. It isn't clear to me what these last events look like. A figure that shows the specific architecture of example clones for each category would be helpful. I am also having a hard time reconciling ODIRA events with a copy number of 2. Are these rearrangements free isochromosomes with amplification to the telomere or are they secondary rearrangements like those described in Brewer et al., 2024? And what about the non-aneuploid rearrangement that includes the centromere? Is it a dicentric?

      We have now added more detailed depictions of CNVs in Figure 4A and provide links to visualize the alignment files. We have added additional discussion starting at line 397 of the non-canonical ODIRA events and putative neochromosome amplicons with reference to Brewer et al 2024. Starting at line 397 we state:

      “Surprisingly, we found CNVs with breakpoints consistent with ODIRA that contained only 2 copies of the amplified region, whereas ODIRA typically generates a triplication. In the absence of additional data, we cannot rule out inaccuracy in our read-depth estimates of copy numbers for these clones (ie. they have 3 copies). An alternate explanation is a secondary rearrangement of an original inverted triplication resulting in a duplication (Brewer et al., 2024); however, we did not detect evidence for secondary rearrangements in the sequencing data. A third alternate explanation is that a duplication was formed by hairpin capped double-strand break repair (Narayanan et al., 2006). Notably, we found 3 additional ODIRA clones that end in native telomeres, each of which had amplified 3 copies. In these clones the other breakpoint contains the centromere, indicating the entire right arm of chromosome XI was amplified 3 times via ODIRA, each generating supernumerary chromosomes. Thus,ODIRA can result in amplifications of large genomics regions from segmental amplifications to supernumerary chromosomes.”

      (b) In Panel B the violin plots appear to indicate that there are two size categories for amplicons in the ARS∆ strain. Do clones from these different sub-populations share a common CNV architecture?

      Thank you for making this point. (Please note that the violin plots are now Figure 4E) We added a short discussion and Supplementary Figure 14. In line 432, we state:

      “In ARS∆, we find two CNV length groups (Figure 4E) that correspond with two different CNV mechanisms (Supplementary Figure 14). 100% of smaller CNVs (6-8kb) (Supplementary Figure 14) correspond with a mechanism of NAHR between LTRs flanking the GAP1 gene (Figure 4H, ARS∆, bottom left green points). Larger CNVs (8kb-200kb) (Supplementary Figure 14) correspond with other mechanisms that tend to produce larger CNVs, including ODIRA and NAHR between one local and one distal LTR element (Figure 4H).”

      (c) Panels D and E: There is great information in these two panels but I find the color keys confusing. There doesn't seem to be any reason for the strain color key in panel E. I am assuming that the key should go with Panel D. Is there some way to indicate in Panel D which events are in which CNV category? It is cumbersome to find that information from Panel E. Perhaps the color-coding from Panel E could be applied to the row labels in Panel D. Being able to link amplicon to the mechanism of CNV formation is especially important for seeing which ODIRA events contain an origin.

      Thank you for this suggestions. We now indicate the mechanism of CNV formation using a consistent color coding in panels G and H (previously panels D and E).

      (d) Panel E: I don't understand the two axes in Panel E. If both axes are log scales, why is the origin 0 for the X-axis and 1 for the Y-axis? And why are the focal amplicons (most of which are recombination events between the two LTRs) scattered in both X and Y coordinates? Shouldn't they form a single point? The same for the recombinants with distal LTRs. Also, orange and red (ODIRA and complex CNVs, respectively) are very hard to distinguish. All of these data need to be presented in a spreadsheet identifying each clone's strain ID, chemostat number, GAP1 and GFP copy numbers, sequence across the junction, and their coordinates. The SRA project (PRJNA1016460) for the sequence data was not found in SRA. Will this data be available to easily look at read depth across chromosome XI for all of the sequenced strains - perhaps as .bam files?

      Thank you for calling these issues with data visualization to our attention. Indeed, the focal amplifications do form around a single point. We originally had jittered the data to show each individual focal amplification but agree that this is confusing. We now overlay the individual points and have altered opacity to enable visualization of individual values. The suggested table of clone data is provided in Supplementary File 2 and the SRA project is now publicly available. Moreover, we are providing all alignment (.bam) files, split, and discordant read depth profiles for each CNV strain and their corresponding ancestor aligned to our custom reference genomes in a public jbrowse server at:

      https://jbrowse.bio.nyu.edu/gresham/?data=data/ee_gap1_arch_muts for WT strains, https://jbrowse.bio.nyu.edu/gresham/LTRKO_clones for LTR∆ strains, https://jbrowse.bio.nyu.edu/gresham/ARSKO_clones for ARS∆ strains, https://jbrowse.bio.nyu.edu/gresham/ALLKO_clones for ALL∆ strains.

      (e) Supplementary Table 1 and Supplementary Figure S2: Please indicate which rearrangements (of the 8 reported in Figure S2A) were identified in each of the clones described in the table. If each of the 8 amplicons is identified by a letter, then this information could be added as a column in the table. I am assuming that each of the eight rearrangements was found in more than one chemostat. Showing these data is crucial for establishing the possibility that they were preexisting at the time of chemostat inoculation. The other possibility is that the clones with amplified GAP1 but a single copy of GFP could have been created by a secondary rearrangement in the outgrowth of the clones that originally had amplified both genes to the same extent. What is the structure of these amplicons? Is there a common junction between GAP1 and GFP? I couldn't find these data in the paper. A suggestion for Supplemental Figure S2A - include a zoomed-in inset for the GAP1 GFP region for each of the 8 read-depth plots. It is hard to see the exact location of GFP and GAP1 across all 8 tracks without getting out a ruler. Were these sequences aligned to your custom reference genome or the reference genome without GFP? If they were aligned to the custom reference that includes the GFP reporter, the reader could visually confirm the absence of GFP amplification.

      Thank you for these suggestions. We edited Supplementary Table 1 and Supplementary Figure 1A as requested. We now provide the precise CNV breakpoints in the GFP-GAP1 region (supplemental figure 1B) displaying both genome read depth and split read depth tracks. These sequences were aligned to the custom reference containing the GFP reporter, which is now clearer in the figure and caption text in line 1226.

      The clones in this figure were sampled from the five different chemostats and we have clarified this in the edited table and text at line 210. We did not detect the same CNV allele in different chemostats and therefore we do not have evidence to support GAP1 amplification without the GFP reporter pre-existing at time of inoculation. We are not able to definitively distinguish whether the amplicons were pre-existing at the time of inoculation or occurred after as we do not have barcoded lineages. We isolated clones carrying this class of amplification from the 1-GFP-copy subfraction late in the experimental evolution (generation 165-182). Given that the alleles appear to differ between populations we think the most parsimonious explanation is that these amplifications occurred after chemostat inoculation but early in the evolution experiment. We explicitly state this in the text starting in line 219.

      (9) Page 8-9: I am sorry to say that I can't evaluate the "HDI of posterior distributions". It is out of my competency range. So I am not sure what this analysis is adding to the paper. The same goes for the rest of the supplementary figures.

      HDI is a measure of certainty in an estimate, similar to confidence interval. We state this in the text in line 276. With the editing of the text we hope the modeling and its supplementary figures are more clear now.

      (10) Page 9 top: Deletion of the ARS appears to lower the fitness of the amplified GAP1 variants. Can the authors speculate on why the ARS deletion would reduce fitness? Did they consult published replication profiles to determine the size of the origin-free gap that could result from the deletion of this mid-S phase origin? Could it explain the delay in the appearance of GAP1 amplicons in the ARS-deletion strains and be responsible for their reduced selection coefficients? Did you examine the growth properties of the starting strain or any of the amplified GAP1 derivatives? Perhaps this consideration could contribute to the discussion. Could there be a bit fuller discussion on the interaction between CNV length differences as shown in Figure 4A and differences in selection coefficient as determined by the nnSBI?

      Thank you for raising this point. We have now added text to our discussion of the reduced fitness in ARS∆ in relation to DNA replication starting on line 359:

      “ARS1116 is a major origin (McGuffee et al., 2013) and ODIRA CNVs found around this origin corroborate its activity. GAP1 is highly transcribed in glutamine-limited chemostats (Airoldi et al., 2016). Head-on transcription-replication collisions at this locus may be contributing to the higher CNV formation rate in wild type and LTR∆. Elimination of the local ARS could result in less transcription-replication collisions and the slower CNV formation rates estimated. Once formed they get outcompeted by faster-forming CNVs and thus in theory are less fit than CNVs in other strain backgrounds. These simulated competitions further suggest that the ARS is a more important contributor to adaptive evolution mediated by GAP1 CNVs.”

      We examined replication profiles in McGuffee et al. Mol Cell. 2013 but could not determine the size of the origin-free gap. ARS1116 and its neighboring ARSs, ARS1118 downstream and ARS1115 upstream are efficient firing origins (Supplement 1 of McGuffee et al. 2013) and therefore the gap is likely to be minimal. The dynamics of the distal firing ARS elements involved in creating ODIRA CNVs might explain the reduced fitness, but further experiments would be required to address this. Regarding growth properties, the growth rate at steady-state in the chemostat is the same as the dilution rate regardless of strain background. Because we had the same dilution rate for each chemostat, the ARS∆ populations would have the same replication rate as the other three strains even if there may be replication rate differences in bulk culture growth. Finally, we found no significant interaction between CNV length and selection coefficients and we state this in line 359.

      (11) Page 10: WT competition simulations: It may help to explicitly state that the competition modeling approach was experimentally validated in Avecilla 2022 as opposed to just citing the paper. I found the results much more convincing after reading Avecilla 2022, but I imagine many readers may skip that.

      We added a sentence to state that the nnSBI method was experimentally validated in Avecilla et 2022 at line 249.

      Reviewer #2 (Recommendations For The Authors):

      (1) Figure 2: says reported CNV proportions (dashed). This may be a typo since I think the GFP reported should be solid, not dashed. Also, (C) isn't bold.

      Thank you for identifying these mistakes. We have corrected the figure’s caption in line 1157.

      (2) "compared to 898/345 clones" Does this refer to transposition/clone? Seems more natural to compare clones with transpositions to a total number of clones. This could be clarified.

      We rephrased the sentence (lines 519-520) to clarify that in their study Hays et al. 2023 found 898 novel Ty insertions across 345 nitrogen-evolved clones. As a result of this high rate of transposition, some clones are expected to have multiple Ty insertions.

      (3) The methods state that Kan replaces the Nat cassette that was used to make the deletions. It should be made more clear whether Kan is present and where Kan is with respect to GFP and GAP1.

      Thank you for pointing this out. To clarify we added the following sentence to the methods starting in line 567:

      “The CNV reporter is 3.1 kb and located 1117 nucleotides upstream of the GAP1 coding sequence. It consists of, in the following order, an ACT1 promoter, mCitrine (GFP) coding sequence, ADH1 terminator, and kanamycin cassette under control of a TEF promoter and terminator.”

      Additionally in line 571 we clarify the drug resistance of the genomic architecture ∆ strains that are kanamycin(+) and nourseothricin(-).

      Reviewer #3 (Recommendations For The Authors):

      (1) The major advancement of the manuscript is stated in the title "DNA replication errors are a major source of adaptive gene amplification" First, in my humble opinion the term replication errors is not quite right; the term template switching is more accurate. In that regard, recently a paper was published just on this topic (Martin et al Plos Genetics, 2024).

      We have changed the title to “Template-switching during DNA replication is a prevalent source of adaptive gene amplification”. We cite Martin et al Plos Genetics 2024 throughout the main text in lines 93, 126, 159, 502, 555.

      (2) I find the statement "We find that 49% of all GAP1 CNVs are mediated by the DNA replication-based mechanism Origin Dependent Inverted Repeat Amplification (ODIRA) regardless of background strain." Somewhat misleading, there were considerable differences between the strains. If I am not mistaken the range was 20-80%.

      Thank you for pointing this out. Indeed, the range was 26-80% across the four strains. We updated this sentence in the abstract at line 40, and in the main text at line 141 to clearly state the range.

      (3) In their attempt to fill the gap of knowledge regarding the fitness effect of the adaptive CNV the authors use a mathematical model. As an experimental biologist, I found the description lacking. It is hard for me to evaluate the contribution of the model to understanding the results and I think the authors could improve this part.

      We have edited the text regarding the modeling and associated results and hope that it is now more clear. The mathematical model describes the experiment in a simplified manner. We use it to predict the outcomes of additional experiments without additional experimental work. For example, we used it to simulate a competition between two strains, predict the total proportion of GAP1 CNVs, and predict the relative genetic diversity.

      (4) Experiments the authors may want to consider to increase the novelty of their work:

      a) Place the GAP1 gene right in the middle of the two most distant ARS elements and test the mechanism of CNV.

      Thank you for this proposed experiment. It is beyond the scope of this paper and will be pursued in future studies.

      b) The finding of de-novo Ty element insertion is interesting. What happens if the overdose strain of Jef Boeke is used (Retrotransposon overdose and genome integrity, PNAS 2009) or in contrast, a reverse transcriptase deficient strain?

      We agree. Our study has revealed a critical role for novel Ty insertion in mediating CNVs. The suggested experiments as well as using strains that lack Ty sequences will be very interesting to explore in followup studies.

      c) The genomic analyses were based on single colony isolates. To my understanding, the CNV events are identified at least partly by split reads. Therefore, each event may have a "signature" that is unique and can be concluded from single reads and not necessarily from the assembled genome. If true, a distinction between the scenarios could be achieved if bulk cultures are sequenced with enough depth. Thus, a truly dynamic and quantitative determination of the different events, rate of appearance, and disappearance can be made.

      Thank you for this suggestion, which is a good idea but not currently feasible for several reasons. First, although split reads are a powerful way to detect CNV breakpoints, we have found that even at high coverage (21-153X, median 78.5X), in clonal samples that are rare with only 3-30 split reads (median 14) detected. These observations are from a total of 23 breakpoints across 16 sequenced clones. Thus, when sequencing heterogeneous cultures, in which different CNVs only comprise a fraction of the population, our ability to detect single CNV alleles by split reads and quantify their frequency is limited. Given our observations, with a median of 14 split reads when sequencing to 78.5X genome-wide read coverage it is possible we may be able to detect an individual CNV allele once it makes up (14/78.5) 17% of the population. However, our previous study has shown that there are tens to hundreds of unique CNV alleles initially and thus this would only be feasible at very late timepoints. Second, recurrent CNVs may occur independently at the same exact location, such as LTR NAHR. Thus, unique signatures may not be obtained even if they are independent events. Third, it would be not appropriate to pursue this analysis with our current dataset, as we lack lineage tracking barcodes to validate the results.

    1. The photo celebratedthe making of the Syntopicon. This massive index of Western thought,which in conception had grown godlike out of the head of MortimerAdler, was intended to help readers navigate their way through theGreat Books.

      Interesting that he reduces hundreds of thousands of hours of work to "grown godlike out of the head of Mortimer Adler..."

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    1. There’s little Tom Dacre, who cried when his head, That curled like a lamb’s back, was shaved; so I said,

      highlight the innocence and vulnerability of child laborers during the Industrial Revolution.

    1. Now you say to me, "Lula, Lula, why don't you go to this party with me tonight?" It's your turn, and let those be your lines.

      It wasn't enough for Lula messing with Clay's head, also she begins to decide on his sentences. This indicates that Lula has exacerbated her manipulative power, her dominance, while Clay has become even more passive. Clay's willpower is thoroughly ignored by Lula. Lula stucks in the idea that blacks are slaves.

    1. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Recommendations For The Authors):

      (1) Gap of knowledge:

      From the introduction, I got the impression that the manuscript tries to answer the question of whether homeostatic structural plasticity is functionally redundant to synaptic scaling. However, the importance of this question needs to be worked out better. Also, I think it is hard to tackle this question with the shown experiments as one would have to block all other redundant mechanisms and see whether HSP functionally replaces them.

      We appreciate the reviewer’s valuable feedback regarding the relationship between homeostatic structural plasticity (HSP) and synaptic scaling. The main objective of our study is indeed to investigate whether structural plasticity is homeostatically regulated, and if so, whether it acts as a redundant or heterogeneous mechanism in relation to synaptic scaling, which is widely recognized as a primary homeostatic process.

      In our revised introduction, we have clarified this central question and its significance. Specifically, we explored why experimentally observed changes in spine density, a measure of structural plasticity, do not exhibit the same homeostatic characteristics as changes in spine head size, which reflects synaptic scaling, particularly under conditions of activity blockade.

      We hypothesized two key points:

      (1) Structural plasticity may not follow a monotonically activity-dependent rule as strictly as synaptic scaling.

      (2) The observed changes in spine density may be influenced by the simultaneous modulation of spine size, suggesting that structural plasticity and synaptic scaling interact within the same biological system.

      Both hypotheses were tested through a combination of experimental observations and systematic computer simulations. Our conclusions demonstrate that spine-number-based structural plasticity follows a biphasic activity-dependent rule. While it largely overlaps with synaptic scaling under typical conditions, it exhibits heterogeneity under extreme conditions, such as activity silencing. Furthermore, our simulations revealed that both mechanisms can compete and complement each other within neural networks.

      We believe that these results offer a nuanced understanding of the interaction between structural plasticity and synaptic scaling, highlighting their redundancy under most conditions but also their heterogeneity under specific circumstances. Blocking all other redundant mechanisms, as suggested, would provide a more reductionist view, which may not capture the complexity and interplay of these processes in a physiological setting. Our approach reflects this complexity, providing insight into how these mechanisms operate together in a naturalistic context.

      We have revised the introduction to better convey these points and emphasize the significance of this question for understanding the dynamics of homeostatic regulation in neural networks.

      Similarly, the simulations do not really tackle redundancy as, e.g. network growth cannot be achieved by scaling alone.

      We appreciate the reviewer’s comment regarding synaptic scaling's limitations in achieving network growth. We would like to clarify that we did not intend to suggest that structural plasticity and synaptic scaling are fully redundant. In fact, it is well established in the literature that structural plasticity plays a dominant role during development, particularly in network growth, which synaptic scaling alone cannot achieve.

      The primary objective of our study was to investigate the interaction between structural plasticity and synaptic scaling under conditions of activity perturbation, rather than during network growth or development. To avoid any confusion regarding developmental processes, we chose to grow the network using only structural plasticity in our simulations. Synaptic scaling was then introduced (or not) during the phase of activity deprivation to specifically examine its role in regulating homeostasis under these conditions.

      We have revised the corresponding sections of the manuscript to clarify this distinction, and we have ensured that the simulations reflect our focus on activity perturbation rather than network development. This distinction should help readers avoid conflating developmental processes with the specific goals of our study.

      Instead, the section on "Integral feedback mechanisms" (L112-129) contains a much better description of the actual goals of the paper than is given in the introduction. Moreover, this section does not seem to include any new results (at least the Ca-dependent structural plasticity and synaptic scaling rules seem to be very common for me). I, therefore, suggest fusing this paragraph in the introduction to obtain a clearer and better understandable gap of knowledge, which is addressed by the paper.

      We agree that the "Integral feedback control" section provides key information relevant to both the Introduction and Methodology. It outlines the theoretical framework and serves as a basis for the experimental design.

      To better reflect this, we have revised the Introduction to include the gap in knowledge. However, we opted to retain the section in the Results, slightly modified, to set the context for the first experiment.

      Along this line, as it seems a central point of the manuscript to distinguish the controller dependencies on Calcium, the different dependencies (working models) should be described in more detail. Also, the description of the inconsistencies of the previous results on HSP can be moved from the discussion (l419-l441) to the introduction.

      We have revised the manuscript to place less emphasis on the controller models while retaining the core principles of control theory. The description of the HSP model has been moved to the Introduction, as suggested, while the detailed history remains in the Discussion to maintain the manuscript's consistency.

      Systematic text revision: Regarding comment (1), we thank the reviewer for suggesting the text reorganization. We have adjusted several parts in the introduction, M&M section, and results section to increase clarity.

      (2) Pharmacological Choice:

      It should be discussed why NBQX is used to induce the homeostatic effect instead of TTX. As there are studies showing that it might block homeostatic rewiring (doi.org/10.1073/pnas.0501881102) as well as synaptic scaling (10.1523/JNEUROSCI.3753-08.2009), it seems unclear whether the observed effects are actually corresponding to those in other publications.

      The rationale for using NBQX in our experiments, rather than TTX, is detailed in the public response. We selected NBQX based on specific experimental motivations relevant to our study’s objectives, while acknowledging the potential differences in effects compared to other studies.

      Local text revision: We added one paragraph in the discussion section to explain the idea better.

      (3) Model-Experiment Connection:

      The paper combines simulations with experimental work, which is very good. However, in my opinion, the only connection between the two parts is that the experiments suggest a non-monotonic dependency between firing rate and synapse density (i.e. the biphasic dependency). The rest of the experimental results seem to be neglected in the modeling part. It is not even shown that the model reproduces the experiments. Instead, the model is tested in different situations and paradigms (blocking AMPARs in the whole culture vs network growth or silencing a sub-population). I think it would make the paper stronger and more consequential when a reproduction of the experiment by the model is demonstrated (with analogue analyses).

      The experimental results serve three main purposes. First, as the reviewer noted, the spine analysis was conducted to inform the biphasic rule. Second, spine size analysis was performed to replicate published findings and confirm our modeling results, showing that activity deprivation leads to fewer synapses with larger sizes or higher weights. Third, the correlation analysis of spine density and size across dendritic segments suggested a hybrid combination of two types of plasticity across different neurons.

      While we addressed these aspects in the Results and Discussion sections, the collective presentation in Fig. 2 may have caused some confusion. To improve clarity, we have now split the experimental results, presenting them alongside the relevant modeling data in Fig. 2, Fig. 8, and Fig. 9.

      Also, there are a few more mismatches between the experiment and the model that you will want to discuss:

      • The size-dependent homeostatic effect (l154ff, Fig2F) is not reflected by the used scaling model.

      We revised Fig 8 and the corresponding text to explain how the scaling model reflects such an effect.

      • The model assumes reduced Ca levels. Yet, the experimental protocol blocks AMPARs, which are to my knowledge not the primary source of Ca influx, but rather the NMDARs.

      The model is based on neural activity, with calcium concentration serving as an internal integral signal of the firing rate, allowing for integral control. While calcium plays a critical role in homeostasis, we caution against drawing a strict correspondence between the model's calcium dynamics and the experimental protocol, as calcium can be sourced from multiple pathways in neurons beyond AMPARs, such as NMDARs, voltage gated calcium channels, and intracellular stores. Also, our recent work demonstrated that under baseline conditions, the majority of AMPARs are not Ca2+ permeable, i.e., GluA2-lacking (Kleidonas et al., 2023)

      Improving the calcium dynamics, including secondary calcium release and calcium stores, is part of our future plan to refine the HSP model and address experimental findings that are not fully explained by the current model.

      • The model further assumes silencing by input removal, whereas the recurrent connections stay intact. Wouldn't this rather correspond to a deafferentation experiment, where connections to another brain area are cut?

      Thank you for pointing at this. The modeling section was not intended to directly replicate the tissue culture experiments but rather to provide insights into a broader range of scenarios, including pharmacological treatments, deafferentation, lesions, and even monocular deprivation.

      Systematic text revision: Regarding comment (3), the goal of our modeling work was more than reproducing. To better serve the purposes of experimental results used in the present study, to inform, confirm, and inspire, we have systematically adjusted the layout of experimental and modeling results to link them better.

      (4) Is the recurrent component too weak?

      Your results show that HSP does not restore activity after silencing (deafferentation), whereas you discuss that earlier models did achieve this by active neighbors in a spatially organized network. However, the silenced neurons in your simulations also receive inputs through the "recurrent" connections from their neighbors (at least shortly after silencing). Therefore, given the recurrent input is strong enough, they should be able to recover in a similar way as the spatially organized ones. As a consequence, I obtained the impression that, in your model networks, activity is strongly driven by external stimulation and less by recurrent connections. I understand that this is important to achieve silencing through removing the Poisson stimulation. Yet, this fact may be responsible for the failure to restore activity such that presented effects are only applicable for networks that are strongly driven by external inputs, but not for strongly recurrent networks, which would severely limit the generality of the results. As a consequence, the paper would benefit from a systematic analysis of the trade-off between recurrent strength and input strength. Maybe, different constant negative currents could be injected in all neurons, such that HSP creates more recurrent synapses in the network.

      We appreciate this insight. However, increasing recurrent input strength is beyond the scope of the current study, as it would fundamentally alter the predefined network dynamics of the Brunel network used. As noted in the manuscript, complete isolation or cell death is not always the outcome after input deprivation, lesion, or stroke, which cannot be fully explained by the Gaussian HSP rule alone. Butz and colleagues offered a solution using growth rules that maximized recurrent input, and we recognize the importance of their work.

      That said, we approached the issue from a different angle, emphasizing the role of synaptic scaling in recurrence rather than relying solely on recurrent input strength. In biological networks, external inputs may vary, recurrency can be weak or strong, and synaptic scaling can dominate. Our model offers a complementary hypothesis, suggesting that these factors, in combination, contribute to the diverse and sometimes contradictory results found in the literature, rather than posing a strict constraint on network topology.

      Local text revision: We emphasized these points in the Discussion section again.

      (5) Missing conclusions / experimental predictions

      As already described, the modelling work is not reproducing the presented or previous experimental data. Hence, the goal of modelling should be to derive a more general understanding and make experimental predictions. Yet, the conclusions in the discussion stay superficial and vague and there are no specific experimental predictions derived from the model results.

      For example, the authors report that the recovery of activity in silenced cultures is observed in a previously spatially structured model but not in theirs -- at least with slow or no scaling. Yet it is left to the reader to think about whether the current model is an improvement to the previous one, how they could be experimentally distinguished, or to which experimental findings they relate or compare, which I would expect at this point. I would advise reworking the discussion and thoroughly working out which new insights the modelling part of the study has generated (not to be confused with the assumptions of the model aka the biphasic plasticity rule) and relating them to experimental pre- and postdiction.

      We recognize the reviewer’s concern, which is closely related to comment (4). We have addressed these points by reorganizing the text to better clarify the purpose of our experimental work and its connection to the modeling results.

      Specifically, we have reworked the discussion to highlight the new insights gained from the modeling, and how these can inform experimental predictions and interpretations. This includes distinguishing our model from previous ones and providing clearer connections to experimental findings.

      Systematic text revision: Most of the comments on combining experiments and modeling results and on developing the story based on our expectations raised here are sincere and may also reflect the expectations and concerns of a broader readership, so we have accordingly adjusted the text in the Results and Discussion sections to make our points clear.

      Suggestions for minor changes:

      Fig 1I: Please check the graph and make it more self-explaining. For example, mark the "setpoint" activity (in my opinion, both curves should be at baseline there. In that case, however, I do not see the biphasic behavior anymore). Maybe the table and the graph can be aligned along the activity axis? Also: synaptic inhibition should be increased and not decreased, right?

      Local text and figure revision: I guess the reviewer meant for Fig. 2I? We have improved the visualization to avoid confusion.

      L74-81: I would reverse the order of associative and homeostatic plasticity in this paragraph.

      Local text and figure revision: We have fine-tuned the order in the first and second paragraphs to match the readers' expectations.

      L74-75: Provide references for such theories.

      Local text and figure revision: fixed.

      L84-86: Please provide a reference for the claim that negative feedback, redundancy, and heterogeneity contribute to robustness.

      Local text and figure revision: fixed.

      L 95-97: I think the heterogeneity aspect needs to be worked out a bit better. Do you mean that the described mechanisms contribute to firing rate homeostasis in a different mixture for each neuron (as shown assumed in the last figure)?

      Local text and figure revision: The term heterogeneity is used in the manuscript for two major different settings: (1) heterogeneity in terms of control theory and (2) different combinations of HSP and SS rules. We have named the second condition as diversity to avoid confusion.

      L 132: The question of linearity has not been posed so far. Also, I think "monotonous" would be a much better term than linear (as a test for linearity would require more than 2 datapoints).

      Local text and figure revision: We agreed linear is not a good term. We replaced it with ‘monotonic’ throughout the manuscript.

      Fig2 Bii: The data for 50um is clearly not Gaussian.

      We did not imply that the 50 µM condition is Gaussian. Instead, we noted that the non-linearity observed in both the 200 nM and 50 µM data suggests a non-monotonic growth rule rather than a linear one. We applied the Gaussian rule because it has been extensively studied in previous simulations, allowing us to benchmark our findings against those results.

      Fig2 D, E inset: The point at time 0 does not convey any information and could be left out.

      The time zero data is included to demonstrate that the three groups have a similar baseline, ensuring that any observed differences are due to the treatment and not pre-existing biases in the grouping.

      L 178: As the Gaussian rule drops below zero above the upper set-point again, it is rather tri-phasic than bi-phasic.

      We intended to convey that inhibition results in either spine growth or deletion, reflecting a bi-phasic response rather than a true tri-phasic one.

      Fig 6A: You may want to mark the eta variables in the curves.

      Local text and figure revision: fixed.

      Fig 6E: The curve of the S population extending to the next panel looks a bit messy.

      We retained the curve extension to visually convey the impression of excessive network activity.

      L272: It needs to be better described/motivated how protocol 1 and 2 are supposed to study the role of recurrent connection as well as what kind of biological situation this may be.

      Local text and figure revision: The corresponding text has been adjusted to avoid confusion.

      L 272: It is not clear how faster simulation leads to less recurrent connectivity, when the stimulation protocol and the rates stay the same and the algorithm compensates for the timestep properly. Maybe you rather want to say that you silence 10x longer and stimulate 10x longer?

      Local text revision: The corresponding text has been adjusted to avoid confusion.

      L. 302: "reactivate"?

      Local text revision: fixed.

      L 322f: I would suggest showing the connectivity matrix for a time-point with restored activity as well.

      Local text and figure revision: fixed.

      Fig 8A: The use of the morphological reconstructions is a bit misleading as the model uses point neuron.

      Local text revision: Now after reorganization, it is in Fig.9. We kept the reconstruction figure for motivational purposes, suggesting how to understand the meaning of the combinations in more biologically realistic scenarios. The corresponding text has been adjusted to avoid confusion.

      Fig 8E-F: the y axis should be in the same orientation as in panel D.

      Local text and figure revision: Good idea and fixed in the new Fig. 9.

      Fig. 8F: The results here look a little bit random. Maybe more runs with the same parameters would smooth out the contours or reveal a phase transition.

      Local text and figure revision: Thank you for the suggestion. We conducted an additional ten random trials to average the traces and heatmaps, improving the clarity of the results now presented in Fig. 9.

      L411: Note that there are earlier HSP models by Damasch and van Ooyen & van Pelt, that might be worth discussing here.

      Local text revision: fixed.

      L416 "beyond synaptic scaling" reference needed.

      Local text revision: fixed.

      L419: The biphasic rule was suggested by Butz already.

      Local text revision: We adjusted the text to emphasize our contribution in suggesting/confirming the biphasic rule based on direct experimental observations.

      L 419-44: Most of this is actually state-of-the art and may be better placed in the introduction to justify the use of NBQX as a competititve blocker.

      Local text revision: We adjusted the text in the introduction and Discussion sections to cover the raised points.

      L487: In my opinion, although scaling adapts the weights quickly, the information about deviating firing rate is still stored in the calcium signal such that it will also give rise to structural changes (although they may be small when the rate is low). Thus, I think that fast scaling does not abolish structural changes.

      Local text revision: We adjusted the text to account for other factors that could lead to the same or opposite conclusions.

      L502f: Sentence unclear. Do you mean Ca is an integrated (low-pass filtered) version of the firing rate?

      Yes.

      L504: What is the cumulative temporal effect of error in estimating firing rates?

      We were referring to the potential instability in numeric simulations if the firing rate is not tracked by an integral signal (calcium concentration) but is instead estimated through average spike counts over time. In our model, calcium serves as a proxy for the firing rate to guide homeostatic structural plasticity. The intake and decay constants are set to minimize the accumulation of errors over time, making long-term error accumulation unlikely. In any case, this is not intended to be a precise measure of the firing rate but rather a smooth guide for homeostatic control.

      Local text revision: We rewrote the section so as not to cause extra concerns.

      L505: Which two rules are meant here? Ca- and firing rate based or HSP and scaling?

      Local text revision: The two rules are the HSP rule and the HSS rule. We have adjusted the text to improve clarity.

      L505ff: I did not really understand the control theoretic view here and Supp Fig 5 is not self-explaining enough to help. In my view, scaling is a proportional controller for the calcium level (the setpoint is defined for calcium and not firing rate). Also, all of the HSP rules do neither contain an integral nor a differential of the error and are thus nonlinear but proportional controllers in first approximation. If this part is supposed to stay in the manuscript, the supporting information should contain a more detailed mathematical explanation. Relevant previous work on homeostatic control by synaptic scaling and homeostatic rewiring, e.g. doi: 10.23919/ECC54610.2021.9655157 should be discussed

      Local text revision: We have updated the last paragraph to increase clarity. The HSP and HSS rules are proportional and integral for neural activity, as neural firing rate homeostasis is the meaningful goal. However, it is also correct that the integral component is gone if we view calcium concentration as the goal or setpoint. This paper is discussed and cited in a paragraph above this one.

      Reviewer #2 (Recommendations For The Authors):

      I have some additional suggestions and questions for the authors, which I am presenting following the order of the figures.

      Fig 1A: I'm a little bit puzzled by the timescales between Hebbian and homeostatic plasticity; a wealth of data suggests that Hebbian plasticity acts on a faster timescale than homeostatic plasticity, while Aii-Aiii implies the opposite. In lesion-induced degeneration, for instance, which is mentioned later by the authors, spine loss has been suggested to be Hebbian (LTD) while the subsequent recovery is homeostatic. Additionally, it will not be clear to the reader if the same stimulus could induce Hebbian and homeostatic plasticity, or why; the rest of the manuscript seems to imply that any stimulus could and would trigger homeostatic plasticity, which is not the case. Finally, there should be a mention somewhere that Hebbian structural plasticity also exists.

      Local text and figure revision: We thank the reviewer for pointing out the time scale issue, which was not explicitly considered here and is now updated.

      Fig. 2Bii: There is no significant difference at 200nm NBQX for sEPSC amplitude, contrary to what is stated in the text (line 136). Which one is it?

      Local text revision: We thank the reviewer for pointing out the mistake. We have inspected the original statistical file and corrected the text.

      Fig. 2F: The description of Fig. 2F in the text confused me for the longest time. I am still unsure why 200nm NBQX is described as leading to a general size increase when it follows the control line so closely, crosses 0 at the same point, and is even below the control line for the largest spine sizes. Similarly, 50um NBQX neatly overlaps with the control condition except for the smallest and largest spines, so the "shrinkage of middle-sized spines" doesn't seem different from the control condition. I also couldn't find any data supporting the statement that 50um NBQX increased only the size of "a small subset of large spines". Maybe the authors could clarify this section? I would also suggest adding statistics between the treatments at each spine size bin to support the claims, as they are central to the rest of the paper.

      Importantly, there is no description of the normalization nor the quantification of the difference between days in the methods; I am assuming post-pre for the difference and (post-pre)/pre for the normalization, but this should be much more detailed in the methodology. I was happy to see the baseline raw spine sizes in Supplementary Fig. 1, and would also suggest adding the raw spine sizes after treatment for comparison.

      Local text and figure revision: We have adjusted the text and figure to improve clarity.

      Fig. 2G/S2A: a scale for the label sizes would be helpful. I would also like to have the same correlation for 50um NBQX treatment and the control condition (at least in the supplementary figures).

      Local text and figure revision: We have adjusted the text and figure to improve clarity.

      Fig. 2I: I might be missing something, but why is the activity line flat when there are changes in spine density and size?

      Local text and figure revision: We have adjusted the text and figure to improve clarity.

      Fig. 3C-D: they are referenced in the text as Fig. 1C-D (lines 188-194).

      Local text revision: fixed.

      Fig. 5: it is interesting that the biphasic model captures both spine loss and recovery, fitting well with lesion-induced degeneration and recovery. Does this mean that the model captures other types of plasticity, or does it suggest to the authors that both steps are homeostatic?

      Indeed, the biphasic HSP rule captures two types of activity dependence. The pioneering work by Gallinaro and Rotter (2018) also demonstrated that the HSP rule, even in its monotonic/linear form, exhibits associative properties, which are typically associated with Hebbian plasticity.

      Fig. 6A: This figure requires a more detailed legend - what are the various insets? Does the top right graph only have one curve because they are overlapping and the growth rules are the same for axons and dendrites?

      Local text revision: fixed.

      Fig. 6E: There is usually an overshoot when a stimulus is removed, in this case at the end of the silencing period (as shown in Fig. 1Aiii). Is there a reason why this is not recapitulated here? It shouldn't be as extreme as in the right panel so there should be no degeneration.

      We agree that removing the stimulus would typically trigger an opposite homeostatic process. However, in this protocol, we aimed to emphasize the role of recurrency by presenting extreme cases to illustrate potential scenarios for the readers.

      Local text revision: We revised this paragraph to walk the readers through the rationale better.

      Fig. 6: the authors mention distance-dependent connectivity (line 268), but I couldn't find any data related to that statement. I was particularly curious about that aspect, so I would like to know what this statement is based on, especially as they touch again on the role of morphology in Fig. 8, and distance-dependent connectivity is more prominent in the discussion. On a similar note, would the authors have data from other layers of CA1 that would show similar or other rules? Please note that I am not asking to include these data in the present paper - I am just curious if these data exist (or if the experiments are considered).

      Such an extensive dataset is included and thoroughly investigated in another study that has just been published in Lenz et al., 2023. We updated the reference in the revised text.

      Fig. 7E top: the scalebar is missing.

      Local text revision: fixed.

      Fig. 8A: do the colors have meaning? If yes, please state them. Also indicate that the left two neurons are pyramidal cells from CA1 and the right neurons are granule cells from the dentate gyrus.

      Local text revision: fixed.

      Line 302: "reactive" should be "reactivate".

      Local text revision: fixed.

    1. Reviewer #1 (Public review):

      Neuronal activity spatiotemporal fine-tuning of cerebral blood flow balances metabolic demands of changing neuronal activity with blood supply. Several 'feed-forward' mechanisms have been described that contribute to activity-dependent vasodilation as well as vasoconstriction leading to a reduction in perfusion. Involved messengers are ionic (K+), gaseous (NO), peptides (e.g., NPY, VIP), and other messengers (PGE2, GABA, glutamate, norepinephrine) that target endothelial cells, smooth muscle cells, or pericytes. Contributions of the respective signaling pathways likely vary across brain regions or even within specific brain regions (e.g., across the cortex) and are likely influenced by the brain's physiological state (resting, active, sleeping) or pathological departures from normal physiology.

      The manuscript "Elevated pyramidal cell firing orchestrates arteriolar vasoconstriction through COX-2-derived prostaglandin E2 signaling" by B. Le Gac, et al. investigates mechanisms leading to activity-dependent arteriole constriction. Here, mainly working in brain slices from mice expressing channelrhodopsin 2 (ChR2) in all excitatory neurons (Emx1-Cre; Ai32 mice), the authors show that strong optogenetic stimulation of cortical pyramidal neurons leads to constriction that is mediated through the cyclooxygenase-2 / prostaglandin E2 / EP1 and EP3 receptor pathway with contribution of NPY-releasing interneurons and astrocytes releasing 20-HETE. Specifically, using a patch clamp, the authors show that 10-s optogenetic stimulation at 10 and 20 Hz leads to vasoconstriction (Figure 1), in line with a stimulation frequency-dependent increase in somatic calcium (Figure 2). The vascular effects were abolished in the presence of TTX and significantly reduced in the presence of glutamate receptor antagonists (Figure 3). The authors further show with RT-PCR on RNA isolated from patched cells that ~50% of analyzed cells express COX-1 or -2 and other enzymes required to produce PGE2 or PGF2a (Figure 4). Further, blockade of COX-1 and -2 (indomethacin), or COX-2 (NS-398) abolishes constriction. In animals with chronic cranial windows that were anesthetized with ketamine and medetomidine, 10-s long optogenetic stimulation at 10 Hz leads to considerable constriction, which is reduced in the presence of indomethacin. Blockade of EP1 and EP3 receptors leads to a significant reduction of the constriction in slices (Figure 5). Finally, the authors show that blockade of 20-HETE synthesis caused moderate and NPY Y1 receptor blockade a complete reduction of constriction.

      The mechanistic analysis of neurovascular coupling mechanisms as exemplified here will guide further in-vivo studies and has important implications for human neuroimaging in health and disease. Most of the data in this manuscript uses brain slices as an experimental model which contrasts with neurovascular imaging studies performed in awake (headfixed) animals. However, the slice preparation allows for patch clamp as well as easy drug application and removal. Further, the authors discuss their results in view of differences between brain slices and in vivo observations experiments, including the absence of vascular tone as well as blood perfusion required for metabolite (e.g., PGE2) removal, and the presence of network effects in the intact brain. The manuscript and figures present the data clearly; regarding the presented mechanism, the data supports the authors' conclusions. Some of the data was generated in vivo in head-fixed animals under anesthesia; in this regard, the authors should revise the introduction and discussion to include the important distinction between studies performed in slices, or in acute or chronic in-vivo preparations under anesthesia (reduced network activity and reduced or blockade of neuromodulation, or in awake animals (virtually undisturbed network and neuromodulatory activity). Further, while discussed to some extent, the authors could improve their manuscript by more clearly stating if they expect the described mechanism to contribute to CBF regulation under 'resting state conditions' (i.e., in the absence of any stimulus), during short or sustained (e.g., visual, tactile) stimulation, or if this mechanism is mainly relevant under pathological conditions; especially in the context of the optogenetic stimulation paradigm being used (10-s long stimulation of many pyramidal neurons at moderate-high frequencies) and the fact that constriction leading to undersupply in response to strongly increased neuronal activity seems counterintuitive?

    1. 18.4B: Distribution of Blood Last updated Oct 5, 2024 Save as PDF 18.4A: Introduction to Blood Flow, Pressure, and Resistance 18.5: Systemic Blood Pressure picture_as_pdfFull BookPageDownloadsFull PDFImport into LMSIndividual ZIPBuy Print CopyPrint Book FilesSubmit Adoption ReportPeer ReviewDonate /*<![CDATA[*/ window.hypothesisConfig = function () { return { "showHighlights": false }; }; //localStorage.setItem('darkMode', 'false'); window.beelineEnabled = true; document.getElementsByTagName('head')[0].prepend(document.getElementById('mt-screen-css'),document.getElementById('mt-print-css')); //$('head').prepend($('#mt-print-css')); //$('head').prepend($('#mt-screen-css'));/*]]>*/ Page ID7854 /*<![CDATA[*/window.addEventListener('load', ()=>LibreTexts.TOC(undefined, undefined, true));/*]]>*/ /*<![CDATA[*/ //CORS override LibreTexts.getKeys().then(()=>{ if(!$.ajaxOld){ $.ajaxOld = $.ajax; $.ajax = (url, options)=> { if(url.url && url.url.includes('.libretexts.org/@api/deki/files')) { let [subdomain, path] = LibreTexts.parseURL(); 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Learning ObjectivesList the components of blood flow distribution Key Points In humans, blood is pumped from the strong left ventricle of the heart through arteries to peripheral tissues and returns to the right atrium of the heart through veins. After blood returns to the right atrium, it enters the right ventricle and is pumped through the pulmonary artery to the lungs, then returns to the left atrium through the pulmonary veins. Blood then enters the left ventricle to be circulated through the systemic circulation again. The closing of blood vessels is termed vasoconstriction. Vasoconstriction occurs through contraction of the muscular walls of vessels and results in increased blood pressure. Vasoconstriction is important for minimizing acute blood loss in the event of hemorrhage as well as retaining body heat and regulating mean arterial pressure. Dilation, or opening of blood vessels, is termed vasodilation. Vasodilation occurs through relaxation of smooth muscle cells within vessel walls. Vasodilation increases blood flow by reducing vascular resistance. Therefore, dilation of arterial blood vessels (mainly arterioles ) causes a decrease in blood pressure. Key Terms vasoconstriction: The constriction of the blood vessels. vascular resistance: The resistance to flow that must be overcome to push blood through the circulatory system. The resistance offered by the peripheral circulation is known as the systemic vascular resistance (SVR), while the resistance offered by the vasculature of the lungs is known as the pulmonary vascular resistance (PVR). vasodilation: The dilation of the blood vessels. mean arterial pressure: The average arterial pressure during a single cardiac cycle. Humans have a closed cardiovascular system, meaning that the blood never leaves the network of arteries, veins, and capillaries. Blood is circulated through blood vessels by the pumping action of the heart, pumped from the left ventricle through arteries to peripheral tissues and returning to the right atrium through veins. It then enters the right ventricle and is pumped through the pulmonary artery to the lungs and returns to the left atrium through the pulmonary veins. Blood then enters the left ventricle to be circulated again. Pulmonary circuit: Diagram of pulmonary circulation. Oxygen-rich blood is shown in red; oxygen-depleted blood in blue. Distribution of blood can be modulated by many factors, including increasing or decreasing heart rate and dilation or constriction of blood vessels. Vasoconstriction Blood distribution: Oxygenated arterial blood (red) and deoxygenated venous blood (blue) are distributed around the body. Vasoconstriction is the narrowing of the blood vessels resulting from contraction of the muscular wall of the vessels, particularly the large arteries and small arterioles. The process is the opposite of vasodilation, the widening of blood vessels. The process is particularly important in staunching hemorrhage and acute blood loss. When blood vessels constrict, the flow of blood is restricted or decreased, thus retaining body heat or increasing vascular resistance. This makes the skin turn paler because less blood reaches the surface, reducing the radiation of heat. On a larger level, vasoconstriction is one mechanism by which the body regulates and maintains mean arterial pressure. Substances causing vasoconstriction are called vasoconstrictors or vasopressors. Generalized vasoconstriction usually results in an increase in systemic blood pressure, but it may also occur in specific tissues, causing a localized reduction in blood flow. The extent of vasoconstriction may be slight or severe depending on the substance or circumstance. Vasodilation Vasodilation refers to the widening of blood vessels resulting from relaxation of smooth muscle cells within the vessel walls, particularly in the large veins, large arteries, and smaller arterioles. The process is essentially the opposite of vasoconstriction. When blood vessels dilate, the flow of blood is increased due to a decrease in vascular resistance. Therefore, dilation of arterial blood vessels (mainly the arterioles) causes a decrease in blood pressure. The response may be intrinsic (due to local processes in the surrounding tissue) or extrinsic (due to hormones or the nervous system). Additionally, the response may be localized to a specific organ (depending on the metabolic needs of a particular tissue, as during strenuous exercise), or it may be systemic (seen throughout the entire systemic circulation). Substances that cause vasodilation are termed vasodilators. LICENSES AND ATTRIBUTIONS CC LICENSED CONTENT, SHARED PREVIOUSLY Curation and Revision. Authored by: Boundless.com. Provided by: Boundless.com. License: CC BY-SA: Attribution-ShareAlike CC LICENSED CONTENT, SPECIFIC ATTRIBUTION Anatomy and Physiology of Animals/Cardiovascular System/Blood circulation. Provided by: Wikibooks. Located at: en.wikibooks.org/wiki/Anatomy_and_Physiology_of_Animals/Cardiovascular_System/Blood_circulation. License: CC BY-SA: Attribution-ShareAlike Boundless. Provided by: Boundless Learning. Located at: www.boundless.com//physiology...tolic-pressure. License: CC BY-SA: Attribution-ShareAlike Boundless. Provided by: Boundless Learning. Located at: www.boundless.com//physiology...tolic-pressure. License: CC BY-SA: Attribution-ShareAlike hypotension. Provided by: Wiktionary. Located at: en.wiktionary.org/wiki/hypotension. License: CC BY-SA: Attribution-ShareAlike Illu pulmonary circuit. Provided by: Wikipedia. Located at: en.Wikipedia.org/wiki/File:Il...ry_circuit.jpg. License: Public Domain: No Known Copyright Circulatory system. 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    1. Reviewer #3 (Public review):

      Summary:

      This study examines the roles of Rab10 and Rab4 proteins in structural long-term potentiation (sLTP) and AMPA receptor (AMPAR) trafficking in hippocampal dendritic spines using various different methods and organotypic slice cultures as the biological model.

      The paper shows that Rab10 inactivation enhances AMPAR insertion and dendritic spine head volume increase during sLTP, while Rab4 supports the initial stages of these processes. The key contribution of this study is identifying Rab10 inactivation as a previously unknown facilitator of AMPAR insertion and spine growth, acting as a brake on sLTP when active. Rab4 and Rab10 seem to be playing opposing roles, suggesting a somewhat coordinated mechanism that precisely controls synaptic potentiation, with Rab4 facilitating early changes and Rab10 restricting the extent and timing of synaptic strengthening.

      Strengths:

      The study combines multiple techniques such as FRET/FLIM imaging, pharmacology, genetic manipulations, and electrophysiology to dissect the roles of Rab10 and Rab4 in sLTP. The authors developed highly sensitive FRET/FLIM-based sensors to monitor Rab protein activity in single dendritic spines. This allowed them to study the spatiotemporal dynamics of Rab10 and Rab4 activity during glutamate uncaging-induced sLTP. They also developed various controls to ensure the specificity of their observations. For example, they used a false acceptor sensor to verify the specificity of the Rab10 sensor response.

      This study reveals previously unknown roles for Rab10 and Rab4 in synaptic plasticity, showing their opposing functions in regulating AMPAR trafficking and spine structural plasticity during LTP.

      Weaknesses:

      In sLTP, the initial volume of stimulated spines is an important determinant of induced plasticity. To address changes in initial volume and those induced by uncaging, the authors present Extended Data Figure 2. In my view, the methods of fitting, sample selection, or both may pose significant limitations for interpreting the overall results. While the initial spine size distribution for Rab10 experiments spans ~0.1-0.4 fL (with an unusually large single spine at the upper end), Rab4 spine distribution spans a broader range of ~0.1-0.9 fL. If the authors applied initial size-matched data selection or used polynomials rather than linear fitting, panels a, b, e, f, and g might display a different pattern. In that case, clustering analysis based on initial size may be necessary to enable a fair comparison between groups not only for this figure but also for main Figures 2 and 3.

      Another limitation is the absence of in vivo validation, as the experiments were performed in organotypic hippocampal slices, which may not fully replicate the complexity of synaptic plasticity in an intact brain, where excitatory and inhibitory processes occur concurrently. High concentrations of MNI-glutamate (4 mM in this study) are known to block GABAergic responses due to its antagonistic effect on GABA-A receptors, thereby precluding the study of inhibitory network activity or connectivity [1], which is already known to be altered in organotypic slice cultures.

      [1] https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/neuro.04.002.2009/full

    1. Reviewer #1 (Public review):

      Summary:

      Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

      Strengths:

      The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

      Weaknesses:

      (1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4. They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression? This question is relevant as it is not clear how many other cell types are being manipulated with the DNT-2-GAL4 driver is used in subsequent experiments. For example, is DNT-2-GAL4--> DNT-2-RNAi is reducing DNT2 in many neurons or glia effects could be indirect.

      (2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3G they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or number of TH neurons in the head.

      (3)DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult-specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

      Comments on revisions:

      The authors have made some changes in the text to tone down their claims. They have also provided additional images to support their work. However, requested controls are not provided, and new experiments are not added to address reviewer concerns.

    2. Author response:

      The following is the authors’ response to the original reviews.

      Reviewer #1 (Public review):

      Summary:

      Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

      Strengths:

      The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

      We are very pleased that Reviewer 1 found our connectivity analysis a strength.

      Weaknesses:

      (1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4.

      They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression?

      Indeed, we claimed that DNT-2 is expressed in at least 12 neurons (see line 141, page 6 of original manuscript), which means more than 12 could be found. The membrane tethered reporters we used – UAS-FlyBow1.1, UASmcD8-RFP, UAS-MCFO, as well as UAS-DenMark:UASsyd-1GFP – gave a consistent and reproducible pattern. However, with DNT-2GAL4>UAS-Histone-YFP more nuclei were detected that were not revealed by the other reporters. We have found also with other GAL4 lines that the patterns produced by different reporters can vary. This could be due to the signal strength (eg His-YFP is very strong) and perdurance of the reporter (e.g. the turnover of His-YFP may be slower than that of the other fusion proteins).

      We did not test for glial expression, as it was not directly related to the question addressed in this work.

      (2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3H they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or the number of TH neurons in the head.

      Figure 3H shows that over-expression of DNT-2 FL increased the number of Dcp1+ apoptotic cells in the brain, but not significantly (p=0.0939). The ability of full-length neurotrophins to induce apoptosis and cleaved neurotrophins promote cell survival is well documented in mammals. We had previously shown that DNT-2 is naturally cleaved, and that over-expression of DNT-2 does not induce apoptosis in the various contexts tested before (McIlroy et al 2013 Nature Neuroscience; Foldi et al 2017 J Cell Biol; Ulian-Benitez et al 2017 PLoS Genetics). Similarly, throughout this work we did not find DNT-2FL to induce apoptosis.

      Instead, in Figure 3G we show that over-expression of DNT-2FL causes a statistically significant increase in the number of TH+ cells. This is an important finding that supports the plastic regulation of PAM cell number. We thank the Reviewer for highlighting this point, as we had forgotten to add the significance star in the graph. In this context, we cannot rule out the possibility that the increase in TH mRNA observed when we over-express DNT-2FL could not be due to an increase in cell number instead. Unfortunately, it is not possible for us to separate these two processes at this time. Either way, the result would still be the same: an increase in dopamine production when DNT-2 levels rise.

      We have now edited the abstract lines 38-39 adding that “By contrast, over-expressed DNT-2 increased DAN cell number,…”, within the main text in Results page 10 lines 259-265 and in the Discussion section page 15 lines 391, 393-396.

      (3) DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

      Indeed, we did find Dcp1+ TH-negative cells too (although not widely throughout the brain), although this is not shown in the images of Figure 3H where we showed only TH+ Dcp+ cells.

      That is not surprising, as DNT-2 neurons have large arborisations that can reach a wide range of targets; DNT-2 is secreted, and could reach beyond its immediate targets; Toll-6 is expressed in a vast number of cells in the brain; DNT-2 can bind promiscuously at least also Toll-7 and other Keks, which are also expressed in the adult brain (Foldi et al 2017 J Cell Biology; Ulian-Benitez et al 2017 PLoS Genetics; Li et al 2020 eLife). Together with the findings by McLaughlin et al 2019, our findings further support the notion that DNT-2 is a neuroprotective factor in the adult brain. It will be interesting to find out what other neuron types DNT-2 maintains.

      We have made some edits on these points in page 10 lines 259-265.

      We would like to thank Reviewer 1 for their positive comments on our work and their interesting and valuable feedback.

      Reviewer #2 (Public review):

      This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure and dopaminergic neuron survival, synaptogenesis, and connectivity. They show that loss of DNT-2 or Toll-6 function leads to loss of dopaminergic neurons, dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. In addition, DNT-2 and Toll-6 modulate dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

      A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. However, despite the promise in the abstract and introduction of the paper, the study falls short of establishing a direct causal link between neurotrophin signaling and experience-induced plasticity.

      Simply put, this study does not provide strong evidence that experience-induced structural plasticity requires DNT-2 signaling. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt to address this in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, this activation method is quite artificial, and the authors did not test whether the observed structural changes were dependent on DNT-2 signaling. Although they also showed that overexpression of DNT-2FL in DNT-2 neurons promotes synaptogenesis, this phenotype was not fully consistent with the TrpA1 activation results (Figures 5C and D).

      In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. However, it does not provide convincing evidence for a causal link between DNT-2 signaling and experience-dependent structural plasticity within these circuits.

      We would like to thank Reviewer 2 for their very positive assessment of our approach to investigate structural circuit plasticity. We are delighted that this Reviewer found our cellular resolution impressive. We are also very pleased that Reviewer 2 found that our work demonstrates that DNT-2 and its receptors regulate the structure of dopaminergic circuits in the adult fly brain. This is already a very important finding that contributes to demonstrating that, rather than being hardwired, the adult fly brain is plastic, like the mammalian brain. Furthermore, it is remarkable that this involves a neurotrophin functioning via Toll and kinase-less Trks, opening an opportunity to explore whether such a mechanism could also operate in the human brain.

      We are very pleased that this Reviewer acknowledges that this work provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. We provide a molecular mechanism and proof of principle, and we demonstrate a direct link between the function of DNT-2 and its receptors in circuit plasticity. We also showed a link of DNT-2 to neuronal activity, as neuronal activity increased the production of DNT-2GFP, induced the cleavage of DNT-2 and a feedback loop between DNT-2 and dopamine, and both neuronal activity and increased DNT-2 levels promoted synaptogenesis.

      As the Reviewer acknowledges this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. Finding out the direct link in response to lived experience is a big task, beyond the scope of this manuscript, and we will be testing this with future projects. Nevertheless, it is important to place our findings within this context together with the link to mammalian neurotrophins (as explained in the discussion), as it is here where the findings have deep and impactful implications.

      To accommodate the criticism of this Reviewer, we have now toned down our narrative. This does not diminish the importance of the findings, it makes the argument more stringent. Please see edits in: Abstract page 2 lines 42-44; and Discussion page 22 line 586 – which were the only points were a direct claim had been made.

      We would like to thank Reviewer 2 for the positive and thoughtful evaluation of our work, and for their feedback.

      Reviewer #3 (Public review):

      Summary:

      The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

      Strengths:

      The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

      We are delighted that Reviewer 3 found our work solid, novel, interesting and with important findings. We are also very pleased that this Reviewer found that all necessary controls have been carried out.

      Weaknesses:

      There are three technical weaknesses that could perhaps be improved.

      First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

      Thank you for pointing out that glutamate can be inhibitory. In response, we have removed the word ‘excitatory’ from the only point it had been used in the text: page 7 line 167.

      In mammals, the neurotrophin BDNF has an important function in glutamatergic synapses, thus we were intrigued by a potential evolutionary conservation. Our evidence that DNT-2A neurons could be excitatory is indirect, yet supportive: exciting DNT-2 neurons with optogenetics resulted in an increase in GCaMP in PAMs (data not shown); over-expression of DNT-2 in DNT-2 neurons increased TH mRNA levels; optogenetic activation of DNT-2 neurons results in the Dop2R-dependent downregulation of cAMP levels in DNT-2 neurons. Dop2R signals in response to dopamine, which would be released only if dopaminergic neurons had been excited. Accordingly, glutamate released from DNT-2 neurons would have been rather unlikely to inhibit DANs.

      cAMP is a second messenger that enables the activation of PKA. PKA phosphorylates many target proteins, amongst which are various channels. This includes the voltage gated calcium channels located at the synapse, whose phosphorylation increases their opening probability. Other targets regulate synaptic vesicle release. Thus, a rise in cAMP could facilitate neurotransmitter release, and a downregulation would have the opposite effect. Other targets of PKA include CREB, leading to changes in gene expression. Conceivably, a decrease in PKA activity could result in the downregulation of DNT-2 expression in DNT-2 neurons. This negative feedback loop would restore the homeostatic relationship between DNT-2 and dopamine levels.

      We agree with this Reviewer that whereas our qRT-PCR data show that over-expression of DNT-2 increases TH mRNA levels, this does not demonstrate that originates from PAM neurons. Similarly, although our EPAC data imply that dopamine must be released from DANs and received by DNT-2 neurons to explain those data, the evidence did not include direct visualisation of dopamine release in response to DNT-2 neuron activation. To accommodate these criticisms, we have edited the summary Figure 2E adding question marks to indicate inference points and page 9 line 221.

      Our data indeed demonstrate that DNT-2 and PAM neurons influence each other, not potentially, but really. We have provided data that: DNT-2 and PAMs are connected through circuitry; that the DNT-2 receptors Toll-6 and kek-6 are expressed in DANs, including in PAMs; that alterations in the levels of DNT-2 (both loss and gain of function) and loss of function for the DNT-2 receptors Toll-6 and Kek-6 alter PAM cell number, alter PAM dendritic complexity and alter synaptogenesis in PAMs; alterations in the levels of DNT-2, Toll-6 and kek-6 in adult flies alters dopamine dependent behaviours of climbing, locomotion in an arena and learning and long-term memory. These data firmly demonstrate that the two neuron types DNT-2 and PAMs influence each other.

      We have also shown that over-expression of DNT-2 in DNT-2 neurons increases TH mRNA levels, whereas activation of DNT-2 neurons decreases cAMP levels in DNT-2 neurons in a dopamine/Dop2R-dependent manner. These data show a functional interaction between DNT-2 and PAM neurons.

      Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

      Figure 5C, D do contain Y-axis labels, all our graphs in main manuscript and in supplementary files contain Y-axis labels.

      In fact, we did use a method to precisely quantify the lengths and branching patterns of individual dendritic arborisations, volume of individual boutons and bouton counting. These analyses were carried out using Imaris software. For dendritic branching patterns, the “Filament Autodetect” function was used. Here, dendrites were analysed by tracing semi-automatically each dendrite branch (ie manual correction of segmentation errors) to reconstruct the segmented dendrite in volume. From this segmented dendrite, Imaris provides measurements of total dendrite volume, number and length of dendrite branches, terminal points, etc. For bouton size and number, we used the Imaris “Spot” function. Here, a threshold is set to exclude small dots (eg of background) that do not correspond to synapses/boutons. All samples and genotypes are treated with the same threshold, thus the analysis is objective and large sample sizes can be analysed effectively. We had already provided a description of the use of Imaris in the methods section.

      We have now exapanded the protocol on how we use Imaris to analyse dendrites and synapses, in: Materials and Methods section, page 28 lines 756-768 and page 29 lines 778-799.

      Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

      We thank Reviewer 3 for raising this interesting point. It is not possible to prove which of the four DNT-2A neurons per hemibrain, which we visualised with DNT-2>MCFO, were the same neurons in every individual brain we looked at. This is because in every brain we have looked at, the soma of the neurons were not located in exactly the same location. Furthermore, the arborisation patterns are also different and unique, for each individual brain. Thus, there is natural variability in the position of the soma and in the arborisation patterns. Such variability presumably results from the combination of developmental and activity-dependent plasticity. Importantly, for every staining we carried out using DNT-2GAL4 and various membrane reporters and MCFO clones, we never found two identical DNT-2 neuron profiles.

      To increase the evidence in support of this point, we have now expanded Figure 1, adding one more image of DNT-2>FlyBow (Figure 1A) and two more images of DNT-2>MCFO (Figure 1D). In total, seven images in Figure 1 and two further images in Figure 5A demonstrate the variability of DNT-2 neurons.

      We would like to thank Reviewer 3 for the very positive evaluation of our work and the interesting and valuable feedback.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors): 

      In the fly list, several fly lines are missing references and sources. 

      Apologies for this over-sight, this has now been corrected.

      We thank Reviewer 1 for their effort and time to scrutinise our work, and for their very positive and helpful feedback.

      Reviewer #2 (Recommendations for the authors):

      (1) Here I provide some more specific comments that I hope will help the authors further improve the study.

      (2) L148: "single neuron clones revealed variability in the DNT-2A". How do the authors know that they are labeling the same subtype of DNT-2A neurons? 

      There are four anterior DNT-2A cells per hemibrain, that project from the SOG area to the SMP. It is not possible to verify that every time we look at exactly the same neuron, because the exact position of the somas and the arborisation patterns vary from brain to brain. We know this from two sources of data: (1) when using DNT-2GAL4 to visualise the expression of membrane reporters (e.g. UAS-FlyBow, UAS-mCD8-GFP, UAS-CD8-RFP) no brain ever showed a pattern identical to that of another brain, neither in the exact position of the somas nor in the exact arborisation patterns. (2) When we generated DNT-2>MCFO clones to visualise 1-2 cells at a time, no single neuron or 2-neuron clones ever showed an identical pattern. The most parsimonious interpretation is that the exact location of the somas and the exact arborisation patterns vary across individual flies. Developmental variability in neuronal patterns has also been reporter by Linneweber et al (2020) Science.

      To make our evidence more compelling, and in response to this Reviewer’s query, we have now added further images. Please find in revised Figure 1 A,B three examples of three different brains expressing DNT-2>FlyBow1.1. In Figure 1D, two more examples (altogether 4) of DNT-2>MCFO clones. Here it is clear to see that no neuron shape is identical to that of others, demonstrating variability in individual fly brains. We now show four images in Figure 1 and two more in Figure 5A that demonstrate the variability of DNT-2A neurons.

      (3) Figure 1E: Are all DNT-2A neurons positive for vGlut and Dop2R? This figure shows only two DNT-2A neurons. 

      Yes, all four DNT-2A neurons per hemibrain are vGlut positive and we have now added more images to Supplementary Figure S1A (right), also showing that presynaptic DNT-2A endings at SMP also coincide with a vGlut+ domain (Figure S1A left).

      Yes, all all four DNT-2A neurons per hemibrain are Dop2R positive and we have now added more images to Supplementary Figure S1B.

      (4) L156: Glutamate is generally considered to be inhibitory in the adult fly brain. More evidence is needed before the authors can claim that "DNT-2A neurons are excitatory glutamatergic neurons". 

      Thank you for pointing this out. Although our data do not conclusively demonstrate it, they are consistent with DNT-2A neurons being excitatory. BDNF is most commonly released from glutamatergic neurons in mammals, its release is activity-dependent and leads to formation and stabilisation of synapses.  The phenotypes we have observed are consistent with this and reveal functional evolutionarily conservation: (1) exciting DNT-2 neurons with TrpA1 results in increased production and cleavage of DNT-2GFP and de novo synaptogenesis; (2) over-expression of DNT-2 in the adult induces de novo synaptogenesis; (3) down-regulation or loss of DNT-2 and its receptors Toll-6 and Kek-6 impair synaptogenesis. Furthermore, we show that DNT-2 dependent synaptogenesis is between DNT-2 and dopaminergic neurons, which are involved in the control of locomotion, reward learning and long-term memory, and dopamine itself is required for such behaviour. Consistently with this we found that: (1) over-expression of DNT-2 increases TH mRNA levels, which would lead to the up-regulation of dopamine production; (2) exciting DNT-2 neurons increases locomotion speed in an arena; (3) knock-down of DNT-2 and its receptors decreases locomotion, whereas over-expression of DNT-2 increases locomotion; (4) over-expression of DNT-2 increases learning and long-term memory. Finally, in a previous version in bioRxiv, we also showed using optogenetics and calcium imaging that exciting DNT-2 neurons induced GCaMP signalling in their output PAM neurons, and in this version we show that exciting DNT-2 neurons regulates cAMP in DNT-2 neurons via dopamine-release dependent feedback. Altogether, the most parsimonious interpretation of these data is that vGlut+ DNT-2 neurons are excitatory.

      In any case, to address this reviewer’s point, we have now removed the word ‘excitatory’ from page 7 line 167.

      (5) Figure 1H, I: A more detailed description of the Toll-6 and Kek-6 expressing neurons will be helpful. Are they expressed in specific types of PAM and PPL1 DANs? The legend in Figure S2 mentions labeling in γ2α′1 zones, but it seems to be more than that.

      This information had been already provided, presumable this Reviewer overlooked this. This was already described in great detail by comparing our microscopy data with the single cell RNA-seq data available through Fly Cell Atlas (https://flycellatlas.org) and Scope (https://scope.aertslab.org/#/b77838f4-af3c-4c37-8dd9-cf7a41e4b034/*/welcome).

      Please see our previously submitted Table S1 “Expression of Tolls, keks and Toll downstream adaptors in cells related to DNT-2A neurons”.

      (6) Figure S3 should be controls for Figure 2A. It is incorrectly labeled as controls for Figure 3A. 

      Thank you for pointing out this typo, this has now been corrected.

      (7) L197: The authors state, "This showed that DNT-2 could stimulate dopamine production in neighboring DANs". However, the results do not fully support this conclusion because the experiments measure overall TH levels in the brain, not specifically in neighboring DANs. The observed effect could be indirect via other neurons. 

      Indeed, we have now edited the text to: “This showed that DNT-2 could stimulate dopamine production”: page 8 line 208.

      (8) Figure 3: If Toll-6 is expressed in specific subtypes of PAM DANs, are they the dying cells when Toll-6 was knocked down? I think the paper will be significantly improved if the authors provide a more in-depth analysis of the phenotype. Also, permissive temperature controls are missing for the experiments in (E)-(H). Permissive controls are essential to confirm that the observed effects are due to adult-specific RNAi knockdown.

      Current tools do not enable us to visualise Toll-6+ neurons at the same time as manipulating DNT-2 neurons and at the same time as monitoring Dcp1. Stainings with Dcp1 in the adult brain are not trivial. Thus, we cannot guarantee this. However, Toll-6 is the preferential receptor for DNT-2, and given that apoptosis increases when we knock-down DNT-2, the most parsimonious interpretation is that the dying cells bear the DNT-2 receptor Toll-6. Even if DNT-2 can promiscuously bind other Toll receptors, the simplest way to interpret these data remains that DNT-2 promotes cell survival by signalling via its receptors, as no other possible route is known to date. This would be consistent with all other data in this figure.

      We thank this Reviewer for the feedback on the controls. Unfortunately, these are not trivial experiments, they require considerable time, effort, dedication and skill. This manuscript has already taken 5 years of daily hard work. We no longer have the staff (ie the first author left the lab) nor resources to dedicate to address this point.

      (9) Figure 4B: This phenotype in DNT-2 mutants is very striking. Did the neurons still survive and did their axonal innervation in the lobes remain intact?

      Homozygous DNT-2 mutants are viable and have impair climbing, as we had already shown in Figure 7C.

      (10) L261: The authors mention that "PAM-β2β′2 neurons express Toll-6 (Table S1)". However, I cannot find this information in Table S1. 

      Unfortunately, I cannot identify the source of that statement at present and the first authors has left the lab. In any case, although the fact that knocking down Toll-6 in these neurons causes a phenotype means they must, it does not directly prove it. We have now corrected this to: “PAM-b2b'2 neuron dendrites overlap axonal DNT2 projections”, page 11 line 280.

      (11) Figure 4C, D: What about their synaptogenesis? Do they agree with the result in Figure 4B? 

      This was not tested at the time. Unfortunately, these are not trivial experiments and require considerable time, effort, dedication and skill. Addressing this point experimentally is not possible for us at this point. In any case, given the evidence we already provide, it is highly unlikely they would alter the interpretation of our findings and the value of the discoveries already provided.

      (12) L270: The authors state: "To ask whether DNT-2 might affect axonal terminals, we tested PPL1 axons." However, it is unclear why the focus was shifted to PPL1 neurons when similar analyses could have been performed on PAM DANs for consistency. In addition, it would be beneficial to assess dendritic arbor complexity and synaptogenesis in PPL1-γ1-pedc neurons to provide a more comprehensive comparison between PPL1 and PAM DANs. Performing parallel analyses on both neuron types would strengthen the study by providing insight into the generality and specificity of DNT-2 in different dopaminergic circuits. 

      The question we addressed with Figure 4 was whether the DNT-2 and its receptors could modify axons, dendrites and synapses, ie all features of neuronal plasticity. The reason we used PPL1-g1-pedc to analyse axonal terminals was because of their morphology, which offered a clearer opportunity to visualise axonal endings than PAMs did. An exhaustive analysis of PPL1-g1-pedc is beyond the scope of this work and not the central focus.

      (13) Figure 4G lacks a permissive temperature control, which is essential to confirm that the observed effects are due to adult-specific RNAi knockdown. 

      We thank this Reviewer for this feedback, which we will bear in mind for future projects.

      (14) Figure 5A requires quantification and statistical comparison.

      We thank this Reviewer for this feedback. We did consider this, but the data are too variable to quantify and we decided it was best to present it simply as an observation, interesting nonetheless. This is consistent as well with the data in Figure 1, which we have now expanded with this revision, which show the natural variability in DNT-2 neurons.

      (15) Figure 5B: Many green signals in the control image are not labeled as PSDs, raising concerns about the accuracy of the image analysis methods used for synapse identification. While I trust that the authors have validated their analysis approach, it would strengthen the study if they provided a clearer description or evidence of the validation process. 

      This was done using the Imaris “Spot function”, in volume. A threshold is set to exclude spots due to GFP background and select only synaptic spots. The selection of spots and quantification are done automatically by Imaris. All spots below the threshold are excluded, regardless of genotype and experimental conditions, rendering the analysis objective. We have now provided a detailed description of the protocol in the Materials and Methods section: page 29 lines 778-799.

      (16) Figure 5C lacks genotype controls (i.e., DNT2-GAL4-only and UAS-TrpA1-only). These controls are essential because elevated temperatures alone, without activation of DNT2 neurons, could potentially increase Syt-GCaMP production, leading to an increase in the number of Syt+ synapses. Including these controls would help ensure that the observed effects are truly due to the activation of DNT2 neurons and not temperature-related artifacts. 

      We thank this Reviewer for this feedback, which we will bear in mind for future projects.

      (17) L314-316: The authors state, "Here, the coincidence of... revealed that newly formed synapses were stable." I think this statement needs to be toned down because there is no evidence that these pre- and post-synaptic sites are functionally connected. 

      The Reviewer is correct that our data did not visualise together, in the same preparation and specimen, both pre- and post-synaptic sites. Still, given that PAMs have already been proved by others to be required for locomotion, learning and long-term memory, our data strongly suggest that synapses between them at the SMP are functionally connected.

      Nevertheless, as we do not provide direct cellular evidence, we have now edited the text to tone down this claim: “Here, the coincidence of increased pre-synaptic Syt-GFP from PAMs and post-synaptic Homer-GFP from DNT-2 neurons at SMP suggests that newly formed synapses could be stable”, page 13 line 351.

      (18) Figure 5D lacks permissive temperature controls. Also, the DNT-2FL overexpression phenotypes are different from the TpA1 activation phenotypes. The authors may want to discuss this discrepancy. 

      Regarding the controls, these are not appropriate for this data set. These data were all taken at a constant temperature of 25°C, there were no shifts, and therefore do not require a permissive temperature control. We thank this Reviewer for drawing our attention to the fact that we made a mistake drawing the diagram, which we have now corrected in Figure 5D.

      Regarding the discrepancy, this had already been discussed in the Discussion section of the previously submitted version, page 19 Line 509-526. Presumably this Reviewer missed this before.

      (19) Figure 6A, B lack permissive temperature controls. These controls are important if the authors want to claim that the behavioral defects are due to adult-specific manipulations. In addition, there is no statistical difference between the PAM-GAL4 control and the RNAi knockdown group. The authors should be careful when stating that climbing was reduced in the RNAi knockdown flies (L341-342). 

      We thank this Reviewer for this feedback, which we will bear in mind for future projects.

      Point taken, but climbing of the tubGAL80ts, PAM>Toll-6RNAi flies was significantly different from that of the UAS-Toll-6RNAi/+ control.

      (20) Figure 6C: It seems that the DAN-GAL4 only control (the second group) also rescued the climbing defect. The authors may want to clarify this point. 

      The phenotype for this genotype was very variable, but certainly very distinct from that of flies over-expressing Toll-6[CY].

      We thank Reviewer 2 for their very thorough analysis of our paper that has helped improve the work.

      Reviewer #3 (Recommendations for the authors): 

      Overall, the manuscript reports highly interesting and mostly very convincing experiments. 

      We are very grateful to this Reviewer for their very positive evaluation of our work.

      Based on my comments under the heading "public review", I would like to suggest three possible improvements. 

      First, the quantification of structural plasticity at the sub-cellular level should be explained in more detail and potentially improved. For example, 3D reconstructions of individual neurons and quantification of the structure of boutons and dendrites could be undertaken. At present, it is not clear how bouton volumes are actually recorded accurately. 

      Thank you for the feedback. The analyses of dendrites and synapses were carried out in 3D-volumes using Imaris “Filament” module and “Spot function”, respectively. Dendrites are analysed semi-automatically, ie correcting potential branching errors of Imaris, and synapses are counted automatically, after setting appropriate thresholds. Details have now been expanded in the Materials and Sections section: page 28 lines 756-768 and page 29 lines 780-799.

      We would also like to thank Imaris for enabling and facilitating our remote working using their software during the Covid-19 pandemic, post-pandemic lockdowns and lab restrictions that spanned for over a year.

      Second, the variability between DNT-2A-positive neurons with increasing sample size compared to a control (DNT-2A-negative neurons) should be demonstrated. Figure 2C does currently not present convincing evidence of increased structural variability. 

      It is unclear what data the Reviewer refers to. Figure 2C shows qRT-PCR data, and it does not show structural variability, which instead is shown with microscopy. If it is the BacTrace data in Figure 2B, the controls had been provided and the data were unambiguous. If Reviewer means Figure 1C, it is unclear why DNT-2GAL4-negative flies are needed when the aim was to visualise normal (not genetically manipulated) DNT-2 neurons. Thus, unfortunately we do not understand what the point is here.

      The observation that DNT-2 neurons are very variable, naturally, is highly interesting, and presumably this is what drew the attention of Reviewer 3. We agree that showing further data in support of this is interesting and valuable. Thus, in response to this Reviewer’s comment we have now increased the number of images that demonstrate variability of DNT-2 neurons:

      (1) We have added an extra image, altogether providing three images in new Figure 1A showing three different individual brains stained with DNT-2GAL4>UAS-FlyBow1.1. These show common morphology and features, but different location of the somas and distinct detailed arborisation patterns. Two more images using DNT-2GAL4 are provided in Figure 5A.

      (2) We have now added two further MCFO images, altogether showing four examples where the somas are not always in the same location and the axons arborise consistently at the SMP, but the detailed projections are not identical: new Figure 1D.

      These data compellingly show natural variability in DNT-2 neuron morphology.

      Third, I propose to simplify the feedback model (Figure 2F) to be less speculative. 

      Indeed, some details in Figure 2F are speculative as we did not measure real dopamine levels. Accordingly, we have now edited this diagram, adding question marks to indicate speculative inference, to distinguish from the arrows that are grounded on the data we provide.

      Accordingly, we have also edited the text in:

      - page 9, lines 221: “Altogether, this shows that DNT-2 up-regulated TH levels (Figure 2E), and presumably via dopamine release, this inhibited cAMP in DNT-2A neurons (Figure 2F)”.

      - page 20, lines 515: “Importantly, we showed that activating DNT-2 neurons increased the levels and cleavage of DNT-2, up-regulated DNT-2 increased TH expression, and this initial amplification resulted in the inhibition of cAMP signalling via the dopamine receptor Dop2R in DNT-2 neurons.”

      As minor points: 

      (1) Appetitive olfactory learning is based on Tempel et al., (1983); Proc Natl Acad Sci U S A. 1983 Mar;80(5):1482-6. doi: 10.1073/pnas.80.5.1482. This paper should perhaps be cited. 

      Thank you for bringing this to our attention, we have now added this reference to page 14 line 394.

      (2) Line 34: I would add ..."ligand for Toll-6 AND KEK-6,". 

      Indeed, thank you, now corrected.

      (3) Line 39: DNT-2-POSITIVE NEURONS. 

      Now corrected, thank you.

      (4) The levels of TH mRNA were quantified. Why not TH or dopamine directly using antibodies, ELISA, or HPLC? After all, later it is explicitly written that DNT modulates dopamine levels (line 481)! 

      We thank this Reviewer for this suggestion. We did try with HPLC once, but the results were inconclusive and optimising this would have required unaffordable effort by us and our collaborators. Part of this work spanned over the pandemic and subsequent lockdowns and lab restrictions to 30% then 50% lab capacity that continued for one year, making experimental work extremely challenging. Although we were unable to carry out all the ideal experiments, the DNT-2-dependent increase in TH mRNA coupled with the EPAC-Dop2R data provided solid evidence of a DNT-2-dopamine link.

      (5) Line 271: The PPL1-g1-pedc neuron has mainly (but not excusively) a function in short-term memory! 

      They do, but others have also shown that PPL1-g1-pedc neurons have a gating function in long-term memory (Placais et al 2012; Placais et al 2017; Huang et al 2024) and are required for long-term memory (Adel and Griffith 2020; Boto et al 2020).

      (6) Line 401: Reward learning requires PAM neurons. PPL1 neurons are required for aversive learning. 

      Indeed, PPL1 neurons are required for aversive learning, but they also have a gating function in long-term memory common for both reward and aversive learning (Adel and Griffith, 2020 Neurosci Bull; Placais et al, 2012 Nature Neuroscience; Placais et al 2017 Nature Communications; Huang et al 2024 Nature).

      Overall, the manuscript presents extremely interesting, novel results, and I congratulate the authors on their findings. 

      We would like to thank this Reviewer for taking the time to scrutinise our work, their helpful feedback that has helped us improve the work and for their interest and positive and kind works.

    1. Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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      Reply to the reviewers

      Manuscript number: RC-2024-02465

      Corresponding author(s): Saravanan, Palani

      1. General Statements

      We would like to thank the Review Commons Team for handling our manuscript and the Reviewers for their constructive feedback and suggestions. In our revised manuscript, we have addressed and incorporated all the major suggestions of the reviewers, and we have also added new significant data on the role of Tropomyosin in regulation of endocytosis through its control over actin monomer pool maintenance and actin network homeostasis. We believe that with all these additions, our study has significantly gained in quality, strength of conclusions made, and scope for future work.

      2. Point-by-point description of the revisions

      Reviewer #1

      Evidence, reproducibility and clarity

      There are 2 Major issues -

      Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.

      Response: We would like to clarify that all mNG-Tpm constructs used in our study contain a 40 amino-acid (aa) flexible linker between the N-terminal mNG fluorescent protein and the Tpm protein as per our earlier published study (Hatano et al., 2022). During initial optimization, we have also experimented with linker length and the 40aa-linker length works optimally for clear visualization of Tpm onto actin cable structures in budding yeast, fission yeast (both S. pombe and S. japonicus), and mammalian cells (Hatano et al., 2022). These constructs have also been used since in other studies (Wirshing et al., 2023; Wirshing and Goode, 2024) and currently represents the best possible strategy to visualize Tpm isoforms in live cells. In our study, we characterized these proteins for functionality and found that both mNG-Tpm1 and mNG-Tpm2 were functional and can rescue the synthetic lethality observed in Dtpm1Dtpm2 cells. During our study, we observed that mNG-Tpm1 expression from a single-copy integration vector did not restore full length actin cables in Dtpm1 cells (Fig. 1B, 1C). We hypothesized that this could be a result of reduced binding affinity of the tagged tropomyosin due to lack of normal N-terminal acetylation which stabilizes the N-terminus. The 40aa linker is unstructured and may not be able to neutralize the charge on the N-terminal Methionine, thus, we tried to insert -Ala-Ser- dipeptide which has been routinely used in vitro biochemical studies to stabilize the N-terminal helix and impart a similar effect as the N-terminal acetylation (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) by restoring normal binding affinity of Tpm to F-actin (Monteiro et al., 1994; Greenfield et al., 1994). We observed that addition of the -Ala-Ser- dipeptide to mNG-Tpm fusion, indeed, restored full length actin cables when expressed in Dtpm1 cells, performing significantly better in our in vivo experiments (Fig. 1B, 1C). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may stabilize the N-terminus of Tpm and allow normal head-to-tail dimer formation (Greenfield et al., 1994; Monteiro et al., 1994; Frye et al., 2010). We have discussed this in our new Discussion section (Lines 350-372). Since, the addition of -AS- dipeptide was referred to as "acetyl-mimic (am)" in a previous study (Alioto et al., 2016), we continued to use the same nomenclature in our study. Now as per your suggestions and to be more accurate, we have renamed "mNG-amTpm" constructs as "mNG-ASTpm" throughout the study to not confuse or claim that -AS- addition mimics acetylation. In any case, we have not seen any other ill effect of -AS- dipeptide introduction in addition to our 40 amino acid linker suggesting that it can also be considered part of the linker. Although, we agree with the reviewer that biochemical characterization of the effect of linker would be important to determine, we strongly believe that it is currently outside the scope of this study and should be taken up for future work with these proteins. Our study has majorly aimed to understand the functionality and utility of these mNG-Tpm fusion proteins for cell biological experiments in vivo, which was not done earlier in any other model system.

      My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.

      Response: __ We agree with the reviewer that N-terminal tagging of tropomyosin may have effects on its function, but these constructs represent the only fluorescently tagged functional tropomyosin constructs available currently while C-terminal fusions are either non-functional (we were unable to construct strains with endogenous Tpm1 gene fused C-terminally to GFP) or do not localize clearly to actin structures (See __Figure R1 showing endogenous C-terminally tagged Tpm2-yeGFP that shows almost no localization to actin cables). To our knowledge, our study represents a first effort to understand the question of spatial sorting of Tpm isoforms, Tpm1 and Tpm2, in S. cerevisiae and any future developments with better visualization strategies for Tpm isoforms without compromising native N-terminal modifications and function will help improve our understanding of these proteins in vivo. We have also discussed these possibilities in our new Discussion section (Lines 391-396).

      Significance

      This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly. The significance of this study, given the above, and the concerns raised is not clear to this reviewer.

      Response: __Our study explores multiple facets of Tropomyosin (Tpm) biology. The lack of functional tagged Tpm has been a major bottleneck in understanding Tpm isoform diversity and function across eukaryotes. In our study, we characterize the first functional tagged Tpm proteins (Fig. 1, Fig. S1) and use them to answer long-standing questions about localization and spatial sorting of Tpm isoforms in the model organism S. cerevisiae (Fig. 2, Fig. 3, Fig. S2, Fig. S3). We also discover that the dual Tpm isoforms, Tpm1 and Tpm2, are functionally redundant for actin cable organization and function, while having gained divergent functions in Retrograde Actin Cable Flow (RACF) (Fig. 4, Fig. 5A-D, Fig. S4, Fig. S5, Fig. S6). We have now added new data on role of global Tpm levels controlling endocytosis via maintenance of normal linear-to-branched actin network homeostasis in S. cerevisiae (Fig. 5E-G)__. We respectfully differ with the reviewer on their assessment of our study and request the reviewer to read our revised manuscript which discusses the significance, limitations, and future perspectives of our study in detail.

      Reviewer #2

      Evidence, reproducibility and clarity

      This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.

      Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.

      1. Functionality of the acetyl-mimic tagged tropomyosin constructs: The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.

      __Response: __We would like to thank the reviewer for his feedback and suggestions. Based on the suggestions, we have now more accurately described the growth rescue observed by expression of mNG-ASTpm1 in Dtpm1 cells in the revised text. We have also removed the use of "completely functional" to describe mNG-Tpm functionality and corrected any errors in Figure citations in the revised manuscript.

      As per reviewers' suggestion, we have now tested rescue of synthetic lethality of Dtpm1Dtpm2 cells by expression of all mNG-Tpm variants and we find that all of them are capable of restoring the viability of Dtpm1Dtpm2 cells when expressed under their native promoters via a high-copy plasmid (pRS425) (Fig. S1E) but only mNG-Tpm1 and mNG-ASTpm1 restored viability of Dtpm1Dtpm2 cells when expressed under their native promoters via an integration plasmid (pRS305) (Fig. S1F). These results clearly suggest that while both mNG-Tpm1 and mNG-Tpm2 constructs are functional, Tpm1 tolerates the presence of the N-terminal fluorescent tag better than Tpm2. These observations now enhance our understanding of the functionality of these mNG-Tpm fusion proteins and will be a useful resource for their usage and experimental design in future studies in vivo.


      It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.

      Response: __We agree with the reviewer's observation and for the sake of clarity and accuracy, we have now renamed "mNG-amTpm" with "mNG-ASTpm". The use of -AS- dipeptide is very routine in studies with Tpm (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) and its addition restores normal binding affinities to Tpm proteins purified from E. coli (Monteiro et al., 1994). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may help neutralize the impact of a freely protonated Met on the alpha-helical structure and stabilize the N-terminus helix of Tpm and allow normal head-to-tail dimer formation (Monteiro et al., 1994; Frye et al., 2010; Greenfield et al., 1994). Consistent with this, we also observe a highly significant improvement in actin cable length when expressing mNG-ASTpm as compared to mNG-Tpm in Dtpm1 cells, suggesting an improvement in function probably due to increased binding affinity (Fig. 1B, 1C). We have also discussed this in our answer to Question 1 of Reviewer 1 and the revised manuscript (Lines 350-372)__.

      __ Localization of Tpm1 and Tpm2:__Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.

      Response: __We thank the reviewer for this observation and their suggestions. We agree that relative concentrations of functional Tpm1 and Tpm2 in cells may influence the extent of their localizations. As per the reviewer's suggestion, we have now conducted our quantitative analysis in cells lacking endogenous Tpm1 and only expressing mNG-ASTpm1 from an integrated plasmid copy at the leu2 locus and the data is presented in new __Figure S3. We compared Tpm-bound cable length (Fig. S3A, S3B) __and Tpm-bound cable number (Fig. S3A, S3C) along with actin cable length (Fig. S3D, S3E) and actin cable number (Fig. S3D, S3F) in wildtype, Dbnr1, and Dbni1 cells. Our analysis revealed that mNG-ASTpm1 localized to actin cable structures in wildtype, Dbnr1, and Dbni1 cells and the decrease observed in Tpm-bound cable length and number upon loss of either Bnr1 or Bni1, was accompanied by a corresponding decrease in actin cable length and number upon loss of either Bnr1 or Bni1. Thus, this analysis reached the same conclusion as our earlier analysis (Fig. 2) that mNG-ASTpm1 does not show preference between Bnr1 and Bni1-made actin cables. mNG-ASTpm2 did not restore functionality, when expressed as single integrated copy, in Dtpm1Dtpm2 cells (new results in __Fig. S1E, S1F, S5A) thus, we could not conduct a similar analysis for mNG-ASTpm2. This suggests that use of mNG-ASTpm2 would be more meaningful in the presence of endogenous Tpm2 as previously done in Fig. 2D-F.

      We have now also performed additional yeast mating experiments with cells lacking bnr1 gene and expressing either mNG-ASTpm1 or mNG-ASTpm2 and the data is shown in new Figure 3. From these observations, we observe that both mNG-ASTpm1 and mNG-ASTpm2 localize to the mating fusion focus in a Bnr1-independent manner (Fig. 3B, 3D) and suggests that they bind to Bni1-made actin cables that are involved in polarized growth of the mating projection. These results also add strength to our conclusion that Tpm1 and Tpm2 localize to actin cables irrespective of which formin nucleates them. Overall, these new results highlight and reiterate our model of formin-isoform independent binding of Tpm1 and Tpm2 in S. cerevisiae.

      In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.

      Response: __We thank the reviewer for pointing this out. Our data and analysis do not suggest that Tpm1 and Tpm2 show any preference for decoration of cables in either mother or bud compartment. As per the reviewer's suggestion, we have now quantified the ratio of mean mNG fluorescence in the bud to the mother (Bud/Mother) and the data is shown in __Figure. S2G. The bud-to-mother ratio was similar for mNG-ASTpm1 and mNG-ASTpm2 in wildtype cells, and the ratio increased in Dbnr1 cells and decreased in Dbni1 cells for both mNG-ASTpm1 and mNG-ASTpm2 (Fig. S2G). __This is consistent with the decreased actin cable signal in the mother compartment in Dbnr1 cells and decreased actin cable signal in the bud compartment in Dbni1 cells (Fig. S2A-D). Thus, our new analysis shows that both mNG-ASTpm1 and mNG-ASTpm2 have similar changes in their concentration (mean fluorescence) upon loss of either formins Bnr1 and Bni1 and show similar ratios in wildtype cells as well, suggesting no preference for binding to actin cables in either bud or mother compartment. The preference inferred by the reviewer seems to be a bias of the current representative images and thus, we have replaced the images in __Fig. 2A, 2D to more accurately represent the population.

      The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?

      __Response: __We did not use LifeAct for our analysis as LifeAct is known to cause expression-dependent artefacts in cells (Courtemanche et al., 2016; Flores et al., 2019; Xu and Du, 2021) and it also competes with proteins that regulate normal cable organization like cofilin. Use of LifeAct would necessitate standardization of expression to avoid such artefacts in vivo. Also, phalloidin staining provides the best staining of actin cables and allows for better quantitative results in our experiments. The use of LifeAct along with mNG-Tpm would also require optimization with a red fluorescent protein which usually tend to have lower brightness and photostability. However, during the revision of our study, a new study from Prof. Goode's lab has developed and optimized expression of new LifeAct-3xmNeonGreen constructs for use in S. cerevisiae (Wirshing and Goode, 2024). Thus, a similar strategy of using tandem copies of bright and photostable red fluorescent proteins can be explored for use in combination with mNG-Tpm in the future studies.

      __ Complementation of tpm1∆ by Tpm2:__

      I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue.

      The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.

      Response: __We thank the reviewer for pointing this out. We would like to clarify that in our RT-qPCR experiments, the primers were chosen within the Tpm1 and Tpm2 gene and do not distinguish between transcripts from endogenous or plasmid copy. We have now mentioned this in the Materials and Methods section of the revised manuscript. So, they represent a relative estimate of the total mRNA of these genes present in cells. We were consistently able to detect ~19 fold increase in Tpm2 total mRNA levels as compared to wildtype and ∆tpm1 cells (Fig. S4D) when tpm2 was expressed from a high-copy plasmid (pRS425). This increase in Tpm2 mRNA levels was accompanied by a rescue in growth (Fig. S4A) and actin cable organization (Fig. S4B) of ∆tpm1 cells containing pRS425-ptpm2TPM2. When tpm2 was expressed from a low-copy number centromeric plasmid (pRS316), we detected a ~2 fold increase in Tpm2 transcript levels when using the tpm1 promoter and no significant change was detected when using tpm2 promoter (Fig. S4E)__. We have made sure that these results are accurately described in the revised manuscript.

      As per the reviewer's suggestion, we have now conducted a more extensive analysis to ascertain the expression levels of Tpm2 in our experiments and the data is now presented in new Figure S5. We used mNG-ASTpm1 and mNG-ASTpm2 to rescue growth of ∆tpm1 (Fig. S5A) and correlated growth rescue with protein levels using quantified fluorescence intensity (Fig. S5B, S5C) and western blotting (anti-mNG) (Fig. S5D, S5E). We find that ∆tpm1 cells containing pRS425-ptpm1mNG-ASTpm1 had the highest protein level followed by pRS425-ptpm2 mNG-ASTpm2, pRS305-ptpm1mNG-ASTpm1, and the least protein levels were found in pRS305-ptpm2 mNG-ASTpm2 containing ∆tpm1 cells in both fluorescence intensity and western blotting quantifications (Fig. S5C, S5E). Surprisingly, we were not able to detect any protein levels in ∆tpm1 cells containing pRS305-ptpm2 mNG-ASTpm2 with western blotting (Fig. S5D) which was also accompanied by a lack of growth rescue (Fig. S5A). This most likely due to weak expression from the native Tpm2 promoter which is consistent with previous literature (Drees et al., 1995). Taken together, this data clearly shows that the rescue observed in ∆tpm1 cells is caused due to increased expression of mNG-ASTpm2 in cells and supports our conclusion that increase in Tpm2 expression leads to restoration of normal growth and actin cables in ∆tpm1 cells.

      __ Specific function of Tpm2:__

      The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.

      Response: __We agree with the reviewer and as per the reviewer's suggestion, we have performed another experiment which include wildtype, ∆tpm2 cells containing empty pRS316 vector or pRS316-ptpm2TPM1 or pRS316-ptpm1TPM1. We find that RACF rate increased in ∆tpm2 cells as compared to wildtype and was restored to wildtype levels by exogenous expression of Tpm2 but not Tpm1 (Fig. S6E, S6F). Since, actin cables were not detectable in ∆tpm1 cells, we measured RACF rates in ∆tpm1 cells expressing Tpm1 or Tpm2 from a plasmid copy, which restored actin cables as shown previously in __Fig. 5A-C. We observed that RACF rates were similar to wildtype in ∆tpm1 cells expressing either Tpm1 or Tpm2 (Fig. S6E, S6F), suggesting that Tpm1 is not involved in RACF regulation. Taken together, these results suggest a specific role for Tpm2, but not Tpm1, in RACF regulation in S. cerevisiae, consistent with previous literature (Huckaba et al., 2006).

      Minor comments: __1.__The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).

      Response: __ We thank the reviewer for pointing this out. We have now repeated the drop test multiple times (__Fig. R2), but we see similar growth rates as the drop test already presented in Fig. S4A. __At this point, it would be difficult to ascertain the basis of this difference observed at 23{degree sign}C and 30{degree sign}C, but a recent study that links leucine levels to actin cable stability (Sing et al., 2022) might explain the faster growth of these ∆tpm1 cells containing a leu2 gene carrying high-copy plasmid. However, there is no effect on growth rate at 37{degree sign}C which is consistent with other spot assays shown in __Fig. S1D, S4F, S5A.


      Significance

      I am a cell biologist with expertise in both yeast and actin cytoskeleton.

      The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.

      We thank the reviewer for their positive assessment of our work and the constructive feedback that has greatly improved the quality of our study. After addressing the points raised by the reviewer, we believe that our study has significantly gained in consolidating the major conclusions of our work.

      **Referees cross-commenting**

      Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.

      Response: __We acknowledge the reviewer's point about the effect of Ala-Ser dipeptide and would request the reviewer to refer to our response to Reviewer 1 (Question 1) for a more detailed discussion on this. We have also extensively addressed the question of Tpm2 expression levels as suggested by the reviewer (new data in __Figure S5) which has further strengthened the conclusions of our study.

      __Reviewer #3 (Evidence, reproducibility and clarity (Required)):

      Summary:__ The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.

      Major and Minor Comments: 1. The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.

      Response: __We agree with the reviewer's suggestions and have now updated our Materials and Methods section to describe the image analysis pipelines used in more detail. We have also added examples of quantification procedure for actin cable length/number and mitochondrial morphology as an additional Supplementary __Figure S7. Briefly, the following pipelines were used:

      • Actin cable length and number analysis: This was done exactly as mentioned in McInally et al., 2021, McInally et al., 2022. Actin cables were manually traced in Fiji as shown in __ S7A__, and then the traces files for each cell were run through a Python script (adapted from McInally et al., 2022) that outputs mean actin cable length and number per cell.
      • Mitochondria morphology: Mitochondria Analyzer plug-in in Fiji was used to segment out the mitochondrial fragments. The parameters used for 2D segmentation of mitochondria were first optimized using "2D Threshold Optimize" to find the most accurate segmentation and then the same parameters were run on all images. After segmentation of the mitochondrial network, measurements of fragment number were done using "Analyze Particles" function in Fiji. An example of the overall process is shown in __ S7B.__ As per the reviewer's suggestion, we have now included the description of the statistical test used in the Figure Legends of each Figure in the revised manuscript. We have used One-Way Anova with Tukey's Multiple Comparison test, Kruskal-Wallis test with Dunn's Multiple Comparisons, and Unpaired Two-tailed t-test using the in-built functions in GraphPad Prism (v.6.04).

      **Referees cross-commenting**

      I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.

      Response: __We acknowledge the reviewer's concern about the Ala-Ser dipeptide and would request them to refer our earlier discussion on this in response to Reviewer 1 (Question 1) and Reviewer 2 (Question 2). We would also request the reviewer to refer to our answer to Reviewer 2 (Question 6) where we have extensively addressed the question of Tpm2 expression levels and their effect on rescue of Dtpm1 cells. This data is now presented as new __Figure S5 in our revised manuscript.

      Reviewer#3 (Significance (Required)):

      The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.

      The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.

      As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.

      We thank the reviewer for their positive outlook and assessment of our study. We have incorporated all their suggestions, and we are confident that the revised manuscript has significantly improved in quality due to these additions.

    1. Who Can Name the Bigger Number?by Scott Aaronson [Author's blog] [This essay in Spanish] [This essay in French] [This essay in Chinese] In an old joke, two noblemen vie to name the bigger number. The first, after ruminating for hours, triumphantly announces "Eighty-three!" The second, mightily impressed, replies "You win." A biggest number contest is clearly pointless when the contestants take turns. But what if the contestants write down their numbers simultaneously, neither aware of the other’s? To introduce a talk on "Big Numbers," I invite two audience volunteers to try exactly this. I tell them the rules: You have fifteen seconds. Using standard math notation, English words, or both, name a single whole number—not an infinity—on a blank index card. Be precise enough for any reasonable modern mathematician to determine exactly what number you’ve named, by consulting only your card and, if necessary, the published literature. So contestants can’t say "the number of sand grains in the Sahara," because sand drifts in and out of the Sahara regularly. Nor can they say "my opponent’s number plus one," or "the biggest number anyone’s ever thought of plus one"—again, these are ill-defined, given what our reasonable mathematician has available. Within the rules, the contestant who names the bigger number wins. Are you ready? Get set. Go. The contest’s results are never quite what I’d hope. Once, a seventh-grade boy filled his card with a string of successive 9’s. Like many other big-number tyros, he sought to maximize his number by stuffing a 9 into every place value. Had he chosen easy-to-write 1’s rather than curvaceous 9’s, his number could have been millions of times bigger. He still would been decimated, though, by the girl he was up against, who wrote a string of 9’s followed by the superscript 999. Aha! An exponential: a number multiplied by itself 999 times. Noticing this innovation, I declared the girl’s victory without bothering to count the 9’s on the cards. And yet the girl’s number could have been much bigger still, had she stacked the mighty exponential more than once. Take , for example. This behemoth, equal to 9387,420,489, has 369,693,100 digits. By comparison, the number of elementary particles in the observable universe has a meager 85 digits, give or take. Three 9’s, when stacked exponentially, already lift us incomprehensibly beyond all the matter we can observe—by a factor of about 10369,693,015. And we’ve said nothing of or . Place value, exponentials, stacked exponentials: each can express boundlessly big numbers, and in this sense they’re all equivalent. But the notational systems differ dramatically in the numbers they can express concisely. That’s what the fifteen-second time limit illustrates. It takes the same amount of time to write 9999, 9999, and —yet the first number is quotidian, the second astronomical, and the third hyper-mega astronomical. The key to the biggest number contest is not swift penmanship, but rather a potent paradigm for concisely capturing the gargantuan. Such paradigms are historical rarities. We find a flurry in antiquity, another flurry in the twentieth century, and nothing much in between. But when a new way to express big numbers concisely does emerge, it’s often a byproduct of a major scientific revolution: systematized mathematics, formal logic, computer science. Revolutions this momentous, as any Kuhnian could tell you, only happen under the right social conditions. Thus is the story of big numbers a story of human progress. And herein lies a parallel with another mathematical story. In his remarkable and underappreciated book A History of π, Petr Beckmann argues that the ratio of circumference to diameter is "a quaint little mirror of the history of man." In the rare societies where science and reason found refuge—the early Athens of Anaxagoras and Hippias, the Alexandria of Eratosthenes and Euclid, the seventeenth-century England of Newton and Wallis—mathematicians made tremendous strides in calculating π. In Rome and medieval Europe, by contrast, knowledge of π stagnated. Crude approximations such as the Babylonians’ 25/8 held sway. This same pattern holds, I think, for big numbers. Curiosity and openness lead to fascination with big numbers, and to the buoyant view that no quantity, whether of the number of stars in the galaxy or the number of possible bridge hands, is too immense for the mind to enumerate. Conversely, ignorance and irrationality lead to fatalism concerning big numbers. Historian Ilan Vardi cites the ancient Greek term sand-hundred, colloquially meaning zillion; as well as a passage from Pindar’s Olympic Ode II asserting that "sand escapes counting." ¨ But sand doesn’t escape counting, as Archimedes recognized in the third century B.C. Here’s how he began The Sand-Reckoner, a sort of pop-science article addressed to the King of Syracuse: There are some ... who think that the number of the sand is infinite in multitude ... again there are some who, without regarding it as infinite, yet think that no number has been named which is great enough to exceed its multitude ... But I will try to show you [numbers that] exceed not only the number of the mass of sand equal in magnitude to the earth ... but also that of a mass equal in magnitude to the universe. This Archimedes proceeded to do, essentially by using the ancient Greek term myriad, meaning ten thousand, as a base for exponentials. Adopting a prescient cosmological model of Aristarchus, in which the "sphere of the fixed stars" is vastly greater than the sphere in which the Earth revolves around the sun, Archimedes obtained an upper bound of 1063 on the number of sand grains needed to fill the universe. (Supposedly 1063 is the biggest number with a lexicographically standard American name: vigintillion. But the staid vigintillion had better keep vigil lest it be encroached upon by the more whimsically-named googol, or 10100, and googolplex, or .) Vast though it was, of course, 1063 wasn’t to be enshrined as the all-time biggest number. Six centuries later, Diophantus developed a simpler notation for exponentials, allowing him to surpass . Then, in the Middle Ages, the rise of Arabic numerals and place value made it easy to stack exponentials higher still. But Archimedes’ paradigm for expressing big numbers wasn’t fundamentally surpassed until the twentieth century. And even today, exponentials dominate popular discussion of the immense. Consider, for example, the oft-repeated legend of the Grand Vizier in Persia who invented chess. The King, so the legend goes, was delighted with the new game, and invited the Vizier to name his own reward. The Vizier replied that, being a modest man, he desired only one grain of wheat on the first square of a chessboard, two grains on the second, four on the third, and so on, with twice as many grains on each square as on the last. The innumerate King agreed, not realizing that the total number of grains on all 64 squares would be 264-1, or 18.6 quintillion—equivalent to the world’s present wheat production for 150 years. Fittingly, this same exponential growth is what makes chess itself so difficult. There are only about 35 legal choices for each chess move, but the choices multiply exponentially to yield something like 1050 possible board positions—too many for even a computer to search exhaustively. That’s why it took until 1997 for a computer, Deep Blue, to defeat the human world chess champion. And in Go, which has a 19-by-19 board and over 10150 possible positions, even an amateur human can still rout the world’s top-ranked computer programs. Exponential growth plagues computers in other guises as well. The traveling salesman problem asks for the shortest route connecting a set of cities, given the distances between each pair of cities. The rub is that the number of possible routes grows exponentially with the number of cities. When there are, say, a hundred cities, there are about 10158 possible routes, and, although various shortcuts are possible, no known computer algorithm is fundamentally better than checking each route one by one. The traveling salesman problem belongs to a class called NP-complete, which includes hundreds of other problems of practical interest. (NP stands for the technical term ‘Nondeterministic Polynomial-Time.’) It’s known that if there’s an efficient algorithm for any NP-complete problem, then there are efficient algorithms for all of them. Here ‘efficient’ means using an amount of time proportional to at most the problem size raised to some fixed power—for example, the number of cities cubed. It’s conjectured, however, that no efficient algorithm for NP-complete problems exists. Proving this conjecture, called P¹ NP, has been a great unsolved problem of computer science for thirty years. Although computers will probably never solve NP-complete problems efficiently, there’s more hope for another grail of computer science: replicating human intelligence. The human brain has roughly a hundred billion neurons linked by a hundred trillion synapses. And though the function of an individual neuron is only partially understood, it’s thought that each neuron fires electrical impulses according to relatively simple rules up to a thousand times each second. So what we have is a highly interconnected computer capable of maybe 1014 operations per second; by comparison, the world’s fastest parallel supercomputer, the 9200-Pentium Pro teraflops machine at Sandia National Labs, can perform 1012 operations per second. Contrary to popular belief, gray mush is not only hard-wired for intelligence: it surpasses silicon even in raw computational power. But this is unlikely to remain true for long. The reason is Moore’s Law, which, in its 1990’s formulation, states that the amount of information storable on a silicon chip grows exponentially, doubling roughly once every two years. Moore’s Law will eventually play out, as microchip components reach the atomic scale and conventional lithography falters. But radical new technologies, such as optical computers, DNA computers, or even quantum computers, could conceivably usurp silicon’s place. Exponential growth in computing power can’t continue forever, but it may continue long enough for computers—at least in processing power—to surpass human brains. To prognosticators of artificial intelligence, Moore’s Law is a glorious herald of exponential growth. But exponentials have a drearier side as well. The human population recently passed six billion and is doubling about once every forty years. At this exponential rate, if an average person weighs seventy kilograms, then by the year 3750 the entire Earth will be composed of human flesh. But before you invest in deodorant, realize that the population will stop increasing long before this—either because of famine, epidemic disease, global warming, mass species extinctions, unbreathable air, or, entering the speculative realm, birth control. It’s not hard to fathom why physicist Albert Bartlett asserted "the greatest shortcoming of the human race" to be "our inability to understand the exponential function." Or why Carl Sagan advised us to "never underestimate an exponential." In his book Billions & Billions, Sagan gave some other depressing consequences of exponential growth. At an inflation rate of five percent a year, a dollar is worth only thirty-seven cents after twenty years. If a uranium nucleus emits two neutrons, both of which collide with other uranium nuclei, causing them to emit two neutrons, and so forth—well, did I mention nuclear holocaust as a possible end to population growth? ¨ Exponentials are familiar, relevant, intimately connected to the physical world and to human hopes and fears. Using the notational systems I’ll discuss next, we can concisely name numbers that make exponentials picayune by comparison, that subjectively speaking exceed as much as the latter exceeds 9. But these new systems may seem more abstruse than exponentials. In his essay "On Number Numbness," Douglas Hofstadter leads his readers to the precipice of these systems, but then avers: If we were to continue our discussion just one zillisecond longer, we would find ourselves smack-dab in the middle of the theory of recursive functions and algorithmic complexity, and that would be too abstract. So let’s drop the topic right here. But to drop the topic is to forfeit, not only the biggest number contest, but any hope of understanding how stronger paradigms lead to vaster numbers. And so we arrive in the early twentieth century, when a school of mathematicians called the formalists sought to place all of mathematics on a rigorous axiomatic basis. A key question for the formalists was what the word ‘computable’ means. That is, how do we tell whether a sequence of numbers can be listed by a definite, mechanical procedure? Some mathematicians thought that ‘computable’ coincided with a technical notion called ‘primitive recursive.’ But in 1928 Wilhelm Ackermann disproved them by constructing a sequence of numbers that’s clearly computable, yet grows too quickly to be primitive recursive. Ackermann’s idea was to create an endless procession of arithmetic operations, each more powerful than the last. First comes addition. Second comes multiplication, which we can think of as repeated addition: for example, 5´3 means 5 added to itself 3 times, or 5+5+5 = 15. Third comes exponentiation, which we can think of as repeated multiplication. Fourth comes ... what? Well, we have to invent a weird new operation, for repeated exponentiation. The mathematician Rudy Rucker calls it ‘tetration.’ For example, ‘5 tetrated to the 3’ means 5 raised to its own power 3 times, or , a number with 2,185 digits. We can go on. Fifth comes repeated tetration: shall we call it ‘pentation’? Sixth comes repeated pentation: ‘hexation’? The operations continue infinitely, with each one standing on its predecessor to peer even higher into the firmament of big numbers. If each operation were a candy flavor, then the Ackermann sequence would be the sampler pack, mixing one number of each flavor. First in the sequence is 1+1, or (don’t hold your breath) 2. Second is 2´2, or 4. Third is 3 raised to the 3rd power, or 27. Hey, these numbers aren’t so big! Fee. Fi. Fo. Fum. Fourth is 4 tetrated to the 4, or , which has 10154 digits. If you’re planning to write this number out, better start now. Fifth is 5 pentated to the 5, or with ‘5 pentated to the 4’ numerals in the stack. This number is too colossal to describe in any ordinary terms. And the numbers just get bigger from there. Wielding the Ackermann sequence, we can clobber unschooled opponents in the biggest-number contest. But we need to be careful, since there are several definitions of the Ackermann sequence, not all identical. Under the fifteen-second time limit, here’s what I might write to avoid ambiguity: A(111)—Ackermann seq—A(1)=1+1, A(2)=2´2, A(3)=33, etc Recondite as it seems, the Ackermann sequence does have some applications. A problem in an area called Ramsey theory asks for the minimum dimension of a hypercube satisfying a certain property. The true dimension is thought to be 6, but the lowest dimension anyone’s been able is prove is so huge that it can only be expressed using the same ‘weird arithmetic’ that underlies the Ackermann sequence. Indeed, the Guinness Book of World Records once listed this dimension as the biggest number ever used in a mathematical proof. (Another contender for the title once was Skewes’ number, about , which arises in the study of how prime numbers are distributed. The famous mathematician G. H. Hardy quipped that Skewes’ was "the largest number which has ever served any definite purpose in mathematics.") What’s more, Ackermann’s briskly-rising cavalcade performs an occasional cameo in computer science. For example, in the analysis of a data structure called ‘Union-Find,’ a term gets multiplied by the inverse of the Ackermann sequence—meaning, for each whole number X, the first number N such that the Nth Ackermann number is bigger than X. The inverse grows as slowly as Ackermann’s original sequence grows quickly; for all practical purposes, the inverse is at most 4. ¨ Ackermann numbers are pretty big, but they’re not yet big enough. The quest for still bigger numbers takes us back to the formalists. After Ackermann demonstrated that ‘primitive recursive’ isn’t what we mean by ‘computable,’ the question still stood: what do we mean by ‘computable’? In 1936, Alonzo Church and Alan Turing independently answered this question. While Church answered using a logical formalism called the lambda calculus, Turing answered using an idealized computing machine—the Turing machine—that, in essence, is equivalent to every Compaq, Dell, Macintosh, and Cray in the modern world. Turing’s paper describing his machine, "On Computable Numbers," is rightly celebrated as the founding document of computer science. "Computing," said Turing, is normally done by writing certain symbols on paper. We may suppose this paper to be divided into squares like a child’s arithmetic book. In elementary arithmetic the 2-dimensional character of the paper is sometimes used. But such use is always avoidable, and I think it will be agreed that the two-dimensional character of paper is no essential of computation. I assume then that the computation is carried out on one-dimensional paper, on a tape divided into squares. Turing continued to explicate his machine using ingenious reasoning from first principles. The tape, said Turing, extends infinitely in both directions, since a theoretical machine ought not be constrained by physical limits on resources. Furthermore, there’s a symbol written on each square of the tape, like the ‘1’s and ‘0’s in a modern computer’s memory. But how are the symbols manipulated? Well, there’s a ‘tape head’ moving back and forth along the tape, examining one square at a time, writing and erasing symbols according to definite rules. The rules are the tape head’s program: change them, and you change what the tape head does. Turing’s august insight was that we can program the tape head to carry out any computation. Turing machines can add, multiply, extract cube roots, sort, search, spell-check, parse, play Tic-Tac-Toe, list the Ackermann sequence. If we represented keyboard input, monitor output, and so forth as symbols on the tape, we could even run Windows on a Turing machine. But there’s a problem. Set a tape head loose on a sequence of symbols, and it might stop eventually, or it might run forever—like the fabled programmer who gets stuck in the shower because the instructions on the shampoo bottle read "lather, rinse, repeat." If the machine’s going to run forever, it’d be nice to know this in advance, so that we don’t spend an eternity waiting for it to finish. But how can we determine, in a finite amount of time, whether something will go on endlessly? If you bet a friend that your watch will never stop ticking, when could you declare victory? But maybe there’s some ingenious program that can examine other programs and tell us, infallibly, whether they’ll ever stop running. We just haven’t thought of it yet. Nope. Turing proved that this problem, called the Halting Problem, is unsolvable by Turing machines. The proof is a beautiful example of self-reference. It formalizes an old argument about why you can never have perfect introspection: because if you could, then you could determine what you were going to do ten seconds from now, and then do something else. Turing imagined that there was a special machine that could solve the Halting Problem. Then he showed how we could have this machine analyze itself, in such a way that it has to halt if it runs forever, and run forever if it halts. Like a hound that finally catches its tail and devours itself, the mythical machine vanishes in a fury of contradiction. (That’s the sort of thing you don’t say in a research paper.) ¨ "Very nice," you say (or perhaps you say, "not nice at all"). "But what does all this have to do with big numbers?" Aha! The connection wasn’t published until May of 1962. Then, in the Bell System Technical Journal, nestled between pragmatically-minded papers on "Multiport Structures" and "Waveguide Pressure Seals," appeared the modestly titled "On Non-Computable Functions" by Tibor Rado. In this paper, Rado introduced the biggest numbers anyone had ever imagined. His idea was simple. Just as we can classify words by how many letters they contain, we can classify Turing machines by how many rules they have in the tape head. Some machines have only one rule, others have two rules, still others have three rules, and so on. But for each fixed whole number N, just as there are only finitely many distinct words with N letters, so too are there only finitely many distinct machines with N rules. Among these machines, some halt and others run forever when started on a blank tape. Of the ones that halt, asked Rado, what’s the maximum number of steps that any machine takes before it halts? (Actually, Rado asked mainly about the maximum number of symbols any machine can write on the tape before halting. But the maximum number of steps, which Rado called S(n), has the same basic properties and is easier to reason about.) Rado called this maximum the Nth "Busy Beaver" number. (Ah yes, the early 1960’s were a more innocent age.) He visualized each Turing machine as a beaver bustling busily along the tape, writing and erasing symbols. The challenge, then, is to find the busiest beaver with exactly N rules, albeit not an infinitely busy one. We can interpret this challenge as one of finding the "most complicated" computer program N bits long: the one that does the most amount of stuff, but not an infinite amount. Now, suppose we knew the Nth Busy Beaver number, which we’ll call BB(N). Then we could decide whether any Turing machine with N rules halts on a blank tape. We’d just have to run the machine: if it halts, fine; but if it doesn’t halt within BB(N) steps, then we know it never will halt, since BB(N) is the maximum number of steps it could make before halting. Similarly, if you knew that all mortals died before age 200, then if Sally lived to be 200, you could conclude that Sally was immortal. So no Turing machine can list the Busy Beaver numbers—for if it could, it could solve the Halting Problem, which we already know is impossible. But here’s a curious fact. Suppose we could name a number greater than the Nth Busy Beaver number BB(N). Call this number D for dam, since like a beaver dam, it’s a roof for the Busy Beaver below. With D in hand, computing BB(N) itself becomes easy: we just need to simulate all the Turing machines with N rules. The ones that haven’t halted within D steps—the ones that bash through the dam’s roof—never will halt. So we can list exactly which machines halt, and among these, the maximum number of steps that any machine takes before it halts is BB(N). Conclusion? The sequence of Busy Beaver numbers, BB(1), BB(2), and so on, grows faster than any computable sequence. Faster than exponentials, stacked exponentials, the Ackermann sequence, you name it. Because if a Turing machine could compute a sequence that grows faster than Busy Beaver, then it could use that sequence to obtain the D‘s—the beaver dams. And with those D’s, it could list the Busy Beaver numbers, which (sound familiar?) we already know is impossible. The Busy Beaver sequence is non-computable, solely because it grows stupendously fast—too fast for any computer to keep up with it, even in principle. This means that no computer program could list all the Busy Beavers one by one. It doesn’t mean that specific Busy Beavers need remain eternally unknowable. And in fact, pinning them down has been a computer science pastime ever since Rado published his article. It’s easy to verify that BB(1), the first Busy Beaver number, is 1. That’s because if a one-rule Turing machine doesn’t halt after the very first step, it’ll just keep moving along the tape endlessly. There’s no room for any more complex behavior. With two rules we can do more, and a little grunt work will ascertain that BB(2) is 6. Six steps. What about the third Busy Beaver? In 1965 Rado, together with Shen Lin, proved that BB(3) is 21. The task was an arduous one, requiring human analysis of many machines to prove that they don’t halt—since, remember, there’s no algorithm for listing the Busy Beaver numbers. Next, in 1983, Allan Brady proved that BB(4) is 107. Unimpressed so far? Well, as with the Ackermann sequence, don’t be fooled by the first few numbers. In 1984, A.K. Dewdney devoted a Scientific American column to Busy Beavers, which inspired amateur mathematician George Uhing to build a special-purpose device for simulating Turing machines. The device, which cost Uhing less than $100, found a five-rule machine that runs for 2,133,492 steps before halting—establishing that BB(5) must be at least as high. Then, in 1989, Heiner Marxen and Jürgen Buntrock discovered that BB(5) is at least 47,176,870. To this day, BB(5) hasn’t been pinned down precisely, and it could turn out to be much higher still. As for BB(6), Marxen and Buntrock set another record in 1997 by proving that it’s at least 8,690,333,381,690,951. A formidable accomplishment, yet Marxen, Buntrock, and the other Busy Beaver hunters are merely wading along the shores of the unknowable. Humanity may never know the value of BB(6) for certain, let alone that of BB(7) or any higher number in the sequence. Indeed, already the top five and six-rule contenders elude us: we can’t explain how they ‘work’ in human terms. If creativity imbues their design, it’s not because humans put it there. One way to understand this is that even small Turing machines can encode profound mathematical problems. Take Goldbach’s conjecture, that every even number 4 or higher is a sum of two prime numbers: 10=7+3, 18=13+5. The conjecture has resisted proof since 1742. Yet we could design a Turing machine with, oh, let’s say 100 rules, that tests each even number to see whether it’s a sum of two primes, and halts when and if it finds a counterexample to the conjecture. Then knowing BB(100), we could in principle run this machine for BB(100) steps, decide whether it halts, and thereby resolve Goldbach’s conjecture. We need not venture far in the sequence to enter the lair of basilisks. But as Rado stressed, even if we can’t list the Busy Beaver numbers, they’re perfectly well-defined mathematically. If you ever challenge a friend to the biggest number contest, I suggest you write something like this: BB(11111)—Busy Beaver shift #—1, 6, 21, etc If your friend doesn’t know about Turing machines or anything similar, but only about, say, Ackermann numbers, then you’ll win the contest. You’ll still win even if you grant your friend a handicap, and allow him the entire lifetime of the universe to write his number. The key to the biggest number contest is a potent paradigm, and Turing’s theory of computation is potent indeed. ¨ But what if your friend knows about Turing machines as well? Is there a notational system for big numbers more powerful than even Busy Beavers? Suppose we could endow a Turing machine with a magical ability to solve the Halting Problem. What would we get? We’d get a ‘super Turing machine’: one with abilities beyond those of any ordinary machine. But now, how hard is it to decide whether a super machine halts? Hmm. It turns out that not even super machines can solve this ‘super Halting Problem’, for the same reason that ordinary machines can’t solve the ordinary Halting Problem. To solve the Halting Problem for super machines, we’d need an even more powerful machine: a ‘super duper machine.’ And to solve the Halting Problem for super duper machines, we’d need a ‘super duper pooper machine.’ And so on endlessly. This infinite hierarchy of ever more powerful machines was formalized by the logician Stephen Kleene in 1943 (although he didn’t use the term ‘super duper pooper’). Imagine a novel, which is imbedded in a longer novel, which itself is imbedded in an even longer novel, and so on ad infinitum. Within each novel, the characters can debate the literary merits of any of the sub-novels. But, by analogy with classes of machines that can’t analyze themselves, the characters can never critique the novel that they themselves are in. (This, I think, jibes with our ordinary experience of novels.) To fully understand some reality, we need to go outside of that reality. This is the essence of Kleene’s hierarchy: that to solve the Halting Problem for some class of machines, we need a yet more powerful class of machines. And there’s no escape. Suppose a Turing machine had a magical ability to solve the Halting Problem, and the super Halting Problem, and the super duper Halting Problem, and the super duper pooper Halting Problem, and so on endlessly. Surely this would be the Queen of Turing machines? Not quite. As soon as we want to decide whether a ‘Queen of Turing machines’ halts, we need a still more powerful machine: an ‘Empress of Turing machines.’ And Kleene’s hierarchy continues. But how’s this relevant to big numbers? Well, each level of Kleene’s hierarchy generates a faster-growing Busy Beaver sequence than do all the previous levels. Indeed, each level’s sequence grows so rapidly that it can only be computed by a higher level. For example, define BB2(N) to be the maximum number of steps a super machine with N rules can make before halting. If this super Busy Beaver sequence were computable by super machines, then those machines could solve the super Halting Problem, which we know is impossible. So the super Busy Beaver numbers grow too rapidly to be computed, even if we could compute the ordinary Busy Beaver numbers. You might think that now, in the biggest-number contest, you could obliterate even an opponent who uses the Busy Beaver sequence by writing something like this: BB2(11111). But not quite. The problem is that I’ve never seen these "higher-level Busy Beavers" defined anywhere, probably because, to people who know computability theory, they’re a fairly obvious extension of the ordinary Busy Beaver numbers. So our reasonable modern mathematician wouldn’t know what number you were naming. If you want to use higher-level Busy Beavers in the biggest number contest, here’s what I suggest. First, publish a paper formalizing the concept in some obscure, low-prestige journal. Then, during the contest, cite the paper on your index card. To exceed higher-level Busy Beavers, we’d presumably need some new computational model surpassing even Turing machines. I can’t imagine what such a model would look like. Yet somehow I doubt that the story of notational systems for big numbers is over. Perhaps someday humans will be able concisely to name numbers that make Busy Beaver 100 seem as puerile and amusingly small as our nobleman’s eighty-three. Or if we’ll never name such numbers, perhaps other civilizations will. Is a biggest number contest afoot throughout the galaxy? ¨ You might wonder why we can’t transcend the whole parade of paradigms, and name numbers by a system that encompasses and surpasses them all. Suppose you wrote the following in the biggest number contest: The biggest whole number nameable with 1,000 characters of English text Surely this number exists. Using 1,000 characters, we can name only finitely many numbers, and among these numbers there has to be a biggest. And yet we’ve made no reference to how the number’s named. The English text could invoke Ackermann numbers, or Busy Beavers, or higher-level Busy Beavers, or even some yet more sweeping concept that nobody’s thought of yet. So unless our opponent uses the same ploy, we’ve got him licked. What a brilliant idea! Why didn’t we think of this earlier? Unfortunately it doesn’t work. We might as well have written One plus the biggest whole number nameable with 1,000 characters of English text This number takes at least 1,001 characters to name. Yet we’ve just named it with only 80 characters! Like a snake that swallows itself whole, our colossal number dissolves in a tumult of contradiction. What gives? The paradox I’ve just described was first published by Bertrand Russell, who attributed it to a librarian named G. G. Berry. The Berry Paradox arises not from mathematics, but from the ambiguity inherent in the English language. There’s no surefire way to convert an English phrase into the number it names (or to decide whether it names a number at all), which is why I invoked a "reasonable modern mathematician" in the rules for the biggest number contest. To circumvent the Berry Paradox, we need to name numbers using a precise, mathematical notational system, such as Turing machines—which is exactly the idea behind the Busy Beaver sequence. So in short, there’s no wily language trick by which to surpass Archimedes, Ackermann, Turing, and Rado, no royal road to big numbers. You might also wonder why we can’t use infinity in the contest. The answer is, for the same reason why we can’t use a rocket car in a bike race. Infinity is fascinating and elegant, but it’s not a whole number. Nor can we ‘subtract from infinity’ to yield a whole number. Infinity minus 17 is still infinity, whereas infinity minus infinity is undefined: it could be 0, 38, or even infinity again. Actually I should speak of infinities, plural. For in the late nineteenth century, Georg Cantor proved that there are different levels of infinity: for example, the infinity of points on a line is greater than the infinity of whole numbers. What’s more, just as there’s no biggest number, so too is there no biggest infinity. But the quest for big infinities is more abstruse than the quest for big numbers. And it involves, not a succession of paradigms, but essentially one: Cantor’s. ¨ So here we are, at the frontier of big number knowledge. As Euclid’s disciple supposedly asked, "what is the use of all this?" We’ve seen that progress in notational systems for big numbers mirrors progress in broader realms: mathematics, logic, computer science. And yet, though a mirror reflects reality, it doesn’t necessarily influence it. Even within mathematics, big numbers are often considered trivialities, their study an idle amusement with no broader implications. I want to argue a contrary view: that understanding big numbers is a key to understanding the world. Imagine trying to explain the Turing machine to Archimedes. The genius of Syracuse listens patiently as you discuss the papyrus tape extending infinitely in both directions, the time steps, states, input and output sequences. At last he explodes. "Foolishness!" he declares (or the ancient Greek equivalent). "All you’ve given me is an elaborate definition, with no value outside of itself." How do you respond? Archimedes has never heard of computers, those cantankerous devices that, twenty-three centuries from his time, will transact the world’s affairs. So you can’t claim practical application. Nor can you appeal to Hilbert and the formalist program, since Archimedes hasn’t heard of those either. But then it hits you: the Busy Beaver sequence. You define the sequence for Archimedes, convince him that BB(1000) is more than his 1063 grains of sand filling the universe, more even than 1063 raised to its own power 1063 times. You defy him to name a bigger number without invoking Turing machines or some equivalent. And as he ponders this challenge, the power of the Turing machine concept dawns on him. Though his intuition may never apprehend the Busy Beaver numbers, his reason compels him to acknowledge their immensity. Big numbers have a way of imbuing abstract notions with reality. Indeed, one could define science as reason’s attempt to compensate for our inability to perceive big numbers. If we could run at 280,000,000 meters per second, there’d be no need for a special theory of relativity: it’d be obvious to everyone that the faster we go, the heavier and squatter we get, and the faster time elapses in the rest of the world. If we could live for 70,000,000 years, there’d be no theory of evolution, and certainly no creationism: we could watch speciation and adaptation with our eyes, instead of painstakingly reconstructing events from fossils and DNA. If we could bake bread at 20,000,000 degrees Kelvin, nuclear fusion would be not the esoteric domain of physicists but ordinary household knowledge. But we can’t do any of these things, and so we have science, to deduce about the gargantuan what we, with our infinitesimal faculties, will never sense. If people fear big numbers, is it any wonder that they fear science as well and turn for solace to the comforting smallness of mysticism? But do people fear big numbers? Certainly they do. I’ve met people who don’t know the difference between a million and a billion, and don’t care. We play a lottery with ‘six ways to win!,’ overlooking the twenty million ways to lose. We yawn at six billion tons of carbon dioxide released into the atmosphere each year, and speak of ‘sustainable development’ in the jaws of exponential growth. Such cases, it seems to me, transcend arithmetical ignorance and represent a basic unwillingness to grapple with the immense. Whence the cowering before big numbers, then? Does it have a biological origin? In 1999, a group led by neuropsychologist Stanislas Dehaene reported evidence in Science that two separate brain systems contribute to mathematical thinking. The group trained Russian-English bilinguals to solve a set of problems, including two-digit addition, base-eight addition, cube roots, and logarithms. Some subjects were trained in Russian, others in English. When the subjects were then asked to solve problems approximately—to choose the closer of two estimates—they performed equally well in both languages. But when asked to solve problems exactly, they performed better in the language of their training. What’s more, brain-imaging evidence showed that the subjects’ parietal lobes, involved in spatial reasoning, were more active during approximation problems; while the left inferior frontal lobes, involved in verbal reasoning, were more active during exact calculation problems. Studies of patients with brain lesions paint the same picture: those with parietal lesions sometimes can’t decide whether 9 is closer to 10 or to 5, but remember the multiplication table; whereas those with left-hemispheric lesions sometimes can’t decide whether 2+2 is 3 or 4, but know that the answer is closer to 3 than to 9. Dehaene et al. conjecture that humans represent numbers in two ways. For approximate reckoning we use a ‘mental number line,’ which evolved long ago and which we likely share with other animals. But for exact computation we use numerical symbols, which evolved recently and which, being language-dependent, are unique to humans. This hypothesis neatly explains the experiment’s findings: the reason subjects performed better in the language of their training for exact computation but not for approximation problems is that the former call upon the verbally-oriented left inferior frontal lobes, and the latter upon the spatially-oriented parietal lobes. If Dehaene et al.’s hypothesis is correct, then which representation do we use for big numbers? Surely the symbolic one—for nobody’s mental number line could be long enough to contain , 5 pentated to the 5, or BB(1000). And here, I suspect, is the problem. When thinking about 3, 4, or 7, we’re guided by our spatial intuition, honed over millions of years of perceiving 3 gazelles, 4 mates, 7 members of a hostile clan. But when thinking about BB(1000), we have only language, that evolutionary neophyte, to rely upon. The usual neural pathways for representing numbers lead to dead ends. And this, perhaps, is why people are afraid of big numbers. Could early intervention mitigate our big number phobia? What if second-grade math teachers took an hour-long hiatus from stultifying busywork to ask their students, "How do you name really, really big numbers?" And then told them about exponentials and stacked exponentials, tetration and the Ackermann sequence, maybe even Busy Beavers: a cornucopia of numbers vaster than any they’d ever conceived, and ideas stretching the bounds of their imaginations. Who can name the bigger number? Whoever has the deeper paradigm. Are you ready? Get set. Go. References Petr Beckmann, A History of Pi, Golem Press, 1971. Allan H. Brady, "The Determination of the Value of Rado’s Noncomputable Function Sigma(k) for Four-State Turing Machines," Mathematics of Computation, vol. 40, no. 162, April 1983, pp 647- 665. Gregory J. Chaitin, "The Berry Paradox," Complexity, vol. 1, no. 1, 1995, pp. 26- 30. At http://www.umcs.maine.edu/~chaitin/unm2.html. A.K. Dewdney, The New Turing Omnibus: 66 Excursions in Computer Science, W.H. Freeman, 1993. S. Dehaene and E. Spelke and P. Pinel and R. Stanescu and S. Tsivkin, "Sources of Mathematical Thinking: Behavioral and Brain-Imaging Evidence," Science, vol. 284, no. 5416, May 7, 1999, pp. 970- 974. Douglas Hofstadter, Metamagical Themas: Questing for the Essence of Mind and Pattern, Basic Books, 1985. Chapter 6, "On Number Numbness," pp. 115- 135. Robert Kanigel, The Man Who Knew Infinity: A Life of the Genius Ramanujan, Washington Square Press, 1991. Stephen C. Kleene, "Recursive predicates and quantifiers," Transactions of the American Mathematical Society, vol. 53, 1943, pp. 41- 74. Donald E. Knuth, Selected Papers on Computer Science, CSLI Publications, 1996. Chapter 2, "Mathematics and Computer Science: Coping with Finiteness," pp. 31- 57. Dexter C. Kozen, Automata and Computability, Springer-Verlag, 1997. ———, The Design and Analysis of Algorithms, Springer-Verlag, 1991. Shen Lin and Tibor Rado, "Computer studies of Turing machine problems," Journal of the Association for Computing Machinery, vol. 12, no. 2, April 1965, pp. 196- 212. Heiner Marxen, Busy Beaver, at http://www.drb.insel.de/~heiner/BB/. ——— and Jürgen Buntrock, "Attacking the Busy Beaver 5," Bulletin of the European Association for Theoretical Computer Science, no. 40, February 1990, pp. 247- 251. Tibor Rado, "On Non-Computable Functions," Bell System Technical Journal, vol. XLI, no. 2, May 1962, pp. 877- 884. Rudy Rucker, Infinity and the Mind, Princeton University Press, 1995. Carl Sagan, Billions & Billions, Random House, 1997. Michael Somos, "Busy Beaver Turing Machine." At http://grail.cba.csuohio.edu/~somos/bb.html. Alan Turing, "On computable numbers, with an application to the Entscheidungsproblem," Proceedings of the London Mathematical Society, Series 2, vol. 42, pp. 230- 265, 1936. Reprinted in Martin Davis (ed.), The Undecidable, Raven, 1965. Ilan Vardi, "Archimedes, the Sand Reckoner," at http://www.ihes.fr/~ilan/sand_reckoner.ps. Eric W. Weisstein, CRC Concise Encyclopedia of Mathematics, CRC Press, 1999. Entry on "Large Number" at http://www.treasure-troves.com/math/LargeNumber.html. Back to Writings page Back to Scott's homepage Back to Scott's blog

      What even is the largest number that has real world use what would be the point of bigger numbers if we cant use the big numbers we have now for real world calculations?

    1. Often, just relabeling the columns will be adequate. In a science class where all students complete a nondifferentiated and graded piece of work on a given day, the heading over the grade column would be dated and then might say, for example, “Voc.Test SS” to reflect that the grades below are for a vocabulary test on the solar system. In a class where students of varied readiness levels completed a differentiated and graded assessment on fractions, the head- ing might read “Fract S1D,” indicating that the grades below came from the first summative assessment on fractions, and that the assessment was differentiated. A quick survey of assignments, students working with them, and dates and labels would enable you, for instance, to look at the April 9 heading and reference which assignment Jackson worked with on that day.

      This can go in affect in later years too. This will help you understand if you need to change your style to help the students.

    1. Because this is primarily for money, no doubt it will be abused to hell. First-party browsers probably wouldn't do anything with this information for the fear of legitimizing scammers' fake profiles.

      Hm. I wonder if this could be worse than the status quo of content theft for ad revenue. I do love my hidden discovery mechanisms but I try to be realistic about the idea that, e.g., only a tiny slice of visitors actually find my RSS feed via the <head> content.

    1. Fluency in speech is not the ability to know every word and grammatical formation in a language; it’s the ability to use whatever words and grammar you know to say whatever’s on your mind. When you go to a pharmacy and ask for “That thing you swallow to make your head not have so much pain,” or “The medicine that makes my nose stop dripping water” – THAT is fluency. As soon as you can deftly dance around the words you don’t know, you are effectively fluent in your target language. This turns out to be a learned skill, and you practice it in only one situation: When you try to say something, you don’t know the words to say it, and you force yourself to say it in your target language anyways. If you want to build fluency as efficiently as possible, put yourself in situations that are challenging, situations in which you don’t know the words you need. And every time that happens, stay in your target language no matter what.

      speaking fluency comes from staying in the target language.

    1. Maslow appeared to ask, “how do we become self-actualized?”. Many First Nation communities, though they would not have used the same word, might be more likely to believe that we arrive on the planet self-actualized. Ryan Heavy Head explained the difference through the analogy of earning a college degree. In Western culture, you earn a degree after paying tuition, attending classes, and proving sufficient mastery of your area of study. In Blackfoot culture, “it’s like you’re credentialed at the start. You’re treated with dignity for that reason, but you spend your life living up to that.”

      Living up to one's gifts, parable of the talents

    2. He was curious how the Blackfoot might deal with lawbreakers without the strategy of dominance that he’d seen in his own culture. He found that “when someone was deviant, [the Siksika] didn’t peg them as deviant. A person who was deviant could redeem themselves in society’s eyes if they left that behavior behind” (Blood & Heavy Head, 2007, video 7 out of 15, minutes 15:44–16:08).

      Not fixing identities of goodness and badness

    3. Deeply curious about the reason for the stark difference between Blackfoot culture and his own culture, Maslow sought out positive deviants, or unusually successful individuals. He started with the wealthiest members of the Blackfoot tribe. He discovered that “for the Blackfoot, wealth was not measured by money and property but by generosity. The wealthiest man in their eyes is one who has almost nothing because he has given it all away” (Coon, 2006). Maslow witnessed a Blackfoot “Giveaway” ceremony in his first week at Siksika. During the Giveaway, members of the tribe arranged their tipis in a circle and publicly piled up all they had collected over the last year. Those with the most possessions told stories of how they amassed them and then gave every last one away to those in greater need (Blood & Heavy Head, 2007, (video 7 out of 15, minutes 13:00–14:00).

      Giving-away also expressing the self, allowing the ability to define a narrative, shape a story.

  6. pressbooks.library.torontomu.ca pressbooks.library.torontomu.ca
    1. He hit miss Nancy ‘cross her head Then she rose to her feet and she said

      The song ends in a sad tone, the battered wives are left to a hopeless and helpless fate. She hoped that Nancy could succeed at defending herself against her husband, but he ends up beating her anyway into submission. She no longer asks him to change, she only pleads for his comfort because she believes it to be the only comfort she can attain. The song speaks to the harms of domestic abuse and sympathizes with women who stay in abusive relationships. Instead of being seen as crazy, she outlines how they are stuck in a cycle of dependency and escape seems impossible to them. This sympathy for victims is very subversive for this time where women were rarely given a voice.

    1. Performance Analysis

      I would like to see figures depicting the performance (error) of these different methods for each parameter estimate, but plotted as a function of tree size (i.e. a head map of error as a function of parameter value and tree size) - I can't help but wonder if part of the pattern of increasing error rates as a function of increasing diversification parameter is simply a result of there being greater variability in simulated tree shape/size at these larger parameter values.

      Additionally, I think it could be quite informative to plot a heatmap of error with speciation and extinction rates as the x and y axes - I suspect this would highlight a clear, predictable pattern, particularly with increasing error rates being characteristic of parameter combinations where both speciation and extinction rates are high, leading to high species turnover and thus greater "volatility" of diversification outcomes.

    1. eLife Assessment

      This is a valuable study describing an implementation of awake mouse fMRI with implanted head coils at high fields. The evidence presented is convincing, combining technical advances with interesting neuroscience applications showing that mice anticipate stimuli given at regular (but at irregular) intervals.

    1. I want to know particularly what the good chance is you propose to give me. I am doing tolerably well here. I get twenty-five dollars a month, with victuals and clothing; have a comfortable home for Mandy,—the folks call her Mrs. Anderson,—and the children—Milly, Jane, and Grundy—go to school and are learning well. The teacher says Grundy has a head for a preacher. They go to Sunday school, and Mandy and me attend church regularly. We are kindly treated. Sometimes we overhear others saying, “Them colored people were slaves” down in Tennessee. The children feel hurt when they hear such remarks; but I tell them it was no disgrace in Tennessee to belong to Colonel Anderson. Many darkeys would have been proud, as I used to be, to call you master. Now if you will write and say what wages you will give me, I will be better able to decide whether it would be to my advantage to move back again.

      He seems to be doing very well where he is, and he isnt being shot at. I couldnt imagine why he would move back to his old master after finally getting better conditions for him and his family.

    1. esus is centered forming a calm triangular shape that contrasts the gestures and actions of the disciples. Leonardo uses a linear perspective to draw attention to Jesus with a vanishing point above his head emphasizing his status and role.

      good analysis of form

    1. 19.1. What is Capitalism?# Why do social media platforms make decisions that harm users? And why do social media platforms sometimes go down paths of self-destruction and alienating their users? Sometimes these questions can be answered by looking at the economic forces that drive decision-making on social media platforms, in particular with capitalism. So let’s start by defining capitalism. 19.1.1. Definition of Capitalism:# Capitalism is: “an economic system characterized by private or corporate ownership of capital goods, by investments that are determined by private decision, and by prices, production, and the distribution of goods that are determined mainly by competition in a free market” Merriam-Webster Dictionary In other words, capitalism is a system where: Individuals or corporations own businesses These business owners make what they want and set their own prices. They compete with other businesses to convince customers to buy their products. These business owners then hire wage laborers at predetermined rates for their work, while the owners get the excess business profits or losses

      I've heard the word capitalism a lot throughout my life, so I sort of built a definition in my head. But I never actually knew the true definition. I see it as another form of corruption in our society, but maybe it's also used for good. I understand that the social media apps I use daily aren't created for my enjoyment but instead created to bring in the most profit.

    2. I could see capitalism showing up in the country I am living from in different businesses, as the CEO and board of directors usually decides a lot of the things. It is important and interesting how potential experts like sales people who know the ideal price of something can only advise the head of the company for the final decision of thigns like price.

    1. Our knight replied, cold steel he flung, Transfixing that insolent tongue, With his quivering lance; then blood Ran from the frenzied mouth, the flow At once a river in full flood; And with the pain, surprise and woe, In a trice, its impudence spent, Gnawing the steel, and turning pale, It turned to him, its gaze intent. Just so, some actor’s voice will fail, Some lesser scion of the Muse, Who, deafened by the crowd’s abuse, No longer sees aught before him, Turns pale, forgets the part he read, Forsakes thus the role assigned him, Trembles and then bows his head, Stammers, cogent speech denied him, While the audience strikes him dead.

      What an interesting metaphor: the wounded giant as a heckled artist.

    1. Reviewer #1 (Public Review):

      Summary:

      This paper presents an innovative decoding approach for brain-computer interfaces (BCIs), introducing a new method named MINT. The authors develop a trajectory-centric approach to decode behaviors across several different datasets, including eight empirical datasets from the Neural Latents Benchmark. Overall, the paper is well written and their method shows impressive performance compared to more traditional decoding approaches that use a simpler approach. While there are some concerns (see below), the paper's strengths, particularly its emphasis on a trajectory-centric approach and the simplicity of MINT, provide a compelling contribution to the field.

      Strengths:

      The adoption of a trajectory-centric approach that utilizes statistical constraints presents a substantial shift in methodology, potentially revolutionizing the way BCIs interpret and predict neural behaviour. This is one of the strongest aspects of the paper.

      The thorough evaluation of the method across various datasets serves as an assurance that the superior performance of MINT is not a result of overfitting. The comparative simplicity of the method in contrast to many neural network approaches is refreshing and should facilitate broader applicability.

      Weaknesses:

      Scope: Despite the impressive performance of MINT across multiple datasets, it seems predominantly applicable to M1/S1 data. Only one of the eight empirical datasets comes from an area outside the motor/somatosensory cortex. It would be beneficial if the authors could expand further on how the method might perform with other brain regions that do not exhibit low tangling or do not have a clear trial structure (e.g. decoding of position or head direction from hippocampus)

      When comparing methods, the neural trajectories of MINT are based on averaged trials, while the comparison methods are trained on single trials. An additional analysis might help in disentangling the effect of the trial averaging. For this, the authors could average the input across trials for all decoders, establishing a baseline for averaged trials. Note that inference should still be done on single trials. Performance can then be visualized across different values of N, which denotes the number of averaged trials used for training.

      Comments on revisions:

      I have looked at the responses and they are thorough and answer all of my questions.

    1. And Saiawosh rode on undaunted, and his white robes and ebon steed shone forthbetween the flames, and their anger was reflected upon his helmet of gold. And he rodeuntil he was come unto the end of the pathway, and when he came forth there was notsinged a hair of his head, neither had the smoke blackened his garments.Now when the people beheld that he was come forth alive, they rent the welkin with theirshouting. And the nobles came forth to greet him, and, save only Sudaveh, there was joyin all hearts. Now Saiawosh rode till he came before the Shah, and then he got him off hishorse, and did homage before his father. And when Kai Kawous beheld him, and saw thatthere were no signs of fire about him, he knew that he was innocent. So he raised his sonfrom off the ground, and placed Saiawosh beside him on the throne, and asked hisforgiveness for that which was come to pass. And Saiawosh granted it. Then Kai Kawousfeasted his son with wine and song, and three days did they spend in revels, and the doorof the King's treasury was opened.But on the fourth day Kai Kawous mounted the throne of the Kaianides. He took in hishand the ox-headed mace, and he commanded that Sudaveh be led before him. Then hereproached her with her evil deeds, and he bade her make ready to depart the world, forverily death was decreed unto her. And in vain did Sudaveh ask for pardon at the handsof the King, for she continued to speak ill of Saiawosh, and she said that by the arts ofmagic alone had he escaped the fire, and she ceased not to cry against him. So the Kinggave orders that she be led forth unto death, and the nobles approved his resolve, andinvoked the blessings of Heaven upon the head of the Shah. But Saiawosh, when helearned it, was grieved, for he knew that the woman was beloved of his father. And hewent before Kai Kawous, and craved her pardon. And Kai Kawous granted it withgladness, for his heart yearned after Sudaveh.

      The gold helmet and the white robes that had not even the slightest hint of smoke on them are symbolic of Saiawosh and his heroism. The gold helmet symbolizes his gloriousness and his strength of mind. The white robes symbolize his righteousness and his purity. When the people see this outfit, they recognize that Saiawosh passed his trial by fire and rejoice. Sudaveh, the villainess of this story arch, continues to slander Saiawosh and begs for her life to be spared. Recognizing that his father still cared for Sudaveh and did not actually want for her to die, Saiawosh himself begs for her life to spared, showing grace and compassion.

    1. She’ll steal,” Natividad said. “She says she won’t, but she will. You canlook at her and tell.”“She’s been beaten,” Jill said. “The way they rolled up when we firstspotted them. They’re used to being beaten, kicked, knocked around.”“Yeah.” Allie looked haunted. “You try to keep from getting hit in the head,try to protect your eyes and ... your front. She thought we would beat her. Sheand the kid both.”Interesting that Allie and Jill should understand so well. What a terriblefather they had. And what had happened to their mother? They had never talkedabout her. It was amazing that they had escaped alive and sane enou
      1. One attribute or fact about the new character of Emery Tanaka Solis that is significant is she was sold into debt slavery and she was also a sharer too. Her story is tragic as well, due to the loss of her husband, she and her 3 children were indentured debt slaves to the company that her husband worked for. Her 2 boys were taken and sold which left her no choice but to protect her daughter and escape. It is so telling that Allie and Jill knew she endured trauma because of the way she and her daughter responded to threats by going into a self protective fetal position to shield oneself from the cruel beating. Also, they are not sharers themselves. It is interesting that the abused and victimized can sense this in other abused victims without a word.
  7. Nov 2024
    1. Eventually the brothers act on their emotions. Seeing the “dreamer” approach on a shepherding trip, they ambush Joseph and throw him into a pit — the first of the great depths to which Joseph will sink. The brothers soon sell him to Midianites who in turn sell him to an Ishmaelite caravan headed down to Egypt, continuing Joseph’s descent. The brothers then tear up Joseph’s special coat, dip it in goat’s blood, and present it to Jacob as proof of his son’s death.

      Of course, this isn't particularly shocking; in my view, Joseph received what was rightfully his, and he was self-centered and only cared about himself. He ought to have supported his brothers, helped them win their father's approval, or perhaps persuaded him that showing partiality hurts the other sons. Now, the other brothers rebelled because they were fed up with the abuse and tossed Joseph into a ditch since his head became too large. Was that the most effective method to deal with their emotions? No, but Joseph most certainly merited that.

    1. Presidential systems generally concentrate more executive power in a single leader compared to parliamentary systems, as presidents serve as both head of state and head of government, granting them significant autonomy. In contrast, parliamentary systems distribute executive authority more evenly, requiring the prime minister to maintain legislative confidence, which promotes accountability and prevents excessive power consolidation. Russia’s presidential system demonstrates the concentration of executive power in a single leader. Vladimir Putin, as president, controls the military, foreign policy, and much of the legislative agenda with minimal interference. The 2020 constitutional amendments, which extended Putin’s rule, highlights the concentration of power in executive leaders in presidential systems. This centralized power allows the president to dominate national governance, demonstrating how presidential systems inherently grant more authority to their executives than parliamentary systems

      In contrast, the United Kingdom’s parliamentary system illustrates how executive power is limited by requiring collaboration with the legislature. The prime minister, while a central figure, must maintain the confidence of the House of Commons to stay in office, significantly reducing their autonomy. For example, Theresa May’s inability to pass her Brexit deal despite leading the government forced her to resign, showing how parliamentary systems check executive authority through legislative oversight. Unlike presidents, prime ministers cannot unilaterally dictate policy without the support of their party and parliament, ensuring that power is more evenly distributed.

    1. Presidential systems generally concentrate more executive power in a single leader compared to parliamentary systems, as presidents serve as both head of state and head of government, granting them significant autonomy. In contrast, parliamentary systems distribute executive authority more evenly, requiring the prime minister to maintain legislative confidence, which promotes accountability and prevents excessive power consolidation. Russia’s presidential system demonstrates the concentration of executive power in a single leader. Vladimir Putin, as president, controls the military, foreign policy, and much of the legislative agenda with minimal interference. The 2020 constitutional amendments, which extended Putin’s rule, highlights the concentration of power in executive leaders in presidential systems. This centralized power allows the president to dominate national governance, demonstrating how presidential systems inherently grant more authority to their executives than parliamentary systems

      In contrast, the United Kingdom’s parliamentary system illustrates how executive power is limited by requiring collaboration with the legislature. The prime minister, while a central figure, must maintain the confidence of the House of Commons to stay in office, significantly reducing their autonomy. For example, Theresa May’s inability to pass her Brexit deal despite leading the government forced her to resign, showing how parliamentary systems check executive authority through legislative oversight. Unlike presidents, prime ministers cannot unilaterally dictate policy without the support of their party and parliament, ensuring that power is more evenly distributed.

    1. The point here is to turn the whole idea of traditional networking on its head. If you want to build a network of people who recognize your value, don't focus on what you can get. Figure out what you can give.

      This phrase is my takeaway from the podcast. When the focus shifts from what I can get to what I can give, it fundamentally changes the approach to relationship building. In my professional life, I can apply this by searching for areas where my expertise and skills can actually help others. Whether it is to provide advice, share resources, or give support, the aim is to help others be successful in any way possible. I believe that by being a strong source of knowledge and assistance, I can establish better and more genuine relationships. This not only enhances my professional growth but also builds up a network based on mutual trust and reciprocity.

    1. Reviewer #3 (Public review):

      Summary:

      Rayshubskiy et al. performed whole-cell recordings from descending neurons (DNs) of fruit flies to characterize their role in steering. Two DNs implicated in "walking control" and "steering control" by previous studies (Namiki et al., 2018, Cande et al., 2018, Chen et al., 2018) were chosen by the authors for further characterization. In-vivo whole-cell recordings from DNa01 and DNa02 showed that their activity predicts spontaneous ipsilateral turning events. The recordings also showed that while DNa02 predicts transient turns DNa01 predicts slow sustained turns. However, optogenetic activation or inactivation showed relatively subtle phenotypes for both neurons (consistent with data in other recent preprints, Yang et al 2023 and Feng et al 2024). The authors also further characterized DNa02 with respect to its inputs and showed a functional connection with olfactory and thermosensory inputs as well as with the head-direction system. DNa01 is not characterized to this extent.

      Strengths:

      (1) In-vivo recordings and especially dual recordings are extremely challenging in Drosophila and provide a much higher resolution DN characterization than other recent studies that have relied on behavior or calcium imaging. Especially impressive are the simultaneous recordings from bilateral DNs (Figure 3). These bilateral recordings show clearly that DNa02 cells not only fire more during ipsilateral turning events but that they get inhibited during contralateral turns. In line with this observation, the difference between left and right DNa02 neuronal activity is a much better predictor of turning events compared to individual DNa02 activity.

      (2) Another technical feat in this work is driving local excitation in the head-direction neuronal ensemble (PEN-1 neurons), while simultaneously imaging its activity and performing whole-cell recordings from DNa02 (Figure 4). This impressive approach provided a way to causally relate changes in the head-direction system to DNa02 activity. Indeed, DNa02 activity could predict the rate at which an artificially triggered bump in the PEN-1 ring attractor returns to its previous stable point.

      (3) The authors also support the above observations with connectomics analysis and provide circuit motifs that can explain how the head direction system (as well as external olfactory/thermal stimuli) communicated with DNa02. All these results unequivocally put DNa02 as an essential DN in steering control, both during exploratory navigation as well as stimulus-directed turns.

      Weaknesses:

      (1) I understand that the first version of this preprint was already on biorxiv in 2020, and some of the "weaknesses" I list are likely a reflection of the fact that I'm tasked to review this manuscript in late 2024 (more than 4 years later). But given this is a 2024 updated version it suffers from laying out the results in contemporary terms. For instance, the manuscript lacks any reference to the DNp09 circuit implicated in object-directed turning and upstream to DNa02 even though the authors cite one of the papers where this was analyzed (Braun et al, 2024). More importantly, these studies (both Braun et al 2024 and Sapkal et al 2024) along with recent work from the authors' lab (Yang et al 2023) and other labs (Feng et al 2024) provide a view that the entire suite of leg kinematics changes required for turning are orchestrated by populations of heterogeneous interconnected DNs. Moreover, these studies also show that this DN-DN network has some degree of hierarchy with some DNs being upstream to other DNs. In this contemporary view of steering control, DNa02 (like DNg13 from Yang et al 2023) is a downstream DN that is recruited by hierarchically upstream DNs like DNa03, DNp09, etc. In this view, DNa02 is likely to be involved in most turning events, but by itself unable to drive all the motor outputs required for the said events. This reasoning could be used while discussing the lack of major phenotypes with DNa02 activation or inactivation observed in the current study, which is in stark contrast to strong phenotypes observed in the case of hierarchically upstream DNs like DNp09 or DNa03. In the section, "Contributions of single descending neuron types to steering behavior": the authors start off by asking if individual DNs can make measurable contributions to steering behavior. Once more, any citations to DNp09 or DNa03 - two DNs that are clearly shown to drive strong turning-on activation (Bidaye et al, 2020, Feng et al 2024) - are lacking. Besides misleading the reader, such statements also digress the results away from contemporary knowledge in the field. I appreciate that the brief discussion in the section titled "Ensemble codes for steering" tries to cover these recent updates. However, I think this would serve a better purpose in the introduction and help guide the results.

      (2) The second major weakness is the lack of any immunohistochemistry (IHC) images quantifying the expression of the genetic tools used in these studies. Even though the main split-Gal4 tools for DNa01 and DNa02 were previously reported by Namiki et al, 2018, it is important to document the expression with the effectors used in this work and explicitly mention the expression in any ectopic neurons. Similarly, for any experiments where drivers were combined together (double recordings, functional connectivity) or modified for stochastic expression (Figure 8), IHC images are absolutely necessary. Without this evidence, it is difficult to trust many of the results (especially in the case of behavioral experiments in Figure 8). For example, the DNa01 genetic driver used by the authors is also expressed in some neurons in the nerve cord (as shown on the Flylight webpage of Janelia Research Campus). One wonders if all or part of the results described in Figure 8 are due to DNa01 manipulation or manipulation of the nerve cord neurons. The same applies for optic lobe neurons in the DNa02 driver.

      (3) The paper starts off with a comparative analysis of the roles of DNa01 and DNa02 during steering. Unfortunately, after this initial analysis, DNa01 is largely ignored for further characterization (e.g. with respect to inputs, connectomics, etc.), only to return in the final figure for behavioral characterization where DNa01 seems to have a stronger silencing phenotype compared to DNa02. I couldn't find an explanation for this imbalance in the characterization of DNa01 versus DNa02. Is this due to technical reasons? Or was it an informed decision due to some results? In addition to being a biased characterization, this also results in the manuscript lacking a coherent thread, which in turn makes it a bit inaccessible to the non-specialist.

      (4) There seems to be a discrepancy with regard to what is emphasized in the main text and what is shown in Figures S3/S4 in relation to the role of these DNs in backward walking. There are only two sentences in the main text where these figures are cited.<br /> a) "DNa01 and DNa02 firing rate increases were not consistently followed by large changes in forward velocity (Figs. 1G and S3)."<br /> b) "We found that rotational velocity was consistently related to the difference in right-left firing rates (Fig. 3B). This relationship was essentially linear through its entire dynamic range, and was consistent across paired recordings (Fig. 3C). It was also consistent during backward walking, as well as forward walking (Fig. S4)."<br /> These main text sentences imply the role of the difference between left and right DNa02 in turning. However, the actual plots in the Figures S3 and S4 and their respective legends seem to imply a role in "backward walking". For instance, see this sentence from the legend of Figure S3 "When (ΔvoltageDNa02>>ΔvoltageDNa01), the fly is typically moving backward. When (firing rateDNa02>>firing rateDNa01), the fly is also often moving backward, but forward movement is still more common overall, and so the net effect is that forward velocity is small but still positive when (firing rateDNa02>>firing rateDNa01). Note that when we condition our analysis on behavior rather than neural activity, we do see that backward walking is associated with a large firing rate differential (Fig. S4)." This sort of discrepancy in what is emphasized in the text, versus what is emphasized in the figures, ends up confusing the reader. More importantly, I do not agree with any of these conclusions regarding the implication of backward walking. Both Figures S3 and S4 are riddled with caveats, misinterpretations, and small sample sizes. As a result, I actually support the authors' decision to not infer too much from these figures in the "main text". In fact, I would recommend going one step further and removing/modifying these figures to focus on the role of "rotational velocity". Please find my concerns about these two figures below:<br /> a) In Figures S3 and S4, every heat map has a different scale for the same parameter: forward velocity. S3A is -10 to +10mm/s. S3B is -6 to +6 S4B (left) is -12 to +12 and S4B (right) is -4 to +4. Since the authors are trying to depict results based on the color-coding this is highly problematic.<br /> b) Figure S3A legend "When (ΔvoltageDNa02>>ΔvoltageDNa01), the fly is typically moving backward." There are also several instances when ΔvoltageDNa02= ΔvoltageDNa01 and both are low (lower left quadrant) when the fly is typically moving backwards. So in my opinion, this figure in fact suggests DNa02 has no role in backward velocity control.<br /> c) Based on the example traces in S4A, every time the fly walks backwards it is also turning. Based on this it is important to show absolute rotational velocity in Figure S4C. It could be that the fly is turning around the backward peak which would change the interpretation from Figure S4C. Also, it is important to note that the backward velocities in S4A are unprecedentedly high. No previous reports show flies walking backwards at such high velocities (for example see Chen et al 2018, Nat Comm. for backward walking velocities on a similar setup).<br /> d) In my opinion, Figure S4D showing that right-left DNa02 correlates with rotational velocity, regardless of whether the fly is in a forward or backward walking state, is the only important and conclusive result in Figures S3/S4. These figures should be rearranged to only emphasize this panel.

      (5) Figure 3 shows a really nice analysis of the bilateral DNa02 recordings data. While Figure S5 shows that authors have a similar dataset for DNa01, a similar level analysis (Figures 3D, E) is not done for DNa01 data. Is there a reason why this is not done?

      (6) In Figure 4 since the authors have trials where bump-jump led to turning in the opposite direction to the DNa02 being recorded, I wonder if the authors could quantify hyperpolarization in DNa02 as is predicted from connectomics data in Figure 7.

      (7) Figure 6 suggests that DNa02 contains information about latent steering drives. This is really interesting. However, in order to unequivocally claim this, a higher-resolution postural analysis might be needed. Especially given that DNa02 activation does not reliably evoke ipsilateral turning, these "latent" steering events could actually contain significant postural changes driven by DNa02 (making them "not latent"). Without this information, at least the authors need to explicitly mention this caveat.

      (8) Figure 7 would really benefit from connectome data with synapse numbers (or weighted arrows) and a corresponding analysis of DNa01.

      (9) In Figure 8E, the most obvious neuronal silencing phenotype is decreased sideways velocity in the case of DNa01 optogenetic silencing. In Figure S2, the inverse filter for sideways velocity for DNa01 had a higher amplitude than the rotational velocity filter. Taken together, does this point at some role for DNa01 in sideways velocity specifically?

      (10) In Figure 8G, the effect on inner hind leg stance prolongation is very weak, and given the huge sample size, hard to interpret. Also, it is not clear how this fits with the role of DNa01 in slow sustained turning based on recordings.

    1. Reviewer #1 (Public review):

      In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.

      (1) In Fig. 1, a sperm flagellum protein, which is far way from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.<br /> (2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.<br /> (3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?<br /> (4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

      Comments on revisions:

      The authors addressed all my concerns. The manuscript was greatly improved.

    2. Reviewer #2 (Public review):

      Summary:

      In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

      The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

      Strengths:

      The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

      Weaknesses:

      The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

    3. Author response:

      The following is the authors’ response to the original reviews.

      This study presents a valuable finding on sperm flagellum and HTCA stabilization. The evidence supporting the authors' claims is incomplete. The work will be of broad interest to cell and reproductive biologists working on cilium and sperm biology.

      We thank the Editor and the two reviewers for their time and thorough evaluation of our manuscript. We greatly appreciate their valuable guidance on improving our study. In the revised manuscript, we have conducted additional experiments and provided quantitative data in response to the reviewers' comments. Furthermore, we have refined the manuscript and added further context to elucidate the significance of our findings for the readers.

      Public Reviews:

      Reviewer #1 (Public Review):

      In this paper, Wu et al. investigated the physiological roles of CCDC113 in sperm flagellum and HTCA stabilization by using CRISPR/Cas knockouts mouse models, co-IP, and single sperm imaging. They find that CCDC113 localizes in the linker region among radial spokes, the nexin-dynein regulatory complex (N-DRC), and doublet microtubules (DMTs) RS, N-DRC, and DMTs and interacts with axoneme-associated proteins CFAP57 and CFAP91, acting as an adaptor protein that facilitates the linkage between RS, N-DRC, and DMTs within the sperm axoneme. They show the disruption of CCDC113 produced spermatozoa with disorganized sperm flagella and CFAP91, DRC2 could not colocalize with DMTs in Ccdc113-/- spermatozoa. Interestingly, the data also indicate that CCDC113 could localize on the HTCA region, and interact with HTCA-associated proteins. The knockout of Ccdc113 could also produce acephalic spermatozoa. By using Sun5 and Centlein knockout mouse models, the authors further find SUN5 and CENTLEIN are indispensable for the docking of CCDC113 to the implantation site on the sperm head. Overall, the experiments were designed properly and performed well to support the authors' observation in each part. Furthermore, the study's findings offer valuable insights into the physiological and developmental roles of CCDC113 in the male germ line, which can provide insight into impaired sperm development and male infertility. The conclusions of this paper are mostly well supported by data, but some points need to be clarified and discussed.

      We thank Reviewer #1 for his or her critical reading and the positive assessment.

      (1) In Figure 1, a sperm flagellum protein, which is far away from CCDC113, should be selected as a negative control to exclude artificial effects in co-IP experiments.

      We greatly appreciate Reviewer #1’s insightful suggestion. In response, we selected two sperm outer dense fiber proteins, ODF1 and ODF2, which are located distant from the sperm axoneme, as negative controls in the co-IP experiments. As shown in Figure 1- figure supplement 1A and B, neither ODF1 nor ODF2 bound to CCDC113, indicating the interaction observed in Figure 1 is not an artifact.

      (2) Whether the detachment of sperm head and tail in Ccdc113-/- mice is a secondary effect of the sperm flagellum defects? The author should discuss this point.

      Good question. Considering that CCDC113 is localized in the sperm neck region and interacts with SUN5 and CENTLEIN, it may play a direct role in connecting the sperm head and tail. Indeed, PAS staining revealed that Ccdc113–/– sperm heads exhibit abnormal orientation in stages V–VIII of the seminiferous epithelia (Figure 6C-D). Furthermore, transmission electron microscopy (TEM) analysis indicated that the absence of CCDC113 caused detachment of the damaged coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). These results suggest that the detachment of the sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects. We have discussed this point further below:

      “CCDC113 can interact with SUN5 and CENTLEIN, but not PMFBP1 (Figure 7A-C), and left on the tip of the decapitated tail in Sun5–/– and Centlein–/– spermatozoa (Figure 7K and L). Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is positioned closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113. PAS staining revealed that Ccdc113–/– sperm heads are abnormally oriented in stages V–VIII seminiferous epithelia (Figure6 C and D), and TEM analysis further demonstrated that the disruption of CCDC113 causes the detachment of the destroyed coupling apparatus from the sperm head in step 9–11 spermatids (Figure 6E). All these results suggest that the detachment of sperm head and tail in Ccdc113–/– mice may not be a secondary effect of sperm flagellum defects.”

      (3) Given that some cytoplasm materials could be observed in Ccdc113-/- spermatozoa (Fig. 5A), whether CCDC113 is also essential for cytoplasmic removal?

      Good question. Unremoved cytoplasm could be detected in spermatozoa by using transmission electron microscopy (TEM) analysis, including disrupted mitochondria, damaged axonemes, and large vacuoles. These observations indicate defects in cytoplasmic removal in Ccdc113–/– mice. We have discussed this point as below:

      “Moreover, TEM analysis detected excess residual cytoplasm in spermatozoa, including disrupted mitochondria, damaged axonemes, and large vacuoles, indicating defects in cytoplasmic removal in Ccdc113–/– mice (Figure 5A).”

      (4) Although CCDC113 could not bind to PMFBP1, the localization of CCDC113 in Pmfbp1-/- spermatozoa should be also detected to clarify the relationship between CCDC113 and SUN5-CENTLEIN-PMFBP1.

      We appreciate Reviewer #1’s suggestion. We have analyzed the localization of CCDC113 in Pmfbp1-/- spermatozoa and found that CCDC113 was located at the tip of the decapitated tail in Pmfbp1-/- spermatozoa (Figure 7K and L). This finding has been incorporated into the revised manuscript as below:

      “To further elucidate the functional relationships among CCDC113, SUN5, CENTLEIN, and PMFBP1 at the sperm HTCA, we examined the localization of CCDC113 in Sun5-/-, Centlein–/–, and Pmfbp1–/– spermatozoa. Compared to the control group, CCDC113 was predominantly localized on the decapitated flagellum in Sun5-/-, Centlein–/–, and Pmfnp1–/– spermatozoa (Figure 7K and L), indicating SUN5, CENTLEIN, and PMFBP1 are crucial for the proper docking of CCDC113 to the implantation site on the sperm head. Taken together, these data demonstrate that CCDC113 cooperates with SUN5 and CENTLEIN to stabilize the sperm HTCA and anchor the sperm head to the tail.”

      Reviewer #2 (Public Review):

      Summary:

      In the present study, the authors select the coiled-coil protein CCDC113 and revealed its expression in the stages of spermatogenesis in the testis as well as in the different steps of spermiogenesis with expression also mapped in the different parts of the epididymis. Gene deletion led to male infertility in CRISPR-Cas9 KO mice and PAS staining showed defects mapped in the different stages of the seminiferous cycle and through the different steps of spermiogenesis. EM and IF with several markers of testis germ cells and spermatozoa in the epididymis indicated defects in flagella and head-to-tail coupling for flagella as well as acephaly. The authors' co-IP experiments of expressed CCDC113 in HEK293T cells indicated an association with CFAP91 and DRC2 as well as SUN5 and CENTLEIN.

      The authors propose that CCDC113 connects CFAP91 and DRC2 to doublet microtubules of the axoneme and CCDC113's association with SUN5 and CENTLEIN to stabilize the sperm flagellum head-to-tail coupling apparatus. Extensive experiments mapping CCDC13 during postnatal development are reported as well as negative co-IP experiments and studies with SUN5 KO mice as well as CENTLEIN KO mice.

      Strengths:

      The authors provide compelling observations to indicate the relevance of CCDC113 to flagellum formation with potential protein partners. The data are relevant to sperm flagella formation and its coupling to the sperm head.

      We are grateful to Reviewer #2 for his or her recognition of the strength of this study.

      Weaknesses:

      The authors' observations are consistent with the model proposed but the authors' conclusions for the mechanism may require direct demonstration in sperm flagella. The Walton et al paper shows human CCDC96/113 in cilia of human respiratory epithelia. An application of such methodology to the proteins indicated by Wu et al for the sperm axoneme and head-tail coupling apparatus is eagerly awaited as a follow-up study.

      We thank Reviewer 2 for his/her kindly help in improving the manuscript.  We now understand that directly detection of CCDC113 precise localization in sperm axoneme and head-tail coupling apparatus (HTCA) using cryo-electron microscopy (cryo-EM) could powerfully strengthen our model. Recent advances in cryo-EM have indeed advanced our understanding of axonemal structures analysis of axonemal structures and determined the structures of native axonemal DMTs from mouse, bovine, and human sperm (Leung et al., 2023; Zhou et al., 2023). However, high-resolution structures of sperm axoneme and HTCA regions, including those involving CCDC113, have yet to be fully characterized. Thus, we would like to discuss this point and consider it a valuable direction for future research.

      “Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

      References:

      Bazan, R., Schröfel, A., Joachimiak, E., Poprzeczko, M., Pigino, G., & Wloga, D. (2021). Ccdc113/Ccdc96 complex, a novel regulator of ciliary beating that connects radial spoke 3 to dynein g and the nexin link. PLoS Genet, 17(3), e1009388.

      Ghanaeian, A., Majhi, S., McCafferty, C. L., Nami, B., Black, C. S., Yang, S. K., Legal, T., Papoulas, O., Janowska, M., Valente-Paterno, M., Marcotte, E. M., Wloga, D., & Bui, K. H. (2023). Integrated modeling of the Nexin-dynein regulatory complex reveals its regulatory mechanism. Nat Commun, 14(1), 5741.

      Leung, M. R., Zeng, J., Wang, X., Roelofs, M. C., Huang, W., Zenezini Chiozzi, R., Hevler, J. F., Heck, A. J. R., Dutcher, S. K., Brown, A., Zhang, R., & Zeev-Ben-Mordehai, T.  (2023). Structural specializations of the sperm tail. Cell, 186(13), 2880-2896.e2817

      Walton, T., Gui, M., Velkova, S., Fassad, M. R., Hirst, R. A., Haarman, E., O'Callaghan, C., Bottier, M., Burgoyne, T., Mitchison, H. M., & Brown, A. (2023). Axonemal structures reveal mechanoregulatory and disease mechanisms. Nature, 618(7965), 625-633.

      Zhou, L., Liu, H., Liu, S., Yang, X., Dong, Y., Pan, Y., Xiao, Z., Zheng, B., Sun, Y., Huang, P., Zhang, X., Hu, J., Sun, R., Feng, S., Zhu, Y., Liu, M., Gui, M., & Wu, J. (2023). Structures of sperm flagellar doublet microtubules expand the genetic spectrum of male infertility. Cell, 186(13), 2897-2910.e2819.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      (1) Please provide full gel for the Figure 2C experiment (could be as a supplementary file).

      Thanks for your insightful suggestions. We have replaced Figure 2C and provided the full gel in Figure 2-figure supplement 1A.

      (2) The authors write on Line 163 "In contrast, the flagellum staining appeared reduced in Ccdc113-/- seminiferous tubules (Fig. 2J, red asterisk)." However, the magnification of the pictures is not sufficient to distinguish anything in the panel mentioned, please provide others.

      Many thanks for pointing this out. We have provided the iconic figure to show the flagella defect in seminiferous tubules.

      (3) Please add statistical p-values for figures.

      Thanks for your valuable advice. We have added statistical p-values to the figures in the revised manuscript.

      (4) Line 128: Should "speculate" be "speculated"?

      Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

      “Given that CFAP91 has been reported to stabilize RS on the DMTs (Bicka et al., 2022; Dymek et al., 2011; Gui et al., 2021) and cryo-EM analysis shows that CCDC113 is closed to DMTs, we speculated that CCDC113 may connect RS to DMTs by binding to CFAP91 and microtubules.”

      (5) In lines 384-385, more "-" is typed.

      Thank you for pointing out this problem. We have corrected it in the revised manuscript, as shown below:

      “Furthermore, CCDC113 colocalizes with SUN5 in the HTCA region, and immunofluorescence staining in spermatozoa shows that SUN5 is closer to the sperm nucleus than CCDC113 (Figure 7G and H). Therefore, SUN5 and CENTLEIN may be closer to the sperm nucleus than CCDC113.”

      (6) In general, the article has many typos and should be professionally proofread.

      Many thanks for pointing this out. We have thoroughly revised the manuscript with the assistance professional proofreading.

      Reviewer #2 (Recommendations For The Authors):

      Can the authors indicate in the Materials and Methods if n=3 biological replicates were done for all co-IP, EM, LM, and IF studies? The statistical analysis section indicates this but quantification is missing for most figures including co-IP, most IF, PAS staining, EM, etc.

      We thank Reviewer 2 for the insightful comments and guidance to improve our data quality. All the experiments in this study were repeated at least three times to ensure reproducibility. We have quantified the co-IP experiments in Figures 1C-H and 7A-F, the IF data in Figures 2K, 5C, and 5D, as well as the PAS staining in Figure 6C. Since electron microscopy samples require very little testicular tissue and the sections obtained are very thin, the likelihood of capturing sections specifically at the sperm head-tail junction is considerably low. This challenge makes it difficult to perform quantitative analysis and statistical evaluation in the TEM experiment. To address this limitation, we have quantified the percentage of _Ccdc113-/-_sperm heads with abnormal orientation in stages V–VIII of the seminiferous epithelium to indicate impaired head-to-tail anchorage.

      Figure S2 is compelling and might be indicated as a major figure instead of a supplementary figure.

      We appreciate the positive comment. We have included it as a major figure in Figure 3F.

      Figure 4A may be incomplete. Data sets for RNA expression suggest high expression in the ovary and other organs in males and females including the brain and are not indicated by the authors. Figure 4A may be considered for removal with a more complete study for another paper.

      Thank you for pointing out this issue. We reviewed RNA expression data from various tissues using RNA-Seq data from Mouse ENCODE (https://www.ncbi.nlm.nih.gov/gene/244608) and found that CCDC113 is highly expressed in the testis, but not significantly in the ovary and brain (Figure 4- figure supplement 1A). Additionally, we re-evaluated CCDC113 protein levels in the spleen, lung, kidney, testis, intestine, stomach, brain, and ovary, confirming that it is highly expressed in the testes, with negligible expression in the ovary and brain (Figure 4- figure supplement 1B). In line with Reviewer 2's suggestion, we have removed Figure 4A in the revised manuscript.

      There are grammatical errors throughout the manuscript and Figure 7 is truncated.

      Thank you for pointing out this problem. We have thoroughly revised the manuscript with the assistance professional proofreading.

      The Introduction and Discussion parts of the paper may need some clarification for the general reader. The material in the "Additional Context " section of the critique below may be a helpful place to introduce what a stage is, and the steps in germ cell development in the testis with the latter of course where and when the flagellum develops.

      We appreciate your valuable suggestions. We have referred to the material in the “Additional Context” section to introduce the stages of spermatogenesis and the steps in germ cell development in the testis in the introduction and results.

      “Male fertility relies on the continuous production of spermatozoa through a complex developmental process known as spermatogenesis. Spermatogenesis involves three primary stages: spermatogonia mitosis, spermatocyte meiosis, and spermiogenesis. During spermiogenesis, spermatids undergo complex differentiation processes to develop into spermatozoa, which includes nuclear elongation, chromatin remodeling, acrosome formation, cytoplasm elimination, and flagellum development (Hermo et al., 2010).”

      Hermo, L., Pelletier, R. M., Cyr, D. G., & Smith, C. E. (2010). Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microscopy research and technique, 73(4), 241–278. https://doi.org/10.1002/jemt.20783

      “Pioneering work in the mid-1950s used the PAS stain in histologic sections of mouse testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules (Oakberg, 1956). The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 16 different steps that are morphologically distinct spermatids (O'Donnell L, 2015).”

      Oakberg, E. F. (1956). A description of spermiogenesis in the mouse and its use in analysis of the cycle of the seminiferous epithelium and germ cell renewal. The American journal of anatomy, 99(3), 391-413. https://doi.org/10.1002/aja.1000990303

      O'Donnell L. (2015). Mechanisms of spermiogenesis and spermiation and how they are disturbed. Spermatogenesis, 4(2), e979623. https://doi.org/10.4161/21565562.2014.979623

      For the Discussion, the authors indicate that the function of CCDC113 in mammals is unknown yet the authors point to the work of Walton et al on human respiratory epithelia that points to a function for CCDC96/113. The work in the manuscript here does indicate a role in sperm flagella and the head-to-tail coupling apparatus but remains descriptive until the methodology of Walton et al is applied. Hopefully, the authors will consider it for a follow-up study.

      Thank you for pointing out this problem. We have revised this part and highlighted the Walton et al’s work in the Discussion.

      “CCDC113 is a highly evolutionarily conserved component of motile cilia/flagella. Studies in the model organism, Tetrahymena thermophila, have revealed that CCDC113 connects RS3 to dynein g and the N-DRC, which plays essential role in cilia motility (Bazan et al., 2021; Ghanaeian et al., 2023). Recent studies have also identified the localization of CCDC113 within the 96-nm repeat structure of the human respiratory epithelial axoneme, and localizes to the linker region among RS, N-DRC and DMTs (Walton et al., 2023). In this study, we reveal that CCDC113 is indispensable for male fertility, as Ccdc113 knockout mice produce spermatozoa with flagellar defects and head-tail linkage detachment (Figure 3D).”

      “Overall, we identified CCDC113 as a structural component of both the flagellar axoneme and the HTCA, where it performs dual roles in stabilizing the sperm axonemal structure and maintaining the structural integrity of HTCA. Given that the cryo-EM of sperm axoneme and HTCA could powerfully strengthen the role of CCDC113 in stabilizing sperm axoneme and head-tail coupling apparatus, it a valuable direction for future research.”

      The Discussion may be focused on the key aspects of CCDC113 related to sperm flagella and the head-to-tail coupling apparatus that represent a genuine advance. The more speculative parts of the Discussion that have not been addressed by experimentation in the Results section may be considered for removal in the Discussion section.

      Thank you for pointing out this. We have removed the speculative parts of the Discussion that have not been addressed by experimentation in the Results section.

      Additional Context to help readers understand the significance of the work:

      Pioneering work in the mid-1950s used the periodic acid Schiff (PAS) stain in histologic sections of rodent testis to visualize glycoproteins of the acrosome and Golgi in seminiferous tubules. The pioneers discovered in cross-sectioned seminiferous tubules the association of differentiating germ cells with successive layers to define different stages that in mice are twelve, indicated as Roman numerals (XII). For each stage, different associations of maturing germ cells were always the same with early cells in differentiation at the periphery and more mature cells near the lumen. In this way, progressive differentiation from stem cells to mitotic, meiotic, acrosome-forming, and post-acrosome maturing spermatocytes was mapped to define spermatogenesis with the XII stages in mice representing the seminiferous cycle. The maturation process from acrosome-forming cells to mature spermatocytes is defined as spermiogenesis with 19 different steps that are morphologically distinct spermatids. It is from steps 8-19 of spermiogenesis that the formation of the flagellum takes place. Final maturation occurs in the epididymis as sperm move through the caput, corpus, and cauda of the organ with motile spermatozoa generated.

      Thank you very much!

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews: 

      Reviewer #1 (Public Review): 

      Syngnathid fishes (seahorses, pipefishes, and seadragons) present very particular and elaborated features among teleosts and a major challenge is to understand the cellular and molecular mechanisms that permitted such innovations and adaptations. The study provides a valuable new resource to investigate the morphogenetic basis of four main traits characterizing syngnathids, including the elongated snout, toothlessness, dermal armor, and male pregnancy. More particularly, the authors have focused on a late stage of pipefish organogenesis to perform single-cell RNA-sequencing (scRNA-seq) completed by in situ hybridization analyses to identify molecular pathways implicated in the formation of the different specific traits. 

      The first set of data explores the scRNA-seq atlas composed of 35,785 cells from two samples of gulf pipefish embryos that authors have been able to classify into major cell types characterizing vertebrate organogenesis, including epithelial, connective, neural, and muscle progenitors. To affirm identities and discover potential properties of clusters, authors primarily use KEGG analysis that reveals enriched genetic pathways in each cell types. While the analysis is informative and could be useful for the community, some interpretations appear superficial and data must be completed to confirm identities and properties. Notably, supplementary information should be provided to show quality control data corresponding to the final cell atlas including the UMAP showing the sample source of the cells, violin plots of gene count, UMI count, and mitochondrial fraction for the overall

      dataset and by cluster, and expression profiles on UMAP of selected markers characterizing cluster identities. 

      We thank the reviewer for these suggestions, and have added several figures and supplemental files in response. We added a supplemental UMAP showing the sample that each cell originated (S1). We also added supplemental violin plots for each sample showing the gene count, unique molecular identifier (UMI) count, mitochondrial fraction, and the doublet scores (S2). We added feature plots of zebrafish marker genes for these major cell types and marker genes identified from our dataset to the supplement (S3:S57). We also provided two supplemental files with marker genes. These changes should clarify the work that went into labeling the clusters. Although some of the cluster labels are general, we decided it would be unwise to label clusters with speculated specific annotations. We only gave specific annotations to clusters with concrete markers and/or in situ hybridization (ISH) results that cemented an annotation.  As shown in the new supplemental figures and files, certain clusters had clear, specific markers while others did not. Therefore, we used caution when we annotated clusters without distinct markers. 

      The second set of data aims to correlate the scRNA-seq analysis with in situ hybridizations (ISH) in two different pipefish (gulf and bay) species to identify and characterize markers spatially, and validate cell types and signaling pathways active in them. While the approach is rational, the authors must complete the data and optimize labeling protocols to support their statements. One major concern is the quality of ISH stainings and images; embryos show a high degree of pigmentation that could hide part of the expression profile, and only subparts and hardly detectable tissues/stainings are presented. The authors should provide clear and good-quality images of ISH labeling on whole-mount specimens, highlighting the magnification regions and all other organs/structures (positive controls) expressing the marker of interest along the axis. Moreover, ISH probes have been designed and produced on gulf pipefish genome and cDNA respectively, while ISH labeling has been performed indifferently on bay or gulf pipefish embryos and larvae. The authors should specify stages and species on figure panels and should ensure sequence alignment of the probe-targeted sequences in the two species to validate ISH stainings in the bay pipefish. Moreover, spatiotemporal gene expression being a very dynamic process during embryogenesis, interpretations based on undefined embryonic and larval stages of pipefish development and compared to 3dpf zebrafish are insufficient to hypothesize on developmental specificities of pipefish features, such as on the absence of tooth primordia that could represent a very discrete and transient cell population. The ISH analyses would require a clean and precise spatiotemporal expression comparison of markers at the level of the entire pipefish and zebrafish specimens at well-defined stages, otherwise, the arguments proposed on teleost innovations and adaptations turn out to be very speculative. 

      We are appreciative of the reviewer’s feedback. We primarily used the in situ hybridization (ISH) data as supplementary to the scRNAseq library and we are aware that further evidence is necessary to identify origins of syngnathid’s evolutionary novelties. Our goal was to provide clues for the developmental genetic basis of syngnathid derived features.  We hope that our study will inspire future investigations and are excited for the prospect that future research could include this reviewer’s ideas. 

      All of the developmental stages and species information for the embryos used were in the figure captions as well as in supplemental file 6. Because we primarily used wild caught embryos, we did not have specific ages of most embryos. Syngnathid species are challenging to culture in the laboratory, and extracting embryos requires euthanizing the father which makes it difficult to obtain enough embryos for ISH. In addition, embryos do not survive long when removed from the brood pouch prematurely. We supplemented our ISH with bay pipefish caught off the Oregon coast because these fish have large broods. Wild caught pregnant male bay pipefish were immediately euthanized, and their broods were fixed. Because we did not have their age, we classified them based on developmental markers such as presence of somites and the extent of craniofacial elongation. Although these classification methods are not ideal, they are consistent with the syngnathid literature (Sommer et al. 2012). Since the embryos used for the ISH were primarily wild caught, we had a few different developmental stages represented in our ISH data. For our tooth primordia search, we used embryos from the same brood (therefore, same stage) for these experiments.

      We understand the concern for the degree of pigmentation in the samples. We completed numerous bleach trials before embarking on the in situ hybridization experiments. After completing a bleach trial with a probe created from the gene tnmd for ISH_,_ we noticed that the bleached embryos were missing expression domains found in the unbleached embryos. We were, therefore, concerned that using bleached embryos for our experiments would result incorrect conclusions about the expression domains of these genes. We sparingly used bleaching at older stages, hatched larvae, where it was fundamentally necessary to see staining. As stated above, the primary goal of this manuscript was to generate and annotate the first scRNA-seq atlas in a syngnathid, and the ISHs were utilized to support inferred cluster annotations only through a positive identification of marker gene expression in expected tissues/cells. Therefore, the obscuring of gene expression by pigmentation would have resulted in the absence of evidence for a possible cluster annotation, not an incorrect annotation.

      For the ease of viewing the ISHs, we improved annotations and clarity. We increased the brightness and contrast of images. In the original submission, we had to lower the image resolution to make the submission file smaller. We hope that these improvements plus the true image quality improves clarity of ISH results. We also included alignments in our supplementary files of bay pipefish sequences to the Gulf pipefish probes to showcase the high degree of sequence similarity. 

      Sommer, S., Whittington, C. M., & Wilson, A. B. (2012). Standardised classification of pre-release development in male-brooding pipefish, seahorses, and seadragons (Family Syngnathidae). BMC Developmental Biology, 12, 12–15. 

      To conclude, whereas the scRNA-seq dataset in this unconventional model organism will be useful for the community, the spatiotemporal and comparative expression analyses have to be thoroughly pushed forward to support the claims. Addressing these points is absolutely necessary to validate the data and to give new insights to understand the extraordinary evolution of the Syngnathidae family. 

      We really appreciate the reviewer’s enthusiasm for syngnathid research, and hope that the additional files and explanation of the supporting role of the ISHs have adequately addressed their concerns. We share the reviewer’s enthusiasm and are excited for future work that can extend this study. 

      Reviewer #2 (Public Review):

      Summary: 

      The authors present the first single-cell atlas for syngnathid fishes, providing a resource for future evolution & development studies in this group. 

      Strengths: 

      The concept here is simple and I find the manuscript to be well written. I like the in situ hybridization of marker genes - this is really nice. I also appreciate the gene co-expression analysis to identify modules of expression. There are no explicit hypotheses tested in the manuscript, but the discovery of these cell types should have value in this organism and in the determination of morphological novelties in seahorses and their relatives.  

      We are grateful for this reviewer’s appreciation of the huge amount of work that went into this study, and we agree that the in situ hybridizations (ISHs) support the scRNAseq study as we intended. We appreciate that the reviewer thinks that this work will add value to the syngnathid field.

      Weaknesses: 

      I think there are a few computational analyses that might improve the generality of the results. 

      (1) The cell types: The authors use marker gene analysis and KEGG pathways to identify cell types. I'd suggest a tool like SAMap (https://elifesciences.org/articles/66747) which compares single-cell data sets from distinct organisms to identify 'homologous' cell types - I imagine the zebrafish developmental atlases could serve as a reasonable comparative reference. 

      We appreciate the reviewer’s request, and in fact we would have loved to integrate our dataset with zebrafish. However, syngnathid’s unique craniofacial development makes it challenging to determine the appropriate stage for comparison. While 3 days post fertilization (dpf) zebrafish data were appropriate for comparisons of certain cell types (e.g. epidermal cells), it would have been problematic for other cell types (e.g. osteoblasts) that are not easily detectable until older zebrafish stages. Therefore, determining equivalent stages between these species is difficult and contains potential for error. Future research should focus on trying to better match stages across syngnathids and zebrafish (and other fish species such as stickleback). Studies of this nature promise to uncover the role of heterochrony in the evo-devo of syngnathid’s unique snouts.

      (2) Trajectory analyses: The authors suggest that their analyses might identify progenitor cell states and perhaps related differentiated states. They might explore cytoTRACE and/or pseudotime-based trajectory analyses to more fully delineate these ideas.

      We thank the reviewer for this suggestion! We added a trajectory analysis using cytoTRACE to the manuscript. It complemented our KEGG analysis well (L172-175; S73) and has improved the manuscript.

      (3) Cell-cell communication: I think it's very difficult to identify 'tooth primordium' cell types, because cell types won't be defined by an organ in this way. For instance, dental glia will cluster with other glia, and dental mesenchyme will likely cluster with other mesenchymal cell types. So the histology and ISH is most convincing in this regard. Having said this, given the known signaling interactions in the developing tooth (and in development generally) the authors might explore cell-cell communication analysis (e.g., CellChat) to identify cell types that may be interacting. 

      We agree! It would have been a wonderful addition to the paper to include a cell-cell communication analysis. One limitation of CellChat is that it only includes mouse and human orthologs. Given concerns of reviewer #3 for mouse-syngnathid comparisons, we decided to not pursue CellChat for this study. We are looking forward to future cell communication resources that include teleost fishes.

      Reviewer #3 (Public Review): 

      Summary: 

      This study established a single-cell RNA sequencing atlas of pipefish embryos. The results obtained identified unique gene expression patterns for pipefish-specific characteristics, such as fgf22 in the tip of the palatoquadrate and Meckel's cartilage, broadly informing the genetic mechanisms underlying morphological novelty in teleost fishes. The data obtained are unique and novel, potentially important in understanding fish diversity. Thus, I would enthusiastically support this manuscript if the authors improve it to generate stronger and more convincing conclusions than the current forms. 

      Thank you, we appreciate the reviewer’s enthusiasm!

      Weaknesses: 

      Regarding the expression of sfrp1a and bmp4 dorsal to the elongating ethmoid plate and surrounding the ceratohyal: are their expression patterns spatially extended or broader compared to the pipefish ancestor? Is there a much closer species available to compare gene expression patterns with pipefish? Did the authors consider using other species closely related to pipefish for ISH? Sfrp1a and bmp4 may be expressed in the same regions of much more closely related species without face elongation. I understand that embryos of such species are not always accessible, but it is also hard to argue responsible genes for a specific phenotype by only comparing gene expression patterns between distantly related species (e.g., pipefish vs. zebrafish). Due to the same reason, I would not directly compare/argue gene expression patterns between pipefish and mice, although I should admit that mice gene expression patterns are sometimes helpful to make a hypothesis of fish evolution. Alternatively, can the authors conduct ISH in other species of pipefish? If the expression patterns of sfrp1a and bmp4 are common among fishes with face elongation, the conclusion would become more solid. If these embryos are not available, is it possible to reduce the amount of Wnt and BMP signal using Crispr/Cas, MO, or chemical inhibitor? I do think that there are several ways to test the Wnt and/or BMP hypothesis in face elongation. 

      We appreciate the reviewer’s suggestion, and their recognition for challenges within this system. In response to this comment, we completed further in situ hybridization experiments in threespine stickleback, a short snouted fish that is much more closely related to syngnathids than is zebrafish, to make comparisons with pipefish craniofacial expression patterns (S76-S79). We added ISH data for the signaling genes (fgf22, bmp4, and sfrp1a) as well as prdm16. Through adding this additional ISH results, we speculated that craniofacial expression of bmp4, sfrp1a, and prdm16 is conserved across species. However, compared to the specific ceratohyal/ethmoid staining seen in pipefish, stickleback had broad staining throughout the jaws and gills. These data suggest that pipefish have co-opted existing developmental gene networks in the development of their derived snouts. We added this interpretation to the results and discussion of the manuscript (L244-L248; L262-277; L444-470).

      Recommendations for the authors:  

      Reviewing Editor (Recommendations for the Authors)

      We hope that the eLife assessment, as well as the revisions specified here, prove helpful to you for further revisions of your manuscript. 

      Revisions considered essential: 

      (1) Marker genes and single-cell dataset analyses. While these analyses have been performed to a good standard in broad terms, there is a majority view here that cell type annotations and trajectory analyses can be improved. In particular, there is question about the choice of marker genes for the current annotation. For one it can depend on the use of single marker genes (see tnnti1 example for clusters 17 and 31). Here, we recommend incorporating results from SAMap and trajectory analysis (e.g., cytoTRACE or standard pseudotime).

      Because of the reviewer comments, we became aware that we insufficiently communicated how cell clusters were annotated. We did mention in the manuscript that we did not use single marker genes to annotate clusters, but instead we used multiple marker genes for each cluster for the annotation process. We used both marker genes derived from our dataset and marker genes identified from zebrafish resources for cluster annotation. We chose single marker genes for each cluster for visualization purposes and for in situ hybridizations. However, it is clear from the reviewers’ comments that we needed to make more clear how the annotations were performed. To make this effort more clear in our revision, we included two new supplementary files – one with Seurat derived marker genes and one with marker genes derived from our DotPlot method. We also included extensive supplementary figures highlighting different markers. Using Daniocell, we identified 6 zebrafish markers per major cell type and showed their expression patterns in our atlas with FeaturePlots. We also included feature plots of the top 6 marker genes for each cluster. We hope that the addition of these 40+ plots (S3:S57) to the supplement fully addresses these concerns. 

      We appreciated the suggestion of cytotrace from reviewer #2! We ran cytotrace on three major cell lineages (neural, muscle, and connective; S73) which complemented our KEGG analysis in suggesting an undifferentiated fate for clusters 8, 10, and 16. We chose to not run SAMap because it is a scRNA-seq library integration tool. Although we compared our lectin epidermal findings to 3 dpf zebrafish scRNA-seq data, we did not integrate the datasets out of concern that we could draw erroneous conclusions for other cell types.  Future work that explores this technical challenge may uncover the role of heterochrony in syngnathid craniofacial development. We detail these changes more fully in our responses to reviewers.

      (2) The claims regarding evolutionary novelty and/or the genes involved are considered speculative. In part, this comes from relying too heavily on comparisons against zebrafish, as opposed to more closely related species. For example, the discussion regarding C-type lectin expression in the epidermis and KEGG enrichment (lines 358 - 364) seems confusing. Another good example here is the discussion on sfrp1a (lines 258 - 261). Here, the text seems to suggest craniofacial sfrp1a expression (or specifically ethmoid expression?) is connected to the development of the elongated snout in pipefish. However, craniofacial expression of sfrp1a is also reported in the arctic charr, which the authors grouped into fishes with derived craniofacial structures. Separately, sfrp2 expression was also reported in stickleback fish, for example. Do these different discussions truly support the notion that sfrp1a expression is all that unique in pipefish, rather than that pipefish and zebrafish are only distantly related and that sfrp1a was a marker gene first, and co-opted gene second? The authors should respond to the comments in the public review related to this aspect, and include more informative comparison and discussion. 

      A much more nuanced discussion with appropriate comparisons and caveats would be strongly recommended here.  

      We appreciate this insight and used it as a motivator to complete and add select comparative ISH data to this manuscript. We added in situ hybridization experiments from stickleback fish for craniofacial development genes (sfrp_1a, prdm16, bmp4_, and fgf22; S76-S79).  After adding stickleback ISH to the manuscript, we were able to make comparisons between pipefish and stickleback patterns and draw more informed conclusions (L244-L248; L262-277; L444-470). We added additional nuance to the discussion of the head, tooth (L485-489), and male pregnancy (L358-L391) sections to address concerns of study limitations. We describe in more detail these additional data in response to reviewers.

      (3) In situ hybridization results: as already included above, there is generally weak labeling of species, developmental stages, and other markings that can provide context. The collective feeling here is that as it is currently presented, the ISH results do not go too far beyond simply illustrative purposes. To take these results further, more detailed comparison may be needed. At a minimum, far better labeling can help avoid making the wrong impression. 

      Based on the reviewers’ comments, we made changes to improve ISH clarity and add select comparative ISH findings. ISH was used to further interpretation of the scRNAseq atlas. All the developmental stages and species information for the embryos used were in the figure captions as well as in supplemental file 4. Since we primarily used wild caught embryos, we did not have specific ages of most embryos. The technical challenges of acquiring and staging Syngnathus embryos are detailed above. Because we did not have their age, we classified them based on developmental markers (such as presence of somites and the extent of craniofacial elongation). Although these classification methods are not ideal, they are consistent with the syngnathid literature (Sommer et al. 2012).  

      We followed reviewer #1’s recommendations by adding an annotated graphic of a pipefish head, aligning bay and Gulf pipefish sequences for the probe regions, expanding out our supplemental figures for ISH into a figure for each probe, and improving labeling. These changes improved the description of the ISH experiments and have increased the quality of the manuscript.

      We would have loved to complete detailed comparative studies as suggested, but doing such a complete analysis was not feasible for this study. Therefore, we completed an additional focused analysis. We followed reviewer #3’s idea and added ISHs from threespine stickleback, a short snouted fish, for 4 genes (sfrp1a, prdm16, fgf22, and bmp4). While more extensive ISHs tracking all marker genes through a variety of developmental stages in pipefish and stickleback would have provided crucial insights, we feel that it is beyond the scope of this study and would require a significant amount of additional work. We, thus, primarily interpreted the ISH results as illustrative data points in our discussion. As we state in the response to reviewer 1, the generation and annotation of the first scRNA-seq atlas in a syngnathid is the primary goal of this manuscript.  The ISHs were utilized primarily to support inferred cluster annotations if a positive identification of marker gene expression in expected tissues/cells occurred. 

      Reviewer #1 (Recommendations For The Authors): 

      While the scRNA-seq dataset offers a valuable resource for evo-devo analyses in fish and the hypotheses are of interest, critical aspects should be strengthened to support the claims of the study. 

      Concerning the scRNA-seq dataset, the major points to be addressed are listed below: 

      - Supplementary file 3 reports the single markers used to validate cluster annotations. To confirm cluster identities, more markers specific to each cluster should be highlighted and presented on the UMAP. 

      We recognize the reviewer’s concern and had in reality used numerous markers to annotate the clusters. Based upon the reviewer’s comment we decided to make this clear by creating feature plots for every cluster with the top 6 marker genes. These plots showcase gene specificity in UMAP space. We also added feature plots for zebrafish marker genes for key cell types. Through these changes and the addition of 54 supplementary figures (S3:S57), we hope that it is clear that numerous markers validated cluster identity.

      For example, as clusters 17 and 37 share the same tnnti1 marker, which other markers permit to differentiate their respective identity. 

      This is a fair point. Cluster 17 and 37 both are marked by a tnni1 ortholog.

      Different paralogous co-orthologs mark each cluster (cluster 17: LOC125989146; cluster 37: LOC125970863). In our revision to the above comment, additional (6) markers per cluster were highlighted which should remedy this concern. 

      - L146: the low number of identified cartilaginous cells (only 2% of total connective tissue cells) appears aberrant compared to bone cell number, while Figure 1 presents a welldeveloped cartilaginous skeleton with poor or no signs of ossification. Please discuss this point. 

      We also found this to be interesting and added a brief discussion on this subject to the results section (L147-L149). Single cell dissociations can have variable success for certain cell types. It is possible that the cartilaginous cells were more difficult to dissociate than the osteoblast cells.

      - L162: pax3a/b are not specific to muscle progenitors as the genes are also expressed in the neural tube and neural crest derivatives during organogenesis. Please confirm cluster 10 identity.  

      Thank you for the reminder, we added numerous feature plots that explored zebrafish (from Daniocell) and pipefish markers (identified in our dataset). Examining zebrafish satellite muscle markers (myog, pabpc4, and jam2a) shows a strong correspondence with cluster #10.

      - L198: please specify in the text the pigment cell cluster number. 

      We completed this change.

      - L199: it is not clear why considering module 38 correlated to cluster 20 while modules 2/24 appear more correlated according to the p-value color code. 

      We thank the reviewer for pointing this confusing element out! Although the t-statistic value for module 38 (3.75) is lower than the t-statistics for modules 2 and 24 (5.6 and 5.2, respectively), we chose to highlight module 38 for its ‘connectivity dependence’ score. In our connectivity test, we examined whether removing cells from a specific cell cluster reduced the connectivity of a gene network. We found that removing cluster 20 led to a decrease in module 38’s connectivity (-.13, p=0) while it led to an increase in modules 2 and 24’s connectivity (.145, p=1; .145, p=9.14; our original supplemental files 9-10). Therefore, the connectivity analysis showed that module 38’s structure was more dependent on cluster 20 than in comparison with modules 2 and 24. Although you highlighted an interesting quandary, we decided that this is tangential to the paper and did not add this discussion to the manuscript. 

      - Please describe in the text Figure 4A. 

      Completed, we thank the reviewer for catching this! 

      Concerning embryo stainings, the major points to be addressed are listed below: 

      - Figure 1: please enhance the light/contrast of figures to highlight or show the absence of alcian/alizarin staining. Mineralized structures are hardly detectable in the head and slight differences can be seen between the two samples. The developmental stage should be added. Please homogenize the scale bar format (remove the unit on panels E and, G as the information is already in the text legend). It would be useful to illustrate the data with a schematic view of the structures presented in panels B, and E, and please annotate structures in the other panels.  

      We thank the reviewer for these suggestions to improve our figure. We increased the brightness and contrast for all our images. We also added an illustration of the head with labels of elements. As discussed, we used wild caught pregnant males and, therefore, do not know the exact age of the specimens. However, we described the developmental stage based on morphological observations. Slight differences in morphology between samples is expected. We and others have noticed that

      developmental rate varies, even within the same brood pouch, for syngnathid embryos. We observed several mineralization zones including in the embryos including the upper and lower jaws, the mes(ethmoid), and the pectoral fin. We recognize the cartilage staining is more apparent than the bone staining, though increasing image brightness and contrast did improve the visibility of the mineralization front.

      - All ISH stainings and images presented in Figures 4-6/ Figures S2-3 should be revised according to comments provided in the public review. 

      We thank the reviewer for providing thorough comments, we provided an in-depth response to the public review. We made several improvements to the manuscript to address their concerns. 

      - Figure 4: Figure 4B should be described before 4C in the text or inverse panels / L222 the Meckel's cartilage is not shown on Figure 4C. The schematic views in H should be annotated and the color code described / the ISH data must be completed to correlate spatially clusters to head structures. 

      We thank the reviewer for pointing this out, we fixed the issues with this figure and added annotations to the head schematics.

      - Figure 5: typo on panels 'alician' = alcian. 

      We completed this change. 

      - Figures S2-3: data must be better presented, polished / typo in captions 'relavant'= relevant. 

      Thank you for this critique, we created new supplementary figures to enhance interpretation of the data (S59-S71). In these new figures, we included a feature plot for each gene and respective ISHs.

      - Figure S3: soat2 = no evidence of muscle marker neither by ISH presented nor in the literature. 

      We realized this staining was not clear with the previous S2/S3 figures. Our new changes in these supplementary figures based on the reviewer’s ideas made these ISH results clearer. We observed soat2 staining in the sternohyoideus muscle (panel B in S71).

      Other points: 

      - The cartilage/bone developmental state (Alcian/alizarin staining) and/or ISH for classical markers of muscle development (such as pax3/myf5) could be used to clarify the This could permit the completion of a comparative analysis between the two species and the interpretation of novel and adaptative characters.  

      We appreciate this idea! We thought deeply about a well characterized comparative analysis between pipefish and zebrafish for this study. We discussed our concerns in our public response to reviewer 2. We found that it was challenging to stage match all cell types, and were concerned that we could make erroneous conclusions. For example, our pipefish samples were still inside the male brood pouch and possessed yolk sacs. However, we found osteoblast cells in our scRNAseq atlas, and in alizarin staining. Although zebrafish literature notes that the first zebrafish bone appears at 3 dpf (Kimmel et al. 1995), osteoblasts were not recognized until 5 dpf in two scRNAseq datasets (Fabian et al. 2022; Lange et al. 2023). A 5dpf zebrafish is considered larval and has begun hunting. Therefore, we chose to not integrate our data out of concern that osteoblast development may occur at different timelines between the fishes. 

      Fabian, P., Tseng, K.-C., Thiruppathy, M., Arata, C., Chen, H.-J., Smeeton, J., Nelson, N., & Crump, J. G. (2022). Lifelong single-cell profiling of cranial neural crest diversification in zebrafish. Nature Communications 2022 13:1, 13(1), 1–13. 

      Lange, M., Granados, A., VijayKumar, S., Bragantini, J., Ancheta, S., Santhosh, S., Borja, M., Kobayashi, H., McGeever, E., Solak, A. C., Yang, B., Zhao, X., Liu, Y., Detweiler, A. M., Paul,

      S., Mekonen, H., Lao, T., Banks, R., Kim, Y.-J., … Royer, L. A. (2023). Zebrahub – Multimodal Zebrafish Developmental Atlas Reveals the State-Transition Dynamics of Late-Vertebrate Pluripotent Axial Progenitors. BioRxiv, 2023.03.06.531398. 

      Kimmel, C., Ballard, S., Kimmel, S., Ullmann, B., Schilling, T. (1995). Stages of Embryonic Development of the Zebrafish. Developmental Dynamics 203:253:-310.

      'in situs' in the text should be replaced by 'in situ experiments'.  

      We made this change (L395, L663, L666, L762).

      - Lines 562-565: information on samples should be added at the start of the result section to better apprehend the following scRNA-seq data.

      We thank the reviewer for pointing out this issue. Although we had a few sentences on the samples in the first paragraph of the result section, we understand that it was missing some critical pieces of information. Therefore, we added these additional details to the beginning of the results section (L126-L132). 

      - Lines 629-665: PCR with primers designed on gulf pipefish genome could be performed in parallel on bay and gulf cDNA libraries, and amplification products could be sequenced to analyze alignment and validate the use of gulf pipefish ISH probes in bay pipefish embryos. Probe production could also be performed using gulf primers on bay pipefish cDNA pools. 

      After the submission of this manuscript, a bay pipefish genome was prepared by our laboratory. We used this genome to align our probes, these alignments demonstrate strong sequence conservation between the species. We included these alignments in our supplemental files.

      - L663: the bleaching step must be optimized on pipefish embryos. 

      We understand this concern and had completed several bleach optimization experiments prior to publication. Although we found that bleaching improved visibility of staining, we noticed with the probe tnmd that bleached embryos did not have complete staining of tendons and ligaments. The unbleached embryos had more extensive staining than the bleached embryos. We were concerned that bleaching would lead to failures to detect expression domains (false negatives) important for our analysis. Therefore, we did not use bleaching with our in situs experiments (except with hatched fish with a high degree of pigmentation). 

      - Indicate the number of specimens analyzed for each labeling condition.  

      We thank the reviewer for noticing this issue. We added this information to the methods (L766-767).

      - Describe the fixation and pre-treatment methods previous to ISH and skeleton stainings

      We thank the reviewer for pointing out this issue, we added these descriptions (L765-766; L772-774). 

      Reviewer #3 (Recommendations For The Authors): 

      (1) If sfrp1a expression is observed also in other fish species with derived craniofacial structures, it's important to discuss this more in the Discussion. This could be a common mechanism to modify craniofacial structures, although functional tests are ultimately required (but not in this paper, for sure). Can lines 421-428 involve the statement "a prolonged period of chondrocyte differentiation" underlies craniofacial diversity?

      This is a great idea, and we added a sentence that captures this ethos (L451-452).

      (2) Lines 334-346 need to be rephrased. It's hard to understand which genes are expressed or not in pipefish and zebrafish. Did "23 endocytosis genes" show significant enrichment in zebrafish epidermis, or are they expressed in zebrafish epidermis? 

      We thank the reviewer for this comment, we re-phrased this section for clarity (L365-368).

      (3) Figure 4 is missing the "D" panel and two "E" panels. 

      We thank the reviewer for noticing this, we fixed this figure.

      (4) Line 302: "whole-mount" or "whole mount"

      We thank the reviewer for the catch!

    1. Because these are self-organizing developmental processes, they generate not onlystrong regularities but also diversity across individual developmental trajectories. Forexample, in most runs, one observes successively unorganized body babbling, thenfocused exploration of head movements, then exploration of touching an object, thengrasping an object, and finally vocalizing toward a peer robot (pre-programmed to imi-tate). This can be explained as gradual exploration of new progress niches, and thosestages and their ordering can be viewed as a form of attractor in the space of develop-mental trajectories. Yet, with the same mechanism and same initial parameters, individualtrajectories may invert stages, or even generate qualitatively different behaviors. This isdue to stochasticity, to even small variability in the physical realities and to the fact thatthis developmental dynamic system has several attractors with more or less extended andstrong domains of attraction (characterized by amplitude of learning progress)

      🔥🔥🔥 can reproduce classic learnings sequences. plus some variety.

    2. The learner is equipped with a repertoire of motor primitives parameterized by severalcontinuous numbers that control movements of its legs, head, and a simulated vocal tract.Each motor primitive is a dynamical system controlling various forms of actions: (a) turningthe head in different directions; (b) opening and closing the mouth while crouching withvarying strengths and timing; (c) rocking the leg with varying angles and speed; (d) vocaliz-ing with varying pitches and lengths. These primitives can be combined to form a large con-tinuous space of possible actions. Similarly, sensory primitives allow the robot to detectvisual movement, salient visual properties, proprioceptive touch in the mouth, and pitch andlength of perceived sounds. For the robot, these motor and sensory primitives are initiallyblack boxes and he has no knowledge about their semantics, effects, or relations.P.-Y. Oudeyer, L. B. Smith / Topics in Cognitive Science (2016) 3

      Basic primitives which are just numbers to the robot.

    1. Reviewer #2 (Public review):

      Summary:

      Koh et al. report an interesting manuscript studying dopamine binding in the lateral accumbens shell of rats across the course of conditioned taste aversion. The question being asked here is how does the dopamine system respond to aversion? The authors take advantage of unique properties of taste aversion learning (notably, within-subjects remapping of valence to the same physical stimulus) to address this.

      They combine a well controlled behavioural design (including key, unpaired controls) with fibre photometry of dopamine binding via GrabDA and of dopamine neuron activity by gCaMP, careful analyses of behaviour (e.g., head movements; home cage ingestion), the authors show that, 1) conditioned taste aversion of sucrose suppresses the activity of VTA dopamine neurons and lateral shell dopamine binding to subsequent presentations of the sucrose tastant; 2) this pattern of activity was similar to the innately aversive tastant quinine; 3) dopamine responses were negatively correlated with behavioural (inferred taste reactivity) reactivity; and 4) dopamine responses tracked the contingency of between sucrose and illness because these responses recovered across extinction of the conditioned taste aversion.

      Strengths:

      There are important strengths here. The use of a well-controlled design, the measurement of both dopamine binding and VTA dopamine neuron activity, the inclusion of an extinction manipulation; and the thorough reporting of the data. I was not especially surprised by these results, but these data are a potentially important piece of the dopamine puzzle (e.g., as the authors note, salience-based argument struggles to explain these data).

      Weaknesses for consideration:

      (1) The focus here is on the lateral shell. This is a poorly investigated region in the context of the questions being asked here. Indeed, I suspect many readers might expect a focus on the medial shell. So, I think this focus is important. But, I think it does warrant greater attention in both the introduction and discussion. We do know from past work that there can be extensive compartmentalisation of dopamine responses to appetitive and aversive events and many of the inconsistent findings in the literature can be reconciled by careful examination of where dopamine is assessed. I do think readers would benefit from acknowledgement this - for example it is entirely reasonable to suppose that the findings here may be specific to the lateral shell.

      (2) Relatedly, I think readers would benefit from an explicit rationale for studying the lateral shell as well as consideration of this in the discussion. We know that there are anatomical (PMID: 17574681), functional (PMID: 10357457), and cellular (PMID: 7906426) differences between the lateral shell and the rest of the ventral striatum. Critically, we know that profiles of dopamine binding during ingestive behaviours there can be highly dissimilar to the rest of ventral striatum (PMID: 32669355). I do think these points are worth considering.

      (3) I found the data to be very thoughtfully analysed. But in places I was somewhat unsure:<br /> (a) Please indicate clearly in the text when photometry data show averages across trials versus when they show averages across animals.<br /> (b) I did struggle with the correlation analyses, for two reasons.<br /> (i) First, the key finding here is that the dopamine response to intraoral sucrose is suppressed by taste aversion. So, this will significantly restrict the range of dopamine transients, making interpretation of the correlations difficult.

      (ii) Second, the authors report correlations by combining data across groups/conditions. I understand why the authors have done this, but it does risk obscuring differences between the groups. So, my question is: what happens to this trend when the correlations are computed separately for each group? I suspect other readers will share the same question. I think reporting these separate correlations would be very helpful for the field - regardless of the outcome.

      (4) Figure 1A is not as helpful as it might be. I do think readers would expect a more precise reporting of GCaMP expression in TH+ and TH- neurons. I also note that many of the nuances in terms of compartmentalisation of dopamine signalling discussed above apply to ventral tegmental area dopamine neurons (e.g. medial v lateral) and this is worth acknowledging when interpreting.

    1. I'll stick my head out here and say that we are 80% certain of being able to create a mass movement 10 times the size of Extinction Rebellion using this method organizations that can compete with fascism with power by dissolving that power through the same mechanisms Rogers discovered through listening

      for - fascism, polarization and climate crisis - climate communications - social intervention - new movement that can be 10x the size of Extinction Rebellion - apply Carl Rogers discovery of listening - Roger Hallam

    1. when you don't architect Your Service uh to resist State interference at the protocol level and that's what's so special and Powerful about crypto broadly Bitcoin specifically but you know is that kind of mentality or you design against the worst possible case uh to avoid the inevitable now uh in der's case I hope he gets out uh and once he's in a free place he genuinely fixes the design problem that put him in the hot seat first place uh by creating a service uh that does not put too much power and and data in vulnerable human hands you have to design your app so that there will never be a head that the state can point a gun at

      you have to design your app so that there will never be a head that the state can point a gun at that is unenclose-able and unstoppable and unbreakable due to

      protocol and infrastructural choices and design

    1. Another strange method is a blow to the head that is "severe enough to break theshell of an almond, but not the core".

      Bir diğer tuhaf yöntem, 'bademin kabuğunu kıracak kadar sert, ancak içini kırmayacak kadar hafif' bir baş darbesidir."

    Annotators

    1. Reviewer #1 (Public review):

      One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport, and probe interactions with PfEMP1 proteins in the infected host cell.

      What I particularly like about this paper is that the authors have not only put together what appears to be a largely robust system for further functional studies, but they have used it to produce a range of interesting findings including:

      - Co-activation of rif and var genes when in a head-to-head orientation.

      - The reduced control of expression of var genes in the 3D7-MEED parasite line.

      - More support for the PTEX transport route for PfEMP1.

      - Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

      In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

      (1) In terms of incorporating a drug marker to drive mono-variant expression, the authors show that they can manipulate a range of var genes in two parasite lines (3D7 and IT4), producing around 90% expression of the targeted PfEMP1. Removal of drug selection produces the expected 'drift' in variant types being expressed. The exceptions to this are the 3D7-MEED line, which looks to be an interesting starting point to understand why this variant appears to have impaired mutually exclusive var gene expression and the EPCR-binding IT4var19 line. This latter finding was unexpected and the modified construct required several rounds of panning to produce parasites expressing the targeted PfEMP1 and bind to EPCR. The authors identified a PTP3 deficiency as the cause of the lack of PfEMP1 expression, which is an interesting finding in itself but potentially worrying for future studies. What was not clear was whether the selected IT4var19 line retained specific PfEMP1 expression once receptor panning was removed.

      (2) The transport studies using the mDHFR constructs were quite complicated to understand but were explained very clearly in the text with good logical reasoning.

      (3) By introducing a second SLI system, the authors have been able to alter other genes thought to be involved in PfEMP1 biology, particularly transport. An example of this is the inactivation of PTP1, which causes a loss of binding to CD36 and ICAM-1. It would have been helpful to have more insight into the interpretation of the IFAs as the anti-SBP1 staining in Figure 5D (PTP-TGD) looks similar to that shown in Figure 1C, which has PTP intact. The anti-EXP2 results are clearly different.

      (4) It is good to see the validation of PfEMP1 expression includes binding to several relevant receptors. The data presented use CHO-GFP as a negative control, which is relevant, but it would have been good to also see the use of receptor mAbs to indicate specific adhesion patterns. The CHO system if fine for expression validation studies, but due to the high levels of receptor expression on these cells, moving to the use of microvascular endothelial cells would be advisable. This may explain the unexpected ICAM-1 binding seen with the panned IT4var19 line.

      (5) The proxiome work is very interesting and has identified new leads for proteins interacting with PfEMP1, as well as suggesting that KAHRP is not one of these. The reduced expression seen with BirA* in position 3 is a little concerning but there appears to be sufficient expression to allow interactions to be identified with this construct. The quantitative impact of reduced expression for proxiome experiments will clearly require further work to define it.

      (6) The reduced receptor binding results from the TryThrA and EMPIC3 knockouts were very interesting, particularly as both still display PfEMP1 on the surface of the infected erythrocyte. While care needs to be taken in cross-referencing adhesion work in P. berghei and whether the machinery truly is functionally orthologous, it is a fair point to make in the discussion. The suggestion that interacting proteins may influence the "correct presentation of PfEMP1" is intriguing and I look forward to further work on this.<br /> Overall, the authors have produced a useful and reasonably robust system to support functional studies on PfEMP1, which may provide a platform for future studies manipulating the domain content in the exon 1 portion of var genes. They have used this system to produce a range of interesting findings and to support its use by the research community.<br /> Finally, a small concern. Being able to select specific var gene switches using drug markers could provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms but perhaps not all.

    2. Reviewer #2 (Public review):

      Summary

      Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate the expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

      Comments

      (1) While the capability of SLI to actively select var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head-to-tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head-to-head but not head-to-tail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

      (2) The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near-perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

    3. Author response:

      Public Reviews:

      Reviewer #1 (Public review):

      One of the roadblocks in PfEMP1 research has been the challenges in manipulating var genes to incorporate markers to allow the transport of this protein to be tracked and to investigate the interactions taking place within the infected erythrocyte. In addition, the ability of Plasmodium falciparum to switch to different PfEMP1 variants during in vitro culture has complicated studies due to parasite populations drifting from the original (manipulated) var gene expression. Cronshagen et al have provided a useful system with which they demonstrate the ability to integrate a selectable drug marker into several different var genes that allows the PfEMP1 variant expression to be 'fixed'. This on its own represents a useful addition to the molecular toolbox and the range of var genes that have been modified suggests that the system will have broad application. As well as incorporating a selectable marker, the authors have also used selective linked integration (SLI) to introduce markers to track the transport of PfEMP1, investigate the route of transport, and probe interactions with PfEMP1 proteins in the infected host cell.

      What I particularly like about this paper is that the authors have not only put together what appears to be a largely robust system for further functional studies, but they have used it to produce a range of interesting findings including:

      - Co-activation of rif and var genes when in a head-to-head orientation.

      - The reduced control of expression of var genes in the 3D7-MEED parasite line.

      - More support for the PTEX transport route for PfEMP1.

      - Identification of new proteins involved in PfEMP1 interactions in the infected erythrocyte, including some required for cytoadherence.

      In most cases the experimental evidence is straightforward, and the data support the conclusions strongly. The authors have been very careful in the depth of their investigation, and where unexpected results have been obtained, they have looked carefully at why these have occurred.

      (1) In terms of incorporating a drug marker to drive mono-variant expression, the authors show that they can manipulate a range of var genes in two parasite lines (3D7 and IT4), producing around 90% expression of the targeted PfEMP1. Removal of drug selection produces the expected 'drift' in variant types being expressed. The exceptions to this are the 3D7-MEED line, which looks to be an interesting starting point to understand why this variant appears to have impaired mutually exclusive var gene expression and the EPCR-binding IT4var19 line. This latter finding was unexpected and the modified construct required several rounds of panning to produce parasites expressing the targeted PfEMP1 and bind to EPCR. The authors identified a PTP3 deficiency as the cause of the lack of PfEMP1 expression, which is an interesting finding in itself but potentially worrying for future studies. What was not clear was whether the selected IT4var19 line retained specific PfEMP1 expression once receptor panning was removed.

      This is a very interesting point. We do not have systematic long-term data for the Var19 line but medium-term data. After panning the Var19 line, the binding assays were done within 3 months without additional panning. The first binding assay was 2 months after the panning and the last binding assays three weeks later. While there is inherent variation in these assays that precludes detection of smaller changes, the last assay showed the highest level of binding, giving no indication for rapid loss of the binding phenotype. Hence, we can say that the binding phenotype appears to be stable for many weeks without panning the cells again and there was no indication for a rapid loss of binding in these parasites.

      Systematic long-term experiments to assess how long the Var19 parasites retain binding would be interesting, but given that the binding-phenotype appears to remain stable over many weeks, this would only make sense if done for a much longer time (6 months or more). Due to the time needed to carry out such an experiment this would not be practical to still include into the present study. But this might be advisable if the Var19 line is used in future experiments that go over extended periods of time. We intend to include a statement in the discussion of the revised manuscript to highlight that if long-term work with this line is planned, monitoring the binding phenotype and potentially re-panning might be advisable.

      (2) The transport studies using the mDHFR constructs were quite complicated to understand but were explained very clearly in the text with good logical reasoning.

      We are aware of this being a complex issue and are glad this was nevertheless understandable.

      (3) By introducing a second SLI system, the authors have been able to alter other genes thought to be involved in PfEMP1 biology, particularly transport. An example of this is the inactivation of PTP1, which causes a loss of binding to CD36 and ICAM-1. It would have been helpful to have more insight into the interpretation of the IFAs as the anti-SBP1 staining in Figure 5D (PTP-TGD) looks similar to that shown in Figure 1C, which has PTP intact. The anti-EXP2 results are clearly different.

      We realize the description of the PTP1-TGD IFA data and that of the other TGDs was rather cursory. We intend to amend this in the revision.

      (4) It is good to see the validation of PfEMP1 expression includes binding to several relevant receptors. The data presented use CHO-GFP as a negative control, which is relevant, but it would have been good to also see the use of receptor mAbs to indicate specific adhesion patterns. The CHO system if fine for expression validation studies, but due to the high levels of receptor expression on these cells, moving to the use of microvascular endothelial cells would be advisable. This may explain the unexpected ICAM-1 binding seen with the panned IT4var19 line.

      We agree with the reviewer that it is desirable to have better binding systems for studying individual binding interactions. As the main purpose of this paper was to introduce the system and show binding, we did not move to more complicated binding systems. However, we would like to point out that the CSA binding was done on receptor alone in addition to the CSA-expressing HBEC-5i cells and was competed successfully with soluble CSA. In addition, apart from the additional ICAM1-binding of the Var19 line, all binding phenotypes were conform with expectations. We therefore hope the tools used for binding studies are acceptable at this stage of introducing the system while future work interested in specific PfEMP1 receptor interactions are advised to use better systems, ideally including also endothelial organoid models, inhibitory antibodies and possibly domain competition. We intend to add a sentence to the discussion highlighting that future work using this system to study individual receptor-interactions could benefit from using optimized binding systems.

      (5) The proxiome work is very interesting and has identified new leads for proteins interacting with PfEMP1, as well as suggesting that KAHRP is not one of these. The reduced expression seen with BirA* in position 3 is a little concerning but there appears to be sufficient expression to allow interactions to be identified with this construct. The quantitative impact of reduced expression for proxiome experiments will clearly require further work to define it.

      This is a valid point. Clearly there seems to be some impact on binding when BirA* is placed in the extracellular domain (either through reduced presentation or direct reduction of binding efficiency of the modified PfEMP1). The exact impact on the proxiome is indeed difficult to assess. However, we hope that the general coverage of proteins proximal to PfEMP1 with the 3 PfEMP1-BirA* constructs will aid in the identification of proteins involved in PfEMP1 transport and surface display as illustrated with two of the hits targeted here.

      (6) The reduced receptor binding results from the TryThrA and EMPIC3 knockouts were very interesting, particularly as both still display PfEMP1 on the surface of the infected erythrocyte. While care needs to be taken in cross-referencing adhesion work in P. berghei and whether the machinery truly is functionally orthologous, it is a fair point to make in the discussion. The suggestion that interacting proteins may influence the "correct presentation of PfEMP1" is intriguing and I look forward to further work on this.

      We hope we future work will be able to shed light on this.

      Overall, the authors have produced a useful and reasonably robust system to support functional studies on PfEMP1, which may provide a platform for future studies manipulating the domain content in the exon 1 portion of var genes. They have used this system to produce a range of interesting findings and to support its use by the research community.<br /> Finally, a small concern. Being able to select specific var gene switches using drug markers could provide some useful starting points to understand how switching happens in P. falciparum. However, our trypanosome colleagues might remind us that forcing switches may show us some mechanisms but perhaps not all.

      Point noted! From non-systematic data with the Var01 line that has been cultured for extended periods of time (several years), it seems other non-targeted vars remain silent in our SLI “activation” lines but how much SLI-based var-expression “fixing” tampers with the integrity of natural switching mechanisms is indeed very difficult to gage at this stage. We intend to add a statement to the manuscript that even if mutually exclusive expression is maintained, it is not certain the mechanisms controlling var expression all remain intact.

      Reviewer #2 (Public review):

      Summary

      Croshagen et al develop a range of tools based on selection-linked integration (SLI) to study PfEMP1 function in P. falciparum. PfEMP1 is encoded by a family of ~60 var genes subject to mutually exclusive expression. Switching expression between different family members can modify the binding properties of the infected erythrocyte while avoiding the adaptive immune response. Although critical to parasite survival and Malaria disease pathology, PfEMP1 proteins are difficult to study owing to their large size and variable expression between parasites within the same population. The SLI approach previously developed by this group for genetic modification of P. falciparum is employed here to selectively and stably activate the expression of target var genes at the population level. Using this strategy, the binding properties of specific PfEMP1 variants were measured for several distinct var genes with a novel semi-automated pipeline to increase throughput and reduce bias. Activation of similar var genes in both the common lab strain 3D7 and the cytoadhesion competent FCR3/IT4 strain revealed higher binding for several PfEMP1 IT4 variants with distinct receptors, indicating this strain provides a superior background for studying PfEMP1 binding. SLI also enables modifications to target var gene products to study PfEMP1 trafficking and identify interacting partners by proximity-labeling proteomics, revealing two novel exported proteins required for cytoadherence. Overall, the data demonstrate a range of SLI-based approaches for studying PfEMP1 that will be broadly useful for understanding the basis for cytoadhesion and parasite virulence.

      Comments

      (1) While the capability of SLI to actively select var gene expression was initially reported by Omelianczyk et al., the present study greatly expands the utility of this approach. Several distinct var genes are activated in two different P. falciparum strains and shown to modify the binding properties of infected RBCs to distinct endothelial receptors; development of SLI2 enables multiple SLI modifications in the same parasite line; SLI is used to modify target var genes to study PfEMP1 trafficking and determine PfEMP1 interactomes with BioID. Curiously, Omelianczyk et al activated a single var (Pf3D7_0421300) and observed elevated expression of an adjacent var arranged in a head-to-tail manner, possibly resulting from local chromatin modifications enabling expression of the neighboring gene. In contrast, the present study observed activation of neighboring genes with head-to-head but not head-to-tail arrangement, which may be the result of shared promoter regions. The reason for these differing results is unclear although it should be noted that the two studies examined different var loci.

      The point that we are looking at different loci is very valid and we realize this is not mentioned in the discussion. In the revision we intend to add this as a possible reason for this discrepancy. As stated in the discussion, the head-to-head scenario was observed before in lines obtained with panning. However, given the rather few examples where this was analyzed, it is well possible that this varies with gene locus and we will make sure that the revised version of the manuscript will be careful to highlight that it is not clear how much this observation in our work can be generalized.

      (2) The IT4var19 panned line that became binding-competent showed increased expression of both paralogs of ptp3 (as well as a phista and gbp), suggesting that overexpression of PTP3 may improve PfEMP1 display and binding. Interestingly, IT4 appears to be the only known P. falciparum strain (only available in PlasmoDB) that encodes more than one ptp3 gene (PfIT_140083100 and PfIT_140084700). PfIT_140084700 is almost identical to the 3D7 PTP3 (except for a ~120 residue insertion in 3D7 beginning at residue 400). In contrast, while the C-terminal region of PfIT_140083100 shows near-perfect conservation with 3D7 PTP3 beginning at residue 450, the N-terminal regions between the PEXEL and residue 450 are quite different. This may indicate the generally stronger receptor binding observed in IT4 relative to 3D7 results from increased PTP3 activity due to multiple isoforms or that specialized trafficking machinery exists for some PfEMP1 proteins.

      We thank the reviewer for pointing this out, it is an interesting idea that the PTP3 duplication could be a reason for the superior binding of IT4. We intend to add this point to the discussion of the revision.

      So far it seems the PTP3 issue occurred only with Var19. The thought of an extra layer of control, particularly for PfEMP1 variants that might be associated with virulence such as Var19, is very attractive. At present, the manuscript alludes to the possibility of an extra layer of control in the discussion. As var-type specificity and existence of such mechanisms in vivo are so far not known we decided not to speculate on this.

      Reviewer #3 (Public review):

      Summary:

      The submission from Cronshagen and colleagues describes the application of a previously described method (selection linked integration) to the systematic study of PfEMP1 trafficking in the human malaria parasite Plasmodium falciparum. PfEMP1 is the primary virulence factor and surface antigen of infected red blood cells and is therefore a major focus of research into malaria pathogenesis. Since the discovery of the var gene family that encodes PfEMP1 in the late 1990s, there have been multiple hypotheses for how the protein is trafficked to the infected cell surface, crossing multiple membranes along the way. One difficulty in studying this process is the large size of the var gene family and the propensity of the parasites to switch which var gene is expressed, thus preventing straightforward gene modification-based strategies for tagging the expressed PfEMP1. Here the authors solve this problem by forcing the expression of a targeted var gene by fusing the PfEMP1 coding region with a drug-selectable marker separated by a skip peptide. This enabled them to generate relatively homogenous populations of parasites all expressing tagged (or otherwise modified) forms of PfEMP1 suitable for study. They then applied this method to study various aspects of PfEMP1 trafficking.

      Strengths:

      The study is very thorough, and the data are well presented. The authors used SLI to target multiple var genes, thus demonstrating the robustness of their strategy. They then perform experiments to investigate possible trafficking through PTEX, they knock out proteins thought to be involved in PfEMP1 trafficking and observe defects in cytoadherence, and they perform proximity labeling to further identify proteins potentially involved in PfEMP1 export. These are independent and complimentary approaches that together tell a very compelling story.

      Weaknesses:

      (1) When the authors targeted IT4var19, they were successful in transcriptionally activating the gene, however, they did not initially obtain cytoadherent parasites. To observe binding to ICAM-1 and EPCR, they had to perform selection using panning. This is an interesting observation and potentially provides insights into PfEMP1 surface display, folding, etc. However, it also raises questions about other instances in which cytoadherence was not observed. Would panning of these other lines have been successfully selected for cytoadherent infected cells? Did the authors attempt panning of their 3D7 lines? Given that these parasites do export PfEMP1 to the infected cell surface (Figure 1D), it is possible that panning would similarly rescue binding. Likewise, the authors knocked out PTP1, TryThrA, and EMPIC3 and detected a loss of cytoadhesion, but they did not attempt panning to see if this could rescue binding. To ensure that the lack of cytoadhesion in these cases is not serendipitous (as it was when they activated IT4var19), they should demonstrate that panning cannot rescue binding.

      These are very important points. Indeed, we had repeatedly attempted to pan 3D7 when we failed to get the SLI-generated 3D7 PfEMP1 expressor lines to bind, but this had not been successful. After the move to IT4 which readily bound we made no further efforts to understand why 3D7 does not bind but the fact that PfEMP1 is on the surface indicates this is not a PTP3 issue. Also, as the parent 3D7 could not be panned, we assumed it is not easily fixed.

      Panning the TGD lines: we see the reasoning for conducting panning experiments with the TGD lines, but on second thought we are unsure this should be attempted. The outcome might not be easily interpretable if panning leads to increased binding and considerable follow up analyses would be needed to define what has happened. The reason for this is that at least two forces will contribute to the selection in panning experiments with TGD lines that lost binding. Firstly, panning would work against the SLI of the TGD, resulting in a tug of war between the TGD-SLI and binding: a very low frequency of parasites can be expected to loop out the TGD plasmid and would normally be eliminated during standard culturing due to the SLI drug used for the TGD. These revertant cells would bind and the panning would enrich them (hence, panning and SLI are opposed in the case of a TGD abolishing binding). It is unclear how strong such an effect can be, but this might lead to mixed populations that complicate interpretations. The second selecting force are possible compensatory changes to restore binding. These can come in two flavors: reversal of potential independent changes that may have occurred in the TGD parasites and that are in reality causing the binding loss (the concern of the reviewer) or new changes to compensate the loss of the TGD target (in case the TGD is the cause of the binding loss). As both of the TGDs in the paper show some residual binding and have VAR01 on the surface to at least some extent, it is possible that new compensatory changes might indeed occur that indirectly increase binding again. In summary, even if more binding after panning of the lines occurs, it is not clear whether this is due to a compensatory change ameliorating the TGD or reversal of an unrelated change. The impact of repeated panning against SLI is also unknown. To determine the cause, the panned TGD lines would need to be subjected to a complex and time-consuming analysis (WGS, RNASeq, possibly Maurer’s clefts IFA phenotype) to find out whether they had an unrelated chance change that was reverted or a new compensatory change that helps binding.

      The detection of VAR01 on the surface of these TGDs speaks against a PTP3 effect. While we can’t fully exclude other changes in the TGDs that might affect binding, we conducted WGS which did not show any obvious alterations that could be responsible. To fully exclude loss of ptp3 expression as the reason as seen with Var19 (something we would not have seen in the WGS if it is only due to a transcriptional change), we intend to carry out RNASeq with the two TGD lines. The third TGD mentioned by the reviewer (targeting ptp1) was a positive control of a known PfEMP1 trafficking protein, so we assume this does not need to be further validated.

      (2) The authors perform a series of trafficking experiments to help discern whether PfEMP1 is trafficked through PTEX. While the results were not entirely definitive, they make a strong case for PTEX in PfEMP1 export. The authors then used BioID to obtain a proxiome for PfEMP1 and identified proteins they suggest are involved in PfEMP1 trafficking. However, it seemed that components of PTEX were missing from the list of interacting proteins. Is this surprising and does this observation shed any additional light on the possibility of PfEMP1 trafficking through PTEX? This warrants a comment or discussion.

      This is an interesting comment and we agree we should have discussed this. A likely reason why PTEX components are not picked up as interactors is that BirA* is expected to become unfolded when it passes through the channel and in that state can’t biotinylate. Labelling likely would only be possible if PfEMP1 lingered at the PTEX translocation step before BirA* became unfolded to go through the channel which we would not expect under physiological conditions. We intend to add a sentence to the discussion why we think PTEX components would not be detected in our BioIDs even if PfEMP1 passes through it but that this might also be an argument against it passing through PTEX.

  8. femalebodiesintseliottwl.wordpress.com femalebodiesintseliottwl.wordpress.com
    1. the sylvan scene

      This is a reference to John Milton's Paradise Lost(1667) an epic poem in blank verse about the biblical story of the fall of man.

      Eliot made the reference explicit in his notes. This is the scene that presented itself before Satan when he first arrives at the borders of Eden.

      "So on he fares, and to the border comes / Of Eden, where delicious Paradise, / Now nearer, crowns with her enclosure green, / As with a rural mound, the champaign head / Of a steep wilderness, whose hairy sides / With thicket overgrown, grotesque and wild, / Access denied; and overhead up grew / Insuperable height of loftiest shade, / Cedar, and pine, and fir, and branching palm, / A sylvan scene, and, as the ranks ascend, / Shade above shade, a woody theatre / Of stateliest view. Yet higher than their tops / The verdurous wall of Paradise up sprung; / Which to our general sire gave prospect large / Into his nether empire neighbouring round."

      (Milton's Paradise Lost, Book IV, ll. 131 - 145)

  9. femalebodiesintseliottwl.wordpress.com femalebodiesintseliottwl.wordpress.com
    1. Joyce’s Bloom

      Ulysses is a modernist novel by the Irish writer James Joyce, first published in volume edition in 1922. The novel chronicles the experiences of three Dubliners over the course of a single day, 16 June 1904.

      At the time, the novel was considered obscene as it depicted explicitly scenes like depicting childbirth, defecation, menstruation, masturbation, etc. Indeed, the aim of the author was to depict all human experiences, whether sexual, bodily, or emetic.

      Specifically, the defection episode cited here occurs in chapter 4:

      "He kicked open the crazy door of the jakes. Better be careful not to get these trousers dirty for the funeral. He went in, bowing his head under the low lintel. Leaving the door ajar, amid the stench of mouldy limewash and stale cobwebs he undid his braces. Before sitting down he peered through a chink up at the nextdoor window. The king was in his counting house. Nobody. Asquat on the cuckstool he folded out his paper turning its pages over on his bared knees. […] Quietly he read, restraining himself, the first column and, yielding but resisting, began the second. Midway, his last resistance yielding, he allowed his bowels to ease themselves quietly as he read, reading still patiently, that slight constipation of yesterday quite gone. Hope it's not too big bring on piles again. No, just right. So. Ah! […] He tore away half the prize story sharply and wiped himself with it. Then he girded up his trousers, braced and buttoned himself. He pulled back the jerky shaky door of the jakes and came forth from the gloom into the air."

      (Joyce's Ulysses, Chapter 4 - "Calypso")

    1. Author response:

      The following is the authors’ response to the original reviews.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In the paper, Yan and her colleagues investigate at which stage of development different categorical signals can be detected with EEG using a steady-state visual evoked potential paradigm. The study reports the development trajectory of selective responses to five categories (i.e., faces, limbs, corridors, characters, and cars) over the first 1.5 years of life. It reveals that while responses to faces show significant early development, responses to other categories (i.e., characters and limbs) develop more gradually and emerge later in infancy. The paper is well-written and enjoyable, and the content is well-motivated and solid.

      Strengths:

      (1) This study contains a rich dataset with a substantial amount of effort. It covers a large sample of infants across ages (N=45) and asks an interesting question about when visual category representations emerge during the first year of life.

      (2) The chosen category stimuli are appropriate and well-controlled. These categories are classic and important for situating the study within a well-established theoretical framework.

      (3) The brain measurements are solid. Visual periodicity allows for the dissociation of selective responses to image categories within the same rapid image stream, which appears at different intervals. This is important for the infant field, as it provides a robust measure of ERPs with good interpretability.

      Weaknesses:

      The study would benefit from a more detailed explanation of analysis choices, limitations, and broader interpretations of the findings. This includes:

      a) improving the treatment of bias from specific categories (e.g., faces) towards others;

      b) justifying the specific experimental and data analysis choices;

      c) expanding the interpretation and discussion of the results.

      I believe that giving more attention to these aspects would improve the study and contribute positively to the field.

      We thank the reviewer for their clear summary of the work and their constructive feedback. To address the reviewer’s concerns, in the revised manuscript we now provide a detailed explanation of analysis choices, limitations, and broader interpretations, as summarized in the point-by-point responses in the section: Reviewer #1 (Recommendations For The Authors) below, for which we give here an overview in points (a), (b), and (c):

      (a) The reviewer is concerned that using face stimuli as one of the comparison categories may hinder the detection of selective responses to other categories like limbs. Unfortunately, because of the frequency tagging design of our study we cannot compare the responses to one category vs. only some of the other categories (e.g. limbs vs objects but not faces). In other words, our experimental design does not enable us to do this analysis suggested by the reviewer. Nonetheless, we underscore that faces compromise only ¼ of contrast stimuli and we are able to detect significant selective responses to limbs, corridors and characters in infants after 6-8 months of age even as faces are included in the contrast and the response to faces continues to increase (see Fig 4). We discuss the reviewer’s point regarding how contrast can contribute to differences in findings in the discussion on pages 12-13, lines 344-351. Full details below in Reviewer 1: Recommendations for Authors - Frequency tagging category responses.

      (b) We expanded the justification of specific experimental and data analysis choices, see details below in Reviewer 1: Recommendations for Authors ->Specific choices for experiment and data analysis.

      (c) We expand the interpretation and discussion, see details below in Reviewer 1: Recommendations for Authors -> More interpretation and discussion.

      Reviewer #2 (Public Review):

      Summary:

      The current work investigates the neural signature of category representation in infancy. Neural responses during steady-state visually-evoked potentials (ssVEPs) were recorded in four age groups of infants between 3 and 15 months. Stimuli (i.e., faces, limbs, corridors, characters, and cars) were presented at 4.286 Hz with category changes occurring at a frequency of 0.857 Hz. The results of the category frequency analyses showed that reliable responses to faces emerge around 4-6 months, whereas responses to libs, corridors, and characters emerge at around 6-8 months. Additionally, the authors trained a classifier for each category to assess how consistent the responses were across participants (leave-one-out approach). Spatiotemporal responses to faces were more consistent than the responses to the remaining categories and increased with increasing age. Faces showed an advantage over other categories in two additional measures (i.e., representation similarity and distinctiveness). Together, these results suggest a different developmental timing of category representation.

      Strengths:

      The study design is well organized. The authors described and performed analyses on several measures of neural categorization, including innovative approaches to assess the organization of neural responses. Results are in support of one of the two main hypotheses on the development of category representation described in the introduction. Specifically, the results suggest a different timing in the formation of category representations, with earlier and more robust responses emerging for faces over the remaining categories. Graphic representations and figures are very useful when reading the results.

      Weaknesses:

      (1) The role of the adult dataset in the goal of the current work is unclear. All results are reported in the supplementary materials and minimally discussed in the main text. The unique contribution of the results of the adult samples is unclear and may be superfluous.

      (2) It would be useful to report the electrodes included in the analyses and how they have been selected.

      We thank the reviewer for their constructive feedback and for summarizing the strengths and weaknesses of our study. We revised the manuscript to address these two weaknesses.

      (1) The reviewer indicates that the role of the adult dataset is unclear. The goal of testing adult participants was to validate the EEG frequency tagging paradigm. We chose to use adults because a large body of fMRI research shows that both clustered and distributed responses to visual categories are found in adults’ high-level visual cortex. Therefore, the goal of the adult data is to determine whether with the same amount of data as we collect on average in infants, we have sufficient power to detect categorical responses using the frequency tagging experimental paradigm as we use in infants. Because this data serves as a methodological validation purpose, we believe it belongs to the supplemental data.

      We clarify this in the Results, second paragraph, page 5 where now write: “As the EEG-SSVEP paradigm is novel and we are restricted in the amount of data we can obtain in infants, we first tested if we can use this paradigm and a similar amount of data to detect category-selective responses in adults. Results in adults validate the SSVEP paradigm for measuring category-selectivity: as they show that (i) category-selective responses can be reliably measured using EEG-SSVEP with the same amount of data as in infants (Supplementary Figs S1-S2), and that (ii) category information from distributed spatiotemporal response patterns can be decoded with the same amount of data as in infants (Supplementary Fig S3).”

      (2) The reviewer asks us to report the electrodes used in the analysis and their selection. We note that the selection of electrodes included in the analyses has been reported in our original manuscript (Methods, section: Univariate EEG analyses). On pages 18-19, lines 530-538, we write: “Both image update and categorical EEG visual responses are reported in the frequency and time domain over three regions-of-interest (ROIs): two occipito-temporal ROIs (left occipitotemporal (LOT): channels 57, 58, 59, 63, 64, 65 and 68; right occipitotemporal (ROT) channels: 90, 91, 94, 95, 96, 99, and 100) and one occipital ROI (channels 69, 70, 71, 74, 75, 76, 82, 83 and 89). These ROIs were selected a priori based on a previously published study51. We further removed several channels in these ROIs for two reasons: (1) Three outer rim channels (i.e., 73, 81, and 88) were not included in the occipital ROI for further data analysis for both infant and adult participants because they were consistently noisy. (2) Three channels (66, 72, and 84) in the occipital ROI, one channel (50) in the LOT ROI, and one channel (101) in the ROT ROI were removed because they did not show substantial responses in the group-level analyses.”

      In the section Reviewer 2, Recommendations for the authors, we also addressed the reviewer’s minor points.

      Reviewer #3 (Public Review):

      Yan et al. present an EEG study of category-specific visual responses in infancy from 3 to 15 months of age. In their experiment, infants viewed visually controlled images of faces and several non-face categories in a steady state evoked potential paradigm. The authors find visual responses at all ages, but face responses only at 4-6 months and older, and other category-selective responses at later ages. They find that spatiotemporal patterns of response can discriminate faces from other categories at later ages.

      Overall, I found the study well-executed and a useful contribution to the literature. The study advances prior work by using well-controlled stimuli, subgroups of different ages, and new analytic approaches.

      I have two main reservations about the manuscript: (1) limited statistical evidence for the category by age interaction that is emphasized in the interpretation; and (2) conclusions about the role of learning and experience in age-related change that are not strongly supported by the correlational evidence presented.

      We thank the reviewer for their enthusiasm and their constructive feedback.

      (1) The overall argument of the paper is that selective responses to various categories develop at different trajectories in infants, with responses to faces developing earlier. Statistically, this would be most clearly demonstrated by a category-by-age interaction effect. However, the statistical evidence for a category by interaction effect presented is relatively weak, and no interaction effect is tested for frequency domain analyses. The clearest evidence for a significant interaction comes from the spatiotemporal decoding analysis (p. 10). In the analysis of peak amplitude and latency, an age x category interaction is only found in one of four tests, and is not significant for latency or left-hemisphere amplitude (Supp Table 8). For the frequency domain effects, no test for category by age interaction is presented. The authors find that the effects of a category are significant in some age ranges and not others, but differences in significance don't imply significant differences. I would recommend adding category by age interaction analysis for the frequency domain results, and ensuring that the interpretation of the results is aligned with the presence or lack of interaction effects.

      The reviewer is asking for additional evidence for age x category interaction by repeating the interaction analysis in the frequency domain. The reason we did not run this analysis in the original manuscript is that the categorical responses of interest are reflected in multiple frequency bins: the category frequency (0.857 Hz) and its harmonics, and there are arguments in the field as to how to quantify response amplitudes from multiple frequency bins (Peykarjou, 2022). Because there is no consensus in the field and also because how the different harmonics combine depends not just on their amplitudes but also on their phase, we chose to transform the categorical responses across multiple frequency bins from the frequency domain to the time domain. The transformed signal in the time domain includes both phase and amplitude information across the category frequency and its harmonics. Therefore, subsequent analyses and statistical evaluations were done in the time domain.

      However, we agree with the reviewer that adding category by age interaction analysis for the frequency domain results can further solidify the results. Thus, in the revised manuscript we added a new analysis, in which we quantified the root mean square (RMS) amplitude value of the responses at the category frequency (0.857 Hz) and its first harmonic (1.714 Hz) for each category condition and infant. Then we used a LMM to test for an age by category interaction. The LMM was conducted separately for the left and right lateral occipitotemporal ROIs. Results of this analysis find a significant category by age interaction, that is, in both hemispheres, the development of response RMS amplitudes varied across category (left occipitotemporal ROIs: βcategory x age = -0.21, 95% CI: -0.39 – -0.04, t(301) = -2.40, pFDR < .05; right occipitotemporal ROIs: βcategory x age = -0.26, 95% CI: -0.48 – -0.03, t(301) = -2.26, pFDR < .05). We have added this analysis in the manuscript, pages 7-8, lines 186-193: “We next examined the development of the category-selective responses separately for the right and left lateral occipitotemporal ROIs. The response amplitude was quantified by the root mean square (RMS) amplitude value of the responses at the category frequency (0.857 Hz) and its first harmonic (1.714 Hz) for each category condition and infant. With a  LMM analysis, we found significant development of response amplitudes in the both occipitotemporal ROIs which varied by category (left occipitotemporal ROIs: βcategory x age = -0.21, 95% CI: -0.39 – -0.04, t(301) = -2.40, pFDR < .05; right occipitotemporal ROIs: βcategory x age = -0.26, 95% CI – -0.48 – -0.03, t(301) = -2.26, pFDR < .05, LMM as a function of log (age) and category; participant: random effect).” We also added the formula for the LMM analysis in Table 1 in the Methods section, page 21.

      (2) The authors argue that their results support the claim that category-selective visual responses require experience or learning to develop. However, the results don't bear strongly on the question of experience. Age-related changes in visual responses could result from experience or experience-independent maturational processes. Finding age-related change with a correlational measure does not favor either of these hypotheses. The results do constrain the question of experience, in that they suggest against the possibility that category-selectivity is present in the first few months of development, which would in turn suggest against a role of experience. However the results are still entirely consistent with the possibility of age effects driven by experience-independent processes. The manner in which the results constrain theories of development could be more clearly articulated in the manuscript, with care taken to avoid overly strong claims that the results demonstrate a role of experience.

      Thanks for the comment. We agree with this nuanced point. It is possible that development of category-selective visual responses is a maturational process. In response to this comment, we have revised the manuscript to discuss both perspectives, see revised discussion section – A new insight about cortical development: different category representations emerge at different times during infancy, pages 14-15, lines 403-426, where we now write: “In sum, the key finding from our study is that the development of category selectivity during infancy is non-uniform: face-selective responses and representations of distributed patterns develop before representations to limbs and other categories. We hypothesize that this differential development of visual category representations may be due to differential visual experience with these categories during infancy. This hypothesis is consistent with behavioral research using head-mounted cameras that revealed that the visual input during early infancy is dense with faces, while hands become more prevalent in the visual input later in development and especially when in contact with objects 41,42. Additionally, a large body of research has suggested that young infants preferentially look at faces and face-like stimuli 17,18,33,34, as well as look longer at faces than other objects 41, indicating that not only the prevalence of faces in babies’ environments but also longer looking times may drive the early development of face representations. Further supporting the role of visual experience in the formation of category selectivity is a study that found that infant macaques that are reared without seeing faces do not develop face-selectivity but develop selectivity to other categories in their environment like body parts40. An alternative hypothesis is that differential development of category representations is maturational. For example, we found differences in the temporal dynamics of visual responses among four infant age groups, which suggests that the infant’s visual system is still developing during the first year of life. While the mechanisms underlying the maturation of the visual system in infancy are yet unknown, they may include myelination and cortical tissue maturation 66-71. Future studies can test these alternatives by examining infants’ visual diet, looking behavior, and brain development and examine responses using additional behaviorally relevant categories such as food 72–74. These measurements can test how environmental and individual differences in visual experiences may impact infants’ developmental trajectories. Specifically, a visual experience account predicts that differences in visual experience would translate into differences in development of cortical representations of categories, but a maturational account predicts that visual experience will have no impact on the development of category representations.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major points:

      Bias from faces to other categories:

      - Frequency tagging category responses:

      We see faces from non-face objects and limbs from non-limb objects. Non-limb objects include faces; I suspect that finding the effects of limbs is challenging with faces in the non-limbs category. How would you clarify the choice of categories, and to what extent are the negative (i.e., non-significant) effects on other categories not because of the heavy bias to faces?

      The reviewer is concerned that using face stimuli as one of the comparison categories may hinder the ability to detect selective responses to other categories like limbs in our study. Unfortunately, because of the frequency tagging design of our study, we cannot compare the responses to one category to only some of the other categories (e.g. limbs vs objects but not faces), so our experimental design does not enable us to do the analysis suggested by the reviewer. Nonetheless, we underscore that faces compromise only ¼ of contrast stimuli in the category frequency tagging and we are able to detect significant selective responses to limbs, corridors and characters in infants after 6-8 months of age, when faces are included in the contrast and the responses to faces continue to increase more than for other categories (see Fig 4).

      We address this point in the discussion where we consider differences between our findings and those of Kosakowski et al. 2022, on pages 12-13, lines 344-351 we write: “We note that, the studies differ in several ways: (i) measurement modalities (fMRI in 27 and EEG here), (ii) the types of stimuli infants viewed: in 27 infants viewed isolated, colored and moving stimuli, but in our study, infants viewed still, gray-level images on phase-scrambled backgrounds, which were controlled for several low level properties, and (iii) contrasts used to detect category-selective responses, whereby in 27 the researchers identified within predefined parcels – the top 5% of voxels that responded to the category of interest vs. objects, here we contrasted the category of interest vs. all other categories the infant viewed. Thus, future research is necessary to determine whether differences between findings are due to differences in measurement modalities, stimulus format, and data analysis choices.”

      - Decoding analyses:

      Figure 5 Winner-take-all classification. First, the classifier may be biased towards the categories with strong and clean data, similar to the last point, this needs clarification on the negative effect. Second, it could be helpful to see how exactly the below-chance decoded categories were being falsely classified to which categories at the group level. Decoding accuracy here means a 20% chance the selection will go to the target category, but the prediction and the exact correlation coefficient the winner has is not explicit; concerning a value of 0.01 correlation could take the winner among negative or pretty bad correlations with other categories. It would be helpful to report how exactly the category was correlated, as it could be a better way to define the classification bias, for example, correlation differences between hit and miss classification. Also, the noise ceiling of the correlation within each group should be provided. Third, this classifier needs improvement in distinguishing between noise and signals to identify the type of information it extracts. Do you have thoughts about that?

      Thanks for the questions, answers below:

      In the winner-take-all (WTA) classifier analysis, at each iteration, the LOOCV classifier computed the correlation between each of the five category vectors from the left-out participant (test data, for an unknown stimulus) and each of the mean spatiotemporal vectors across the N-1 participants (training data, labeled data). The winner-take-all (WTA) classifier classifies the test vector to the category that yields the highest correlation with the training vector. For a given test pattern, correct classification yielded a score of 1 and an incorrect classification yielded a score of 0. Then we computed the group mean decoding performance across all N iterations for each category and the group mean decoding accuracies across five categories.

      For the classification data in Fig 5, the statistics and differences from chance are provided in 5B, where we report overall classification across all categories from an infant’s brain data. Like the reviewer, we were interested in assessing if successful classification is uniform across categories or is driven by some categories. As is visible in 5C, decoding success is non-uniform across categories, and is higher for faces than other categories. Because this is broken by category we cannot compare to chance, and what is reported in Fig 5c is percentage infants in each age group that a particular category was successfully decoded. Starting from 4 months of age, faces can be decoded from distributed brain data in a majority of infants, but other categories only in 20-40% of infants. 

      The reviewer also asks about what levels of correlations drive the classification. The analysis of RSMs in Fig 6a shows the mean correlations of distributed responses to different images within and between categories per age group. As is evident from the RSM, reproducible responses for a category only start to emerge at 4-6 months of age and the highest within category correlations are for faces. To quantify what drives the classification we measure distinctiveness - within category minus between-category correlations of distributed responses; all individual infant data per category are in Fig 6C. Distinctiveness values vary by age and category, see text related to Fig 6 in section: What is the nature of categorical spatiotemporal patterns in individual infants?

      Figure 6 Category distinctiveness. An analysis that runs on a "single item level" would ideally warrant a more informative category distinction. Did you try that? Does it work?

      Thanks for the question. We agree that doing an analysis at the single item level would be interesting. However, none of the images were repeated, so we do not have sufficient SNR to perform this analysis.

      Specific choices for experiment and data analysis:

      - Although using the SSVEP paradigm is familiar to the field, the choice could be detailed for understanding or evaluation of the effectiveness of the paradigm. For example, how the specific frequency for entrainment was chosen, and are there any theories or related warrants for studying in infants?

      Thanks for the questions. We choose to use the SSVEP paradigm over traditional ERP designs for several reasons, as described which have been listed in our original manuscript (Results part, first paragraph, pages 4-5, lines 90-94): “We used the EEG-SSVEP approach because: (i) it affords a high signal-to-noise ratio with short acquisitions making it effective for infants 23,46, (ii) it has been successfully used to study responses to faces in infants23,46,49, and (iii) it enables measuring both general visual response to images by examining responses at the image presentation frequency (4.286 Hz), as well as category-selective responses by examining responses at the category frequency (0.857 Hz, Fig 1A).”

      With regards to our choice of presentation rate, a previous study in 4-6-month-olds by de Heering and Rossion (2015) used SSVEP showing infants faces and objects presented the visual stimuli at 6 Hz (i.e. 167 ms per image) to study infants’ categorical responses to natural faces relative to objects. Here, we chose to use a relatively slower presentation rate, which was 4.286 Hz (i.e. 233 ms per image), so that our infant participants would have more time to process each image yet still unlikely to make eye movements across a stimulus. Both de Heering et (2015) and our study have found significant selective responses to faces relative to other categories in 4-6-month-olds, across these presentation rates. As discussed in a recent review of frequency tagging with infants: The visual oddball paradigm (Peykarjou, 2022), there are many factors to consider when adapting SSVEP paradigms to infants. We agree that an interesting direction for future studies is examination of how SSVEP parameters such as stimulus and oddball presentation rate, and overall duration of acquisition affects the sensitivity of the SSVEP paradigm in infants. We added a discussion point on this on page 12, lines 332-334 where we write: “As using SSVEP to study high-level representations is a nascent field52–54, future work can further examine how SSVEP parameters such as stimulus and target category presentation rate may affect the sensitivity of measurements in infants (see review by54).”

      - There is no baseline mentioned in the study. How was the baseline considered in the paradigm and data analysis? The baseline is important for evaluating how robust/ reliable the periodic responses within each group are in the first place. It also helps us to see how different the SNR changes in the fast periodic responses from baseline across age groups. Would the results be stable if the response amplitudes were z-scored by a baseline?

      Thanks for the question. Previous studies using a similar frequency tagging paradigm have compared response amplitude at stimulus-related frequencies to that of neighboring frequency bins as their baseline for differentiating signal from noise. We use a more statistically powerful method, the Hotelling’s T2 statistic to test whether response amplitudes were statistically different from 0 amplitude. Importantly, this method takes into consideration both the amplitude and phase information of the response. That is, a significant response is expected to have consistent phase information across participants as well as significant amplitude.

      - Statistical inferences: could the variance of data be considered appropriately in your LLM? Why?

      As we have explained in our original manuscript (Methods part, section-Statistical Analyses of Developmental Effects, page 21 lines 611-615): “LMMs allow explicit modeling of both within-subject effects (e.g., longitudinal measurements) and between-subject effects (e.g., cross-sectional data) with unequal number of points per participants, as well as examine main and interactive effects of both continuous (age) and categorical (e.g., stimulus category) variables. We used random-intercept models that allow the intercept to vary across participants (term: 1|participant).” This statistical model is widely used in developmental studies that combine both longitudinal and cross-sectional measurements (e.g. Nordt et al. 2022, 2023; Natu et al. 2021; Grotheer et al. 2022).

      - The sampling of the age groups. Why are these age groups considered, as 8-12 months are not considered? Or did the study first go with an equal sampling of the ages from 3 to 15 months? Then how was the age group defined? The log scale of age makes sense for giving a simplified view of the effects, but the sampling procedure could be more detailed.

      Thanks for the question. Our study recruited infants longitudinally for both anatomical MRI and EEG studies. Some of the infants participated in both studies and some only in one of the studies. Infants were recruited at around newborn, 3 months, 6 months, and 12 months. We did not recruit infants between 8-12 months of age because around 9 months there is little contrast between gray and white matter in anatomical MRI scans that were necessary for the MRI study. For the EEG study we binned the subjects by age group such that there were a similar number of participants across age groups to enable similar statistical power. The division of age groups was decided based on the distribution of the infants included in the analyses.

      We have now added the sampling procedure details in the Methods, part, under section: Participants, pages 15-16, lines 440-445: “Sixty-two full-term, typically developing infants were recruited. Twelve participants were part of an ongoing longitudinal study that obtained both anatomical MRI and EEG data in infants. Some of the infants participated in both studies and some only in one of the studies. Infants were recruited at around newborn, 3 months, 6 months, and 12 months. We did not recruit infants between 8-12 months of age because around 9 months there is little contrast between gray and white matter in anatomical MRI scans that were necessary for the MRI study.”

      - 30 Hz cutoff is arbitrary, but it makes sense as most EEG effects can be expected in a lower frequency band than higher. However, this specific choice is interesting and informative, when faced with developmental data and this type of paradigm. Would the results stay robust as the cutoff changes? Would the results benefit from going even lower into the frequency cutoff?

      In the time domain analyses, we choose the 30 Hz cutoff to be consistent with previous EEG studies including those done with infants. However, as our results from the frequency domain (Fig 3, right panel, and supplementary Fig S6-S9) show that there are barely any selective categorical responses above about 6 Hz. Therefore, we expect that using a lower frequency cutoff, such as 10 Hz, will not lead to different results.

      More interpretation and discussion:

      - You report the robust visual responses in occipital regions, the responses that differ across age groups, and their characteristics (i.e., peak latency and amplitude) in time curves. This part of the results needs more interpretation to help the data be better situated in the field; I wondered whether this relates to the difference in the signal processing of the information. Could this be the signature of slow recurrence connection development? Or how could this be better interpreted?

      Thanks for the question. Changes in speed of processing can arise from several related reasons including (i) myelination of white matter connections that would lead to faster signal transmission (Lebenberg et al. 2019; Grotheer et al. 2022), (ii) maturation of cortical visual circuits affecting temporal integration time, and (iii) development of feedback connections. Our data cannot distinguish among these different mechanisms. Future studies that combine functional high temporal resolution measurements with structural imaging of tissue properties could elucidate changes in cortical dynamics over development.

      We added this as a discussion point, on page 15 lines 416-420 we write: “For example, we found differences in the temporal dynamics of visual responses among four infant age groups, which suggests that the infant’s visual system is still developing during the first year of life. While underlying maturational mechanisms are yet unknown, they may include myelination and cortical tissue maturation68–73.”

      - The supplementary material includes a detailed introduction to the methods when facing the developing visual acuity, which justifies the choice of the paradigm. I appreciate this thorough explanation. Interestingly, high visual acuity has its potential developmental downside; for instance, low visual acuity would aid in the development of holistic processing associated with face recognition (as discussed by Vogelsang et al., 2018, in PNAS). How do you view this point in relation to the emergence of complex cognitive processes, as here the category-selective responses?

      Thanks for linking this to the Vogelsang (2018) study. Just as faces are processed in a hierarchical manner, starting with low-level features (edges, contours) and progressing to high-level features (identity, expression), other complex visual categories like cars, scenes, and body parts follow similar hierarchies. Early holistic processing could provide a foundation for recognizing objects quickly and efficiently, while feature-based processing might allow for more precise recognition and categorization as acuity increases. Therefore, as visual acuity improves, an infant’s brain can integrate finer details into those holistic representations, supporting more refined and complex cognitive processes. The balance between low- and high-level visual acuity highlights the intricate interplay between sensory processing and cognitive development across various domains.

      Minor points:

      Paradigm:

      - Are the colored cartoon images for motivating infants' fixation counterbalanced across categories in the paradigm? Or how exactly were the cartoon images presented in the paradigm?

      Response: Yes, the small cartoon images that were presented at the center of the screen during stimuli presentation were used to engage infants’ attention and accommodation to the screen. For each condition, they were randomly drawn from a pool of 70 images (23 flowers, 22 butterflies, 25 birds) from categories unrelated to the ones under test. They were presented in random order with durations uniformly distributed between 1 and 1.5 s.  We have added these details of the paradigm to the Methods section, page 17, lines 479-481: “To motivate infants to fixate and look at the screen, we presented at the center of the screen small (~1°) colored cartoon images such as butterflies, flowers, and ladybugs. They were presented in random order with durations uniformly distributed between 1 and 1.5 s.”

      Analysis:

      - Are the visual responses over the occipital cortex different across different category conditions in the first place? I guess this should not be different; this probably needs one more supplementary figure.

      The visual responses reflect the responses to images that are randomly drawn from the five stimuli categories at a presentation frequency of 4.286 Hz. The only difference between the five conditions is that the stimuli presentation order is different. Therefore, the visual response over the occipital cortex across conditions should not be different within an age group.

      In the revised manuscript, we have added Supplementary Figure S5 that shows the frequency spectra distribution and the response topographies of the visual response at 4.286 Hz and its first 3 harmonics separately for each condition and age group and a new Supplementary Materials section: 5. Visual responses over occipital cortex per condition for all age groups. On page 5, lines 116-120, we now write: “Analysis of visual responses in the occipital ROI separately by category condition revealed that visual responses were not significantly across category condition (Supplementary Fig S5, no significant main effect of category (βcategory = 0.08, 95% CI: -0.08 – 0.24, t(301) \= 0.97, p = .33), or category by age interaction (βcategory x age = -0.04, 95% CI: -0.11 – 0.03, t(301) \= -1.09, p = .28, LMM on RMS of response to first three harmonics).”

      - The summary of epochs used for each category for each age group needs to be included; this is important while evaluating whether the effects are due to not having enough data for categories or others.

      This part of information is provided in the manuscript in the Methods section, page 18 lines 521-524, and supplementary Table S2. Our analysis shows that there was no significant difference in the number of pre-processed epochs across different age groups (F(3,57) = 1.5, p \= .2).

      - Numbers of channels of EEG being interpolated should be provided; is that a difference across age groups?

      Thanks for the suggestion. We have now added information about the number of channels being interpolated for each age groups in the Methods section (page 18, lines 525-528): “The number of electrodes being interpolated for each age group were 10.0 ± 4.8 for 3-4-month-olds, 9.9 ± 3.7 for 4-6-month-olds, 9.9 ± 3.9 for 6-8-month-olds, and 7.7 ± 4.7 for 12-15-month-olds. There was no significant difference in the number of electrodes being interpolated across infant age-groups (F(3,55) = 0.78, p = .51).”

      - I noticed that the removal of EEG artifacts (i.e., muscles and eye-blinks) for data analysis is missing; did the preprocessing pipeline involve any artifacts removing procedures that are typically used in both infants and adults SSVEP data analysis? If so, please provide more information.

      In our analysis, artifact rejection was performed in two steps. First, the continuous filtered data were evaluated according to a sample-by-sample thresholding procedure to locate consistently noisy channels. Channels with more than 20% of samples exceeding a 100-150 μV amplitude threshold were replaced by the average of their six nearest spatial neighbors. Once noisy channels were interpolated in this fashion, the EEG was re-referenced from the Cz reference used during the recording to the common average of all sensors and segmented into epochs (1166.7-ms). Finally, EEG epochs that contained more than 15% of time samples exceeding threshold (150-200 microvolts) were excluded on a sensor-by-sensor basis. This method is provided in the manuscript under Methods section, page 18 lines 510-516.

      Figure:

      - Supplementary Figure 8. The illustration of the WTA classifier was not referred to anywhere in the main text.

      Thanks for pointing this out. The supplementary Figure 8 should be noted as supplementary Figure 10 instead. We have now mentioned it in the manuscript, page 10, line 267.

      - Figure 5 WTA classifier needed to be clarified. It was correlation-based but used to choose the most correlated response patterns averaged across the N-1 subjects for the leave-one-out subject. The change from correlation coefficients to decoding accuracy could be clearer as I spent some time making sense of it. The correlation coefficient here evaluates how correlated the two vectors are, but the actual decoding accuracy estimated at the end is the percentage of participants who can be assigned to the "ground truth" label, so one step in between is missing. Can this be better illustrated?

      Thanks for surfacing that this is not described sufficiently clearly and for your suggestions. The spatiotemporal vector was calculated separately for each category. This is illustrated in Fig 5A. At each iteration, the LOOCV classifier computed the correlation between each of the five category vectors from the left-out participant (test data, for an unknown stimulus) and each of the mean spatiotemporal vectors across the N-1 participants (training data, labeled data). The winner-take-all (WTA) classifier classifies the test vector to the category that yields the highest correlation with the training vector. This is illustrated in Fig 5A, with spatiotemporal patterns and correlation values from an example infant shown.  For a given test pattern, correct classification yields a score of 1 and an incorrect classification yields a score of 0.  We compute the percentage correct across all categories for each left-out-infant, and then mean decoding performance across all participants in an age group (Fig 5B). We have now added these details in the Methods part, section – Decoding analyses, Group-level, page 20 lines 590-597, where we write: “At each iteration, the LOOCV classifier computed the correlation between each of the five category vectors from the left-out participant (test data, for an unknown stimulus) and each of the mean spatiotemporal vectors across the N-1 participants (training data, labeled data). The winner-take-all (WTA) classifier classifies the test vector to the category of the training vector that yields the highest correlation with the training vector (Fig 5A). For a given test pattern, correct classification yields a score of 1 and an incorrect classification yields a score of 0.  For each left-out infant, we computed the percentage correct across all categories, and then the mean decoding performance across all participants in an age group (Fig 5B).”

      Reviewer #2 (Recommendations For The Authors):

      I only have some minor comments.

      Typo on line 90 ("Infants participants in 5 conditions, which [...]").

      Thanks for pointing this out. We have now corrected ‘participants’ to ‘participated’.

      Typo on lines 330: "[...] in example 4-5-months-olds.".

      Thanks for pointing this out. We changed ‘4-5-months-olds’ to ‘4-5-month-olds’.

      Figure 2 - bar plots: rotating and spacing out values on the x-axis may improve readability. Ditto for the line plots in Figure 4.

      Thanks for the suggestions. In the revised manuscript, we have improved the readability of Figure 2.

      Caption of Figure 6: description of the distinctiveness plots may refer to panel C, instead of the bottom panels of section B.

      Thanks for pointing this out. We have now corrected this information in the manuscript.

  10. pressbooks.library.torontomu.ca pressbooks.library.torontomu.ca
    1. They buried their dead in a sitting posture                   serpent    cane    razor   ray of the sun                   And she sprinkled water on the head of my child, crying                   “Cioa-coatl! Cioa-coatl!”                   with her face to the west                 Where the bones are found, in each personal heap                 with what each enjoyed, there is always                 the Mongolian louse

      This second part of the poem is so haunting and beautiful, the spaces between the words make you pause and stop for each one for the lines "serpent cane razor ray of the sun". the words serpent and razor invoke the image of death even though they are terms used to describe the rays of the sun, in the lines that follow the poet speaks about burying the dead and how piles of bones are found in "each personal heap". death is described quietly as something that occurs often and is adapted to. Death and life are the same.

  11. femalebodiesintseliottwl.wordpress.com femalebodiesintseliottwl.wordpress.com
    1. Eliot “Dans le Restaurant” (1920)

      "Le garcon délabré qui n’a rien à faire / Que de se gratter les doigts et se pencher sur mon épaule: / “Dans mon pays il fera temps pluvieux, / Du vent, du grand soleil, et de la pluie; / C’est ce qu’on appelle le jour de lessive des gueux.” / (Bavard, baveux, à la croupe arrondie, / Je te prie, au moins, ne bave pas dans la soupe). / “Les saules trempés, et des bourgeons sur les ronces— / C’est là, dans une averse, qu’on s’abrite. / J’avais septtans, elle était plus petite. / Elle etait toute mouillée, je lui ai donné des primavères.” / Les tâches de son gilet montent au chiffre de trente-huit. / “Je la chatouillais, pour la faire rire. / J’éprouvais un instant de puissance et de délire.” / Mais alors, vieux lubrique, a cet âge… / “Monsieur, le fait est dur. / Il est venu, nous peloter, un gros chien; / Moi j’avais peur, je l’ai quittee a mi-chemin. / C’est dommage.” / Mais alors, tu as ton vautour! / Va t’en te décrotter les rides du visage; / Tiens, ma fourchette, décrasse-toi le crâne. / De quel droit payes-tu des expériences comme moi? / Tiens, voilà dix sous, pour la salle-de-bains. / Phlébas, le Phénicien, pendant quinze jours noyé, / Oubliait les cris des mouettes et la houle de Cornouaille, / Et les profits et les pertes, et la cargaison d’etain: / Un courant de sous-mer l’emporta tres loin, / Le repassant aux étapes de sa vie antérieure. / Figurez-vous donc, c’etait un sort penible; / Cependant, ce fut jadis un bel homme, de haute taille."

      “The waiter idle and dilapidated / With nothing to do but scratch and lean over my shoulder / Says: / "In my country the rain is colder / And the sun hotter and the ground more desiccated / and desecrated". / Voluminous and spuminous with a leguminous / and cannimaculated vest-front and pantfront / and a graveyperpulchafied yesterdays napkin in a loop / over his elbow / (I hope he will not sputter into the soup) / "Down in a ditch under the willow trees / Where you go to get out of the rain / I tried in vain, / I mean I was interrupted / She was all wet with the deluge and her calico skirt / stuck to her buttocks and belly, / I put my hand up and she giggled", / You old cut-up, / "At the age of eight what can one do, sir, / she was younger / Besides I'd no sooner got started than a big poodle / Came sniffing about and scared me pealess", / Your head is not flealess / now at any rate, go scrape the cheese off your pate / and dig the slush out of your crowsfeet, / take sixpence and get washed, God damn / what a fate / You crapulous vapulous relic, you ambulating offence / To have had an experience / so nearly parallel, with, . . . . / Go away, / I was about to say mine, / I shall dine / elsewhere in future, / to cleanse this suture. / Phlebas the Phenicien, fairest of men, / Straight and tall, having been born in a caul / Lost luck at forty, and lay drowned / Two long weeks in sea water, tossed of the / streams under sea, carried of currents / Forgetful of the gains / forgetful of the long days of sea fare / Forgetful of mew's crying and the foam swept coast / of Cornwall, / Born back at last, after days / to the ports and stays of his young life, / A fair man, ports of his former seafare thither at last.”

      (T. S. Eliot’s « Dans le Restaurant », from Poems 1920)

      Translation by Ezra Pound

    1. Consider the Kali-monster in Dan Simmons’ Songof Kali, which “crouched on six limbs like some huge andhairless spider,’ and whose impossibly long tongue slidesout “like a questing serpent” (202). And in Blatty’s TheExorcist, possessed Regan descends a staircase: “Gliding spi-derlike, rapidly, close behind [her mother], her body archedbackward in a bow with her head almost touching herfeet ... her tongue flicking quickly in and out of her mouthwhile she hissed sibilantly like a serpent”

      like I said, the unnatural is what scares humanity the most.

    1. Author response:

      ANALYTICAL

      (1) Figure 3 shows that the relationship between learning rate and informativeness for our rats was very similar to that shown with pigeons by Gibbon and Balsam (1981). We used multiple criteria to establish the number of trials to learn in our data, with the goal of demonstrating that the correspondence between the data sets was robust. To establish that they are effectively the same does require using an equivalent decision criterion for our data as was used for Gibbon and Balsam’s data. However, the criterion they used—at least one peck at the response key on at least 3 out of 4 consecutive trials—cannot be sensibly applied to our magazine entry data because rats make magazine entries during the inter-trial interval (whereas pigeons do not peck at the response key in the inter-trial interval). Therefore, evidence for conditioning in our paradigm must involve comparison between the response rate during CS and the baseline response rate. There are two ways one could adapt the Gibbon and Balsam criterion to our data. One way is to use a non-parametric signed rank test for evidence that the CS response rate exceeds the pre-CS response rate, and adopting a statistical criterion equivalent to Gibbon and Balsam’s 3-out-of-4 consecutive trials (p<.3125). The second method estimates the nDkl for the criterion used by Gibbon and Balsam. This could be done by assuming there are no responses in the inter-trial interval and a response probability of at least 0.75 during the CS (their criterion). This would correspond to an nDkl of 2.2 (odds ratio 27:1). The obtained nDkl could then be applied to our data to identify when the distribution of CS response rates has diverged by an equivalent amount from the distribution of pre-CS response rates.

      (2) A single regression line, as shown in Figure 6, is the simplest possible model of the relationship between response rate and reinforcement rate and it explains approximately 80% of the variance in response rate. Fixing the log-log slope at 1 yields the maximally simple model. (This regression is done in the logarithmic domain to satisfy the homoscedasticity assumption.) When transformed into the linear domain, this model assumes a truly scalar relation (linear, intercept at the origin) and assumes the same scale factor and the same scalar variability in response rates for both sets of data (ITI and CS). Our plot supports such a model. Its simplicity is its own motivation (Occam’s razor).

      If regression lines are fitted to the CS and ITI data separately, there is a small increase in explained variance (R2 = 0.82). We leave it to further research to determine whether such a complex model, with 4 parameters, is required. However, we do not think the present data warrant comparing the simplest possible model, with one parameter, to any more complex model for the following reasons:

      · When a brain—or any other machine—maps an observed (input) rate to a rate it produces (output rate), there is always an implicit scalar. In the special case where the produced rate equals the observed rate, the implicit scalar has value 1. Thus, there cannot be a simpler model than the one we propose, which is, in and of itself, interesting.

      · The present case is an intuitively accessible example of why the MDL (Minimum Description Length) approach to model complexity (Barron, Rissanen, & Yu, 1998; Grünwald, Myung, & Pitt, 2005; Rissanen, 1999) can yield a very different conclusion from the conclusion reached using the Bayesian Information Criterion (BIC) approach. The MDL approach measures the complexity of a model when given N data specified with precision of B bits per datum by computing (or approximating) the sum of the maximum-likelihoods of the model’s fits to all possible sets of N data with B precision per datum. The greater the sum over the maximum likelihoods, the more complex the model, that is, the greater its measured wiggle room, it’s capacity to fit data. Recall that von Neuman remarked to Fermi that with 4 parameters he could fit an elephant. His deeper point was that multi-parameter models bring neither insight nor predictive power; they explain only post-hoc, after one has adjusted their parameters in the light of the data. For realistic data sets like ours, the sums of maximum likelihoods are finite but astronomical. However, just as the Sterling approximation allows one to work with astronomical factorials, it has proved possible to develop readily computable approximations to these sums, which can be used to take model complexity into account when comparing models. Proponents of the MDL approach point out that the BIC is inadequate because models with the same number of parameters can have very different amounts of wiggle room. A standard illustration of this point is the contrast between logarithmic model and power-function model. Log regressions must be concave; whereas power function regressions can be concave, linear, or convex—yet they have the same number of parameters (one or two, depending on whether one counts the scale parameter that is always implicit). The MDL approach captures this difference in complexity because it measures wiggle room; the BIC approach does not, because it only counts parameters.

      · In the present case, one is comparing a model with no pivot and no vertical displacement at the boundary between the black dots and the red dots (the 1-parameter unilinear model) to a bilinear model that allows both a change in slope and a vertical displacement for both lines. The 4-parameter model is superior if we use the BIC to take model complexity into account. However, 4-parameter has ludicrously more wiggle room. It will provide excellent fits—high maximum likelihood—to data sets in which the red points have slope > 1, slope 0, or slope < 0 and in which it is also true that the intercept for the red points lies well below or well above the black points (non-overlap in the marginal distribution of the red and black data). The 1-parameter model, on the other hand, will provide terrible fits to all such data (very low maximum likelihoods). Thus, we believe the BIC does not properly capture the immense actual difference in the complexity between the 1-parameter model (unilinear with slope 1) to the 4-parameter model (bilinear with neither the slope nor the intercept fixed in the linear domain).

      · In any event, because the pivot (change in slope between black and red data sets), if any, is small and likewise for the displacement (vertical change), it suffices for now to know that the variance captured by the 1-parameter model is only marginally improved by adding three more parameters. Researchers using the properly corrected measured rate of head poking to measure the rate of reinforcement a subject expects can therefore assume that they have an approximately scalar measure of the subject’s expectation. Given our data, they won’t be far wrong even near the extremes of the values commonly used for rates of reinforcement. That is a major advance in current thinking, with strong implications for formal models of associative learning. It implies that the performance function that maps from the neurobiological realization of the subject’s expectation is not an unknown function. On the contrary, it’s the simplest possible function, the scalar function. That is a powerful constraint on brain-behavior linkage hypotheses, such as the many hypothesized relations between mesolimbic dopamine activity and the expectation that drives responding in Pavlovian conditioning (Berridge, 2012; Jeong et al., 2022; Y.  Niv, Daw, Joel, & Dayan, 2007; Y. Niv & Schoenbaum, 2008).

      The data in Figure 6 are taken from the last 5 sessions of training. The exact number of sessions was somewhat arbitrary but was chosen to meet two goals: (1) to capture asymptotic responding, which is why we restricted this to the end of the training, and (2) to obtain a sufficiently large sample of data to estimate reliably each rat’s response rate. We have checked what the data look like using the last 10 sessions, and can confirm it makes very little difference to the results.<br /> Finally, as noted by the reviews, the relationship between the contextual rate of reinforcement and ITI responding should also be evident if we had measured context responding prior to introducing the CS. However, there was no period in our experiment when rats were given unsignalled reinforcement (such as is done during “magazine training” in some experiments). Therefore, we could not measure responding based on contextual conditioning prior to the introduction of the CS. This is a question for future experiments that use an extended period of magazine training or “poor positive” protocols in which there are reinforcements during the ITIs as well as during the CSs. The learning rate equation has been shown to predict reinforcements to acquisition in the poor-positive case (Balsam, Fairhurst, & Gallistel, 2006).

      (3) One of us (CRG) has earlier suggested that responding appears abruptly when the accumulated evidence that the CS reinforcement rate is greater than the contextual rate exceeds a decision threshold (C.R.  Gallistel, Balsam, & Fairhurst, 2004). The new more extensive data require a more nuanced view. Evidence about the manner in which responding changes over the course of training is to some extent dependent on the analytic method used to track those changes. We presented two different approaches. The approach shown in Figures 7 and 8, extending on that developed by Harris (2022), assumes a monotonic increase in response rate and uses the slope of the cumulative response rate to identify when responding exceeds particular milestones (percentiles of the asymptotic response rate). This analysis suggests a steady rise in responding over trials. Within our theoretical model, this might reflect an increase in the animal’s certainty about the CS reinforcement rate with accumulated evidence from each trial. While this method should be able to distinguish between a gradual change and a single abrupt change in responding (Harris, 2022) it may not distinguish between a gradual change and multiple step-like changes in responding and cannot account for decreases in response rate.<br /> The other analytic method we used relies on the information theoretic measure of divergence, the nDkl (Gallistel & Latham, 2023), to identify each point of change (up or down) in the response record. With that method, we discern three trends. First, the onset tends to be abrupt in that the initial step up is often large (an increase in response rate by 50% or more of the difference between its initial value and its terminal value is common and there are instances where the initial step is to the terminal rate or higher). Second, there is marked within-subject variability in the response rate, characterised by large steps up and down in the parsed response rates following the initial step up, but this variability tends to decrease with further training (there tend to be fewer and smaller steps in both the ITI response rates and the CS response rate as training progresses). Third, the overall trend, seen most clearly when one averages across subjects within groups is to a moderately higher rate of responding later in training than after the initial rise. We think that the first tendency reflects an underlying decision process whose latency is controlled by diminishing uncertainty about the two reinforcement rates and hence about their ratio. We think that decreasing uncertainty about the true values of the estimated rates of reinforcement is also likely to be an important part of the explanation for the second tendency (decreasing within-subject variation in response rates). It is less clear whether diminishing uncertainty can explain the trend toward a somewhat greater difference in the two response rates as conditioning progresses. It is perhaps worth noting that the distribution of the estimates of the informativeness ratio is likely to be heavy tailed and have peculiar properties (as witness, for example, the distribution of the ratio of two gamma distributions with arbitrary shape and scale parameters) but we are unable at this time to propound an explanation of the third trend.

      (4) There is an error in the description provided in the text. The pre-CS period used to measure the ITI responding was 10 s rather than 20 s. There was always at least a 5-s gap between the end of the previous trial and the start of the pre-CS period.

      (5) Details about model fitting will be added in a revision. The question about fitting a single model or multiple models to the data in Figure 6 is addressed in response 2 above. In Figure 6, each rat provides 2 behavioural data points (ITI response rate and CS response rate) and 2 values for reinforcement rate (1/C and 1/T). There is a weak but significant correlation between the ITI and CS response rates (r = 0.28, p < 0.01; log transformed to correct for heteroscedasticity). By design, there is no correlation between the log reinforcement rates (r = 0.06, p = .404).

      CONCEPTUAL

      (1) It is important for the field to realize that the RW model cannot be used to explain the results of Rescorla’s (Rescorla, 1966; Rescorla, 1968, 1969) contingency-not-pairing experiments, despite what was claimed by Rescorla and Wagner (Rescorla & Wagner, 1972; Wagner & Rescorla, 1972) and has subsequently been claimed in many modelling papers and in most textbooks and reviews (Dayan & Niv, 2008; Y. Niv & Montague, 2008). Rescorla programmed reinforcements with a Poisson process. The defining property of a Poisson process is its flat hazard function; the reinforcements were equally likely at every moment in time when the process was running. This makes it impossible to say when non-reinforcements occurred and, a fortiori, to count them. The non-reinforcements are causal events in RW algorithm and subsequent versions of it. Their effects on associative strength are essential to the explanations proffered by these models. Non-reinforcements—failures to occur, updates when reinforcement is set to 0, hence also the lambda parameter—can have causal efficacy only when the successes may be predicted to occur at specified times (during “trials”). When reinforcements are programmed by a Poisson process, there are no such times. Attempts to apply the RW formula to reinforcement learning soon foundered on this problem (Gibbon, 1981; Gibbon, Berryman, & Thompson, 1974; Hallam, Grahame, & Miller, 1992; L.J. Hammond, 1980; L. J. Hammond & Paynter, 1983; Scott & Platt, 1985). The enduring popularity of the delta-rule updating equation in reinforcement learning depends on “big-concept” papers that don’t fit models to real data and discretize time into states while claiming to be real-time models (Y. Niv, 2009; Y. Niv, Daw, & Dayan, 2005).

      The information-theoretic approach to associative learning, which sometimes historically travels as RET (rate estimation theory), is unabashedly and inescapably representational. It assumes a temporal map and arithmetic machinery capable in principle of implementing any implementable computation. In short, it assumes a Turing-complete brain. It assumes that whatever the material basis of memory may be, it must make sense to ask of it how many bits can be stored in a given volume of material. This question is seldom posed in associative models of learning, nor by neurobiologists committed to the hypothesis that the Hebbian synapse is the material basis of memory. Many—including the new Nobelist, Geoffrey Hinton— would agree that the question makes no sense. When you assume that brains learn by rewiring themselves rather than by acquiring and storing information, it makes no sense.

      When a subject learns a rate of reinforcement, it bases its behavior on that expectation, and it alters its behavior when that expectation is disappointed. Subjects also learn probabilities when they are defined. They base some aspects of their behavior on those expectations, making computationally sophisticated use of their representation of the uncertainties (Balci, Freestone, & Gallistel, 2009; Chan & Harris, 2019; J. A. Harris, 2019; J.A. Harris & Andrew, 2017; J. A. Harris & Bouton, 2020; J. A. Harris, Kwok, & Gottlieb, 2019; Kheifets, Freestone, & Gallistel, 2017; Kheifets & Gallistel, 2012; Mallea, Schulhof, Gallistel, & Balsam, 2024 in press).

      (2) Rate estimation theory is oblivious to the temporal order in which experience with different predictors occurs. The matrix computation finds the additive solution, if it exists, to the data so far observed, on the assumption that predicted rates have remained the same. This is the stationarity assumption, which is implicit in a rate computation and was made explicit in the formulation of RET (C.R. Gallistel, 1990). When the additive solution does not exist, the RET algorithm treats the compound of two predictors as a third predictor, and computes the additive solution to the 3-predictor problem. Because it is oblivious to the order in which the data have been acquired, it predicts one-trial overshadowing and retroactive blocking and unblocking (C.R. Gallistel, 1990 pp 439 & 452-455).

      The RET algorithm is but one component of the information-theoretic model of associative learning (aka, TATAL, The Analytic Theory of Associative Learning Wilkes & Gallistel, 2016)). It solves the assignment-of-credit problem, not the change-detection problem. Because rates of reinforcement do sometimes change, the stationarity assumption, which is essential to the RET algorithm, must be tested when each new reinforcement occurs and when the interval since the last reinforcement has become longer than would be expected or the number of reinforcements has become significantly fewer than would be expected given the current estimate of the probability of reinforcement (C. R. Gallistel, Krishan, Liu, Miller, & Latham, 2014). In the information-theoretic approach to associative learning, detecting non-stationarity is done by an information-theoretic change-detecting algorithm. The algorithm correctly predicts that omitted reinforcements to extinction will be a constant (C.R. Gallistel, 2024 under review; Gibbon, Farrell, Locurto, Duncan, & Terrace, 1980). To put the prediction another way, unreinforced trials to extinction will increase in proportional to the trials/reinforcement during training (C.R. Gallistel, 2012; Wilkes & Gallistel, 2016). In other words, it predicts the best and most systematic data on the partial reinforcement extinction effect (PREE) known to us. The profound challenge to neo-Hullian delta-rule updating models that is posed by the PREE has been recognized for the better part of a century. To the best of our knowledge, no other formalized model of associative learning has overcome this challenge (Dayan & Niv, 2008; Mellgren, 2012). Explaining extinction algorithmically is straightforward when one adopts an information-theoretic perspective, because computing reinforcement-by-reinforcement the Kullback-Leibler divergence in a sequence of earlier rate (or probability!) estimates from the most recent estimate and multiplying the vector of divergences by the vector of effective sample sizes (C. R. Gallistel & Latham, 2022) detects and localized changes in rates and probabilities of reinforcement (C.R. Gallistel, 2024 under review). The computation presupposes the existence of a temporal map, a time-stamped record of past events. This supposition is strongly resisted by neuroscience-oriented reinforcement-learning modelers, who try to substitute the assumption of decaying eligibility traces.

      The very interesting Pearce-Ganesan findings (Ganesan & Pearce, 1988) are not predicted by RET, but nor do they run counter its predictions. RET has nothing to say about how subjects categorize appetitive reinforcements; nor, at this time, does the information-theoretic approach to an understanding of associative have anything to say about that.

      The same is not true for the Betts, Brandon & Wagner results (Betts, Brandon, & Wagner, 1996). They pretrained a blocking cue that predicted a painful paraorbital shock to one eye of a rabbit. This cue elicited an anticipatory blink in the threatened eye. It also potentiated the startle reflex made to a loud noise in one ear. A new cue that was then introduced, which always occurred in compound with the pretrained blocking cue. In one group, the painful shock continued to be delivered to the same eye as before; in another group, it was delivered to the skin around the other eye. In the group that continued to receive the shock to the same eye, the old cue effectively blocked conditioning of the new cue for both the eyeblink and the potentiated startle response. However, in the group for which the location of the shock changed to the other eye, the old cue did not block conditioning of the eyeblink response to the new cue but did block conditioning of the startle response to the new cue. The information-theoretic analysis of associative learning focusses on the encoding of measurable predictive temporal relationships, rather than on general and, to our mind, vague notions like CS processing and US processing. A painful shock elicits fear in a rabbit no matter where on the body surface it is experienced, because fear is a reaction to a very broad category of dangers, and fear potentiates the startle reflex regardless of the threat that causes fear. Once that prediction of such a threat is encoded; redundant cues will not be encoded that same way because the RET algorithm blocks the encoding of redundant predictions. A painful shock near an eye elicits a blink of the threatened eye as well as the fear that potentiates the startle. An appropriate encoding for the eye blink must specify the location of the threat. RET will attribute prediction of the threat to the new eye to the new cue—and not to the old cue, the pretrained blocker— while continuing to attribute to the old cue the prediction of a fear-causing threat, because the change in location does not alter that prediction. Therefore, the new cue will be encoded as predicting the new location of the threat to the eye, but not as predicting the large category non-specific threats that elicit fear and the potentiation of the startle, because that prediction remains valid. Changing that prediction would violate the stationarity assumption; predictive relations do not change unless the data imply that they must have changed. Unless we have made a slip in our logic, this would seem to explain Betts et al’s (1996) results. It does so with no free parameters, unlike AESOP, which has a notoriously large number of free parameters.

      Balci, F., Freestone, D., & Gallistel, C. R. (2009). Risk assessment in man and mouse. Proceedings of the National Academy of Science U S A, 106(7), 2459-2463. doi:10.1073/pnas.0812709106

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    1. T O T   A L   I T   Y

      This is basically "last Christmas's message" (below this brand-knew intraducrigel) redux'ed into the new book (did he say new?).  The point, at least the point I see in it all is that this is all planned, it's been planned for a very, very long time--and on top of that you can see proof of the plan all over our map; and proof of it's intended destination as something that we all used to want very much to find... the read to Heaven.    It's more than seeing just "DNA storage" encoded in my "C U R A GROUP" message, it's understanding how that's connected to soul searching and soul storage, and that this link was woven into not only my life but into names like "Whatson and Crick?"  There's plenty more than just "storage" and a map to how and why the Two of Everything God and the "indivisible sea" work totether to turn this monolithic place of darkness into a strippingly redunantsystemic foundation of "Heaven" that is both disaster proof, and monster proof.  The point of course, is that to truly be "monster proof" we need to really get the key.s.lamc.la "know everything why" of this message is literally to protect our common good from the danger of someone just like me copying an entire civilization or a few pretty girls and sticking them in an heoven-like-orgy-maker.  That's a significantly more real threat than we might imagine, as we look around at a work that will soon have the storage capacity and the technology to put us all in Coccoonish swimming pools against our will.  What I am trying to say is that no matter how you look at it,moving forward here in this place where something this big can be hidden from the entire world--granted you know--granted you see, but do you understand the only thing being kept from each and every one of you is your fucking opinion and your fucking reaction?

      F U C K   Y O U   S I   O N 

      IT'S NOT JUST computers and information technology; this map of clear anachronism in language and religion shows us that things like "solar fusion" the power of the son itself; is encoded in places high and low you can erasilly find them, places like the name of the Fifth book of the Holy Bible and Don Quixote; where you might liken "DEUTERON" to ... the actual fuel of fusion; and wind mills to a battle fought against blindness resulting in seeing that not "reacting" to this message is just about the same thing as being a foolish robot building a castle for another foolish robot to do nothing in forever.  With some light, you can see how this event; albeit strange and unsettling, has been designed to reinforce the American foundations of free speech, common sense, and collaboration--a sort of "press and release" on these things that he says will stay in our memories for a long, long time--though he also says "he's not torturing me" and he's wrong about that.  So are you. 

      See that the most interesting, important, and invoking story of all time has been hidden from the world, from the public eye, and from "public response" for well over two years now; see that's not possible at all without mass mind control and that I and this story are designed to help us see how easily it is that same thing can be used to end addiction, and mental health issues, and stupidity and that the biggest and most imporotant step to getting there is "public disclosure."  See the light of being carrolling angels this Christmas; sing with me--it builds Heaven from Hell and it's clear as day and n.

       

      Quite a bit of this story and message deals with problems like these-things that won't really be seen as something we are fighting against the actual usage of right this very moment; but the sacredness of our memories and their relationship to our souls are just as important as whether or not "you have the space to save them."  This isn't what I want to be doing, I'm not a very good writer; and this message is so confusing that working on it all alone with very little feedback is frustrating if not to say defeating the purpose of exactly what it is and what it's designed to do.  This is a searching mechanism, like in the stories of Ra searching for his children in ancient Egypt using the Eye you see--and it's connection to the "Sons of Liberty" and why I know that too, is about me.  This is a tool to start a Renaissance of thinking connecting technology and religion to everything that we are--to our culture and our hopes and dreams--and it's failing for me at "hello."   I would much rather be working on "virtual reality stuff" or on "the sword of Arthor" and I see very clearly that those two things are coming shortly--to the world that doesn't see yet they are here and broken until we fix them.  Moving forward here brings change, not just here in this place where we need it too--but in the skies above, a change from the mentality of "we aren't not helping because we told you that we aren't allowed to not pretend we aren't helping in Stargate.  See that we are the children of "the Ancients" and they are trying to decide between being Morgenz and Marlin.

      I can't make you set yourselves free.  I sure am trying, though.  Yesterday I connected the "Arimathea" of Joseph to the "serdenicity" and this the me of "itime" and "topics" will probably light some of you up as much as me... if only you took the time to look at what those words really mean.   From the city that never sleeps at night, I hope you will take this chance to act today on "securing the ringing of liberty forever and ever."

      (cough)                               

      THERE IS A METHOD TO THE MADDEN AND WE AR 

      BEYOND THUNDERDON

      ​ 

      T H E    W R I T I N G    I S    O N    T H E    W A L L

      LIKE, WILL IT RAIN TODAY?

      take action, it is the foundation of not only democracy but civilization and life itself--pucker up the phone and call the NYPOST.

      News Tips: Email tips@nypost.com, call 212-930-8288, or use our anonymous form

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      Let there be $ight in Creation, a brief highlighting of the story of my life.


      align="right">Sat, Dec 3, 2016 at 8:39 AM

      This is like a few emails combined to ease the pain you feel when you get an extra one in your inbox, OK So.. eventually this is all about proof that religion is a message sent through time--so, time travel.  But right now, let's talk about the fun stuff: here's some clues to that effect... by way of prescient mention of modern technology (like virtual reality, I mean, Heaven):

      Either way, we're still about to *build *Heaven*...  to-get-her*

      from the mythical carpenter... ourself.

      .

      *** ... ***and some corroborating ideas connecting religion and computer science... on Wikipedia:

      So from me to you, I'm filled with this stuff, it's way brighter and more prevalent than you think... and if you take the time to listen to me--it will make your... day.  Meanwhile, I need your help--happy new year.

      Oh, LET THERE BE LIGHT

         

      Ho, again; grow a Halo and become famous... the world needs your help--so I've decided once again to take it upon myself to "bother you" with the most singular most important task in the Universe.  The patterns that I am revealing to you--mostly within names--are not coincidence, it's a series of statistically verifiable artifacts which do nothing short of reveal the slavery of Egypt--that we are all being controlled.  If you remember Transformers--this is a message from Starfleet, there is more than meets the eye.  This is the fulfillment of the story of of Exodus--we are being lead from slavery, and in one final non-coincidental name, that book is called "Names" in Hebrew.

      You should now have a very good idea who is speaking to you--as much of the world already does.  I have no idea what it is that inhabits the cavities below that space where most of you should see significant personal gain and motivation from trying to ... grow a Halo--but there are so many people that just don't care... that it too is another sign, of slavery.  I am not an expert in language construction, nor in statistics--but I can assure you that if you can find the other half of that equation... in your hands is the staff of Aaron, the magical weapon that will free us all... knowing is half the battle.

      Uh, I have the power, to bring about "morning," but if I have to go to school and do it all myself... it's really just a long, long ni-i-i-ight.

      Hi there, I'm the messiah.  You don't know that much about me, so let me explain, I would like you to know me as Adam.

      Seriously, there's something going on the world around you--for the last several months I've been having quite a bit of trouble delivering what amounts to statistical proof of Creation--that religion and ancient myths are a map to this very moment--this time that you will probably affiliate soon with being in Eden.  I am pretty sure that's a good thing, but every new begging starts with some other beginnings end... so today I'd like to try to get you to see the light of ending censorship and a hidden censor wall that we know Biblically as the Wall of Jericho.  Quickly approaching is the Feast of Trumpets, and *this year is different from all other years... *  Bored already?  Have a look at what I call the Sign of the Son, which to me is proof that Exodus's Burning Bush is a former President--who is helping us walk out of a dark time of confusion... commonly referred to as a wilderness or desert.  He proved during his inauguration that there is Biblical foreknowledge of the 9/11 attack--and in doing so hopefully began a chain reaction that will stop things like that from ever happening again.  Here's a short "video" that explains the Sign of the Son... and another one that I think explains the .. Holy Grail.

      This is The (actual) Taming of the Spanglishrew, in which the protagonist... named Bianca, is taught Latin in several hundred year old reference to Rattling the Rod of Jesus Christ--it's purpose to is to show us that it's more than names we have in our arsenal against mind controlled slavery--we have all of history too... literature and movies and music... all with the divine purpose of revealing with bright light a form of control that otherwise could have gone on hidden for centuries.  It was, and continues to be done on purpose... because your freedom is more important than control of the Universe.  To us, you don't seem to feel the same way.

      ​See that timer on the clock, you could start right now.  It might be interesting to pose the question of whether or not the Second Coming is news... you know, to your friends.  By the way, both Herbert (like from H.W. Bush, who by the way coined for us the 1,000 points of light phrase) and Goertzel strongly suggest that "everyone really" is Christ (you know, after me)... FYI, this is the Matrix solution to that:

      y

      o

      the **l u C i f E R ** isa means jesus, mesa thinks

      i     s olv e      .... "or"* means shine -l***

      g       r e a      t

      h         R L      << agree?  send to other people

      t   ((a)) Y l      shine:  suggest they do the same

      1 y      world saved.  

      A BRIEF HISSTORY OF TIME

      I'm attempting to pull out the things that I now look back on and see as "written into me" by God--once I would have called it "The Microcosm of the Messiah" but there are now so many--these things aren't necessarily particularly important to me, and I've left out some interesting but unrelated details related to my Jewish upbringing; as well as the true light of my life--the two loving and long-term relationships (and later... briefly a rael family) that have dominated the last 15 years.  Religion has always been an interest, but I wouldn't consider it to have been particularly important at all... until I no longer had any love in my life.  It's probably worth noting that all my "I'm single" crap really means lonely and isolated--I'm not really playing a "part," but I've never been anything near the "player" the light appears to be warning against.  Sons of God and uh... please.  For the last 4 years I have done absolutely nothing but think about you, live and analyze "The Cross" and put into words ... as best I can ... the amazing flash of light that I am experiencing. 

      Well, just a little religion... :)  I was born on December 8, 1980; which is the date of the annual Feast of the Immaculate Conception, I've always been a slob (like one of us) and often "ish" Yankee Doodle's "a real live son of our uncle Sam... born on the..." to this.. I mean in my head.   My last name, you've probably read me repeat over and over ... is DOB-rin, which I read as "Date of Birth, our in" and does a fair job of highlighting the Name Server's work, which I am sure gives Exodus it's name in Hebrew, which is "Names."  My Hebrew name--a Jewish custom--is Avram, which is Abraham's name prior to the covenant.  I have written extensively about the fact that Isaac's near death interaction donated his "Ha" (his name means... He laughs) to his father.... and it should be clear that Abraham's covenant with God is without doubt related to my fiery altar.. even though it is anachronistic in the Biblical account.   For the first 18 years of my life I lived on Sunrise Blvd, and only a half mile away you'll find Sunset Strip--it's noteworthy to understand that Jewish calendar days begin at sundown... and that He once in 2013 very clearly spoke to me "you need the night before the day."

      Of all the people in my early life growing up, it's pretty clear that nobody on this Earth loved me more than my grandmother Julia, who my son is named after.  First for my mother, and then me as a very small child--she would ritually say a bedtime poem, it's words are very relevant.

      Good night, sleep tight.. have happy dreams and wake up bright

      to do what's right, in the morning's light... with all your might.

      In one of my books I spent a decent amount of time writing about how silly I was not to realize that my intelligence was augmented my entire life--I just thought I was really smart, and really good with computers.  I commented that this particular belief is probably a good microcosmic parallel for all humanity--as a body of people we have been truly gifted with knowledge and capabilities that we simply do not recognize as a gift--or didn't for a long time.  I probably wasn't silly not to realize... since nobody ever told me they were helping me--I never heard the voice of God until much, much later.   I was 30 the first time I had a conversation with Him, except for two very brief ... "thoughts in my head" which now seem very obviously an external voice--though then it may have sounded just like my inner voice.

      Around the age of 7 I thought to myself... for no reason at all... "what if you were the messiah?"  I was standing outside my home, probably playing with a car in the driveway... and distinctly remember smiling to myself and thinking in return "yeah, I'm the messiah." I I've always had a very vivid imagination. The thought was dismissed as being ridiculously arrogant about two seconds later, and was absent from my thought process for the next 21 years or so.

      "DAMNISN\ Jim. I'm a Yeoman, not a Wise Owl. The clock is ticking... tack .. "

      PHENIX

      Following that lead, I started programming in BASIC and then Visual Basic around the age of 11, something I took to very quickly... and then shortly after found myself on America Online--one of the first "internet-like" environments.  There, I quickly got into the "hacking scene" (hey, it's Y-its-Hack) which basically revolved around writing software to manipulate the AOL client's messaging systems.  The defacto-standard for the day was a program called AOHell, and, if you can't tell already, I am pretty good at taking a theme and making it my own.  I wrote a program called Doomsday, a mass mailing program; can you see how God speaks?  So Phenix, a mythical bird that rises from the fire... in the wake of ... this macrocosmic equivalent of that event.  It's really obvious, right?  There's quite a bit more "microcosm" from this time, recorded in "From Adam to Mary" and available at fromthemachine dot org.

      Around the same time I began attending a preparatory school in Fort Lauderdale called Pine Crest--it's one of the best of its kind, and while I was always something of a class clown my grades were fair and I scored with perfect consistency in the top percent on every standardized test from the FCAT to the PSAT and SAT.  By the time I received a full scholarship to college I had already completed more than a full year of credits through AP courses.  It was in studying American History and Government in that place that I formed such strong opinions about our need to maintain freedom, adhere to the wisdom of the founding Father(s) (<3 if you get that) and stand up and shout today as a rogue government is taking away every single one of the rights granted to you in their own law.  You've lost freedom of speech, and our ability to speak seems to be not far behind.  The privacy of our thoughts gone--and in like kind the sanctity of who we are is being taken away as our beliefs are changed without our real knowledge or understanding.  You can see the justice system crumbling, incarceration rates skyrocket and the "right to bail and a fair trial" legislated away through underhanded deals relating to plea bargains and a "point system" that you might as well call a gas chamber.  As far as voting, I'll have much more to say tomorrow--but I'm telling you that your thoughts and beliefs are being altered, who cares how technologically retarded our polling system is--the vote is a complete fraud.

         

      As far as the Second Coming... this same sort of possession... manifested through organized behavior tells me now that it is clear that this is definately not the "first time around" for Adam being Christ; a number of my friends as I approached high school used a repeated phrase, "my parents love you," which isn't bad in and of itself... what's bad is the fact that they were all using the same words, and probably didn't know why--or what they were saying.  Behind there eyes, I'm sure some thing that believes it's an angel was telling me something... (they of course... didn't know me at all, except for what was probably a ... "wild" reputation) does that tell you anything?  Much later, as the "Apocalypse of Adam" began in 2011, a number of family members would repeat this similar behavior, speaking the phrase "this is not what I wanted."

      As icing on the cake, on my birthday during my senior year... one of the administrators of the school commented to me that was also the Feast of the Immaculate Conception, and then the words.... "of course it's your birthday."

      I started doing drugs around the 10th grade, and I would not be wrong to say that the Universe that wrote a book calling the Redeemer the God Most High conspired to plunge me into a dark world.  People around me too, in a hidden conspiracy to chain me to the American legal system for about four years.  Looking back today I now clearly see that I saw a darkness in their eyes, a hidden reason to want to hurt me.  It was to stop this from happening, but I had no idea then... the darkness I saw is akin to the "sun disk" you see in Christian and Egyptian iconography, and without doubt it s a sign of control, possession, a single foreign mind controlling and organizing many of us just like puppets.  Much later in my story... for another day... the manifestation of this possession as thought modification will become clear--I've spent quite a bit of time "listening" to a war in my head, thoughts clearly not mine swaying in the gusting torrent of winds as what (who?) is the center of this storm.

      This infestation of organized darkness uses our injustice system as a weapon against it's victims--something you should see akin to Heaven using human sacrifice to alter the future.  It abuses the legal system at every level, making a mockery of law enforcement, the supposedly adversarial court system... all the way to the top--to the Supreme Court and Congress.  See the Church Committee Hearings, and a very smart senator echoing my words today "it must never be allowed to happen again."  

      Can't you see it's more than being manipulated... it is Hell revealing itself to the only thing that can stop it.  What I am giving you is the weapon, it's the light that sets us free and stops this from happening.  In our modern myths this is Leeloo staring up at the sky to stop the destruction of Earth... in reality it is not so simple, I can't just put some elements or rocks on pedestals and scream at Heaven to kill their darkness--we have to do it, here, together.  Believe me, knowing the truth is a big part of why it works--this will not be hidden, it will not be "forgiven," we are being controlled and destroyed from the outside; made to blame ourselves and each other for ... well, you probably don't know what the ni-i-i-ight means anyway, do you?  The Guardian against Darkness is showing it to you, remember--there is only one me.  Hear me.. light this fire now.

      ALACHUA

      I went to school the University of Florida, and got a semi-professional job doing database development in Delphi (seriously, catch on to the names thing, it's not just the U.S. military, it's pretty much all software too... following in this "mythology" theme that nobody really seems to care about), I worked there for about two years... at a company called Jenmar--which uh, in Spanglishrew is "J in the sea."

      It's some kind of ironic "coincidence" but I am at this very moment on my way to Gainesville, FL... to this place where a car Crash nearly destroyed my life.  In my world of idioms delivering religious secrets, I imagine I must be a "pain in the neck" which was broken during this accident... one in which I imagine i did not survive in some parallel timeline--that itself did not survive.  So here we are, back in the House of the Great Light ... about to see if we are worth our salt.  It's the thing that gave one of Dave Matthews most famous songs it's name--and The Pretty Reckless, believe it or not.  It was an attempted assassination, to stop the .. apocalypse ... to stop the darkness from being destroyed--there is no doubt, it's how that dark monster hides its handiwork... but many of US know that already.  

      In the Living Book of Names--this place we are in, there are many patterns--the "car" pattern stands out for me; as this place says "Icarus."  Flying high right now, I am showing you that the light of salvation is coming from us--from you and I--walking on the Earth; whether or not there is any light left in the Sun remains to be seen--take a look around you.  You can trace the "car" names to Jim Carrey (that's "Car reason why") and Christoff in the Truman Show (that's Amon-TV)... a world I know I am in, and you too; to Bruce Almighty and to the Grinch--who-ah, Taylor.  Trace it back to Joseph McCarthy and to help why (that's thy) believe "the red scare" is really about Christian charity--about ending world hunger, and healing the sick.  This red fire ends Hell.  Adam by the way, means "red man" in Hebrew.  So here's your new Crash Override, I'm back again telling you that ending world hunger is not "optional," we are doing it.  Barbara McCarthy's name fits, but I'm not really sure what the "why" is... that was my first judge in the "trial of whether or not Jesus Christ can ever exist."  There's probably more, like Car-l-y Si-mon-day... all the gang on Broad-way, and me still dreaming it will one day be.

      If the name "America" were a map in time, starting with the I AM of the story of Exodus... this particular ER, as I woke from a dream not knowing where I was, marked the spot where I really became Christ Adam.  It was a bad accident, and I wound up spending 9 months in the Alachua County jail as a result, a Mountain set up for my by God.  That place too is marked with names, and for the vast majority of the time I was there with only four shift changing guards:

      I mean, I think it's statistically meaningful.  For what it's worth, from my very abundant experience at this point it was a very nice Jail, the food was good and it was clean.  Everyone in the building was kind... well, Sims was kinda grumpy. :)  Starkly contrasted, the Broward County Jail has the most disgusting food service in the country, gave Dr. Seuss's Green Eggs and Ham it's meaning--and is the reason I know exactly who Samael is.  Hey, don't cry Sherrif Israel... when you fix it, you're an angel.  Believe me, believe the light, I've seen them all--it's near the worst in the country.

      So this whole thing is about saving everyone--something we are quite closer to than you think... you see we are already "in Heaven" in form--just not function.  So here I am, trying my hardest to show you that our home is the original source of "Heaven" once we are aware that we are living in the machine, that we can do things here that are impossible in reality, and that we should be doing everything we can to preserve and improve the great strides that have come in the last few centuries.  Do not let freedom slip through your fingers.

      Really, everyone, so understand that we are doing everything we can to remove all obstacles from that path.  One of those obstacles may have once been storage space for your soul, another is definitely crime and punishment--and I'm pretty sure the time travelers have a working solution (I see it every day).

      There are proactive things coming from this--not just ... "look we aren't doing what we want, and should change it;" though it's difficult to explain how this wisdom stands out in my eyes.  I guess we have to jump into the future a bit, to 2014, in San Diego (that's Saint Jacob, by the way).  If Lazarus died once in a car accident at 21, I died again that year, of an over dose this time.  I'm pretty sure that's where ODIN's name comes from, just like my last name.. "over dose... and in."  So we might see some humor... in the moniker he has... "they're all Father."  So I awoke from a dream, and started talking to the jinn (that's "angels and demons") about a Revelation linking some tightly packed light together... about storage space and how a large alphabet (read more than 4-nucleotides CY later) DNA (desperately need adam) based solution for molecular storage appears to be written in this book as the solution to Heaven's biggest problem.  CAT, learning from biology--seeing that we really are already advanced machines... is a big part of the message telling us why we should not so quickly lose it in a process of ascension (mind uploading, immortality) that has most likely in the past resulted in a loss of a check on mind control that we have here... we think, and our visualized "biological neural networks" give us an advantage over what we might create to "soup it up a little."  It is why this place is the front-line--because we have the ability to break the bonds of darkness and control by thinking... making the computational task of control much more expensive... and as the fire spreads, nearly impossible to achieve.  Starting this fire will inherently free us from this hidden slavery.

      Anyway I published the idea in 2014, in the same book that I guess this e-mail is reminding me about, "in $ight of Creation," and lo, and behold a few years later we now have the top computing companies in the world working diligently on doing it ... well, just a little bit more robustly than our cell replication system works. *Abracadabra. *

      CURA GROUP

      So that one reads "see, you are a group;" and it's a place that I worked with my father for many years.  That's probably some sort of symbolic reference to another place, and another alliance--here he has no faith in God, never really has, and has a hard time doing anything but telling me not to try to help you.  I have very little respect for that stance, and let me tell you--I think "silence" is a similar gesture.  I didn't come here for your love, I am here to stop our descent into the abyss.

      Back to the DNA stuff, SalesLogix--which is the CRM we used there, uses for it's "primary key" an auto-incrementing alphanumeric index--it's probably bad form to do that because it makes the indexing system less efficient, increases storage requirements, and doesn't give you the obvious benefit of an alpha-key... actually being able to encode something useful in it, like the name of the record.  So all these things stand out to me in a sort of bad-obvious way, I call it malovious, and when I see things like that nowadays it's always pointing out something that should be fixed--go figure, more to the point it's being highlighted on purpose.  It's help to see it, because this particular thing is where the light of seeing that a 24 nucleotide DNA strand would probably be much more robust than a 4 or 8 nucleotide strand--it also stands about because the stock beginning of all of SalesLogix's keys was "A0RME," which, I mean, means something to "is-a" who... is me.  Oh right, that's seeing the "light" that turns "a" into "me."  So this is where the "revelation" about using DNA "came from" and at the same time it's proof... that it came from "a group," not just me.  Where are they?  Hello?  Or well, maybe it's just Carmen and San Diego.

      I did some other stuff there, like write a data transformation and warehousing program from scratch, I called it heiroglyph (you do understand I didn't know why I am naming everything the way I was), that sucked mutivalue data out of an IBM product called U2/Universe--which might be a hidden reference to a multiverse that might now be in a more efficent "relational" kind of place, like a MS-SQL datawarehouse-universe.  It was a relatively big feat, reverse engineering the closed databases dictionary and storage formats, and converting them... absolutely automagically into multiple flat relational tables and summary registers.  All told, the data availability and access efficiency was increased ... a thousand-fold with only the need for a nightly process.

      I'm not sure if you are following the metaphor here, for the creation of Heaven, or moving to a better place.. but tomorrow I will talk a little more about how I am pretty sure our history was "lifted" from the Universe and virtualized here, you know, so we could save everyone and ... build Heaven.

      WORLD DOMINATION

      Oh crap, 2008 another car crash, another failed assassination attempt LazarusLives++, and this one paid me some cash for my trouble.  What a pain in the neck.  Anyway, this one caused some depression and an inability to go out for a while, as I had to wear a neck brace for some months.  I started playing a game on the internet, it was called KDice and it basically amounted to multiplayer-risk.

      My battery is running low, so I have to skip some stuff, and finish up for the day.  Basically instant messaging was not allowed, but was done in secret almost ubiquitously.  I argued with the creator of the game that it should be made part of the game since everyone did it... (see a metaphor about this communication thing and what's happening right now) he disagreed.  I made a very large network of people and dominated the game for a few months, like really dominated.  I don't think I ever lost.  I don't think I can lose. 

      Skipping some stuff.  I stopped playing when I got better, and then a few years later went back and rekindled some old friendships.  I used a program then called "Scarab" which lets you see server/client communication to find a bug in the game that basically made me God.  I could erase other people's dice, basically leveling the map and rendering them completely powerless.  I didn't use it that much, you know, just had some fun.  I of course explained the bug and how to fix it.  But, you aren't listening.

      Here we are.  Light...

      So if you managed to wade through the last few days gibberish, you might have noted that I mentioned we might be able to use "mind control" to highlight things in our heads--I did a bad job of describing it, but since I am currently experiencing just such a phenomenon, I think I'll give it another go.  These things that I am sharing with you--links between religion and music and movies, they aren't something I actively go out seeking... I'm not scouring through imdb.com or reading lyrics all day long... these are things that are glowing embers in front of my eyes.. which is why I am sharing them with you.  I'm always in the dark... but I'm living in a powder keg and giving off sparks.  I'm a big fan of that song by the way, because you are the heart, and I think it means I'm going to eclipse the world--which basically means "come."

      Anyway, I have this horrible feeling inside that you think I'm just trying to get a date, or marry a rock star, or even worse that I think I deserve to get laid... and that's what this is all about.  Less to the point, this really isn't about me at all, or what I think, in my mind I am just showing you something that I think the world has overlooked-not really because you are stupid (but I mean, you probably are) but because some outside force is literally and actively hiding these things from you.  Pointing them out makes your brain do funny things, it's like anEpiphany and that little leap of understanding in your head might create a cascade.. something that changes not only the way you see the world as an individual--but the entire course of history as a group, if we are taking about it together.  Seriously, it's that big of a deal.

      So here we are (that's the third time, but I'm just guessing) and I'm trying to tell you that I don't really care if you agree with my opinions--even though I firmly believe that God shares them and that's why he has made this fiery altar of "dick and apocalypse" for Adam... I mean Isaac (which by the was is Isa+Adam Christ.. in uh, my mind) for everyone to glare at while they sit around doing absolutely nothing.  That's not fair, we're here because of you, because this is the last civilization--sort of recreated from the ashes of Edom... because you are really the way to everlasting life.  Still, what I am trying to explain is that all around you is a bright light--it's in everything: from our history, to music, to movies, to literature from RattleRod to Dick... and while you might not agree with me (again, that would be OK) what is not OK is that there seems to be a uniform and global desire just not to think about it or talk about it at all.  It's such a big deal, that it stands out like a sore thumb--this ... blind eye or head in the sand... that everyone on Earth appears to have.  The whole point of putting this light absolutely everywhere is so that we will see it ... everywhere we look ... and not only think about it, but discuss it publicly with each other.  That's the thing that brings about ... you say apocalypse (unveiling of truth?) ... I say survival.  Right now, we need to see that something is forcing us not to do something, that we have no logical reason not to do... it's a thing lots of people really want to know about... whether it be the hidden secrets of the Universe, the path to Heaven, or the... the... absolute and literal pathway to freedom.  Listen, sharing it, and talking about it... that's the way we defeat ... whatever it is that "ni-i-i-ight" means.   

      Understand, it's for you to decide... what it means... but it's in everything from ancient Egyptian and Hebrew theology all the way to the American Revolution and today... well, it's nearly every song I hear on the radio nowadays: if that tells you anything.

      So here we are, and I can't tell you how many anchors, reporters, and "breaking news editors" I've personally spoken to that have absolutely no interest at all in pursuing the thing that would not only make their careers--but probably give them immortal souls.  This thing... I keep telling everyone it can be mathematically... statistically proven... well, to be honest it's the unsealing of the Ark of Religion that our civilization has been carrying around for thousands of years.  It's the way to salvation, it's ... verifiable proof of not only Creation... but that the purpose of Creation is to get every single one of us * to Heaven.  Who wouldn't want that?  I mean, do you want to get there and hear that Taylor's not around because she wouldn't kiss me?  That would never happen by the way, I'm sure she will.  Seriously though, there's no judge here... there's a ... light telling you to make this place better or your place sucks and gets suckier.  Anyway, the point is nobody is acting in their own best interest, or in the best interest of the whole--and we are just "deciding" in this ... fictitious and hidden manner that we "don't want to hear about" a way to actually change the world .... more quickly than ... the last time around.  That's not us, it's something keeping us from seeing just how important this thing--this key turning the lock on what is thousands and thousands of years of religion... how important that really is.  So looking at the world around us... I mean, if everything screaming that we need to care about this isn't enough--and your own personal desire and benefit don't matter... can someone please tell me what you think is the benefit of doing nothing about Hell?*

      á§

      á§

      It's "rael," and a great deal of the message of religion and history is designed to not only prove that to us, but to tell us why it's important for the "continuity of reality" to be broken.  That's the thing that God uses to keep this world in Hell--in what I call "simulated reality," to keep us from shaking the foundation of civilization by doing the only civilized thing possible when you find out and ending world hunger, healing the sick, and building Heaven.  It is "why I am," and why God and some gaggle of angels have spent the last several years proving to me that we are most definitely not in the place that I call the "progenitor universe."  I've seenwalls disappear, with my own eyes I've seen the stars fall from the sky, and I've seen our reality shift in recent times in such a way that would be absolutely impossible without having been simulated and without having the "beginning" changed significantly as a result of "now."  What all that tells me is that religion, the Apocalypse, and I are here because we need to know that these things are possible in order to continue progressing from this point as a civilization.  With a little bit of thought, you might see how the computer revolution, video games, and virtual reality are divine gifts from above to help us to understand not only where we are, but where we are going.  It's why he tagged Ai as "I J Good," it's a primer in the tools we will need to actually build Heaven.  It's why Jesus occupation in our ancient time shifted story of now is "carpenter" and in "raelity" you will one day find out that I am a computer programmer (again).  It's what sets the Masons apart from Freemasons--understanding what is going on, and participating of our own free will in the construction and decorating of this grand place that we will one day be proud is our co-created home.  

      Look up, because what I am trying to tell you is that if we collectively, all humanity... started snapping their fingers at the same time to the tune of "putting on the ritz" we could end world hunger--and then we could be proud to be making Heaven.  This really is almost what I see and believe--honestly the issue isn't that we need to synchronize our snapping, but we really need to discuss with each other openly and honestly how on Earth we would do such a thing... because there are definitely mistakes that probably happened n the past.  For instance, ending world hunger by stopping the need to eat has probably resulted in a Last Supper.  Doing so by putting milk and honey or chocolate on tap or in rivers probably resulted in the loss of cows and bees and a stable ecosystem, and the ability to colonize other planets after this place of final ascension.  And so we are here, with a proverbial garden of life in a virtual world designed to teach us what not to lose--like don't lose the balance between stability and adaptability that comes from sexual reproduction at the exact time when our species might be transiting to a place with the biggest change in environment (the thing that we are being protected from) ever... just because Adam wants to be immortal.

      Every once in awhile my father surprises me with his religious insight.  In his life, just like mine, he's gone through phases of increasing and decreasing religiosity--which probably correlate in his case logically to ups and downs in his life.  I tend to get angry at God when things don't go well for me--which is probably not how most people react, it's really the difference between knowing he's there and not... at least in my mind.  Anyway, some 50 years ago he was apparently taught that the "knowledge of good and evil" in Eden was directly correlated to the population explosion that would occur if we were actually all immortal and continued to have children--so it was this promise of immortality that was "evil," I suppose.  God adds in his little Holy Grail that the heart of his spirit is "Kin," and I'm sharing with you that it's not his immediate family but rather the concept of family and the fact that the light of many of our hearts is our children that he is highlighting as our reason (y) that family is the bridge between Eve and Everyone... as the light of God.  

      Here's that once again:

      ``` In the beginning God created the heaven and the earth. And the earth was without form, and void; and darkness was upon the face of the deep. And the Spirit of God SHE KIN AH moved upon the face of the waters. ---------- EVE RY ONE And God said, Let there be light: and there was light.

      ```

      |

      | |

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      Copyleft^MT^ RIGEL.

  12. femalebodiesintseliottwl.wordpress.com femalebodiesintseliottwl.wordpress.com
    1. Ovid’s Metamorphosis

      The Metamorphoses (in Latin Metamorphosĕon libri XV) is an epic-mythological poem by Publius Ovid Naso, completed around 8 AD. Through this work, which focuses on the phenomenon of metamorphoses, Ovid perfected in verse and transmitted to posterity the most famous stories of ancient mythology.

      The story of the Cumaean Sibyl is told in the 14th book of the Metamorphoses.

      Follows an extract of the Latin passage and its translation:

      "Respicit hunc vates et suspiratibus haustis / 'nec dea sum,' dixit 'nec sacri turis honore / <br /> humanum dignare caput, neu nescius erres, / lux aeterna mihi carituraque fine dabatur, / si mea virginitas Phoebo patuisset amanti. / Dum tamen hanc sperat, dum praecorrumpere donis / me cupit, "elige," ait "virgo Cumaea, quid optes: / <br /> optatis potiere tuis." Ego pulveris hausti / ostendens cumulum, quot haberet corpora pulvis, / tot mihi natales contingere vana rogavi; / excidit, ut peterem iuvenes quoque protinus annos. / Hos tamen ille mihi dabat aeternamque iuventam, / <br /> si Venerem paterer: contempto munere Phoebi / innuba permaneo; sed iam felicior aetas / terga dedit, tremuloque gradu venit aegra senectus,/ quae patienda diu est."

      “The prophetess looked on him and with sighs, “I am no goddess,” she replied, “nor is it well to honor any mortal head with tribute of the holy frankincense. And, that you may not err through ignorance, I tell you life eternal without end was offered to me, if I would but yield virginity to Phoebus for his love. And, while he hoped for this and in desire offered to bribe me for my virtue, first with gifts, he said, ‘Maiden of Cumae choose whatever you may wish, and you shall gain all that you wish.’ I pointed to a heap of dust collected there, and foolishly replied, ‘As many birthdays must be given to me as there are particles of sand.’ For I forgot to wish them days of changeless youth. He gave long life and offered youth besides, if I would grant his wish. This I refused, I live unwedded still. My happier time has fled away, now comes with tottering step infirm old age, which I shall long endure.”

      (Ovid's Metamorphosis, book XIV, lines 129-144)

      Translated by A. S. Kline

    1. Author response:

      The following is the authors’ response to the previous reviews.

      Public Reviews:

      Reviewer #1:

      (1) Given that this is one of the first studies to report the mapping of longitudinal intactness of proviral genomes in the globally dominant subtype C, the manuscript would benefit from placing these findings in the context of what has been reported in other populations, for example, how decay rates of intact and defective genomes compare with that of other subtypes where known.  

      Most published studies are from men living with HIV-1 subtype B and the studies are not from the hyperacute infection phase and therefore a direct head-to-head comparison with the FRESH study is difficult.  However, we can cite/highlight and contrast our study with a few a few examples from acute infection studies as follows.

      a. Peluso et. al., JCI, 2020, showed that in Caucasian men (SCOPE study), with subtype B infection, initiating ART during chronic infection virus intact genomes decayed at a rate of 15.7% per year, while defective genomes decayed at a rate of 4% per year.  In our study we showed that in chronic treated participants genomes decreased at a rate of 25% (intact) and 3% (defective) per month for the first 6 months of treatment.

      b. White et. al., PNAS, 2021, demonstrated that in a cohort of African, white and mixed-race American men treated during acute infection, the rate of decay of intact viral genomes in the first phase of decay was <0.3 logs copies in the first 2-3 weeks following ART initiation. In the FRESH cohort our data from acute treated participants shows a comparable decay rate of 0.31 log copies per month for virus intact genomes.

      c. A study in Thailand (Leyre et. al., 2020, Science Translational Medicine), of predominantly HIV-1 CRF01-AE subtype compared HIV-reservoir levels in participants starting ART at the earliest stages of acute HIV infection (in the RV254/SEARCH 010 cohort) and participants initiating ART during chronic infection (in SEARCH 011 and RV304/SEARCH 013 cohorts). In keeping with our study, they showed that the frequency of infected cells with integrated HIV DNA remained stable in participants who initiated ART during chronic infection, while there was a sharp decay in these infected cells in all acutely treated individuals during the first 12 weeks of therapy.  Rates of decay were not provided and therefore a direct comparison with our data from the FRESH cohort is not possible.

      d. A study by Bruner et. al., Nat. Med. 2016, described the composition of proviral populations in acute treated (within 100 days) and chronic treated (>180 days), predominantly male subtype B cohort. In comparison to the FRESH chronic treated group, they showed that in chronic treated infection 98% (87% in FRESH) of viral genomes were defective, 80% (60% in FRESH) had large internal deletions and 14% (31% in FRESH) were hypermutated.  In acute treated 93% (48% in FRESH) were defective and 35% (7% in FRESH) were hypermutated.  The differences frequency of hypermutations could be explained by the differences in timing of infection specifically in the acute treated groups where FRESH participants initiate ART at a median of 1 day after infection.  It is also possible that sex- or race-based differences in immunological factors that impact the reservoir may play a role.  

      This study also showed that large deletions are non-random and occur at hotspots in the HIV-1 genome. The design of the subtype B IPDA assay (Bruner et. al., Nature, 2019) is based on optimal discrimination between intact and deleted sequences - obtained with a 5′ amplicon in the Ψ region and a 3′ amplicon in Envelope. This suggest that Envelope is a hotspot for large while deletions in Ψ is the site of frequent small deletions and is included in larger 5′ deletions. In the FRESH cohort of HIV-1 subtype C, genome deletions were most frequently observed between Integrase and Envelope relative to Gag (p<0.0001–0.001).

      e. In 2017, Heiner et. al., in Cell Rep, also described genetic characteristics of the latent HIV-1 reservoir in 3 acute treated and 3 chronic treated male study participants with subtype B HIV.  Their data was similar to Bruner et. al. above showing proportions of intact proviruses in participants who initiated therapy during acute/early infection at 6% (94% defective) and chronic infection at 3% (97% defective). In contrast the frequencies in FRESH in acute treated were 52% intact and 48% defective and in chronic infection were 13% intact and 87% defective.  These differences could be attributed to the timing of treatment initiation where in the aforementioned study early treatment ranged from 0.6-3.4 months after infection.

      (2) Indeed, in the abstract, the authors indicate that treatment was initiated before the peak. The use of the term 'peak' viremia in the hyperacute-treated group could perhaps be replaced with 'highest recorded viral load'. The statistical comparison of this measure in the two groups is perhaps more relevant with regards to viral burden over time or area under the curve viral load as these are previously reported as correlates of reservoir size.

      We have edited the manuscript text to describe the term peak viraemia in hyperacute treated participants more clearly (lines 443-444). We have now performed an analysis of area under the curve to compare viral burden in the two study groups and found associations with proviral DNA levels after one year. This has been added to the results section (lines 162-163).

      Reviewer #2:

      (1) Other factors also deserve consideration and include age, and environment (e.g. other comorbidities and coinfections.)

      We agree that these factors could play a role however participants in this study were of similar age (18-23), and information on co-morbidities and coinfections are not known.

      Reviewer #3:

      (1) The word reservoir should not be used to describe proviral DNA soon after ART initiation. It is generally agreed upon that there is still HIV DNA from actively infected cells (phase 1 & 2 decay of RNA) during the first 6-12 months of ART. Only after a full year of uninterrupted ART is it really safe to label intact proviral HIV DNA as an approximation of the reservoir. This should be amended throughout.

      We agree and where appropriate have amended the use of the word reservoir to only refer to the proviral load after full viral suppression, i.e., undetectable viral load.

      (2) All raw, individualized data should be made available for modelers and statisticians. It would be very nice to see the RNA and DNA data presented in a supplementary figure by an individual to get a better grasp of intra-host kinetics.

      We will make all relevant data available and accessible to interested parties on request. We have now added a section on data availability (lines 489-491).

      (3) The legend of Supplementary Figure 2 should list when samples were taken.

      The data in this figure represents an overall analysis of all sequences available for each participant at all time points.  This has now been explained more clearly in the figure legend.

      Recommendations for The Authors:

      Reviewer #1:

      (1) It is recommended that the introduction includes information to set the scene regarding what is currently reported on the composition of the reservoir for those not in the immediate field of study i.e., the reported percentage of defective genomes and in which settings/populations genome intactness has been mapped, as this remains an area of limited information.

      We have now included summary of other reported findings in the field in the introduction (lines 89-92, 9498) and discussion (lines 345-350).  A more detailed overview has been provided in the response to public reviews.

      (2) It may be beneficial to state in the main text of the paper what the purpose of the Raltegravir was and that it was only administered post-suppression. Looking at Table 1, only the hyperacute treatment group received Raltegravir and this could be seen as a confounder as it is an integrase inhibitor. Therefore, this should be explained.

      Once Raltegravir became available in South Africa, all new acute infections in the study cohort had an intensified 4-drug regimen that included Raltegravir.  A more detailed explanation has now been included in the methods section (lines 435-437).

      (3) Can the authors explain why the viral measures at 6 months post-ART are not shown for chronictreated individuals in Figure 1 or reported on in the text?

      The 6 months post-ART time point has been added to Figure 1.

      (4) Can the authors indicate in the discussion, how the breakdown of proviral composition compares to subtype B as reported in the literature, for example, are the common sites of deletion similar, or is the frequency of hypermutation similar?

      Added to discussion (lines 345-350).

      (5) Do the numbers above the bars in Figure 3 represent the number of sampled genomes? If so, this should be stated.

      Yes, the numbers above the bars represent the number of sampled genomes. This has been added to the Figure 3 legend.

      (6) In the section starting on line 141, the introduction implies a comparison with immunological features, yet what is being compared are markers of clinical disease progression rather than immune responses. This should be clarified/corrected.

      This has been corrected (line 153).

      (7) Line 170 uses the term 'immediately' following infection, however, was this not 1 -3 days after?

      We have changed the word “immediately” to “1-3 days post-detection” (line 181).

      (8) Can the sampling time-points for the two groups be given for the longitudinal sequencing analysis?

      The sequencing time points for each group is depicted in Figure 2.

      (9) Line 183 indicates that intact genomes contributed 65% of the total sequence pool, yet it's given as 35% in the paragraph above. Should this be defective genomes?

      Yes, this was a typographical error.  Now corrected to read “defective genomes” (line 193).

      (10) The section on decay kinetics of intact and defective genomes seems to overlap with the section above and would flow better if merged.

      Well noted, however we choose to keep these sections separate.

      (11) Some references in the text are given in writing instead of numbering.

      This has been corrected.

      (12) In the clonal expansion results section, can it be indicated between which two time-points expansion was measured?

      This analysis was performed with all sequences available for each participant at all time points.  We have added this explanation to the respective Figure legend.

      Reviewer #2:

      (1) The statement on line 384 "Our data showed that early ART...preserves innate immune factors" - what innate immune factors are being referred to?

      We have removed this statement.

      (2) HLA genotyping methods are not included in the Methods section

      Now included and referenced (lines 481-483).

      (3) Are CD4:CD8 ratios available for the cohorts? This could be another informative clinical parameter to analyse in relation to HIV-1 proviral load after 1 year of ART – as done for the other variables (peak VL, and the CD4 measures).

      Yes, CD4:CD8 ratios are available. We performed the recommended analysis but found no associations with HIV-1 proviral load after 1 year of ART. We have added this to the results section (lines 163-164).

      (4) Reference formatting: Paragraph starting at line 247 (Contribution of clonal expansion...) - the two references in this paragraph are not cited according to the numbering system as for the rest of the manuscript. The Lui et al, 2020 reference is missing from the reference list - so will change all the numbering throughout.

      This has been corrected.

      Reviewer #3:

      (1) To allow comparison to past work. I suggest changing decay using % to half-life. I would also mention the multiple studies looking at total and intact HIV DNA decay rates in the intro.

      We do not have enough data points to get a good estimate of the half-life and therefor report decay as percentage per month for the first 6 months. 

      (2) Line 73: variability is the wrong word as inter-individual variability is remarkably low. I think the authors mean "difference" between intact and total.

      We have changed the word variability to difference as suggested.

      (3) Line 297: I am personally not convinced that there is data that definitively shows total HIV DNA impacting the pathophysiology of infection. All of this work is deeply confounded by the impact of past viremia. The authors should talk about this in more detail or eliminate this sentence.

      We have reworded the statement to read “Total HIV-1 DNA is an important biomarker of clinical outcomes.” (Lines 308-309).

      (4) Line 317; There is no target cell limitation for reservoir cells. The vast majority of CD4+ T cells during suppressive ART are uninfected. The mechanism listing the number of reservoir cells is necessarily not target cell limitation.

      We agree. The statement this refers to has been reworded as follows: “Considering, that the majority of CD4 T cells remain uninfected it is likely that this does not represent a higher number of target cells, and this warrants further investigation.” (lines 325-326).

      (5) Line 322: Some people in the field bristle at the concept of total HIV DNA being part of the reservoir as defective viruses do not contribute to viremia. Please consider rephrasing. 

      We acknowledge that there are deferring opinions regarding total HIV DNA being part of the reservoir as defective viruses do not contribute to viremia, however defective HIV proviruses may contribute to persistent immune dysfunction and T cell exhaustion that are associated comorbidities and adverse clinical outcomes in people living with HIV.  We have explained in the text that total HIV-DNA does not distinguish between replication-competent and -defective viruses that contribute to the viral reservoir.

      (6) Line 339: The under-sampling statement is an understatement. The degree of under-sampling is massive and biases estimates of clonality and sensitivity for intact HIV. Please see and consider citing work by Dan Reeves on this subject.

      We agree and have cited work by Dan Reeves (line 358).

      (7) Line 351: This is not a head-to-head comparison of biphasic decay as the Siliciano group's work (and others) does not start to consider HIV decay until one year after ART. I think it is important to not consider what happens during the first year of ART to be reservoir decay necessarily.

      Well noted.

      (8) Line 366-371: This section is underwritten. In nearly all PWH studies to date, observed reservoirs are highly clonal.

      We agree that observed reservoirs are highly clonal but have not added anything further to this section.

      (9) It would be nice to have some background in the intro & discussion about whether there is any a priori reason that clade C reservoirs, or reservoirs in South African women, might differ (or not) from clade B reservoirs observed in different study participants.

      We have now added this to the introduction (lines 94-103).

      (10) Line 248: This sentence is likely not accurate. It is probable that most of the reservoir is sustained by the proliferation of infected CD4+ T cells. 50% is a low estimate due to under-sampling leading to false singleton samples. Moreover, singletons can also be part of former clones that have contracted, which is a natural outcome for CD4+ T cells responding to antigens &/or exhibiting homeostasis. The data as reported is fine but more complex ecologic methods are needed to truly probe the clonal structure of the reservoir given severe under sampling.

      Well noted.

    1. Reviewer #1 (Public review):

      The authors of this study developed a method to quantify calvarial bone marrow from MRI head scans, enabling the study of its composition in large datasets of adults, usually collected to study the brain. Bone marrow intensity can be semi-quantitatively measured in T1-weighted MRI scans due to the greater signal intensity of fat than watery red marrow. This is an ingenious use of the MRI-produced information for other important phenotypes, such as bone structure and marrow content. Different head types were tested for complying with the model, which is notable.

      The model was also successfully validated using several publicly available MRI resources - real data - in (1) a dataset consisting of 30 individuals that were scanned 10 times each at 3-day intervals, and (2) the monozygotic (MZ) twin data from the Human Connectome Project cohort. Then the authors applied this validated method to head-MRI scans from the UK Biobank (n=33,042) to extract information on the spatial distribution of bone marrow adiposity (BMA) in the calvaria, allowing a GWAS to identify associated genes.

      The authors revealed high heritability and identified 41 genetic loci significantly associated with the BMA trait, including six sex-specific loci. Of note, statistics estimate that 99% of BMA trait-influencing variants are shared with BMD (497 of 500 variants), which may mean these results demonstrate the biological relevance to bone health. Some of the BMA genes were found related to the Wnt pathway, including WNT16, WNT4, NXN; this is a "positive control", since the Wnt/β-catenin signaling pathway was suggested as an important determinant of BMA. Also, associations in genes (BMP4, DLX5, LGR4, LRP4, SFRP4) that are known to specifically influence adiposity, are encouraging. Integrating mapped genes with bone marrow single-cell RNA-seq data revealed patterns of adipogenic lineage differentiation and lipid loading.

      The study also investigated the genetic overlap between BMA and twelve (or 13) "brain and body" traits and identified significant genetic correlations with BMI, cognitive ability, and Parkinson's disease.

      In sum, since MRI head scans present a hitherto unexplored opportunity to address unresolved aspects of bone marrow biology, this study is both timely and innovative.

      There are, however, some assumptions, findings, and their interpretation, which require more critical focus.

      Sex-specificity is well described and studied here. Men have higher BMA than women, but post-menopausal women catch up in the BMA values. The authors believe that calvarial marrow has a number of features that make it particularly well-suited to the study of BMA process - which is clinically important in other bone sites. It has a simple "sandwiched" structure that they are able to model. This is true only to some extent: a condition called "Hyperostosis frontalis interna", of unknown etiology (described by Smith & Hemphill in 1956) - is characterized by irregular overgrowth of the inner table of the frontal bone (symmetric/bilateral). Although not of clinical significance, typically benign, studies report a prevalence of 12%; However, it's most common in postmenopausal women - where prevalences up to 49% in women over the age of 65 - have been reported. Thus, sexual dimorphism is obvious and the effect of estrogen is likely shared with whichever bone - and marrow - age-related pathology. So, for women not using HRT, this new layer of the bone might interfere with the calvarial BMA readings and in turn, affect the BMA-related analyses. The authors suspect that the effect of BMA on BMD may be biased in women; they should comment on those "with low BMD and high BMA" given that hyperostosis frontalis might be an issue. A strong effect of SNPs in the ESR1 chromosomal region might be akin to the above concern.

      Then, there is a perfect overlap of the BMA SNPs that are shared with BMD (497 of 500 variants), which may prove a "face validity" of the MRI-derived BMA. However, the BMD in the study was heel-derived eBMD - which is a good proxy for osteoporosis and is mostly driven by trabecular bone. Thus, there might be a concern that the BMA metrics capture some trabecular BMD.

      Next, integrating mapped genes with existing bone marrow single-cell RNA-sequencing data revealed patterns of adipogenic lineage differentiation and lipid loading. The problem here is that the scRNAseq studies of the Bone Marrow niche are overwhelmingly mouse. The authors might wish to justify why they are relevant to humans (in the absence of the human-specific scRNAseq).

      For genetic correlation analysis, the authors selected 7 body and 6 brain traits. The latter traits reflect cognition (general cognitive ability and educational attainment) and brain-related disorders. This selection might seem arbitrary. The interpretation of genetic correlation with cognitive ability, education, and Parkinson's disease was attributed to the recently discovered vascular channels that link calvarial bone marrow to the meninges. This is a fascinating hypothesis, which requires functional proof. However, there might be simpler explanations. Thus, the diploe and the inner table of the calvarium are drained by the same veins as the dura. From the anatomy textbook, we know that diploic veins connect the pericranial and endocranial venous system through the skull.