Reviewer #2 (Public Review):

Summary:

The manuscript by Kaneda et al "FBXO24 ensures male fertility by preventing abnormal accumulation 2 of membraneless granules in sperm flagella" is a significant paper on the role of FBXO24 in murine male germ cell development and sperm ultrastructure and function. The body of experimental evidence that the authors present is extraordinarily strong in both breadth and depth. The authors investigate the protein's functions in male germ cells and sperm using a wide variety of approaches but focusing predominantly on their novel mouse model featuring deletion of the Fbxo24 gene and its product. Using this mouse, and a cross of it with another model that expresses reporters in the head and midpiece, they logically build from one experiment to the next. Together, their data show that this protein is involved in the regulation of membraneless electron-dense structures; loss of FBXO24 led to an accumulation of these materials and defects in the sperm flagellum and fertilizing ability. Interestingly, the authors found that several of the best-known components of electron-dense ribonucleoprotein granules that are found in the intermitochondrial cement and chromatoid body were not disrupted in the Fbxo24 knockout, suggesting that the electron-dense material and these structures are not all the same, and the biology is more complicated than some might have thought. They found evidence for the most changes in IPO5 and KPNB1, and biochemical evidence that FBXO24 and IPO5 could interact.

Strengths:

The authors are to be commended for the thoroughness of their experimental approaches and the extent to which they investigated impacts on sperm function and potential biochemical mechanisms. Very briefly, they start by showing that the Fbxo24 message is present in spermatids and that the protein can interact with SKP1, in a way that is dependent on its F-box domain. This points toward a potential function in protein degradation. To test this, they next made the knockout mouse, validated it, and found the males to be sterile, although capable of plugging a female. Looking at the sperm, they identified a number of ultrastructural and morphological abnormalities, which they looked at in high resolution using TEM. They also cross their model with RBGS mice so that they have reporters in both the acrosome and mitochondria. The authors test a variety of sperm functions, including motility parameters, ability to fertilize by IVF, cumulus-free IVF, zona-free-IVF, and ICSI. They found that ICSI could rescue the knockout but not other assisted reproductive technologies. Defects in male fertility likely resulted from motility disruption and failure to get through the utero-tubal junction but defects in acrosome exocytosis also were noted. The authors performed thorough investigations including both targeted and unbiased approaches such as mass spectrometry. These enabled them to show that although the loss of the FBXO24 protein led to more RNA and elevated levels of some proteins, it did not change others that were previously identified in the electron-dense RNP material.

The manuscript will be highly significant in the field because the exact functions of the electron-dense RNP materials have remained somewhat elusive for decades. Much progress has been made in the past 15 years but this work shows that the situation is more complex than previously recognized. The results show critical impacts of protein degradation in the differentiation process that enables sperm to change from non-descript round cells into highly polarized and compartmentalized mature sperm, with an equally highly compartmentalized flagellum. This manuscript also sets a high bar for the field in terms of how thorough it is, which reveals wide-ranging impacts on processes such as mitochondrial compaction and arrangement in the midpiece, the correct building of the major cytoskeletal elements in the flagellum, etc.

Weaknesses:

There are no real weaknesses in the manuscript that result from anything in the control of the authors. They attempted to rescue the knockout by expressing a FLAG-tagged Fbxo24 transgene, but that did not rescue the phenotype, either because of inappropriate levels/timing/location of expression, or because of interference by the tag. They also could not make anti-FBXO24 that worked for co-immunoprecipitation experiments, so relied on the FLAG epitope, an approach that successfully showed co-IP with IPO5 and SKP1.

Certainly! The way it works is when you create a new note and associate it with a user, you will typically provide the user's unique identifier (ID) as the value for the user field. In a typical scenario, this user ID would come from the "user" collection in your database.

Here's a brief example in code:

```javascript // Assume you have a User model and you have obtained a user's ID const userId = "someUserId"; // This would be the actual ID of the user in your database

// Creating a new note and associating it with the user by providing the user ID const newNote = new Notes({ user: userId, title: "Sample Note", description: "This is a sample note.", tag: "General", deadline: new Date(), deadlinetime: "12:00 PM", });

// Save the new note to the database newNote.save() .then((savedNote) => { console.log("Note saved successfully:", savedNote); }) .catch((error) => { console.error("Error saving note:", error); }); ```

In this example, the user field of the new note is set to the userId obtained from the "user" collection. When you save this note to the database, the user field will store the provided user ID. Later, when you retrieve this note, you can use this user ID to identify which user the note is associated with.

It's important to ensure that the user ID you provide for the user field actually corresponds to an existing user in the "user" collection. This way, you maintain the relationship between the "notes" and "user" collections in your MongoDB database.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Koumoundourou et al., identify a pathway downstream of Bcl11b that controls synapse morphology and plasticity of hippocampal mossy fiber synapses. Using an elegant combination of in vivo, ex vivo, and in vitro approaches, the authors build on their previous work that indicated C1ql2 as a functional target of Bcl11b (De Bruyckere et al., 2018). Here, they examine the functional implications of C1ql2 at MF synapses in Bcl11b cKO mice and following C1ql2 shRNA. The authors find that Bcl11b KO and shRNA against C1ql2 significantly reduces the recruitment of synaptic vesicles and impairs LTP at MF synapses. Importantly, the authors test a role for the previously identified C1ql2 binding partner, exon 25b-containing Nrxn3 (Matsuda et al., 2016), as relevant at MF synapses to maintain synaptic vesicle recruitment. To test this, the authors developed a K262E C1ql2 mutant that disrupts binding to Nrxn3. Curiously, while Bcl11b KO and C1ql2 KD largely phenocopy (reduced vesicle recruitment and impaired LTP), only vesicle recruitment is dependent on C1ql2-Nrxn3 interactions. These findings provide new insight into the functional role of C1ql2 at MF synapses. While the authors convincingly demonstrate a role for C1ql2-Nrxn3(25b+) interaction for vesicle recruitment and a Nrxn3(25b+)independent role for C1ql2 in LTP, the underlying mechanisms remain inconclusive. Additionally, a discussion of how these findings relate to previous work on C1ql2 at mossy fiber synapses and how the findings contribute to the biology of Nrxn3 would increase the interpretability of this work.

As suggested by reviewer #1, we extended our discussion of previous work on C1ql2 and additionally discussed the biology of Nrxn3 and how our work relates to it. Moreover, we extended our mechanistic analysis of how Bcl11b/C1ql2/Nrxn3 pathway controls synaptic vesicle recruitment as well as LTP (please see also response to reviewer #2 points 5 and 8 and reviewer #3 point 4 of public reviews below for detailed discussion).

Reviewer #2 (Public Review):

This manuscript describes experiments that further investigate the actions of the transcription factor Bcl11b in regulating mossy fiber (MF) synapses in the hippocampus. Prior work from the same group had demonstrated that loss of Bcl11b results in loss of MF synapses as well as a decrease in LTP. Here the authors focus on a target of Bcl11b a secreted synaptic organizer C1ql2 which is almost completely lost in Bcl11b KO. Viral reintroduction of C1ql2 rescues the synaptic phenotypes, whereas direct KD of C1ql2 recapitulates the Bcl1 phenotype. C1ql2 itself interacts directly with Nrxn3 and replacement with a binding deficient mutant C1q was not able to rescue the Bcl11b KO phenotype. Overall there are some interesting observations in the study, however there are also some concerns about the measures and interpretation of data.

The authors state that they used a differential transcriptomic analysis to screen for candidate targets of Bcl11b, yet they do not present any details of this screen. This should be included and at the very least a table of all DE genes included. It is likely that many other genes are also regulated by Bcl11b so it would be important to the reader to see the rationale for focusing attention on C1ql2 in this study.

The transcriptome analysis mentioned in our manuscript was published in detail in our previous study (De Bruyckere et al., 2018), including chromatin-immunoprecipitation that revealed C1ql2 as a direct transcriptional target of Bcl11b. Upon revision of the manuscript, we made sure that this was clearly stated within the main text module to avoid future confusion. In the same publication (De Bruyckere et al., 2018), we discuss in detail several identified candidate genes such as Sema5b, Ptgs2, Pdyn and Penk as putative effectors of Bcl11b in the structural and functional integrity of MFS. C1ql2 has been previously demonstrated to be almost exclusively expressed in DG neurons and localized to the MFS.

There it bridges the pre- and post-synaptic sides through interaction with Nrxn3 and KAR subunits, respectively, and regulates synaptic function (Matsuda et al., 2016). Taken together, C1ql2 was a very good candidate to study as a potential effector downstream of Bcl11b in the maintenance of MFS structure and function. However, as our data reveal, not all Bcl11b mutant phenotypes were rescued by C1ql2 (see supplementary figures 2d-f of revised manuscript). We expect additional candidate genes, identified in our transcriptomic screen, to act downstream of Bcl11b in the control of MFS.

All viral-mediated expression uses AAVs which are known to ablate neurogenesis in the DG (Johnston DOI: 10.7554/eLife.59291) through the ITR regions and leads to hyperexcitability of the dentate. While it is not clear how this would impact the measurements the authors make in MF-CA3 synapses, this should be acknowledged as a potential caveat in this study.

We agree with reviewer #2 and are aware that it has been demonstrated that AAV-mediated gene expression ablates neurogenesis in the DG. To avoid potential interference of the AAVs with the interpretability of our phenotypes, we made sure during the design of the study that all of our control groups were treated in the same way as our groups of interest, and were, thus, injected with control AAVs. Moreover, the observed phenotypes were first described in Bcl11b mutants that were not injected with AVVs (De Bruyckere et al., 2018). Finally, we thoroughly examined the individual components of the proposed mechanism (rescue of C1ql2 expression, over-expression of C1ql3 and introduction of mutant C1ql2 in Bcl11b cKOs, KD of C1ql2 in WT mice, and Nrxn123 cKO) and reached similar conclusions. Together, this strongly supports that the observed phenotypes occur as a result of the physiological function of the proteins involved in the described mechanism and not due to interference of the AAVs with these biological processes. We have now addressed this point in the main text module of the revised ms.

The authors claim that the viral re-introduction "restored C1ql2 protein expression to control levels. This is misleading given that the mean of the data is 2.5x the control (Figure 1d and also see Figure 6c). The low n and large variance are a problem for these data. Moreover, they are marked ns but the authors should report p values for these. At the least, this likely large overexpression and variability should be acknowledged. In addition, the use of clipped bands on Western blots should be avoided. Please show the complete protein gel in primary figures of supplemental information.

We agree with reviewer #2 that C1ql2 expression after its re-introduction in Bcl11b cKO mice was higher compared to controls and that this should be taken into consideration for proper interpretation of the data. To address this, based also on the suggestion of reviewer #3 point 1 below, we overexpressed C1ql2 in DG neurons of control animals. We found no changes in synaptic vesicle organization upon C1ql2 over-expression compared to controls. This further supports that the observed effect upon rescue of C1ql2 expression in Bcl11b cKOs is due to the physiological function of C1ql2 and not as result of the overexpression. These data are included in supplementary figure 2g-j and are described in detail in the results part of the revised manuscript.

Additionally, we looked at the effects of C1ql2 overexpression in Bcl11b cKO DGN on basal synaptic transmission. We plotted fEPSP slopes versus fiber volley amplitudes, measured in slices from rescue animals, as we had previously done for the control and Bcl11b cKO (Author response image 1a). Although regression analysis revealed a trend towards steeper slopes in the rescue mice (Author response image 1a and b), the observation did not prove to be statistically significant, indicating that C1ql2 overexpression in Bcl11b cKO animals does not strongly alter basal synaptic transmission at MFS. Overall, our previous and new findings support that the observed effects of the C1ql2 rescue are not caused by the artificially elevated levels of C1ql2, as compared to controls, but are rather a result of the physiological function of C1ql2.

Following the suggestion of reviewer #2 all western blot clipped bands were exchanged for images of the full blot. This includes figures 1c, 4c, 6b and supplementary figure 2g of the revised manuscript. P-value for Figure 1d has now been included.

Author response image 1.

C1ql2 reintroduction in Bcl11b cKO DGN does not significantly alter basal synaptic transmission at mossy fiber-CA3 synapses. a Input-output curves generated by plotting fEPSP slope against fiber volley amplitude at increasing stimulation intensities. b Quantification of regression line slopes for input-output curves for all three conditions. Control+EGFP, 35 slices from 16 mice; Bcl11b cKO+EGFP, 32 slices from 14 mice; Bcl11b cKO+EGFP-2A-C1ql2, 22 slices from 11 mice. The data are presented as means, error bars represent SEM. Kruskal-Wallis test (non-parametric ANOVA) followed by Dunn’s post hoc pairwise comparisons. p=0.106; ns, not significant.

Measurement of EM micrographs: As prior work suggested that MF synapse structure is disrupted the authors should report active zone length as this may itself affect "synapse score" defined by the number of vesicles docked. More concerning is that the example KO micrographs seem to have lost all the densely clustered synaptic vesicles that are away from the AZ in normal MF synapses e.g. compare control and KO terminals in Fig 2a or 6f or 7f. These terminals look aberrant and suggest that the important measure is not what is docked but what is present in the terminal cytoplasm that normally makes up the reserve pool. This needs to be addressed with further analysis and modifications to the manuscript.

As requested by reviewer #2 we analyzed and reported in the revised manuscript the active zone length. We found that the active zone length remained unchanged in all conditions (control/Bcl11b cKO/C1ql2 rescue, WT/C1ql2 KD, control/K262E and control/Nrxn123 cKO), strengthening our results that the described Bcl11b/C1ql2/Nrxn3 mechanism is involved in the recruitment of synaptic vesicles. These data have been included in supplementary figures 2c, 4h, 5f and 6g and are described in the results part of the revised manuscript.

We want to clarify that the synapse score is not defined by the number of docked vesicles to the plasma membrane. The synapse score, which is described in great detail in our materials and methods part and has been previously published (De Bruyckere et al., 2018), rates MFS based on the number of synaptic vesicles and their distance from the active zone and was designed according to previously described properties of the vesicle pools at the MFS. The EM micrographs refer to the general misdistribution of SV in the proximity of MFS. Upon revision of the manuscript, we made sure that this was clearly stated in the main text module to avoid further confusion.

The study also presents correlated changes in MF LTP in Bcl11b KO which are rescued by C1ql2 expression. It is not clear whether the structural and functional deficits are causally linked and this should be made clearer in the manuscript. It is also not apparent why this functional measure was chosen as it is unlikely that C1ql2 plays a direct role in presynaptic plasticity mechanisms that are through a cAMP/ PKA pathway and likely disrupted LTP is due to dysfunctional synapses rather than a specific LTP effect.

The inclusion of functional experiments in this and our previous study (de Bruyckere et al., 2018) was first and foremost intended to determine whether the structural alterations observed at MFB disrupt MFS signaling. From the signaling properties we tested, basal synaptic transmission (this study) and short-term potentiation (de Bruyckere et al., 2018) were unaltered by Bcl11b KO, whereas MF LTP was found to be abolished (de Bruyckere et al., 2018). Indeed, because MF LTP largely depends on presynaptic mechanisms, including the redistribution of the readily releasable pool and recruitment of new active zones (Orlando et al., 2021; Vandael et al., 2020), it appears to be particularly sensitive to the specific structural changes we observed. We therefore believe that it is valuable information that MF LTP is affected in Bcl11b cKO animals - it conveys a direct proof for the functional importance of the observed morphological alterations, while basic transmission remains largely normal. Furthermore, it subsequently provided a functional marker for testing whether the reintroduction of C1ql2 in Bcl11b cKO animals or the KD of C1ql2 in WT animals can functionally recapitulate the control or the Bcl11b KO phenotype, respectively.

We fully agree with the reviewer that C1ql2 is unlikely to directly participate in the cAMP/PKA pathway and that the ablation of C1ql2 likely disrupts MF LTP through an alternative mode of action. Our original wording in the paragraph describing the results of the forskolin-induced LTP experiment might have overstressed the importance of the cAMP pathway. We have now rephrased that paragraph to better describe the main idea behind the forskolin experiment, namely to circumvent the initial Ca2+ influx in order to test whether deficient presynaptic Ca2+ channel/KAR signaling might be responsible for the loss of LTP in Bcl11b cKO. The results are strongly indicative of a downstream mechanism and further investigation is needed to determine the specific mechanisms by which C1ql2 regulates MFLTP, especially in light of the result that C1ql2.K262E rescued LTP, while it was unable to rescue the SV recruitment at the MF presynapse. This raises the possibility that C1ql2 can influence MF-LTP through additional, yet uncharacterized mechanisms, independent of SV recruitment. As such, a causal link between the structural and functional deficits remains tentative and we have now emphasized that point by adding a respective sentence to the discussion of our revised manuscript. Nevertheless, we again want to stress that the main rationale behind the LTP experiments was to assess the functional significance of structural changes at MFS and not to elucidate the mechanisms by which MF LTP is established.

The authors should consider measures that might support the role of Bcl11b targets in SV recruitment during the depletion of synapses or measurements of the readily releasable pool size that would complement their findings in structural studies.

We fully agree that functional measurements of the readily releasable pool (RRP) size would be a valuable addition to the reported redistribution of SV in structural studies. We have, in fact, attempted to use high-frequency stimulus trains in both field and single-cell recordings (details on single-cell experiments are described in the response to point 8) to evaluate potential differences in RRP size between the control and Bcl11b KO (Figure for reviewers 2a and b). Under both recording conditions we see a trend towards lower values of the intersection between a regression line of late responses and the y-axis. This could be taken as an indication of slightly smaller RRP size in Bcl11b mutant animals compared to controls. However, due to several technical reasons we are extremely cautious about drawing such far-reaching conclusions based on these data. At most, they suffice to conclude that the availability of release-ready vesicles in the KO is likely not dramatically smaller than in the control.

The primary issue with using high-frequency stimulus trains for RRP measurements at MFS is the particularly low initial release probability (Pr) at these synapses. This means that a large number of stimulations is required to deplete the RRP. As the RRP is constantly replenished, it remains unclear when steady state responses are reached (reviewed by Kaeser and Regehr, 2017). This is clearly visible in our single-cell recordings (Author response image 2b), which were additionally complicated by prominent asynchronous release at later stages of the stimulus train and by a large variability in the shapes of cumulative amplitude curves between cells. In contrast, while the cumulative amplitude curves for field potential recordings do reach a steady state (Author response image 2a), field potential recordings in this context are not a reliable substitute for single cell or, in the case of MFB, singlebouton recordings. Postsynaptic cells in field potential recordings are not clamped, meaning that the massive release of glutamate due to continuous stimulation depolarizes the postsynaptic cells and reduces the driving force for Na+, irrespective of depletion of the RRP. This is supported by the fact that we consistently observed a recovery of fEPSP amplitudes later in the trains where RRP had presumably been maximally depleted. In summary, high-frequency stimulus trains at the field potential level are not a valid and established technique for estimating RRP size at MFS.

Specialized laboratories have used highly advanced techniques, such as paired recordings between individual MFB and postsynaptic CA3 pyramidal cells, to estimate the RRP size of MFB (Vandael et al., 2020). These approaches are outside the scope of our present study which, while elucidating functional changes following Bcl11b depletion and C1ql2 rescue, does not aim to provide a high-end biophysical analysis of the presynaptic mechanisms involved.

Author response image 2.

Estimation of RRP size using high-frequency stimulus trains at mossy fiber-CA3 synapses. a Results from field potential recordings. Cumulative fEPSP amplitude in response to a train of 40 stimuli at 100 Hz. All subsequent peak amplitudes were normalized to the amplitude of the first peak. Data points corresponding to putative steady state responses were fit with linear regression (RRP size is indirectly reflected by the intersection of the regression line with the yaxis). Control+EGFP, 6 slices from 5 mice; Bcl11b cKO+EGFP, 6 slices from 3 mice. b Results from single-cell recordings. Cumulative EPSC amplitude in response to a train of 15 stimuli at 50 Hz. The last four stimuli were fit with linear regression. Control, 5 cells from 4 mice; Bcl11b cKO, 3 cells from 3 mice. Note the shallow onset of response amplitudes and the subsequent frequency potentiation. Due to the resulting increase in slope at higher stimulus numbers, intersection with the y-axis occurs at negative values. The differences shown were not found to be statistically significant; unpaired t-test or Mann-Whitney U-test.

Bcl11b KO reduces the number of synapses, yet the I-O curve reported in Supp Fig 2 is not changed. How is that possible? This should be explained.

We agree with reviewer #2– this apparent discrepancy has indeed struck us as a counterintuitive result. It might be that synapses that are preferentially eliminated in Bcl11b cKO are predominantly silent or have weak coupling strength, such that their loss has only a minimal effect on basal synaptic transmission. Although perplexing, the result is fully supported by our single-cell data which shows no significant differences in MF EPSC amplitudes recorded from CA3 pyramidal cells between controls and Bcl11b mutants (Author response image 3; please see the response below for details and also our response to Reviewer #1 question 2).

Matsuda et al DOI: 10.1016/j.neuron.2016.04.001 previously reported that C1ql2 organizes MF synapses by aligning postsynaptic kainate receptors with presynaptic elements. As this may have consequences for the functional properties of MF synapses including their plasticity, the authors should report whether they see deficient postsynaptic glutamate receptor signaling in the Bcl11b KO and rescue in the C1ql2 re-expression.

We agree that the study by Matsuda et al. is of key importance for our present work. Although MF LTP is governed by presynaptic mechanisms and we previously did not see differences in short-term plasticity between the control and Bcl11b cKO (De Bruyckere et al., 2018), the clustering of postsynaptic kainate receptors by C1ql2 is indeed an important detail that could potentially alter synaptic signaling at MFS in Bcl11b KO. We, therefore, re-analyzed previously recorded single-cell data by performing a kinetic analysis on MF EPSCs recorded from CA3 pyramidal cells in control and Bcl11b cKO mice (Figure for reviewers 3a) to evaluate postsynaptic AMPA and kainate receptor responses in both conditions. We took advantage of the fact that AMPA receptors deactivate roughly 10 times faster than kainate receptors, allowing the contributions of the two receptors to mossy fiber EPSCs to be separated (Castillo et al., 1997 and reviewed by Lerma, 2003). We fit the decay phase of the second (larger) EPSC evoked by paired-pulse stimulation with a double exponential function, yielding a fast and a slow component, which roughly correspond to the fractional currents evoked by AMPA and kainate receptors, respectively. Analysis of both fast and slow time constants and the corresponding fractional amplitudes revealed no significant differences between controls and Bcl11b mutants (Figure for reviewers 3e-h), indicating that both AMPA and kainate receptor signaling is unaffected by the ablation of C1ql2 following Bcl11b KO.

Importantly, MF EPSC amplitudes evoked by the first and the second pulse (Author response image 3b), paired-pulse facilitation (Author response image 3c) and failure rates (Author response image 3d) were all comparable between controls and Bcl11b mutants. These results further corroborate our observations from field recordings that basal synaptic transmission at MFS is unaltered by Bcl11b KO.

We note that the results from single cell recordings regarding basal synaptic transmission merely confirm the observations from field potential recordings, and that the attempted measurement of RRP size at the single cell level was not successful. Thus, our single-cell data do not add new information about the mechanisms underlying the effects of Bcl11b-deficiency and we therefore decided not to report these data in the manuscript.

Author response image 3.

Basal synaptic transmission at mossy fiber-CA3 synapses is unaltered in Bcl11b cKO mice. a Representative average trace (20 sweeps) recorded from CA3 pyramidal cells in control and Bcl11b cKO mice at minimal stimulation conditions, showing EPSCs in response to paired-pulse stimulation (PPS) at an interstimulus interval of 40 ms. The signal is almost entirely blocked by the application of 2 μM DCG-IV (red). b Quantification of MF EPSC amplitudes in response to PPS for both the first and the second pulse. c Ratio between the amplitude of the second over the first EPSC. d Percentage of stimulation events resulting in no detectable EPSCs for the first pulse. Events <5 pA were considered as noise. e Fast decay time constant obtained by fitting the average second EPSC with the following double exponential function: I(t)=Afaste−t/τfast+Aslowe−t/τslow+C, where I is the recorded current amplitude after time t, Afast and Aslow represent fractional current amplitudes decaying with the fast (τfast) and slow (τslow) time constant, respectively, and C is the offset. Starting from the peak of the EPSC, the first 200 ms of the decaying trace were used for fitting. f Fractional current amplitude decaying with the fast time constant. g-h Slow decay time constant and fractional current amplitude decaying with the slow time constant. For all figures: Control, 8 cells from 4 mice; Bcl11b cKO, 8 cells from 6 mice. All data are presented as means, error bars indicate SEM. None of the differences shown were found to be statistically significant; Mann-Whitney U-test for nonnormally and unpaired t-test for normally distributed data.

Reviewer #3 (Public Review):

Overall, this is a strong manuscript that uses multiple current techniques to provide specific mechanistic insight into prior discoveries of the contributions of the Bcl11b transcription factor to mossy fiber synapses of dentate gyrus granule cells. The authors employ an adult deletion of Bcl11b via Tamoxifen-inducible Cre and use immunohistochemical, electron microscopy, and electrophysiological studies of synaptic plasticity, together with viral rescue of C1ql2, a direct transcriptional target of Bcl11b or Nrxn3, to construct a molecular cascade downstream of Bcl11b for DG mossy fiber synapse development. They find that C1ql2 re-expression in Bcl11b cKOs can rescue the synaptic vesicle docking phenotype and the impairments in MF-LTP of these mutants. They also show that C1ql2 knockdown in DG neurons can phenocopy the vesicle docking and plasticity phenotypes of the Bcl11b cKO. They also use artificial synapse formation assays to suggest that C1ql2 functions together with a specific Nrxn3 splice isoform in mediating MF axon development, extending these data with a C1ql2-K262E mutant that purports to specifically disrupt interactions with Nrxn3. All of the molecules involved in this cascade are disease-associated and this study provides an excellent blueprint for uncovering downstream mediators of transcription factor disruption. Together this makes this work of great interest to the field. Strengths are the sophisticated use of viral replacement and multi-level phenotypic analysis while weaknesses include the linkage of C1ql2 with a specific Nrxn3 splice variant in mediating these effects.

Here is an appraisal of the main claims and conclusions:

1) C1ql2 is a downstream target of Bcl11b which mediates the synaptic vesicle recruitment and synaptic plasticity phenotypes seen in these cKOs. This is supported by the clear rescue phenotypes of synapse anatomy (Fig.2) and MF synaptic plasticity (Fig.3). One weakness here is the absence of a control assessing over-expression phenotypes of C1ql2. It's clear from Fig.1D that viral rescue is often greater than WT expression (totally expected). In the case where you are trying to suppress a LoF phenotype, it is important to make sure that enhanced expression of C1ql2 in a WT background does not cause your rescue phenotype. A strong overexpression phenotype in WT would weaken the claim that C1ql2 is the main mediator of the Bcl11b phenotype for MF synapse phenotypes.

As suggested by reviewer #3, we carried out C1ql2 over-expression experiments in control animals. We show that the over-expression of C1ql2 in the DG of control animals had no effect on the synaptic vesicle organization in the proximity of MFS. This further supports that the observed effect upon rescue of C1ql2 expression in Bcl11b cKOs is due to the physiological function of C1ql2 and not a result of the artificial overexpression. These data are now included in supplementary figure 2g-j and are described in detail in the results part of the revised manuscript. Please also see response to point 3 of reviewer #2.

2) Knockdown of C1ql2 via 4 shRNAs is sufficient to produce the synaptic vesicle recruitment and MFLTP phenotypes. This is supported by clear effects in the shRNA-C1ql2 groups as compared to nonsense-EGFP controls. One concern (particularly given the use of 4 distinct shRNAs) is the potential for off-target effects, which is best controlled for by a rescue experiment with RNA insensitive C1ql2 cDNA as opposed to nonsense sequences, which may not elicit the same off-target effects.

We agree with reviewer #3 that the usage of shRNAs could potentially create unexpected off-target effects and that the introduction of a shRNA-insensitive C1ql2 in parallel to the expression on the shRNA cassette would be a very effective control experiment. However, the suggested experiment would require an additional 6 months (2 months for AAV production, 2-3 months from animal injection to sacrifice and 1-2 months for EM imaging/analysis and LTP measurements) and a high number of additional animals (minimum 8 for EM and 8 for LTP measurements). We note here, that before the production of the shRNA-C1ql2 and the shRNA-NS, the individual sequences were systematically checked for off-target bindings on the murine exome with up to two mismatches and presented with no other target except the proposed (C1ql2 for shRNA-C1ql2 and no target for shRNA-NS). Taking into consideration our in-silico analysis, we feel that the interpretation of our findings is valid without this (very reasonable) additional control experiment.

3) C1ql2 interacts with Nrxn3(25b+) to facilitate MF terminal SV clustering. This claim is theoretically supported by the HEK cell artificial synapse formation assay (Fig.5), the inability of the K262-C1ql2 mutation to rescue the Bcl11b phenotype (Fig.6), and the altered localization of C1ql2 in the Nrxn1-3 deletion mice (Fig.7). Each of these lines of experimental evidence has caveats that should be acknowledged and addressed. Given the hypothesis that C1ql2 and Nrxn3b(25b) are expressed in DG neurons and work together, the heterologous co-culture experiment seems strange. Up till now, the authors are looking at pre-synaptic function of C1ql2 since they are re-expressing it in DGNs. The phenotypes they are seeing are also pre-synaptic and/or consistent with pre-synaptic dysfunction. In Fig.5, they are testing whether C1ql2 can induce pre-synaptic differentiation in trans, i.e. theoretically being released from the 293 cells "post-synaptically". But the post-synaptic ligands (Nlgn1 and and GluKs) are not present in the 293 cells, so a heterologous synapse assay doesn't really make sense here. The effect that the authors are seeing likely reflects the fact that C1ql2 and Nrxn3 do bind to each other, so C1ql2 is acting as an artificial post-synaptic ligand, in that it can cluster Nrxn3 which in turn clusters synaptic vesicles. But this does not test the model that the authors propose (i.e. C1ql2 and Nrxn3 are both expressed in MF terminals). Perhaps a heterologous assay where GluK2 is put into HEK cells and the C1ql2 and Nrxn3 are simultaneously or individually manipulated in DG neurons?

C1ql2 is expressed by DG neurons and is then secreted in the MFS synaptic cleft, while Nrxn3, that is also expressed by DG neurons, is anchored at the presynaptic side. In our work we used the well established co-culture system assay and cultured HEK293 cells secreting C1ql2 (an IgK secretion sequence was inserted at the N-terminus of C1ql2) together with hippocampal neurons expressing Nrxn3(25b+). We used the HEK293 cells as a delivery system of secreted C1ql2 to the neurons to create regions of high concentration of C1ql2. By interfering with the C1ql2-Nrxn3 interaction in this system either by expression of the non-binding mutant C1ql2 variant in the HEK cells or by manipulating Nrxn expression in the neurons, we could show that C1ql2 binding to Nrxn3(25b+) is necessary for the accumulation of vGlut1. However, we did not examine and do not claim within our manuscript that the interaction between C1ql2 and Nrxn3(25b+) induces presynaptic differentiation. Our experiment only aimed to analyze the ability of C1ql2 to cluster SV through interaction with Nrxn3. Moreover, by not expressing potential postsynaptic interaction partners of C1ql2 in our system, we could show that C1ql2 controls SV recruitment through a purely presynaptic mechanism. Co-culturing GluK2-expressing HEK cells with simultaneous manipulation of C1ql2 and/or Nrxn3 in neurons would not allow us to appropriately answer our scientific question, but rather focus on the potential synaptogenic function of the Nrxn3/C1ql2/GluK2 complex and the role of the postsynaptic ligand in it. Thus, we feel that the proposed experiment, while very interesting in characterization of additional putative functions of C1ql2, may not provide additional information for the point we were addressing. In the revised manuscript we tried to make the aim and methodological approach of this set of experiments more clear.

4) K262-C1ql2 mutation blocks the normal rescue through a Nrxn3(25b) mechanism (Fig.6). The strength of this experiment rests upon the specificity of this mutation for disrupting Nrxn3b binding (presynaptic) as opposed to any of the known postsynaptic C1ql2 ligands such as GluK2. While this is not relevant for interpreting the heterologous assay (Fig.5), it is relevant for the in vivo phenotypes in Fig.6. Similar approaches as employed in this paper can test whether binding to other known postsynaptic targets is altered by this point mutation.

It has been previously shown that C1ql2 together with C1ql3 recruit postsynaptic GluK2 at the MFS. However, loss of just C1ql2 did not affect the recruitment of GluK2, which was disrupted only upon loss of both C1ql2 and C1ql3 (Matsuda et al., 2018). In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 can recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (KARs and BAI3; Fig.5; please also see response above). Furthermore, we have now performed a kinetic analysis on single-cell data which we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b KO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling is altered upon the loss of C1ql2 following Bcl11b cKO (Author response image 3e-h; please also see our response to reviewer #2 point 8). Thus, we have no experimental evidence supporting the idea that a loss of interaction between C1ql2.K262E and GluK2 would interfere with the examined phenotype. However, to exclude that the K262E mutation disrupts interaction between C1ql2 and GluK2, we performed co-immunoprecipitation from protein lysate of HEK293 cells expressing GluK2myc-flag and GFP-C1ql2 or GluK2-myc-flag and GFP-K262E and could show that both C1ql2 and K262E had GluK2 bound when precipitated. These data are included in supplementary figure 5k of the revised manuscript.

5) Altered localization of C1ql2 in Nrxn1-3 cKOs. These data are presented to suggest that Nrx3(25b) is important for localizing C1ql2 to the SL of CA3. Weaknesses of this data include both the lack of Nrxn specificity in the triple a/b KOs as well as the profound effects of Nrxn LoF on the total levels of C1ql2 protein. Some measure that isn't biased by this large difference in C1ql2 levels should be attempted (something like in Fig.1F).

We acknowledge that the lack of specificity in the Nrxn123 model makes it difficult to interpret our data. We have now examined the mRNA levels of Nrxn1 and Nrxn2 upon stereotaxic injection of Cre in the DG of Nrxn123flox/flox animals and found that Nrxn1 was only mildly reduced. At the same time Nrxn2 showed a tendency for reduction that was not significant (data included in supplementary figure 6a of revised manuscript). Only Nrxn3 expression was strongly suppressed. Of course, this does not exclude that the mild reduction of Nrxn1 and Nrxn2 interferes with the C1ql2 localization at the MFS. We further examined the mRNA levels of C1ql2 in control and Nrxn123 mutants to ensure that the observed changes in C1ql2 protein levels at the MFS are not due to reduced mRNA expression and found no changes (data are included in supplementary figure 6b of the revised manuscript), suggesting that overall protein C1ql2 expression is normal.

The reduced C1ql2 fluorescence intensity at the MFS was first observed when non-binding C1ql2 variant K262E was introduced to Bcl11b cKO mice that lack endogenous C1ql2 (Fig.6). In these experiments, we found that despite the overall high protein levels of C1ql2.K262E in the hippocampus (Fig. 6c), its fluorescence intensity at the SL was significantly reduced compared to WT C1ql2 (Fig. 6d-e). The remaining signal of the C1ql2.K262E at the SL was equally distributed and in a punctate form, similar to WT C1ql2. Together, this suggests that loss of C1ql2-Nrxn3 interaction interferes with the localization of C1ql2 at the MFS, but not with the expression of C1ql2. Of course, this does not exclude that other mechanisms are involved in the synaptic localization of C1ql2, beyond the interaction with Nrxn3, as both the mutant C1ql2 in Bcl11b cKO and the endogenous C1ql2 in Nrxn123 cKOs show residual immunofluorescence at the SL. Further studies are required to determine how C1ql2-Nrxn3 interaction regulates C1ql2 localization at the MFS.

Reviewer #1 (Recommendations For The Authors):

In addition to addressing the comments below, this study would benefit significantly from providing insight and discussion into the relevant potential postsynaptic signaling components controlled exclusively by C1ql2 (postsynaptic kainate receptors and the BAI family of proteins).

We have now performed a kinetic analysis on single-cell data that we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b cKO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling differ between controls and upon the loss of C1ql2 following Bcl11b cKO (Author response image 3e-h; please also see our response to Reviewer #2 point 8). This agrees with previous findings that C1ql2 regulates postsynaptic GluK2 recruitment together with C1ql3 and only loss of both C1ql2 and C1ql3 results in a disruption of KAR signaling (Matsuda et al., 2018). In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 can recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (KARs and BAI3; Fig.5; please also see our response to reviewer #3 point 4 above). We believe that further studies are needed to fully understand both the pre- and the postsynaptic functions of C1ql2. Because the focus of this manuscript was on the role of the C1ql2-Nrxn3 interaction and our investigation on postsynaptic functions of C1ql2 was incomplete, we did not include our findings on postsynaptic current kinetics in our revised manuscript. However, we increased the discussion on the known postsynaptic partners of C1ql2 in the revised manuscript to increase the interpretability of our results.

Major Comments:

The authors demonstrate that the ultrastructural properties of presynaptic boutons are altered after Bcl11b KO and C1ql2 KD. However, whether C1ql2 functions as part of a tripartite complex and the identity of the postsynaptic receptor (BAI, KAR) should be examined.

Matsuda and colleagues have nicely demonstrated in their 2016 (Neuron) study that C1ql2 is part of a tripartite complex with presynaptic Nrxn3 and postsynaptic KARs. Moreover, they demonstrated that C1ql2, together with C1ql3, recruit postsynaptic KARs at the MFS, while the KO of just C1ql2 did not affect the KAR localization. In our study we demonstrate a purely presynaptic function of C1ql2 through Nrxn3 in the synaptic vesicle recruitment. This function is independent of C1ql3, as C1ql3 expression is unchanged in all of our models and its over-expression did not compensate for C1ql2 functions (Fig. 2, 3a-c). Our in vitro experiments also reveal that C1ql2 is able to recruit both Nrxn3 and vGlut1 in the absence of any known postsynaptic C1ql2 partner (Fig. 5; please also see our response to reviewer #3 point 4 above). Moreover, we were able to show that the SV recruitment depends on C1ql2 interaction with Nrxn3 through the expression of a non-binding C1ql2 (Fig. 6) that retains the ability to interact with GluK2 (supplementary figure 5k of revised manuscript) or by KO of Nrxns (Fig. 7). Furthermore, we have now performed a kinetic analysis on single-cell data which we had previously collected to evaluate postsynaptic AMPA and kainate receptor responses in both the control and Bcl11b cKO. Our analysis reveals no significant differences in postsynaptic current kinetics, making it unlikely that AMPA and kainate receptor signaling differ between controls and Bcl11b mutants (Author response image 3e-h; please also see our response to Reviewer #2 question 8). Together, we have no experimental evidence so far that would support that the postsynaptic partners of C1ql2 are involved in the observed phenotype. While it would be very interesting to characterize the postsynaptic partners of C1ql2 in depth, we feel this would be beyond the scope of the present study.

Figure 1f: For a more comprehensive understanding of the Bcl11b KO phenotype and the potential role for C1ql2 on MF synapse number, a complete quantification of vGlut1 and Homer1 for all conditions (Supplement Figure 2e) should be included in the main text.

In our study we focused on the role of C1ql2 in the structural and functional integrity of the MFS downstream of Bcl11b. Bcl11b ablation leads to several phenotypes in the MFS that have been thoroughly described in our previous study (De Bruyckere et al., 2018). As expected, re-expression of C1ql2 only partially rescued these phenotypes, with full recovery of the SV recruitment (Fig. 2) and of the LTP (Fig. 3), but had no effect on the reduced numbers of MFS nor the structural complexity of the MFB created by the Bcl11b KO (supplementary figure 2d-f of revised manuscript). We understand that including the quantification of vGlut1 and Homer1 co-localization in the main figures would help with a better understanding of the Bcl11b mutant phenotype. However, in our manuscript we investigate C1ql2 as an effector of Bcl11b and thus we focus on its functions in SV recruitment and LTP. As we did not find a link between C1ql2 and the number of MFS/MFB upon re-expression of C1ql2 in Bcl11b cKO or now also in C1ql2 KD (see response to comment #4 below), we believe it is more suitable to present these data in the supplement.

Figure 3/4: Given the striking reduction in the numbers of synapses (Supplement Figure 2e) and docked vesicles (Figure 2d) in the Bcl11b KO and C1ql2 KD (Figure 4e-f), it is extremely surprising that basal synaptic transmission is unaffected (Supplement Figure 2g). The authors should determine the EPSP input-output relationship following C1ql2 KD and measure EPSPs following trains of stimuli at various high frequencies.

We fully acknowledge that this is an unexpected result. It is, however, well feasible that the modest displacement of SV fails to noticeably influence basal synaptic transmission. This would be the case, for example, if only a low number of vesicles are released by single stimuli, in line with the very low initial Pr at MFS. In contrast, the reduction in synapse numbers in the Bcl11b mutant might indeed be expected to reflect in the input-output relationship. It is possible, however, that synapses that are preferentially eliminated in Bcl11b cKO are predominantly silent or have weak coupling strength, such that their loss has only a minimal effect on basal synaptic transmission. Finally, we cannot exclude compensatory mechanisms (homeostatic plasticity) at the remaining synapses. A detailed analysis of these potential mechanisms would be a whole project in its own right.

As additional information, we can say that the largely unchanged input-output-relation in Bcl11b cKO is also present in the single-cell level data (Author response image 3; details on single-cell experiments are described in the response to Reviewer #2 point 8).

As suggested by the reviewer, we have now additionally analyzed the input-output relationship following C1ql2 KD and again did not observe any significant difference between control and KD animals. We have incorporated the respective input-output curves into the revised manuscript under Supplementary figure 3c-d.

Figure 4: Does C1ql2 shRNA also reduce the number of MFBs? This should be tested to further identify C1ql2-dependent and independent functions.

As requested by reviewer #1 we quantified the number of MFBs upon C1ql2 KD. We show that C1ql2 KD in WT animals does not alter the number of MFBs. The data are presented in supplementary figure 4d of the revised manuscript. Re-expression of C1ql2 in Bcl11b cKO did not rescue the loss of MFS created by the Bcl11b mutation. Moreover, C1ql2 re-expression did not rescue the complexity of the MFB ultrastructure perturbed by the Bcl11b ablation. Together, this suggests that Bcl11b regulates MFs maintenance through additional C1ql2-independent pathways. In our previously published work (De Bruyckere et al., 2018) we identified and discussed in detail several candidate genes such as Sema5b, Ptgs2, Pdyn and Penk as putative effectors of Bcl11b in the structural and functional integrity of MFS (please also see response to reviewer #2- point 1 of public reviews).

Figure 5: Clarification is required regarding the experimental design of the HEK/Neuron co-culture: 1. C1ql2 is a secreted soluble protein - how is the protein anchored to the HEK cell membrane to recruit Nrxn3(25b+) binding and, subsequently, vGlut1?

C1ql2 was secreted by the HEK293 cells through an IgK signaling peptide at the N-terminus of C1ql2. The high concentration of C1ql2 close to the secretion site together with the sparse coculturing of the HEK293 cells on the neurons allows for the quantification of accumulation of neuronal proteins. We have now described the experimental conditions in greater detail in the main text module of the revised manuscript

2) Why are the neurons transfected and not infected? Transfection efficiency of neurons with lipofectamine is usually poor (1-5%; Karra et al., 2010), while infection of neurons with lentiviruses or AAVs encoding cDNAs routinely are >90% efficient. Thus, interpretation of the recruitment assays may be influenced by the density of neurons transfected near a HEK cell.

We agree with reviewer #1 that viral infection of the neurons would have been a more effective way of expressing our constructs. However, due to safety allowances in the used facility and time limitation at the time of conception of this set of experiments, a lipofectamine transfection was chosen.

However, as all of our examined groups were handled in the same way and multiple cells from three independent experiments were examined for each experimental set, we believe that possible biases introduced by the transfection efficiency have been eliminated and thus have trust in our interpretation of these results.

3) Surface labeling of HEK cells for wild-type C1ql2 and K262 C1ql2 would be helpful to assess the trafficking of the mutant.

We recognize that potential changes to the trafficking of C1ql2 caused by the K262E mutation would be important to characterize, in light of the reduced localization of the mutant protein at the SL in the in vivo experiments (Fig. 6e). In our culture system, C1ql2 and K262E were secreted by the HEK cells through insertion of an IgK signaling peptide at the N-terminus of the myc-tagged C1ql2/K262E. Thus, trafficking analysis on this system would not be informative, as the system is highly artificial compared to the in vivo model. Further studies are needed to characterize C1ql2 trafficking in neurons to understand how C1ql2-Nrxn3 interaction regulates the localization of C1ql2. However, labeling of the myc-tag in C1ql2 or K262E expressing HEK cells of the co-culture model reveals a similar signal for the two proteins (Fig. 5a,c). Nrxn-null mutation in neurons co-cultured with C1ql2-expressing HEK cells disrupted C1ql2 mediated vGlut1 accumulation in the neurons. Selective expression of Nrxn3(25b) in the Nrxn-null neurons restored vGlut1 clustering was (Fig. 5e-f). Together, these data suggest that it is the interaction between C1ql2 and Nrxn3 that drives the accumulation of vGlut1.

Figure 6: Bcl11b KO should also be included in 6f-h.

As suggested by reviewer #1, we included the Bcl11b cKO in figures 6f-h and in corresponding supplementary figures 5c-j.

Figure 7b: What is the abundance of mRNA for Nrxn1 and Nrxn2 as well as the abundance of Nrxns after EGFP-Cre injection into DG?

We addressed this point raised by reviewer #1 by quantifying the relative mRNA levels of Nrxn1 and Nrxn2 via qPCR upon Nrxn123 mutation induction with EGFP-Cre injection. We have now examined the mRNA levels of Nrxn1 and Nrxn2 upon stereotaxic injection of Cre in the DG of Nrxn123flox/flox animals and found that Nrxn1 was only mildly reduced. At the same time Nrxn2 showed a tendency for reduction that was not significant. The data are presented in supplementary figure 6a of the revised maunscript.

Minor Comments for readability:

Synapse score is referred to frequently in the text and should be defined within the text for clarification.

'n' numbers should be better defined in the figure legends. For example, for protein expression analysis in 1c, n=3. Is this a biological or technical triplicate? For electrophysiology (e.g. 3c), does "n=7" reflect the number of animals or the number of slices? n/N (slices/animals) should be presented.

Figure 7a: Should the diagrams of the cre viruses be EGFP-Inactive or active Cre and not CRE-EGFP as shown in the diagram?

Figure 7b: the region used for the inset should be identified in the larger image.

All minor points have been fixed in the revised manuscript according to the suggestions.

Reviewer #3 (Recommendations For The Authors):

-Please describe the 'synapse score' somewhere in the text - it is too prominently featured to not have a clear description of what it is.

The description of the synapse score has been included in the main text module of the revised manuscript.

-The claim that Bcl11b controls SV recruitment "specifically" through C1ql2 is a bit stronger than is warranted by the data. Particularly given that C1ql2 is expressed at 2.5X control levels in their rescue experiments. See pt.2

Please see response to reviewer #3 point 1 of public reviews. To address this, we over-expressed C1ql2 in control animals and found no changes in the synaptic vesicle distribution (supplementary figure 2g-j of revised manuscript). This supports that the observed rescue of synaptic vesicle recruitment by re-expression of C1ql2 is due to its physiological function and not due to the artificially elevated protein levels. Of course, we cannot exclude the possibility that other, C1ql2-independent, mechanisms also contribute to the SV recruitment downstream of Bcl11b. Our data from the C1ql2 rescue, C1ql2 KD, the in vitro experiments and the interruption of C1ql2-Nrxn3 in vivo, strongly suggest C1ql2 to be an important regulator of SV recruitment.

-Does Bcl11b regulate Nrxn3 expression? Considering the apparent loss of C1ql2 expression in the Nrxn KO mice, this is an important detail.

We agree with reviewer #3 that this is an important point. We have previously done differential transcriptomics from DG neurons of Bcl11b cKOs compared to controls and did not find Nrxn3 among the differentially expressed genes. To further validate this, we now quantified the Nrxn3 mRNA levels via qPCR in Bcl11b cKOs compared to controls and found no differences. These data are included in supplementary figure 5a of the revised manuscript.

-It appears that C1ql2 expression is much lower in the Nrxn123 KO mice. Since the authors are trying to test whether Nrxn3 is required for the correct targeting of C1ql2, this is a confounding factor. We can't really tell if what we are seeing is a "mistargeting" of C1ql2, loss of expression, or both. If the authors did a similar analysis to what they did in Figure 1 where they looked at the synaptic localization of C1ql2 (and quantified it) that could provide more evidence to support or refute the "mistargeting" claim.

Please also see response to reviewer #3 point 5 of public reviews. To exclude that reduction of fluorescence intensity of C1ql2 at the SL in Nrxn123 KO mice is due to loss of C1ql2 expression, we examined the mRNA levels of C1ql2 in control and Nrxn123 mutants and found no changes (data are included in supplementary figure 6b of the revised manuscript), suggesting that C1ql2 gene expression is normal. The reduced C1ql2 fluorescence intensity at the MFS was first observed when non-binding C1ql2 variant K262E was introduced to Bcl11b cKO mice that lack endogenous C1ql2 (Fig.6). In these experiments, we found that despite the overall high protein levels of C1ql2.K262E in the hippocampus (Fig. 6c), its fluorescence intensity at the SL was significantly reduced compared to WT C1ql2 (Fig. 6d-e). The remaining C1ql2.K262E signal in the SL was equally distributed and in a punctate form, similar to WT C1ql2. Together, this indicates that the loss of C1ql2-Nrxn3 interaction interferes with the localization of C1ql2 along the MFS, but not with expression of C1ql2. Of course, this does not exclude that additional mechanisms regulate C1ql2 localization at the synapse, as both the mutant C1ql2 in Bcl11b cKO and the endogenous C1ql2 in Nrxn123 cKO show residual immunofluorescence at the SL.

We note here that we have not previously quantified the co-localization of C1ql2 with individual synapses. C1ql2 is a secreted molecule that localizes at the MFS synaptic cleft. However, not much is known about the number of MFS that are positive for C1ql2 nor about the mechanisms regulating C1ql2 targeting, transport, and secretion to the MFS. Whether C1ql2 interaction with Nrxn3 is necessary for the protection of C1ql2 from degradation, its surface presentation and transport or stabilization to the synapse is currently unclear. Upon revision of our manuscript, we realized that we might have overstated this particular finding and have now rephrased the specific parts within the results to appropriately describe the observation and have also included a sentence in the discussion referring to the lack of understanding of the mechanism behind this observation.

-Title of Figure S5 is "Nrxn KO perturbs C1ql2 localization and SV recruitment at the MFS", but there is no data on C1ql2 localization.

This issue has been fixed in the revised manusript.

-S5 should be labeled more clearly than just Cre+/-

This issue has been fixed in the revised manuscript.

References

Castillo, P.E., Malenka, R.C., Nicoll, R.A., 1997. Kainate receptors mediate a slow postsynaptic current in hippocampal CA3 neurons. Nature 388, 182–186. https://doi.org/10.1038/40645

De Bruyckere, E., Simon, R., Nestel, S., Heimrich, B., Kätzel, D., Egorov, A.V., Liu, P., Jenkins, N.A., Copeland, N.G., Schwegler, H., Draguhn, A., Britsch, S., 2018. Stability and Function of Hippocampal Mossy Fiber Synapses Depend on Bcl11b/Ctip2. Front. Mol. Neurosci. 11. https://doi.org/10.3389/fnmol.2018.00103

Kaeser, P.S., Regehr, W.G., 2017. The readily releasable pool of synaptic vesicles. Curr. Opin. Neurobiol. 43, 63–70. https://doi.org/10.1016/j.conb.2016.12.012

Lerma, J., 2003. Roles and rules of kainate receptors in synaptic transmission. Nat. Rev. Neurosci. 4, 481–495. https://doi.org/10.1038/nrn1118

Orlando, M., Dvorzhak, A., Bruentgens, F., Maglione, M., Rost, B.R., Sigrist, S.J., Breustedt, J., Schmitz, D., 2021. Recruitment of release sites underlies chemical presynaptic potentiation at hippocampal mossy fiber boutons. PLoS Biol. 19, e3001149. https://doi.org/10.1371/journal.pbio.3001149

Vandael, D., Borges-Merjane, C., Zhang, X., Jonas, P., 2020. Short-Term Plasticity at Hippocampal Mossy Fiber Synapses Is Induced by Natural Activity Patterns and Associated with Vesicle Pool Engram Formation. Neuron 107, 509-521.e7. https://doi.org/10.1016/j.neuron.2020.05.013

Author Response

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

Reviewer #1 (Recommendations For The Authors):

It is somewhat speculative that the structure represents the EIIa-bound regulatory state. There's a strong enough case that it should be analyzed in the discussion, but I don't think it is firmly established. Therefore, the title of the paper should be changed.

Our answer: Thank you for the comment. We have changed the title to “Mobile barrier mechanisms for Na+-coupled symport in an MFS sugar transporter”

Reading through the manuscript, it was challenging to distinguish what is new in the current manuscript and what has been done previously. There were a lot of parts where it was hard for me to identify the main point of the current study among all the details of previous studies. It would also benefit from shortening. For example:

-Page 6: Nb725 binding has already been characterized extensively in the very nice JBC paper earlier this year. It's important to test 725-4 for binding, but since it doesn't change the binding interaction, and probably wouldn't be expected to, the entire section could be written more succinctly. The main point, which is that 725-4 behaves like 725, is lost among all the details

Our answer: Thanks for this instructive suggestion. We have shortened the description in this section.

-Page 9-10. I don't understand what summarizing all of the results from the previous D59C studies adds to the current story. It's important because it provides an indication of the substrate binding site, but its mechanism of action does not seem relevant to the current work.

Our answer: We have shortened the description of the sugar-binding site and moved the previous Fig. 3b to supplementary figure sFig. 11. According to your comment about showing the location of the binding sites, which is also suggested by Reviewer #2, we modified Fig. 3 and added two panels to map the location of the bound Na+ in the inward-facing structure and the bound sugar in the outward-facing structure.

The sugar-binding site identified in the published structure is critical to construct the mobile barrier mechanism. The sugar-binding residues identified in the published structure provided essential data to support the conclusion that the sugar-binding pocket is broken in the inward-facing structure. Thus, this published structure is mechanistically relevant to the current study.

-Page 12. Too much summary of the previous outward structure. Since this is already part of the literature, it would be more efficient to reference the previous data when it is important to interpret the new data (or show as a figure).

Our answer: The introduction of the previous sugar-binding sit is important for the detailed comparison between the two states as discussed above, but we agree with this reviewer and have significantly shortened the paragraph by moving the detailed description into the legend to the sFig. 11.

-Instead of providing the PDB ID in figures of the current structure, just say "current work" or similar. Then it is obvious you are not citing a previous structure.

Our answer: To distinguish clearly the new data and published results, the citation of the cryoEM structure [PDP ID 8T60] has been completely removed from the main text but kept in sTable 1.

-An entire panel of Figure 3 is dedicated to ligand binding in a previous outward-facing structure.

Showing it in the overlay would be sufficient.

Our answer: It is the first time for us to show a structure with a bound-Na+. Fig. 3 also illustrates the spatial relationship between the sugar-binding pocket and the cation-binding pocket since both binding sites are determined now. As stated above, according to two reviewers’ comments, we have modified the Figures and the Fig. 3d is the overlay.

Please increase the size of the font in all figures. It should be 6-8 point when printed on a standard sheet of paper. Labels in Figure 3, distances in Figure 4, and everything in Figure 5 is hard to see.

Our answer: Thank you for the comments and the enlargement of the figure size and label font in all figures have been made.

Figure 2: would be helpful to show Figure S8 in the main text, orienting the reader to the approximate location of substrate binding. What is known about the EIIA-Glc binding interface? Has anyone probed this by mutagenesis? Where are these residues on the overall structure, and are they somewhere other than the nanobody interface?

Our answer: Thank you for this comment. We have added a panel for orienting the readers about the substrate location in MelB in Figure 3c. The sFig. 8 actually focuses on the details of Nb interactions with MelB. Our current data strongly supported the notion that the Nb-bound MelBSt structure mimics the EIIAGlc-bound MelB but is not structurally resolved, so we have tuned down our statement on EIIAGlc. There is one study suggesting the C-terminal tail helix may be involved in the EIIAGlc binding, which has been added to the discussion.

Can Figure 5 be split into 2 figures and simplified?

Our answer: thanks for the suggestion. We have split it into Figs. 5b and 6 and also moved the peptide mapping to the Fig 5a.

What is the difference between cartoon and ribbon rendering?

Our answer: Ribbon: illustrating the structure; cartoon: highlighting the positions with statistically significant protection or deprotection. The statistically significant changes are implied by the ribbon representation; Sphere: not covered by labeled peptides.

Can the panels showing the kinetic data be enlarged? I don't think they need to surround the molecule. An array underneath would be fine.

Our answer: We have enlarged all figures and labels. The placement of selected plots around the model could clearly show the difference in deuterium uptake rates between the transmembrane domain and extra-membrane regions. We will maintain this arrangement.

Do colors in panel A correspond with colors in panel B?

Our answer: The color usage in both are different. Now the two panels have been separated.

Do I understand correctly that in the HDX experiments, negative values indicate positions that exchange more quickly in the nanobody-free protein relative to the nanobody-bound protein?

Our answer: Your understanding is correct.

I assume some of this is due to the protein changing conformation, but some of it might be due to burial at the nanobody-binding interface. Can those peptides be indicated?

Our answer: Thank you for this comment. We have marked the peptide carrying the Nb-binding residues on uptake plots in Figs.6 and Extended Fig. 1. There are only three Nb-binding residues covered by many overlapping peptides. Most are not covered, either not carried by the labeled peptides (Tyr205, Ser206, and Ser207) or with insignificant changes (Pro132 and Thr133), except for Asp137, Lys138, and Arg141 which are presented in 8 labeled peptides.

Few buried positions in the outward-facing state are expected to be solvent in the inward-facing state; unfortunately, inward-facing state they are buried by Nb binding.

Make figure legends easier to interpret by removing non-essential methods details (like buffer conditions).

Our answer: We removed the detailed method descriptions in most figure legends. Thank you.

Check throughout for typos.

ie page 9 Lue Leu

Page 9 like likely

Our answer: We have corrected them. Thank you!

Reviewer #2 (Recommendations For The Authors):

I have mostly minor questions/remarks.

• Why not do the hdx-ms experiments in the presence of sugar? That would give a proper distinction between two conformational states, instead of an ensemble of states vs one state.

Our answer: MelB conformation induced by sugar is also multiple states, and likely most are outward-facing states and occluded intermediate states. This is also supported by the new finding of an inward state with low sugar affinity. The ideal design should be one inward and one outward to understand the inward-outward transition. We have not identified an outward-facing mutant while we can obtain the inward by the Nb. WT MelBSt with bound Na+ favors the outward-facing state. Although our design is not ideal, we do have one state vs a predominant outward-facing WT with bound Na+.

Minor comments:

• Fig 5 is misleading as the peptide number does not match with the amino acid sequence. I would suggest putting a heat map with coverage on top. Or showing deuterium uptake per peptide. See examples below.

Our answer: The peptide number should not match with sequence number. We have 155 overlapping peptides that cover the entire amino acid sequence including the 10-His tag, and there are 60 residues with no data because they are not covered by a labeled peptide. The residue positions that are covered by peptides are estimated by bars on the top. The cylinder length does not correspond to the length of the transmembrane helix, just for mapping purposes.

• Can the authors explain how they found that the Nbs bind to the cytoplasmic side (before obtaining the structure)?

Our answer: Our in vivo two-hybrid assay between the Nb and MelBSt indicated their interaction on the cytoplasmic surface of MelBSt, which is further confirmed by the melibiose fermentation and transport assay, where the transport activities were completely inhibited by intracellularly coexpressed Nb and MelBSt. Thanks for raising this question.

• The authors use the word "substrate" indifferently for sugar and Na+ binding, which is a bit confusing. Technically, only sugar is the substrate and Na+ is a ligand, or cotransported-ion, that powers the reaction of transport. This might sound like nit-picking but it can lead to misunderstandings (at some point I thought two sugars were transported, and then I was looking for the second Na+ binding site).

Our answer: We used to call the sugar and Na as co-substrate but we agree with this comment.

We have changed by using substrate for the cargo sugar and coupling cation for the driving cation.

• Abstract "only the inner barrier" - the is missing.

Thanks. We have corrected this.

• p.3 intro "and identified that the positive cooperativity of cation and melibiose, " something is missing.

Thanks again. We missed the “as the core symport mechanism”.

• P.6 Nb275_4 instead of Nb725_4

Thank you very much for your careful reading.

• P.7. Also, affinity affinities

Thank you very much. We changed to “; and also, the -NPG affinity decreased by 21~32-fold for both Nbs”

• P.8 " contains 417 MelBSt residues (positions 2-210, 219-355, and 364-432). This does not sum up to 417 residues.

Thanks for your critical reading. We changed 364-432 to 262-432.

• p.9 Lue 54

We have corrected it to Leu54.

• I find fig.3 hard to read. Can the authors show the Na+ binding pockets and sugar binding pockets within the structure? Especially figure 3b. why are the residues in different colors?

Our answer: We have moved Fig 3b into sFig. 11. We colored the residues in the previous Fig 3B to match the hosting helices. We have added two panels to show the location of both sugar and Na in the molecular. Thank you for your comments.

• Fig4 bcef. Colored circles at the end of the helices. What are they for?

Our answer: We revised the legend. “The paired helices involved in either barrier formation were highlighted in the same colored circles.”

• 86% coverage includes the his-tag - it would be good to clarify that.

Our answer: Yes, it includes the 10-His tag.

• Fig.7 - anti clockwise cycle of transport is counter-intuitive.

Our answer: We have re-arranged. Our model was constructed originally to explain efflux due to limited information at the earlier state. Now more data are available allowing us to explain inflow and active transport.

• Where are all the uptake plots per peptide for the HDX-MS data?

Our answer: We have added the course raw data and prepared all uptake plots for all 71 peptides with statistically significant changes as an Extended Fig. 1.

• P.22 protein was concentrated to 50 mg/mL. Really? That is a lot.

This is correct. We can even concentrate MelBSt protein to greater than 50 mg/ml.

• Have the authors looked into the potential role of lipids in regulating the conformational transition? Since the structure was obtained in nanodiscs, have they observed some unexplained densities? The role of lipid-protein interactions in regulating such transitions was observed for several transporters including MFS (Gupta K, et al. The role of interfacial lipids in stabilizing membrane protein oligomers. Nature. 2017 10.1038/nature20820. Martens C, et al. Direct protein-lipid interactions shape the conformational landscape of secondary transporters. Nat Commun. 2018 10.1038/s41467-018-06704-1.). Furthermore, I see the authors have already observed lipid specific functional regulation of MelB (ref: Hariharan, P., et al BMC Biol 16, 85 (2018). https://doi.org/10.1186/s12915-018-0553-0). A few words about this previous work, and even commenting on the absence of lipid-protein interactions in this current work is worthwhile.

Our answer: Thanks for this very relevant comment. We paid attention to the unmodelled densities. There is one with potential but it is challenging to model it. We have added a sentence “There is no unexplained density that can be clearly modeled by lipids.” in the method to address this concern.

Reviewer #3 (Recommendations For The Authors):

1) In the following sentence, the authors report high errors for the Kd value. The anti-Fab Nb binding to NabFab was two-fold poorer than Nb725_4 at a Kd value of 0.11 {plus minus} 0.16 μM. The figure however indicates that the error value is 0.016 µM. Pls correct.

Our answer: Thank you. You are correct. The error has been corrected. 0.16 ± 0.02 uM. In this revised manuscript, we present the data in nM units.

2) Is the stoichiometry of the MelB:Na+ symport clearly known in this transporter. It can be mentioned in the discussion with appropriate references.

Our answer: Yes, the stoichiometry of unity has been clearly determined, which was included in the second paragraph of the previous version.

3) In the last section of results, the authors seem to suggest a greater movement within their Cterminal helical bundle compared to N-terminal helices. Is there evidence to suggest an asymmetry in the rocker switch between the two states of the transporter?

Our answer: Our structural data revealed that the C-terminal bundle is more dynamic compared with the N-terminal bundle where hosts the residues for specific binding of galactoside and Na+. The HDX data showed that the most dynamic regions are the structurally unresolved C-terminal tail by either method, the conserved tail helix and the middle-loop helix. transmembrane helices are relatively less dynamic with similar distributions on both transmembrane bundles. Since the most dynamic regions are peripheral element associated with the C-terminal domain, it might give a wrong impression. With regard to the symmetric or asymmetric movement, which will certainly affect the dynamic interactions between the transporter and the lipids, we favor the notion that MelBSt performs symmetric movement during the rocker switch between inward and outward states at the least cost for the protein-lipids interaction.

4) Figure 1. Are the thermograms exothermic or endothermic? clarify

Our answer: In our thermograms, all positive peaks are exothermic due to the direct detection of the heat release by the TA instrument. We clarified this in Method and now we stress this in figure legends to avoid confusion.

5) Figure 4a,d. Please put in a membrane bilayer and depict cytosolic and extracellular compartments for clarity.

Thank you. We have added a bilayer and labeled the sidedness in this figure and other related figures.

6) Fig 7. Melibiose symport cannot be referred to as Melibiose efflux transport in the legend as the latter refers to antiport. Pls rectify.

Our answer: Influx and efflux are conventionally used to describe the direction of movement of a substrate. The use of symport and antiport indicates the directions of the coupling reaction for the cargo and cation. For the symporter MelB, melibiose efflux means that sugar with the coupled cation moves out, which is driven by the melibiose concentration. During the steady state of melibiose active transport, efflux rate = influx rate.

7) Page 11 "A common feature of carrier transporters". The authors can use either carriers or transporters. Need not use both simultaneously.

Sorry for overlooking this. We have deleted carriers. Thank you very much for your time.

8) Several typos were noticed in this manuscript. some are listed below. pls correct.

Page 4- last paragraph "Furthermore"

We have corrected it. Thank you again!

Page 7 - second para one repharse "affinity reduced by 21~32 fold/units.." pls clarify

Added 21~32 fold.

Page 9 - "so it is highly likely that inward-open conformation" pls correct.

We have corrected to “likely”.

Fig. S9c - correct the spelling "Distance".

We have corrected to “Distance”

https://youtu.be/3Lru4k9Q55k?si=2TsGAi8iMEMunKii&t=15

I opened the image in Youtube and then under share link I choose the start at time ootion. Please note the three tags I added. Note, once you have created the required tag of physical-computing it will auotcomplete. Also, you need to hit enter after you type in each tag, be sure to check the tags got added, as you are being graded on your ability to tag web content.

Certainly! Let's break it down:

1. What is root.unmount()?

2. root.unmount() is a function in React that you can use to clean up and remove a rendered tree inside a React root. It's like saying, "Hey React, I'm done with this part, please clean it up."

3. When do you need it?

4. You might need it when the DOM node where your React app lives is going to be removed by some other code. For example, if you have a tab panel made with jQuery that removes inactive tabs, calling root.unmount() helps React know that it should stop managing the components inside the removed tab.

5. What does it do?

6. When you call root.unmount(), it removes all the React components in that part of the app and disconnects React from the corresponding DOM node. This cleanup includes removing event handlers and state.

7. How to use it?

8. If your entire app is built with React, you typically create one root at the beginning, and you don't need to call root.unmount(). You set it up once, and React takes care of everything. javascript const root = createRoot(document.getElementById('root')); root.render(<App />);

9. Cautions and Restrictions:

10. After calling root.unmount(), you can't call root.render() again on the same root. You would need to create a new root for the same DOM node.

11. If you're using server rendering, use hydrateRoot instead of createRoot for the initial setup.

12. Troubleshooting Tips:

13. If nothing is displayed, make sure you actually called root.render(<App />);.
14. If you get a "Target container is not a DOM element" error, check if the DOM node you're passing to createRoot is valid.
15. If you get a "Functions are not valid as a React child" error, ensure you're passing a React component, not just a function.

In simple terms, root.unmount() is like telling React, "I'm done with this part of the app, clean it up," and you typically use it in specific situations where parts of your app might be removed dynamically.

Certainly! Let's break down the information in a simpler way with examples:

1. createElement Function:
2. What it does: createElement is a function in React that allows you to create a React element. It's an alternative to using JSX.

3. Example: javascript const element = createElement('h1', { className: 'greeting' }, 'Hello');

4. Parameters:

5. type: Specifies the type of element you want to create. It can be a tag name string (e.g., 'div', 'span') or a React component.
6. props: An object that holds the properties (attributes) for the element.
7. ...children: Optional. Represents child elements or content.

8. Example: javascript createElement('h1', { className: 'greeting' }, 'Hello', createElement('i', null, 'World'));

9. Returns:

10. The function returns a React element object with properties like type, props, ref, and key.

11. Example: javascript const element = createElement('h1', { className: 'greeting' }, 'Hello'); console.log(element.type); // 'h1' console.log(element.props); // { className: 'greeting' }

12. Usage Caveats:

13. React elements and their props should be treated as immutable. They should not be changed after creation.
14. JSX tags should start with a capital letter for custom components.
15. When using JSX, dynamic children should be passed as an array to ensure React warns about missing keys for dynamic lists.

16. Example: javascript createElement('ul', {}, listItems); // Dynamic children as an array

17. Creating an Element Without JSX:

18. If you're not using JSX, you can use createElement to create elements.

19. Example: javascript function Greeting({ name }) { return createElement('h1', { className: 'greeting' }, 'Hello ', createElement('i', null, name), '. Welcome!'); }

20. Comparison with JSX:

21. JSX is a more concise and readable way to create elements compared to using createElement.

22. Example: javascript function Greeting({ name }) { return <h1 className="greeting">Hello <i>{name}</i>. Welcome!</h1>; }

23. Rendering Your Own Component:

24. You can use createElement to render your own React components.
25. Example: javascript export default function App() { return createElement(Greeting, { name: 'Taylor' }); }
26. With JSX: javascript export default function App() { return <Greeting name="Taylor" />;

In summary, createElement is a foundational function in React for creating elements, and it is often used behind the scenes when JSX is transpiled. While it's useful, JSX provides a more readable syntax for creating React elements. You can choose the style that best fits your project.

PE is the field I am going into. After reading this article I have learned a couple new things. Responsibility, respectfulness, and gratefulness are very important in any classrroom. I like the things talked about, and even the videos of the frozen tag game he explained.

20% der Schneemasse auf der Nordhalbkugel bedecken Gebiete, die im Winter meist wärmer sind als 8°. In diesen Gebieten hat die Schneedecke in den letzten Jahrzehnten bereits deutlich abgenommen. Für ihre Zukunft ist jedes Zehntelgrad mehr oder weniger Erhitzung entscheidend. Der Verlust der Schneedecke führt zu Problemen bei der Wasserversorgung etwa der Donau und des Mississippi. https://www.liberation.fr/environnement/y-aura-t-il-encore-de-la-neige-en-2050-20240117_UPOQVWROIZEBVDQRD5JBRA4EH4/

Mehr zur selben Studie: https://hypothes.is/search?q=tag%3A%22Evidence%20of%20human%20influence%20on%20Northern%20Hemisphere%20snow%20loss%22

Die OPEC geht davon aus, dass sich die Nachfrage nach Öl in diesem Jahr um 2,25 Millionen Barrel pro Tag erhöhen wird. Für das kommende Jahr erwartet die OPEC eine Steigerung um 1,85 Millionen Barrel am Tag. Die Prognosen der OPEC liegen deutlich höher als die der IEA. Die USA haben in der zweiten Januarwoche mit mit 13,3 Millionen Barrel pro Tag einen neuen Rekord in der Ölproduktion aufgestellt. https://www.reuters.com/business/energy/oil-prices-edge-higher-opec-demand-estimate-while-cold-hits-us-output-2024-01-18/

Author Response

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

Reviewer #1 (Public Review):

In this study, the authors attempt to describe alterations in gene expression, protein expression, and protein phosphorylation as a consequence of chronic adenylyl cyclase 8 overexpression in a mouse model. This model is claimed to have resilience to cardiac stress.

Major strengths of the study include 1) the large dataset generated which will have utility for further scientific inquiry for the authors and others in the field, 2) the innovative approach of using cross-analyses linking transcriptomic data to proteomic and phosphoproteomic data. One weakness is the lack of a focused question and clear relevance to human disease. These are all critical biological pathways that the authors are studying and essentially, they have compiled a database that could be surveyed to generate and test future hypotheses.

Thank you for your efforts to review our manuscript, we are delighted to learn that you found our approach to link transcriptomic, proteomic and phosphoproteome data in our analysis to be innovative. Your comment that we have not focused on a question with clear relevance to human disease is “right on point!”

During chronic pathophysiologic states e.g., chronic heart failure (CHF) in humans, AC/cAMP/PKA/Ca2+ signaling increases progressively the degree of heart failure progresses, leading to cardiac inflammation, mediated in part, by cyclic-AMP- induced up- regulation of renin-angiotensin system (RAS) signaling. Standard therapies for CHF include β-adrenoreceptor blockers and RAS inhibitors, which although effective, are suboptimal in amelioration of heart failure progression. One strategy to devise novel and better therapies for heart failure, would be to uncover the full spectrum of concentric cardio- protective adaptations that becomes activated in response to severe, chronic AC/cAMP/PKA/Ca2+ -induced cardiac stress.

We employed unbiased omics analyses, in our prior study (https://elifesciences.org/articles/80949v1) of the mouse harboring cardiac specific overexpression of adenylyl cyclase type 8 (TGAC8), and identified more than 2,000 transcripts and proteins, comprising a broad array of biological processes across multiple cellular compartments, that differed in TGAC8 left ventricle compared to WT. These bioinformatic analyses revealed that marked overexpression of AC8 engages complex, concentric adaptation "circuity" that has evolved in mammalian cells to confer resilience to stressors that threaten health or life. The main human disease category identified in these analyses was Organismal Injury and Abnormalities, suggesting that defenses against stress were activated as would be expected, in response to cardiac stress. Specific concentric signaling pathways that were enriched and activated within the TGAC8 protection circuitry included cell survival initiation, protection from apoptosis, proliferation, prevention of cardiac-myocyte hypertrophy, increased protein synthesis and quality control, increased inflammatory and immune responses, facilitation of tissue damage repair and regeneration and increased aerobic energetics. These TGAC8 stress response circuits resemble many adaptive mechanisms that occur in response to the stress of disease states and may be of biological significance to allow for proper healing in disease states such as myocardial infarction or failure of the heart. The main human cardiac diseases identified in bioinformatic analyses were multiple types cardiomyopathies, again suggesting that mechanisms that confer resilience to the stress of chronic increased AC-PKA-Ca2+ signaling are activated in the absence of heart failure in the super-performing TGAC8 heart at 3-months of age.

In the present study, we performed a comprehensive in silico analysis of transcription, translation, and post-translational patterns, seeking to discover whether the coordinated transcriptome and proteome regulation of the adaptive protective circuitry within the AC8 heart that is common to many types of cardiac disease states identified in our previous study (https://elifesciences.org/articles/80949v1) extends to the phosphoproteome.

Reviewer #2 (Public Review):

In this study, the investigators describe an unbiased phosphoproteomic analysis of cardiac-specific overexpression of adenylyl cyclase type 8 (TGAC8) mice that was then integrated with transcriptomic and proteomic data. The phosphoproteomic analysis was performed using tandem mass tag-labeling mass spectrometry of left ventricular (LV) tissue in TGAC8 and wild-type mice. The initial principal component analysis showed differences between the TGAC8 and WT groups. The integrated analysis demonstrated that many stress-response, immune, and metabolic signaling pathways were activated at transcriptional, translational, and/or post-translational levels.

The authors are to be commended for a well-conducted study with quality control steps described for the various analyses. The rationale for following up on prior transcriptomic and proteomic analyses is described. The analysis appears thorough and well-integrated with the group's prior work. Confirmational data using Western blot is provided to support their conclusions. Their findings have the potential of identifying novel pathways involved in cardiac performance and cardioprotection.

Thank you for your efforts to review our manuscript, we are delighted to learn that you found our approach to link transcriptomic, proteomic and phosphoproteome data in our analysis. We are delighted that you found our work to be well-conducted, to have been well performed, and that our analysis was thorough and well-integrated with our prior work in this arena and that are findings have the potential of identifying novel pathways involved in cardiac performance and cardioprotection.

Reviewer #1 (Recommendations For The Authors):

I humbly suggest that the authors reconsider the title, as it could be more clear as to what they are studying. Are the authors trying to highlight pathways related to cardiac resilience? Resilience might be a clearer word than "performance and protection circuitry".

Thank you for this important comment. We have revised the title accordingly: Reprogramming of cardiac phosphoproteome, proteome and transcriptome confers resilience to chronic adenylyl cyclase-driven stress.

Perhaps the text can be reviewed in detail by a copy-editor, as there are many grammatically 'awkward' elements (for example, line 56: "mammalians" instead of mammals), inappropriate colloquialisms (for example, line 73: "port-of-call"), and stylistic unevenness that make it difficult to read.

We have reviewed the text in detail, with the assistance of a copy editor, in order to identify and correct awkward elements and to search for other colloquialisms. Finally, although “stylistic unevenness” to which you refer may be difficult for us to identify during our re-edits, we have tried our best to identify and revise them.

The best-written sections are the first few paragraphs of the discussion section, which finally clarify why the TGAC8 mouse is important in understanding cardiac resilience to stress and how the present study leverages this model to disentangle the biological processes underlying the resilience. I wish this had been presented in this manner earlier in the paper, (in the abstract and introduction) so I could have had a clearer context in which to interpret the data. It would also be helpful to point out whether the TGAC8 mouse has any correlates with human disease.

Thank you for this very important comment. Well put! In addition to recasting the title to include the concept of resilience, we have revised both the abstract and introduction to feature what you consider to be important to the understanding of cardiac resilience to stress, and how the present study leverages this model to disentangle the biological processes underlying the resilience.

Reviewer #2 (Recommendations For The Authors):

1. How were the cutoffs determined to distinguish between upregulated/downregulated phosphoproteins and phosphopeptides?

Thank you for this important question. We used the same criteria to distinguish differences between TGAC8 and WT for unnormalized and normalized phosphoproteins, -log10(p-value) > 1.3, and log2FoldChange <= -0.4 (down) or log2FoldChange >= 0.4 (up), as stated in the methods section, main text and figure legend. The results were consistent across all analyses and selectively verified by experiments.

1. Were other models assessed for correlation between transcriptome and phosphoproteome other than a linear relationship of log2 fold change?

Thank you for this comment. In addition to a linear relationship of log2 fold change of molecule expression, we also compared protein activities, e.g., Fig 4F, and pathways enriched from different omics, e.g., Fig 3D, 5J, 6B and 6F.

1. Figures 1A and 5G seem to show outliers. How many biological and technical replicates would be needed to minimize error?

Thank you for the question. Figures 1A and 5G were PCA plots which, as expected, manifested some genetic variability among the same genotypes. The PCA plots, however, are useful in determining how the identified items separated, both within and among genotypes. For bioinformatics analysis such as ours, 4-5 samples are sufficient to accomplish this, as demonstrated by separation, by genotype, of samples in PCA. Thus, in addition to discovery of true heterogeneity among the samples, our results are still able to robustly discover the true differences between the genotypes.

1. Were the up/downregulated genes more likely to be lowly expressed (which would lead to larger log2 changes identified)?

In response to your query, we calculated the average expression of phosphorylation levels across all samples to observe whether they were expressed in low abundance in all samples. We also generated the MA plots, an application of a Bland–Altman plot, to create a visual representation of omics data. The MA plots in Author response image 1 illustrate that the target molecules with significantly changed phosphorylation levels did not aggregate within the very low abundance. To confirm this conclusion, we adopted two sets of cutoffs: (1) change: -log10(p-value) > 1.3, and log2FoldChange < 0 (down) or log2FoldChange > 0 (up); and (2) change_2: -log10(p-value) > 1.3, and log2FoldChange <= -0.4 (down) or log2FoldChange >= 0.4 (up).

Author response image 1.

1. "We verified some results through wet lab experiments" in the abstract is vague.

Thank you for the good suggestion. What we meant to indicate here was that identified genotypic differences in selected proteins, phosphoproteins and RNAs discovered in omics were verified by western blots, protein synthesis detection, proteosome activity detection, and protein soluble and insoluble fractions detection. However, we have deleted the reference to the wet lab experiments in the revised manuscript.

1. There are minor syntactical errors throughout the text.

Thank you very much for the suggestion. As noted in our response, we have edited and revised those errors throughout the text.

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

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Reply to the reviewers

1. General Statements [optional]

We are thankful to the reviewers for the time and effort invested in assessing our manuscript and for their suggestions to improve it. We have now considered the points raised by them, carried out additional experiments, and modified the text and figures to address them. We feel that the new experiments and modifications have been able to solve all concerns raised by the reviewers and have improved the manuscript substantially, strengthening and extending our conclusions.

The main modifications include:

• We have extended the analysis of the overexpression strains to highly stringent conditions, which revealed a mild acidification defect for the strain overexpressing Oxr1. In addition, we have included in our analysis a strain in which both proteins are overexpressed, which resulted in a further growth defect.
• We have analyzed the recruitment of Rtc5 to the vacuole under additional conditions: deletion of the main subunit of the RAVE complex RAV1, medium containing galactose as the sole carbon source and pharmacological inhibition of the V-ATPase. These experiments allowed us to strengthen and extend our conclusions regarding the requirements for Rtc5 targeting to the vacuole.
• We have analyzed V-ATPase disassembly in intact cells, by addressing the localization to the vacuole of subunit C (Vma5) in glucose and galactose-containing medium. The results strengthen our conclusion that both Rtc5 and Oxr1 promote an in vivo state of lower V-ATPase assembly.
• We have extended our analyses of V-ATPase function to medium containing galactose as a carbon source, since glucose availability is one of the main regulators of V-ATPase function in vivo. The results are consistent with what we observed in glucose-containing medium.
• We have included a diagram of the structure of the V-ATPase for reference.
• We have included a diagram and a paragraph describing Oxr1 and Rtc5 regarding protein length and domain architecture and comparing them to other TLDc domain-containing proteins.
• We have made changes to the text and figures to improve clarity and accuracy, including a methods section that was missing. We include below a point-by-point response to the reviewers´ comments.

2. Point-by-point description of the revisions

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

__ __Suggestions:

1. The authors observed that knockout of Rtc5p or Oxr1p does not affect vacuolar pH. If Rtc5p and Oxr1p both cooperate to dissociate V-ATPase, the authors may wish to characterize the effect of a ∆Rtc5p∆Oxr1p double knockout on vacuolar pH. The double mutant ∆rtc5∆oxr1 was already included in the original manuscript (the growth test is shown in Figure 5 B and the BCECF staining is shown in Figure 5C). This strain behaved like wt in both of these assays. Of note, what we observe for the deletion strains is increased assembly (Figure 5 D - G), so we expect that it would be hard to observe a difference in vacuole acidity or growth in the presence of metals.

Therefore, we have now also included a strain with the double overexpression of Oxr1 and Rtc5. Since overexpression of the proteins results in decreased assembly, it is more likely that this strain will show impaired growth under conditions that strongly rely on V-ATPase activity. Indeed, we observed that the overexpression of Oxr1 alone resulted in a slight growth defect in media containing high concentrations of ZnCl2 and the double overexpression strain showed an even further defect (Figure 6 A and C).

The manuscript would benefit from a well-labelled diagram showing the subunits of V-ATPase (e.g. in Figure 2D).

We agree with the reviewer and we have now added a diagram of the structure of the V-ATPase labeling the different subunits in Figure 2B.

The images of structures, especially in Figure 1-Supplement 1B, are not particularly clear and could be improved (e.g. by removing shadows or using transparency).

We are thankful to the reviewer for this suggestion. To improve the clarity of the structures in Figure 1 C and Figure 1 – Supplement 1A, we are now presenting the different subunits in the structures with different shades of blue and grey.

The authors should clearly describe the differences between Rtc5p and Oxr1p in terms of protein length, sequence identity, domain structure, etc.

We are thankful for this suggestion and we have now included a diagram of the domain architecture and protein length of Rtc5 and Oxr1, comparing with two human proteins containing a TLDc domain in Figure 5A. In addition, we have added the following paragraph describing the features of the proteins.

“Rtc5 is a 567 residue-long protein. Analysis of the protein using HHPred (Zimmermann et al., 2018), finds homology to the structure of porcine Meak7 (PDB ID: 7U8O, (Zi Tan et al., 2022)) over the whole protein sequence (residues 37-559). For both yeast Rtc5 and human Meak7 (Uniprot ID: Q6P9B6), HHPred detects homology of the C-terminal region to other TLDc domain containing proteins like yeast Oxr1 (PDBID: 7FDE), Drosophila melanogaster Skywalker (PDB ID: 6R82), and human NCOA7 (PDB ID: 7OBP), while the N-terminus has similarity to EF-hand domain calcium-binding proteins (PDB IDs: 1EG3, 2CT9, 1S6C6, Figure 5A). HHPred analysis of the 273 residue long Saccharomyces cerevisiae Oxr1, on the other hand, only detects similarity to TLDc domain containing proteins (PDB IDs: 7U80, 6R82, 7OBP), which spans the majority of the sequence of the protein (residues 71-273). The overall sequence identity between Oxr1 and Rtc5 is 24% according to a ClustalOmega alignment within Uniprot. The Alphafold model that we generated for Rtc5 is in good agreement with the available partial structure of Oxr1 (7FDE) (root mean square deviation (RMSD) of 3.509Å) (Figure 5 - S1 A), indicating they are structurally very similar, in the region of the TLDc domain. Taken together, these analyses suggest that Oxr1 belongs to a group of TLDc domain-containing proteins consisting mainly of just this domain like the splice variants Oxr1-C or NCOA7-B in humans (NP_001185464 and NP_001186551, respectively), while Rtc5 belongs to a group containing an additional N-terminal EF-hand-like domain and a N-myristoylation sequence, like human Meak7 (Finelli & Oliver, 2017) (Figure 5 A).”

Minor:

1. The "O" in VO should be capitalized. This has been corrected.

In Figure 4 supplement 1, the labels "I", "S", and "P" should be defined.

This has been clarified in the figure legend.

Please clarify what is meant by "switched labelling"

This refers to the SILAC vacuole proteomics experiments, for which yeast strains are grown in medium containing either L-Lysine or 13C6;15N2- L-Lysine to produce normal (‘light’) or heavy isotope-labeled (‘heavy’) proteins. This allows comparing two conditions. To increase the robustness of the comparisons, the experiments are done twice with both possible labeling schemes (condition A – light, condition B – heavy + condition A – heavy + condition B – light), which is commonly described as switched labeling or label switching.

We have exchanged the original sentence in the manuscript for:

“Performing the same experiments but switching which strain was labeled with heavy and light amino acids gave consistent results.”

The meaning of the sentence "Indeed, this was the case for both of them" is ambiguous.

We have now replaced this sentence with the following:

“Indeed, overexpression of either Rtc5 or Oxr1 resulted in increased growth defects in the context of Stv1 deletion (Figure 7 H and I).”

For Figure 1-Supplement 1B it is hard to see the crosslink distances.

We have updated this figure to improve the visibility of the cross-links. In addition, we now include a supplemental table (supplemental table 5) with a list of the Cα- Cα distances measured for all the crosslinks we mapped onto high-resolution structures.

The statement "The effects of Oxr1 are greater than those caused by Rtc5" requires more context. Is there a way of quantifying this effect for the reader?

We agree that this sentence was too general and vague. The effects caused by one or the other protein depend on the condition and the assay. We have thus deleted this sentence, and we think it is better to refer to the description of the individual assays performed.

The phrase "negative genetic interaction" should be clarified.

We have included in the text the following explanation of genetic interactions:

“A genetic interaction occurs when the combination of two mutations results in a different phenotype from that expected from the addition of the phenotypes of the individual mutations. For example, deletion of OXR1 or RTC5 has no impact on growth in neutral pH media containing zinc in a control background but improves the growth of RAV1 deletion strains (Figure 7 E and F), so this is a positive genetic interaction. On the other hand, overexpression of either Rtc5 or Oxr1 results in a growth defect in a background lacking Rav1 in neutral media containing zinc, a negative genetic interaction.”

* * In the sentence "Isogenic strains with the indicated modifications in the genome where spotted as serial dilutions in media with pH=5.5, pH=7.5 or pH=7.5 and containing 3 mM ZnCl2", "where" should be "were".

This has been corrected.

Figure 2D: the authors should consider re-coloring these models, as it is challenging to distinguish Rtc5p from the V-ATPase.

We have changed the coloring of this structure and added a diagram of the V-ATPase structure with the same coloring scheme to improve clarity.

Reviewer #1 (Significance (Required)):

The vacuolar protein interaction map alone from this manuscript is a nice contribution to the literature. Experiments establishing colocalization of Rtc5p to the vacuole are convincing, as is dependence of this association on the presence of assembled V-ATPase. Similarly, experiments related to myristoylation are convincing. The observed mislocalization of V-ATPases that contain Stv1p to the vacuole (which is also known to occur when Vph1p has been knocked out) upon knockout of Oxr1p is also extremely interesting. Overall, this is an interesting manuscript that contributes to our understand of TLDc proteins.

We are thankful to the reviewer for their appreciation of the significance of our work, including the interactome map of the vacuole as a resource and the advances on the understanding of the regulation of the V-ATPase by TLDc domain-containing proteins.

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

Major points:

1. The evidence of Oxr1 and Rtc5 as V-ATPase disassembly factors is circumstantial. The authors base their interpretation primarily on increased V1 (but not Vo) at purified vacuoles from Oxr1- or Rtc5-deleted strains, which does not directly address disassembly. Of course, the results regarding Oxr1 confirm detailed disassembly experiments with the purified protein complex (PMID 34918374), but on their own are open to other interpretations, e.g. suppression of V-ATPase assembly. Of note, the authors emphasize that they provide first evidence of the in vivo role of Oxr1, but monitor V1 recruitment with purified vacuoles and do not follow V-ATPase assembly in intact cells. We are thankful to the reviewer for pointing this out. We did not want to express that the molecular activity of the proteins is the disassembly of the complex, as our analyses include in vivo and ex vivo experiments and do not directly address this. We rather meant that both proteins promote an in vivo state of lower assembly of the V-ATPase. We have modified the wording throughout the manuscript to be clearer about this.

In addition, we have added new experiments to monitor V-ATPase assembly in intact cells, as suggested by the reviewer. Previous work has shown that in yeast, only subunit C leaves the vacuole membrane under conditions that promote disassembly, while the other subunits remain at the vacuole membrane (Tabke et al 2014). Our own experiments agree with what was published (Figure 3 D). We have thus monitored Vma5 localization to the vacuole under glucose or after shift to galactose containing media in cells lacking or overexpressing Rtc5 or Oxr1. We observed that cells overexpressing either TLDc domain protein show lower levels of Vma5 recruitment to the vacuole in glucose (Figure 6 D and E). Additionally cells lacking either Rtc5 or Oxr1 contain higher levels of Vma5 at the vacuole after 20 minutes in galactose medium (Figure 5 F and G). Thus, these results re-inforce our conclusions that Rtc5 and Oxr1 promote states of lower assembly.

Oxr1 and Rtc5 have very low sequence similarity. It would be helpful if the authors provided more detail on the predicted structure of the putative TLDc domain of Rtc5 and its relationship to the V-ATPase - Oxr1 structure. Is Rtc5 more closely related to established TLDc domain proteins in other organisms?

We have now included a diagram of the domain architecture of Rtc5 and Oxr1, and comparison to the features of other TLDc domain containing proteins in Figure 5 A, as well as a paragraph describing them:

“Rtc5 is a 567 residue-long protein. Analysis of the protein using HHPred (Zimmermann et al., 2018), finds homology to the structure of porcine Meak7 (PDB ID: 7U8O, (Zi Tan et al., 2022)) over the whole protein sequence (residues 37-559). For both yeast Rtc5 and human Meak7 (Uniprot ID: Q6P9B6), HHPred detects homology of the C-terminal region to other TLDc domain containing proteins like yeast Oxr1 (PDBID: 7FDE), Drosophila melanogaster Skywalker (PDB ID: 6R82), and human NCOA7 (PDB ID: 7OBP), while the N-terminus has similarity to EF-hand domain calcium-binding proteins (PDB IDs: 1EG3, 2CT9, 1S6C6, Figure 5A). HHPred analysis of the 273 residue long Saccharomyces cerevisiae Oxr1, on the other hand, only detects similarity to TLDc domain containing proteins (PDB IDs: 7U80, 6R82, 7OBP), which spans the majority of the sequence of the protein (residues 71-273). The overall sequence identity between Oxr1 and Rtc5 is 24% according to a ClustalOmega alignment within Uniprot. The Alphafold model that we generated for Rtc5 is in good agreement with the available partial structure of Oxr1 (7FDE) (root mean square deviation (RMSD) of 3.509Å) (Figure 5 - S1 A), indicating they are structurally very similar, in the region of the TLDc domain. Taken together, these analyses suggest that Oxr1 belongs to a subfamily of TLDc domain-containing proteins consisting mainly of just this domain like the splice variants Oxr1-C or NCOA7-B in humans (NP_001185464 and NP_001186551, respectively) , while Rtc5 belongs to a subfamily containing an additional N-terminal EF-hand-like domain and a N-myristoylation sequence, like human Meak7 (Finelli & Oliver, 2017) (Figure 5 A).”

The authors conclude vacuolar recruitment of Rtc5 depends on the assembled V-ATPase, based on deletion of different V1 and Vo domain subunits. However, these genetic manipulations likely cause a strong perturbation of vacuolar acidification; indeed, the images show drastically altered vacuolar morphology. To strengthen their conclusion, it would be helpful to show that Rtc5 recruitment is not blocked by inhibition of vacuolar acidification, and that conversely it is blocked by deletion of rav1.

We are thankful to the reviewer for this insightful suggestion and we have now performed both experiments suggested. The experiment regarding rav1Δ is now Figure 3C, and we observed that this mutation also disrupts Rtc5 localization to the vacuole. In addition, we decided to include an experiment showing the subcellular localization of Rtc5 after shifting the cells to galactose containing medium for 20 minutes, as a physiologically relevant condition that results in disassembly of the complex (Figure 3D). We observed that under these conditions Rtc5 re-localizes to the cytosol. This result is particularly interesting given that in yeast only subunit C (but not other V1 subunits) re-localizes to the cytosol under these conditions. In addition, the experiment using Bafilomycin A to inhibit the V-ATPase shows that Rtc5 is still localized at the vacuole membrane under conditions of V-ATPase inhibition (Figure 3 F). Taken together these results allowed us to strengthen our original interpretation that Rtc5 requires an assembled V-ATPase for its localization and extend it to the fact that the V-ATPase does not need to be active.

Reviewer #2 (Significance (Required)):

This is an interesting paper that confirms and extends previous findings on TLDc domain proteins as a novel class of proteins that interact with and regulate the V-ATPase in eukaryotes. The title seems to exaggerate the findings a bit, as the authors do not investigate V-ATPase (dis)assembly directly and only phenotypically describe altered subcellular localization of the Golgi V-ATPase in Oxr1-deleted cells. A recent structural and biochemical characterization of Oxr1 as a V-ATPase disassembly factor (PMID 34918374) somewhat limits the novelty of the results, but the function of Oxr1 in regulating subcellular V-ATPase localization and the identification of a second potential TLDc domain protein in yeast provide relevant insights into V-ATPase regulation. This paper will be of interest to cell biologists and biochemists working on lysosomal biology, organelle proteomics and V-ATPase regulation.

We thank the reviewer for the assessment of our work, and for recognizing the novel insights that we provide. Regarding the previous biochemical work on Oxr1 and the V-ATPase, we have clearly cited this work in the manuscript. In our opinion, our results complement and extend this article, showing that the function in disassembly is relevant in vivo. Additionally, this is only one of five major points of the article, the other four being

• The interactome map of the vacuole as a resource
• The identification of Rtc5 as a second yeast TLDc domain containing protein and interactor of the V-ATPase.
• The identification of the role of Rtc5 in V-ATPase assembly.
• The identification of the role of Oxr1 in Stv1 subcellular localization. We believe these additional points add important insights to researchers interested in lysosomes, the V-ATPase, intracellular trafficking and TLDc-domain containing proteins.

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

Major comments

__1) Re: A cross-linking mass spectrometry map of vacuolar protein interactions (results) __ While XL-MS is a very powerful method, it is a high-throughput approach and there should be some kind of negative control in these experiments. In cross-linking experiments, non-cross-linked samples are usually used as negative controls. What was the negative control in cross-linking mass-spectrometry experiments here? If there was no negative control, how the specificity of interactions was evaluated? Maybe the authors analyzed the dataset for highly improbable interactions and found very few of them?

We fully agree that it is crucial to ensure the specificity of the interactions detected by XL-MS. To achieve this, one needs to control (1) the specificity of the data analysis (i.e. that the recorded mass spectrometry data are correctly matched to cross-linked peptides from the sequence database) and (2) the biological specificity (i.e. that the cross-linking captured natively occurring interactions).

To ascertain that criterion (1) is met, cross-link identifications are filtered to a pre-defined false-discovery rate (FDR) – an approach that the XL-MS field adopted from mass spectrometry-based proteomics. As a result, low-confidence identifications (e.g. cross-linked peptides that are only supported by a few signals in a given mass spectrum) are removed from the dataset. FDR filtering in XL-MS is a rather complex matter as it can be done at different points during data analysis and the optimal FDR cut-off depends on the specific scientific question at hand (for more details see for example Fischer and Rappsilber, Anal Chem, 2017). Generally speaking, an overly restrictive FDR cut-off would remove a lot of correct identifications, thereby greatly limiting the sensitivity of the analysis. On the other hand, a too relaxed FDR cut-off would dilute the correct identifications with a high number of false-positives, which would impair the robustness and specificity of the dataset. While many XL-MS study control the FDR on the level of individual spectrum matches, we opted for a 2% FDR cut-off on the level of unique residue pairs, which is more stringent (see Fischer and Rappsilber, Anal Chem, 2017). Our FDR parameters are described in the Methods section (Cross-linking mass spectrometry of isolated vacuoles - Data analysis). Of note, we have made all raw mass spectrometry data publicly available through the PRIDE repository (https://www.ebi.ac.uk/pride/ ; accession code PXD046792; login details during peer review: Username = reviewer_pxd046792@ebi.ac.uk, Password = q1645lTP). This will allow other researchers to re-analyze our data with the data analysis settings of their choice in the future.

To ascertain that criterion (2) is met, we mapped the identified cross-links onto existing high-resolution structures of vacuolar protein complexes. Taking into account the length of our cross-linking reagent, the side-chain length of the cross-linkable amino acids (i.e. lysines), and a certain degree of in-solution flexibility, cross-links can reasonably occur between lysines with a mutual Cα-Cα distance of up to 35 Å. Using this cut-off, the lysine-lysine pairs in the high-resolution structures we studied can be split into possible cross-linking partners (Cα-Cα distance 35 Å). Of all cross-links we could map onto high-resolution structures, 95.2% occurred between possible cross-linking partners. In addition, our cross-links reflect numerous known vacuolar protein interactions that have not yet been structurally characterized. These lines of evidence increase our confidence that our XL-MS approach captured genuine, natively occurring interactions. These analyses are described in more detail in the first Results sub-section (“A cross-linking mass spectrometry map of vacuolar protein interactions”).

In addition, the high purity of vacuole preparation is critical. How was it assessed by the authors?

We disagree that the purity of the vacuole preparation is critical for this analysis to be valid. The accuracy of the protein-protein interactions detected will depend on their preservation during sample preparation until the sample encounters the cross-linker, and the data analysis, as described above. The experiment would have been equally valid if performed on whole cell lysates without any enrichment of vacuoles, but the coverage of vacuolar proteins would have likely been very low. For this reason, we decided to use the vacuole isolation procedure to obtain better coverage of the proteins of this particular organelle. The use of the Ficoll gradient protocol (Haas, 1995) was based on that it is a protocol that yields strong enrichment of proteins annotated with the GO Term “vacuole” (Eising et al, 2019) and that it preserves the functionality of the organelle, as evidenced by its use for multiple functional assays (vacuole-vacuole fusion (Haas, 1995), autophagosome-vacuole fusion (Gao et al, 2018), polyphosphate synthesis by the VTC complex (Desfougéres et al, 2016), among others).

2) Re: Rtc5 and Oxr1 counteract the function of the RAVE complex (results)

Taken together, data, presented in this section of the manuscript, provide strong evidence that Rtc5 and Oxr1 negatively regulate V-ATPase activity, counteracting the V-ATPase assembly, facilitated by the activity of the RAVE complex. However, the complete deletion of the major RAVE subunit Rav1p was required to observe this effect in vivo in yeast. The other way to induce V-ATPase disassembly in yeast is glucose deprivation. It will be interesting to study if there is a synergistic effect between glucose deprivation and RTC5/OXR1 deletion on V-ATPase assembly, vacuolar pH, and growth of single oxr1Δ, rtc5Δ or double oxr1Δrtc5Δ mutants (OPTIONAL). Glucose deprivation is a more physiologically relevant condition than a deletion of an entire gene.

We would like to point out that an effect on assembly is observed without deleting the RAVE complex: deletions of Oxr1 or Rtc5 resulted in increased V-ATPase assembly in vivo in the presence of glucose and of the RAVE complex (Figures 5 D and E). We have now also added the experiments showing that the overexpression strains have a mild growth defect under conditions that force cells to strongly rely on V-ATPase activity (Figures 6 A and C).

Nevertheless, we agree that addressing the effect of changing the levels of Oxr1 and Rtc5 under low-glucose conditions is an interesting physiologically relevant question. We have now included growth assays and BCECF staining in medium containing galactose as the carbon source (Figures 5 – Supplement 1 B and C, and Figure 6 C and Figure 6- Supplement 1A). In addition, we have addressed the vacuolar localization of Vma5 in medium containing glucose or after shifting to medium containing galactose for 20 minutes, as a proxy for V-ATPase disassembly in intact cells (Figure 5 F and G, Figure 6 D and E). Taken together, these analyses reinforce our conclusions that both Rtc5 and Oxr1 promote an in vivo state of lower V-ATPase assembly, based on the following observations:

• Higher localization of Vma5 to the vacuole after 20 mins in galactose in cells lacking Oxr1 or Rtc5 (Figure 5 F and G).
• Lower localization of Vma5 to the vacuole in medium containing glucose in cells overexpressing Oxr1 or Rtc5 (Figure 6 D and E).
• Growth defect of the strain overexpressing Oxr1 in medium containing galactose with pH = 7.5 and zinc chloride, with a further growth defect caused by additional overexpression of Rtc5 (Figure 6 C). 3) Re: Figure 6 - supplement 1. The title is relevant to panel D only, it should be renamed to reflect the results of the disassembly of V-ATPase in rav1Δ mutant strains, while results about the stv1Δ-based strains (Panel D) should be shown together with similar experiments in Figure 7 - supplement 2 for clarity.

We have shifted the Panel D from the original Figure 6 – Supplement 1 to the main Figure (now Figure 7 – H and I). Regarding the title of the Figure, whether Supplemental Figures have titles or not will depend on the journal where the manuscript is published. For now, we have removed all titles from supplemental figures, as they are conceived to complement the main Figures.

4) Re: Figure 7 - supplement 1, Panel A. The proper assay to show that Stv1-mNeonGreen is functional is to express it in double mutant vph1Δstv1Δ to see if the growth defect is reversed. In addition, the vph1Δ growth defect is not changed (improved or worsened) in the presence of Stv1-mNeonGreen, so it means that the expression of Stv1-mNeonGreen does not further compromise the V-ATPase function, but it does not mean that it improves its function.

It is clear from the experiment suggested by the reviewer that they think that we have expressed Stv1-mNeonGreen from a plasmid. This was not the case, Stv1 was C-terminally tagged with mNeonGreen in the genome. It is thus the only expressed version in the strain. The experiment we have performed is thus equivalent to the one suggested by the reviewer, but for genomically expressed variants. For reference, the genotypes of all the strains used can be found in Supplemental Table 1.

5) Re: Figure 7 - supplement 2. This figure should be combined with Fig. 6- suppl 1, panel D as also mentioned above. The figure seems to lack some labels, and conclusions are not accurate as discussed below. However, this data provides important additional information about relationships between isoform-specific subunits of V-ATPase Vph1 and Stv1 and both Rtc5 and Oxr1 and should be repeated if it is not done yet to have a better idea about these relationships.

Panel B: Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of VPH1, since double deletion mutant vph1Δ rtc5Δ grows worse than each individual mutant. Although it also means that there is no positive interaction, it is not the same.

Indeed, there is a negative genetic interaction between the deletion of RTC5 and VPH1. We have replaced the growth tests in this figure (Figure 8 – Supplement 2 A in the new manuscript) to show this negative genetic interaction better. This effect is reproducible, as shown in the repetitions of the experiments.

Panel C: Same as for panel B. Based on this picture, the deletion of OXR1 has a weak negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ oxr1Δ grows worse than each individual mutant at 6 mM ZnCl2.

Panel D: Same as for panels B and C. Based on this picture, deletion of RTC5 has a negative genetic interaction with the deletion of STV1, since double deletion mutant stv1Δ rtc5Δ grows worse than each individual mutant at 6 mM ZnCl2. There is no label in the middle panel (growth conditions) and no growth assay data in the presence of CaCl2.

However, these results will be then in contradiction with the results from Figure 6 - Supplement 1, panel D, showing negative genetic interaction between the overexpression of Rtc5 or Oxr1 and deletion of Stv1, since both deletion and overexpression of Rtc5 or Oxr1 would have negative genetic interactions with Stv1.

For both Panels C and D (Now Figure 8 - Supplement 2 B and C). The effect pointed out by the reviewer (slightly stronger growth defect for the double mutants than for the single mutants) is very mild. We have attempted to make it more evident by assessing growth in medium with higher and lower concentrations of zinc and this was not possible. This is in contrast with the very clear positive genetic interaction that we observe between the deletion of OXR1 and VPH1 (Now Figure 8 H). This is the reason that we decided to report the lack of a positive genetic interaction instead of the presence of a negative one, as we do not want to draw conclusions based on results that are borderline detectable.

In addition, there is no label for the media in the middle panel, is it just YPAD pH=7.5, without the addition of any metals?

Indeed, the media is YPAD pH=7.5, without the addition of any metals. The line drawn above several images based on this media indicated this. Since this form of labeling appears to be confusing, we have now replaced it and placed the label directly above the image.

Why there is no growth assay in the presence of CaCl2, like in panels A and B?

Every growth test shown in the manuscript was performed including growth in YPD pH=5,5 as a control of a permissive condition for lack of V-ATPase activity, and then in YPD pH=7,5 including a broad range of Zinc Chloride and Calcium chloride concentrations. From all these pictures, the conditions where the differences among strains were clearly visible were chosen to assemble the figures. Conditions that did not provide any information for that particular experiment were not included in the figure to avoid making them unnecessarily large and crowded.

Re: Figure 7 - supplement 2, continued. How many times all these experiments were repeated? These experiments should be repeated at least 3 times, which is especially necessary for the experiments in panel C, because the effects are borderline. If results are reproducible and statistically significant, although small, the conclusion should be changed from "no positive genetic interactions" to "negative genetic interactions", which is more precise and informative.

All growth tests shown in the manuscript were repeated at least three times for the conditions shown. We are thankful to the reviewer for pointing out that this was not mentioned, and we have added this to the methods section. We have assembled a file with all repetitions of the shown growth tests and added it at the end of this file. In doing so, these are already available for the public. These repetitions show that all effects reported are reproducible. We will then discuss with the editors of the journal where this manuscript is published about the necessity of including it with the final article.

Regarding reporting the lack of a positive genetic interaction vs. a negative one, we have discussed this above. Shortly, for Panel B (Figure 8 – Supplement 2 A in the new manuscript) we have changed the conclusion to “negative genetic interaction” as adjusting the zinc chloride concentration allowed us to show this clearly and reproducibly, as shown by the repetitions of the experiments. For panels C and D (Now Figure 8 - Supplement 2 B and C), the effect is really mild and barely detectable, even when we tried a wide range of zinc chloride concentrations. For this reason, we would prefer to maintain the “no positive genetic interaction” conclusion.

Re: Methods. There is no description of yeast serial dilution growth assay at all. In addition, why the specific media (neutral pH, in the presence of high concentrations of calcium or zinc) was used is not explained either in the results or methods. Appropriate references should be included, for example, PMID: 2139726, PMID: 1491236.

We apologize for the oversight of the missing methods section, which we have now included.

Regarding the explanation of the media used, the following section was already a part of the results section, before the description of the first growth test:

“The V-ATPase is not essential for viability in yeast cells, and mutants lacking subunits of this complex grow similarly to a wt strain in acidic media. However, when cells grow at near-neutral pH or in the presence of divalent cations such as calcium and zinc, the mutants lacking V-ATPase function show a strong growth impairment (Kane et al, 2006).”

We have now replaced this with the following, more complete version:

“As a first approach for addressing the role of these proteins, we tested growth phenotypes related to V-ATPase function in strains lacking or overexpressing them. The V-ATPase is not essential for viability in yeast cells, and mutants lacking subunits of this complex grow similarly to a wt strain in acidic media, but display a growth defect at near-neutral pH the mutants (Nelson & Nelson, 1990). In addition, the proton gradient across the vacuole membrane generated by the V-ATPase energizes the pumping of metals into the vacuole, as a mechanism of detoxification. Thus, increasing concentrations of divalent cations such as calcium and zinc, generate conditions in which growth is increasingly reliant on V-ATPase activity (Förster & Kane, 2000; MacDiarmid et al, 2002; Kane, 2006).”

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MINOR COMMENTS

Yeast proteins are named with "p" at the end, such as "Rtc5p".

This nomenclature rule is falling into disuse during the last decades, as the use of capitals vs lowercase and italics allows to distinguish between genes proteins and strains (OXR1 = gene, Oxr1 = protein, oxr1Δ = strain). As an example, I include a list of the latest papers by some of the major yeast labs around the world, all of which use the same nomenclature as we do (in alphabetical order). This list even includes some work in the field of the V-ATPase.

• Alexey Merz, USA. PMID: 33225520
• Benoit Kornmann, UK. PMID: 35654841
• Christian Ungermann, Germany. PMID: 37463208
• Claudio de Virgilio, Switzerland. PMID: 36749016
• Daniel E. Gottschling, USA. PMID: 37640943
• David Teis, Austria. PMID: 32744498
• Elizabeth Conibear, Canada. PMID: 35938928
• Fulvio Reggiori, Denmark. PMID: 37060997
• J Christopher Fromme, USA. PMID: 37672345
• Maya Schuldiner, Israel. PMID: 37073826
• Patricia Kane, USA. PMID: 36598799
• Scott Emr, USA. PMID: 35770973
• W Mike Henne, USA. PMID: 37889293
• Yoshinori Ohsumi, Japan. PMID: 37917025 In addition, we would prefer to keep the nomenclature that we already use, to keep consistency with other published articles from our lab.

Re: Introduction. In the introduction it should be indicated that Rtc5 was originally discovered as a "restriction of telomere capping 5", using screening of temperature-sensitive cdc13-1 mutants combined with the yeast gene deletion collection [PMID: 18845848]. A couple of sentences should be written about the RAVE complex and its role in V-ATPase assembly.

We are thankful for this suggestion and we have now included both pieces of information in the introduction.

*“The re-assembly of the V1 onto the VO complex when glucose becomes again available, is aided by a dedicated chaperone complex known as the RAVE complex, which also likely has a general role in V-ATPase assembly (Seol et al, 2001; Smardon et al, 2002, 2014).” *

“In our cross-linking mass spectrometry interactome map of isolated vacuoles we found that the only other TLDc-domain containing protein of yeast, Rtc5, is a novel interactor of the V-ATPase. Rtc5 is a protein of unknown function, originally described in a genetic screen for genes related to telomere capping (Addinall et al, 2008)”

Re: The TLDc domain-containing protein of unknown function Rtc5 is a novel interactor of the vacuolar V-ATPase (results)

1) It is important to understand, that Oxr1 was co-purified before with the V1 domain of V-ATPase from a certain mutant strain, not wild-type yeast [PMID: 34918374]. It may explain why the authors did not identify it in their original protein-protein interactions screen here.

The structural work on the V1 domain bound to Oxr1 (Khan et al, 2022) showed that the binding of Oxr1 prevented V1 from assembling onto the Vo. Since our experiments rely on the purification of vacuoles, they should contain mainly only V1 assembled onto the VO, and not the free soluble V1. This is likely the reason that we do not detect Oxr1, in addition to it being less abundant. We have clarified this now in the manuscript and added the fact that Oxr1 was co-purified with a V1 containing a mutant version of the H subunit.

“In a previous study, Oxr1 was co-purified with a V1 domain containing a mutant version of the H subunit, and its presence prevented the in vitro assembly of this V1 domain onto the VO domain and promoted disassembly of the holocomplex (Khan et al., 2022). This is likely the reason why we do not detect Oxr1 in our experiments, which rely on isolated vacuoles and thus would only include V1 domains that are assembled onto the membrane. In addition, Oxr1 is less abundant in yeast cells than Rtc5 according to the protein abundance database PaxDb (Wang et al, 2015).”

2) It is a wrong conclusion that because Rtc5 was co-purified with both V1 and V0 domain subunits it interacts with the assembled V-ATPase, this does not exclude a possibility that Rtc5 also interacts with separate V1 sector or separate V0 sector of V-ATPase.

We agree with the reviewer that the co-purification of Rtc5 with both V1 and VO domain subunits does not necessarily mean that it interacts with the assembled V-ATPase. Thus, we have modified the text in this part to:

“The fact that we can co-enrich Rtc5 both with Vma2 and with Vph1 indicates that it can interact either with both the VO and V1 domains or with the assembled V-ATPase.”

However, other results throughout the manuscript can be taken into account to strengthen this idea:

1. Rtc5 requires an assembled V-ATPase to localize to the vacuole membrane, and thus seems not to interact with free VO domains, which would be available when we delete V1 subunits or in medium containing galactose.
2. Rtc5 becomes cytosolic in galactose-containing media. This would indicate that it also does not interact with free V1 domains, which are still localized to the vacuole membrane under these conditions. Taken together with the pull-downs, these results suggest that Rtc5 interacts with the assembled V1-VO V-ATPase. Thus, we have included the following sentence after Figure 3, which shows the subcellular localization experiments.

*“Taking into account that Rtc5 is co-enriched with subunits of both the VO and V1 domain, and that it localizes at the vacuole membrane dependent on an assembled V-ATPase, we suggest that Rtc5 interacts with the assembled V-ATPase complex.” *

Re: Figure 1, Panel C. Is it possible to show individual proteins in different colors for clarity?

Panel D. How were cross-link distances measured? It is not obvious if you are not an expert in the field and it is not described in the methods.

We have modified Figure 1 C and Figure 1 – Supplement 1B (now Figure 1 – Supplement 1 A) to present the different subunits in the structures with different shades of blue and grey.

Furthermore, we have clarified the distance measurement approach in the methods section and in the legend of Fig 1D: “Ca-Ca distances were determined using the measuring function in Pymol v.2.5.2 (Schrodinger LLC).”

__Re: Figure 1 - Supplement 1, __

Panel A. What scientific information are we getting from this picture?

This panel was just a visual representation of the complexity of the protein network we obtained. Indeed, there was no specific scientific message, so we have decided to remove this panel from the revised manuscript.

Panel B. Why are these complexes shown separately from the complexes in Figure 1, panel C? Also, can individual proteins be colored differently here as well?

We did not want to overload Fig 1C, so we decided to show some of the protein complexes in Fig 1 – Supplement 1B. The most important information is the histogram showing that 95% of the mapped cross-links fall within the expected length range, and this is shown in the main Figure (Figure 1D). As stated above, we have adjusted the subunit coloring in Figure 1 C to improve clarity.

Re: Figure 3. It will be nice to show the localization of the untagged protein as well if antibodies are available (OPTIONAL).

Unfortunately, there are no available antibodies for either Rtc5 or Oxr1. This hinders us from detecting the endogenous untagged proteins. We would like to point out that we have been very careful in showing which tagged proteins are functional (C-terminally tagged Rtc5) and which are not (C-terminally tagged Oxr1), so that the reader can know how to interpret the localization data.

Re: Figure 4. Why different tags were used in panels A (GFP), C (msGFP2) and D

(mNeonGreen)?

In general, we prefer to use mNeonGreen as a tag for microscopy experiments because it is brighter and more stable, and msGFP2 as a tag for experiments involving Western blots because we have better antibodies available. There was a mistake in the labeling, and actually, all constructs labeled as GFP were msGFP2. We have now corrected this. Of note, we have tested the functionality of both tagged version (mNeonGreen and msGFP2).

Panels B and C. Were Rtc5 fusions detected using anti-GFP antibodies?

Indeed, Rtc5-msGFP2 was detected with an anti-GFP antibody. We have now indicated next to each Western blot membrane the primary antibody used. In addition, all antibodies are detailed in Supplemental Figure 3.

The authors should have full-size Western blots available, not just cut-out bands, as some journals and reviewers require them for publication.

For all western blots, we always showed a good portion of the membrane and not cut-out bands. The cropping was performed to avoid making figures unnecessarily large. The whole membranes are of course available and will be included in an “extended data file” if required by the journal.

Re: Figure 4 - Supplement 1, Panel A. Does "-" and "+" mean -/+ Azido-Myr?

Indeed. We have now added this label to the figure.

Panel B. There is no blot with a membrane protein marker (Vam3 or Vac8), it should be included.

We have replaced this western blot for a different repetition of this experiment in which a membrane protein marker was included. Of note, the two other repetitions of the experiment shown (Figure 4 – Supplement 1 panel C and Figure 4 panel C) also include both a membrane protein marker and a soluble protein marker.

Re: Figure 5. The title does not describe all results in this figure and should be modified accordingly.

The original data from Figure 5 is now separated into Figures 5 and 6 because of the additional experiments included during revisions. We have modified the Figure titles to be descriptive of the overall message of the Figures.

Panel C. Statistical significance value for *** should be indicated in the legend.

This has been indicated in the Figure legend.

It is not clear how many times yeast growth assays were repeated. Usually, all experiments should be done in triplicates or more.

All shown growth tests were performed at least three times for the conditions shown. We have now indicated this in the materials and methods section. In addition, we now provide in this response a file with all repetitions of growth tests, which will be appended to the article if deemed necessary by the editors.

Re: Figure 5 - supplement 1. No title

Re: Figure 5 - supplement 2. No title

Whether the supplemental Figures should have a title or not will depend on the style of the journal where the manuscript is finally published. The current idea of the supplemental Figures is that they complement the corresponding main Figure. For this reason, we have removed all titles from supplemental Figures.

Re: Figure 6. There is a typo on the second lane in the legend: "...the genome were", not "...the genome where".

This has been corrected.

Panel C. Why the analysis of BCECF vacuole staining of double mutants oxr1Δrav1Δ and rtc5Δrav1Δ is not shown? Was it done at all?

We had not included this piece of data, as we thought that the genetic interaction of RTC5 and OXR1 and rav1Δ was sufficiently well supported with the included data (growth tests in combination with the deletion, growth tests in combination with the overexpression, vacuole proteomics in combination with overexpression, and BCECF staining in combination with the overexpression). Because of the request of the reviewer, we have now included this experiment as Figure 7 G.

Re: Figure 6 - Supplement 2. Why were two different tags (2xmNG and msGFP2) used?

We tried both tags to see if one of them would be functional. Unfortunately, they both resulted in non-functional proteins, as shown by the corresponding growth tests.

Did the authors study N-terminally tagged Oxr1? Was it functional?

We have tagged Oxr1 N-terminally, and this unfortunately resulted in a protein that was not completely functional. We show below the localization of N-terminally mNeon-tagged Oxr1, under the control of the TEF1 promoter. The protein appears cytosolic (Panel A) but is not completely functional (Panel B). The localization of Oxr1 had already been misreported by using a tagged version that we now show to be non-functional. For this reason, we preferred not to include this data in the manuscript, to avoid again including in the literature subcellular localizations that correspond to non-functional or partially functional proteins.

Panel B. Results for the untagged TEF1pr-Oxr1 overexpression are not shown, thus tagged and untagged proteins can't be compared. Are they available? What is the promoter for the expression of 2xmNG fusion constructs?

Oxr1-2xmNG was C-terminally tagged in the genome, which means that the promoter is the endogenous one, it was not modified. For this reason, the correct controls are a strain expressing Oxr1 at endogenous levels (the wt strain) and a strain lacking Oxr1. Both controls were included in the Figure, and in all repetitions made of this experiment. For reference, all the genotypes of the strains used are found in Supplemental Table 1.

Re: Methods. Were vacuoles prepared differently for XL-MS and SILAC-based vacuole proteomics (there are different references) and why? Methods for XL-MS and quantitative SILAC-based proteomics can be placed together for clarity.

The basis for the method of vacuole purification is the same, from (Haas, 1995). This reference was included in both protocols that include vacuole purifications. However, modifications of this method were performed to fit the crosslinking method (higher pH, no primary amines) or to fit the SILAC labeling (combination of two differentially labeled samples in one purification). The reference for the vacuole proteomics (Eising et al 2022) corresponds to a paper in which the SILAC-based comparison of vacuoles from different mutant strains was optimized, and includes not only the vacuole purification but the growth conditions and downstream processing of the vacuoles.

Since both the SILAC-based vacuole proteomics and the XL-MS are multi-step methods, containing numerous parameters including the sample preparation, processing for MS, MS run and data analysis, we would prefer to keep them separate. We think this would allow a person attempting to reproduce these methods to go through them step by step.

What is CMAC dye? Why was it used to stain the vacuolar lumen?

We apologize for this oversight, we have included the definition of CMAC as 7-Amino-4-Chlormethylcumarin. It is a standard-used organelle marker for the lumen of the vacuole.

Some abbreviations (TEAB, ACN) are not explained.

We apologize for this oversight. We have now replaced these abbreviations with the full names of the compounds in the article.

What is 0% Ficoll?

We used the term 0% Ficoll, because this is the name given to the buffer in the original Haas 1995 paper on vacuole purifications. However, we agree that the term is misleading and we have now added the composition of the buffer (10 mM PIPES/KOH pH=6.8, 0.2 M Sorbitol).

Reviewer #3 (Significance (Required)):

The vacuolar-type proton ATPase, V-ATPase, is the key proton pump, that hydrolases ATP and uses this energy to pump protons across membranes. Amazingly, this proton pump and its function are conserved in eukaryotes from yeast to mammals. While V-ATPase structure and function have been studied for more than 30 years in various organisms, its regulation is not completely understood. The very recent discoveries of two new V-ATPase interacting proteins in yeast, first Oxr1 (OXidative Resistance 1), and now Rtc5 (Restriction of Telomere Capping 5), both the only two members of TLDc (The Tre2/Bub2/Cdc16 (TBC), lysin motif (LysM), domain catalytic) proteins in yeast, provide new insights in V-ATPase regulation in yeast, and because the interaction is conserved in mammals its relevance to mammalian V-ATPases regulation as well.

TLDc proteins are best known for their role in protection from oxidative stress, in particular in yeast and in the nervous system in mammals. The discovery of the novel Rtc5-V-ATPase interaction points to the role of V-ATPase not only in protection from oxidative stress but also in restriction of telomere capping in yeast and most likely higher species. The studies of other species also highlight the possible conserved role of V-ATPase in lifespan determination and Torc1 signaling, mediated through these interactions. Thus, the discovery of this new functionally important interaction between the second TLDc family member in yeast, Rtc5, and V-ATPase will shed light on the molecular mechanisms of all these essential biological processes and pathways.

In addition, because the authors performed a comprehensive proteomics protein-protein interaction study of the purified yeast vacuole it provides a valuable resource for all researchers who study vacuoles and/or related to them lysosomes.

The follow-up functional studies using the rav1Δ strain clearly demonstrated that Rtc5 and Oxr1 disassemble V-ATPase and counteract the function of V-ATPase assembly RAVE complex in vivo in yeast. Thus, they are essentially the first discovered endogenous eukaryotic protein inhibitors of V-ATPase. Moreover, because the authors obtained the evidence that Oxr1 is the regulator of the specific subunit isoform of V-ATPase Stv1p in vivo in yeast, it suggests that different TLDc proteins may regulate different specific V-ATPase subunit isoforms in cell- and tissue-specific manner in higher eukaryotes. The mechanism of this isoform-specific regulation in yeast and other species needs further investigation in the future.

Because of the conservation of the TLDc-V-ATPase interactions, all this information can be extrapolated to higher species, all the way to humans, in whom genetic mutations in various TLDc proteins are known to cause devastating diseases and syndromes.

We are thankful to the reviewer for their positive comments about the significance of our work.

Author Response

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

Reviewer #1 (Public Review):

The biogenesis of outer membrane proteins (OMPs) into the outer membranes of Gram-negative bacteria is still not fully understood, particularly substrate recognition and insertion by beta-assembly machinery (BAM). In the studies, the authors present their studies that in addition to recognition by the last strand of an OMP, sometimes referred to as the beta-signal, an additional signal upstream of the last strand is also important for OMP biogenesis.

Strengths:

1. Overall the manuscript is well organized and written, and addresses an important question in the field. The idea that BAM recognizes multiple signals on OMPs has been presented previously, however, it was not fully tested.

2. The authors here re-address this idea and propose that it is a more general mechanism used by BAM for OMP biogenesis.

3. The notion that additional signals assist in biogenesis is an important concept that indeed needs fully tested in OMP biogenesis.

4. A significant study was performed with extensive experiments reported in an attempt to address this important question in the field.

5. The identification of important crosslinks and regions of substrates and Bam proteins that interact during biogenesis is an important contribution that gives clues to the path substrates take en route to the membrane.

Weaknesses:

Major critiques (in no particular order):

1. The title indicates 'simultaneous recognition', however no experiments were presented that test the order of interactions during OMP biogenesis.

We have replaced the word “Simultaneous” with “Dual” so as not to reflect on the timing of the recognition events for the distinct C-terminal signal and -5 signal.

1. Aspects of the study focus on the peptides that appear to inhibit OmpC assembly, but should also include an analysis of the peptides that do not to determine this the motif(s) present still or not.

We thank the reviewer for this comment. Our study focuses on the peptides which exhibited an inhibitory effect in order to elucidate further interactions between the BAM complex and substrate proteins, especially in early stage of the assembly process. In the case of peptide 9, which contains all of our proposed elements but did not have an inhibitory effect, there is the presence of an arginine residue at the polar residue next to hydrophobic residue in position 0 (0 Φ). As seen in Fig S5, S6, and S7, there are no positively charged amino acids in the polar residue positions in the -5 or last strands. This might be the reason why peptide 9, as well as peptide 24, the β-signal derived from the mitochondrial OMP Tom40 and contains a lysine at the polar position, did not display an inhibitory effect. Incorporating the reviewer's suggestions might elucidate conditions that should not be added to the elements, but this is not the focus of this paper and was not discussed to avoid complicating the paper.

1. The β-signal is known to form a β-strand, therefore it is unclear why the authors did not choose to chop OmpC up according to its strands, rather than by a fixed peptide size. What was the rationale for how the peptide lengths were chosen since many of them partially overlap known strands, and only partially (2 residues) overlap each other? It may not be too surprising that most of the inhibitory peptides consist of full strands (#4, 10, 21, 23).

A simple scan of known β-strands would have been an alternative approach, however this comes with the bias of limiting the experiments to predicted substrate (strand) sequences, and it presupposes that the secondary structure element would be formed by this tightly truncated peptide.

Instead, we allowed for the possibility that OMPs meet the BAM complex in an unfolded or partially folded state, and that the secondary structure (β-strand) might only form via β-argumentation after the substrate is placed in the context of the lateral gate. We therefore used peptides that mapped right across the entirety of OmpC, with a two amino acid overlap.

To clarify this important point regarding the unbiased nature of our screen, we have revised the text:

(Lines 147-151) "We used peptides that mapped the entirety of OmpC, with a two amino acid overlap. This we considered preferable to peptides that were restricted by structural features, such as β-strands, in consideration that β-strand formation may or may not have occurred in early-stage interactions at the BAM complex."

1. It would be good to have an idea of the propensity of the chosen peptides to form β-stands and participate in β-augmentation. We know from previous studies with darobactin and other peptides that they can inhibit OMP assembly by competing with substrates.

We appreciate the reviewer's suggestion. However, we have not conducted biophysical characterizations of the peptides to calculate the propensity of each peptide to form β-stands and participate in β-augmentation. The sort of detailed biophysical analysis done for Darobactin (by the Maier and Hiller groups, The antibiotic darobactin mimics a β-strand to inhibit outer membrane insertase Nature 593:125-129) was a Nature publication based on this single peptide. A further biophysical analysis of all of the peptides presented here goes well beyond the scope of our study.

1. The recognition motifs that the authors present span up to 9 residues which would suggest a relatively large binding surface, however, the structures of these regions are not large enough to accommodate these large peptides.

The β-signal motif (ζxGxx[Ω/Φ]x[Ω/Φ]) is an 8-residue consensus, some of the inhibitory peptides include additional residues before and after the defined motif of 8 residues, and the lateral gate of BamA has been shown interact with a 7-residue span (eg. Doyle et al, 2022). Cross-linking presented in our study showed BamD residues R49 and G65 cross-linked to the positions 0 and 6 of the internal signal in OmpC (Fig. 6D).

We appreciate this point of clarification and have modified the text to acknowledge that in the final registering of the peptide with its binding protein, some parts of the peptide might sit beyond the bounds of the BamD receptor’s binding pocket and the BamA lateral gate:

(Lines 458-471) "The β-signal motif (ζxGxx[Ω/Φ]x[Ω/Φ]) is an eight-residue consensus, and internal signal motif is composed of a nine-residue consensus. Recent structures have shown the lateral gate of BamA interacts with a 7-residue span of substrate OMPs. Interestingly, inhibitory compounds, such as darobactin, mimic only three resides of the C-terminal side of β-signal motif. Cross-linking presented here in our study showed that BamD residues R49 and G65 cross-linked to the positions 0 and 6 of the internal signal in OmpC (Fig. 6D). Both signals are larger than the assembly machineries signal binding pocket, implying that the signal might sit beyond the bounds of the signal binding pocket in BamD and the lateral gate in BamA. These finding are consistent with similar observations in other signal sequence recognition events, such as the mitochondrial targeting presequence signal that is longer than the receptor groove formed by the Tom20, the subunit of the translocator of outer membrane (TOM) complex (Yamamoto et al., 2011). The presequence has been shown to bind to Tom20 in several different conformations within the receptor groove (Nyirenda et al., 2013)."

Moreover, the distance between amino acids of BamD which cross-linked to the internal signal, R49 and Y62, is approximately 25 Å (pdbID used 7TT3). The distance of the maximum amino acid length of the internal signal of OmpC, from F280 to Y288, is approximately 22 Å (pdbID used 2J1N). This would allow for the signal to fit within the confines of the TRP motif of BamD.

Author response image 1.

1. The authors highlight that the sequence motifs are common among the inhibiting peptides, but do not test if this is a necessary motif to mediate the interactions. It would have been good to see if a library of non-OMP related peptides that match this motif could also inhibit or not.

With respect, this additional work would not address any biological question relevant to the function of BamD. To randomize sequences and then classify those that do or don’t fit the motif would help in refining the parameters of the β-signal motif, but that was not our intent.

We have identified the peptides from within the total sequence of an OMP, shown which peptides inhibit in an assembly assay, and then observed that the inhibitory peptides conform to a previously published (β-signal) motif.

1. In the studies that disrupt the motifs by mutagenesis, an effect was observed and attributed to disruption of the interaction of the 'internal signal'. However, the literature is filled with point mutations in OMPs that disrupt biogenesis, particular those within the membrane region. F280, Y286, V359, and Y365 are all residues that are in the membrane region that point into the membrane. Therefore, more work is needed to confirm that these mutations are in parts of a recognition motif rather than on the residues that are disrupting stability/assembly into the membrane.

As the reviewer pointed out, the side chains of the amino acids constituting the signal elements we determined were all facing the lipid side, of which Y286 and Y365 were important for folding as well as to be recognized. However, F280A and V359A had no effect on folding, but only on assembly through the BAM complex. The fact that position 0 functions as a signal has been demonstrated by peptidomimetics (Fig. 1) and point mutant analysis (Fig. 2). We appreciate this clarification and have modified the text to acknowledge that the all of the signal element faces the lipid side, which contributes to their stability in the membrane finally, and before that the BAM complex actively recognizes them and determines their orientation:

(Lines 519-526) After OMP assembly, all elements of the internal signal are positioned such that they face into the lipid-phase of the membrane. This observation may be a coincidence, or may be utilized by the BAM complex to register and orientate the lipid facing amino acids in the assembling OMP away from the formative lumen of the OMP. Amino acids at position 6, such as Y286 in OmpC, are not only component of the internal signal for binding by the BAM complex, but also act in structural capacity to register the aromatic girdle for optimal stability of the OMP in the membrane.

1. The title of Figure 3 indicates that disrupting the internal signal motif disrupts OMP assembly, however, the point mutations did not seem to have any effect. Only when both 280 and 286 were mutated was an effect observed. And even then, the trimer appeared to form just fine, albeit at reduced levels, indicating assembly is just fine, rather the rate of biogenesis is being affected.

We appreciate this point and have revised the title of Figure 3 to be:

(Lines 1070-1071) "Modifications in the putative internal signal slow the rate of OMP assembly in vivo."

1. In Figure 4, the authors attempt to quantify their blots. However, this seems to be a difficult task given the lack of quality of the blots and the spread of the intended signals, particularly of the 'int' bands. However, the more disturbing trend is the obvious reduction in signal from the post-urea treatment, even for the WT samples. The authors are using urea washes to indicate removal of only stalled substrates. However a reduction of signal is also observed for the WT. The authors should quantify this blot as well, but it is clear visually that both WT and the mutant have obvious reductions in the observable signals. Further, this data seems to conflict with Fig 3D where no noticeable difference in OmpC assembly was observed between WT and Y286A, why is this the case?

We have addressed this point by adding a statistical analysis on Fig. 4A. As the reviewer points out, BN-PAGE band quantification is a difficult task given the broad spread of the bands on these gels. Statistical analysis showed that the increase in intermediates (int) was statistically significant for Y286A at all times until 80 min, when the intermediate form signals decrease.

(Lines 1093-1096) "Statistical significance was indicated by the following: N.S. (not significant), p<0.05; , p<0.005; *. Exact p values of intermediate formed by Wt vs Y286A at each timepoint were as follows; 20 minutes: p = 0.03077, 40 minutes: p = 0.02402, 60 minutes: p = 0.00181, 80 minutes: p = 0.0545."

Further regarding the Int. band, we correct the statement as follows.

(Lines 253-254) "Consistent with this, the assembly intermediate which was prominently observed at the OmpC(Y286A) can be extracted from the membranes with urea;"

OMP assembly in vivo has additional periplasmic chaperones and factors present in order to support the assembly process. Therefore, it is likely that some proteins were assembled properly in vivo compared to their in vitro counterparts. Such a decrease has been observed not only in E. coli but also in mitochondrial OMP import (Yamano et al., 2010).

1. The pull-down assays with BamA and BamD should include a no protein control at the least to confirm there is no non-specific binding to the resin. Also, no detergent was mentioned as part of the pull downs that contained BamA or OmpC, nor was it detailed if OmpC was urea solubilized.

We have performed pull down experiments with a no-protein (Ni-NTA only) control as noted (Author response image 1). The results showed that the amount of OmpC carrying through on beads only was significantly lower than the amount of OmpC bound in the presence of BamD or BamA. The added OmpC was not treated with urea, but was synthesized by in vitro translation; the in vitro translated OmpC is the standard substrate in the EMM assembly assay (Supp Fig. S1) where it is recognized by the BAM complex. Thus, we used it for pull-down as well and, to make this clearer, we have revised as follows:

Author response image 2.

Pull down assay of radio-labelled OmpC with indicated protein or Ni-NTA alone (Ni-NTA) . T; total, FT; Flow throw, W; wash, E; Elute.

(Lines 252-265) "Three subunits of the BAM complex have been previously shown to interact with the substrates: BamA, BamB, and BamD (Hagan et al., 2013; Harrison, 1996; Ieva et al., 2011). In vitro pull-down assay showed that while BamA and BamD can independently bind to the in vitro translated OmpC polypeptide (Fig .S9A), BamB did not (Fig. S9B)."

11.

• The neutron reflectometry experiments are not convincing primarily due to the lack controls to confirm a consistent uniform bilayer is being formed and even if so, uniform orientations of the BamA molecules across the surface.

• Further, no controls were performed with BamD alone, or with OmpC alone, and it is hard to understand how the method can discriminate between an actual BamA/BamD complex versus BamA and BamD individually being located at the membrane surface without forming an actual complex.

• Previous studies have reported difficulty in preparing a complex with BamA and BamD from purified components.

• Additionally, little signal differences were observed for the addition of OmpC. However, an elongated unfolded polypeptide that is nearly 400 residues long would be expected to produce a large distinct signal given that only the C-terminal portion is supposedly anchored to BAM, while the rest would be extended out above the surface.

• The depiction in Figure 5D is quite misleading when viewing the full structures on the same scales with one another.

We have addressed these five points individually as follows.

i. The uniform orientation of BamA on the surface is guaranteed by the fixation through a His-tag engineered into extracellular loop 6 of BamA and has been validated in previous studies as cited in the text. Moreover, to explain this, we reconstructed another theoretical model for BamA not oriented well in the system as below. However, we found that the solid lines (after fitting) didn’t align well with the experimental data. We therefore assumed that BamA has oriented well in the membrane bilayer.

Author response image 3.

Experimental (symbols) and fitted (curves) NR profiles of BamA not oriented well in the POPC bilayer in D2O (black), GMW (blue) and H2O (red) buffer.

ii. There would be no means by which to do a control with OmpC alone or BamD alone as neither protein binds to the lipid layer chip. OmpC is diluted from urea and then the unbound OmpC is washed from the chip before NR measurements. BamD does not have an acyl group to anchor it to the lipid layer, without BamA to anchor to, it too is washed from the chip before NR measurements. We have reconstructed another theoretical model for both of BamA + BamD embedding in the membrane bilayer, and the fits were shown below. Apparently, the fits didn’t align well with the experimental data, which discriminate the BamA/BamD individually being located at the membrane surface without forming an actual complex.

Author response image 4.

Experimental (symbols) and fitted (curves) NR profiles of BamA+D embedding together in the POPC bilayer in D2O (black), GMW (blue) and H2O (red) buffer.

iii. The previous studies that reported difficulty in preparing a complex with BamA and BamD from purified components were assays done in aqueous solution including detergent solubilized BamA, or with BamA POTRA domains only. Our assay is superior in that it reports the binding of BamD to a purified BamA that has been reconstituted in a lipid bilayer.

iv. The relatively small signal differences observed for the addition of OmpC are expected, since OmpC is an elongated, unfolded polypeptide of nearly 400 residues long which, in the context of this assay, can occupy a huge variation in the positions at which it will sit with only the C-terminal portion anchored to BAM, and the rest moving randomly about and extended from the surface.

v. We appreciate the point raised and have now added a note in the Figure legend that these are depictions of the results and not a scale drawing of the structures.

1. In the crosslinking studies, the authors show 17 crosslinking sites (43% of all tested) on BamD crosslinked with OmpC. Given that the authors are presenting specific interactions between the two proteins, this is worrisome as the crosslinks were found across the entire surface of BamD. How do the authors explain this? Are all these specific or non-specific?

The crosslinking experiment using purified BamD was an effective assay for comprehensive analysis of the interaction sites between BamD and the substrate. However, as the reviewer pointed out, cross-linking was observed even at the sites that, in the context of the BAM complex, interact with BamC as a protein-protein interaction and would not be available for substrate protein-protein interactions. To complement this, analysis and to address this issue, we also performed the experiment in Fig. 6C.

In Fig. 6C, the interaction of BamD with the substrate is examined in vivo, and the results demonstrate that if BPA is introduced into the site, we designated as the substrate recognition site, it is cross-linked to the substrate. On the other hand, position 114 was found to crosslink with the substrate in vitro crosslinking, but not in vivo. It should be noted that position 114 has also been confirmed to form cross-link products with BamC, we believe that BamD-substrate interactions in the native state have been investigated. To explain the above, we have added the following description to the Results section.

(Lines 319-321) "Structurally, these amino acids locate both the lumen side of funnel-like structure (e.g. 49 or 62) and outside of funnel-like structure such as BamC binding site (e.g. 114) (fig. S12C). (Lines 350-357) Positions 49, 53, 65, and 196 of BamD face the interior of the funnel-like structure of the periplasmic domain of the BAM complex, while position 114 is located outside of the funnel-like structure (Bakelar et al., 2016; Gu et al., 2016; Iadanza et al., 2016). We note that while position 114 was cross-linked with OmpC in vitro using purified BamD, that this was not seen with in vivo cross-linking. Instead, in the context of the BAM complex, position 114 of BamD binds to the BamC subunit and would not be available for substrate binding in vivo (Bakelar et al., 2016; Gu et al., 2016; Iadanza et al., 2016)."

1. The study in Figure 6 focuses on defined regions within the OmpC sequence, but a more broad range is necessary to demonstrate specificity to these regions vs binding to other regions of the sequence as well. If the authors wish to demonstrate a specific interaction to this motif, they need to show no binding to other regions.

The region of affinity for the BAM complex was determined by peptidomimetic analysis, and the signal region was further identified by mutational analysis of OmpC. Subsequently, the subunit that recognizes the signal region was identified as BamD. In other words, in the process leading up to Fig. 6, we were able to analyze in detail that other regions were not the target of the study. We have revised the text to make clear that we focus on the signal region including the internal signal, and have not also analyzed other parts of the signal region:

(Lines 329-332) "As our peptidomimetic screen identified conserved features in the internal signal, and cross-linking highlighted the N-terminal and C-terminal TPR motifs of BamD as regions of interaction with OmpC, we focused on amino acids specifically within the β-signals of OmpC and regions of BamD which interact with β-signal."

1. The levels of the crosslinks are barely detectable via western blot analysis. If the interactions between the two surfaces are required, why are the levels for most of the blots so low?

These are western blots of cross-linked products – the efficiency of cross-linking is far less than 100% of the interacting protein species present in a binding assay and this explains why the levels for the blots are ‘so low’. We have added a sentence to the revised manuscript to make this clear for readers who are not molecular biologists:

(Lines 345-348) "These western blots reveal cross-linked products representing the interacting protein species. Photo cross-linking of unnatural amino acid is not a 100% efficient process, so the level of cross-linked products is only a small proportion of the molecules interacting in the assays."

15.

• Figure 7 indicates that two regions of BamD promote OMP orientation and assembly, however, none of the experiments appears to measure OMP orientation?

• Also, one common observation from panel F was that not only was the trimer reduced, but also the monomer. But even then, still a percentage of the trimer is formed, not a complete loss.

(i) We appreciate this point and have revised the title of Figure 7 to be:

(Lines 1137-1138) "Key residues in two structurally distinct regions of BamD promote β-strand formation and OMP assembly."

(ii) In our description of Fig. 7F (Lines 356-360) we do not distinguish between the amount of monomer and trimer forms, since both are reflective of the overall assembly rate i.e. assembly efficiency. Rather, we state that:

"The EMM assembly assay showed that the internal signal binding site was as important as the β-signal binding site to the overall assembly rates observed for OmpC (Fig. 7F), OmpF (fig. S15D), and LamB (fig. S15E). These results suggest that recognition of both the C-terminal β-signal and the internal signal by BamD is important for efficient protein assembly."

16.

• The experiment in Fig 7B would be more conclusive if it was repeated with both the Y62A and R197A mutants and a double mutant. These controls would also help resolve any effect from crowding that may also promote the crosslinks.

• Further, the mutation of R197 is an odd choice given that this residue has been studied previously and was found to mediate a salt bridge with BamA. How was this resolved by the authors in choosing this site since it was not one of the original crosslinking sites?

As stated in the text, the purpose of the experiment in Figure 7B is to measure the impact of pre-forming a β-strand in the substrate (OmpC) before providing it to the receptor (BamD). We thank the reviewer for the comment on the R197 position of BamD. The C-terminal domain of BamD has been suggested to mediate the BamA-BamD interface, specifically BamD R197 amino acid creates a salt-bridge with BamA E373 (Ricci et al., 2012). It had been postulated that the formation of this salt-bridge is not strictly structural, with R197 highlighted as a key amino acid in BamD activity and this salt-bridge acts as a “check-point” in BAM complex activity (Ricci et al., 2012, Storek et al., 2023). Our results agree with this, showing that the C-terminus of BamD acts in substrate recognition and alignment of the β-signal (Fig. 6, Fig S12). We show that amino acids in the vicinity of R197 (N196, G200, D204) cross-linked well to substrate and mutations to the β-signal prevent this interaction (Fig S12B, D). For mutational analysis of BamD, we looked then at the conservation of the C-terminus of BamD and determined R197 was the most highly conserved amino acid (Fig 6C). In order to account for this, we have adjusted the manuscript:

(Lines 376-377) "R197 has previously been isolated as a suppressor mutation of a BamA temperature sensitive strain (Ricci et al., 2012)."

(Lines 495-496) "This adds an additional role of the C-terminus of BamD beyond a complex stability role (Ricci et al., 2012; Storek et al., 2023)."

1. As demonstrated by the authors in Fig 8, the mutations in BamD lead to reduction in OMP levels for more than just OmpC and issues with the membrane are clearly observable with Y62A, although not with R197A in the presence of VCN. The authors should also test with rifampicin which is smaller and would monitor even more subtle issues with the membrane. Oddly, no growth was observed for the Vec control in the lower concentration of VCN, but was near WT levels for 3 times VCN, how is this explained?

While it would be interesting to correlate the extent of differences to the molecular size of different antibiotics such as rifampicin, such correlations are not the intended aim of our study. Vancomycin (VCN) is a standard measure of outer membrane integrity in our field, hence its use in our tests for membrane integrity.

We apologize to the reviewer as Figure 8 D-G may have been misleading. Figure 8D,E are using bamD shut-down cells expressing plasmid-borne BamD mutants. Whereas Figure 8F, G are the same strain as used in Figure 3. We have adjusted the figure as well as the figure legend: (Lines 1165-1169) D, E E coli bamD depletion cells expressing mutations at residues, Y62A and R197A, in the β-signal recognition regions of BamD were grown with of VCN. F, G, E coli cells expressing mutations to OmpC internal signal, as shown in Fig 3, grown in the presence of VCN. Mutations to two key residues of the internal signal were sensitive to the presence of VCN.

1. While Fig 8I indeed shows diminished levels for FY as stated, little difference was observed for the trimer for the other mutants compared to WT, although differences were observed for the dimer. Interestingly, the VY mutant has nearly WT levels of dimer. What do the authors postulate is going on here with the dimer to trimer transition? How do the levels of monomer compare, which is not shown?

The BN-PAGE gel system cannot resolve protein species that migrate below ~50kDa and the monomer species of the OMPs is below this size. We can’t comment on effects on the monomer because it is not visualized. The non-cropped gel image is shown here. Recently, Hussain et al., has shown that in vitro proteo-liposome system OmpC assembly progresses from a “short-lived dimeric” form before the final process of trimerization (Hussain et al., 2021). However, their findings suggest that LPS plays the final role in stimulation of dimer-to-trimer, a step well past the recognition step of the β-signals. Mutations to the internal signal of OmpC results in the formation of an intermediate, the substrate stalled on the BAM complex. This stalling, presumably, causes a hinderance to the BAM complex resulting in reduced timer and loss of dimer OmpF signal in the EMM of cells expressing OmpC double mutant strain, FY. cannot resolve protein species that migrate below ~50kDa and the monomer species of the OMPs is below this size. We can’t comment on effects on the monomer because it is not visualized. The non-cropped gel image is shown here. We have noted this in the revised text:

Author response image 5.

Non-cropped gel of Fig. 8I. the asterisk indicates a band observed in the sample loading wells at the top of the gel.

(Lines 417-418) "The dimeric form of endogenous OmpF was prominently observed in both the OmpC(WT) as well as the OmpC(VY) double mutant cells."

1. In the discussion, the authors indicate they have '...defined an internal signal for OMP assembly', however, their study is limited and only investigates a specific region of OmpC. More is needed to definitively say this for even OmpC, and even more so to indicate this is a general feature for all OMPs.

We acknowledge the reviewer's comment on this point and have expanded the statement to make sure that the conclusion is justified with the specific evidence that is shown in the paper and the supplementary data. We now state:

(Lines 444-447) "This internal signal corresponds to the -5 strand in OmpC and is recognized by BamD. Sequence analysis shows that similar sequence signatures are present in other OMPs (Figs. S5, S6 and S7). These sequences were investigated in two further OMPs: OmpF and LamB (Fig. 2C and D)."

Note, we did not state that this is a general feature for all OMPs. That would not be a reasonable proposition.

20.

• In the proposed model in Fig 9, it is hard to conceive how 5 strands will form along BamD given the limited surface area and tight space beneath BAM.

• More concerning is that the two proposal interaction sites on BamD, Y62 and R197, are on opposite sides of the BamD structure, not along the same interface, which makes this model even more unlikely.

• As evidence against this model, in Figure 9E, the two indicates sites of BamD are not even in close proximity of the modeled substrate strands.

We can address the reviewer’s three concerns here:

i. The first point is that the region (formed by BamD engaged with POTRA domains 1-2 and 5 of BamA) is not sufficient to accommodate five β-strands. Structural analysis reveals that the interaction between the N-terminal side of BamD and POTRA1-2 is substantially changed the conformation by substrate binding, and that this surface is greatly extended. This surface does have enough space to accommodate five beta-strands, as now documented in Fig. 9D, 9E using the latest structures (7TT5 and 7TT2) as illustrations of this. The text now reads:

(Lines 506-515) "Spatially, this indicates the BamD can serve to organize two distinct parts of the nascent OMP substrate at the periplasmic face of the BAM complex, either prior to or in concert with, engagement to the lateral gate of BamA. Assessing this structurally showed the N-terminal region of BamD (interacting with the POTRA1-2 region of BamA) and the C-terminal region of BamD (interacting with POTRA5 proximal to the lateral gate of BamA) (Bakelar et al., 2016; Gu et al., 2016; Tomasek et al., 2020) has the N-terminal region of BamD changing conformation depending on the folding states of the last four β-strands of the substrate OMP, EspP (Doyle et al., 2022). The overall effect of this being a change in the dimensions of this cavity change, a change which is dependent on the folded state of the substrate engaged in it (Fig 9 B-E)."

ii. The second point raised regards the orientation of the substrate recognition residues of BamD. Both Y62A and R197 were located on the lumen side of the funnel in the EspP-BAM transport intermediate structure (PDBID;7TTC); Y62A is relatively located on the edge of BamD, but given that POTRA1-2 undergoes a conformational change and opens this region, as described above, both are located in locations where they could bind to substrates. This was explained in the following text in the results section of revised manuscript.

(Lines 377-379) "Each residue was located on the lumen side of the funnel-like structure in the EspP-BAM assembly intermediate structure (PDBID; 7TTC) (Doyle et al., 2022)."

**Reviewer #2 (Public Review):"

Previously, using bioinformatics study, authors have identified potential sequence motifs that are common to a large subset of beta-barrel outer membrane proteins in gram negative bacteria. Interestingly, in that study, some of those motifs are located in the internal strands of barrels (not near the termini), in addition to the well-known "beta-signal" motif in the C-terminal region.

Here, the authors carried out rigorous biochemical, biophysical, and genetic studies to prove that the newly identified internal motifs are critical to the assembly of outer membrane proteins and the interaction with the BAM complex. The author's approaches are rigorous and comprehensive, whose results reasonably well support the conclusions. While overall enthusiastic, I have some scientific concerns with the rationale of the neutron refractory study, and the distinction between "the intrinsic impairment of the barrel" vs "the impairment of interaction with BAM" that the internal signal may play a role in. I hope that the authors will be able to address this.

Strengths:

1. It is impressive that the authors took multi-faceted approaches using the assays on reconstituted, cell-based, and population-level (growth) systems.

2. Assessing the role of the internal motifs in the assembly of model OMPs in the absence and presence of BAM machinery was a nice approach for a precise definition of the role.

Weaknesses:

1. The result section employing the neutron refractory (NR) needs to be clarified and strengthened in the main text (from line 226). In the current form, the NR result seems not so convincing.

What is the rationale of the approach using NR?

We have now modified the text to make clear that:

(Lines 276-280) "The rationale to these experiments is that NR provides: (i) information on the distance of specified subunits of a protein complex away from the atomically flat gold surface to which the complex is attached, and (ii) allows the addition of samples between measurements, so that multi-step changes can be made to, for example, detect changes in domain conformation in response to the addition of a substrate."

What is the molecular event (readout) that the method detects?

We have now modified the text to make clear that:

(Lines 270-274) "While the biochemical assay demonstrated that the OmpC(Y286A) mutant forms a stalled intermediate with the BAM complex, in a state in which membrane insertion was not completed, biochemical assays such as this cannot elucidate where on BamA-BamD this OmpC(Y286A) substrate is stalled."

What are "R"-y axis and "Q"-x axis and their physical meanings (Fig. 5b)?

The neutron reflectivity, R, refers to the ratio of the incoming and exiting neutron beams and it is measured as a function of Momentum transfer Q, which is defined as Q=4π sinθ/λ, where θ is the angle of incident and λ is the neutron wavelength. R(Q)is approximately given byR(Q)=16π2/ Q2 |ρ(Q)|2, where R(Q) is the one-dimensional Fourier transform of ρ(z), the scattering length density (SLD) distribution normal to the surface. SLD is the sum of the coherent neutron scattering lengths of all atoms in the sample layer divided by the volume of the layer. Therefore, the intensity of the reflected beams is highly dependent on the thickness, densities and interface roughness of the samples. This was explained in the following text in the method section of revised manuscript.

(Lines 669-678) "Neutron reflectivity, denoted as R, is the ratio of the incoming to the exiting neutron beams. It’s calculated based on the Momentum transfer Q, which is defined by the formula Q=4π sinθ/λ, where θ represents the angle of incidence and λ stands for the neutron wavelength. The approximate value of R(Q) can be expressed as R(Q)=16π2/ Q2 |ρ(Q)|2, where R(Q) is the one-dimensional Fourier transform of ρ(z), which is the scattering length density (SLD) distribution perpendicular to the surface. SLD is calculated by dividing the sum of the coherent neutron scattering lengths of all atoms in a sample layer by the volume of that layer. Consequently, factors such as thickness, volume fraction, and interface roughness of the samples significantly influence the intensity of the reflected beams."

How are the "layers" defined from the plot (Fig. 5b)?

The “layers” in the plot (Fig. 5b) represent different regions of the sample being studied. In this study, we used a seven-layer model to fit the experimental data (chromium - gold - NTA - HIS8 - β-barrel - P3-5 - P1-2. This was explained in the following text in the figure legend of revised manuscript. (Lines 1115-1116) The experimental data was fitted using a seven-layer model: chromium - gold - NTA - His8 - β-barrel - P3-5 - P1-2.

What are the meanings of "thickness" and "roughness" (Fig. 5c)?

We used neutron reflectometry to determine the relative positions of BAM subunits in a membrane environment. The binding of certain subunits induced conformational changes in other parts of the complex. When a substrate membrane protein is added, the periplasmic POTRA domain of BamA extends further away from the membrane surface. This could result in an increase in thickness as observed in neutron reflectometry measurements.

As for roughness, it is related to the interface properties of the sample. In neutron reflectometry, the intensity of the reflected beams is highly dependent on the thickness, densities, and interface roughness of the samples. An increase in roughness could suggest changes in these properties, possibly due to protein-membrane interactions or structural changes within the membrane.

(Lines 1116-1120) "Table summarizes of the thickness, roughness and volume fraction data of each layer from the NR analysis. The thickness refers to the depth of layered structures being studied as measured in Å. The roughness refers to the irregularities in the surface of the layered structures being studied as measured in Å."

What does "SLD" stand for?

We apologize for not explaining abbreviation when the SLD first came out. We explained it in revised manuscript. (Line 298)

1. In the result section, "The internal signal is necessary for insertion step of assembly into OM" This section presents an important result that the internal beta-signal is critical to the intrinsic propensity of barrel formation, distinct from the recognition by BAM complex. However, this point is not elaborated in this section. For example, what is the role of these critical residues in the barrel structure formation? That is, are they involved in any special tertiary contacts in the structure or in membrane anchoring of the nascent polypeptide chains?

We appreciate the reviewer's comment on this point. Both position 0 and position 6 appear to be important amino acids for recognition by the BAM complex, since mutations introduced at these positions in peptide 18 prevent competitive inhibition activity.

In terms of the tertiary structure of OmpC, position 6 is an amino acid that contributes to the aromatic girdle, and since Y286A and Y365A affected OMP folding as measured in folding experiments, it is perhaps their position in the aromatic girdle that contributes to the efficiency of β-barrel folding in addition to its function as a recognition signal. We have added a sentence in the revised manuscript:

(Lines 233-236) "Position 6 is an amino acid that contributes to the aromatic girdle. Since Y286A and Y365A affected OMP folding as measured in folding experiments, their positioning into the aromatic girdle may contributes to the efficiency of β-barrel folding, in addition to contributing to the internal signal."

The mutations made at position 0 had no effect on folding, so this residue may function solely in the signal. Given the register of each β-strand in the final barrel, the position 0 residues have side-chains that face out into the lipid environment. From examination of the OmpC crystal structure, the residue at position 0 makes no special tertiary contacts with other, neighbouring residues.  

Reviewer #1 (Recommendations For The Authors):

Minor critiques (in no particular order):

1. Peptide 18 was identified based on its strong inhibition for EspP assembly but another peptide, peptide 23, also shows inhibition and has no particular consensus.

We would correct this point. Peptide 23 has a strong consensus to the canonical β-signal. We had explained the sequence consensus of β-signal in the Results section of the text. In the third paragraph, we have added a sentence indicating the relationship between peptide 18 and peptide 23.

(Lines 152-168) "Six peptides (4, 10, 17, 18, 21, and 23) were found to inhibit EspP assembly (Fig. 1A). Of these, peptide 23 corresponds to the canonical β-signal of OMPs: it is the final β-strand of OmpC and it contains the consensus motif of the β-signal (ζxGxx[Ω/Φ]x[Ω/Φ]). The inhibition seen with peptide 23 indicated that our peptidomimetics screening system using EspP can detect signals recognized by the BAM complex. In addition to inhibiting EspP assembly, five of the most potent peptides (4, 17, 18, 21, and 23) inhibited additional model OMPs; the porins OmpC and OmpF, the peptidoglycan-binding OmpA, and the maltoporin LamB (fig. S3). Comparing the sequences of these inhibitory peptides suggested the presence of a sub-motif from within the β-signal, namely [Ω/Φ]x[Ω/Φ] (Fig. 1B). The sequence codes refer to conserved residues such that: ζ, is any polar residue; G is a glycine residue; Ω is any aromatic residue; Φ is any hydrophobic residue and x is any residue (Hagan et al., 2015; Kutik et al., 2008). The non-inhibitory peptide 9 contained some elements of the β-signal but did not show inhibition of EspP assembly (Fig. 1A).

Peptide 18 also showed a strong sequence similarity to the consensus motif of the β-signal (Fig. 1B) and, like peptide 23, had a strong inhibitory action on EspP assembly (Fig. 1A). Variant peptides based on the peptide 18 sequence were constructed and tested in the EMM assembly assay (Fig. 1C)."

1. It is unclear why the authors immediately focused on BamD rather than BamB, given that both were mentioned to mediate interaction with substrate. Was BamB also tested?

We thank the reviewer for this comment. Following the reviewer's suggestion, we have now performed a pull-down experiment on BamB and added it to Fig. S9. We also modified the text of the results as follows.

(Lines 262-265) "Three subunits of the BAM complex have been previously shown to interact with the substrates: BamA, BamB, and BamD (Hagan et al., 2013; Harrison, 1996; Ieva et al., 2011). In vitro pull-down assay showed that while BamA and BamD can independently bind to the in vitro translated OmpC polypeptide (Fig .S9A), BamB did not (Fig. S9B)."

1. For the in vitro folding assays of the OmpC substrates, labeled and unlabeled, no mention of adding SurA or any other chaperone which is known to be important for mediating OMP biogenesis in vitro.

We appreciate the reviewer’s concerns on this point, however chaperones such as SurA are non-essential factors in the OMP assembly reaction mediated by the BAM complex: the surA gene is not essential and the assembly of OMPs can be measured in the absence of exogenously added SurA. It remains possible that addition of SurA to some of these assays could be useful in detailing aspects of chaperone function in the context of the BAM complex, but that was not the intent of this study.

1. For the supplementary document, it would be much easier for the reader to have the legends groups with the figures.

Following the reviewer's suggestion, we have placed the legends of Supplemental Figures together with each Figure.

1. Some of the figures and their captions are not grouped properly and are separated which makes it hard to interpret the figures efficiently.

We thank the reviewer for this comment, we have revised the manuscript and figures to properly group the figures and captions together on a single page.

1. The authors begin their 'Discussion' with a question (line 454), however, they don't appear to answer or even attempt to address it; suggest removing rhetorical questions.

As per the reviewers’ suggestion, we removed this question.

1. Line 464, 'unbiased' should be removed. This would imply that if not stated, experiments are 'negatively' biased.

We removed this word and revised the sentence as follows:

(Lines 431-433) "In our experimental approach to assess for inhibitory peptides, specific segments of the major porin substrate OmpC were shown to interact with the BAM complex as peptidomimetic inhibitors."

1. Lines 466-467; '...go well beyond expected outcomes.' What does this statement mean?

Our peptidomimetics led to unexpected results in elucidating the additional essential signal elements. The manuscript was revised as follows:

(Lines 433-435) "Results for this experimental approach went beyond expected outcomes by identifying the essential elements of the signal Φxxxxxx[Ω/Φ]x[Ω/Φ] in β-strands other than the C-terminal strand."

1. Line 478; '...rich information that must be oversimplified...'?

We appreciate the reviewer’s pointed out. For more clarity, the manuscript was revised as follows:

(Lines 450-453) "The abundance of information which arises from modeling approaches and from the multitude of candidate OMPs, is generally oversimplified when written as a primary structure description typical of the β-signal for bacterial OMPs (i.e. ζxGxx[Ω/Φ]x[Ω/Φ]) (Kutik et al., 2008)."

1. There are typos in the supplementary figures.

We have revised and corrected the Supplemental Figure legends.  

Reviewer #2 (Recommendations For The Authors):

1. In Supplementary Information, I recommend adding the figure legends directly to the corresponding figures. Currently, it is very inconvenient to go back and forth between legends and figures.

Following the reviewer's suggestion, we have placed the legends of Supplemental Figures together with each Figure.

1. Line 94 (p.3): "later"

Lateral?

Yes. We have corrected this.

1. Line 113 (p.3): The result section, "Peptidomimetics derived from E. coli OmpC inhibit OMP assembly" Rationale of the peptide inhibition assay is not clear. How can the peptide sequence that effectively inhibit the assembly interpreted as the b-assembly signal? By competitive binding to BAM or by something else? What is the authors' hypothesis in doing this assay?

In revision, we have added following sentence to explain the aim and design of the peptidomimetics:

(Lines 140-145) "The addition of peptides with BAM complex affinity, such as the OMP β-signal, are capable of exerting an inhibitory effect by competing for binding of substrate OMPs to the BAM complex (Hagan et al., 2015). Thus, the addition of peptides derived from the entirety of OMPs to the EMM assembly assay, which can evaluate assembly efficiency with high accuracy, expects to identify novel regions that have affinity for the BAM complex."

1. Line 113- (p.3) and Fig. S1: The result section, "Peptidomimetics derived from E. coli OmpC inhibit OMP assembly"

Some explanation seems to be needed why b-barrel domain of EspP appears even without ProK?

We appreciate the reviewer’s pointed out. We added following sentence to explain:

(Lines 128-137) "EspP, a model OMP substrate, belongs to autotransporter family of proteins. Autotransporters have two domains; (1) a β-barrel domain, assembled into the outer membrane via the BAM complex, and (2) a passenger domain, which traverses the outer membrane via the lumen of the β-barrel domain itself and is subsequently cleaved by the correctly assembled β-barrel domain (Celik et al., 2012). When EspP is correctly assembled into outer membrane, a visible decrease in the molecular mass of the protein is observed due to the self-proteolysis. Once the barrel domain is assembled into the membrane it becomes protease-resistant, with residual unassembled and passenger domains degraded (Leyton et al., 2014; Roman-Hernandez et al., 2014)."

1. Line 186 (p.6): "Y285"

Y285A?

We have corrected the error, it was Y285A.

1. Lines 245- (p. 7)/ Lines 330- (p. 10)

It needs to be clarified that the results described in these paragraphs were obtained from the assays with EMM.

We appreciate the reviewer’s concerns on these points. For the first half, the following text was added at the beginning of the applicable paragraph to indicate that all of Fig. 4 is the result of the EMM assembly assay.

(Line 241) "We further analyzed the role of internal β-signal by the EMM assembly assay. At the second half, we used purified BamD but not EMM. We described clearly with following sentence."

(Lines 316-318) "We purified 40 different BPA variants of BamD, and then irradiated UV after incubating with 35S-labelled OmpC."

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Author Response

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

We are very grateful to both reviewers for taking the time to review our manuscript and data in great detail. We thank you for the fair assessment of our work, the helpful feedback, and for recognizing the value of our work. We have done our best to address your concerns below:

eLife assessment This work reports a valuable finding on glucocorticoid signaling in male and female germ cells in mice, pointing out sexual dimorphism in transcriptomic responsiveness. While the evidence supporting the claims is generally solid, additional assessments would be required to fully confirm an inert GR signaling despite the presence of GR in the female germline and GR-mediated alternative splicing in response to dexamethasone treatment in the male germline. The work may interest basic researchers and physician-scientists working on reproduction and

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Cincotta et al set out to investigate the presence of glucocorticoid receptors in the male and female embryonic germline. They further investigate the impact of tissue-specific genetically induced receptor absence and/or systemic receptor activation on fertility and RNA regulation. They are motivated by several lines of research that report inter and transgenerational effects of stress and or glucocorticoid receptor activation and suggest that their findings provide an explanatory mechanism to mechanistically back parental stress hormone exposure-induced phenotypes in the offspring.

Strengths:

A chronological immunofluorescent assessment of GR in fetal and early life oocyte and sperm development.

RNA seq data that reveal novel cell type specific isoforms validated by q-RT PCR E15.5 in the oocyte.

2 alternative approaches to knock out GR to study transcriptional outcomes. Oocytes: systemic GR KO (E17.5) with low input 3-tag seq and germline-specific GR KO (E15.5) on fetal oocyte expression via 10X single cell seq and 3-cap sequencing on sorted KO versus WT oocytes both indicating little impact on polyadenylated RNAs

2 alternative approaches to assess the effect of GR activation in vivo (systemic) and ex vivo (ovary culture): here the RNA seq did show again some changes in germ cells and many in the soma.

They exclude oocyte-specific GR signaling inhibition via beta isoforms.

Perinatal male germline shows differential splicing regulation in response to systemic Dex administration, results were backed up with q-PCR analysis of splicing factors. Weaknesses:

COMMENT #1: The presence of a protein cannot be entirely excluded based on IF data

We agree that very low levels of GR could escape the detection by IF and confocal imaging. We feel that our IF data do match transcript data in our validation studies of the GR KO using (1) qRT-PCR on fetal ovary in Fig 2E and (2) scRNA-seq in germ cells and ovarian soma in Fig S2B.

COMMENT #2: (staining of spermatids is referred to but not shown).

You are correct that this statement was based on a morphological identification of spermatids using DAPI morphology. We have performed a co-stain for GR with the spermatocyte marker SYCP3, and the spermatid/spermatozoa marker PNA (Peanut Agglutinin; from Arachis hypogaea) in adult testis tissue. We have updated Figure 4D to reflect this change, as well as the corresponding text in the Results section.

COMMENT #3: The authors do not consider post-transcriptional level a) modifications also triggered by GR activation b) non-coding RNAs (not assessed by seq).

We thank the reviewer for raising this very important point about potential post-transcriptional (non-genomic) effects of GR in the fetal oocyte. We agree that while our RNA-seq results show only a minimal transcriptional response, we cannot rule out a non-canonical signaling function of GR, such as the regulation of cellular kinases (as reviewed elsewhere1), or the regulation of non coding RNAs at the post-transcriptional level, and we have amended the discussion to include a sentence on this point. However, while we fully acknowledge the possibility of GR regulating non-genomic level cellular signaling, we chose not to explore this option further based on the lack of any overall functional effect on meiotic progression when GR signaling was perturbed- either by KO (Figure 2D) or dex-mediated activation (Figure S3C).

COMMENT #4: Sequencing techniques used are not total RNA but either are focused on all polyA transcripts (10x) or only assess the 3' prime end and hence are not ideal to study splicing

We thank the reviewer for raising this concern, however this statement is not correct and we have clarified this point in the Results section to explain how the sequencing libraries of the male germ cell RNA-seq were prepared. We agree that certain sequencing techniques (such as 3’ Tag-Seq) that generate sequencing libraries from a limited portion of an entire transcript molecule are not appropriate for analysis of differential splicing. This was not the case, however, for the RNA-seq libraries prepared on our male germ cells treated with dexamethasone. These libraries were constructed using full length transcripts that were reverse transcribed using random hexamer priming, thus accounting for sequencing coverage across the full transcript length. As a result, this type of library prep technique should be sufficient for capturing differential splicing events along the length of the transcript. We do, however, point out that these libraries were constructed on polyA-enriched transcripts. Thus while we obtained full length transcript coverage for these polyA transcripts, any differential splicing taking place in non poly-adenylated RNA moieties were not captured. While we are excited about the possibility of exploring GR-mediated splicing regulation of other RNA species in the future, we chose to focus the scope of our current study on polyA mRNA molecules specifically.

COMMENT #5: The number of replicates in the low input seq is very low and hence this might be underpowered

While the number of replicates (n=3-4 per condition) is sufficient for performing statistical analysis of a standard RNA-seq experiment, we do acknowledge and agree with the reviewer that low numbers of FACS-sorted germ cells from individual embryos combined with the low input 3’ Tag-Seq technique could have led to higher sample variability than desired. Given that we validated our bulk RNA-seq analysis of GR knockout ovaries using an orthogonal single-cell RNA-seq approach, we feel that our conclusions regarding a lack of transcriptional changes upon GR deletion remain valid.

COMMENT #6: Since Dex treatment showed some (modest) changes in oocyte RNA - effects of GR depletion might only become apparent upon Dex treatment as an interaction.

We may be missing the nuance of this point, but our interpretation of an effect that is seen only when the KO is treated with Dex would be that the mechanism would not be autonomous in germ cells but indirect or off-target.

COMMENT #7: Effects in oocytes following systemic Dex might be indirect due to GR activation in the soma.

As both the oocytes and ovarian soma express GR during the window of dex administration, we agree that it is possible that the few modest changes seen in the oocyte transcriptome are the result of indirect effects following robust GR signaling in the somatic compartment. However, given that these modest oocyte transcript changes in response to dex treatment did not significantly alter the ability of oocytes to progress through meiosis, we chose not to explore this mechanism further.

COMMENT #8: Even though ex vivo culture of ovaries shows GR translocation to the nucleus it is not sure whether the in vivo systemic administration does the same.

AND

The conclusion that fetal oocytes are resistant to GR manipulation is very strong, given that "only" poly A sequencing and few replicates of 3-prime sequencing have been analyzed and information is lacking on whether GR is activated in germ cells in the systemically dex-injected animals.

If we understand correctly, the first part refers to a technical limitation and the second part takes issue with our interpretation of the data. For the former, we appreciate this astute insight on the conundrum of detecting a response to systemic dex in fetal oocytes, which is generally monitored by nuclear translocation of GR. As shown in Figure 1A and 1B, GR localization is overwhelmingly nuclear in fetal oocytes of WT animals at E13.5 without addition of any dex. We could not, therefore, use GR translocation as a proxy for activation in response to dex treatment. We instead used ex vivo organ culture to monitor localization changes, as we were able to maintain fetal ovaries ex vivo in hormone-depleted and ligand negative conditions. As shown in Fig. 3, these defined culture conditions elicited a shift of GR to the cytoplasm of fetal oocytes. This led us to conclude that GR is capable of translocating between nucleus and cytoplasm in fetal oocytes, and we were able to counteract this loss in nuclear localization by providing dex ligand in the media.

We feel that our conclusion that oocytes are resistant to manipulation of glucocorticoid signaling despite their possession of the receptor and capacity for nuclear translocation is substantiated by multiple results: meiotic phenotyping, bulk RNA-seq and scRNA-seq analysis of both GR KO and dex dosed mice. Our basis for testing the timing and fidelity of meiotic prophase I was the coincident onset of GR expression in female germ cells at E13, and the disappearance of GR in neonatal oocytes as they enter meiotic arrest. The lack of transcriptional changes observed in oocytes in response to dex has made it even more challenging to demonstrate a bona fide “activation” of GR. Observation of a dose-dependent induction of the canonical GR response gene Fkbp5 in the somatic cells of the fetal ovary (Figure S3A and 3A) affirmed that dex traverses the placenta. We agree with the reviewer that it remains possible that dex or GR KO could lead to changes in epigenetic marks or small RNAs in oocytes, and have mentioned these possibilities in the discussion, but we note that even epigenetic perturbations during oocyte development such as the loss of Tet1 or Dnmt1 result in measurable changes in the transcriptome and the timing of meiotic prophase 2–4.

COMMENT #9: This work is a good reference point for researchers interested in glucocorticoid hormone signaling fertility and RNA splicing. It might spark further studies on germline-specific GR functions and the impact of GR activation on alternative splicing. While the study provides a characterization of GR and some aspects of GR perturbation, and the negative findings in this study do help to rule out a range of specific roles of GR in the germline, there is still a range of other potential unexplored options. The introduction of the study eludes to implications for intergenerational effects via epigenetic modifications in the germline, however, it does not mention that the indirect effects of reproductive tissue GR signaling on the germline have indeed already been described in the context of intergenerational effects of stress.

The reviewer raises an excellent point that we have not made sufficient distinction in our manuscript between prior studies of gestational stress and preconception stress and the light that our work may shed on those findings. We have revised the introduction to clarify this difference, and added reference to an outstanding study that identifies glucocorticoid-induced changes to microRNA cargo of extracellular vesicles shed by epididymal epithelial cells that when transferred to mature sperm can induce changes in the HPA axis and brain of offspring 5. Interestingly, this GR-mediated effect in the epididymal epithelial cells concurs with our observation in the adult testis that GR can be detected only cKit+ spermatogonia but not in subsequent stages of spermatids.

COMMENT #10: Also, the study does not assess epigenetic modifications.

We agree with the reviewer that exploring the role of GR in regulating epigenetic modifications within the germline is an area of extreme interest given the potential links between stress and transgenerational epigenetic inheritance. As this is a broader topic that requires a more thorough and comprehensive set of experiments, we have intentionally chosen to keep this work separate from the current study, and hope to expand upon this topic in the future.

COMMENT #11: The conclusion that the persistence of a phenotype for up to three generations suggests that stress can induce lasting epigenetic changes in the germline is misleading. For the reader who is unfamiliar with the field, it is important to define much more precisely what is referred to as "a phenotype". Furthermore, this statement evokes the impression that the very same epigenetic changes in the germline have been observed across multiple generations.

We see how this may be misleading, and we have amended the text of the introduction and discussion accordingly to avoid the use of the term “phenotype”.

COMMENT #12: The evidence of the presence of GR in the germline is also somewhat limited - since other studies using sequencing have detected GR in the mature oocyte and sperm.

As described above in response to Comment #2, we have included immunostaining of adult testis in a revised Figure 4D and shown that we detect GR in PLZF+ and cKIT+ spermatogonia. We also show low/minimal expression in some (SYCP3+) early meiotic spermatocytes, but not in (Lectin+) spermatids. We are not aware of any studies that have shown expression of GR protein in the mature oocyte.

COMMENT #13: The discussion ends again on the implications of sex-specific differences of GR signaling in the context of stress-induced epigenetic inheritance. It states that the observed differences might relate to the fact that there is more evidence for paternal lineage findings, without considering that maternal lineage studies in epigenetic inheritance are generally less prevalent due to some practical factors - such as more laborious study design making use of cross-fostering or embryo transfer.

We thank the reviewer for this valid point, and we have amended the discussion section.

Reviewer #2 (Public Review):

Summary:

There is increasing evidence in the literature that rodent models of stress can produce phenotypes that persist through multiple generations. Nevertheless, the mechanism(s) by which stress exposure produces phenotypes are unknown in the directly affected individual as well as in subsequent offspring that did not directly experience stress. Moreover, it has also been shown that glucocorticoid stress hormones can recapitulate the effects of programmed stress. In this manuscript, the authors test the compelling hypothesis that glucocorticoid receptor (GR)-signaling is responsible for the transmission of phenotypes across generations. As a first step, the investigators test for a role of GR in the male and female germline. Using knockouts and GR agonists, they show that although germ cells in male and female mice have GR that appears to localize to the nucleus when stimulated, oocytes are resistant to changes in GR levels. In contrast, the male germline exhibits changes in splicing but no overt changes in fertility.

Strengths:

Although many of the results in this manuscript are negative, this is a careful and timely study that informs additional work to address mechanisms of transmission of stress phenotypes across generations and suggests a sexually dimorphic response to glucocorticoids in the germline. The work presented here is well-done and rigorous and the discussion of the data is thoughtful. Overall, this is an important contribution to the literature.

Reviewer #1 (Recommendations For The Authors):

RECOMMENDATION #1: To assess whether in females the systemic Dex administration directly activates GR in oocytes it would be great to assess GR activation following Dex administration, and ideally to see the effects abolished when Dex is administered to germline-specific KO animals.

In regard to the recommendation to assess GR activation in response to systemic dex administration, we refer the reviewer back to our response in Comment #8 highlighting the difficulties defining and measuring GR activation in the germline.

This therefore has made it difficult to assess whether any of the modest effects seen in response to dex are abolished in our germline-specific KO animals. While repeating our RNA-seq experiment in dex-dosed germline KO animals would address whether the ~60 genes induced in oocytes are the result of oocyte-intrinsic GR activity, we have decided not to explore this mechanism further due to the overall lack of a functional effect on meiotic progression in response to dex (Figure S3C).

RECOMMENDATION #2: To further strengthen the link between GR and alternative splicing it would be great to see the dex administration experiment repeated in germline specific GR KO's.

While we understand the reviewer’s suggestion to explore whether deletion of GR in the spermatogonia is sufficient to abrogate the dex-mediated decreases in splice factor expression, we chose not to explore the details of this mechanism given that deletion of GR in the male germline does not impair fertility (Figure 6).

RECOMMENDATION #3: I am wondering how much a given reduction in one of the splicing factors indeed affects splicing events. Can the authors relate this to literature, or maybe an in vitro experiment can be done to see whether the level of differential splicing events detected is in a range that can be expected in the case of the magnitude of splicing factor reduction?

It has been shown in many instances in the literature that a full genetic deletion of a single splice factor leads to impairments in spermatogenesis, and ultimately infertility 6–16. We suspect that dex treatment leads to fewer differential splicing events than a full splice factor deletion, given that dex treatment causes a broader decrease in splice factor expression without entirely abolishing any single splice factor. We have amended the discussion section to include this point. While we share the reviewer’s curiosity to compare the effects of dex vs genetic deletion of splicing machinery on the overall magnitude of differential splicing events, we unfortunately do not have access to mice with a floxed splice factor at this time. While we have considered knocking out one or more splice factors in an ex vivo cultured testis to compare alongside dex treatment, our efforts to date have proven unsuccessful due to high cell death upon culture of the postnatal testis for more than 24 hours.

RECOMMENDATION #4: It is unclear from the methods whether in germline-specific KO's also the controls received tamoxifen.

We thank the reviewer for catching this missing piece of information. All control embryos that were assessed received an equivalent dose of tamoxifen to the germline-specific KO embryos. The only difference between cKOs and controls was the presence of the Cre transgene. We have updated the Materials and Methods 3’ Tag-Seq sample preparation section to include the sentence: “Both GRcKO/cKO and control GRflox/flox embryos were collected from tamoxifen-injected dams, and thus were equally exposed to tamoxifen in utero”.

Reviewer #2 (Recommendations For The Authors):

I just have only a few comments/questions.

RECOMMENDATION #5: It is somewhat surprising that GR is expressed in female germ cells, yet there doesn't seem to be a requirement. Is there any indication of what it does? Is the long-term stability of the germline compromised?

We thank the reviewer for these questions, and we agree that it was quite surprising to find a lack of GR function in the female germline despite its robust expression. The question of whether loss of GR affects the long-term stability of the female germline is interesting, given that similar work in GR KO zebrafish has shown impairments to female reproductive capacity, yet only upon aging 17–19.

While we have shared interest in this question, technical limitations thus far have prevented us from properly assessing the effect of GR loss in aged females. Homozygous deletion of GR results in embryonic lethality at approximately E17.5. Conditional deletion of GR using Oct4-CreERT2 with a single dose of tamoxifen (2.5 mg / 20g mouse) at E9.5 results in complete deletion of GR by E10.5, although dams consistently suffer from dystocia and are no longer able to deliver viable pups. While using the more active tamoxifen metabolite (4OHT) at 0.1 mg / 20g has allowed for successful delivery, the resulting deletion rate is very poor (see qPCR results in panel below, left). While using half the dose of standard tamoxifen (1.25 mg / 20g mouse) at E9.5 has on rare occasions led to a successful delivery, the resulting recombination efficiency is insufficient (Author response image 1 right panel).

Author response image 1.

While a Blimp1-Cre conditional KO model was used to assess male fertility on GR deletion, we believe this model may not be ideal for studying fertility in the context of aging. While Blimp1-Cre is highly specific to the germ cells within the gonad, there are many cell types outside of the gonad that express Blimp1, including the skin and certain cells of the immune system. It is unclear, particularly over the course of aging, whether any effects on fertility seen would be due to an oocyte-intrinsic effect, or the result of GR loss elsewhere in the body. While we hope to explore the role of GR in the aging oocyte further using alternative Cre models in the future, this is currently outside the scope of this work.

RECOMMENDATION #6: Figure 5b: what is the left part of that panel? Is it the same volcano plot for germ cells as shown in part a but with splicing factors?

We apologize if this panel was unclear. Yes, the left panel of Figure 5B is in fact the same volcano plot in 5A, labeled with splicing factors instead of top genes. We have edited Figure 5B and corresponding figure legend to clarify this.

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prova + tag

Prova dei tag

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors develop a method to fluorescently tag peptides loaded onto dendritic cells using a two-step method with a tetracystein motif modified peptide and labelling step done on the surface of live DC using a dye with high affinity for the added motif. The results are convincing in demonstrating in vitro and in vivo T cell activation and efficient label transfer to specific T cells in vivo. The label transfer technique will be useful to identify T cells that have recognised a DC presenting a specific peptide antigen to allow the isolation of the T cell and cloning of its TCR subunits, for example. It may also be useful as a general assay for in vitro or in vivo T-DC communication that can allow the detection of genetic or chemical modulators.

Strengths:

The study includes both in vitro and in vivo analysis including flow cytometry and two-photon laser scanning microscopy. The results are convincing and the level of T cell labelling with the fluorescent pMHC is surprisingly robust and suggests that the approach is potentially revealing something about fundamental mechanisms beyond the state of the art.

Weaknesses:

The method is demonstrated only at high pMHC density and it is not clear if it can operate at at lower peptide doses where T cells normally operate. However, this doesn't limit the utility of the method for applications where the peptide of interest is known. It's not clear to me how it could be used to de-orphan known TCR and this should be explained if they want to claim this as an application. Previous methods based on biotin-streptavidin and phycoerythrin had single pMHC sensitivity, but there were limitations to the PE-based probe so the use of organic dyes could offer advantages.

We thank the reviewer for the valuable comments and suggestions. Indeed, we have shown and optimized this labeling technique for a commonly used peptide at rather high doses to provide a proof of principle for the possible use of tetracysteine tagged peptides for in vitro and in vivo studies. However, we completely agree that the studies that require different peptides and/or lower pMHC concentrations may require preliminary experiments if the use of biarsenical probes is attempted. We think it can help investigate the functional and biological properties of the peptides for TCRs deorphaned by techniques. Tetracysteine tagging of such peptides would provide a readily available antigen-specific reagent for the downstream assays and validation. Other possible uses for modified immunogenic peptides could be visualizing the dynamics of neoantigen vaccines or peptide delivery methods in vivo. For these additional uses, we recommend further optimization based on the needs of the prospective assay.

Reviewer #2 (Public Review):

Summary:

The authors here develop a novel Ovalbumin model peptide that can be labeled with a site-specific FlAsH dye to track agonist peptides both in vitro and in vivo. The utility of this tool could allow better tracking of activated polyclonal T cells particularly in novel systems. The authors have provided solid evidence that peptides are functional, capable of activating OTII T cells, and that these peptides can undergo trogocytosis by cognate T cells only.

Strengths:

-An array of in vitro and in vivo studies are used to assess peptide functionality.

-Nice use of cutting-edge intravital imaging.

-Internal controls such as non-cogate T cells to improve the robustness of the results (such as Fig 5A-D).

-One of the strengths is the direct labeling of the peptide and the potential utility in other systems.

Weaknesses:

1. What is the background signal from FlAsH? The baselines for Figure 1 flow plots are all quite different. Hard to follow. What does the background signal look like without FLASH (how much fluorescence shift is unlabeled cells to No antigen+FLASH?). How much of the FlAsH in cells is actually conjugated to the peptide? In Figure 2E, it doesn't look like it's very specific to pMHC complexes. Maybe you could double-stain with Ab for MHCII. Figure 4e suggests there is no background without MHCII but I'm not fully convinced. Potentially some MassSpec for FLASH-containing peptides.

We thank the reviewer for pointing out a possible area of confusion. In fact, we have done extensive characterization of the background and found that it has varied with the batch of FlAsH, TCEP, cytometer and also due to the oxidation prone nature of the reagents. Because Figure 1 subfigures have been derived from different experiments, a combination of the factors above have likely contributed to the inconsistent background. To display the background more objectively, we have now added the No antigen+Flash background to the revised Fig 1.

It is also worthwhile noting that nonspecific Flash incorporation can be toxic at increasing doses, and live cells that display high backgrounds may undergo early apoptotic changes in vitro. However, when these cells are adoptively transferred and tracked in vivo, the compromised cells with high background possibly undergo apoptosis and get cleared by macrophages in the lymph node. The lack of clearance in vitro further contributes to different backgrounds between in vitro and in vivo, which we think is also a possible cause for the inconsistent backgrounds throughout the manuscript. Altogether, comparison of absolute signal intensities from different experiments would be misleading and the relative differences within each experiment should be relied upon. We have added further discussion about this issue.

1. On the flip side, how much of the variant peptides are getting conjugated in cells? I'd like to see some quantification (HPLC or MassSpec). If it's ~10% of peptides that get labeled, this could explain the low shifts in fluorescence and the similar T cell activation to native peptides if FlasH has any deleterious effects on TCR recognition. But if it's a high rate of labeling, then it adds confidence to this system.

We agree that mass spectrometry or, more specifically tandem MS/MS, would be an excellent addition to support our claim about peptide labeling by FlAsH being reliable and non-disruptive. Therefore, we have recently undertaken a tandem MS/MS quantitation project with our collaborators. However, this would require significant time to determine the internal standard based calibration curves and to run both analytical and biological replicates. Hence, we have decided pursuing this as a follow up study and added further discussion on quantification of the FlAsH-peptide conjugates by tandem MS/MS.

1. Conceptually, what is the value of labeling peptides after loading with DCs? Why not preconjugate peptides with dye, before loading, so you have a cleaner, potentially higher fluorescence signal? If there is a potential utility, I do not see it being well exploited in this paper. There are some hints in the discussion of additional use cases, but it was not clear exactly how they would work. One mention was that the dye could be added in real-time in vivo to label complexes, but I believe this was not done here. Is that feasible to show?

We have already addressed preconjugation as a possible avenue for labeling peptides. In our hands, preconjugation resulted in low FlAsH intensity overall in both the control and tetracysteine labeled peptides (Author response image 1). While we don’t have a satisfactory answer as to why the signal was blunted due to preconjugation, it could be that the tetracysteine tagged peptides attract biarsenical compounds better intracellularly. It may be due to the redox potential of the intracellular environment that limits disulfide bond formation. (PMID: 18159092)

Author response image 1.

Preconjugation yields poor FlAsH signal. Splenic DCs were pulsed with peptide then treated with FlAsH or incubated with peptide-FlAsH preconjugates. Overlaid histograms show the FlAsH intensities on DCs following the two-step labeling (left) and preconjugation (right). Data are representative of two independent experiments, each performed with three biological replicates.

1. Figure 5D-F the imaging data isn't fully convincing. For example, in 5F and 2G, the speeds for T cells with no Ag should be much higher (10-15micron/min or 0.16-0.25micron/sec). The fact that yours are much lower speeds suggests technical or biological issues, that might need to be acknowledged or use other readouts like the flow cytometry.

We thank the reviewer for drawing attention to this technical point. We would like to point out that the imaging data in fig 5 d-f was obtained from agarose embedded live lymph node sections. Briefly, the lymph nodes were removed, suspended in 2% low melting temp agarose in DMEM and cut into 200 µm sections with a vibrating microtome. Prior to imaging, tissue sections were incubated in complete RPMI medium at 37 °C for 2 h to resume cell mobility. Thus, we think the cells resuming their typical speeds ex vivo may account for slightly reduced T cell speeds overall, for both control and antigen-specific T cells (PMID: 32427565, PMID: 25083865). We have added text to prevent the ambiguity about the technique for dynamic imaging. The speeds in Figure 2g come from live imaging of DC-T cell cocultures, in which the basal cell movement could be hampered by the cell density. Additionally, glass bottom dishes have been coated with Fibronectin to facilitate DC adhesion, which may be responsible for the lower average speeds of the T cells in vitro.

Reviewer #1 (Recommendations For The Authors):

Does the reaction of ReAsH with reactive sites on the surface of DC alter them functionally? Functions have been attributed to redox chemistry at the cell surface- could this alter this chemistry?

We thank the reviewer for the insight. It is possible that the nonspecific binding of biarsenical compounds to cysteine residues, which we refer to as background throughout the manuscript, contribute to some alterations. One possible way biarsenicals affect the redox events in DCs can be via reducing glutathione levels (PMID: 32802886). Glutathione depletion is known to impair DC maturation and antigen presentation (PMID: 20733204). To avoid toxicity, we have carried out a stringent titration to optimize ReAsH and FlAsH concentrations for labeling and conducted experiments using doses that did not cause overt toxicity or altered DC function.

Have the authors compared this to a straightforward approach where the peptide is just labelled with a similar dye and incubated with the cell to load pMHC using the MHC knockout to assess specificity? Why is this that involves exposing the DC to a high concentration of TCEP, better than just labelling the peptide? The Davis lab also arrived at a two-step method with biotinylated peptide and streptavidin-PE, but I still wonder if this was really necessary as the sensitivity will always come down to the ability to wash out the reagents that are not associated with the MHC.

We agree with the reviewer that small undisruptive fluorochrome labeled peptide alternatives would greatly improve the workflow and signal to noise ratio. In fact, we have been actively searching for such alternatives since we have started working on the tetracysteine containing peptides. So far, we have tried commercially available FITC and TAMRA conjugated OVA323-339 for loading the DCs, however failed to elicit any discernible signal. We also have an ongoing study where we have been producing and testing various in-house modified OVA323-339 that contain fluorogenic properties. Unfortunately, at this moment, the ones that provided us with a crisp, bright signal for loading revealed that they have also incorporated to DC membrane in a nonspecific fashion and have been taken up by non-cognate T cells from double antigen-loaded DCs. We are actively pursuing this area of investigation and developing better optimized peptides with low/non-significant membrane incorporation.

Lastly, we would like to point out that tetracysteine tags are visible by transmission electron microscopy without FlAsH treatment. Thus, this application could add a new dimension for addressing questions about the antigen/pMHCII loading compartments in future studies. We have now added more in-depth discussion about the setbacks and advantages of using tetracysteine labeled peptides in immune system studies.

The peptide dosing at 5 µM is high compared to the likely sensitivity of the T cells. It would be helpful to titrate the system down to the EC50 for the peptide, which may be nM, and determine if the specific fluorescence signal can still be detected in the optimal conditions. This will not likely be useful in vivo, but it will be helpful to see if the labelling procedure would impact T cell responses when antigen is limited, which will be more of a test. At 5 µM it's likely the system is at a plateau and even a 10-fold reduction in potency might not impact the T cell response, but it would shift the EC50.

We thank the reviewer for the comment and suggestion. We agree that it is possible to miss minimally disruptive effects at 5 µM and titrating the native peptide vs. modified peptide down to the nM doses would provide us a clearer view. This can certainly be addressed in future studies and also with other peptides with different affinity profiles. A reason why we have chosen a relatively high dose for this study was that lowering the peptide dose had costed us the specific FlAsH signal, thus we have proceeded with the lowest possible peptide concentration.

In Fig 3b the level of background in the dsRed channel is very high after DC transfer. What cells is this associated with and does this appear be to debris? Also, I wonder where the ReAsH signal is in the experiments in general. I believe this is a red dye and it would likely be quite bright given the reduction of the FlAsH signal. Will this signal overlap with signals like dsRed and PHK-26 if the DC is also treated with this to reduce the FlAsH background?

We have already shown that ReAsH signal with DsRed can be used for cell-tracking purposes as they don’t get transferred to other cells during antigen specific interactions (Author response image 2). In fact, combining their exceptionally bright fluorescence provided us a robust signal to track the adoptively transferred DCs in the recipient mice. On the other hand, the lipophilic membrane dye PKH-26 gets transferred by trogocytosis while the remaining signal contributes to the red fluorescence for tracking DCs. Therefore, the signal that we show to be transferred from DCs to T cells only come from the lipophilic dye. To address this, we have added a sentence to elaborate on this in the results section. Regarding the reviewer’s comment on DsRed background in Figure 3b., we agree that the cells outside the gate in recipient mice seems slightly higher that of the control mice. It may suggest that the macrophages clearing up debris from apoptotic/dying DCs might contribute to the background elicited from the recipient lymph node. Nevertheless, it does not contribute to any DsRed/ReAsH signal in the antigen-specific T cells.

Author response image 2.

ReAsH and DsRed are not picked up by T cells during immune synapse. DsRed+ DCs were labeled with ReAsH, pulsed with 5 μM OVACACA, labeled with FlAsH and adoptively transferred into CD45.1 congenic mice mice (1-2 × 106 cells) via footpad. Naïve e450-labeled OTII and e670-labeled polyclonal CD4+ T cells were mixed 1:1 (0.25-0.5 × 106/ T cell type) and injected i.v. Popliteal lymph nodes were removed at 42 h post-transfer and analyzed by flow cytometry. Overlaid histograms show the ReAsh/DsRed, MHCII and FlAsH intensities of the T cells. Data are representative of two independent experiments with n=2 mice per group.

In Fig 5b there is a missing condition. If they look at Ea-specific T cells for DC with without the Ova peptide do they see no transfer of PKH-26 to the OTII T cells? Also, the FMI of the FlAsH signal transferred to the T cells seems very high compared to other experiments. Can the author estimate the number of peptides transferred (this should be possible) and would each T cell need to be collecting antigens from multiple DC? Could the debris from dead DC also contribute to this if picked up by other DC or even directly by the T cells? Maybe this could be tested by transferring DC that are killed (perhaps by sonication) prior to inoculation?

To address the reviewer’s question on the PKH-26 acquisition by T cells, Ea-T cells pick up PKH-26 from Ea+OVA double pulsed DCs, but not from the unpulsed or single OVA pulsed DCs. OTII T cells acquire PKH-26 from OVA-pulsed DCs, whereas Ea T cells don’t (as expected) and serve as an internal negative control for that condition. Regarding the reviewer’s comment on the high FlAsH signal intensity of T cells in Figure 5b, a plausible explanation can be that the T cells accumulate pMHCII through serial engagements with APCs. In fact, a comparison of the T cell FlAsH intensities 18 h and 36-48 h post-transfer demonstrate an increase (Author response image 3) and thus hints at a cumulative signal. As DCs are known to be short-lived after adoptive transfer, the debris of dying DCs along with its peptide content may indeed be passed onto macrophages, neighboring DCs and eventually back to T cells again (or for the first time, depending on the T:DC ratio that may not allow all T cells to contact with the transferred DCs within the limited time frame). We agree that the number and the quality of such contacts can be gauged using fluorescent peptides. However, we think peptides chemically conjugated to fluorochromes with optimized signal to noise profiles and with less oxidation prone nature would be more suitable for quantification purposes.

Author response image 3.

FlAsH signal acquisition by antigen specific T cells becomes more prominent at 36-48 h post-transfer. DsRed+ splenic DCs were double-pulsed with 5 μM OVACACA and 5 μM OVA-biotin and adoptively transferred into CD45.1 recipients (2 × 106 cells) via footpad. Naïve e450-labeled OTII (1 × 106 cells) and e670-labeled polyclonal T cells (1 × 106 cells) were injected i.v. Popliteal lymph nodes were analyzed by flow cytometry at 18 h or 48 h post-transfer. Overlaid histograms show the T cell levels of OVACACA (FlAsH). Data are representative of three independent experiments with n=3 mice per time point

Reviewer #2 (Recommendations For The Authors):

As mentioned in weaknesses 1 & 2, more validation of how much of the FlAsH fluorescence is on agonist peptides and how much is non-specific would improve the interpretation of the data. Another option would be to preconjugate peptides but that might be a significant effort to repeat the work.

We agree that mass spectrometry would be the gold standard technique to measure the percentage of tetracysteine tagged peptide is conjugated to FlAsH in DCs. However, due to the scope of such endevour this can only be addressed as a separate follow up study. As for the preconjugation, we have tried and unfortunately failed to get it to work (Reviewer Figure 1). Therefore, we have shifted our focus to generating in-house peptide probes that are chemically conjugated to stable and bright fluorophore derivates. With that, we aim to circumvent the problems that the two-step FlAsH labeling poses.

Along those lines, do you have any way to quantify how many peptides you are detecting based on fluorescence? Being able to quantify the actual number of peptides would push the significance up.

We think two step procedure and background would pose challenges to such quantification in this study. although it would provide tremendous insight on the antigen-specific T cell- APC interactions in vivo, we think it should be performed using peptides chemically conjugated to fluorochromes with optimized signal to noise profiles.

In Figure 3D or 4 does the SA signal correlate with Flash signal on OT2 cells? Can you correlate Flash uptake with T cell activation, downstream of TCR, to validate peptide transfers?

To answer the reviewer’s question about FlAsH and SA correlation, we have revised the Figure 3d to show the correlation between OTII uptake of FlAsH, Streptavidin and MHCII. We also thank the reviewer for the suggestion on correlating FlAsH uptake with T cell activation and/or downstream of TCR activation. We have used proliferation and CD44 expressions as proxies of activation (Fig 2, 6). Nevertheless, we agree that the early events that correspond to the initiation of T-DC synapse and FlAsH uptake would be valuable to demonstrate the temporal relationship between peptide transfer and activation. Therefore, we have addressed this in the revised discussion.

Author response image 4.

FlAsH signal acquisition by antigen specific T cells is correlates with the OVA-biotin (SA) and MHCII uptake. DsRed+ splenic DCs were double-pulsed with 5 μM OVACACA and 5 μM OVA-biotin and adoptively transferred into CD45.1 recipients (2 × 106 cells) via footpad. Naïve e450-labeled OTII (1 × 106 cells) and e670-labeled polyclonal T cells (1 × 106 cells) were injected i.v. Popliteal lymph nodes were analyzed by flow cytometry. Overlaid histograms show the T cell levels of OVACACA (FlAsH) at 48 h post-transfer. Data are representative of three independent experiments with n=3 mice.

Minor:

Figure 3F, 5D, and videos: Can you color-code polyclonal T cells a different color than magenta (possibly white or yellow), as they have the same look as the overlay regions of OT2-DC interactions (Blue+red = magenta).

We apologize for the inconvenience about the color selection. We have had difficulty in assigning colors that are bright and distinct. Unfortunately, yellow and white have also been easily mixed up with the FlAsH signal inside red and blue cells respectively. We have now added yellow and white arrows to better point out the polyclonal vs. antigen specific cells in 3f and 5d.

• Who: The author of the post, @mrenglish
• What: The post is a guide for beginners on blogging and discusses the definition and advantages of blogs.
• Why: The post is submitted to @clixmoney's weekly DCC tag contest focused on blogs and blogging.
• When: The post was published recently, as it mentions the current political transition in Nigeria.
• How: The author explains what a blog is, the difference between a blog and a website, the advantages of having a blog (such as running campaigns, making money, running a business, and socializing), and how blogging can help improve one's expertise. The author also promotes another user's blog post and includes an excerpt from it.

Die Repubblica interviewt Carlo Buontempo, den Leiter des europäischen Klimaservice Copernicus. 20 23 wurden viele Anomalien beobachtet. Jeder Tag war mindestens ein Grad wärmer als in der Vergleichsperiode, fast die Hälfte der Tage 1,5 und zwei sogar zwei Grad. Der Juli war der heißeste je gemessene Monat. Es sei noch unklar, ob es sich dabei um Ausnahmen handelt oder um den Beginn einer neuen Phase. https://www.repubblica.it/economia/2024/01/15/news/clima_cambiamenti_climatici_caldo_record_carlo_buontempo_copernicus-421876070/

Author Response

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

eLife assessment:

This important study represents a comprehensive computational analysis of Plasmodium falciparum gene expression, with a focus on var gene expression, in parasites isolated from patients; it assesses changes that occur as the parasites adapt to short-term in vitro culture conditions. The work provides technical advances to update a previously developed computational pipeline. Although the findings of the shifts in the expression of particular var genes have theoretical or practical implications beyond a single subfield, the results are incomplete and the main claims are only partially supported.

The authors would like to thank the reviewers and editors for their insightful and constructive assessment. We particularly appreciate the statement that our work provides a technical advance of our computational pipeline given that this was one of our main aims. To address the editorial criticisms, we have rephrased and restructured the manuscript to ensure clarity of results and to support our main claims. For the same reason, we removed the var transcript differential expression analysis, as this led to confusion.

Public Reviews:

Reviewer #1:

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching..." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites...". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Thank you for this comment. We were happy to modify the wording in the abstract to have consistency with the results presented by highlighting that modest but unpredictable var gene switching was observed while substantial changes were found in the core transcriptome. Moreover, any differences observed in core transcriptome between ex vivo samples from naïve and pre-exposed patients are diminished after one cycle of cultivation making inferences about parasite biology in vivo impossible.

Therefore, – to our opinion – the statement in the last sentence is well supported by the data presented.

Line 43–47: “Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.” Nevertheless, we would like to note that this study was in a unique position to assess changes at the individual patient level as we had successive parasite generations. This comparison is not done in most cross-sectional studies and therefore these small, unpredictable changes in the var transcriptome are missed.

Reviewer #2:

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

We are grateful for this suggestion and agree that a table comparing the pros and cons of all existing methods would be helpful for the general reader and also highlight the key advantages of our new approach. A table comparing previous methods for var gene and transcript characterisation has been added to the manuscript and is referenced in the introduction (line 107).

Author response table 1.

Comparison of previous var assembly approaches based on DNA- and RNA-sequencing.

Reviewer #3:

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately, there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

We agree with the reviewer that the different methods and results of the var transcriptome analysis can be difficult to reconcile. To address this, we have included a summary table with a brief description of the rationale and results of each approach in our analysis pipeline.

Author response table 2.

Summary of the different levels of analysis performed to assess the effect of short-term parasite culturing on var and core gene expression, their rational, method, results, and interpretation.

Additionally, the var transcript differential expression analysis was removed from the manuscript, because this study was in a unique position to perform a more focused analysis of var transcriptional changes across paired samples, meaning the per-patient approach was more suitable. This allowed for changes in the var transcriptome to be identified that would have gone unnoticed in the traditional differential expression analysis.

We thank the reviewer for his highly important comment about adjusting for life cycle stage. Var gene expression is highly stage-dependent, so any quantitative comparison between samples does need adjustment for developmental stage. All life cycle stage adjustments were done using the mixture model proportions to be consistent with the original paper, described in the results and methods sections:

• Line 219–221: “Due to the potential confounding effect of differences in stage distribution on gene expression, we adjusted for developmental stage determined by the mixture model in all subsequent analyses.”

• Line 722–725: “Var gene expression is highly stage dependent, so any quantitative comparison between samples needs adjustment for developmental stage. The life cycle stage proportions determined from the mixture model approach were used for adjustment.“

The rank-expression analysis did not have adjustment for life cycle stage as the values were determined as a percentage contribution to the total var transcriptome. The var group level and the global var gene expression analyses were adjusted for life cycle stages, by including them as an independent variable, as described in the results and methods sections.

Var group expression:

• Line 321–326: “Due to these results, the expression of group A var genes vs. group B and C var genes was investigated using a paired analysis on all the DBLα (DBLα1 vs DBLα0 and DBLα2) and NTS (NTSA vs NTSB) sequences assembled from ex vivo samples and across multiple generations in culture. A linear model was created with group A expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. The same was performed using group B and C expression levels.“

• Line 784–787: “DESeq2 normalisation was performed, with patient identity and life cycle stage proportions included as covariates and differences in the amounts of var transcripts of group A compared with groups B and C assessed (Love et al., 2014). A similar approach was repeated for NTS domains.”

Gobal var gene expression:

• Line 342–347: “A linear model was created (using only paired samples from ex vivo and generation 1) (Supplementary file 1) with proportion of total gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. This model showed no significant differences between generations, suggesting that differences observed in the raw data may be a consequence of small changes in developmental stage distribution in culture.”

• Line 804–806: “Significant differences in total var gene expression were tested by constructing a linear model with the proportion of gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as an independent variables and the patient identity included as a random effect.“

The analysis of the conserved var gene expression was adjusted for life cycle stage:

• Line 766–768: “For each conserved gene, Salmon normalised read counts (adjusted for life cycle stage) were summed and expression compared across the generations using a pairwise Wilcoxon rank test.”

And life cycle stage estimates were included as covariates in the design matrix for the domain differential expression analysis:

• Line 771–773: “DESeq2 was used to test for differential domain expression, with five expected read counts in at least three patient isolates required, with life cycle stage and patient identity used as covariates.”

Reviewer #1:

1. In the legend to Figure 1, the authors cite "Deitsch and Hviid, 2004" for the classification of different var gene types. This is not the best reference for this work. Better citations would be Kraemer and Smith, Mol Micro, 2003 and Lavstsen et al, Malaria J, 2003.

We agree and have updated the legend in Figure 1 with these references, consistent with the references cited in the introduction.

1. In Figures 2 and 3, each of the boxes in the flow charts are largely filled with empty space while the text is nearly too small to read. Adjusting the size of the text would improve legibility.

We have increased the size of the text in these figures.

1. My understanding of the computational method for assessing global var gene expression indicates an initial step of identifying reads containing the amino acid sequence LARSFADIG. It is worth noting that VAR2CSA does not contain this motif. Will the pipeline therefore miss expression of this gene, and if so, how does this affect the assessment of global var gene assessment? This seems relevant given that the authors detect increased expression of var2csa during adaptation to culture.

To address this question, we have added an explanation in the methods section to better explain our analysis. Var2csa was not captured in the global var gene expression analysis, but was analyzed separately because of its unique properties (conservation, proposed role in regulating var gene switching, slightly divergent timing of expression, translational repression).

• Line 802/3: “Var2csa does not contain the LARSFADIG motif, hence this quantitative analysis of global var gene expression excluded var2csa (which was analysed separately).”

1. In Figures 4 and 7, panels a and b display virtually identical PCA plots, with the exception that panel A displays more generations. Why are both panels included? There doesn't appear to be any additional information provided by panel B.

We agree and have removed Figure 7b for the core transcriptome PCA as it did not provide any new information. The var transcript differential analysis (displayed in Figure 4) has been removed from the manuscript.

1. On line 560-567, the authors state "However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at higher levels." What are the high levels being referred to here?

We have replaced this sentence to make it clearer what the different levels are (global var gene expression, var domain and var type).

• Line 526/7: “However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at the var domain, var type and global var gene expression level.”

Reviewer #2:

The authors make no mention or assessment of previously published var gene assembly methods from clinical samples that focus on genomic or transcriptomic approaches. These include:

https://pubmed.ncbi.nlm.nih.gov/28351419/

https://pubmed.ncbi.nlm.nih.gov/34846163/

These methods should be compared to the method for var gene assembly outlined by the co-authors, especially as the authors say that their method "overcomes previous limitations and outperforms current methods" (128-129). The second reference above appears to be a method to measure var expression in clinical samples and so should be particularly compared to the approach outlined by the authors.

Thank you for pointing this out. We have included the second reference in the introduction of our revised manuscript, where we refer to var assembly and quantification from RNA-sequencing data. We abstained from including the first paper in this paragraph (Dara et al., 2017) as it describes a var gene assembly pipeline and not a var transcript assembly pipeline.

• Line 101–105: “While approaches for var assembly and quantification based on RNA-sequencing have recently been proposed (Wichers et al., 2021; Stucke et al., 2021; Andrade et al., 2020; TonkinHill et al., 2018, Duffy et al., 2016), these still produce inadequate assembly of the biologically important N-terminal domain region, have a relatively high number of misassemblies and do not provide an adequate solution for handling the conserved var variants (Table S1).”

Additionally, we have updated the manuscript with a table (Table S1) comparing these two methods plus other previously used var transcript/gene assembly approaches (see comment to the public reviews).

But to address this particular comment in more detail, the first paper (Dara et al., 2017) is a var gene assembly pipeline and not a var transcript assembly pipeline. It is based on assembling var exon 1 from unfished whole genome assemblies of clinical samples and requires a prior step for filtering out human DNA. The authors used two different assemblers, Celera for short reads (which is no longer maintained) and Sprai for long reads (>2000bp), but found that Celera performed worse than Sprai, and subsequently used Sprai assemblies. Therefore, this method does not appear to be suitable for assembling short reads from RNA-seq.

The second paper (Stucke et al. 2021) focusses more on enriching for parasite RNA, which precedes assembly. The capture method they describe would complement downstream analysis of var transcript assembly with our pipeline. Their assembly pipeline is similar to our pipeline as they also performed de novo assembly on all P. falciparum mapping and non-human mapping reads and used the same assembler (but with different parameters). They clustered sequences using the same approach but at 90% sequence identity as opposed to 99% sequence identity using our approach. Then, Stucke et al. use 500nt as a cut-off as opposed to the more stringent filtering approach used in our approach. They annotated their de novo assembled transcripts with the known amino acid sequences used in their design of the capture array; our approach does not assume prior information on the var transcripts. Finally, their approach was validated only for its ability to recover the most highly expressed var transcript in 6 uncomplicated malaria samples, and they did not assess mis-assemblies in their approach.

For the methods (619–621), were erythrocytes isolated by Ficoll gradient centrifugation at the time of collection or later?

We have updated the methods section to clarify this.

• Line 586–588: “Blood was drawn and either immediately processed (#1, #2, #3, #4, #11, #12, #14, #17, #21, #23, #28, #29, #30, #31, #32) or stored overnight at 4oC until processing (#5, #6, #7, #9, #10, #13, #15, #16, #18, #19, #20, #22, #24, #25, #26, #27, #33).”

Was the current pipeline and assembly method assessed for var chimeras? This should be described.

Yes, this was quantified in the Pf 3D7 dataset and also assessed in the German traveler dataset. For the 3D7 dataset it is described in the result section and Figure S1.

• Line 168–174: “However, we found high accuracies (> 0.95) across all approaches, meaning the sequences we assembled were correct (Figure 2 – Figure supplement 1b). The whole transcript approach also performed the best when assembling the lower expressed var genes (Figure 2 – Figure supplement 1e) and produced the fewest var chimeras compared to the original approach on P. falciparum 3D7. Fourteen misassemblies were observed with the whole transcript approach compared to 19 with the original approach (Table S2). This reduction in misassemblies was particularly apparent in the ring-stage samples.” - Figure S1:

Author response image 1.

Performance of novel computational pipelines for var assembly on Plasmodium falciparum 3D7: The three approaches (whole transcript: blue, domain approach: orange, original approach: green) were applied to a public RNA-seq dataset (ENA: PRJEB31535) of the intra-erythrocytic life cycle stages of 3 biological replicates of cultured P. falciparum 3D7, sampled at 8-hour intervals up until 40hrs post infection (bpi) and then at 4-hour intervals up until 48 (Wichers al., 2019). Boxplots show the data from the 3 biological replicates for each time point in the intra-erythrocytic life cycle: a) alignment scores for the dominantly expressed var gene (PF3D7_07126m), b) accuracy scores for the dominantly var gene (PF3D7_0712600), c) number of contigs to assemble the dominant var gene (PF3D7_0712600), d) alignment scores for a middle ranking expressed vargene (PF3D7_0937800), e) alignment scores for the lowest expressed var gene (PF3D7_0200100). The first best blast hit (significance threshold = le-10) was chosen for each contig. The alignment score was used to evaluate the each method. The alignment score represents √accuracy* recovery. The accuracy is the proportion of bases that are correct in the assembled transcript and the recovery reflects what proportion of the true transcript was assembled. Assembly completeness of the dominant vargene (PF3D7 071200, length = 6648nt) for the three approaches was assessed for each biological f) biological replicate 1, g) biological replicate 2, h) biological replicate 3. Dotted lines represent the start and end of the contigs required to assemble the vargene. Red bars represent assembled sequences relative to the dominantly whole vargene sequence, where we know the true sequence (termed “reference transcript”).

For the ex vivo samples, this has been discussed in the result section and now we also added this information to Table 1.

• Line 182/3: “Remarkably, with the new whole transcript method, we observed a significant decrease (2 vs 336) in clearly misassembled transcripts with, for example, an N-terminal domain at an internal position.”

• Table 1:

Author response table 3.

Statistics for the different approaches used to assemble the var transcripts. Var assembly approaches were applied to malaria patient ex vivo samples (n=32) from (Wichers et al., 2021) and statistics determined. Given are the total number of assembled var transcripts longer than 500 nt containing at least one significantly annotated var domain, the maximum length of the longest assembled var transcript in nucleotides and the N50 value, respectively. The N50 is defined as the sequence length of the shortest var contig, with all var contigs greater than or equal to this length together accounting for 50% of the total length of concatenated var transcript assemblies. Misassemblies represents the number of misassemblies for each approach. **Number of misassemblies were not determined for the domain approach due to its poor performance in other metrics.

Line 432: "the core gene transcriptome underwent a greater change relative to the var transcriptome upon transition to culture." Can this be shown statistically? It's unclear whether the difference in the sizes of the respective pools of the core genome and the var genes may account for this observation.

We found 19% of the core transcriptome to be differentially expressed. The per patient var transcript analysis revealed individually highly variable but generally rather subtle changes in the var transcriptome. The different methods for assessing this make it difficult to statistically compare these two different results.

The feasibility of this approach for field samples should be discussed in the Discussion.

In the original manuscript we reflected on this already several times in the discussion (e.g., line 465/6; line 471–475; line 555–568). We now have added another two sentences at the end of the paragraph starting in line 449 to address this point. It reads now:

• Line 442–451: “Our new approach used the most geographically diverse reference of var gene sequences to date, which improved the identification of reads derived from var transcripts. This is crucial when analysing patient samples with low parasitaemia where var transcripts are hard to assemble due to their low abundancy (Guillochon et al., 2022). Our approach has wide utility due to stable performance on both laboratory-adapted and clinical samples. Concordance in the different var expression profiling approaches (RNA-sequencing and DBLα-tag) on ex vivo samples increased using the new approach by 13%, when compared to the original approach (96% in the whole transcript approach compared to 83% in Wichers et al., 2021. This suggests the new approach provides a more accurate method for characterising var genes, especially in samples collected directly from patients. Ultimately, this will allow a deeper understanding of relationships between var gene expression and clinical manifestations of malaria.”

MINOR

The plural form of PfEMP1 (PfEMP1s) is inconsistently used throughout the text.

Corrected.

404-405: statistical test for significance?

Thank you for this suggestion. We have done two comparisons between the original analysis from Wichers et al., 2021 and our new whole transcript approach to test concordance of the RNAseq approaches with the DBLα-tag approach using paired Wilcoxon tests. These comparisons suggest that our new approach has significantly increased concordance with DBLα-tag data and might be better at capturing all expressed DBLα domains than the original analysis (and the DBLα-approach), although not statistically significant. We describe this now in the result section.

• Line 352–361: “Overall, we found a high agreement between the detected DBLα-tag sequences and the de novo assembled var transcripts. A median of 96% (IQR: 93–100%) of all unique DBLα-tag sequences detected with >10 reads were found in the RNA-sequencing approach. This is a significant improvement on the original approach (p= 0.0077, paired Wilcoxon test), in which a median of 83% (IQR: 79–96%) was found (Wichers et al., 2021). To allow for a fair comparison of the >10 reads threshold used in the DBLα-tag approach, the upper 75th percentile of the RNA-sequencingassembled DBLα domains were analysed. A median of 77.4% (IQR: 61–88%) of the upper 75th percentile of the assembled DBLα domains were found in the DBLα-tag approach. This is a lower median percentage than the median of 81.3% (IQR: 73–98%) found in the original analysis (p= 0.28, paired Wilcoxon test) and suggests the new assembly approach is better at capturing all expressed DBLα domains.”

Figure 4: The letters for the figure panels need to be added.

The figure has been removed from the manuscript.

Reviewer #3:

It is difficult from Table S2 to determine how many unique var transcripts would have enough coverage to be potentially assembled from each sample. It seems unlikely that 455 distinct vars (~14 per sample) would be expressed at a detectable level for assembly. Why not DNA-sequence these samples to get the full repertoire for comparison to RNA? Why would so many distinct transcripts be yielded from fairly synchronous samples?

We know from controlled human malaria infections of malaria-naive volunteers, that most var genes present in the genomic repertoire of the parasite strain are expressed at the onset of the human blood phase (heterogenous var gene expression) (Wang et al., 2009; Bachmann et al, 2016; Wichers-Misterek et al., 2023). This pattern shifts to a more restricted, homogeneous var expression pattern in semi-immune individuals (expression of few variants) depending on the degree of immunity (Bachmann et al., 2019).

Author response image 2.

In this cohort, 15 first-time infections are included, which should also possess a more heterogenous var gene expression in comparison to the pre-exposed individuals, and indeed such a trend is already seen in the number of different DBLa-tag clusters found in both patient groups (see figure panel from Wichers et al. 2021: blue-first-time infections; grey–pre-exposed). Moreover, Warimwe et al. 2013 have shown that asymptomatic infections have a more homogeneous var expression in comparison to symptomatic infections. Therefore, we expect that parasites from symptomatic infections have a heterogenous var expression pattern with multiple var gene variants expressed, which we could assemble due to our high read depth and our improved var assembly pipeline for even low expressed variants.

Moreover, the distinct transcripts found in the RNA-seq approach were confirmed with the DBLα tag data. To our opinion, previous approaches may have underestimated the complexity of the var transcriptome in less immune individuals.

Mapping reads to these 455 putative transcripts and using this count matrix for differential expression analysis seems very unlikely to produce reliable results. As acknowledged on line 327, many reads will be mis-mapped, and perhaps most challenging is that most vars will not be represented in most samples. In other words, even if mapping were somehow perfect, one would expect a sparse matrix that would not be suitable for statistical comparisons between groups. This is likely why the per-patient transcript analysis doesn't appear to be consistent. I would recommend the authors remove the DE sections utilizing this approach, or add convincing evidence that the count matrix is useable.

We agree that this is a general issue of var differential expression analysis. Therefore, we have removed the var differential expression analysis from this manuscript as the per patient approach was more appropriate for the paired samples. We validated different mapping strategies (new Figure S6) and included a paragraph discussing the problem in the result section:

• Line 237–255: “In the original approach of Wichers et al., 2021, the non-core reads of each sample used for var assembly were mapped against a pooled reference of assembled var transcripts from all samples, as a preliminary step towards differential var transcript expression analysis. This approach returned a small number of var transcripts which were expressed across multiple patient samples (Figure 3 – Figure supplement 2a). As genome sequencing was not available, it was not possible to know whether there was truly overlap in var genomic repertoires of the different patient samples, but substantial overlap was not expected. Stricter mapping approaches (for example, excluding transcripts shorter than 1500nt) changed the resulting var expression profiles and produced more realistic scenarios where similar var expression profiles were generated across paired samples, whilst there was decreasing overlap across different patient samples (Figure 3 – Figure supplement 2b,c). Given this limitation, we used the paired samples to analyse var gene expression at an individual subject level, where we confirmed the MSP1 genotypes and alleles were still present after short-term in vitro cultivation. The per patient approach showed consistent expression of var transcripts within samples from each patient but no overlap of var expression profiles across different patients (Figure 3 – Figure supplement 2d). Taken together, the per patient approach was better suited for assessing var transcriptional changes in longitudinal samples. It has been hypothesised that more conserved var genes in field isolates increase parasite fitness during chronic infections, necessitating the need to correctly identify them (Dimonte et al., 2020, Otto et al., 2019). Accordingly, further work is needed to optimise the pooled sample approach to identify truly conserved var transcripts across different parasite isolates in cross-sectional studies.” - Figure S6:

Author response image 3.

Var expression profiles across different mapping. Different mapping approaches Were used to quantify the Var expression profiles of each sample (ex Vivo (n=13), generation I (n=13), generation 2 (n=10) and generation 3 (n=l). The pooled sample approach in Which all significantly assembled van transcripts (1500nt and containing3 significantly annotated var domains) across samples were combined into a reference and redundancy was removed using cd-hit (at sequence identity = 99%) (a—c). The non-core reads of each sample were mapped to this pooled reference using a) Salmon, b) bowtie2 filtering for uniquely mapping paired reads with MAPQ and c) bowtie2 filtering for uniquely mapping paired reads with a MAPQ > 20. d) The per patient approach was applied. For each patient, the paired ex vivo and in vitro samples were analysed. The assembled var transcripts (at least 1500nt and containing3 significantly annotated var domains) across all the generations for a patient were combined into a reference, redundancy was removed using cd-hit (at sequence identity: 99%), and expression was quantified using Salmon. Pie charts show the var expression profile With the relative size of each slice representing the relative percentage of total var gene expression of each var transcript. Different colours represent different assembled var transcripts with the same colour code used across a-d.

For future cross-sectional studies a per patient analysis that attempts to group per patient assemblies on some unifying structure (e.g., domain, homology blocks, domain cassettes etc) should be performed.

Line 304. I don't understand the rationale for comparing naïve vs. prior-exposed individuals at ex-vivo and gen 1 timepoints to provide insights into how reliable cultured parasites are as a surrogate for var expression in vivo. Further, the next section (per patient) appears to confirm the significant limitation of the 'all sample analysis' approach. The conclusion on line 319 is not supported by the results reported in figures S9a and S9b, nor is the bold conclusion in the abstract about "casting doubt" on experiments utilizing culture adapted

We have removed this comparison from the manuscript due to the inconsistencies with the var per patient approach. However, the conclusion in the abstract has been rephrased to reflect the fact we observed 19% of the core transcript differentially expressed within one cycle of cultivation.

Line 372/391 (and for the other LMM descriptions). I believe you mean to say response variable, rather than explanatory variable. Explanatory variables are on the right hand side of the equation.

Thank you for spotting this inaccuracy, we changed it to “response variable” (line 324, line 343, line 805).

Line 467. Similar to line 304, why would comparisons of naïve vs. prior-exposed be informative about surrogates for in vivo studies? Without a gold-standard for what should be differentially expressed between naïve and prior-exposed in vivo, it doesn't seem prudent to interpret a drop in the number of DE genes for this comparison in generation 1 as evidence that biological signal for this comparison is lost. What if the generation 1 result is actually more reflective of the true difference in vivo, but the ex vivo samples are just noisy? How do we know? Why not just compare ex vivo vs generation 1/2 directly (as done in the first DE analysis), and then you can comment on the large number of changes as samples are less and less proximal to in vivo?

In the original paper (Wichers et al., 2021), there were differences between the core transcriptome of naïve vs previously exposed patients. However, these differences appeared to diminish in vitro, suggesting the in vivo core transcriptome is not fully maintained in vitro.

We have added a sentence explaining the reasoning behind this analysis in the results section:

• Lines 414–423: “In the original analysis of ex vivo samples, hundreds of core genes were identified as significantly differentially expressed between pre-exposed and naïve malaria patients. We investigated whether these differences persisted after in vitro cultivation. We performed differential expression analysis comparing parasite isolates from naïve (n=6) vs pre-exposed (n=7) patients, first between their ex vivo samples, and then between the corresponding generation 1 samples. Interestingly, when using the ex vivo samples, we observed 206 core genes significantly upregulated in naïve patients compared to pre-exposed patients (Figure 7 – Figure supplement 3a). Conversely, we observed no differentially expressed genes in the naïve vs pre-exposed analysis of the paired generation 1 samples (Figure 7 – Figure supplement 3b). Taken together with the preceding findings, this suggests one cycle of cultivation shifts the core transcriptomes of parasites to be more alike each other, diminishing inferences about parasite biology in vivo.”

Overall, I found the many DE approaches very frustrating to interpret coherently. If not dropped in revision, the reader would benefit from a substantial effort to clarify the rationale for each approach, and how each result fits together with the other approaches and builds to a concise conclusion.

We agree that the manuscript contains many different complex layers of analysis and that it is therefore important to explain the rationale for each approach. Therefore, we now included the summary Table 3 (see comment to public review). Additionally, we have removed the var transcript differential expression due to its limitations, which we hope has already streamlined our manuscript.

Author Response

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

We sincerely thank the reviewers for their in-depth consideration of our manuscript and their helpful reviews. Their efforts have made the paper much better. We have responded to each point. The previously provided public responses have been updated they are included after the private response for convenience.

Reviewer #1 (Recommendations For The Authors):

1. In general, the manuscript will benefit from copy editing and proof reading. Some obvious edits;

2. Page 6 line 140. Do the authors mean Cholera toxin B?

Response: We corrected this error and went through the entire paper carefully correcting for grammar and increased clarity.

• Page 8 line 173. Methylbetacyclodextrin is misspelled.

Response: Yes, corrected.

• Figure 4c is missing representative traces for electrophysiology data.

• Figure 4. Please check labeling ordering in figure legend as it does not match the panels in the figure.

Thank you for the correction and we apologize for the confusion in figure 4. We uploaded an incomplete figure legend, and the old panel ‘e’ was not from an experiment that was still in the figure. It was removed and the figure legends are now corrected.

• Please mention the statistical analysis used in all figure legends.

Response: Thank you for pointing out this omission, statistics have been added.

• Although the schematics in each figure helps guide readers, they are very inconsistent and sometimes confusing. For example, in Figure 5 the gating model is far-reaching without conclusive evidence, whereas in Figure 6 it is over simplified and unclear what the image is truly representing (granted that the downstream signaling mechanism and channel is not known).

Response: Figure 5d is the summary figure for the entire paper. We have made this clearer in the figure legend and we deleted the title above the figure that gave the appearance that the panel relates to swell only. It is the proposed model based on what we show in the paper and what is known about the activation mechanism of TREK-1.

Figure 6 is supposed to be simple. It is to help the reader understand that when PA is low mechanical sensitivity is high. Without the graphic, previous reviewers got confused about threshold going down and mechanosensitivity going up and how the levels of PA relate. Low PA= high sensitivity. We’ve added a downstream effector to the right side of the panel to avoid any biased to a putative downstream channel effector. The purpose of the experiment is to show PLD has a mechanosensitive phenotype in vivo.

Reviewer #2 (Recommendations For The Authors):

This manuscript outlines some really interesting findings demonstrating a mechanism by which mechanically driven alterations in molecular distributions can influence a) the activity of the PLD2 molecule and subsequently b) the activation of TREK-1 when mechanical inputs are applied to a cell or cell membrane.

The results presented here suggest that this redistribution of molecules represents a modulatory mechanism that alters either the amplitude or the sensitivity of TREK-1 mediated currents evoked by membrane stretch. While the authors do present values for the pressure required to activate 50% of channels (P50), the data presented provides incomplete evidence to conclude a shift in threshold of the currents, given that many of the current traces provided in the supplemental material do not saturate within the stimulus range, thus limiting the application of a Boltzmann fit to determine the P50. I suggest adding additional context to enable readers to better assess the limitations of this use of the Boltzmann fit to generate a P50, or alternately repeating the experiments to apply stimuli up to lytic pressures to saturate the mechanically evoked currents, enabling use of the Boltzmann function to fit the data.

Response: We thank the reviewer for pointing this out. We agree the currents did not reach saturation. Hence the term P50 could be misleading, so we have removed it from the paper. We now say “half maximal” current measured from non-saturating pressures of 0-60 mmHg. We also deleted the xPLD data in supplemental figure 3C since there is insufficient current to realistically estimate a half maximal response.

In my opinion, the conclusions presented in this manuscript would be strengthened by an assessment of the amount of TREK-1 in the plasma membrane pre and post application of shear. While the authors do present imaging data in the supplementary materials, these data are insufficiently precise to comment on expression levels in the membrane. To strengthen this conclusion the authors could conduct cell surface biotinylation assays, as a more sensitive and quantitative measure of membrane localisation of the proteins of interest.

1. Response: as mentioned previously, we do not have an antibody to the extracellular domain. Nonetheless to better address this concern we directly compared the levels of TREK-1, PIP2, and GM1; in xPLD2, mPLD2, enPLD2 with and without shear. The results are in supplemental figure 2. PLD2 is known to increase endocytosis1 and xPLD2 is known to block both agonist induced and constitutive endocytosis of µ-opioid receptor2. The receptor is trapped on the surface. This is true of many proteins including Rho3, ARF4, and ACE21 among others. In agreement with this mechanism, in Figure S2C,G we show that TREK increases with xPLD and the localization can clearly be seen at the plasma membrane just like in all of the other publications with xPLD overexpression. xPLD2 would be expected to inhibit the basal current but we presume the increased expression likely has compensated and there is sufficient PA and PG from other sources to allow for the basal current. It is in this state that we then conduct our ephys and monitor with a millisecond time resolution and see no activation. We are deriving conclusion from a very clear response—Figure 1b shows almost no current, even at 1-10 ms after applying pressure. There is little pressure current when we know the channel is present and capable of conducting ion (Figure 1d red bar). After shear there is a strong decrease in TREK-1 currents on the membrane in the presence of xPLD2. But it is not less than TREK-1 expression with mPLD2. And since mouse PLD2 has the highest basal current and pressure activation current. The amount of TREK-1 present is sufficient to conduct large current. To have almost no detective current would require at least a 10 fold reduction compared to mPLD2 levels before we would lack the sensitivity to see a channel open. Lasty endocytosis typically in on the order of seconds to minutes, no milliseconds.

2. We have shown an addition 2 independent ways that TREK-1 is on the membrane during our stretch experiments. Figure 1d shows the current immediately prior to applying pressure for wt TREK-1. When catalytically dead PLD is present (xPLD2) there is almost normal basal current. The channel is clearly present. And then in figure 1a we show within a millisecond there is no pressure current. As a control we added a functionally dead TREK-1 truncation (xTREK). Compared to xPLD2 there is clearly normal basal current. If this is not strong evidence the channel was available on the surface for mechanical activation please help us understand why. And if you think within 2.1 ms 100% of the channel is gone by endocytosis please provide some evidence that this is possible so we can reconsider.

3. We have TIRF super resolution imaging with ~20 nm x-y resolution and ~ 100nm z resolution and Figure 2b clearly shows the channel on the membrane. When we apply pressure in 1b, the channel is present.

4. Lastly, In our previous studies we showed activation of PLD2 by anesthetics was responsible for all of TREK-1’s anesthetic sensitivity and this was through PLD2 binding to the C-terminus of TREK-15. We showed this was the case by transferring anesthetic sensitivity to an anesthetic insensitive homolog TRAAK. This established conclusively the basic premise of our mechanism. Here we show the same C-terminal region and PLD2 are responsible for the mechanical current observed by TREK-1. TRAAK is already mechanosensitive so the same chimera will not work for our purposes here. But anesthetic activation and mechanical activation are dramatically different stimuli, and the fact that the role of PLD is robustly observed in both should be considered.

The authors discuss that the endogenous levels of TREK-1 and PLD2 are "well correlated: in C2C12 cells, that TREK-1 displayed little pair correlation with GM1 and that a "small amount of TREK-1 trafficked to PIP2". As such, these data suggest that the data outlined for HEK293T cells may be hampered by artefacts arising from overexpression. Can TREK-1 currents be activated by membrane stretch in these cells C2C12 cells and are they negatively impacted by the presence of xPLD2? Answering this question would provide more insight into the proposed mechanism of action of PLD2 outlined by the authors in this manuscript. If no differences are noted, the model would be called into question. It could be that there are additional cell-specific factors that further regulate this process.

Response: The low pair correlation of TREK-1 and GM1 in C2C12 cells was due to insufficient levels of cholesterol in the cell membrane to allow for robust domain formation. In Figure 4b we loaded C2C12 cells with cholesterol using the endogenous cholesterol transport protein apoE and serum (an endogenous source of cholesterol). As can be seen in Fig. 4b, the pair correlation dramatically increased (purple line). This was also true in neuronal cells (N2a) (Fig 4d, purple bar). And shear (3 dynes/cm2) caused the TREK-1 that was in the GM1 domains to leave (red bar) reversing the effect of high cholesterol. This demonstrates our proposed mechanism is working as we expect with endogenously expressed proteins.

There are many channels in C2C12 cells, it would be difficult to isolate TREK-1 currents, which is why we replicated the entire system (ephys and dSTORM) in HEK cells. Note, in figure 4c we also show that adding cholesterol inhibits TREK-1 whole cell currents in HEK293cells.

As mentioned in the public review, the behavioural experiments in D. melanogaster can not solely be attributed to a change in threshold. While there may be a change in the threshold to drive a different behaviour, the writing is insufficiently precise to make clear that conclusions cannot be drawn from these experiments regarding the functional underpinnings of this outcome. Are there changes in resting membrane potential in the mutant flys? Alterations in Nav activity? Without controlling for these alternate explanations it is difficult to see what this last piece of data adds to the manuscript, particularly given the lack of TREK-1 in this organism. At the very least, some editing of the text to more clearly indicate that these data can only be used to draw conclusions on the change in threshold for driving the behaviour not the change in threshold of the actual mechanotransduction event (i.e. conversion of the mechanical stimulus into an electrochemical signal).

Response: We agree; features other than PLDs direct mechanosensitivity are likely contributing. This was shown in figure 6g left side. We have an arrow going to ion channel and to other downstream effectors. We’ve added the putative alteration to downstream effectors to the right side of the panel. This should make it clear that we no more speculate the involvement of a channel than any of the other many potential downstream effectors. As mentioned above, the figure helps the reader coordinate low PA with increased mechanosensitivity. Without the graphic reviewers got confused that PA increased the threshold which corresponds to a decreased sensitivity to pain. Nonetheless we removed our conclusion about fly thresholds from the abstract and made clearer in the main text the lack of mechanism downstream of PLD in flies including endocytosis. Supplemental Figure S2H also helps emphasize this. .

Nav channels are interesting, and since PLD contribute to endocytosis and Nav channels are also regulated by endocytosis there is likely a PLD specific effect using Nav channels. There are many ways PA likely regulates mechanosensitive thresholds, but we feel Nav is beyond the scope of our paper. Someone else will need to do those studies. We have amended a paragraph in the conclusion which clearly states we do not know the specific mechanism at work here with the suggestions for future research to discover the role of lipid and lipid-modifying enzymes in mechanosensitive neurons.

There may be fundamental flaws in how the statistics have been conducted. The methods section indicates that all statistical testing was performed with a Student's t-test. A visual scan of many of the data sets in the figures suggests that they are not normally distributed, thus a parametric test such as a Student's t-test is not valid. The authors should assess if each data set is normally distributed, and if not, a non-parametric statistical test should be applied. I recommend assessing the robustness of the statistical analyses and adjusting as necessary.

Response: We thank the reviewer for pointing this out, indeed there is some asymmetry in Figure 6C-d. The p values with Mann Whitney were slightly improved p=0.016 and p=0.0022 for 6c and 6d respectively. For reference, the students t-test had slightly worse statistics p=0.040 and p=0.0023. The score remained the same 1 and 2 stars respectively.

The references provided for the statement regarding cascade activation of the TRPs are incredibly out of date. While it is clear that TRPV4 can be activated by a second messenger cascade downstream of osmotic swelling of cells, TRPV4 has also been shown to be activated by mechanical inputs at the cell-substrate interface, even when the second messenger cascade is inhibited. Recommend updating the references to reflect more current understanding of channel activation.

Response: We thank the reviewer for pointing this out. We have updated the references and changed the comment to “can be” instead of “are”. The reference is more general to multiple ion channel types including KCNQ4. This should avoid any perceived conflict with the cellsubstrate interface mechanism which we very much agree is a correct mechanism for TRP channels.

Minor comments re text editing etc:

The central messages of the manuscript would benefit from extensive work to increase the precision of the writing of the manuscript and the presentation of data in the figures, such textual changes alone would help address a number of the concerns outlined in this review, by clarifying some ambiguities. There are numerous errors throughout, ranging from grammatical issues, ambiguities with definitions, lack of scale bars in images, lack of labels on graph axes, lack of clarity due to the mode of presentation of sample numbers (it would be far more precise to indicate specific numbers for each sample rather than a range, which is ambiguous and confusing), unnecessary and repeat information in the methods section. Below are some examples but this list is not exhaustive.

Response: Thank you, reviewer # 1 also had many of these concerns. We have gone through the entire paper and improved the precision of the writing of the manuscript. We have also added the missing error bar to Figure 6. And axis labels have been added to the inset images. The redundancy in cell culture methods has been removed. Where a range is small and there are lots of values, the exact number of ‘n’ are graphically displayed in the dot plot for each condition.

Text:

I recommend considering how to discuss the various aspects of channel activation. A convention in the field is to use mechanical activation or mechanical gating to describe that process where the mechanical stimulus is directly coupled to the channel gating mechanism. This would be the case for the activation of TREK-1 by membrane stretch alone. The increase in activation by PLD2 activity then reflects a modulation of the mechanical activation of the channel, because the relevant gating stimulus is PA, rather than force/stretch. The sum of these events could be described as shear-evoked or mechanically-evoked, TREK-1 mediated currents (thus making it clear that the mechanical stimulus initiates the relevant cascade, but the gating stimulus may be other than direct mechanical input.) Given the interesting and compelling data offered in this manuscript regarding the sensitisation of TREK-1 dependent mechanicallyevoked currents by PLD2, an increase in the precision of the language would help convey the central message of this work.

Response; We agree there needs to be convention. We have taken the suggestion of mechanically evoked and we suggest the following definitions:

1. Mechanical activation of PLD2: direct force on the lipids releasing PLD2 from nonactivating lipids.

2. Mechanical activation/gating of TREK1: direct force from lipids from either tension or hydrophobic mismatch that opens the channel.

3. Mechanically evoked: a mechanical event that leads to a downstream effect. The effect is mechanically “evoked”.

4. Spatial patterning/biochemistry: nanoscopic changes in the association of a protein with a nanoscopic lipid cluster or compartment.

An example of where discussion of mechanical activation is ambiguous in the text is found at line 109: "channel could be mechanically activated by a movement from GM1 to PIP2 lipids." In this case, the sentence could be suggesting that the movement between lipids provides the mechanical input that activates the channel, which is not what the data suggest.

Response: Were possible we have replaced “movement” with “spatial patterning” and “association” and “dissociation” from specific lipid compartment. This better reflects the data we have in this paper. However, we do think that a movement mechanically activates the channel, GM1 lipids are thick and PIP2 lipids are thin, so movement between the lipids could activate the channel through direct lipid interaction. We will address this aspect in a future paper.

Inconsistencies with usage:

• TREK1 versus TREK-1

Response: corrected to TREK-1

• mPLD2 versus PLD2

Response: where PLD2 represents mouse this has been corrected.

• K758R versus xPLD2

Response: we replaced K758R in the methods with xPLD2.

• HEK293T versus HEK293t Response: we have changed all instances to read HEK293T.

• Drosophila melanogaster and D. melanogaster used inconsistently and in many places incorrectly

Response: we have read all to read the common name Drosophila.

Line 173: misspelled methylbetacyclodextrin

Response corrected

Line 174: degree symbol missing

Response corrected

Line 287: "the decrease in cholesterol likely evolved to further decrease the palmate order in the palmitate binding site"... no evidence, no support for this statement, falsely attributes intention to evolutionary processes .

Response: we have removed the reference to evolution at the request of the reviewer, it is not necessary. But we do wish to note that to our knowledge, all biological function is scientifically attributed to evolution. The fact that cholesterol decreases in response to shear is evidence alone that the cell evolved to do it.

Line 307: grammatical error

Response: the redundant Lipid removed.

Line 319: overinterpreted - how is the mechanosensitivy of GPCRs explained by this translocation?

Response: all G-alpha subunits of the GPCR complex are palmitoylated. We showed PLD (which has the same lipidation) is mechanically activated. If the palmitate site is disrupted for PLD2, then it is likely disrupted for every G-alpha subunit as well.

Line 582: what is the wild type referred to here?

Response: human full length with a GFP tag.

Methods:

• Sincere apologies if I missed something but I do not recall seeing any experiments using purified TREK-1 or flux assays. These details should be removed from the methods section

Response: Removed.

• There is significant duplication of detail across the methods (three separate instances of electrophysiology details) these could definitely be consolidated.

Response: Duplicates removed.

Figures:

• Figure 2- b box doesn't correspond to inset. Bottom panel should provide overview image for the cell that was assessed with shear. In bottom panel, circle outlines an empty space.

Response: We have widened the box slightly to correspond so the non shear box corresponds to the middle panel. We have also added the picture for the whole cell to Fig S2g and outlined the zoom shown in the bottom panel of Fig 2b as requested. The figure is of the top of a cell. We also added the whole cell image of a second sheared cell.

Author response image 1.

• Figure 3 b+c: inset graph lacking axis labels

Response; the inset y axis is the same as the main axis. We added “pair corr. (5nM)” and a description in the figure legend to make this clearer. The purpose of the inset is to show statistical significance at a single point. The contrast has been maximized but without zooming in points can be difficult to see.

• Figure 5: replicate numbers missing and individual data points lacking in panels b + c, no labels of curve in b + c, insets, unclear what (5 nm) refers to in insets.

Response: Thank you for pointing out these errors. The N values have been added. Similar to figure 3, the inset is a bar graph of the pair correlation data at 5 nm. A better explanation of the data has been added to the figure legend.

• Figure 6: no scale bar, no clear membrane localization evident from images presented, panel g offers virtually nothing in terms of insight

Response: We have added scale bars to figure 6b. Figure 6g is intentionally simplistic, we found that correlating decreased threshold with increased pain was confusing. A previous reviewer claimed our data was inconsistent. The graphic avoids this confusion. We also added negative effects of low PA on downstream effects to the right panel. This helps graphically show we don’t know the downstream effects.

Reviewer #3 (Recommendations For The Authors):

Minor suggestions:

1. line 162, change 'heat' to 'temperature'.

Response: changed.

1. in figure 1, it would be helpful to keep the unit for current density consistent among different panels. 1e is a bit confusing: isn't the point of Figure 1 that most of TREK1 activation is not caused by direct force-sensing?

Response: Yes, the point of figure 1 is to show that in a biological membrane over expressed TREK-1 is a downstream effector of PLD2 mechanosensation which is indirect. We agree the figure legend in the previous version of the paper is very confusing.

There is almost no PLD2 independent current in our over expressed system, which is represented by no ions in the conduction pathway of the channel despite there being tension on the membrane.

Purified TREK-1 is only mechanosensitive in a few select lipids, primarily crude Soy PC. It was always assumed that HEK293 and Cos cells had the correct lipids since over expressed TREK-1 responded to mechanical force in these lipids. But that does not appear to be correct, or at least only a small amount of TREK-1 is in the mechanosensitive lipids. Figure 1e graphically shows this. The arrows indicate tension, but the channel isn’t open with xPLD2 present. We added a few sentences to the discussion to further clarify.

Panels c has different units because the area of the tip was measured whereas in d the resistance of the tip was measured. They are different ways for normalizing for small differences in tip size.

1. line 178, ~45 of what?

Response: Cells were fixed for ~30 sec.

1. line 219 should be Figure 4f?

Response: thank you, yes Figure 4f.

Previous public reviews with minor updates.

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

Response: The images were provided are at 300 dpi. Perhaps the reviewer is referring to contrast in Figure 2? We are happy to increase the contrast or resolution.

As a side note, we feel the main conclusion of the paper, mechanical activation of TREK-1 through PLD2, depended primarily on the electrophysiology in Figure 1b-c, not the dSTORM. But both complement each other.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling.

Response: The fly experiment shows that PLD mechanosensitivity is more evolutionarily conserved than TREK-1 mechanosensitivity. We have added this observation to the paper.

-Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.

Response: Figure 6B is a confocal image, it is not dSTORM. There is no dSTORM in Figure 6. We have added the error bars to make this more obvious. For reference, only a few cells would fit in the field of view with dSTORM.

-Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

Response: Yes, 1g is physiologically relevant. It is almost the force needed to wake a fly from sleep (1.2-3.2g). See ref 33. Murphy Nature Pro. 2017.

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Response: Yes, osmotic swell activates TREK1. This was shown in ref 19 (Patel et al 1998). We agree the 70 mOsm is a high degree of stress. This needs to be stated better in the paper.

Reviewer #2 (Public Review):

This manuscript by Petersen and colleagues investigates the mechanistic underpinnings of activation of the ion channel TREK-1 by mechanical inputs (fluid shear or membrane stretch) applied to cells. Using a combination of super-resolution microticopy, pair correlation analysis and electrophysiology, the authors show that the application of shear to a cell can lead to changes in the distribution of TREK-1 and the enzyme PhospholipaseD2 (PLD2), relative to lipid domains defined by either GM1 or PIP2. The activation of TREK-1 by mechanical stimuli was shown to be sensi>zed by the presence of PLD2, but not a catalytically dead xPLD2 mutant. In addition, the activity of PLD2 is increased when the molecule is more associated with PIP2, rather than GM1 defined lipid domains. The presented data do not exclude direct mechanical activation of TREK-1, rather suggest a modulation of TREK-1 activity, increasing sensitivity to mechanical inputs, through an inherent mechanosensitivity of PLD2 activity. The authors additionally claim that PLD2 can regulate transduction thresholds in vivo using Drosophila melanogaster behavioural assays. However, this section of the manuscript overstates the experimental findings, given that it is unclear how the disruption of PLD2 is leading to behavioural changes, given the lack of a TREK-1 homologue in this organism and the lack of supporting data on molecular function in the relevant cells.

Response: We agree, the downstream effectors of PLD2 mechanosensitivity are not known in the fly. Other anionic lipids have been shown to mediate pain see ref 46 and 47. We do not wish to make any claim beyond PLD2 being an in vivo contributor to a fly’s response to mechanical force. We have removed the speculative conclusions about fly thresholds from the abstract.

That said we do believe we have established a molecular function at the cellular level. We showed PLD is robustly mechanically activated in a cultured fly cell line (BG2-c2) Figure 6a of the manuscript. And our previous publication established mechanosensation of PLD (Petersen et. al. Nature Com 2016) through mechanical disruption of the lipids. At a minimum, the experiments show PLDs mechanosensitivity is evolutionarily better conserved across species than TREK1.

This work will be of interest to the growing community of scientists investigating the myriad mechanisms that can tune mechanical sensitivity of cells, providing valuable insight into the role of functional PLD2 in sensi>zing TREK-1 activation in response to mechanical inputs, in some cellular systems.

The authors convincingly demonstrate that, post application of shear, an alteration in the distribution of TREK-1 and mPLD2 (in HEK293T cells) from being correlated with GM1 defined domains (no shear) to increased correlation with PIP2 defined membrane domains (post shear). These data were generated using super-resolution microticopy to visualise, at sub diffraction resolution, the localisation of labelled protein, compared to labelled lipids. The use of super-resolution imaging enabled the authors to visualise changes in cluster association that would not have been achievable with diffraction limited microticopy. However, the conclusion that this change in association reflects TREK-1 leaving one cluster and moving to another overinterprets these data, as the data were generated from sta>c measurements of fixed cells, rather than dynamic measurements capturing molecular movements.

When assessing molecular distribution of endogenous TREK-1 and PLD2, these molecules are described as "well correlated: in C2C12 cells" however it is challenging to assess what "well correlated" means, precisely in this context. This limitation is compounded by the conclusion that TREK-1 displayed little pair correlation with GM1 and the authors describe a "small amount of TREK-1 trafficked to PIP2". As such, these data may suggest that the findings outlined for HEK293T cells may be influenced by artefacts arising from overexpression.

The changes in TREK-1 sensitivity to mechanical activation could also reflect changes in the amount of TREK-1 in the plasma membrane. The authors suggest that the presence of a leak currently accounts for the presence of TREK-1 in the plasma membrane, however they do not account for whether there are significant changes in the membrane localisation of the channel in the presence of mPLD2 versus xPLD2. The supplementary data provide some images of fluorescently labelled TREK-1 in cells, and the authors state that truncating the c-terminus has no effect on expression at the plasma membrane, however these data provide inadequate support for this conclusion. In addition, the data reporting the P50 should be noted with caution, given the lack of saturation of the current in response to the stimulus range.

Response: We thank the reviewer for his/her concern about expression levels. We did test TREK-1 expression. mPLD decreases TREK-1 expression ~two-fold (see Author response image 2 below). We did not include the mPLD data since TREK-1 was mechanically activated with mPLD. For expression to account for the loss of TREK-1 stretch current (Figure 1b), xPLD would need to block surface expression of TREK-1 prior to stretch. The opposite was true, xPLD2 increased TREK-1 expression (see Figure S2c). Furthermore, we tested the leak current of TREK-1 at 0 mV and 0 mmHg of stretch. Basal leak current was no different with xPLD2 compared to endogenous PLD (Figure 1d; red vs grey bars respectively) suggesting TREK-1 is in the membrane and active when xPLD2 is present. If anything, the magnitude of the effect with xPLD would be larger if the expression levels were equal.

Author response image 2.

TREK expression at the plasma membrane. TREK-1 Fluorescence was measured by GFP at points along the plasma membrane. Over expression of mouse PLD2 (mPLD) decrease the amount of full-length TREK-1 (FL TREK) on the surface more than 2-fold compared to endogenously expressed PLD (enPLD) or truncated TREK (TREKtrunc) which is missing the PLD binding site in the C-terminus. Over expression of mPLD had no effect on TREKtrunc.

Finally, by manipulating PLD2 in D. melanogaster, the authors show changes in behaviour when larvae are exposed to either mechanical or electrical inputs. The depletion of PLD2 is concluded to lead to a reduction in activation thresholds and to suggest an in vivo role for PA lipid signaling in setting thresholds for both mechanosensitivity and pain. However, while the data provided demonstrate convincing changes in behaviour and these changes could be explained by changes in transduction thresholds, these data only provide weak support for this specific conclusion. As the authors note, there is no TREK-1 in D. melanogaster, as such the reported findings could be accounted for by other explanations, not least including potential alterations in the activation threshold of Nav channels required for action potential generation. To conclude that the outcomes were in fact mediated by changes in mechanotransduction, the authors would need to demonstrate changes in receptor potential generation, rather than deriving conclusions from changes in behaviour that could arise from alterations in resting membrane potential, receptor potential generation or the activity of the voltage gated channels required for action potential generation.

Response: We are willing to restrict the conclusion about the fly behavior as the reviewers see fit. We have shown PLD is mechanosensitivity in a fly cell line, and when we knock out PLD from a fly, the animal exhibits a mechanosensation phenotype. We tried to make it clear in the figure and in the text that we have no evidence of a particular mechanism downstream of PLD mechanosensation.

This work provides further evidence of the astounding flexibility of mechanical sensing in cells. By outlining how mechanical activation of TREK-1 can be sensitised by mechanical regulation of PLD2 activity, the authors highlight a mechanism by which TREK-1 sensitivity could be regulated under distinct physiological conditions.

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing.

1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.

Response: yes, there is endogenous PLD (enPLD). We calculated the relative expression of xPLD2 vs enPLD. xPLD2 is >10x more abundant (Fig. S3d of Pavel et al PNAS 2020, ref 14 of the current manuscript). Hence, as with anesthetic sensitivity, we expect the xPLD to out compete the endogenous PLD, which is what we see. We added the following sentence and reference : “The xPLD2 expression is >10x the endogenous PLD2 (enPLD2) and out computes the TREK-1 binding site for PLD25.”

1. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

Response: If the PLD2 binding site is removed (TREK-1trunc), yes, the trafficking to the plasma membrane is unaffected by the expression of xPLD and mPLD (Author response image 2 above). For full length TREK1 (FL-TREK-1), co-expression of mPLD decreases TREK expression (Author response image 2) and coexpression with xPLD increases TREK expression (Figure S2f). This is exactly opposite of what one would expect if surface expression accounted for the change in pressure currents. Hence, we conclude surface expression does not account for loss of TREK-1 mechanosensitivity with xPLD2. A few sentences was added to the discussion. We also performed dSTORM on the TREKtruncated using EGFP. TREK-truncated goes to PIP2 (see figure 2 of 6)

Author response image 3.

To better compare the levels of TREK-1 before and after shear, we added a supplemental figure S2f where the protein was compared simultaneously in all conditions. 15 min of shear significantly decreased TREK-1 except with mPLD2 where the levels before shear were already lowest of all the expression levels tested.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the >me gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

Response: Shear was maintained during the fixing. xPLD2 blocks endocytosis, presumably endocytosis and or release of other lipid modifying enzymes affect the system. The change in TREK-1 levels appears to be directly through an interaction with PLD as TREK trunc is not affected by over expression of xPLD or mPLD.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Par>cularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.

Response: The question “how does PLD2 sense mechanical force on the membrane” we addressed and published in Nature Comm. In 2016. The title of that paper is “Kinetic disruption of lipid rafts is a mechanosensor for phospholipase D” see ref 13 Petersen et. al. PLD is a soluble protein associated to the membrane through palmitoylation. There is no transmembrane domain, which narrows the possible mechanism of its mechanosensation to disruption.

The Nature Comm. reviewer identified as “an expert in PLD signaling” wrote the following of our data and the proposed mechanism:

“This is a provocative report that identi0ies several unique properties of phospholipase D2 (PLD2). It explains in a novel way some long established observations including that the enzyme is largely regulated by substrate presentation which 0its nicely with the authors model of segregation of the two lipid raft domains (cholesterol ordered vs PIP2 containing). Although PLD has previously been reported to be involved in mechanosensory transduction processes (as cited by the authors) this is the 0irst such report associating the enzyme with this type of signaling... It presents a novel model that is internally consistent with previous literature as well as the data shown in this manuscript. It suggests a new role for PLD2 as a force transduction tied to the physical structure of lipid rafts and uses parallel methods of disrup0on to test the predic0ons of their model.”

Regarding cholesterol. We use a fluorescent cholesterol oxidase assay which we described in the methods. This is an appropriate assay for determining cholesterol levels in a cell which we use routinely. We have published in multiple journals using this method, see references 28, 30, 31. Working out the metabolic fate of cholesterol after sheer is indeed interesting but well beyond the scope of this paper. Furthermore, we indirectly confirmed our finding using dSTORM cluster analysis (Figure 3d-e). The cluster analysis shows a decrease in GM1 cluster size consistent with our previous experiments where we chemically depleted cholesterol and saw a similar decrease in cluster size (see ref 13). All the data are internally consistent, and the cholesterol assay is properly done. We see no reason to reject the data.

Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

Response: We previously established a kinetic model for PLD2 activation see ref 13 (Petersen et al Nature Comm 2016). In that publication we discussed both entropy and heat as mechanisms of disruption. Here we controlled for heat which narrowed that model to entropy (i.e., shear thinning) (see Figure 3c). We provide an overall justification below. But this is a small refinement of our previous paper, and we prefer not to complicate the current paper. We believe the proper rheological term is shear thinning. The following justification, which is largely adapted from ref 13, could be added to the supplement if the reviewer wishes.

Justification: To establish shear thinning in a biological membrane, we initially used a soluble enzyme that has no transmembrane domain, phospholipase D2 (PLD2). PLD2 is a soluble enzyme and associated with the membrane by palmitate, a saturated 16 carbon lipid attached to the enzyme. In the absence of a transmembrane domain, mechanisms of mechanosensation involving hydrophobic mismatch, tension, midplane bending, and curvature can largely be excluded. Rather the mechanism appears to be a change in fluidity (i.e., kinetic in nature). GM1 domains are ordered, and the palmate forms van der Waals bonds with the GM1 lipids. The bonds must be broken for PLD to no longer associate with GM1 lipids. We established this in our 2016 paper, ref 13. In that paper we called it a kinetic effect, however we did not experimentally distinguish enthalpy (heat) vs. entropy (order). Heat is Newtonian and entropy (i.e., shear thinning) is non-Newtonian. In the current study we paid closer attention to the heat and ruled it out (see Figure 3c and methods). We could propose a mechanism based on kinetic disruption, but we know the disruption is not due to melting of the lipids (enthalpy), which leaves shear thinning (entropy) as the plausible mechanism.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Ouen, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Response: Comment noted. We trust the reviewer is correct. In 1998 osmotic shock was used to activate the channel. We only intended to show that the system is consistent with previous electrophysiologic experiments.

References cited:

1 Du G, Huang P, Liang BT, Frohman MA. Phospholipase D2 localizes to the plasma membrane and regulates angiotensin II receptor endocytosis. Mol Biol Cell 2004;15:1024–30. htps://doi.org/10.1091/mbc.E03-09-0673.

2 Koch T, Wu DF, Yang LQ, Brandenburg LO, Höllt V. Role of phospholipase D2 in the agonist-induced and constistutive endocytosis of G-protein coupled receptors. J Neurochem 2006;97:365–72. htps://doi.org/10.1111/j.1471-4159.2006.03736.x.

3 Wheeler DS, Underhill SM, Stolz DB, Murdoch GH, Thiels E, Romero G, et al. Amphetamine activates Rho GTPase signaling to mediate dopamine transporter internalization and acute behavioral effects of amphetamine. Proc Natl Acad Sci U S A 2015;112:E7138–47. htps://doi.org/10.1073/pnas.1511670112.

4 Rankovic M, Jacob L, Rankovic V, Brandenburg L-OO, Schröder H, Höllt V, et al. ADP-ribosylation factor 6 regulates mu-opioid receptor trafficking and signaling via activation of phospholipase D2. Cell Signal 2009;21:1784–93. htps://doi.org/10.1016/j.cellsig.2009.07.014.

5 Pavel MA, Petersen EN, Wang H, Lerner RA, Hansen SB. Studies on the mechanism of general anesthesia. Proc Natl Acad Sci U S A 2020;117:13757–66. htps://doi.org/10.1073/pnas.2004259117.

6 Call IM, Bois JL, Hansen SB. Super-resolution imaging of potassium channels with genetically encoded EGFP. BioRxiv 2023. htps://doi.org/10.1101/2023.10.13.561998.

image above this is missing for me (Decorative screenshot of a financial staement) also a typo in its Alt tag "staement"

“根据前人研究发现，tag cloud这一工具可以用在读前的预测/讨论阶段”。 运用前人研究证实工具确实可以运用于阅读教学。 下面就开始解释tag cloud这一工具如何具体使用，或者如何在阅读阶段使用。

读前阶段对应的是tag cloud这一工具

This is very interesting to look at after the recent downfall of twitter. It makes me wonder if these people are still using twitter or if they've moved to another platform

may need new tag: combining/bringing different audiences together in the same conversation/context/tool

Author Response

eLife assessment

This important work describes the first high-resolution structure of HGSNAT, a lysosomal membrane protein required for the degradation of heparan sulfate (HS). Through careful structural analysis, this work proposes potential reasons why certain mutations in HGSNAT lead to lysosomal storage disorders and outlines the enzyme's catalytic mechanism. The experimental evidence presented provides incomplete support for the proposed molecular mechanism of the HS acetylation reaction and the impact of disease-causing mutations.

We thank the editors and reviewers for taking the time to provide a critical assessment of our manuscript. We appreciate the input and suggestions to improve the analysis. Included here are only our provisional responses. We will address the concerns raised in more detail and incorporate them in the revised version of the manuscript.

Reviewer #1 (Public Review):

This article by Navratna et al. reports the first structure of human HGSNAT in an acetyl-CoAbound state. Through careful structural analysis, the authors propose potential reasons why certain human mutations lead to lysosomal storage disorders and outline a catalytic mechanism. The structural data are of good quality, and the manuscript is clearly written. This study represents an important step toward understanding the mechanism of HGSNAT and is valuable to the field. I have the following suggestions:

We thank the reviewer for their encouraging and positive overall assessment of our work.

1. The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function.

Thank you for highlighting this concern. The cryo-EM sample was prepared without the exogenous addition of ligand, as noted in the manuscript; the acetyl-CoA that we see in the structure was intrinsically bound to the protein, indicating the ability of GFP-tagged HGSNAT protein to bind the ligand. We purified the protein at a pH optimal for acetyl-CoA binding, as suggested by Bame, K. J. and Rome, L. H. (1985) and Meikle, P. J. et al., (1995). Because we see acetyl-CoA in a structure obtained using a GFP fusion, we argue that GFP does not interfere with protein stability and ability to bind to the co-substrate. As demonstrated by existing literature HGSNAT catalyzed reaction is compartmentalized spatially and conditionally. The binding of acetyl-CoA happens towards the cytosol and is optimal at pH 7-0.8.0, while the transfer of the acetyl group to heparan sulfate occurs towards the luminal side and is optimal at pH 5.0-6.0. We are working on establishing a robust assay to study this complicated and compartmentalized acetyl transfer assay.

1. In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, like a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer?

The acetyl-CoA bound structure presented in the paper does not conclusively support a potential for isomerization and conformational dynamics. We agree with the reviewer that the reaction schematic presented in Figure 5 is speculative. We acknowledge in the discussion that our structure represents only a single step of the reaction, and defining the precise mechanism of acetyl transfer needs additional work. However, we will reword the discussion and change Figure 5 to address this concern raised by multiple reviewers.

Reviewer #2 (Public Review):

Summary:

This work describes the structure of Heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT), a lysosomal membrane protein that catalyzes the acetylation reaction of the terminal alpha-D-glucosamine group required for the degradation of heparan sulfate (HS). HS degradation takes place during the degradation of the extracellular matrix, a process required for restructuring tissue architecture, regulation of cellular function, and differentiation. During this process, HS is degraded into monosaccharides and free sulfate in lysosomes.

HGSNAT catalyzes the transfer of the acetyl group from acetyl-CoA to the terminal non-reducing amino group of alpha-D-glucosamine. The molecular mechanism by which this process occurs has not been described so far. One of the main reasons to study the mechanism of HGSNAT is that multiple mutations spanning the entire sequence of the protein, such as nonsense mutations, splicesite variants, and missense mutations lead to dysfunction that causes abnormal accumulation of HS within the lysosomes. This accumulation is a cause of mucopolysaccharidosis IIIC (MPS IIIC), an autosomal recessive neurodegenerative lysosomal storage disorder, for which there are no approved drugs or treatment strategies.

This paper provides a 3.26A structure of HGSNAT, determined by single-particle cryo-EM. The structure reveals that HGSNAT is a dimer in detergent micelles and a density assigned to acetylCoA. The authors speculate about the molecular mechanism of the acetylation reaction, map the mutations known to cause MPS IIIC on the structure and speculate about the nature of the HGSNAT disfunction caused by such mutations.

Strengths:

The description of the architecture of HGSNAT is the highlight of the paper since this corresponds to the first description of the structure of a member of the transmembrane acyl transferase (TmAT) superfamily. The high resolution of an HGSNAT bound to acetyl-CoA is an important leap in our understanding of the HGSNAT mechanism. The density map is of high quality, except for the luminal domain. The location of the acetyl-CoA allows speculation about the mechanistic role of multiple residues surrounding this molecule. The authors thoroughly describe the architecture of HGSNAT and map the mutations leading to MPS IIIC. The description of the dimeric interphase is a novel result, and future studies are left to confirm the importance of oligomerization for function.

We thank the reviewer for their time and for highlighting both the quality and novelty of the structure presented in this work.

Weaknesses:

Apart from the cryo-EM structure, the article does not provide any other experimental evidence to support or explain a molecular mechanism. Due to the complete absence of functional assays, mutagenesis analysis, or other structures such as a ternary complex or an acetylated enzyme intermediate, the mechanistic model depicted in Figure 5 should be taken with caution.

Thank you for pointing out this concern. The proposed mechanistic model in Figure 5 is a hypothesis based on previously reported biochemical characterization of HGSNAT by Rome & Crain (1981), Rome et al, (1983), Miekle et al., (1995) and Fan et al., (2011). However, we agree with the reviewer that this schematic is not experimentally proven and is speculative at best. Especially because our structure presents only a single step of the reaction, which does not conclusively support either ping-pong or random-order bi-substrate reactions. We will rephrase this section of our discussion and edit Figure 5 to address this concern.

The authors discuss that H269 is an essential residue that participates in the acetylation reaction, possibly becoming acetylated during the process. However, there is no solid experimental evidence, e.g. mutagenesis analysis or structural analysis, in this or previous articles, that demonstrates this to be the case.

H269, as a crucial catalytic residue, was suggested by monitoring the effect of chemical modifications of amino acids on acetylation of HGSNAT membranes by Bame, K. J. and Rome, L. H. (1986). We agree that mutagenesis, catalysis, and structural evidence for the same are not currently available. We are pursuing a more thorough exploration of the role of both H269 (previous studies) and N258 (from this study) on the stability and function of HGSNAT.

In the discussion part, the authors mention previous studies in which it was postulated that the catalytic reaction can be described by a random order mechanistic model or a Ping Pong Bi Bi model. However, the authors leave open the question of which of these mechanisms best describes the acetylation reaction. The structure presented here does not provide evidence that could support one mechanism or the other.

We agree with the reviewer’s observation that the structure doesn’t indeed support one reaction mechanism or another. We are pursuing the structural and kinetic characterization of HGSNAT in the presence of other co-substrates and multiple pHs that are required to address this concern thoroughly.

Although the authors map the mutations leading to MPS IIIC on the structure and use FoldX software to predict the impact of these mutations on folding and fold stability, there is no experimental evidence to support FoldX's predictions.

We are working on assessing the impact of specific mutations on the stability of HGSNAT and will add them to the revised version of the manuscript. We thank the reviewer for this suggestion.

Reviewer #3 (Public Review):

Summary:

Navratna et al. have solved the first structure of a transmembrane N-acetyltransferase (TNAT), resolving the architecture of human heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT) in the acetyl-CoA bound state using single particle cryo-electron microscopy (cryoEM). They show that the protein is a dimer and define the architecture of the alpha- and beta- GSNAT fragments, as well as convincingly characterizing the binding site of acetyl-CoA.

Strengths:

This is the first structure of any member of the transmembrane acyl transferase superfamily, and as such it provides important insights into the architecture and acetyl-CoA binding site of this class of enzymes.

The structural data is of a high quality, with an isotropic cryoEM density map at 3.3Å facilitating the building of a high-confidence atomic model. Importantly, the density of the acetyl-CoA ligand is particularly well-defined, as are the contacting residues within the transmembrane domain.

The open-to-lumen structure of HSGNAT presented here will undoubtedly lay the groundwork for future structural and functional characterization of the reaction cycle of this class of enzymes.

We thank the reviewer for their positive assessment of the data presented in this work. We really appreciate and agree with the reviewer's comment that the “structure of HSGNAT presented here will undoubtedly lay the groundwork for future structural and functional studies.”

Weaknesses:

While the structural data for the open-to-lumen state presented in this work is very convincing, and clearly defines the binding site of acetyl-CoA, to get a complete picture of the enzymatic mechanism of this family, additional structures of other states will be required.

We agree with the reviewers’ assessment and are heavily invested in pursuing the structures of all the steps of acetyl transfer by HGSNAT.

A potentially significant weakness of the study is the lack of functional validation. The enzymatic activity of the enzyme characterized was not measured, and the enzyme lacks native proteolytic processing, so it is a little unclear whether the structure represents an active enzyme.

We thank the reviewer for this comment. While the proteolytic cleavage of the protein remains debated, we find no evidence of such an event in our purification (SDS-PAGE and SEC). Studies like Durand et al., (2010) and Fan et al., (2011) suggest that even the ER retained monomeric HGSNAT is active. Because we see acetyl-CoA (co-substrate) bound to the protein in our structure, we surmise that proteolysis is not necessary for function, at least not for substrate binding. However, we are working towards the structural and kinetic characterization of recombinant α- and β-HGSNAT construct to explore the role of proteolysis on HGSNAT stability and function.

Reviewer #1 (Public Review):

This article by Navratna et al. reports the first structure of human HGSNAT in an acetyl-CoA-bound state. Through careful structural analysis, the authors propose potential reasons why certain human mutations lead to lysosomal storage disorders and outline a catalytic mechanism. The structural data are of good quality, and the manuscript is clearly written. This study represents an important step toward understanding the mechanism of HGSNAT and is valuable to the field. I have the following suggestions:

1. The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function.

2. In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, similar to a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer?

Author Response

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

eLife assessment

This research advance arctile describes a valuable image analysis method to identify individual cells (neurons) within a population of fluorescently labeled cells in the nematode C. elegans. The findings are solid and the method succeeds to identify cells with high precision. The method will be valuable to the C. elegans research community.

Public Reviews:

Reviewer #1 (Public Review):

In this paper, the authors developed an image analysis pipeline to automatically identify individual neurons within a population of fluorescently tagged neurons. This application is optimized to deal with multi-cell analysis and builds on a previous software version, developed by the same team, to resolve individual neurons from whole-brain imaging stacks. Using advanced statistical approaches and several heuristics tailored for C. elegans anatomy, the method successfully identifies individual neurons with a fairly high accuracy. Thus, while specific to C. elegans, this method can become instrumental for a variety of research directions such as in-vivo single-cell gene expression analysis and calcium-based neural activity studies.

The analysis procedure depends on the availability of an accurate atlas that serves as a reference map for neural positions. Thus, when imaging a new reporter line without fair prior knowledge of the tagged cells, such an atlas may be very difficult to construct. Moreover, usage of available reference atlases, constructed based on other databases, is not very helpful (as shown by the authors in Fig 3), so for each new reporter line a de-novo atlas needs to be constructed.

We thank the reviewer for pointing out a place where we can use some clarification. While in principle that every new reporter line would need fair prior knowledge, atlases are either already available or not difficult to construct. If one can make the assumption that the anatomy of a particular line is similar to existing atlases (Yemini 2021,Nejatbakhsh 2023,Toyoshima 2020), the cell ID can be immediately performed. Even in the case that one suspects the anatomy may have changes from existing atlases (e.g. in the case of examining mutants), existing atlases can serve as a starting point to provide a draft ID, which facilitates manual annotation. Once manual annotations on ~5 animals are available as we have shown in this work (which is a manageable number in practice), this new dataset can be used to build an updated atlas that can be used for future inferences. We have added this discussion in the manuscript: “If one determines that the anatomy of a particular animal strain is substantially different from existing atlases, new atlases can be easily constructed using existing atlases as starting points.” (page 18).

I have a few comments that may help to better understand the potential of the tool to become handy.

1. I wonder the degree by which strain mosaicism affects the analysis (Figs 1-4) as it was performed on a non-integrated reporter strain. As stated, for constructing the reference atlas, the authors used worms in which they could identify the complete set of tagged neurons. But how senstiive is the analysis when assaying worms with different levels of mosaicism? Are the results shown in the paper stem from animals with a full neural set expression? Could the authors add results for which the assayed worms show partial expression where only 80%, 70%, 50% of the cells population are observed, and how this will affect idenfication accuracy? This may be important as many non-integrated reporter lines show high mosaic patterns and may therefore not be suitable for using this analytic method. In that sense, could the authors describe the mosaic degree of their line used for validating the method.

We appreciate the reviewer for this comment. We want to clarify that most of the worms used in the construction of the atlas are indeed affected by mosaicism and thus do not express the full set of candidate neurons. We have added such a plot as requested (Figure 3 – figure supplement 2, copied below). Our data show that there is no correlation between the fraction of cells expressed in a worm and neuron ID correspondence. We agree with the reviewer this additional insight may be helpful; we have modified the text to include this discussion: “Note that we observed no correlation between the degree of mosaicism and neuron ID correspondence (Figure 3- figure supplement 2).” (page 10).

Author response image 1.

No correlation between the degree of mosaicism (fraction of cells expressed in the worm) and neuron ID correspondence.

1. For the gene expression analysis (Fig 5), where was the intensity of the GFP extracted from? As it has no nuclear tag, the protein should be cytoplasmic (as seen in Fig 5a), but in Fig 5c it is shown as if the region of interest to extract fluorescence was nuclear. If fluorescence was indeed extracted from the cytoplasm, then it will be helpful to include in the software and in the results description how this was done, as a huge hurdle in dissecting such multi-cell images is avoiding crossreads between adjacent/intersecting neurons.

For this work, we used nuclear-localized RFP co-expressed in the animal, and the GFP intensities were extracted from the same region RFP intensities were extracted. If cytosolic reporters are used, one would imagine a membrane label would be necessary to discern the border of the cells. We clarified our reagents and approach in the text: “The segmentation was done on the nuclear-localized mCherry signals, and GFP intensities were extracted from the same region.” (page21).

1. In the same mater: In the methods, it is specified that the strain expressing GCAMP was also used in the gene expression analysis shown in Figure 5. But the calcium indicator may show transient intensities depending on spontaneous neural activity during the imaging. This will introduce a significant variability that may affect the expression correlation analysis as depicted in Figure 5.

We apologize for the error in text. The strain used in the gene expression analysis did not express GCaMP. We did not analyze GCaMP expression in figure 5. We have corrected the error in the methods.

Reviewer #2 (Public Review):

The authors succeed in generalizing the pre-alignment procedure for their cell idenfication method to allow it to work effectively on data with only small subsets of cells labeled. They convincingly show that their extension accurately identifies head angle, based on finding auto fluorescent tissue and looking for a symmetric l/r axis. They demonstrate that the method works to identify known subsets of neurons with varying accuracy depending on the nature of underlying atlas data. Their approach should be a useful one for researchers wishing to identify subsets of head neurons in C. elegans, for example in whole brain recording, and the ideas might be useful elsewhere.

The authors also strive to give some general insights on what makes a good atlas. It is interesting and valuable to see (at least for this specific set of neurons) that 5-10 ideal examples are sufficient. However, some critical details would help in understanding how far their insights generalize. I believe the set of neurons in each atlas version are matched to the known set of cells in the sparse neuronal marker, however this critical detail isn't explicitly stated anywhere I can see.

This is an important point. We have made text modifications to make it clear to the readers that for all atlases, the number of entities (candidate list) was kept consistent as listed in the methods. In the results section under “CRF_ID 2.0 for automatic cell annotation in multi-cell images,” we added the following sentence: “Note that a truncated candidate list can be used for subse-tspecific cell ID if the neuronal expression is known” (page 3). In the methods section, we added the following sentence: “For multi-cell neuron predictions on the glr-1 strain, a truncated atlas containing only the above 37 neurons was used to exclude neuron candidates that are irrelevant for prediction” (Page 20).

In addition, it is stated that some neuron positions are missing in the neuropal data and replaced with the (single) position available from the open worm atlas. It should be stated how many neurons are missing and replaced in this way (providing weaker information).

We modified the text in the result section as follows: “Eight out of 37 candidate neurons are missing in the neuroPAL atlas, which means 40% of the pairwise relationships of neurons expressing the glr-1p::NLS-mcherry transgene were not augmented with the NeuroPAL data but were assigned the default values from the OpenWorm atlas” (page 10).

It also is not explicitly stated that the putative identities for the uncertain cells (designated with Greek letters) are used to sample the neuropal data. Large numbers of openworm single positions or if uncertain cells are misidentified forcing alignment against the positions of nearby but different cells would both handicap the neuropal atlas relative to the matched florescence atlas. This is an important question since sufficient performance from an ideal neuropal atlas (subsampled) would avoid the need for building custom atlases per strain.

The putative identities are not used to sample the NeuroPAL data. They were used in the glr-1 multi-cell case to indicate low confidence in manual identification/annotation. For all steps of manual annotation and CRF_ID predictions, we used real neuron labels, and the Greek labels were used for reporting purposes only. It is true that the OpenWorm values (40% of the atlas) would be a handicap for the neuroPAL atlas. This is mainly due to the difficulty of obtaining NeuroPAL data as it requires 3-color fluorescence microscopy and significant time and labor to annotate the large set of neurons. This is one reason to take a complementary approach as we do in this paper.

Reviewer #1 (Recommendations For The Authors):

1. Figure 3, there is a confusion in the legend relating to panels c-e (e.g. panel c is neuron ID accuracy but it is described per panel e in the legend.

We made the necessary changes.

1. Figure 3, were statistical tests performed for panels d-e? if so, and the outcome was not significant, then it might be good to indicate this in the legend.

We have added results of statistical tests in the legend as the following sentence: “All distributions in panel d and e had a p-value of less than 0.0001 for one sample t-test against zero.” One sample t-tests were performed because what is plotted already represents each atlas’ differences to the glr-1 25 dataset atlas, we didn’t think the statistical analyses between the other atlases would add significant value.

1. Figure 4, no asterisks are shown in the figure so it is possible to remove the sentence in the legend describing what the asterisk stands for.

Thank you. We made the necessary changes.

Reviewer #2 (Recommendations For The Authors):

Comparison with deep learning approaches could be more nuanced and structured, the authors (prior) approach extended here combines a specific set of comparative relationship measurements with a general optimization approach for matching based on comparative expectations. Other measurements could be used whether explicit (like neighbor expectations) or learned differences in embeddings. These alternate measurements would both need to be extensively re-calibrated for different sets of cells but might provide significant performance gains. In addition deep learning approaches don't solve the optimization part of the matching problem, so the authors approach seems to bring something strong to the table even if one is committed to learned methods (necessary I suspect for human level performance in denser cell sets than the relatively small number here). A more complete discussion of these themes might better frame the impact of the work and help readers think about the advantages and disadvantages or different methods for their own data.

We thank the reviewer for bringing up this point. We apologize perhaps not making the point clearer in the original submission. This extension of the original work (Chaudhary et al) is not changing the CRF-based framework, but only augmenting the approach with a better defined set of axes (solely because in multicell and not whole-brain datasets, the sparsity of neurons degrades the axis definition and consequently the neuron ID predictions). We are not fundamentally changing the framework, and therefore all the advantages (over registration-based approaches for example) also apply here. The other purpose of this paper is to demonstrate a couple of use-cases for gene expression analysis, which is common in studies in C. elegans (and other organisms). We hope that by showing a use-case others can see how this approach is useful for their own applications.

We have clarified these points in the paper (page 18). “The fundamental framework has not been changed from CRF_ID 1.0, and therefore the advantages of CRF_ID outlined in the original work apply for CRF_ID 2.0 as well.”

The atribution of anatomical differences to strain is interesting, but seems purely speculative, and somewhat unlikely. I would suspect the fundamentally more difficult nature of aligning N items to M>>N items in an atlas accounts for the differences in using the neuroPAL vs custom atlas here. If this is what is meant, it could be stated more clearly.

It is important to note that the same neuron candidate list (listed in methods) was used for all atlases, so there is no difference among the atlases in terms of the number of cells in the query vs. candidate list. In other words, the same values for M and for N are used regardless of the reference atlas used.

We have preliminary data indicating differences between the NeuroPAL and custom atlas. For instance, the NeuroPAL atlas scales smaller than the custom glr-1 atlas. Since direct comparisons of the different atlases are beyond the scope of this paper, we will leave the exact comparisons for future work. We suspect that the differences are from a combination of differences in anatomy and imaging conditions. While NeuroPAL atlas may not be exactly fitting for the custom dataset, it can serve as a good starting point for guesses when no custom atlases are available, as we have discussed earlier (response to Public Comments from Reviewer 1 Point 1). As explained earlier, we have added these discussions in the paper (see page 18).

I was also left wondering if the random removal of landmarks had to be adjusted in this work given it is (potentially) helping cope with not just occasional weak cells but the systematic loss of most of the cells in the atlas. If the parameters of this part of the algorithm don't influence the success for N to M>>N alignment (here when the neuroPAL or OpenWorm atlas is used) this seems interesting in itself and worth discussing. Conversely, if these parameters were opitmized for the matched atlas and used for the others, this would seem to bias performance results.

We may have failed to make this clear in the main text. As we have stated in our responses in the public review section, we do systematically limit the neuron labels in the candidate list to neurons that are known to be expressed by the promotor. The candidate list, which is kept consistent for all atlases, has more neurons than cells in the query, so it is always an N-to-M matching where M>N. We did not use landmarks, but such usage is possible and will only improve the matching.

We have attempted to clarify these points in the manuscript. In the results section under “CRF_ID 2.0 for automatic cell annotation in multi-cell images,” we added the following sentence: “Note that a truncated candidate list can be used for subset-specific cell ID if the neuronal expression is known” (page 3). In the methods section, we added the following sentence: “For multi-cell neuron predictions on the glr-1 strain, a truncated atlas containing only the above 37 neurons was used to exclude neuron candidates that are irrelevant for prediction” (Page 20).

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100% true.

The real question is: how to exclude some results?

For example, I have few annotations under tag search results, but there are some of mine, and I want to see only those which are not mine, but others. How can I accomplish that?

It would be helpful, because it provides solution for excluding from results users or websites which are unworthy or spammed with worthless annotations.

Author Response

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

Note to all Reviewers

We appreciate the reviewers’ comments and suggestions for improving the manuscript. Below is a summary of new data added and a brief description of the major new results. A detailed pointby-point response follows.

New data:

• Figure 1f

• Figure 2b, f, g

• Figure 4b

• Figure S7 • Figure S8

• Figure S9

Summary of major new results/edits:

• At the request of Reviewer #1 we have updated the name of the degradation tag to be more specific and we now call it the “LOVdeg” tag.

• We have added new controls demonstrating that light stimulation does not cause photobleaching or toxicity issues (Fig. S7).

• We now show that LOVdeg can function at various points in the growth cycle, demonstrating robust degradation (Fig. 1f, Fig. S8).

• We have included relevant controls for the AcrB-LOVdeg efflux pump results (Fig. 2f-g).

• We have included important benchmarking controls, such as an EL222-only control and SsrA tag control to provide a clearer view of how LOVdeg performance compares to other systems (Fig. S9, Fig. 4b).

Additional note:

• While repeating experiments during the revision process we found that the results for the combined action of EL222 and the LOVdeg tag were not as dramatic as in our original measurements, though the overall findings are consistent with our original results. Specifically, we still find that the combination of EL222 and the LOVdeg tag produces a lower signal than either on their own. We have updated these data in the revised manuscript (Fig. 4b).

Reviewer #1:

Public Review:

Specifically controlling the level of proteins in bacteria is an important tool for many aspects of microbiology, from basic research to protein production. While there are several established methods for regulating transcription or translation of proteins with light, optogenetic protein degradation has so far not been established in bacteria. In this paper, the authors present a degradation sequence, which they name "LOVtag", based on iLID, a modified version of the blue-light-responsive LOV2 domain of Avena sativa phototropin I (AsLOV2). The authors reasoned that by removing the three C-terminal amino acids of iLID, the modified protein ends in "-E-A-A", similar to the "-L-A-A" C-terminus of the widely used SsrA degradation tag. The authors further speculated that, given the light-induced unfolding of the C-terminal domain of iLID and similar proteins, the "-E-A-A" C-terminus would become more accessible and, in turn, the protein would be more efficiently degraded in blue light than in the dark.

Indeed, several tested proteins tagged with the "LOVtag" show clearly lower cellular levels in blue light than in the dark. While the system works efficiently with mCherry (10-20x lower levels upon illumination), the effect is rather modest (2-3x lower levels) in most other cases. Accordingly, the authors propose to use their system in combination with other light-controlled expression systems and provide data validating this approach. Unfortunately, despite the claim that the "LOVtag" should work faster than optogenetic systems controlling transcription or translation of protein, the degradation kinetics are not consistently shown; in the one case where this is done, the response time and overall efficiency are similar or slightly worse than for EL222, an optogenetic expression system.

The manuscript and the figures are generally very well-composed and follow a clear structure. The schematics nicely explain the underlying principles. However, limitations of the method in its main proposed area of use, protein production, should be highlighted more clearly, e.g., (i) the need to attach a C-terminal tag of considerable size to the protein of interest, (ii) the limited efficiency (slightly less efficient and slower than EL222, a light-dependent transcriptional control mechanism), and (iii) the incompletely understood prerequisites for its application. In addition, several important controls and measurements of the characteristics of the systems, such as the degradation kinetics, would need to be shown to allow a comparison of the system with established approaches. The current version also contains several minor mistakes in the figures.

We thank reviewer #1 for the feedback and suggestions to strengthen the manuscript. We have addressed these comments in the points that follow and now include important controls and benchmarks for our molecular tool.

Major points

1. The quite generic name "LOVtag" may be misleading, as there are many LOV-based tags for different purposes.

We appreciate that it would be beneficial to have a more specific name. We have updated the name to “LOVdeg” tag, which captures both the inclusion of LOV and the degradation function of the tag.

Updated throughout the manuscript and figures

1. Throughout the manuscript, the authors use "expression levels". As protein degradation is a post-expression mechanism, "protein levels" should be used instead.

We have transitioned to using “protein levels” at many points in the manuscript.

Updated throughout the manuscript

1. Degradation dynamics (time course experiments) should be shown. The only time this is done in the current version (in Fig. 4), degradation appears to be in the same range (even a bit slower) than for EL222, which does not support the claim that the "LOVtag" acts faster than other optogenetic systems controlling protein levels.

In the revised manuscript, time course data are now shown at multiple points. These include new data in Fig. 1f and Fig. S8 that demonstrate degradation at various stages of growth. Fig. S4 also shows the dynamics of degradation when comparing to the addition of exogenously expressed ClpA. We have added text in the results section to point the reader to these data. In addition, we have made minor modifications to the text in the Introduction to avoid making claims about speed comparisons. Fig. 1f, Fig. S8, Fig. S4

Results: Design and characterization of the AsLOV2-based degradation tag, Introduction

1. "Frequency" is used incorrectly for Fig. 3. A series of 5 seconds on, 5 seconds off corresponds to a frequency of 0.1 Hz (1 illumination round / 10 s), not of 0.5 Hz. What the authors indicate as "frequency" is the fraction of illumination time. However, the (correct) frequency should be given, as this is likely the more important factor.

We have changed how we calculate frequency to use the proposed definition of one pulse per time period. We updated the values in the text and in the figure. Fig. 3c

Results: Tuning frequency response of the LOVdeg tag

1. To properly evaluate the system, several additional controls are needed:

a. To test for photobleaching of mCherry by blue light illumination, untagged controls should be shown for the mCherry-based experiments. Fluorescence always seems to be lower upon illumination, except for the AsLOV2*(546) data, where it cannot be excluded that fluorescence readings are saturated. Relatedly, the raw data for OD and fluorescence should be included. Showing a Western blot against mCherry in at least one case would allow to separate the effects of photobleaching and degradation.

We appreciate the suggestion and have conducted these important controls. We now include new data demonstrating that light induction does not change fluorescence levels using an untagged mCherry control, nor does it significantly affect endpoint OD levels. Based on these results, we did not perform a Western blot because there were no effects to separate. Fig. S7

b. In Fig. 2b, light + IPTG should be shown to estimate the activity of the system at higher expression levels.

We have added these to the figure. Light + IPTG modestly increases expression compared to IPTG only, likely due to the saturating level of IPTG added, which achieves near full induction. Fig. 2b

c. In Fig. 4, EL222 alone should be shown to allow a comparison with the LOVtag. From the data presented, it looks like EL222 is both slightly faster and more efficient than the LOVtag.

We have added the EL222-only case for comparison with LOVdeg only and EL222 + LOVdeg. We note that Reviewer #3 raised a similar concern. Fig. 4b

d. The effect of the used light on bacterial viability under exponential and stationary conditions should be shown.

In this revision, we have added new data on light exposure at various points during exponential and stationary phase (Fig. 1f, Fig. S8). These OD data show that growth curves are similar for all cultures, regardless of the time light is applied during the growth phase. Additionally, we also now include ODs for the photobleaching experiments. These data also show that growth is not significantly altered under continuous light exposure. Figure 1f, Fig. S7b

1. The claim that "Post-translational control of protein function typically requires extensive protein engineering for each use case" is not correct. The authors should discuss alternative options, e.g. based on dimerization, more extensively and in a less biased manner.

We have toned down the language in this location and at other points in the manuscript. However, we maintain that other types of post-translational control, such as dimerization or LOV2 domain insertion, require more protein engineering than inserting a degradation tag. For example, we and others have directly demonstrated this in previous work (e.g. DOI: 10.1021/acssynbio.9b00395, 10.1101/2023.05.26.542511, 10.1038/s41467-023-38993-6), where numerous split site or insertion variants need to be screened and fine-tuned for successful light control. In contrast, a degradation mechanism has the potential to require less fine tuning to achieve a light response. We have included the above sources to clarify this point. Introduction, Results: Modularity of the LOVdeg tag

Minor points

1. In Suppl. Fig. 1, amino acid numbers seem to be off. Also, the alterations in iLID (compared to AsLOV2) that are not used in "LOVtag" appear to be missing and the iLID sequence incorrect, as a consequence.

Thank you for catching this. The number indices in Fig. S1 have been corrected. We also realized we were reporting the iLID(C530M) variant in our amino acid sequence and have reverted the 530M back to C. Fig. S1

1. Why is AsLOV2(543) more efficiently degraded than AsLOV2(543) (blue column in Fig. 1d) when the dark state should be stabilized in AsLOV2(543)?

We are not sure of the exact reason for the increased degradation response in the AsLOV2*(543) variant. It may be that the dark-state stabilizing mutations introduced also have more favorable interactions with degradation machinery, although this is highly speculative.

1. Why does the addition of EL222 reduce protein levels so strongly in the dark for CpFatB1* (Fig. 5)?

We believe this effect stems from the EL222 responsive promoter (PEL222). With LOVdeg only, CpFatB1* is expressed from an IPTG inducible promoter (PlacUV5) whereas EL222 responsive constructs necessitate a promoter switch containing an EL222 binding site. We have clarified this point and expanded our discussion of these results.

Results: Optogenetic control of octanoic acid production

1. Fig. 2f / S10 are difficult to interpret. Why does illumination only lead to a significant effect at 2.5 and 5 µg/ml and not at lower concentrations, where the degradation system would be expected to be most efficient?

We have expanded our discussion on these results to explain that this likely stems from basal protein levels of AcrB-LOVdeg in the light that can provide resistance at low antibiotic concentrations. We have also added new controls to this figure to show the chloramphenicol sensitivity of a ΔacrB strain and a ΔacrB strain with an IPTG-inducible version of acrB with no induction, demonstrating the lowest achievable chloramphenicol resistance from a standard inducible system.

Results: Modularity of the LOVdeg tag, Fig. 2f-g

1. Fig. 2f / S10 do not measure the MIC (which is a clearly defined value), but the sensitivity to Chloramphenicol.

We have changed the text to use the term chloramphenicol sensitivity instead of MIC. Results: Modularity of the LOVdeg tag

1. "***" in Fig. S1 should be explained.

We have removed the ‘***’ to avoid confusion. Fig. S1

1. The fold-change differences between light and dark, indicated in some selected cases, should be listed for all figures.

We have added fold-change values where appropriate. Fig 1d, Fig. 2b

Reviewer #2:

Public Review:

In this manuscript the authors present and characterize LOVtag, a modified version of the bluelight sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVtag, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. They demonstrate modularity of this LOVtag by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVtag is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVtag to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVtag is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVtag with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 269-fold (relative to 15-fold with LOVtag alone). Finally, the authors apply LOVtag to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVtag allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVtag, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division), however it is not clear from the available data that the cells were in stationary phase during light exposure. Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

We thank Reviewer #2 for the constructive feedback. In the updated manuscript, we now include data demonstrating degradation at different growth stages and address other points brought up in the review to improve understanding of the degradation tag.

Overall, the authors present a well written manuscript that characterizes an interesting and likely very useful tool for bacterial synthetic biology and metabolic engineering. I have a few suggestions that could improve the presentation of the material.

Major Comments:

• Could the authors clarify, perhaps through OD measurements, that the cultures in the octanoic acid experiment are actually in stationary phase during the relevant light induction. It isn't clear from the methods.

We have updated the Methods to clarify that the cells are entering stationary phase (OD600 = 0.6) when light is either kept on or turned off for production experiments. Production is continued for the following 24 hours. Note that we now show OD measurements in a separate set of experiments (Fig. 1f, Fig. S8).

Methods: Octanoic acid production experiment. Fig. 1f, Fig. S8

• Can the authors clarify why there is an overall decrease in protein in the clpX deletion? And is it this initial reduction that is the source of the change in fold in 1C? Similarly, for hslU is it because overall protein levels are higher with the tag? In general, I feel that the interpretation of Supplemental Figures S6-S10 could be moved in more detail to the main text, or at least the main takeaway points. But this is a personal preference, and not necessary to the major flow of the story which is about the utility of the LOVtag tool.

As shown in Fig. S5, expression of mCherry without any degradation tag is decreased in a clpX knockout strain compared to wild type. This difference may be the result of reduced cell health, and we now note this in the text. The strains shown in Fig. 1c are in wild type cells with normal expression, so this is not the source of the fold change. As for hslU, we agree it is interesting that expression seems to increase. However, the increase is modest and could stem from gene network regulation differences in that strain compared to wild type and may not be related to LOVdeg tag degradation. Each endogenous protease is involved in a wide range of functions within the cell, and it is unknown how global gene expression is impacted. We acknowledge the suggestion of moving the protease results to the main text, but we have ultimately elected to keep these data in the Supplementary Information to maintain the flow in the manuscript. However, we have added additional text pointing the reader to the Supplemental Text and include a brief summary of the findings in the main text.

Results: Design and characterization of the AsLOV2-based degradation tag

• What is the source of the poor repression in Figure 2D?

Presumably, this stems from low levels of the CRISPRa MCP-SoxS activator, even in the presence of light. We have added this point to the text.

Results: Modularity of the LOVdeg tag

• In general, it would be nice to have light-only controls for many of the experiments to validate that light is not affecting the indicated proteins or their function.

We thank the reviewer for this suggestion and note that Reviewer #1 raised a similar concern. We have now included light-only data for a strain containing IPTG-inducible mCherry without the LOVdeg tag (Fig. S7). These data show that light itself, at the levels used in this study, does not affect mCherry expression or cell growth. This strain serves as a direct control for data presented in Fig. 1 and Fig. 2b, as the systems are identical except for the addition of the LOVdeg tag onto either mCherry or the LacI repressor. Additionally, the control translates to other experiments since mCherry is used as a reporter for other systems in this study. Fig. S7

• It would be nice to directly measure the function of the tool at different phases of E. coli growth to show directly that protein degradation works at stationary phase, rather than the more indirect measurements used in the octanoic acid experiment.

We thank the reviewer for this suggestion, which significantly strengthens our results. We have added an experiment that tests the LOVdeg tag at different phases of growth (Fig. 1f, Fig. S8). In this experiment, cultures are growth from early exponential to stationary phase, and light is introduced at various points. Exposure windows of 4 hours, ranging from early exponential to stationary phase, all show functional light inducible degradation. Fig. 1f, Fig. S8.

Results: Design and characterization of the AsLOV2-based degradation tag

Minor Comments:

• It would be nice to make clear that the data in S6d and S7 is repeated, but with the HslUV data in S7.

We clarified this point in the caption of Fig. S4 (the former Fig. S7 in the original manuscript). Fig. S4 caption

• Why was 5s picked for the frequency response in Figure 3

We picked 5s because 1) it is a substantially shorter timescale than overall degradation dynamics seen for the LOVdeg tag, and 2) we found that shorter pulses could not be reliably achieved with the light stimulation hardware and software we used (Light Plate Apparatus with Iris software). To ensure high fidelity pulses, we opted for 5 second pulses that we empirically determined to be stable throughout long experiments. We have added text clarifying this. Results: Tuning frequency response of the LOVdeg tag

Reviewer #3:

Public Review:

The authors present the mechanism, validation, and modular application of LOVtag, a light-responsive protein degradation tag that is processed by the native degradosome of Escherichia coli. Upon exposure to blue light, the c-terminal alpha helix unfolds, essentially marking the protein for degradation. The authors demonstrate the engineered tag is modular across multiple complex regulatory systems, which shows its potential widespread use throughout the synthetic biology field. The step-by-step rational design of identifying the protein that was most dark stabilized as well as most light-responsive for degradation, was useful in terms of understanding the key components of this system. The most compelling data shows that the engineered LOVTag can be fused to multiple proteins and achieve light-based degradation, without affecting the original function of the fused protein; however, results are not benchmarked against similar degradation tagging and optogenetic control constructs. Creating fusion proteins that do not alter either of the original functions, is often difficult to achieve, and the novelty of this should be expanded upon to drive further impact.

We appreciate the feedback from Reviewer #3 to improve the manuscript. We have included important controls and benchmarking experiments to address the reviewer’s concerns, which are detailed in the points below.

Benchmarking:

The similarity between the L-A-A sequence of SsrA and the E-A-A sequence of LOVtag is one of the pieces of evidence that led the authors to their current protein design. The differences in degradation efficiency between the SsrA degradation tag and LOVtag are not shown, and benchmarking against SsrA would be a valuable way to demonstrate the utility of this construct relative to an established protein tagging tool.

We thank the reviewer for suggesting an experiment to benchmark performance. We have added new experimental data where a full length SsrA tag is added to a fusion protein of nearly identical size (mCherry-iLID), allowing us to directly compare performance to mCherryLOVdeg (Fig. S9). These results show that light inducible control with LOVdeg tag decreases protein expression levels to near those achieved with the native SsrA tag. Fig. S9.

Results: Design and characterization of the AsLOV2-based degradation tag

Additionally, there is a lack of an EL222-only control presented in Figure 4b and in the results section beginning with "Integrating the LOVtag with EL222...". Without benchmarking against this control the claim that "EL222 and the LOVtag work coherently to decrease expression" is unsubstantiated. No assumptions of synergy can be made.

We appreciate this comment and note that Reviewer #1 raised a similar concern. We have added data to Fig. 4b with an EL222-only control for comparison. Fig. 4b

The dramatic change in dark octanoic acid titer between the EL222, LOVtag and combined conditions are surprising, especially in comparison to the lack of change in the dark mCherry expression shown in Figure 4b. This data is the only to suggest that LOVtag may perform better than EL222. However, the inconsistencies in dark state regulation presented in the two experiments, and between conditions in this experiment bring the latter claim to question. A recommendation is that the authors either repeat this experiment, or comment on the observed discrepancy in dark state octanoic acid titers in their discussion.

First, a key difference between the data presented in Fig. 4 and Fig. 5 is that the production experiment is conducted over a long time period (24 hours) and the EL222/LOVdeg reporter experiment is conducted over 5 hours. Likely, performance differences between EL222 and the LOVdeg tag become more pronounced as protein accumulation occurs. Second, the LOVdeg only construct is expressed from a non-EL222 promoter which is able to achieve higher expression (see response to Reviewer #1, Minor point #3). Lastly, a convoluting factor is that the relationship between expression of CpFatB1 and octanoic acid production is not completely linear, and there are likely thresholds or expressions windows that result in similar endpoint titers. We agree a more detailed examination of how CpFatB1 changes over the course of the production period would be very interesting. However, this is beyond the scope of the present study, whose goal is to introduce and showcase the utility of the LOVdeg tag as a tool. We have added new discussion on this in the Results section to clarify some of these points. We have also repeated all experiments in Fig. 4 and consistently see the LOVdeg tag performing as well as or better than EL222. As noted in the remarks to all reviewers, these data have been updated in the revised manuscript.

Results: Optogenetic control of octanoic acid production. Fig. 4d

Based on the methodology presented, no change in the duration in light exposure was tested, even though this may be an important part of the system response. The on/off, for example in Figure 4b, is either all light or all dark, but they claim that their system is beneficial especially at stationary phase. The authors should consider showing the effects of shifting from dark to light at set intervals. (i.e. 1 hr dark then light, 2hr dark until light, etc.) This data would also aid in supporting the utility of this tag for controlling expression during different growth phases, where light may be used after the cells have reached a certain phase.

We have added new data showing the effect of light stimulation at different times in the growth cycle (see response to Reviewer #2, bullet point #5). These data demonstrate that the LOVdeg tag performs well at various points in the growth cycle. Fig. 1f, Fig. S8.

Results: Design and characterization of the AsLOV2-based degradation tag

Minor Revisions Figures:

• Figure 1:

• More clarity is needed in the naming conventions for this figure and in the body of the text. For example, a different convention than 546 and 543 should be used to refer to the full and truncated lengths of the tag. It would greatly aid understanding for this to be made more clear. The authors could simply continue to use "full" and "truncated" to refer to them. In addition, the term "stabilizing mutations" in 1c could be changed to read "dark state stabilizing mutations" to aid in clarity.

When describing the design of the LOVdeg tag, we opted towards a more technically accurate description over clarity in order to make our engineering process easily comparable to other LOV2 systems. As such, we kept the number-based nomenclature (543 or 546) to represent the domain within the phototropin 1 protein from Avena sativa (AsLOV2). The domain used in this study, and many other studies, are only amino acids 404-546, i.e. not the full sequence, thus saying simply ‘full’ or ‘truncated’ is not technically accurate. We believe the detailed nomenclature, which is limited to one section, is important to provide clarity on exactly what we used for protein engineering. In the revised version we introduce the nickname “LOVdeg” tag earlier and use it throughout the rest of the manuscript.

Results: Design and characterization of the AsLOV2-based degradation tag

• 1b It is not clear that this is the dark state stabilized structure in the figure, but is referred to as such only in the body of the text.

We have added text in the manuscript to clarify this is AsLOV2, not iLID, and have labeled it in the figure caption as well.

Results: Design and characterization of the AsLOV2-based degradation tag

• 1d. Fold change is reported in Figure 2d, and may be relevant to include those values in 1d as well.

Done. Fig. 1d

• 1e. It is not clear which tag is being used in this bar plot. Please specify that this is the dark state stabilized, truncated tag.

We have added a title to the plot and language to the caption, both of which clarify this point. Fig. 1e

• In addition, the microscopy images provided in supplemental material should be included in the first figure as it adds a compelling observation of LOVtag activity.

We are pleased to hear that the microscopy results are beneficial, however we elected to leave them in Supplementary to preserve the flow of the manuscript in the text surrounding Fig. 1.

• Figure 2:

• 2d. It is unclear what the 2.5x fold change is relative to (the baseline or the dark)

We have added a line in the figure to clarify the comparison being made. Fig. 2d

• 2f. More discussion can be added to describe what concentration of chloramphenicol is biologically/bioreactor relevant.

Our previous studies on the relationship between AcrAB expression and mutation rate (cited in the text) were carried out at a concentration within the range in which the LOVdeg tag is effective (5 μg/ml), suggesting this range to be relevant to tolerance and resistance.

• Figure 3:

• We recommend that this data and discussion are better suited for supplementary figures. The results shown here essentially recapitulate the same findings of Zoltowski et al., 2009. In addition, the paper describing this mutation should be cited in this figure caption in addition to the body of the text

Although these results are in line with previous findings, we believe this dataset is important for several reasons. First, the agreement with known mutations validates the unfolding-based mechanism for degradation control. Second, degradation that is contingent on unfolding of LOV2 offers a direct actuating mechanism of photocycle properties. Other systems, like that in Zoltowski et al., examine properties of purified proteins but lack the mechanism to translate its effect in live cells. This figure demonstrates how degradation can do so and lays the groundwork for degradation-based frequency processing circuits. Last, there are discrepancies between photocycle kinetics in situ, as reported by Li et al. (DOI: 10.1038/s41467-020-18816-8), and in cell-free studies such as in Zoltowski et al. The studies use different methods of measuring photocycle kinetics (in situ vs cell-free). This dataset substantiates relaxation times from Li et al. and suggests cell-free relaxation time constants are over estimated relative to our live cell results.

• Figure 4:

• There is a lack of an EL222-only control presented in Figure 4b. Without this data present, the claim that "EL222 and the LOVtag work coherently to decrease expression" is unsubstantiated. No assumptions of synergy can be made.

We have added EL222-only data to the figure; we note that Reviewer #1 made a similar request. Figure 4b

Manuscript

Results

• Design and characterization...

• Due to the extensive discussion of ClpX at the beginning of this section, more of the results on evaluating the binding partners and mechanism of LOVtag degradation should be presented in the main body of the manuscript and not in supplementary materials.

To maintain flow of the manuscript and focus on how the LOVdeg tag works as a synthetic biology tool, we have opted to keep this section in the Supplement Information, but have several lines in the text related to Fig. 1 that point the reader to this material. Results: Design and characterization of the AsLOV2-based degradation tag

• In the second paragraph of this section, the authors theorize that the C-terminal truncated E-AA sequence will "remain caged as part of the folded helix". How did the authors determine this? Was there any evidence to suggest that the truncated state would be any more responsive than the full length sequence? More data or rationale may need to be introduced to support the overall hypothesis presented in this paragraph.

We determined this by examining the crystal structure which shows that the E-A-A sequence is part of the folded helix. As seen in Fig. 1b, addition of amino acids after the EAAKEL sequence would not be part of the folded helix which ends prior to the terminal leucine. We added text to clarify our logic.

Results: Design and characterization of the AsLOV2-based degradation tag

• The similarity between the L-A-A sequence of SsrA and the E-A-A sequence of LOVtag is one of the pieces of evidence that brought the authors to their current protein design. The differences in degradation efficiency between the SsrA degradation tag and LOVtag are not clear, and benchmarking against SsrA would be a valuable way to demonstrate the utility of this construct relative to an established protein tagging tool.

We added an SsrA comparison to benchmark the system. Fig. S9

Results: Design and characterization of the AsLOV2-based degradation tag

• Tuning frequency and response...

• Overall the results presented in this section essentially recapitulate the effects that mutation presented in Zoltowski et. al., 2009 have on AsLOV2 dark state recovery and although this is a useful observation of LOVtag performance, a recommendation is to move this into a supplementary section.

See above response to Fig. 3 comment.

• Integrating the LOVtag with EL222...

• The claim is made in this section that LOVtag and EL222 work synergistically, however the experiments presented do not test repression due to EL222 activity alone. Without benchmarking against this control, the claim of synergy is not supported and we recommend that the authors perform this experiment again with the EL222-only control.

We have added this important control. Fig. 4b

Discussion

• The statement "the LOVtag can easily be integrated with existing optogenetic systems to enhance their function" is not substantiated without benchmarking LOVtag against an EL222- only control. As mentioned above this condition should be included in the experiments discussed in Figure 4 and in the section "Integrating the LOVtag with EL222.."

We added EL222-only regulation to benchmark the LOVdeg tag and LOVdeg + EL222 experiments. Fig. 4b

Experiments

Applications:

The application of this tag to the metabolic control of octanoic acid production could be more impactful. For instance, using the LOVtag with two different enzymes to change the composition of long/short chain fatty acids with light induction., Or possibly integrating the tag into a switch to activate production. However, the authors address that "decreasing titers is not the overall goal in metabolic engineering" in their discussion, and therefore the pursuit of this additional experiment is up to the authors' discretion.

We appreciate the suggestions for further applications of the LOVdeg tag. We envision that follow up studies will focus on the application of the LOVdeg tag in metabolic engineering. However, this will require significant development of production systems. We believe this to be out of the scope of this work, where the goal is to present the design and function of the LOVdeg tag as a tool.

eLife assessment

This valuable study reports on a new tool that allows for light-controlled protein degradation in Escherichia coli. With the improved light-responsive protein tag, endogenous protein levels can be reduced severalfold. The methodology is convincing and will be of interest to the fields of gene expression regulation in bacteria and, more generally to synthetic biologists.

Reviewer #1 (Public Review):

Specifically controlling the level of proteins in bacteria is an important tool for many aspects of microbiology, from basic research to protein production. While there are several established methods for regulating transcription or translation of proteins with light, optogenetic protein degradation has so far not been established in bacteria. In this paper, the authors present a degradation sequence, which they name "LOVdeg", based on iLID, a modified version of the blue-light-responsive LOV2 domain of Avena sativa phototropin I (AsLOV2). The authors reasoned that by removing the three C-terminal amino acids of iLID, the modified protein ends in "-E-A-A", similar to the "-L-A-A" C-terminus of the widely used SsrA degradation tag. The authors further speculated that, given the light-induced unfolding of the C-terminal domain of iLID and similar proteins, the "-E-A-A" C-terminus would become more accessible and, in turn, the protein would be more efficiently degraded in blue light than in the dark.

Indeed, several tested LOVdeg-tagged proteins show clearly lower cellular levels in blue light than in the dark. Depending on the nature and expression level of the target protein, protein levels are reduced modestly to strongly (2 to 20x lower levels upon illumination). Accordingly, the authors propose to use their system in combination with other light-controlled expression systems and provide data validating this approach. The LOVdeg system allows to modulate protein levels to a similar degree and with comparable kinetics as optogenetic systems controlling transcription or translation of protein, and can be combined with such systems.

The manuscript and the figures are generally very well-composed and follow a clear structure. The schematics nicely explain the underlying principles. Besides the advantages of the LOVdeg approach, including its complementarity to controlled expression of proteins, the revised version of the manuscript also highlights the limitations of the method more clearly, e.g., (i) the need to attach a C-terminal tag of considerable size to the protein of interest, (ii) the limited efficiency (slightly less efficient and slower than EL222, a light-dependent transcriptional control mechanism), and (iii) the incompletely understood prerequisites for its application. Taken together, this manuscripts describes the LOVdeg system as a valuable addition to the tool box for controlling protein levels in prokaryotic cells.

Reviewer #2 (Public Review):

In this manuscript the authors present and characterize LOVdeg, a modified version of the blue-light sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVdeg, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. The LOVdeg is able to target mCherry for protein degradation across different phases of bacterial growth, which is important for regulating processes at stationary phase and a potential additional advantage over transcriptional repression systems. They demonstrate modularity of this LOVdeg by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVdeg is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVdeg to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVdeg is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVdeg with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 23-fold (relative to 7-fold with LOVdeg alone). Finally, the authors apply LOVdeg to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVdeg allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVdeg, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division). Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

Reviewer #3 (Public Review):

The authors present the mechanism, validation, and modular application of LOVtag, a light-responsive protein degradation tag that is processed by the native degradosome of Escherichia coli. Upon exposure to blue light, the c-terminal alpha helix unfolds, essentially marking the protein for degradation. The authors demonstrate the engineered tag is modular across multiple complex regulatory systems, which shows its potential widespread use throughout the synthetic biology field. The step-by-step rational design of identifying the protein that was most dark-stabilized as well as most light-responsive for degradation, was useful in terms of understanding the key components of this system. The most compelling data shows that the engineered LOVTag can be fused to multiple proteins and achieve light-based degradation, without affecting the original function of the fused protein.

Letterboxd has called itself “Goodreads for movies” but it has far surpassed that initial tag line, having figured out how to create a smooth and intuitive user experience, provide a pleasant and inviting community and earn revenue from both optional paid memberships and advertisers, including studios that produce the films being discussed.

Says Maris Kreizman in NYT on 24 Dec 2023.

https://www.flickr.com/photos/hawkexpress/189972899/in/album-72157594200490122/

Despite being used primarily as a productivity tool the PoIC system also included some features of personal knowledge management with "discovery cards" and "citation cards". Discovery cards were things which contained one's own ideas while the citation cards were the ideas of others and included bibliographic information. Citation cards were tagged on the 5th block as an indicator within the system.

Question: How was the information material managed? Was it separate from the date-based system? On first blush it would appear not, nor was there a subject index which would have made it more difficult for one to find data within the system.

Introduction to the Pile of Index Cards method.

These are more similar to zettelkasten and commonplacing traditions. They comprise the majority of the system.

Reviewer #1 (Public Review):

Tiedje et al. investigated the transient impact of indoor residual spraying (IRS) followed by seasonal malaria chemoprevention (SMC) on the plasmodium falciparum parasite population in a high transmission setting. The parasite population was characterized by sequencing the highly variable DBL$\alpha$ tag as a proxy for var genes, a method known as varcoding. Varcoding presents a unique opportunity due to the extraordinary diversity observed as well as the extremely low overlap of repertoires between parasite strains. The authors also present a new Bayesian approach to estimating individual multiplicity of infection (MOI) from the measured DBL$\alpha$ repertoire, addressing some of the potential shortcomings of the approach that have been previously discussed. The authors also present a new epidemiological endpoint, the so-called "census population size", to evaluate the impact of interventions.

This study provides a nice example of how varcoding technology can be leveraged, as well as the importance of using diverse genetic markers for characterizing populations, especially in the context of high transmission. The data are robust and clearly show the transient impact of IRS in a high transmission setting, however, some aspects of the analysis are confusing.

1) Approaching MOI estimation with a Bayesian framework is a well-received addition to the varcoding methodology that helps to address the uncertainty associated with not knowing the true repertoire size. It's unfortunate that while the authors clearly explored the ability to estimate the population MOI distribution, they opted to use only MAP estimates. Embracing the Bayesian methodology fully would have been interesting, as the posterior distribution of population MOI could have been better explored.

2) The "census population size" endpoint has unclear utility. It is defined as the sum of MOI across measured samples, making it sensitive to the total number of samples collected and genotyped. This means that the values are not comparable outside of this study, and are only roughly comparable between strata in the context of prevalence where we understand that approximately the same number of samples were collected. In contrast, mean MOI would be insensitive to differences in sample size, why was this not explored? It's also unclear in what way this is a "census". While the sample size is certainly large, it is nowhere near a complete enumeration of the parasite population in question, as evidenced by the extremely low level of pairwise type sharing in the observed data.

3) The extraordinary diversity of DBL$\alpha$ presents challenges to analyzing the data. The authors explore the variability in repertoire richness and frequency over the course of the study, noting that richness rapidly declined following IRS and later rebounded, while the frequency of rare types increased, and then later declined back to baseline levels. The authors attribute this to fundamental changes in population structure. While there may have been some changes to the population, the observed differences in richness as well as frequency before and after IRS may also be compatible with simply sampling fewer cases, and thus fewer DBL$\alpha$ sequences. The shift back to frequency and richness that is similar to pre-IRS also coincides with a similar total number of samples collected. The authors explore this to some degree with their survival analysis, demonstrating that a substantial number of rare sequences did not persist between timepoints and that rarer sequences had a higher probability of dropping out. This might also be explained by the extreme stochasticity of the highly diverse DBL$\alpha$, especially for rare sequences that are observed only once, rather than any fundamental shifts in the population structure.

Inspired by https://en.wikipedia.org/wiki/Template:Should_be_SVG.

See https://w3c.github.io/csvw/.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript by Heyndrickx et al describes protein crystal formation and function that bears similarity to Charcot-Leyden crystals made of galectin 10, found in humans under similar conditions. Therefore, the authors set out to investigate CLP crystal formation and their immunological effects in the lung. The authors reveal the crystal structure of both Ym1 and Ym2 and show that Ym1 crystals trigger innate immunity, activated dendritic cells in the lymph node, enhancing antigen uptake and migration to the lung, ultimately leading to induction of type 2 immunity.

Strengths:

We know a lot about expression levels of CLPs in various settings in the mouse but still know very little about the functions of these proteins, especially in light of their ability to form crystal structures. As such data presented in this paper is a major advance to the field.

Resolving the crystal structure of Ym2 and the comparison between native and recombinant CLP crystals is a strength of this manuscript that will be a very powerful tool for further evaluation and understanding of receptor, binding partner studies including the ability to aid mutant protein generation.

The ability to recombinantly generate CLP crystals and study their function in vivo and ex vivo has provided a robust dataset whereby CLPs can activate innate immune responses, aid activation and trafficking of antigen presenting cells from the lymph node to the lung and further enhances type 2 immunity. By demonstrating these effects the authors directly address the aims for the study. A key point of this study is the generation of a model in which crystal formation/function an important feature of human eosinophilic diseases, can be studied utilising mouse models. Excitingly, using crystal structures combined with understanding the biochemistry of these proteins will provide a potential avenue whereby inhibitors could be used to dissolve or prevent crystal formation in vivo.

The data presented flows logically and formulates a well constructed overall picture of exactly what CLP crystals could be doing in an inflammatory setting in vivo. This leaves open a clear and exciting future avenue (currently beyond the scope of this work) for determining whether targeting crystal formation in vivo could limit pathology.

Weaknesses:

Although resolving the crystal structure of Ym2 in particular is a strength of the authors work, the weaknesses are that further work or even discussion of Ym2 versus Ym1 has not been directly demonstrated. The authors suggest Ym2 crystals will likely function the same as Ym1, but there is insufficient discussion (or data) beyond sequence similarity as to why this is the case. If Ym1 and Ym2 crystals function the same way, from an evolutionary point, why do mice express two very similar proteins that are expressed under similar conditions that can both crystalise and as the authors suggest act in a similar way. Some discussion around these points would add further value.

We agree with reviewer. We have further elaborated the discussion section including these points, stating clearly that more research needs to be done using Ym2 crystals before we can draw parallels in vivo.

Additionally, the crystal structure for Ym1 has been previously resolved (Tsai et al 2004, PMID 15522777) and it is unclear whether the data from the authors represents an advance in the 3D structure from what is previously known.

The crystal structure of Ym1 has indeed been previously solved, and we refer to that paper. In addition, we also provide the crystal structure of in vitro grown Ym1, ashowing biosimilarity. This, for the field of crystallography is a major finding, since it validates the concept that crystal structures generated in vitro can reflect in vivo grown structures. Moreover, the in vivo crystallization of Ym2 was unknown prior to this work, and is now clear as revealed by the ex vivo X-ray crystallography. The strength of our story is that we can now compare Ym1 and Ym2 crystals structures in detail.

Whilst also generating a model to understand Charcot-Leyden crystals (CLCs), the authors fail to discuss whether crystal shape may be an important feature of crystal function. CLCs are typically needle like, and previous publications have shown using histology and TEM that Ym1 crystals are also needle like. However, the crystals presented in this paper show only formation of plate like structures. It is unclear whether these differences represent different methodologies (ie histology is 2D slides), or differences in CLP crystals that are intracellular versus extracellular. These findings highlight a key question over whether crystal shape could be important for function and has not been addressed by the authors.

In contrast to the bipyramidal, needle-like CLC crystals formed by human galectin-10 protein (hexagonal space group P6522), the in vivo grown Ym1 and Ym2 crystals we were able to isolate for X-ray diffraction experiments had a plate-like morphology with identical crystallographic parameters as recombinant Ym1/Ym2 crystals (space group P21). We note that depending on the viewing orientation of the thin plate-like Ym1 crystals, they may appear needle-like in histology and TEM images. In addition, we can fully not exclude that both Ym1 or Ym2 may crystallize in vivo in different space groups (which could result in different crystal morphologies for Ym1/Ym2) but we have no data to support this. It is finally also a possibility that plate like structures can break up in vivo along a long axis as a result of mechanical forces, and end up as rod-or needle like shapes.

Ym1/Ym2 crystals are often observed in conditions where strong eosinophilic inflammation is present. However, soluble Ym1 delivery in naïve mice shows crystal formation in the absence of a strong immune response. There is no clear discussion as to the conditions in which crystal formation occurs in vivo and how results presented in the paper in terms of priming or exacerbating an immune response align with what is known about situations where Ym1 and Ym2 crystals have been observed.

Although Ym1 and Ym2 crystals are often observed in mice at sites of eosinophilic inflammation, they are not made by eosinophils, but mainly by macrophages and epithelial cells, respectively. In vitro, protein crystallization typically starts from supersaturated solutions that support crystal nucleation. Several factors such as temperature and pH can affect the solubility of Ym1 and Ym2 in vivo and thus affect the nucleation and crystallization process. For Ym1 and Ym2 we noticed in vitro that a small drop in pH facilitates the crystallization process. Although the physiological pH is 7.4, during inflammation, there is a drop in pH. This drop in pH is the result of the infiltration and activation of inflammatory cells in the tissue, which leads to an increased energy and oxygen demand, accelerated glucose consumption via glycolysis and thus increased lactic acid secretion. In addition, we cannot exclude that in vivo, the nucleation process for Ym1/Ym2 is facilitated by interaction with ligands in the extracellular space (e.g. polysaccharide ligands or other – yet to be identified – specific ligands to Ym1/Ym2).

Reviewer #2 (Public Review):

Summary:

This interesting study addresses the ability of Ym1 protein crystals to promote pulmonary type 2 inflammation in vivo, in mice.

Strengths:

The data are extremely high quality, clearly presented, significantly extending previous work from this group on the type 2 immunogenicity of protein crystals.

Weaknesses:

There are no major weaknesses in this study. It would be interesting to see if Ym2 crystals behave similarly to Ym1 crystals in vivo. Some additional text in the Introduction and Discussion would enrich those sections.

We agree that this would be interesting to investigate, however, we choose to not include recombinant Ym2 crystal data in this report. However, we have further elaborated the discussion section including this point.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for improved experiments and to strengthen findings:

I think additional data on the ability of Ym2 crystals to induce an immune response would be advantageous. I'm not by any means suggesting the authors repeat all the experiments with Ym2 crystals, but even just the ability to show that Ym2 could promote type 2 immunity in the acute OVA model, would help to strengthen the argument that these crystals in general function in a similar way. Alternatively, a discussion on whether these protein crystals may function in different scenarios/tissues or conditions could help in light of additional data

We agree that this is an interesting point to investigate, however, we choose to not include recombinant Ym2 crystal data in this report. However, we have further elaborated the discussion section including this point.

Measuring IL-33 in lung tissue is difficult to interpret as cells will express intracellular IL-33 that is not active and may explain why the results in Fig 2D are not overly convincing. It could just be that Ym1 crystals are changing the number of cells expressing IL-33 (e.g macrophages, or type 2 pneumocytes) Did the authors also measure active IL-33 release in the BAL fluid which may give a better indication of Ym1's ability to activate DAMPs?

We also measured active IL-33 release in the BAL fluid, but due to the limited sample availability we could only measure this in one of the two repeat experiments, resulting in non-significant results for the BAL fluid. However, certainly for the 6h timepoint we saw a similar trend in the BAL fluid as in the lung tissue, meaning higher levels of IL-33 in the Ym1 crystal group compared to the PBS and soluble Ym1 group.

Crystals in Fig 2F staining with Ym1 appear to be brighter in the soluble Ym1 group. Is this related to increased packing of Ym1 in the crystals formed in vivo as opposed to those formed in vitro? Aside from reduced amount of crystals that form when you give soluble Ym1, could the type of crystal also be influencing the ability of soluble Ym1 crystals to generate an immune response?

Our X-ray diffraction experiments show that the packing of Ym1 is identical for in vivo and in vitro grown crystals. Possibly the apparent difference in brightness is caused by stochastic staining by the antibody. In this regard we note that the crystals formed from soluble Ym1 after 24h also can appear as less bright in a similar fashion as recombinant Ym1 crystals.

Overall, the data and writing of the manuscript is presented to a very high standard

A few minor points:

• Fig 2F - a little unsure what the number in the left top corner of the images represented.

These numbers represent the picture numbers generated by the software, but as they don’t have any added value for the story, we removed these numbers from the images.

• Not clear why two different expression vectors were used - one for Ym1 and one for Ym2?

Because we observed that recombinant Ym2 is more poorly secreted in the mammalian cell culture supernatant as compared to recombinant Ym1, we produced Ym2 with an N-terminal hexahistidine-tag followed by a Tobacco Etch Virus (TEV)-protease cleavage site to facilitate its purification.

Reviewer #2 (Recommendations For The Authors):

The authors briefly outline in their Introduction potential Sources of Ym1/2 in vivo, highlighting monocytes, M2 macrophages, alveolar macrophages, neutrophils and epithelial cells. Do DCs also make detectable/meaningful amounts of Ym1/2 in vivo, particularly in type 2 settings?

In the introduction we only highlighted the main cellular sources of Ym1 and Ym2, but there is literature available stating/showing that Ym1/2 is not only expressed by macrophages, neutrophils, monocytes and epithelial cells, but can also be induced in DCs and mast cells. We added the word ‘mainly’ to this sentence in the introduction, to make clear that macrophages, neutrophils and monocytes are not the only sources of Ym1.

Given the nicely demonstrated similarity of recombinant Ym1 and Ym2 crystals, I think it is important for the authors to include at least initial data on the outcome of recombinant Ym2 crystal admin to mice, in comparison to their Ym1 data.

We agree that this is an interesting point to investigate, however, we choose to not include recombinant Ym2 crystal data in this report. However, we have further elaborated the discussion section including this point.

Given the generation of crystals following in vivo administration of soluble Ym1, albeit at a lower level than when crystals were administered, it would be interesting to see if increased concentrations of soluble material show a dose dependent increase in lung inflammation readouts.

We agree that this would be an interesting point to investigate. Alongside this we could also titrate down the crystal dose, to see if there is a dose dependent decrease in lung inflammation readouts. However, at this time, we choose to not investigate this further.

I couldn't easily follow the authors' Discussion about potential ability of anti Ym-1/2 Abs to dissolve Ym1/2 crystals (similar to what they have demonstrated for Abs vs Gal10 crystals). Have they addressed this possibility experimentally? If so, addition of such data to the manuscript would be extremely interesting, given the obvious potential Ym1/2 crystal dissolving Abs for investigation of the role of these in a range of different murine models of type 2 inflammation.

We agree that the phrasing of this part of the discussion can be unclear/confusing. We rephrased this part to make it clearer. However, we did not address the possibility of Ym1/2 crystal dissolving antibodies experimentally.

In the Results section, the authors briefly comment on the pro-type 2 nature of Ym1 crystals in relation to their previous work with uric acid and Gal10 crystals, proposing that the pulmonary type 2 response may be a 'generic response to crystals of different chemical composition'. The Discussion would be enriched by deeper exploration of this comment.

We have further elaborated the discussion section including this point.

Author Response

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

We agree with the reviewer that the statistics are buried in a dense excel file without a read-me page. We will address this by making a summary excel page for p-values during the production process.

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

eLife assessment

This important study uses genomically-engineered glypican alleles to demonstrate convincingly that Dally (not Dally-like protein [Dlp]) is the key contributor to formation of the Dpp/BMP morphogen gradient in the wing disc of Drosophila. The authors provide solid genetic evidence that, surprisingly, the core domain of Dally appears to suffice to trap Dpp at the cell surface. They conclude with a model according to which Dally modulates the range of Dpp signaling by interfering with Dpp's internalization by the Dpp receptor Thickveins.

Public Reviews:

Reviewer #1 (Public Review):

How morphogens spread within tissues remains an important question in developmental biology. Here the authors revisit the role of glypicans in the formation of the Dpp gradient in wing imaginal discs of Drosophila. They first use sophisticated genome engineering to demonstrate that the two glypicans of Drosophila are not equivalent despite being redundant for viability. They show that Dally is the relevant glypican for Dpp gradient formation. They then provide genetic evidence that, surprisingly, the core domain of Dally suffices to trap Dpp at the cell surface (suggesting a minor role for GAGs). They conclude with a model that Dally modulates the range of Dpp signaling by interfering with Dpp's degradation by Tkv. These are important conclusions, but more independent (biochemical/cell biological) evidence is needed.

As indicated above, the genetic evidence for the predominant role of Dally in Dpp protein/signalling gradient formation is strong. In passing, the authors could discuss why overexpressed Dlp has a negative effect on signaling, especially in the anterior compartment. The authors then move on to determine the role of GAG (=HS) chains of Dally. They find that in an overexpression assay, Dally lacking GAGs traps Dpp at the cell surface and, counterintuitively, suppresses signaling (fig 4 C, F). Both findings are unexpected and therefore require further validation and clarification, as outlined in a and b below.

a. In loss of function experiments (dallyDeltaHS replacing endogenous dally), Dpp protein is markedly reduced (fig 4R), as much as in the KO (panel Q), suggesting that GAG chains do contribute to trapping Dpp at the cell surface. This is all the more significant that, according to the overexpression essays, DallyDeltaHS seems more stable than WT Dally (by the way, this difference should also be assessed in the knock-ins, which is possible since they are YFP-tagged). The authors acknowledge that HS chains of Dally are critical for Dpp distribution (and signaling) under physiological conditions. If this is true, one can wonder why overexpressed dally core 'binds' Dpp and whether this is a physiologically relevant activity.

According to the overexpression assay, DallyDeltaHS seems more stable than WT Dally (Fig. 4B’, E’, 5A’, B’). As the reviewer suggested, we addressed the difference using the two knock-in alleles and found that DallyDeltaHS is more stable than WT Dally (Fig.4 L, M inset), further emphasizing the insufficient role of core protein of Dally for extracellular Dpp distribution.

In summary, we showed that, although Dally interacts with Dpp mainly through its core protein from the overexpression assay (Fig. 4E, I), HS chains are essential for extracellular Dpp distribution (Fig. 4R). Thus, the core protein of Dally alone is not sufficient for extracellular Dpp distribution under physiological conditions. These results raise a question about whether the interaction of core protein of Dally with Dpp is physiologically relevant. Since the increase of HS upon dally expression but not upon dlp expression resulted in the accumulation of extracellular Dpp (Fig. 2) and this accumulation was mainly through the core protein of Dally (Fig. 4E, I), we speculate that the interaction of the core protein of Dally with Dpp gives ligand specificity to Dally under physiological conditions.

To understand the importance of the interaction of core protein of Dally with Dpp under physiological conditions, it is important to identify a region responsible for the interaction. Our preliminary results overexpressing a dally mutant lacking the majority of core protein (but keeping the HS modified region intact) showed that HS chains modification was also lost. Although this is consistent with our results that enzymes adding HS chains also interact with the core protein of Dally (Fig. 4D), the dally mutant allele lacking the core protein would hamper us from distinguishing the role of core protein of Dally from HS chains.

Nevertheless, we can infer the importance of the interaction of core protein of Dally with Dpp using dally[3xHA-dlp, attP] allele, where dlp is expressed in dally expressing cells. Since Dally-like is modified by HS chains but does not interact with Dpp (Fig. 2, 4), dally[3xHA-dlp, attP] allele mimics a dally allele where HS chains are properly added but interaction of core protein with Dpp is lost. As we showed in Fig.3O, S, the allele could not rescue dallyKO phenotypes, consistent with the idea that interaction of core protein of Dally with Dpp is essential for Dpp distribution and signaling and HS chain alone is not sufficient for Dpp distribution.

b. Although the authors' inference that dallycore (at least if overexpressed) can bind Dpp. This assertion needs independent validation by a biochemical assay, ideally with surface plasmon resonance or similar so that an affinity can be estimated. I understand that this will require a method that is outside the authors' core expertise but there is no reason why they could not approach a collaborator for such a common technique. In vitro binding data is, in my view, essential.

We agree with the reviewer that a biochemical assay such as SPR helps us characterize the interaction of core protein of Dally and Dpp (if the interaction is direct), although the biochemical assay also would not demonstrate the interaction under the physiological conditions.

However, SPR has never been applied in the case of Dpp, probably because purifying functional refolded Dpp dimer from bacteria has previously been found to be stable only in low pH and be precipitated in normal pH buffer (Groppe J, et al., 1998)(Matsuda et al., 2021). As the reviewer suggests, collaborating with experts is an important step in the future.

Nevertheless, SPR was applied for the interaction between BMP4 and Dally (Kirkpatrick et al., 2006), probably because BMP4 is more stable in the normal buffer. Although the binding affinity was not calculated, SPR showed that BMP4 directly binds to Dally and this interaction was only partially inhibited by molar excess of exogenous HS, suggesting that BMP4 can interact with core protein of Dally as well as its HS chains. In addition, the same study applied Co-IP experiments using lysis of S2 cells and showed that Dpp and core protein of Dally are co-immunoprecipitated, although it does not demonstrate if the interaction is direct.

In a subsequent set of experiments, the authors assess the activity of a form of Dpp that is expected not to bind GAGs (DppDeltaN). Overexpression assays show that this protein is trapped by DallyWT but not dallyDeltaHS. This is a good first step validation of the deltaN mutation, although, as before, an invitro binding assay would be preferable.

Our overexpression assays actually showed that DppDeltaN is trapped by DallyWT and by dallyDeltaHS at similar levels (Fig. 5C), indicating that interaction of DppDeltaN and HS chains of Dally is largely lost but DppDeltaN can still interact with core protein of Dally.

We thank the reviewer for the suggesting the in vitro experiment. Although we decided not to develop biophysical experiments such as SPR for Dpp in this study due to the reasons discussed above, we would like to point out that our result is consistent with a previous Co-IP experiment using S2 cells showing that DppDeltaN loses interaction with heparin (Akiyama2008).

However, in contrast to our results, the same study also proposed by Co-IP experiments using S2 cells that DppDeltaN loses interaction with Dally (Akiyama2008). Although it is hard to conclude since western blotting was too saturated without loading controls and normalization (Fig. 1C in Akiyama 2008), and negative in vitro experiments do not necessarily demonstrate the lack of interaction in vivo. One explanation why the interaction was missed in the previous study is that some factors required for the interaction of DppDeltaN with core protein of Dally are missing in S2 cells. In this case, in vivo interaction assay we used in this study has an advantage to robustly detect the interaction.

Nevertheless, the authors show that DppDeltaN is surprisingly active in a knock-in strain. At face value (assuming that DeltaN fully abrogates binding to GAGs), this suggests that interaction of Dpp with the GAG chains of Dally is not required for signaling activity. This leads to authors to suggest (as shown in their final model) that GAG chains could be involved in mediating the interactions of Dally with Tkv (and not with Dpp. This is an interesting idea, which would need to be reconciled with the observation that the distribution of Dpp is affected in dallyDeltaHS knock-ins (item a above). It would also be strengthened by biochemical data (although more technically challenging than the experiments suggested above). In an attempt to determine the role of Dally (GAGs in particular) in the signaling gradient, the paper next addresses its relation to Tkv. They first show that reducing Tkv leads to Dpp accumulation at the cell surface, a clear indication that Tkv normally contributes to the degradation of Dpp. From this they suggest that Tkv could be required for Dpp internalisation although this is not shown directly. The authors then show that a Dpp gradient still forms upon double knockdown (Dally and Tkv). This intriguing observation shows that Dally is not strictly required for the spread of Dpp, an important conclusion that is compatible with early work by Lander suggesting that Dpp spreads by free diffusion. These result show that Dally is required for gradient formation only when Tkv is present. They suggest therefore that Dally prevents Tkv-mediated internalisation of Dpp. Although this is a reasonable inference, internalisation assays (e.g. with anti-Ollas or anti-HA Ab) would strengthen the authors' conclusions especially because they contradict a recent paper from the Gonzalez-Gaitan lab.

Thanks for suggesting the internalization assay. As we discussed in the discussion, our results suggest that extracellular Dpp distribution is severely reduced in dally mutants due to Tkv mediated internalization of Dpp (Fig. 6). Thus, extracellular Dpp available for labelling with nanobody is severely reduced in dally mutants, which can explain the reduced internalization of Dpp in dally mutants in the internalization assay. Therefore, we think that the nanobody internalization assay would not distinguish the two contradicting possibilities.

The paper ends with a model suggesting that HS chains have a dual function of suppressing Tkv internalisation and stimulating signaling. This constitutes a novel view of a glypican's mode of action and possibly an important contribution of this paper. As indicated above, further experiments could considerably strengthen the conclusion. Speculation on how the authors imagine that GAG chains have these activities would also be warranted.

Thank you very much!

Reviewer #2 (Public Review):

The authors are trying to distinguish between four models of the role of glypicans (HSPGs) on the Dpp/BMP gradient in the Drosophila wing, schematized in Fig. 1: (1) "Restricted diffusion" (HSPGs transport Dpp via repetitive interaction of HS chains with Dpp); (2) "Hindered diffusion" (HSPGs hinder Dpp spreading via reversible interaction of HS chains with Dpp); (3) "Stabilization" (HSPGs stabilize Dpp on the cell surface via reversible interaction of HS chains with Dpp that antagonizes Tkv-mediated Dpp internalization); and (4) "Recycling" (HSPGs internalize and recycle Dpp).

To distinguish between these models, the authors generate new alleles for the glypicans Dally and Dally-like protein (Dlp) and for Dpp: a Dally knock-out allele, a Dally YFP-tagged allele, a Dally knock-out allele with 3HA-Dlp, a Dlp knock-out allele, a Dlp allele containing 3-HA tags, and a Dpp lacking the HS-interacting domain. Additionally, they use an OLLAS-tag Dpp (OLLAS being an epitope tag against which extremely high affinity antibodies exist). They examine OLLAS-Dpp or HA-Dpp distribution, phospho-Mad staining, adult wing size.

They find that over-expressed Dally - but not Dlp - expands Dpp distribution in the larval wing disc. They find that the Dally[KO] allele behaves like a Dally strong hypomorph Dally[MH32]. The Dally[KO] - but not the Dlp[KO] - caused reduced pMad in both anterior and posterior domains and reduced adult wing size (particularly in the Anterior-Posterior axis). These defects can be substantially corrected by supplying an endogenously tagged YFP-tagged Dally. By contrast, they were not rescued when a 3xHA Dlp was inserted in the Dally locus. These results support their conclusion that Dpp interacts with Dally but not Dlp.

They next wanted to determine the relative contributions of the Dally core or the HS chains to the Dpp distribution. To test this, they over-expressed UAS-Dally or UAS-Dally[deltaHS] (lacking the HS chains) in the dorsal wing. Dally[deltaHS] over-expression increased the distribution of OLLAS-Dpp but caused a reduction in pMad. Then they write that after they normalize for expression levels, they find that Dally[deltaHS] only mildly reduces pMad and this result indicates a major contribution of the Dally core protein to Dpp stability.

Thanks for the comments. We actually showed that compared with Dally overexpression, Dally[deltaHS] overexpression only mildly reduces extracellular Dpp accumulation (Fig. 4I). This indicates a major contribution of the Dally core protein to interaction with Dpp, although the interaction is not sufficient to sustain extracellular Dpp distribution and signaling gradient.

The "normalization" is a key part of this model and is not mentioned how the normalization was done. When they do the critical experiment, making the Dally[deltaHS] allele, they find that loss of the HS chains is nearly as severe as total loss of Dally (i.e., Dally[KO]). Additionally, experimental approaches are needed here to prove the role of the Dally core.

Since the expression level of Dally[deltaHS] is higher than Dally when overexpressed, we normalized extracellular Dpp distribution (a-Ollas staining) against GFP fluorescent signal (Dally or Dally[deltaHS]). To do this, we first extracted both signal along the A-P axis from the same ROI in the previous version. The ratio was calculated by dividing the intensity of a-Ollas staining with the intensity of GFP fluorescent signal at a given position x. The average profile from each normalized profile was generated and plotted using the script described in the method (wingdisc_comparison.py) as other pMad or extracellular staining profiles.

Although this analysis provides normalized extracellular Dpp accumulation at different positions along the A-P axis, we are more interested in the total amount of Dpp or DppDeltaN accumulation upon Dally or dallyDeltaHS expression. Therefore, in the revised ms, we decided to normalize total amount of extracellular Dpp against the level of Dally or Dally[deltaHS] by dividing total signal intensity of extracellular Dpp staining (ExOllas staining) by total GFP fluorescent signal (Dally or Dally[deltaHS]) around the Dpp producing cells in each wing disc. Statistical analysis showed that accumulation of extracellular Dpp is only slightly reduced without HS chains (Fig.4I), indicating that Dally interacts with Dpp mainly through its core protein.

We agree with the reviewer that additional experimental approaches are needed to address the role of the core protein of Dally. As we discussed in the response to the reviewer1, to understand the importance of the interaction of core protein of Dally with Dpp, it is important to identify a region responsible for the interaction. Our preliminary results overexpressing a dally mutant lacking the majority of core protein (but keeping the HS modified region intact) showed that HS chains modification was also lost. Although this is consistent with our results that enzymes adding HS chains also interact with the core protein of Dally (Fig. 4D), the dally mutant allele lacking the core protein would hamper us from distinguishing the role of the core protein of Dally from HS chains.

Nevertheless, we can infer the importance of the interaction of core protein of Dally with Dpp using dally[3xHA-dlp, attP] allele, where dlp is expressed in dally expressing cells. Since Dally-like is modified by HS chains but does not interact with Dpp (Fig. 2, 4), dally[3xHA-dlp, attP] allele mimics a dally allele where HS chains are properly added but interaction of core protein with Dpp is lost. As we showed in Fig.3O, S, the allele could not rescue dallyKO phenotypes, consistent with the idea that interaction of core protein of Dally with Dpp is essential for Dpp distribution and signaling.

Prior work has shown that a stretch of 7 amino acids in the Dpp N-terminal domain is required to interact with heparin but not with Dpp receptors (Akiyama, 2008). The authors generated an HA-tagged Dpp allele lacking these residues (HA-dpp[deltaN]). It is an embryonic lethal allele, but they can get some animals to survive to larval stages if they also supply a transgene called “JAX” containing dpp regulatory sequences. In the JAX; HA-dpp[deltaN] mutant background, they find that the distribution and signaling of this Dpp molecule is largely normal. While over-expressed Dally can increase the distribution of HA-dpp[deltaN], over-expression of Dally[deltaHS] cannot. These latter results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

Our overexpression assays actually showed that both Dally and Dally[deltaHS] can accumulate Dpp upon overexpression and the accumulation of Dpp is comparable after normalization (Fig. 5C), consistent with the idea that interaction of DppdeltaN and HS chains are largely lost. As the reviewer pointed out, these results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

In the last part of the results, they attempt to determine if the Dpp receptor Thickveins (Tkv) is required for Dally-HS chains interaction. The 2008 (Akiyama) model posits that Tkv activates pMad downstream of Dpp and also internalizes and degrades Dpp. A 2022 (Romanova-Michaelides) model proposes that Dally (not Tkv) internalizes Dpp.

To distinguish between these models, the authors deplete Tkv from the dorsal compartment of the wing disc and found that extracellular Dpp increased and expanded in that domain. These results support the model that Tkv is required to internalize Dpp.

They then tested the model that Dally antagonizes Tkv-mediated Dpp internalization by determining whether the defective extracellular Dpp distribution in Dally[KO] mutants could be rescued by depleting Tkv. Extracellular Dpp did increase in the D vs V compartment, potentially providing some support for their model. However, there are no statistics performed, which is needed for full confidence in the results. The lack of statistics is particularly problematic (1) when they state that extracellular Dpp does not rise in ap>tkv RNAi vs ap>tkv RNAi, dally[KO] wing discs (Fig. 6E) or (2) when they state that extracellular Dpp gradient expanded in the dorsal compartment when tkv was dorsally depleted in dally[deltaHS] mutants (Fig. 6I). These last two experiments are important for their model but the differences are assessed only visually. In fact, extracellular Dpp in ap>tkv RNAi, dally[KO] (Fig. 6B) appears to be lower than extracellular Dpp in ap>tkv RNAi (Fig. 6A) and the histogram of Dpp in ap>tkv RNAi, dally[KO] is actually a bit lower than Dpp in ap>tkv RNAi, But the author claim that there is no difference between the two. Their conclusion would be strengthened by statistical analyses of the two lines.

We provided statistics for all the quantifications for pMad and extracellular Dpp distribution as supplementary data. In the previous version, we argued that extracellular Dpp level in ap>tkvRNAi, dallyKO (Fig.6B) does not increase compared with that in ap>tkvRNAi (Fig.6A). Statistical analysis (t-test) showed that the extracellular Dpp level in Fig. 6B is similar to or lower than that in Fig. 6A (Fig. 6E), confirming our conclusion. Statistical analysis (t-test) also confirmed that extracellular Dpp distribution expanded when tkv was knocked down in dallyHS mutants (Fig. 6I).

Strengths:

1. New genomically-engineered alleles

A considerable strength of the study is the generation and characterization of new Dally, Dlp and Dpp alleles. These reagents will be of great use to the field.

Thanks. We hope that these resources are indeed useful to the field.

1. Surveying multiple phenotypes

The authors survey numerous parameters (Dpp distribution, Dpp signaling (pMad) and adult wing phenotypes) which provides many points of analysis.

Thanks!

Weaknesses:

1. Confusing discussion regarding the Dally core vs HS in Dpp stability. They don't provide any measurements or information on how they "normalize" for the level of Dally vs Dally[deltaHS]? This is important part of their model that currently is not supported by any measurements.

We explained how we normalized in the above section and updated the method section in the revised ms.

1. Lacking quantifications and statistical analyses:

a. Why are statistical significance for histograms (pMad and Dpp distribution) not supplied? These histograms provide the key results supporting the authors' conclusions but no statistical tests/results are presented. This is a pervasive shortcoming in the current study.

Thanks. We provided t-test analyses together with the raw data as supplementary data.

b. dpp[deltaN] with JAX transgene - it would strengthen the study to supply quantitative data on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAK transgene

In this study, we are interested in the role of dpp[deltaN] during the wing disc development. Therefore, we decided not to perform the detailed analysis on the percent survival/lethal stage of dpp[deltaN] mutants with or without the JAX transgene in the current study. Nevertheless, the fact that dpp[deltaN] allele is maintained with a balanced stock and JAX;dpp[deltaN] allele can be maintained as homozygous stock indicates that the lethality of dpp[deltaN] allele comes from the early stages. Indeed, our preliminary results showed that pMad signal is severely lost in the dpp[deltaN] embryo without JAX (data not shown), indicating that the allele is lethal at early embryonic stages.

c. The graphs on wing size etc should start at zero.

Thanks. We corrected this in the current ms.

d. The sizes of histograms and graphs in each figure should be increased so that the reader can properly assess them. Currently, they are very small.

Thanks. We changed the sizes in the current ms.

The authors' model is that Dally (not Dlp) is required for Dpp distribution and signaling but that this is not due to a direct interaction with Dpp. Rather, they posit that Dally-HS antagonize Tkv-mediated Dpp internalization. Currently the results of the experiments could be considered consistent with their model, but as noted above, the lack of statistical analyses of some parameters is a weakness.

Thanks. We now performed and provided the statistical analyses in the revised ms.

One problematic part of their result for me is the role of the Dally core protein (Fig. 7B). There is a mis-match between the over-expression results and Dally allele lacking HS (but containing the core). Finally, their results support the idea that one or more as-yet unidentified proteins interact with Dally-HS chains to control Dpp distribution and signaling in the wing disc.

Our results simply suggest that Dpp can interact with Dally mainly through core protein but this interaction is not sufficient to sustain extracellular Dpp gradient formation under physiological conditions (dallyDeltaHS) (Fig. 4Q). We find that the mis-match is not problematic if the role of Dally is not simply mediated through interaction with Dpp. We speculate that interaction of Dpp and core protein of Dally is transient and not sufficient to sustain the Dpp gradient without HS chains of Dally stabilizing extracellular Dpp distribution by blocking Tkv-mediated Dpp internalization.

There is much debate and controversy in the Dpp morphogen field. The generation of new, high quality alleles in this study will be useful to Drosophila community, and the results of this study support the concept that Tkv but not Dally regulate Dpp internalization. Thus the work could be impactful and fuel new debates among morphogen researchers.

Thanks.

The manuscript is currently written in a manner that really is only accessible to researchers who work on the Dpp gradient. It would be very helpful for the authors to re-write the manuscript and carefully explain in each section of the results (1) the exact question that will be asked, (2) the prior work on the topic, (3) the precise experiment that will be done, and (4) the predicted results. This would make the study more accessible to developmental biologists outside of the morphogen gradient and Drosophila communities.

Thanks. We modified texts and changed the order of Fig.5. We hope that the changes make this study more accessible to developmental biologists outside of the field.

Joint Public Review:

The authors are trying to distinguish between four models of the role of glypicans (HSPGs) on the Dpp/BMP gradient in the Drosophila wing, schematized in Fig. 1: (1) "Restricted diffusion" (HSPGs transport Dpp via repetitive interaction of HS chains with Dpp); (2) "Hindered diffusion" (HSPGs hinder Dpp spreading via reversible interaction of HS chains with Dpp); (3) "Stabilization" (HSPGs stabilize Dpp on the cell surface via reversible interaction of HS chains with Dpp that antagonizes Tkv-mediated Dpp internalization); and (4) "Recycling" (HSPGs internalize and recycle Dpp).

To distinguish between these models, the authors generate new alleles for the glypicans Dally and Dally-like protein (Dlp) and for Dpp: a Dally knock-out allele, a Dally YFP-tagged allele, a Dally knock-out allele with 3HA-Dlp, a Dlp knock-out allele, a Dlp allele containing 3-HA tags, and a Dpp lacking the HS-interacting domain. Additionally, they use an OLLAS-tag Dpp (OLLAS being an epitope tag against which extremely high affinity antibodies exist). They examine OLLAS-Dpp or HA-Dpp distribution, phospho-Mad staining, adult wing size.

They find that over-expressed Dally - but not Dlp - expands Dpp distribution in the larval wing disc. They find that the Dally[KO] allele behaves like a Dally strong hypomorph Dally[MH32]. The Dally[KO] - but not the Dlp[KO] - caused reduced pMad in both anterior and posterior domains and reduced adult wing size (particularly in the Anterior-Posterior axis). These defects can be substantially corrected by supplying an endogenously tagged YFP-tagged Dally. By contrast, they were not rescued when a 3xHA Dlp was inserted in the Dally locus. These results support their conclusion that Dpp interacts with Dally but not Dlp.

They next wanted to determine the relative contributions of the Dally core or the HS chains to the Dpp distribution. To test this, they over-expressed UAS-Dally or UAS-Dally[deltaHS] (lacking the HS chains) in the dorsal wing. Dally[deltaHS] over-expression increased the distribution of OLLAS-Dpp but caused a reduction in pMad. They do a critical experiment, making the Dally[deltaHS] allele, they find that loss of the HS chains is nearly as severe as total loss of Dally (i.e., Dally[KO]). These results indicate that the HS are critical for Dally's role in Dpp distribution and signaling.

Prior work has shown that a stretch of 7 amino acids in the Dpp N-terminal domain is required to interact with heparin but not with Dpp receptors (Akiyama, 2008). The authors generated an HA-tagged Dpp allele lacking these residues (HA-dpp[deltaN]). It is an embryonic lethal allele, but they can get some animals to survive to larval stages if they also supply a transgene called "JAK" containing dpp regulatory sequences. In the JAK; HA-dpp[deltaN] mutant background, they find that the distribution and signaling of this Dpp molecule is largely normal. While over-expressed Dally can increase the distribution of HA-dpp[deltaN], over-expression of Dally[deltaHS] cannot. These latter results support the model that the HS chains in Dally are required for Dpp function but not because of a direct interaction with Dpp.

In the last part of the results, they attempt to determine if the Dpp receptor Thickveins (Tkv) is required for Dally-HS chains interaction. The 2008 (Akiyama) model posits that Tkv activates pMad downstream of Dpp and also internalizes and degrades Dpp. A 2022 (Romanova-Michaelides) model proposes that Dally (not Tkv) internalizes Dpp. To distinguish between these models, the authors deplete Tkv from the dorsal compartment of the wing disc and found that extracellular Dpp increased and expanded in that domain. These results support the model that Tkv is required to internalize Dpp. They then tested the model that Dally antagonizes Tkv-mediated Dpp internalization by determining whether the defective extracellular Dpp distribution in Dally[KO] mutants could be rescued by depleting Tkv. Extracellular Dpp did increase in the D vs V compartment, potentially providing some support for their model. The results are statistically significant but the statistics are buried in an excel file without a read-me page. The code for the statistics is available from Github. These p values should be made more readily accessible and/or intelligible to the reader.

Strengthens:<br /> 1. New genomically-engineered alleles<br /> A considerable strength of the study is the generation and characterization of new Dally, Dlp and Dpp alleles. These reagents will be of great use to the field.

2. Surveying multiple phenotypes<br /> The authors survey numerous parameters (Dpp distribution, Dpp signaling (pMad) and adult wing phenotypes) which provides many points of analysis.

Weaknesses (minor):<br /> 1. The results are statistically significant but the statistics are buried in a dense excel file without a read-me page. The code for the statistics is available from Github. These p values should be made more readily accessible to the reader.

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions.<br /> The authors' model is that Dally (not Dlp) is required for Dpp distribution and signaling but that this is not due to a direct interaction with Dpp. Rather, they posit that Dally-HS antagonize Tkv-mediated Dpp internalization. Currently the results of the experiments could be considered consistent with their model. Finally, their results support the idea that one or more as-yet unidentified proteins interact with Dally-HS chains to control Dpp distribution and signaling in the wing disc.

There is much debate and controversy in the Dpp morphogen field. The generation of new, high quality alleles in this study will be useful to Drosophila community, and the results of this study support the concept that Tkv but not Dally regulate Dpp internalization. Thus the work could be impactful and fuel new debates among the morphogen researchers.

Ryder, has a tag called #inbox inside of Roam. All the stuff that he reads, goes inside of ReadWise, and from ReadWise to the Inbox inside of Roam where he processes that information.

Author Response:

Reviewer #1 (Public Review):

[...] Weaknesses:

1. I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d). It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

The authors would like to clarify that the velocity-nulling gradient is NOT designed to suppress all the contributions from intravascular blood. Instead, we tried to find a balance so that the VN gradient maximally suppressed the macrovascular signal in unspecific veins but minimally attenuated the microvascular signal in specific capillary bed. We acknowledge the reviewer's concern regarding the potential extravascular contributions from large, non-specific vessels. This aspect will be thoroughly evaluated and addressed in the revised manuscript. Additionally, we will make clarifications in other parts that may have cause the reviewer’s misunderstandings.

1. The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions. VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

We acknowledge that the degree of velocity nulling varies across the cortical folding pattern. We intend to discuss potential solutions to address this variance, and these may be implemented in the revised manuscript as appropriate. Furthermore, we will provide a comprehensive discussion on the advantages and disadvantages of both CBV-based and BOLD-based approaches.

1. The comparison with VASO is misleading. The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years. Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation). Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab? Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

Those literatures will be included and discussed in the revised manuscript. Furthermore, we are considering the exclusion of the LGN results presented in Figure 6, as they may divert attention from the primary focus of the study.

We are enthusiastic about sharing our imaging sequence, provided its usefulness is conclusively established. However, it's important to note that without an online reconstruction capability, such as the ICE, the practical utility of the sequence may be limited. Unfortunately, we currently don’t have the manpower to implement the online reconstruction. Nevertheless, we are more than willing to share the offline reconstruction codes upon request.

1. The repeatability of the results is questionable. The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa. The methods are not described in enough detail to reproduce these results. The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination. No data are shared for reproduction of the analysis.

There may have been a misunderstanding regarding the presentation in Figure 4, which illustrates the impact of TEs and the VN gradient. To enhance clarity and avoid further confusion, we will redesign this figure for improved comprehension.

The authors are open to sharing the MATLAB codes associated with our study. However, we were limited by manpower for refining and enhancing the readability of these codes for broader use.

Regarding the coil combination, we utilized an adaptive coil combination approach as described in the paper by Walsh DO, Gmitro AF, and Marcellin MW, titled 'Adaptive reconstruction of phased array MR imagery' (Magnetic Resonance in Medicine 2000; 43:682-690). The MATLAB code for this method was implemented by Dr. Diego Hernando. We will include a link for downloading this code in the revised manuscript for the convenience of interested readers.

1. The application of NODRIC is not validated. Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

During our internal testing, we observed that the NORDIC denoising process did not alter the activation patterns. These findings will be incorporated into the revised manuscript. The details of NORDIC will be provided as well.

Reviewer #2 (Public Review):

[...] The well-known double peak feature in M1 during finger tapping was used as a test-bed to evaluate the spatial specificity. They were indeed able to demonstrate two distinct peaks in group-level laminar profiles extracted from M1 during finger tapping, which was largely free from superficial bias. This is rather intriguing as, even at 7T, clear peaks are usually only seen with spatially specific non-BOLD sequences. This is in line with their simple simulations, which nicely illustrated that, in theory, intravascular macrovascular signals should be suppressible with only minimal suppression of microvasculature when small b-values of the VN gradients are employed. However, the authors do not state how ROIs were defined making the validity of this finding unclear; were they defined from independent criteria or were they selected based on the region mostly expressing the double peak, which would clearly be circular? In any case, results are based on a very small sub-region of M1 in a single slice - it would be useful to see the generalizability of superficial-bias-free BOLD responses across a larger portion of M1.

Given the individual variations in the location of the M1 region, we opted for manual selection of the ROI. In the revised manuscript, we plan to explore and implement an independent criterion for ROI selection to enhance the objectivity and reproducibility of our methodology.

As repeatedly mentioned by the authors, a laminar fMRI setup must demonstrate adequate functional sensitivity to detect (in this case) BOLD responses. The sensitivity evaluation is unfortunately quite weak. It is mainly based on the argument that significant activation was found in a challenging sub-cortical region (LGN). However, it was a single participant, the activation map was not very convincing, and the demonstration of significant activation after considerable voxel-averaging is inadequate evidence to claim sufficient BOLD sensitivity. How well sensitivity is retained in the presence of VN gradients, high acceleration factors, etc., is therefore unclear. The ability of the setup to obtain meaningful functional connectivity results is reassuring, yet, more elaborate comparison with e.g., the conventional BOLD setup (no VN gradients) is warranted, for example by comparison of tSNR, quantification and comparison of CNR, illustration of unmasked-full-slice activation maps to compare noise-levels, comparison of the across-trial variance in each subject, etc. Furthermore, as NORDIC appears to be a cornerstone to enable submillimeter resolution in this setup at 3T, it is critical to evaluate its impact on the data through comparison with non-denoised data, which is currently lacking.

We appreciate the reviewer’s comments. Those issues will be addressed carefully.

Reviewer #3 (Public Review):

[...] Weaknesses: - Although the VASO acquisition is discussed in the introduction section, the VN-sequence seems closer to diffusion-weighted functional MRI. The authors should make it more clear to the reader what the differences are, and how results are expected to differ. Generally, it is not so clear why the introduction is so focused on the VASO acquisition (which, curiously, lacks a reference to Lu et al 2013). There are many more alternatives to BOLD-weighted imaging for fMRI. CBF-weighted ASL and GRASE have been around for a while, ABC and double-SE have been proposed more recently.

The principal distinction between DW-fMRI and our methodology lies in the level of the b-value employed. DW-fMRI typically measures cellular swelling by utilizing a b-value greater than 1000 s/mm^2 (e.g. 1800). Conversely, our Velocity Nulling functional MRI (VN-fMRI) approach continues to assess hemodynamic responses, utilizing a smaller b-value specifically for the suppression of signals from draining veins. In addition, other layer-fMRI methods will be discussed.

• The comparison in Figure 2 for different b-values shows % signal changes. However, as the baseline signal changes dramatically with added diffusion weighting, this is rather uninformative. A plot of t-values against cortical depth would be much more insightful.
• Surprisingly, the %-signal change for a b-value of 0 is not significantly different from 0 in the gray matter. This raises some doubts about the task or ROI definition. A finger-tapping task should reliably engage the primary motor cortex, even at 3T, and even in a single participant.
• The BOLD weighted images in Figure 3 show a very clear double-peak pattern. This contradicts the results in Figure 2 and is unexpected given the existing literature on BOLD responses as a function of cortical depth.

In our study, the TE in Figure 2 is shorter than that in Figure 3 (33 ms versus 43 ms). It has been reported in the literature that BOLD fMRI with a shorter TE tends to include a greater intravascular contribution. Acknowledging this, we plan to repeat the experiments with a controlled TE to ensure consistency in our results.

• Given that data from Figures 2, 3, and 4 are derived from a single participant each, order and attention affects might have dramatically affected the observed patterns. Especially for Figure 4, neither BOLD nor VN profiles are really different from 0, and without statistical values or inter-subject averaging, these cannot be used to draw conclusions from.

The order of the experiments were randomized to ensure unbiased results.

It is important to note that the error bars presented in Figures 2, 3, and 4 do not represent the standard deviation of the residual fitting error. Instead, they illustrate the variation across voxels within a specific layer. This approach may lead to the error bars being influenced by the selection of the Region of Interest (ROI). In light of this, we intend to refine our statistical methodologies in the revised manuscript to address this issue.

• In Figure 5, a phase regression is added to the data presented in Figure 4. However, for a phase regression to work, there has to be a (macrovascular) response to start with. As none of the responses in Figure 4 are significant for the single participant dataset, phase regression should probably not have been undertaken. In this case, the functional 'responses' appear to increase with phase regression, which is contra-intuitive and deserves an explanation.
• Consistency of responses is indeed expected to increase by a removal of the more variable vascular component. However, the microvascular component is always expected to be smaller than the combination of microvascular + macrovascular responses. Note that the use of %signal changes may obscure this effect somewhat because of the modified baseline. Another expected feature of BOLD profiles containing both micro- and microvasculature is the draining towards the cortical surface. In the profiles shown in Figure 7, this is completely absent. In the group data, no significant responses to the task are shown anywhere in the cortical ribbon.
• Although I'd like to applaud the authors for their ambition with the connectivity analysis, I feel that acquisitions that are so SNR starved as to fail to show a significant response to a motor task should not be used for brain wide directed connectivity analysis.

We agree that exploring brain-wide directed functional connectivity may be overly ambitious at this stage, particularly before the VN-fMRI technique has been comprehensively evaluated and validated. In the revised manuscript, we will focus more on examining the characteristics of the layer-dependent BOLD signal rather than delving into layer-dependent functional connectivity.

Reviewer #1 (Public Review):

Summary:

This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T), it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

3T layer-fMRI papers that are not cited:<br /> Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.<br /> The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

1. I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).<br /> It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

2. The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.<br /> VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

3. The comparison with VASO is misleading.<br /> The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.<br /> Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).<br /> Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?<br /> Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

4. The repeatability of the results is questionable.<br /> The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa.<br /> The methods are not described in enough detail to reproduce these results.<br /> The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.<br /> No data are shared for reproduction of the analysis.

5. The application of NODRIC is not validated.<br /> Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

Use your 12 questions as a tag in your project.

superficially and carelessly?

sticking too much to policy/habit instead of addressing the specific needs of individuals? too much eagerness to close / mark as duplicate?

Joint Public Review:

Summary:

Cincotta et al set out to investigate the presence of glucocorticoid receptors in the male and female embryonic germline. They further investigate the impact of tissue specific genetically induced receptor absence and/or systemic receptor activation on fertility and RNA regulation. They are motivated by several lines of research that report inter and transgenerational effects of stress and or glucocorticoid receptor activation and suggest that their findings provide an explanatory mechanism to mechanistically back parental stress hormone exposure induced phenotypes in the offspring.

Strengths:

- A chronological immunofluorescent assessment of GR in fetal and early life oocyte and sperm development.<br /> - RNA seq data that reveal novel cell type specific isoforms validated by q-RT PCR E15.5 in the oocyte.<br /> - 2 alternative approaches to knock out GR to study transcriptional outcomes. Oocytes: systemic GR KO (E17.5) with low input 3-tag seq and germline specific GR KO (E15.5) on fetal oocyte expression via 10X single cell seq and 3-cap sequencing on sorted KO versus WT oocytes - both indicating little impact on polyadenylated RNAs -<br /> - 2 alternative approaches to assess the effect of GR activation in vivo (systemic) and ex vivo (ovary culture): here the RNA seq did show again some changes in germ cells and many in the soma.<br /> - They exclude oocyte specific GR signaling inhibition via beta isoforms<br /> - Perinatal male germline shows differential splicing regulation in response to systemic Dex administration, results were backed up with q-PCR analysis of splicing factors.

Weaknesses:

- Sequencing techniques used are not Total RNA but either are focused on all polyA transcripts (10x) - effects on non-polyA-transcripts are left unexplored.<br /> The number of replicates in the low input seq is very low and hence this might be underpowered. Since Dex treatment showed some (modest) changes in oocyte RNA effects of GR depletion might only become apparent upon Dex treatment as an interaction. Meaning GR activation in the presence of GR shows changes, upon GR depletion those changes are abolished --> statistically speaking an interaction --> conclusion: there are germline GR effects that get abolished when there is no GR hinting on germline GR autonomous effects.<br /> - Effects in oocytes following systemic Dex might be indirect due to GR activation in the soma. The changes observed might be irrelevant to meiosis and thus in the manuscript are deemed irrelevant, but they could still lead to settle consequences. in other terms.

Even though ex vivo culture of ovaries shows GR translocation to nucleus it is not sure whether the in vivo systemic administration does the same. The authors argue in their rebuttal that GR is already nuclear in fetal oocytes hence the<br /> conclusion that fetal oocytes are resistant to GR manipulation is understandable, at least for the readouts that were considered. Yet the question arises: If GR is already nuclear (active) in the absence of additional Dex treatment why does GR knock out not elicit any changes in the considered readouts -> what are we missing.

This work is a good reference point for researchers interested in glucocorticoid hormone signaling fertility and RNA splicing. It might spark further studies on germline-specific GR functions and the impact of GR activation on alternative splicing.<br /> The study provides a characterization of GR and some aspects of GR perturbation, and the negative findings in this study do help to rule out a range of specific roles of GR in the germline. This will help the study of unexplored options. The authors do acknowledge the unexplored options in their discussion.<br /> The intro of the study eludes to implications for intergenerational effects via epigenetic modifications in the germline and points out additional potential indirect effects of reproductive tissue GR signaling on the germline. Future studies might hence focus on further exploration of epigenetic modifications and/or indirect effects.

a semantic tag that contains what is visible to the user

Author Response

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

Reviewer #1 (Public Review):

This work challenges previously published results regarding the presence and abundance of 6mA in the Drosophila genome, as well as the claim that the TET or DMAD enzyme serves as the "eraser" of this DNA methylation mark and its roles in development. This information is needed to clarify these questions in the field. I am less familiar with the biochemical approaches in this work, so my comments are mainly on the genetic analyses. Generally speaking, the methods for fly husbandry and treatment seem to be in accordance with those established in the field.

Response : We thank the reviewer for his/her work and positive assessment of our manuscript.

Reviewer #2 (Public Review):

DNA adenine methylation (6mA) is a rediscovered modification that has been described in a wide range of eukaryotes. However, 6mA presence in eukaryote remains controversial due to the low abundance of its modification in eukaryotic genome. In this manuscript, Boulet et al. re-investigate 6mA presence in drosophila using axenic or conventional fly to avoid contaminants from feeding bacteria. By using these flies, they find that 6mA is rare but present in the drosophila genome by performing LC/MS/MS. They also find that the loss of TET (also known as DMAD) does not impact 6mA levels in drosophila, contrary to previous studies. In addition, the authors find that TET is required for fly development in its enzymatic activity-independent manner.

The strength of this study is, that compared to previous studies of 6mA in drosophila, the authors employed axenic or conventional fly for 6mA analysis. These fly strains make it possible to analyze 6mA presence in drosophila without bacterial contaminant. Therefore, showing data of 6mA abundance in drosophila by performing LC-MS/MS in this manuscript is more convincing as compared with previous studies. Intriguingly, the authors find that the conserved iron-binding motif required for the catalytic activity of TET is dispensable for its function. This finding could be important to reveal TET function in organisms whose genomic 5mC levels are very low.

The manuscript in this paper is well written but some aspects of data analysis and discussion need to be clarified and extended.

1. It is convincing that an increase in 6mA levels is not observed in TETnull presented in Fig1. But it seems 6mA levels are altered in Ax.TET1/2 compared with Ax.TETwt and Ax.TETnull presented in Fig1f (and also WT vs TET1/2 presented in Fig1g). Is it sure that no statistically significant were not observed between Ax.TET1/2 and Ax.TETwt?

2. The representing data of in vitro demethylation assay presented in Fig.3 is convincing, but it is not well discussed and analyzed why these results are contrary to previous reports (Yao et al., 2018 and Zhang et al., 2015).

We thank the reviewer for his/her work and positive assessment of our manuscript.

(1) We repeated our statistical analyses and confirmed that there is no significant difference between wildtype and tet1/2 mutant embryos in axenic conditions (Welch two sample t-test : p=0.075).

(2) We added some elements in the revised manuscript to discuss the possible reasons for the discrepancies with previous reports. Notably both studies performed the in vitro demethylation assays over a much longer time course and with different sources of recombinant proteins. Zhang et al. purified TET catalytic domain from human cells (HEK293T) and observed around 2.5% of 6mA demethylation at 30 min and less than 25% after 10 hours of incubation as measured by HPLC-MS/MS analyses. Yao et al. incubated recombinant TET catalytic domain with 6mA DNA for 3h and observed a 25% decrease in 6mA levels as measured by dot blot. These results suggest that drosophila TET may oxidize 6mA, but with a much lower affinity than 5mC since with observed a near complete oxidation of 5mC after 1 minute and no decrease in 6mA levels after 30 minutes of reaction (for identical concentrations of substrate and enzyme). It is possible too that the preparation of TET catalytic domain in different systems changes its enzymatic activity, potentially in relation with distinct post-translational modifications. Still, as already mentioned in our manuscript, extensive biochemical analyses of the distant TET homolog from the fungus Coprinopsis cinerea (Mu et al., Nature Chem Biol 2022) strongly argue that TET enzymes do not harbor the residues required to serve as 6mA demethylase.

Reviewer #1 (Recommendations For The Authors):

Here are one comment (#1) and a couple of questions (#2-3) that could be addressed in the future, in order to understand the roles of 6mA and TET. Even though #2 and #3 are likely beyond the scope of this paper, #1 should be addressed within the scope of this work and compared with previous reports.

1. The phenotypic analyses in Fig. 4 should use tet_null/Deficiency and tet_CD/Deficiency for their potential phenotypes. This needs to be addressed since both the tet_null and the tet_CD were generated using the same starting fly line (GFP knock-in). Using a deficiency chromosome and testing these alleles in hemizygotes would be helpful to eliminate any secondary effects due to genetic background issues.

Thanks for this comment. Actually, tet_null and tet_CD were not generated using the same starting lines. Whereas tet_cd was generated (by CRISPR) using the tet-GFP knock-in line, tet_null was generated by FRT site recombination between two PBac insertions (Delatte et al. 2016). As for tet1 and tet2 (used in allelic combination in Fig 4 J-L), they correspond to two distinct mutant alleles generated by CRISPR (Zhang et al. 2015). We have clarified this in the M&M (page 9).

1. Regarding the estimated "200 to 400 methylated adenines per haplogenome", is there any insight into where are they located in the genome?

It is an interesting question and we initially used SMRT-seq sequencing to obtain this kind of information. As it turned out that this technique gives a high level of false positive, we should consider with caution the interpretation of these data and we decided not to include them in the manuscript. Still, we characterized the genomic features of the 6mA detected using stringent criteria (mQV>100, cov>25x in the fusion dataset and triplicated across samples of the same genotype). Both in wild type and tet_null, 6mA were dispersed along each chromosome although few of them were found on chromosome X. In both cases there appeared to be a higher accumulation of 6mAs on the histone locus and the transposon-rich tip of chromosome X, but 6mA density remained below 1.3/kb in other genomic regions. Comparisons with annotated genomic regions indicated that 6mA were enriched in long interspersed nuclear elements (LINEs) and satellite repeats, and depleted in 3’UTR and exons, but there was no significant difference in their repartition between the two genetic contexts. Besides, motif analyses showed similar enrichments in both conditions, with GAG triplet accounting for more than one quarter of all the sites. Whether this reflects the specificity of a putative adenine methylase or a technical bias associated the with SMTR-seq technology remains to be established.

1. The TET-GFP and TET-CD-GFP knock-in lines give proper nuclear localization and could be used to identify genomic regions bound with full-length TET and TET-CD using anti-GFP for ChIP-seq or CUT&RUN (or CUT&TAG).

Indeed, this is a line of research that we are following up and will be part of another study. Actually, our ChIP-seq experiments indicate that they bind on the same genomic regions.

Reviewer #2 (Recommendations For The Authors):

• I think the major findings of this paper are showing 6mA present in drosophila by using xenic or conventional breeding conditions and finding that TET function independently of its catalytic activity is essential for fly development. The authors could have been more precise in title and abstract to emphasize these findings.

We have now modified the abstract to try to emphasize these findings.

• The authors claim that any increase of 6mA levels was not observed in both TETnull and TET1/2, but it is not sufficiently convincing. Because it seems 6mA levels were increased in Ax. tet1/2 embryo as compared with in Ax.wt embryo (Fig.1). In this scenario, 6mA abundance in both TETnull and TET1/2 mutant are supposed to be the same. It would be better to re-analyze data carefully and discuss if 6mA levels were significantly increased in TET1/2, and why 6mA levels are different between TETnull and TET1/2. Additionally, the authors describe that the TET null mutant is pupal lethal, while the TET1/2 survivor is available. The text suggests that TET1/2 could have partial functionality on fly development (Fig.4). It would be better to check whether the N-terminus of TET is expressed in the TET1/2 mutant.

Indeed, the increase in 6mA levels in Ax. tet1/2 embryo seems consequent (although it is not statistically significant) and no increase was observed in Ax tet_null embryos. Thus, the putative effect on 6mA levels in tet1/2 embryos may not be directly due to the absence of TET function. We now mention in the revised manuscript (page 6) that “the apparent increase in 6mA levels in tet1/2 axenic embryos was not reproduced in tet_null embryos, suggesting that it does not simply reflect the tet loss of function, and that it was not statistically significant”. Besides, we do not have an antibody to check whether the N-terminus of TET is expressed in the tet1/2 mutants, but the western blot published by Zhang et al 2015 shows that tet2 mutation leads to the expression of TET N-terminal domain. This N-terminal domain could have partial TET functionality and/or interfere with the function of other factors (notably those implicated in 6mA metabolism).

• The authors show that SMRT-seq data did not reveal an increase in 6mA levels in loss of TET (Fig.2). It is convincing that total 6mA abundance was not altered by loss of TET. But were 6mA-accumulated locus/regions observed in WT not altered by loss of TET?

Please refer to our answer to reviewer 1 on that point.

• It remains unclear that the TET proteins the authors prepared do not exhibit 6mA demethylate activity in vitro, contrary to what was reported in previous papers (Fig.3). I think the preparation of recombinant proteins may make different results between this and previous papers. Yao et al., 2018 and Zhang et al., 2015 used recombinant proteins purified from Human cells or insect cells, while the author purified them from E.Coli. Additionally, it's mentioned that VK Rao et al., 2020 demonstrated cdk5-mediated phosphorylation of Tet3 increases its in catalytic activity in vitro. These previous reports suggest modification of TET could change demethylase activity. More analysis and discussion are needed to support the conclusion.

Thanks for your insights. This in an important point and we added the following elements in the revised manuscript to discuss possible reasons for the discrepancies with previous reports (pages 7-8): “Our results contrast with previous reports showing that recombinant drosophila TET demethylates 6mA on dsDNA in vitro (Yao et al. 2018; Zhang et al., 2015a). However, both studies ran much longer reactions (up to 10 hours) and used different sources of recombinant protein (drosophila TET catalytic domain purified from human HEK293T cells). Notably, Zhang et al. (2015a) only found around 2.5% of 6mA demethylation at 30 min and less than 25% after 10 hours of incubation as measured by HPLC-MS/MS analyses. These results suggest that drosophila TET may oxidize 6mA, but with a much lower affinity than 5mC since with observed a near complete oxidation of 5mC after 1 min. and no significant decrease in 6mA levels after 30 min. of reaction (for identical concentrations of substrate and enzyme). It is possible too that the preparation of TET catalytic domain in different systems changes its enzymatic activity, potentially in relation to distinct post-translational modifications.”

Reviewer #1 (Public Review)

This work challenges previously published results regarding the presence and abundance of 6mA in Drosophila genome, as well as the claim that the TET or DMAD enzyme serves as the "eraser" of this DNA methylation mark and its roles in development. This information is needed to clarify these questions in the field. Generally speaking, the methods for fly husbandry and treatment seem to be in accordance with those established ones in the field.

Here are a couple of suggestions that could be discussed with the current work and addressed in the future, in order to better understand the roles of 6mA and TET.

1. Regarding the estimated "200 to 400 methylated adenines per haplogenome", some insights regarding where they are enriched in the genome could inform the potential target sites regulated by 6mA.

2. The TET-GFP and TET-CD-GFP knock-in lines give proper nuclear localization and could be used to identify genomic regions bound with full-length TET and TET-CD using anti-GFP for ChIP-seq or CUT&RUN (or CUT&TAG).

Author Response

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

Reviewer #1

The study provides a complete comparative interactome analysis of α-arrestin in both humans and drosophila. The authors have presented interactomes of six humans and twelve Drosophila α-arrestins using affinity purification/mass spectrometry (AP/MS). The constructed interactomes helped to find α-arrestins binding partners through common protein motifs. The authors have used bioinformatic tools and experimental data in human cells to identify the roles of TXNIP and ARRDC5: TXNIP-HADC2 interaction and ARRDC5-V-type ATPase interaction. The study reveals the PPI network for α-arrestins and examines the functions of α-arrestins in both humans and Drosophila.

Comments

I will like to congratulate the authors and the corresponding authors of this manuscript for bringing together such an elaborate study on α-arrestin and conducting a comparative study in drosophila and humans.

Introduction:

The introduction provides a rationale behind why the comparison between humans and Drosophila is carried out.

• Even though this is a research manuscript, including existing literature on similar comparison of α-arrestin from other articles will invite a wide readership.

Results:

The results cover all the necessary points concluded from the experiments and computational analysis.

1) The authors could point out the similarity of the α-arrestin in both humans and Drosophila. While comparing α-arrestin in both humans and Drosophila If percentage homology between α-arrestin of both Drosophila and humans needs to be calculated.

Thank you for your insightful feedback. As suggested by reviewer, we determined percentage homology of α-arrestin protein sequences from human and Drosophila using Clustal Omega. This homology is now illustrated as a heatmap in revised Figure S5. Please note that only the values with percentage homology of 40% or higher are selectively labeled.

• Citing the direct connecting genes from the network in the text will invite citations and a wider readership.

Figures:

The images are elaborate and well-made.

2) The authors could use a direct connected gene-gene network that pointing interactions. This can be used by other readers working on the same topic and ensure reproducibility and citations.

We appreciate your valuable comment. Based on the reviewer’s suggestion, we have developed a new website in which one can navigate the gene-gene networks of α-arrestins. These direct connected gene-gene networks are housed in the network data exchange (NDEx) project. Additionally, we have included gene ontology and protein class details for α-arrestins’ interactors in these set of networks, offering a more comprehensive view of α-arrestins’ interactomes.

On page 24 lines 15-18, we have revised the manuscript to introduce the newly developed website, as follows.

“Lastly, to assist the research community, we have made comprehensive α-arrestin interactome maps on our website (big.hanyang.ac.kr/alphaArrestin_PPIN). Researchers can search and download their interactomes of interest as well as access information on potential cellular functions and protein class associated with these interactomes.”  

3-1) The co-expression interactions represented as figures should reveal interaction among the α-arrestin and other genes. Which are the sub-network genes does the α- arrestin interact to/ with from the sub-network? The arrows are only pointing at the sub-networks. The figures do not reveal their interaction. Kindly reveal the interaction in the figure with the proper nodes in the figure.

3-2) Figure 2: the network attached in both human and drosophila is well represented. The green lines from α-arrestin indicate the strength of the interaction. Several smaller expression networks are seen. But "α-arrestin" in both organisms seems highly disconnected from all the genes. Connected genes have edges, not arrows. If α-arrestin can be shown connected to these gene-gene networks will help in identifying which genes connect with which gene through α-arrestin. This can be used by other readers working on the same topic and ensure reproducibility and citations.

Thank you for your valuable comment. In response to the reviewer’s recommendation, we’ve added supplementary figure, Figure S4, which illustrates direct interaction between α-arrestin and protein components of clustered complexes (or sub-networks) in addition to the associations shown between α-arrestins and the clustered complexes in Figure 2. We believe that this newly incorporated information regarding direct protein interactions will invite citations and wider readership as the reviewer pointed out.

On page 12 line 27 to page 13 line 5, we have revised the manuscript to cite the direction interactions between ARRDC3 and proteins involved in ubiquitination-dependent proteolysis, as follows.

“While the association of ARRDC3 with these ubiquitination-dependent proteolysis complexes is statistically insignificant, ARRDC3 does interact with individual components of these complexes such as NEDD4, NEDD4L, WWP1, and ITCH (Figure S4A). This suggest their functional relevance in this context, as previously reported in both literatures and databases (Nabhan et al., 2010; Shea et al., 2012; Szklarczyk et al., 2015; Warde-Farley et al., 2010) (Puca & Brou, 2014; Xiao et al., 2018).”

Direct interaction between α-arrestins and protein components of clustered complexes are illustrated in the newly added figure, Figure S4.

4-1) Figure 4. The Protein blot image was blurred. Kindly provide a higher-resolution image.

4-2) Figure 5. B. - The authors can provide images with higher resolution blot images. The bands were not visible.

We appreciate for valuable comment. Unfortunately, the protein blot image was scanned from the original film and the images we provided in the figure represent the highest resolution that we have obtained to date. Raw, uncropped images are shown in Author response image 1 and 2.

Author response image 1.

Raw image of Figure 4B

Author response image 2.

Raw image of Figure 5B

5) Figure: 5. A. - I see non-specific amplifications in the gel images. Are these blotting images? or the gel images that were changed to "Grayscale"? Non-specific amplification may imply that the experiment was not repeated and standardized. Was it gel images or blot images?

We appreciate your insightful comment. The images in Figure 5A represent western blot bands from co-immunoprecipitation assay for analysis of the interaction between TXNIP and HDAC2 proteins. Since immunoblotting using immunoprecipitates can usually detect some non-specific bands from heavy (~ 50 kDa) and light (~25 kDa) chains of the target antibody or from multiple co-immunoprecipitated proteins, we assume that the vague non-specific bands in Figure 5A might be a heavy chain of TXNIP or HDAC2 antibody or an unclear non-specific band. Because target bands showed strong intensity and very clear pattern compared to the non-specific bands in the co-immunoprecipitation assay, we believe that this data is sufficient to support the interaction of TXNIP with HDAC2. Finally, In the revised Figure 5A, we’ve modified the labeling for different experimental conditions, namely siCon and siTXNIP treatments, and added expected size of proteins (kDa), as shown below.

6) Figure 5. A. RT-PCR analysis: What was your expected size of the amplifications? the ladder indicated is in KDa. Is that right?

We appreciate your insightful questions. As mentioned above, Figure 5A shows the blotting images of co-immunoprecipitation analysis, and the ladder indicates the molecular weight (kDa) of protein markers. For clearer interpretation, the expected size of target proteins has been added in Figure 5A in the revised manuscript.

7) How were the band intensities determined?

Thank you for your question. For quantification of immunoblot results, the densities of target protein bands were analyzed with Image J, as we described in the Materials and Methods.

Discussion:

The authors have utilized and discussed the conclusion they draw from their study. But could highlight more on ARRDCs and why it was selected out of the other arrestins. The authors have provided future work directions associated with their work.

8) Why were only ARRDCs presented amongst all the arrestin in the main part of the manuscript?

We’re grateful for your valuable feedback. The reason we focused on α-arrestins was that α-arrestins have been discovered relatively recently, especially when compared to more established visual/ β-arrestin proteins in the same arrestin family but the biological functions of many α-arrestins remain largely unexplored, with notable exceptions in the budding yeast model and a few α-arrestins in mammals and invertebrate species. Most importantly, comparative study highlighting the shared or unique features of α-arrestins is yet to be undertaken. To gain a more comprehensive understanding of these unexplored α-arrestins across multiple species, we’ve centered our research on the ARRDCs within the arrestin protein family.

On page 21 lines 8-17, we’ve edited the manuscript to emphasize the importance of a comparative study on α-arrestins, as detailed below.

“According to a phylogenetic analysis of arrestin family proteins, α-arrestins were shown to be ubiquitously conserved from yeast to human (Alvarez, 2008). However, compared to the more established visual/ β-arrestin proteins, α-arrestins have been discovered more recently and much of their molecular mechanisms and functions remain mostly unexplored except for budding yeast model (Zbieralski & Wawrzycka, 2022). Based on the high-confidence interactomes of α-arrestins from human and Drosophila, we identified conserved and specific functions of these α-arrestins. Furthermore, we uncovered molecular functions of newly discovered function of human specific α-arrestins, TXNIP and ARRDC5. We anticipate that the discovery made here will enhance current understanding of α-arrestins.”

9) The discussion could be elaborated more by utilizing the data.

We appreciate your insightful feedback. Based on the reviewer’s suggestion, we’ve enhanced the discussion in the manuscript to provide a clearer interpretation of our results. First, we’ve added description of conserved protein complexes significantly associated with α-arrestins, stated on page 22 lines 5-12 and lines 23-26.

Page 22 lines 5-12: “The integrative map of protein complexes also highlighted both conserved and unique relationships between α-arrestins and diverse functional protein complexes. For instance, protein complexes involved in ubiquitination-dependent proteolysis, proteasome, RNA splicing, and intracellular transport (motor proteins) were prevalently linked with α-arrestins in both human and Drosophila. To more precisely identify conserved PPIs associated with α-arrestins, we undertook ortholog predictions within the α-arrestins’ interactomes. This revealed 58 orthologous interaction groups that were observed to be conserved between human and Drosophila (Figure 3).”

Page 22 lines 23-26: “Additionally, interaction between α-arrestins and entities like motor proteins, small GTPase, ATP binding proteins, and endosomal trafficking components were identified to be conserved. Further validation of these interactions could unveil molecular mechanisms consistently associated with these cellular functions.”

Secondly, we’ve added description of role of ARRDC5 in osteoclast maturation, as stated on page 23 lines 22-24.

“Conversely, depletion of ARRDC5 reduces osteoclast maturation, underscoring the pivotal role of ARRDC5 in osteoclast development and function (Figure S9A and B).”

Lastly, we examined the association between α-arrestins’ interactomes and human diseases, incorporating our findings into the discussion. The newly introduced figure based on the result is Figure S10.

On page 24 lines 10-14, we’ve added discussion on Figure S10 as follows.

“We further explored association between α-arrestins’ interactomes and disease pathways (Figure S10). Notably, the interactomes of α-arrestins in human showed clear links to specific diseases. For instance, ARRDC5 is closely associated with disease resulting from viral infection and cardiovascular conditions. ARRDC2, ARRDC4, and TXNIP share common association with certain neurodegenerative diseases, while ARRDC1 is implicated in cancer.”

Supplementary figures:

The authors have a rigorous amount of work added together for the success of this manuscript.

10) The reference section needs editing before publication. Maybe the arrangement was disturbed during compiling.

Thank you for your valuable comment. Based on the reviewer’s suggestion, we have rearranged the reference section to enhance its clarity. Below are excerpts from the update reference section in the manuscript.

“Adenuga, D., & Rahman, I. (2010). Protein kinase CK2-mediated phosphorylation of HDAC2 regulates co-repressor formation, deacetylase activity and acetylation of HDAC2 by cigarette smoke and aldehydes. Arch Biochem Biophys, 498(1), 62-73. doi:10.1016/j.abb.2010.04.002

Adenuga, D., Yao, H., March, T. H., Seagrave, J., & Rahman, I. (2009). Histone Deacetylase 2 Is Phosphorylated, Ubiquitinated, and Degraded by Cigarette Smoke. American Journal of Respiratory Cell and Molecular Biology, 40(4), 464-473. doi:10.1165/rcmb.2008-0255OC

Akalin, A., Franke, V., Vlahovicek, K., Mason, C. E., & Schubeler, D. (2015). Genomation: a toolkit to summarize, annotate and visualize genomic intervals. Bioinformatics, 31(7), 1127-1129. doi:10.1093/bioinformatics/btu775

Alvarez, C. E. (2008). On the origins of arrestin and rhodopsin. BMC Evol Biol, 8, 222. doi:10.1186/1471-2148-8-222”

11) many important references were missing.

We appreciate and agree with the reviewer’s comment. In response to the reviewer’s recommendation, we’ve thoroughly reviewed the manuscript and below are sections of the manuscript where around 20 new references have been added.

On page 8 lines 12-14:

“Utilizing the known affinities between short linear motifs in α-arrestins and protein domains in interactomes(El-Gebali et al., 2019; UniProt Consortium, 2018) “

On page 8 lines 19-22:

“One of the most well-known short-linear motifs in α-arrestin is PPxY, which is reported to bind with high affinity to the WW domain found in various proteins, including ubiquitin ligases (Ingham, Gish, & Pawson, 2004; Macias et al., 1996; Sudol, Chen, Bougeret, Einbond, & Bork, 1995)”

On page 9 lines 3-6:

“Next, we conducted enrichment analyses of Pfam proteins domains (El-Gebali et al., 2019; Huang da, Sherman, & Lempicki, 2009b) among interactome of each α-arrestin to investigate known and novel protein domains commonly or specifically associated (Figure S3A; Table S5).”

On page 9 lines 7-10:

“HECT and C2 domains are well known to be embedded in the E3 ubiquitin ligases such as NEDD4, HECW2, and ITCH along with WW domains (Ingham et al., 2004; Melino et al., 2008; Rotin & Kumar, 2009; Scheffner, Nuber, & Huibregtse, 1995; Weber, Polo, & Maspero, 2019)”

On page 10 lines 12-16:

“In fact, the known binding partners, NEDD4, WWP2, WWP1, and ITCH in human and CG42797, Su(dx), Nedd4, Yki, Smurf, and HERC2 in Drosophila, that were detected in our data are related to ubiquitin ligases and protein degradation (C. Chen & Matesic, 2007; Ingham et al., 2004; Y. Kwon et al., 2013; Marin, 2010; Melino et al., 2008; Rotin & Kumar, 2009) (Figure 1E; Figure S2F).”

On page 13 lines 20-21:

“Given that α-arrestins are widely conserved in metazoans (Alvarez, 2008; DeWire, Ahn, Lefkowitz, & Shenoy, 2007), “

On page 14 lines 12-17:

“The most prominent functional modules shared across both species were the ubiquitin-dependent proteolysis, endosomal trafficking, and small GTPase binding modules, which are in agreement with the well-described functions of α-arrestins in membrane receptor degradation through ubiquitination and vesicle trafficking (Dores et al., 2015; S. O. Han et al., 2013; Y. Kwon et al., 2013; Nabhan et al., 2012; Puca & Brou, 2014; Puca et al., 2013; Shea et al., 2012; Xiao et al., 2018; Zbieralski & Wawrzycka, 2022) (Figure 3).”  

Reviewer #2

In this manuscript, the authors present a novel interactome focused on human and fly alpha-arrestin family proteins and demonstrate its application in understanding the functions of these proteins. Initially, the authors employed AP/MS analysis, a popular method for mapping protein-protein interactions (PPIs) by isolating protein complexes. Through rigorous statistical and manual quality control procedures, they established two robust interactomes, consisting of 6 baits and 307 prey proteins for humans, and 12 baits and 467 prey proteins for flies. To gain insights into the gene function, the authors investigated the interactors of alpha-arrestin proteins through various functional analyses, such as gene set enrichment. Furthermore, by comparing the interactors between humans and flies, the authors described both conserved and species-specific functions of the alpha-arrestin proteins. To validate their findings, the authors performed several experimental validations for TXNIP and ARRDC5 using ATAC-seq, siRNA knockdown, and tissue staining assays. The experimental results strongly support the predicted functions of the alpha-arrestin proteins and underscore their importance. `

I would like to suggest the following analyses to further enhance the study:

1) It would be valuable if the authors could present a side-by-side comparison of the interactomes of alpha-arrestin proteins, both before and after this study. This visual summary network would demonstrate the extent to which this work expanded the existing interactome, emphasizing the overall contribution of this study to the investigation of the alpha-arrestin protein family.

We greatly appreciate your insightful feedback. In response to the reviewer’s suggestion, we’ve depicted a network of known PPIs associated with α-arrestins (Figure S2C and D). Furthermore, by comparing our high-confidence PPIs to these known sets, we found that the overlaps are statistically significant and the high-confidence PPIs of α-arrestins broaden the existing interactome (Figure S2E).

From page 7 line 26 to page 8 line 8, we’ve detailed this side-by-side comparisons of existing interactome and newly discovered high-confidence PPIs of α-arrestins, as outline below.

“As a result, we successfully identified many known interaction partners of α-arrestins such as NEDD4, WWP2, WWP1, ITCH and TSG101, previously documented in both literatures and PPI databases (Figure S2C-F) (Colland et al., 2004; Dotimas et al., 2016; Draheim et al., 2010; Mellacheruvu et al., 2013; Nabhan et al., 2012; Nishinaka et al., 2004; Puca & Brou, 2014; Szklarczyk et al., 2015; Warde-Farley et al., 2010; Wu et al., 2013). Additionally, we greatly expanded repertoire of PPIs associated with α-arrestins in human and Drosophila, resulting in 390 PPIs between six α-arrestins and 307 prey proteins in human, and 740 PPIs between twelve α-arrestins and 467 prey proteins in Drosophila (Figure S2E). These are subsequently referred to as ‘high-confidence PPIs’ (Table S3).”

2) While the authors conducted several analyses exploring protein function, there is a need to further explore the implications of the interactome in human diseases. For instance, it would be beneficial to investigate the association of the newly identified interactome members with specific human diseases. Including such investigations would strengthen the link between the interactome and human disease contexts.

Thank you for your valuable comment. As suggested by the reviewer, we examined the association between α-arrestins’ interactomes and human diseases, incorporating our findings into the discussion. The newly introduced figure based on the result is Figure S10.

On page 24 lines 10-14, we’ve added discussion on Figure S10 as follows.

“We further explored association between α-arrestins’ interactomes and disease pathways (Figure S10). Notably, the interactomes of α-arrestins in human showed clear links to specific diseases. For instance, ARRDC5 is closely associated with disease resulting from viral infection and cardiovascular conditions. ARRDC2, ARRDC4, and TXNIP share common association with certain neurodegenerative diseases, while ARRDC1 is implicated in cancer.”

Reviewer #3:

Lee, Kyungtae and colleagues have discovered and mapped out alpha-arrestin interactomes in both human and Drosophila through the affinity purification/mass spectrometry and the SAINTexpress method. They found the high confident interactomes, consisting of 390 protein-protein interactions (PPIs) between six human alpha-arrestins and 307 preproteins, as well as 740 PPIs between twelve Drosophila alpha-arrestins and 467 prey proteins. To define and characterize these identified alpha-arrestin interactomes, the team employed a variety of widely recognized bioinformatics tools. These included protein domain enrichment analysis, PANTHER for protein class enrichment, DAVID for subcellular localization analysis, COMPLEAT for the identification of functional complexes, and DIOPT to identify evolutionary conserved interactomes. Through these analyses, they confirmed known alpha-arrestin interactors' role and associated functions such as ubiquitin ligase and protease. Furthermore, they found unexpected biological functions in the newly discovered interactomes, including RNA splicing and helicase, GTPase-activating proteins, ATP synthase. The authors carried out further study into the role of human TXNIP in transcription and epigenetic regulation, as well as the role of ARRDC5 in osteoclast differentiation. This study holds important value as the newly identified alpha-arrestin interactomes are likely aiding functional studies of this group of proteins. Despite the overall support from data for the paper's conclusions, certain elements related to data quantification, interpretation, and presentation demand more detailed explanation and clarification.

1) In Figure 1B, it is shown that human alpha-arrestins were N-GFP tagged (N-terminal) and Drosophila alpha-arrestins were C-GFP (C-terminal). However, the rationale of why the authors used different tags for human and fly proteins was not explained in the main text and methods.

We appreciate your valuable comment. Both N- and C-terminally tagged α-arrestins have been used previously. Given that our study aims to increase the repertoire of α-arrestin interacting proteins, where GFP is added might not be a concern. We note that GFP is a relatively bulky tag, and tagging a protein with GFP can potentially abolish the interaction with some of the binding proteins. Follow-up studies utilizing different approaches for detecting protein-protein interactions, such as BioID and yeast two-hybrid, will allow us to build more comprehensive α-arrestin interactomes.

2) In Figure 2A, there seems to be an error for labeling the GAL4p/GAL80p complex that includes NOTCH2, NOTCH1 and TSC2.

Thank you for comment. We double-checked COMPLEAT (protein COMPLex Enrichment Analysis Tool) database for the name of protein complex consisting of NOTCH1, NOTCH2, AND TSC2. The database indeed labeled this complex as the “GAL4p/GAL80p complex”. However, given the potential for mis-annotation (since we could not ascertain the relevance of these proteins to the “GAL4p/GAL80p complex”), we chose to exclude this protein complex from the network. The update protein complex network is illustrated in the revised Figure 2A.

3) In Figure 5, given that knockdown of TXNIP did not affect the levels and nuclear localization of HDAC2, the authors suggest that TXNIP might modulate HDAC2 activity. However, the ChiP assay suggest a different model - TXNIP-HDAC2 interaction might inhibit the chromatin occupancy of HDAC2, reducing histone deacetylation and increasing global chromatin accessibly. The authors need to propose a model consistent with these sets of all data.

We greatly appreciate your detailed feedback. Our data indicates a global decrease in chromatin accessibility (Figure 4C-G) and a diminished interaction between TXNIP and HDAC2 under depletion of TXNIP (Figure 5A). Additionally, we observed an increased occupancy of HDAC2 and subsequent histone deacetylation at TXNIP-target promoter regions (Figure 5C) without any changes in the HDAC2 expression level (Figure 5A) in TXNIP- knockdown cells. From these observations, we infer that the interaction between TXNIP-HDAC2 might suppress the function of HDAC2, a major gene silencer affecting the formation of condensed or accessible chromatin by deacetylating activity. Although we checked whether TXNIP could induce cytosolic retention of HDAC2 to inhibit nuclear function of HDAC2, TNXIP knockdown did not alter its subcellular localization (Figure 5B).

To elucidate the mechanism by which TXNIP inhibits the function of HDAC2, we further investigated the effect of TXNIP on the levels of HDAC2 phosphorylation, which is known to be crucial for its deacetylase activity and the formation of transcriptional repressive complex. However, as shown in the Figure S8C and D, the knockdown of TXNIP did not affect the HDAC2 phosphorylation status, as well as the interaction between HDAC2 and other components in NuRD complex in the immunoblotting and co-IP assays, respectively. The results suggest that TXNIP may inhibit the function of HDAC2 independently of these factors.

Following the reviewer’s suggestion, we carefully provided a proposed model describing the possible role of TXNIP in transcriptional regulation through interaction with HDAC2 and co-repressor complex in Figure S8E.

Description of these newly added figures can be found in the revised manuscript from page 18 line 7 to 27, as outlined below.

“HDAC2 typically operates within the mammalian nucleus as part of co-repressor complexes as it lacks ability to bind to DNA directly (Hassig, Fleischer, Billin, Schreiber, & Ayer, 1997). The nucleosome remodeling and deacetylation (NuRD) complex is one of the well-recognized co-repressor complexes that contains HDAC2 (Kelly & Cowley, 2013; Seto & Yoshida, 2014) and we sought to determine if depletion of TXNIP affects interaction between HDAC2 and other components in this NuRD complex. While HDAC2 interacted with MBD3 and MTA1 under normal condition, the interaction between HDAC2 and MBD3 or MTA1 was not affected upon TXNIP depletion (Figure S8C). Next, given that HDAC2 phosphorylation is known to influence its enzymatic activity and stability (Adenuga & Rahman, 2010; Adenuga, Yao, March, Seagrave, & Rahman, 2009; Bahl & Seto, 2021; Tsai & Seto, 2002), we tested if TXNIP depletion alters phosphorylation status of HDAC2. The result indicated, however, that phosphorylation status of HDAC2 does not change upon TXNIP depletion (Figure S8D). In summary, our findings suggest a model where TXNIP plays a role in transcriptional regulation independent of these factors (Figure S8E). When TXNIP is present, it directly interacts with HDAC2, a key component of transcriptional co-repressor complex. This interaction suppresses the HDAC2 ‘s recruitment to target genomic regions, leading to the histone acetylation of target loci possibly through active complex including histone acetyltransferase (HAT). As a result, transcriptional activation of target gene occurs. In contrast, when TXNIP expression is diminished, the interaction between TXNIP and HDAC2 weakens. This restores histone deacetylating activity of HDAC2 in the co-repressor complex, leading to subsequent repression of target gene transcription.”

4) The authors showed that ectopic expression of ARRDC5 increased osteoclast differentiation and function. Does loss of ARDDC5 lead to defects in osteoclast function and fate determination?

We appreciate your valuable comment. We have confirmed the endogenous expression of ARRDC5 in osteoclasts and conducted a loss-of-function study using shARRDC5. As determined by qPCR, ARRDC5 was endogenously expressed very low in osteoclasts. Even during RANKL-induced osteoclast differentiation, the CT value (29-31) for ARRDC5 expression was high in osteoclasts compared to the CT value (17-24) for the expression of marker genes Cathepsin K, TRAP, and NFATc1. Even though its endogenous expression was very low, we generated ARRDC5 knockdown cells by infecting BMMs with lentivirus expressing shRNA of ARRDC5 and subsequently differentiated the cells into mature osteoclasts. After five days of differentiation, we observed a significant decrease in the total number of TRAP-positive multinucleated cells (No. of TRAP+ MNCs) in shARRDC5 cells compared to that in the control cells. This result indicates that the loss of ARRDC5 leads to defects in osteoclast differentiation. Result of this loss-of-function study using shARRDC5 is depicted in Figure S9A and B.

In the revised manuscript, following sentence explaining Figure S9A and B was added on page 19 lines 15-17 as follows.

“Depletion of ARRDC5 using short hairpin RNA (shRNA) impaired osteoclast differentiation, further affirming its crucial role in this differentiation process (Figure S9A and B).”

5) From Figure 6D, the authors argued that ARRDC5 overexpression resulted in more V-ATPase signals: however, there is no quantification. Quantification of the confocal images will foster the conclusion. Also, western blots for V-ATPase proteins will provide an alternative way to determine the effects of ARRDC5.

We appreciate your insightful feedback. As suggested by the reviewer, we quantified V-type ATPase signals using confocal images, which were shown in Figure 6D. The ImageJ program was employed for integrated density measurements, and the integrated density of GFP-GFP overexpressing osteoclasts was set to 1 for relative comparison. The result in the revised Figure 6D revealed a significant increase in V-type ATPase signals in GFP-ARRDC5 overexpressing osteoclasts compared to that in GFP-GFP overexpressing osteoclasts, as outlined below.

We also agree with the reviewer’s comment that Western blot for V-ATPase proteins will be an alternative way to determine the effects of ARRDC5 in osteoclast differentiation. We have confirmed no different expression of V-type ATPase between GFP-GFP and GFP-ARRDC5 overexpressing osteoclasts using qPCR and western blot analysis. The corresponding western blot result is shown in the revised Figure S9C.

In addition, the corresponding qPCR that measures the expression level of V-type ATPase between GFP-GFP and GFP-ARRDC5 overexpressing osteoclasts is shown in Author response image 3.

Author response image 3.

Moreover, based on the references, the V-type ATPase is localized at the plasma membrane during osteoclast differentiation (Toyomura et al., 2003). Although mRNA and protein expression levels were similar in both cells, localization of V-ATPase in plasma membrane was significantly increased in GFP-ARRDC5 overexpressing osteoclasts compared to that in GFP-GFP osteoclasts, as shown in the revised Figure 6D above.

6) The results from Figure 6D did not support the authors' argument that ARRDC5 might control the membrane localization of the V-ATPase, as bafilomycin is the V-ATPase inhibitor. ARRDC5 knockdown experiments will help to determine whether ARRDC5 can control the membrane localization of the V-ATPase in osteoclast.

Thank you for your insightful comment. V-type ATPase has been reported to play an important role in the differentiation and function of osteoclasts (Feng et al., 2009; Qin et al., 2012). Given that various subunits of the V-type ATPase interact with ARRDC5 (Figure 6A), we speculated that ARRDC5 might be involved in the function of this complex and play a role in osteoclast differentiation and function. As answered above, GFP-ARRDC5 overexpressing osteoclasts showed a similar expression level of V-type ATPase to GFP-GFP cells but exhibited increased V-type ATPase signals at the cell membrane compared to those in GFP-GFP cells (Figure 6D). Additionally, co-localization of ARRDC5 and V-type ATPase was observed in the osteoclast membrane (Figure 6D), as predicted by the human ARRDC5-centric PPI network. On the other side, bafilomycin A1, a V-type ATPase inhibitor, not only blocked localization of V-type ATPase to plasma membrane in GFP-ARRDC5 overexpressing osteoclasts, but also reduced ARRDC5 signals (Figure 6D). These results indicate that ARRDC5 plays a role in osteoclast differentiation and function by interacting with V-type ATPase and promoting the localization of V-type ATPase to plasma membrane in osteoclasts.

V-type ATPase present in osteoclast membrane is important to cell fusion, maturation, and function during osteoclast differentiation (Feng et al., 2009; Qin et al., 2012). GFP-ARRDC5 overexpressing osteoclasts showed a significant increase of V-type ATPase signals in the cell membrane compared to GFP-GFP cells (Figure 6D), and also significantly increased cell fusion (No. of TRAP+ MNCs in Figure 6B) and resorption activity (resorption pit formation in Figure 6C). However, ARRDC5 knockdown in osteoclasts (shARRDC5 cells) showed a significant decrease in No. of TRAP+ MNCs compared to that in the control cells, indicating that the loss of ARRDC5 leads to defects in cell fusion during osteoclast differentiation (Figure S9A and B). As described above, the endogenous expression of ARRDC5 was very low in osteoclasts and could be specifically expressed in a certain timepoint during the differentiation. Therefore, to better understand the interaction with V-type ATPase of ARRDC5 in osteoclasts, ARRDC5 overexpression is more suitable than its knockdown.

Part of the manuscript on page 19 line 21 to page 20 line 6 was edited to support our statement, as outlined below.

“The V-type ATPase is localized at the osteoclast plasma membrane (Toyomura et al., 2003) and its localization is important for cell fusion, maturation, and function during osteoclast differentiation (Feng et al., 2009; Qin et al., 2012). Furthermore, its localization is disrupted by bafilomycin A1, which is shown to attenuate the transport of the V-type ATPase to the membrane (Matsumoto & Nakanishi-Matsui, 2019). We analyzed changes in the expression level and localization of V-type ATPase, especially V-type ATPase V1 domain subunit (ATP6V1), in GFP-GFP and GFP-ARRDC5 overexpressing osteoclasts. The level of V-type ATPase expression did not change in osteoclasts regardless of ARRDC5 expression levels (Figure S9C). GFP signals were detected at the cell membrane when GFP-ARRDC5 was overexpressed, indicating that ARRDC5 might also localize to the osteoclast plasma membrane (Figure 6D; Figure S9D). In addition, we detected more V-type ATPase signals at the cell membrane in the GFP-ARRDC5 overexpressing osteoclasts, and ARRDC5 and V-type ATPase were co-localized at the osteoclast membrane (Figure 6D; Figure S9D).”

7) The tables (excel files) do not have proper names for each table S numbers. Please correct the name of excel files for readers.

We appreciate your valuable comments. In response to the reviewer’s suggestion, we’ve renamed excel files to more appropriate titles for easier readability. List of renamed tables (excel files) are shown below.

Table S1. List of α-arrestins from human and Drosophila Table S2. Evaluation sets of α-arrestins PPIs Table S3. Summary tables of SAINTexpress results Table S4. Protein domains and short linear motifs in the α-arrestin interactomes Table S5. Enriched Pfam domains in the α-arrestin interactomes Table S6. Subcellular localizations of α-arrestin interactomes Table S7. Summary of protein complexes and cellular components associated with α-arrestin Table S8. Orthologous relationship of α-arrestin interactomes between human and Drosophila Table S9. Summary of ATAC- and RNA-seq read counts before and after processing Table S10. Differential accessibility of ACRs and gene expression Table S11. Summary of ATAC-seq peaks located in promoters and gene expression level Table S12. List of primer sequences used in this study

8) http://big.hanyang.ac.kr/alphaArrestin_Fly link does not work. Please fix the link.

We appreciate your comment. In response to the reviewer’s comment, we have made comprehensive α-arrestin interactome maps on our new website (big.hanyang.ac.kr/alphaArrestin_PPIN) and confirmed that users can be re-directed to networks housed in NDEx.

Author response image 4.

Screen shot of the first page of the newly developed website.

Website address: big.hanyang.ac.kr/‌‌‌‌‌‍‍‍‌‌alphaArrestin_PPIN

Author response image 5.

Screen shot of the gene-gene network involving α-arrestin in human.

this creates a gap between the class, some people are way above the mark, while others can barely tag along in class

Reviewer #1 (Public Review):

In this study, the authors examined the role of IBTK, a substrate-binding adaptor of the CRL3 ubiquitin ligase complex, in modulating the activity of the eiF4F translation initiation complex. They find that IBTK mediates the non-degradative ubiquitination of eiF4A1, promotes cap-dependent translational initiation, nascent protein synthesis, oncogene expression, and tumor cell growth. Correspondingly, phosphorylation of  IBTK by mTORC1/ S6K1 increases eIF4A1 ubiquitination and sustains oncogenic translation.

Strengths:

This study utilizes multiple biochemical, proteomic, functional, and cell biology assays to substantiate their results.  Importantly, the work nominates IBTK as a unique substrate of mTORC1, and further validates eiF4A1 ( a crucial subunit of the ei44F complex) as a promising therapeutic target in cancer. Since IBTK interacts broadly with multiple members of the translational initial complex - it will be interesting to examine its role in eiF2alpha-mediated ER stress as well as eiF3-mediated translation. Additionally, since IBTK exerts pro-survival effects in multiple cell types, it will be of relevance to characterize the role of IBTK in mediating increased mTORC1 mediated translation in other tumor types, thus potentially impacting their treatment with eiF4F inhibitors.

Limitations/Weaknesses:

The findings are mostly well supported by data, but some areas need clarification and could potentially be enhanced with further experiments:

1) Since eiF4A1 appears to function downstream of IBTK1, can the effects of IBTK1 KO/KD in reducing puromycin incorporation (in Fig 3A),  cap-dependent luciferase reporter activity (Fig 3G), reduced oncogene expression ( Fig 4A) or 2D growth/ invasion assays (Fig 4) be overcome or bypassed by overexpressing eiF4A1? These could potentially be tested in future studies. 

2) The decrease in nascent protein synthesis in puromycin incorporation assays in Figure 3A suggest that the effects of IBTK KO are comparable to and additive with silvesterol. It would be of interest to examine whether silvesterol decreases nascent protein synthesis or increases stress granules in the IBTK KO cells stably expressing IBTK as well. 

3) The data presented in Figure 5 regarding the role of mTORC1 in IBTK-mediated eiF4A1 ubiquitination needs further clarification on several points:

- It is not clear if the experiments in Figure 5F with Phos-tag gels are using the FLAG-IBTK deletion mutant or the peptide containing the mTOR sites as it is mentioned on line 517, page 19 "To do so, we generated an IBTK deletion mutant (900-1150 aa) spanning the potential mTORC1-regulated phosphorylation sites" This needs further clarification.

-It may be of benefit to repeat the Phos tag experiments with full-length FLAG-IBTK and/or endogenous IBTK with molecular weight markers indicating the size of migrated bands.

-Additionally, torin or Lambda phosphatase treatment may be used to confirm the specificity of the band in separate experiments.

-Phos-tag gels with the IBTK CRISPR KO line would also help confirm that the non-phosphorylated band is indeed IBTK. 

-It is unclear why the lower, phosphorylated bands seem to be increasing (rather than decreasing) with AA starvation/ Rapa in Fig 5H.

WAH? Evernote tags cannot be imported into Upnote??? Seriously?

This video contradicts and mentions tags can be imported:

link

後記：根據影片說法，這是新功能，所以電腦玩物當初可能不知道。

Awesome! This is so important. I wonder why Esor in his intro to Upnote said Evernote tags can't be imported to Upnote.

Evernote標籤可匯入Upnote。（牴觸Esor電腦玩物的說法）

Reviewer #1 (Public Review):

This is a review of the manuscript entitled "Pharmacologic hyperstabilisation of the HIV-1 capsid lattice induces capsid failure" by Faysal et al., in this manuscript the authors used an elegant single virion fluorescence assay based on TIRF to measure the stability of mature HIV cores. Virions were biotinylated and captured onto glass coverslips through specific Biotin-Avidin interactions. Immobilized virions were then introduced to the imaging buffer which contained the pore-forming protein DLY, and fluorescently labeled CypA. Mature virions were identified through the binding of CypA which had a red fluorescent tag allowing them to measure the dynamics of GFP trapped within the mature cores as well as the CypA bound outside the core. The authors show that the addition of LEN starting from about 50nM stabilized the mature cores even after cores have ruptured and released their internal GFP. Higher concentration of Len results in ultrastabilization of the cores and rapid rupture leading to the release of GFP at an earlier timepoint. A biochemical assembly assay was performed which showed uM quantities of Len synergized with IP6 to promote CA assembly. Purified mature virions were also treated with 700nM of Len and analyzed by CryoET, this analysis showed an increased representation of irregular cores within the Len-treated sample. Putting all of this together, the authors concluded that Len facilitates core rupture through hyperstabilization of HIV cores, as described in the title.

While I have found this work technically well performed and well explained, I do not believe that the presented data supports the conclusions reached by the authors.

Remove from search engine results

Author Response

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

eLife assessment

This study reports important findings regarding the systemic function of hemocytes controlling whole-body responses to oxidative stress. The evidence in support of the requirement for hemocytes in oxidative stress responses as well as the hemocyte single-nuclei analyses in the presence or absence of oxidative stress are convincing. In contrast, the genetic and physiological analyses that link the non-canonical DDR pathway to upd3/JNK expression and high susceptibility, and the inferences regarding the function of hemocytes in systemic metabolic control are incomplete and would benefit from more rigorous approaches. The work will be of interest to cell and developmental biologists working on animal metabolism, immunity, or stress responses.

We would like to thank the editorial team for these positive comments on our manuscript and the constructive suggestions to improve our manuscript. We are now happy to send you our revised manuscript, which we improved according to the suggestions and valuable comments of the referees.

Public Reviews:

Reviewer #1 (Public Review):

The study examines how hemocytes control whole-body responses to oxidative stress. Using single cell sequencing they identify several transcriptionally distinct populations of hemocytes, including one subset that show altered immune and stress gene expression. They also find that knockdown of DNA Damage Response (DDR) genes in hemocytes increases expression of the immune cytokine, upd3, and that both upd3 overexpression in hemocytes and hemocyte knockdown of DDR genes leads to increased lethality upon oxidative stress.

Strengths

1. The single cell analyses provide a clear description of how oxidative stress can cause distinct transcriptional changes in different populations of hemocytes. These results add to the emerging them in the field that there functionally different subpopulations of hemocytes that can control organismal responses to stress.

2. The discovery that DDR genes are required upon oxidative stress to limit cytokine production and lethality provides interesting new insight into the DDR may play non-canonical roles in controlling organismal responses to stress.

We are grateful to referee 1 to point out the importance and novelty of our snRNA-seq data and our findings on the role of DNA damage-modulated cytokine release by hemocytes during oxidative stress. We further extended these analyses in the revised manuscript by looking deeper into the transcriptomic alterations in fat body cells upon oxidative stress (Figure 4, Figure S4). We further provide additional data to support the connection of DNA damage signaling and regulation of upd3 release from hemocytes (Figure 6F). Here we show that upd3-deficiency can abrogate the increased susceptibility of flies with mei41 and tefu knockdown in hemocytes. In line with this finding, we also show that upd3null mutants show a reduced but not abolished susceptibility to oxidative stress overall (Figure 6F), underlining the role of upd3 as a mediator of oxidative stress response.

Weaknesses

1. In some ways the authors interpretation of the data - as indicated, for example, in the title, summary and model figure - don't quite match their data. From the title and model figure, it seems that the authors suggest that the DDR pathway induces JNK and Upd3 and that the upd3 leads to tissue wasting. However, the data suggest that the DDR actually limits upd3 production and susceptibility to death as suggested by several results:

According to the referee’s suggestion, we revised the manuscript and adjusted our title, abstract and graphical summary to be more precise that DNA damage signaling seem to have a modulatory or regulatory effect on upd3 release. Furthermore, we provide now additional data to support the connection between DNA damage signaling and upd3 release. For example, we added several genetic “rescue” experiments to strengthen the epistasis that modulation of DNA damage signaling and the higher susceptibility of the fly is connected to altered upd3 levels (Figure 6F). We now provide additional data showing that the loss of upd3 rescues the susceptibility to oxidative stress in flies, which are deficient for DDR components in hemocytes.

a. PQ normally doesn't induce upd3 but does lead to glycogen and TAG loss, suggesting that upd3 isn't connected to the PQ-induced wasting.

Even though in our systemic gene expression analysis of upd3 expression, we could not detect a significant induction of upd3 upon PQ feeding. However, we found upd3 expression within our snRNAseq data in a distinct cluster of immune-activated hemocytes (Figure 3B, Cluster 6). Upon knockdown of the DNA damage signaling in hemocytes, the levels then increase to a detectable level in the whole fly. This supports our assumption that upd3 is needed upon oxidative stress to induce energy mobilization from the fat body, but needs to be tightly controlled to balance tissue wasting for energy mobilization. Furthermore, we found evidence in our new analysis of the snRNA-seq data of the fat body cells, that indeed we can find Jak/STAT activation in one cell cluster here, which could speak for an interaction of Cluster 6 hemocytes with cluster 6 fat body cells. A hypothesis we aim to explore in future studies.

b. knockdown of DDR upregulates upd3 and leads to increased PQ-induced death. This would suggest that activation of DDR is normally required to limit, rather than serve as the trigger for upd3 production and death.

Our data support the hypothesis that DDR signaling in hemocytes “modulates” upd3 levels upon oxidative stress. We now carefully revised the text and the graphical summary of the manuscript to emphasize that oxidative stress causes DNA damage, which subsequently induces the DNA damage signaling machinery. If this machinery is not sufficiently induced, for example by knockdown of tefu and mei-41, non-canonical DNA damage signaling is altered which induces JNK signaling and induces release of pro-inflammatory cytokines, including upd3. Whereas DNA damage itself is only slightly increase in the used DDR deficient lines (Figure 5C) and hemocytes do not undergo apoptosis (unaltered cell number on PQ (Figure 5B)), we conclude that loss of tefu, mei-41, or nbs1 causes dysregulation of inflammatory signaling cascades via non-canonical DNA damage signaling. However, oxidative stress itself seems to also induce upd3 release and DNA damage signaling in the same cell cluster, as shown by our snRNA-seq data (Figure 3B). Hence, we think that DNA damage signaling is needed as a rate-limiting step for upd3 release.

c. hemocyte knockdown of either JNK activity or upd3 doesn't affect PQ-induced death, suggesting that they don't contribute to oxidative stress-induced death. It’s only when DDR is impaired (with DDR gene knockdown) that an increase in upd3 is seen (although no experiments addressed whether JNK was activated or involved in this induction of upd3), suggesting that DDR activation prevents upd3 induction upon oxidative stress.

Whereas the double knockdown of upd3 or bsk and DDR genes was resulting in insufficient knockdown efficiencies, we added a rescue experiment where we combined upd3null mutants with knockdown of tefu and mei-41 in hemocytes and found a reduced susceptibility of DDR-deficient flies to oxidative stress.

1. The connections between DDR, JNK and upd3 aren't fully developed. The experiments show that susceptibility to oxidative stress-induced death can be caused by a) knockdown of DDR genes, b) genetic overexpression of upd3, c) genetic activation of JNK. But whether these effects are all related and reflect a linear pathway requires a little more work. For example, one prediction of the proposed model is that the increased susceptibility to oxidative stress-induced death in the hemocyte DDR gene knockdowns would be suppressed (perhaps partially) by simultaneous knockdown of upd3 and/or JNK. These types of epistasis experiments would strengthen the model and the paper.

As mentioned before, we had some technical difficulties combining the knockdown of bsk or upd3 with DDR genes. However, we added a new experiment in which we show that upd3null mutation can rescue the higher susceptibility of hemocytes with tefu and mei41 knockdown.

1. The (potential) connections between DDR/JNK/UPD3 and the oxidative stress effects on depletion of nutrient (lipids and glycogen) stores was also not fully developed. However, it may be the case that, in this paper, the authors just want to speculate that the effects of hemocyte DDR/upd3 manipulation on viability upon oxidative stress involve changes in nutrient stores.

In the revised version of the manuscript, we now provide a more thorough snRNA-seq analysis in the fat body upon PQ treatment to give more insights on the changes in the fat body upon PQ treatment. We added additional histological images of the abdominal fat body on control food and PQ food, to demonstrate the elimination of triglycerides from fat body with Oil-Red-O staining (Figure S1). We also analyzed now hemocyte-deficient (crq-Gal80ts>reaper) flies for their levels of triglycerides and carbohydrates during oxidative stress, to support our hypothesis that hemocytes are key players in the regulation of energy mobilization during oxidative stress. Loss of hemocytes (and therefore also their regulatory input on energy mobilization from the fat body) results in increased triglyceride storage in the fat body during steady state with a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization, which is mostly done in muscle, is not altered in these flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies, which could be due to insufficient energy mobilization from the fat body and subsequently results in a higher susceptibility of these flies on oxidative stress (Figure 1K). Additionally, we aim to point out here that “functional” hemocytes are needed for effective response to oxidative stress, but this response has to be tightly balanced (see also new graphical abstract).

Reviewer #2 (Public Review):

Hersperger et al. investigated the importance of Drosophila immune cells, called hemocytes, in the response to oxidative stress in adult flies. They found that hemocytes are essential in this response, and using state-of-the-art single-cell transcriptomics, they identified expression changes at the level of individual hemocytes. This allowed them to cluster hemocytes into subgroups with different responses, which certainly represents very valuable work. One of the clusters appears to respond directly to oxidative stress and shows a very specific expression response that could be related to the observed systemic metabolic changes and energy mobilization. However, the association of these transcriptional changes in hemocytes with metabolic changes is not well established in this work. Using hemocyte-specific genetic manipulation, the authors convincingly show that the DNA damage response in hemocytes regulates JNK activity and subsequent expression of the JAK/STAT ligand Upd3. Silencing of the DNA damage response or excessive activation of JNK and Upd3 leads to increased susceptibility to oxidative stress. This nicely demonstrates the importance of tight control of JNK-Upd3 signaling in hemocytes during oxidative stress. However, it would have been nice to show here a link to systemic metabolic changes, as the authors conclude that it is tissue wasting caused by excessive Upd3 activation that leads to increased susceptibility, but metabolic changes were not analyzed in the manipulated flies.

We thank the referee for the suggestion to better connect upd3 cytokine levels to energy mobilization from the fat body. We agree that this is an important point to support our hypothesis. First, we added now a detailed analysis of fat body cells in our snRNA-seq data to evaluate the changes induced in the fat body upon oxidative stress. We further added additional metabolic analyses of hemocyte-deficient flies (crq-Gal80ts>reaper) to support our hypothesis that hemocytes are key players in the regulation of energy mobilization during oxidative stress (see also answer to referee 1). Loss of the regulatory role of hemocytes in the energy mobilization and redistribution leads to a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization from muscle, is not affected in hemocyte-deficient flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies compared to controls, which could be due to insufficient energy mobilization from the fat body resulting in a higher susceptibility to oxidative stress (Figure 1K). This data supports our assumption that “functional” hemocytes are needed for effective response to oxidative stress, but this response has to be tightly balanced (see also new graphical summary).

The overall conclusion of this work, as presented by the authors, is that Upd3 expression in hemocytes under oxidative stress leads to tissue wasting, whereas in fact it has been shown that excessive hemocyte-specific Upd3 activation leads to increased susceptibility to oxidative stress (whether due to increased tissue wasting remains a question). The DNA damage response ensures tight control of JNK-Upd3, which is important. However, what role naturally occurring Upd3 expression plays in a single hemocyte cluster during oxidative stress has not been tested. What if the energy mobilization induced by this naturally occurring Upd3 expression during oxidative stress is actually beneficial, as the authors themselves state in the abstract - for potential tissue repair? It would have been useful to clarify in the manuscript that the observed pathological effects are due to overactivation of Upd3 (an important finding), but this does not necessarily mean that the observed expression of Upd3 in one cluster of hemocytes causes the pathology.

We agree with the referee that the pathological effects and increased susceptibility to oxidative stress are mediated by over-activated hemocytes and enhanced cytokine release, including upd3 during oxidative stress. We edited the revised manuscript accordingly to imply a “regulatory” role of upd3, which we suspect and suggest as an important mediator for inter-organ communication between hemocytes and fat body. Whereas our used model for oxidative stress (15mM Paraquat feeding) is a severe insult from which most of the flies will not recover, we could not account and test how upd3 might influence tissue repair after injury, insults and infection. We believe that this is an important factor, we aim to explore in future studies.

Reviewer #3 (Public Review):

In this study, Kierdorf and colleagues investigated the function of hemocytes in oxidative stress response and found that non-canonical DNA damage response (DDR) is critical for controlling JNK activity and the expression of cytokine unpaired3. Hemocyte-mediated expression of upd3 and JNK determines the susceptibility to oxidative stress and systemic energy metabolism required for animal survival, suggesting a new role for hemocytes in the direct mediation of stress response and animal survival.

Strength of the study:

1. This study demonstrates the role of hemocytes in oxidative stress response in adults and provides novel insights into hemocytes in systemic stress response and animal homeostasis.

2. The single-cell transcriptome profiling of adult hemocytes during Paraquat treatment, compared to controls, would be of broad interest to scientists in the field.

We are grateful to these positive comments on our data and are excited that the referee pointed out the importance of our provided snRNA-seq analysis of hemocytes and other cell types during oxidative stress. In the revised, version we now extended this analysis and looked not only into hemocytes but also highlighted induced changes in the fat body (Figure 4).

Weakness of the study:

1. The authors claim that the non-canonical DNA damage response mechanism in hemocytes controls the susceptibility of animals through JNK and upd3 expression. However, the link between DDR-JNK/upd3 in oxidative stress response is incomplete and some of the descriptions do not match their data.

In the revised manuscript, we aimed to strengthen the weaknesses pointed out by the referee. We now included additional genetic crosses to validate the connection of DDR signaling in hemocytes with upd3 release. For example, we added now survival studies where we show that upd3null mutation can rescue the higher susceptibility of flies with tefu and mei41 knockdown in hemocytes during oxidative stress. Furthermore, we added additional data to highlight the importance of hemocytes themselves as essential regulators of susceptibility to oxidative stress. We analyzed the hemocyte-deficient flies (crq-Gal80ts>reaper) for their triglyceride content and carbohydrate levels during oxidative stress (Figure 1 I-L). As outlined above, loss of hemocytes leads to a decreased consumption of these triglycerides on PQ food compared to control flies (Figure 1J). In contrast, glycogen storage and mobilization from muscle, is not affected in hemocyte-deficient flies during oxidative stress (Figure 1L). Interestingly, free glucose levels are drastically reduced in hemocyte-deficient flies, which could be due to insufficient energy mobilization from the fat body resulting in a higher susceptibility to oxidative stress (Figure 1K).

1. The schematic diagram does not accurately represent the authors' findings and requires further modifications.

We carefully revised the text throughout the manuscript describing our results and edited the graphical abstract to display that upd3 levels and hemocytes are essential to balance and modulate response to oxidative stress.

Reviewer #1 (Recommendations For The Authors):

The summary doesn't say too much about what the specific discoveries and results of the study are. The description is limited to just one sentence saying, "Here we describe the responses of hemocytes in adult Drosophila to oxidative stress and the essential role of non-canonical DNA damage repair activity in direct "responder" hemocytes to control JNK-mediated stress signaling, systemic levels of the cytokine upd3 and subsequently susceptibility to oxidative stress" which doesn't provide sufficient explanation of what the results were.

In the revised version of our manuscript, we now provide further information for the reader to outline the findings of our study in a concise way in the summary.

Reviewer #2 (Recommendations For The Authors):

1. To strengthen the conclusion that the DDR response suppresses JNK, and thus Upd3, rescue of DDR by upd3 null mutation would help (knockdown by Hml>upd3IR might not work, RNAi seems problematic).

We would like to thank the referee for this suggestion and included now a genetic experiment where we combined upd3null mutants with hemocyte-specific knockdown of mei-41 and tefu to test their susceptibility to oxidative stress. Our data indeed provide evidence that loss of upd3 rescues the higher susceptibility of flies with hemocyte-specific knockdown for tefu and mei-41 (Figure 6F). Furthermore, we see that upd3null mutants show a diminished susceptibility to oxidative stress compared to control flies (Figure 6F).

1. To link the observed effects to systemic metabolic changes, it would be useful to measure glycogen and triglycerides in these flies as well:

2. crq-Gal80ts>reaper to see what role hemocytes play in the observed metabolic changes.

3. Hml-Upd3 overexpression and Upd3 null mutant (Upd3 RNAi seems to be problematic, we have similar experiences) to see if Upd3 overexpression leads to even more profound changes as suggested, and if Upd3 mutation at least partially suppresses the observed changes.

We agree with the referee that analyzing the connection of hemocyte activation to metabolic changes should be demonstrated in our manuscript to support our claim that hemocytes are important regulators of energy mobilization during oxidative stress. Hence, we analyzed triglycerides and carbohydrate levels in hemocyte-deficient flies (crq-Gal80ts>reaper) during oxidative stress. Indeed, we found substantial differences in energy mobilization in these flies supporting the assumption that the higher susceptibility of hemocyte-deficient flies could be caused by substantial decrease in free glucose and inefficient lysis of triglycerides from the fat body (Figure 1I-K).

1. To test whether the cause of the increased susceptibility to oxidative stress is due to Upd3 overactivation induced by DDR silencing, the authors should attempt to rescue DDR silencing with an Upd3 null mutation.

The suggestion of the reviewer was included in the revised manuscript and as outlined above we now added this data set to our manuscript (Figure 6F). Indeed, we can now provide evidence that upd3null mutation rescues the higher susceptibility of flies with DDR knockdown in hemocytes.

1. Lethality after PQ treatment varies widely (sometimes from 10 to 90%! as in Figure 5D) - is this normal? In some experiments the variability was much lower. In particular, Figure 5D is very problematic and for example the result with upd3 null mutant compared to control is not very convincing. This could be an important result to test whether Upd3, with normal expression likely coming from cluster 6, actually plays a beneficial role, whereas overexpression with Hml leads to pathology.

We agree with the referee that it would be more convincing if the variation cross of survival experiments would be less. However, we included a lot of flies and vials in many individual experiments to test our hypothesis and variation in these survivals was always the case. These effects can be caused by many factors for example the amount of food intake by the flies, genetic background or inserted transgenes. The n-number is quite high across our survivals; so that we are convinced, the seen effects are valid. This reflects also the power of using Drosophila melanogaster as a model organism for such survivals. The high n-number in our data falls into a normal Gauss distribution with a distinct mean susceptibility between the genotypes analyzed.

1. I like the conclusion at the end of the results: line 413: "We show that this oxidative stressmediated immune activation seems to be controlled by non-canonical DNA damage signaling resulting in JNK activation and subsequent upd3 expression, which can render the adult fly more susceptible to oxidative stress when it is over-activated." This is actually a more appropriate conclusion, but in the summary, introduction and discussion along with the overall schematic illustration, this is not actually stated as such, but rather as Upd3 released from cluster 6 causes the pathology. For example: line 435 "Hence, we postulate that hemocyte-derived upd3, most likely released by the activated plasmatocyte cluster C6 during oxidative stress in vivo and subsequently controlling energy mobilization and subsequent tissue wasting upon oxidative stress."

We thank the referee for this suggestion and edited our manuscript and conclusions accordingly.

Reviewer #3 (Recommendations For The Authors):

1. In Figure 2, the authors claim showed that PQ treatment changes the hemocyte clusters in a way that suppresses the conventional Hml+ or Pxn+ hemocytes (cluster1) while expanding hemocyte clusters enriched with metabolic genes such as Lpin, bmm etc. It is not clear whether these cells are comparable to the fat body and if these clusters express any of previously known hemocyte marker genes to claim that these are bona fide hemocytes.

We now included a new analysis of our snRNA-seq data in Figure S4, where we clearly show that all identified hemocyte clusters do not have a fat body signature and are hemocytes, which seem to undergo metabolic adaptations (Figure S4A). Furthermore, we show that the identified fat body cells have a clear fat body signature (Figure S4B) and do not express specific hemocyte markers (Figure S4C).

1. In Figure 4C, the authors showed that comet assays of isolated hemocytes result in a statistically significant increase in DNA damage in DDR-deficient flies before and after PQ treatment. However, the authors conclude that, in lines 324-328, the higher susceptibility of DDR-deficient flies is not due to an increase in DNA damage. To explicitly conclude that "non-canonical" DNA damage response, without any DNA damage, is specifically upregulated during PQ treatment, the authors require further support to exclude the potential activation of canonical DDR.

The referee is correct that we do not provide direct evidence for non-canonical DNA damage signaling. Therefore, we also decided to tune down our statement here a bit and removed that claim from the title. Increase in DNA damage can of course also increase the non-canonical DNA damage signaling pathway, loss of DNA damage signaling genes such as tefu and mei-41 seem to only have minor impacts on the overall amount of DNA damage acquired in hemocytes by oxidative stress. We therefore concluded that the induction in immune activation is most unlikely only caused by increased DNA damage but might be connected to dysregulation in non-canonical DNA damage signaling. Canonical DNA damage signaling leads essentially to DDR, which could be slow in adult hemocytes because they post-mitotic, or to apoptosis, which we could not observe in the analyzed time window in our experiments. Hemocyte number remained stable over the 24h PQ treatment without reduction in cell number (Figure 1H).

1. From Figure 4D-F, the authors showed that loss of DDR in hemocytes induces the expression of unpaired 2 and 3, Socs36E, which represent the JAK/STAT pathway, and thor, InR, Pepck in the InR pathway, and a JNK readout, puc. These results indicate that the DDR pathway normally inhibits the upd-mediated JAK/STAT activation upon PQ treatment, compared to wild-type animals during PQ treatment in Figure 1B-C, which in turn protects the animal during oxidative stress responses. However, the authors claim that "enhanced DNA damage boosts immune activation and therefore susceptibility to oxidative stress (lines 365-366); we show that this oxidative stress-mediated immune activation seems to be controlled by non-canonical DNA damage signaling resulting in JNK activation and subsequent upd3 expression (line 413-416)". These conclusions are not compatible with the authors' data and may require additional data to support or can be modified.

In the revised manuscript, we carefully revised now the text and our statements that it seems that DNA damage signaling in hemocytes has regulatory or modulatory effect on the immune response during oxidative stress. Accordingly, we also adjusted our graphical summary. We agree with the referee and used the term “non-canonical” DNA damage signaling more carefully throughout the manuscript. The slight increase in DNA damage seen after PQ treatment can contribute to immune activation but seems to be not correlative to the induced cytokine levels or the susceptibility of the flies to oxidative stress.

1. In Fig 1I, the authors showed that genetic ablation of hemocytes using UAS-repear induces susceptibility to PQ treatment. It is possible that inducing cell death in hemocytes itself causes the expression of cytokine upd3 or activates the JNK pathway to enhance the basal level of upd3/JNK even without PQ treatment. If this phenotype is solely mediated by the loss of hemocytes, the results should be repeated by reducing the number of hemocytes with alternative genetic backgrounds.

In the different genotypes analyzed across our manuscript we did not detect cell death of hemocytes or a dramatic reduction in hemocytes number (see Figure 1H, Figure 5B, Figure 6C). The higher susceptibility if hemocyte-deficient flies during oxidative stress is most likely caused by the loss of their regulatory role during energy mobilization. We tested triglyceride levels in hemocyte-deficient flies and found a decreased triglyceride consumption (lipolysis), with reduced levels of circulating glucose levels. This findings support our hypothesis that hemocytes are needed to balance the response to oxidative stress. In contrast, the flies with DDR-deficient hemocytes show higher systemic cytokine levels, which most likely enhance energy mobilization from the fat body and therefore result in a higher susceptibility of the fly to oxidative stress. Hence, we claim that hemocytes and their regulation of systemic cytokine levels are important to balance the response to oxidative stress and guarantee the survival of the organism.

1. Lethality of control animals in PQ treatment is variable and it is hard to estimate the effect of animal susceptibility during 15mM PQ feeding. For example, Fig1A shows that control animals exhibit ~10% death during 15mM PQ which is further enhanced by crq-Gal80>reaper expression to 40% (Fig 1I). However, in Fig 5D-E, the basal lethality of wild-type controls already reaches 40~50%, which makes them hard to compare with other genetic manipulations. Related to this, the authors demonstrated that the expression of upd3 in hemocytes is sufficient to aggravate animal survival upon PQ treatment; however, upd3 null mutants do not rescue the lethality, which indicates that upd3 is not required for hampering animal mortality. These data need to be revisited and analyzed.

As outlined above, we find the variability of susceptibility to oxidative stress across all of our experiments. This could be due to different effects such as food intake but also transgene insertion and genetic background. Crq-gal80ts>reaper flies are healthy, but show a shortened life span on normal food (Kierdorf et al., 2020) due to enhanced loss of proteostasis in muscles. We show in the revised manuscript that these flies have a higher susceptibility to oxidative stress and that this effect could be mediated by defects in energy mobilization and redistribution as shown by less triglyceride lysis from the fat body and decreasing levels in free glucose. This would explain the high mortality rate of these flies at 7 days after eclosion. Paraquat treatment (15mM) is a severe inducer of oxidative stress, which results in death of most flies when they are maintained for longer time windows on PQ food. Hence, it is a model, which is not suitable to examine and monitor recovery from this detrimental insult. upd3null mutants were extensively reexamined in this manuscript, and even though we could not see a full protection of these flies from oxidative stress induced death, we found a reduced susceptibility compared to control flies (Figure 6F). Furthermore, when we combined upd3null mutants with flies deficient for tefu and mei-41 in hemocytes, the increased susceptibility to oxidative stress was rescued.

Reviewer #3 (Public Review):

Summary:<br /> This manuscript describes some biochemical experiments on the crucial virulence factor EsxA (ESAT-6) of Mycobacterium tuberculosis. EsxA is secreted via the ESX-1 secretion system. Although this system is recognized to be crucial for virulence the actual mechanisms employed by the ESX-1 substrates are still mostly unknown. The EsxA substrate is attracting the most attention as the central player in virulence, especially phagosomal membrane disruption. EsxA is secreted as a dimer together with EsxB. The authors show that EsxA is also able to form homodimers and even tetramers, albeit at very low pH (below 5). Furthermore, the addition of a nanobody that specifically binds EsxA blocks intracellular survival, as well as if the nanobody is produced in the cytosol of the infected macrophages.

Strengths:<br /> -Decent biochemical characterization of EsxA and identification of a new and interesting tool to study the function of EsxA (nanobody).

-The manuscript is well-written.

Weaknesses:<br /> The findings are not critically evaluated using extra experiments or controls.

For instance, tetrameric EsxA in itself is interesting and could reveal how EsxA works. But one would say that this is a starting point to make small point mutations that specifically affect tetramer formation and then evaluate what the effect is on phagosomal membrane lysis. Also one would like to see experiments to indicate whether these structures can be produced under in vitro conditions, especially because it seems that this mainly happens when the pH is lower than 5, which is not normally happening in phagosomes that are loaded with M. tuberculosis.

Also, the fact that the addition of the nanobody, either directly to the bacteria or produced in the cytosol of macrophages is interesting, but again it is the starting point for further experimentation. As a control, one would like to see the effect on an Esx-1 secretion mutant. Furthermore, does cytosolic production or direct addition of the nanobody affect phagosomal escape? What happens if an EsxA mutant is produced that does not bind the nanobody?

Finally, it is a bit strange that the authors use a non-native version of esxA that has not only an additional His-tag but also an additional 12 amino acids, which makes the protein in total almost 20% bigger. Of course, these additions do not have to alter the characteristics, but they might. On the other hand, they easily discard the natural acetylation of EsxA by mycobacteria itself (proven for M. marinum) as not relevant for the function because it might not happen in (the close homologue) M. tuberculosis.

reply to u/ethanzanemiller at https://www.reddit.com/r/Zettelkasten/comments/185xmuh/comment/kb778dy/?utm_source=reddit&utm_medium=web2x&context=3

Some of your methodology will certainly depend on what questions you're asking, how well you know your area already, and where you'd like to go. If you're taking notes as part of learning a new area, they'll be different and you'll treat them differently than notes you're collecting on ideas you're actively building on or intriguing facts you're slowly accumulating. Often you'll have specific questions in mind and you'll do a literature review to see what's happing around that area and then read and take notes as a means of moving yourself closer to answering your particular questions.

Take for example, the frequently asked questions (both here in this forum and by note takers across history): how big is an idea? what is an atomic note? or even something related to the question of how small can a fact be? If this is a topic you're interested in addressing, you'll make note of it as you encounter it in various settings and see that various authors use different words to describe these ideas. Over time, you'll be able to tag them with various phrases and terminologies like "atomic notes", "one idea per card", "note size", or "note lengths". I didn't originally set out to answer these questions specifically, but my interest in the related topics across intellectual history allowed such a question to emerge from my work and my notes.

Once you've got a reasonable collection, you can then begin analyzing what various authors say about the topic. Bring them all to "terms" to ensure that they're talking about the same things and then consider what arguments they're making about the topic and write up your own ideas about what is happening to answer those questions you had. Perhaps a new thesis emerges about the idea? Some have called this process having a conversation with the texts and their authors or as Robert Hutchins called it participating in "The Great Conversation".

Almost anyone in the forum here could expound on what an "atomic note" is for a few minutes, but they're likely to barely scratch the surface beyond their own definition. Based on the notes linked above, I've probably got enough of a collection on the idea of the length of a note that I can explore it better than any other ten people here could. My notes would allow me a lot of leverage and power to create some significant subtlety and nuance on this topic. (And it helps that they're all shared publicly so you can see what I mean a bit more clearly; most peoples' notes are private/hidden, so seeing examples are scant and difficult at best.)

Some of the overall process of having and maintaining a zettelkasten for creating material is hard to physically "see". This is some of the benefit of Victor Margolin's video example of how he wrote his book on the history of design. He includes just enough that one can picture what's happening despite his not showing the deep specifics. I wrote a short piece about how I used my notes about delving into S.D. Goitein's work to write a short article a while back and looking at the article, the footnotes, and links to my original notes may be illustrative for some: https://boffosocko.com/2023/01/14/a-note-about-my-article-on-goitein-with-respect-to-zettelkasten-output-processes/. The exercise is a tedious one (though not as tedious as it was to create and hyperlink everything), but spend some time to click on each link to see the original notes and compare them with the final text. Some of the additional benefit of reading it all is that Goitein also had a zettelkasten which he used in his research and in leaving copies of it behind other researchers still actively use his translations and notes to continue on the conversation he started about the contents of the Cairo Geniza. Seeing some of his example, comparing his own notes/cards and his writings may be additionally illustrative as well, though take care as many of his notes are in multiple languages.

Another potentially useful example is this video interview with Kathleen Coleman from the Thesaurus Linguae Latinae. It's in the realm of historical linguistics and lexicography, but she describes researchers collecting masses of data (from texts, inscriptions, coins, graffiti, etc.) on cards which they can then study and arrange to write their own articles about Latin words and their use across time/history. It's an incredibly simple looking example because they're creating a "dictionary", but the work involved was painstaking historical work to be sure.

Again, when you're done, remember to go back and practice for yourself. Read. Ask questions of the texts and sources you're working with. Write them down. Allow your zettelkasten to become a ratchet for your ideas. New ideas and questions will emerge. Write them down! Follow up on them. Hunt down the answers. Make notes on others' attempts to answer similar questions. Then analyze, compare, and contrast them all to see what you might have to say on the topics. Rinse and repeat.

As a further and final (meta) example, some of my answer to your questions has been based on my own experience, but the majority of it is easy to pull up, because I can pose your questions not to my experience, but to my own zettelkasten and then quickly search and pull up a variety of examples I've collected over time. Of course I have far more experience with my own zettelkasten, so it's easier and quicker for me to query it than for you, but you'll build this facility with your own over time.

Good luck. 🗃️

Reviewer #1 (Public Review):

Summary:

This manuscript by Warfvinge et al. reports the results of CITE-seq to generate single-cell multi-omics maps from BM CD34+ and CD34+CD38- cells from nine CML patients at diagnosis. Patients were retrospectively stratified by molecular response after 12 months of TKI therapy using European Leukemia Net (ELN) recommendations. They demonstrate heterogeneity of stem and progenitor cell composition at diagnosis, and show that compared to optimal responders, patients with treatment failure after 12 months of therapy demonstrate increased frequency of molecularly defined primitive cells at diagnosis. These results were validated by deconvolution of an independent previously published dataset of bulk transcriptomes from 59 CML patients. They further applied a BCR-ABL-associated gene signature to classify primitive Lin-CD34+CD38- stem cells as BCR:ABL+ and BCR:ABL-. They identified variability in the ratio of leukemic to non-leukemic primitive cells between patients, showed differences in the expression of cell surface markers, and determined that a combination of CD26 and CD35 cell surface markers could be used to prospectively isolate the two populations. The relative proportion of CD26-CD35+ (BCR:ABL-) primitive stem cells was higher in optimal responders compared to treatment failures, both at diagnosis and following 3 months of TKI therapy.

Strengths:

The studies are carefully conducted and the results are very clearly presented. The data generated will be a valuable resource for further studies. The strengths of this study are the application of single-cell multi-omics using CITE-Seq to study individual variations in stem and progenitor clusters at diagnosis that are associated with good versus poor outcomes in response to TKI treatment. These results were confirmed by deconvolution of a historical bulk RNAseq data set. Moreover, they are also consistent with a recent report from Krishnan et al. and are a useful confirmation of those results. The major new contribution of this study is the use of gene expression profiles to distinguish BCR-ABL+ and BCR-ABL- populations within CML primitive stem cell clusters and then applying antibody-derived tag (ADT) data to define molecularly identified BCR:ABL+ and BCR-ABL- primitive cells by expression of surface markers. This approach allowed them to show an association between the ratio of BCR-ABL+ vs BCR-ABL- primitive cells and TKI response and study dynamic changes in these populations following short-term TKI treatment.

Weaknesses:

One of the limitations of the study is the small number of samples employed, which is insufficient to make associations with outcomes with confidence. Although the authors discuss the potential heterogeneity of primitive stem, they do not directly address the heterogeneity of hematopoietic potential or response to TKI treatment in the results presented. Another limitation is that the BCR-ABL + versus BCR-ABL- status of cells was not confirmed by direct sequencing for BCR-ABL. The BCR-ABL status of cells sorted based on CD26 and CD35 was evaluated in only two samples. We also note that the surface markers identified were previously reported by the same authors using different single-cell approaches, which limits the novelty of the findings. It will be important to determine whether the GEP and surface markers identified here are able to distinguish BCR-ABL+ and BCR-ABL- primitive stem cells later in the course of TKI treatment. Finally, although the authors do describe differential gene expression between CML and normal, BCR:ABL+ and BCR:ABL-, primitive stem cells they have not as yet taken the opportunity to use these findings to address questions regarding biological mechanisms related to CML LSC that impact on TKI response and outcomes.

reply to u/atomicnotes at https://www.reddit.com/r/Zettelkasten/comments/1843k2w/comment/kaypbk2/?utm_source=reddit&utm_medium=web2x&context=3

I'm reasonably certain that most of the transmission of the traditions was specifically from person to person rather than from text to person. Yours is an interesting and important (and rare oral) example of person to person zettelkasten transmission, of which I've been collecting some scant examples. (Other examples appreciated, inquire within.)

Interestingly a lot of this transmission is still happening every day (though now more "visibly" online) in fora like Reddit, zettelkasten.de, Discord, in social media, and even smaller group courses. As Annie Murphy Paul indicates in The Extended Mind, people like to imitate rather than innovate. Perhaps Luhmann, being on his own outside of the establishment, was more likely to innovate because he was on his own and took Heyde's advice, but evolved it to his needs rather than asking questions on Reddit?

Reviewer #1 (Public Review):

Summary:<br /> The study by Klug et al. investigated the pathway specificity of corticostriatal projections, focusing on two cortical regions. Using a G-deleted rabies system in D1-Cre and A2a-Cre mice to retrogradely deliver channelrhodopsin to cortical inputs, the authors found that M1 and MCC inputs to direct and indirect pathway spiny projection neurons (SPNs) are both partially segregated and asymmetrically overlapping. In general, corticostriatal inputs that target indirect pathway SPNs are likely to also target direct pathway SPNs, while inputs targeting direct pathway SPNs are less likely to also target indirect pathway SPNs. Such asymmetric overlap of corticostriatal inputs has important implications for how the cortex itself may determine striatal output. Indeed, the authors provide behavioral evidence that optogenetic activation of M1 or MCC cortical neurons that send axons to either direct or indirect pathway SPNs can have opposite effects on locomotion and different effects on action sequence execution. The conclusions of this study add to our understanding of how cortical activity may influence striatal output and offer important new clues about basal ganglia function.

The conceptual conclusions of the manuscript are supported by the data, but the details of the magnitude of afferent overlap and causal role of asymmetric corticostriatal inputs on behavioral outcomes were not yet fully resolved.

After virally labeling either direct pathway (D1) or indirect pathway (D2) SPNs to optogenetically tag pathway-specific cortical inputs, the authors report that a much larger number of "non-starter" D2-SPNs from D2-SPN labeled mice responded to optogenetic stimulation in slices than "non-starter" D1 SPNs from D1-SPN labeled mice did. Without knowing the relative number of D1 or D2 SPN starters used to label cortical inputs, it is difficult to interpret the exact meaning of the lower number of responsive D2-SPNs in D1 labeled mice (where only ~63% of D1-SPNs themselves respond) compared to the relatively higher number of responsive D1-SPNs (and D2-SPNs) in D2 labeled mice. While relative differences in connectivity certainly suggest that some amount of asymmetric overlap of inputs exists, differences in infection efficiency and ensuing differences in detection sensitivity in slice experiments make determining the degree of asymmetry problematic.

It is also unclear if retrograde labeling of D1-SPN- vs D2-SPN- targeting afferents labels the same densities of cortical neurons. This gets to the point of specificity in the behavioral experiments. If the target-based labeling strategies used to introduce channelrhodopsin into specific SPN afferents label significantly different numbers of cortical neurons, might the difference in the relative numbers of optogenetically activated cortical neurons itself lead to behavioral differences?

In general, the manuscript would also benefit from more clarity about the statistical comparisons that were made and sample sizes used to reach their conclusions.

Author Response

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

eLife assessment

This important paper exploits new cryo-EM tomography tools to examine the state of chromatin in situ. The experimental work is meticulously performed and convincing, with a vast amount of data collected. The main findings are interpreted by the authors to suggest that the majority of yeast nucleosomes lack a stable octameric conformation. Despite the possibly controversial nature of this report, it is our hope that such work will spark thought-provoking debate, and further the development of exciting new tools that can interrogate native chromatin shape and associated function in vivo.

We thank the Editors and Reviewers for their thoughtful and helpful comments. We also appreciate the extraordinary amount of effort needed to assess both the lengthy manuscript and the previous reviews. Below, we provide our point-by-point response in bold blue font. Nearly all comments have been addressed in the revised manuscript. For a subset of comments that would require us to speculate, we have taken a conservative approach because we either lack key information or technical expertise: Instead of adding the speculative replies to the main text, we think it is better to leave them in the rebuttal for posterity. Readers will thereby have access to our speculation and know that we did not feel confident enough to include these thoughts in the Version of Record.

Reviewer #1 (Public Review):

This manuscript by Tan et al is using cryo-electron tomography to investigate the structure of yeast nucleosomes both ex vivo (nuclear lysates) and in situ (lamellae and cryosections). The sheer number of experiments and results are astounding and comparable with an entire PhD thesis. However, as is always the case, it is hard to prove that something is not there. In this case, canonical nucleosomes. In their path to find the nucleosomes, the authors also stumble over new insights into nucleosome arrangement that indicates that the positions of the histones is more flexible than previously believed.

Please note that canonical nucleosomes are there in wild-type cells in situ, albeit rarer than what’s expected based on our HeLa cell analysis and especially the total number of yeast nucleosomes (canonical plus non-canonical). The negative result (absence of any canonical nucleosome classes in situ) was found in the histone-GFP mutants.

Major strengths and weaknesses:

Personally, I am not ready to agree with their conclusion that heterogenous non-canonical nucleosomes predominate in yeast cells, but this reviewer is not an expert in the field of nucleosomes and can't judge how well these results fit into previous results in the field. As a technological expert though, I think the authors have done everything possible to test that hypothesis with today's available methods. One can debate whether it is necessary to have 35 supplementary figures, but after working through them all, I see that the nature of the argument needs all that support, precisely because it is so hard to show what is not there. The massive amount of work that has gone into this manuscript and the state-of-the art nature of the technology should be warmly commended. I also think the authors have done a really great job with including all their results to the benefit of the scientific community. Yet, I am left with some questions and comments:

Could the nucleosomes change into other shapes that were predetermined in situ? Could the authors expand on if there was a structure or two that was more common than the others of the classes they found? Or would this not have been found because of the template matching and later reference particle used?

Our best guess (speculation) is that one of the class averages that is smaller than the canonical nucleosome contains one or more non-canonical nucleosome classes. However, we do not feel confident enough to single out any of these classes precisely because we do not yet know if they arise from one non-canonical nucleosome structure or from multiple – and therefore mis-classified – non-canonical nucleosome structures (potentially with other non-nucleosome complexes mixed in). We feel it is better to leave this discussion out of the manuscript, or risk sending the community on wild goose chases.

Our template-matching workflow uses a low-enough cross-correlation threshold that any nucleosome-sized particle (plus minus a few nanometers) would be picked, which is why the number of hits is so large. So unless the noncanonical nucleosomes quadrupled in size or lost most of their histones, they should be grouped with one or more of the other 99 class averages (WT cells) or any of the 100 class averages (cells with GFP-tagged histones). As to whether the later reference particle could have prevented us from detecting one of the non-canonical nucleosome structures, we are unable to tell because we’d really have to know what an in situ non-canonical nucleosome looks like first.

Could it simply be that the yeast nucleoplasm is differently structured than that of HeLa cells and it was harder to find nucleosomes by template matching in these cells? The authors argue against crowding in the discussion, but maybe it is just a nucleoplasm texture that side-tracks the programs?

Presumably, the nucleoplasmic “side-tracking” texture would come from some molecules in the yeast nucleus. These molecules would be too small to visualize as discrete particles in the tomographic slices, but they would contribute textures that can be “seen” by the programs – in particular RELION, which does the discrimination between structural states. We are not sure what types of density textures would side-track RELION’s classification routines.

The title of the paper is not well reflected in the main figures. The title of Figure 2 says "Canonical nucleosomes are rare in wild-type cells", but that is not shown/quantified in that figure. Rare is comparison to what? I suggest adding a comparative view from the HeLa cells, like the text does in lines 195-199. A measure of nucleosomes detected per volume nucleoplasm would also facilitate a comparison.

Figure 2’s title is indeed unclear and does not align with the paper’s title and key conclusion. The rarity here is relative to the expected number of nucleosomes (canonical plus non-canonical). We have changed the title to:

“Canonical nucleosomes are a minority of the expected total in wild-type cells”.

We would prefer to leave the reference to HeLa cells to the main text instead of as a figure panel because the comparison is not straightforward for a graphical presentation. Instead, we now report the total number of nucleosomes estimated for this particular yeast tomogram (~7,600) versus the number of canonical nucleosomes classified (297; 594 if we assume we missed half of them). This information is in the revised figure legend:

“In this tomogram, we estimate there are ~7,600 nucleosomes (see Methods on how the calculation is done), of which 297 are canonical structures. Accounting for the missing disc views, we estimate there are ~594 canonical nucleosomes in this cryolamella (< 8% the expected number of nucleosomes).”

If the cell contains mostly non-canonical nucleosomes, are they really non-canonical? Maybe a change of language is required once this is somewhat sure (say, after line 303).

This is an interesting semantic and philosophical point. From the yeast cell’s “perspective”, the canonical nucleosome structure would be the form that is in the majority. That being said, we do not know if there is one structure that is the majority. From the chromatin field’s point of view, the canonical nucleosome is the form that is most commonly seen in all the historical – and most contemporary – literature, namely something that resembles the crystal structure of Luger et al, 1997. Given these two lines of thinking, we added the following clarification as lines 312 – 316:

“At present, we do not know what the non-canonical nucleosome structures are, meaning that we cannot even determine if one non-canonical structure is the majority. Until we know the non-canonical nucleosomes’ structures, we will use the term non-canonical to describe all the nucleosomes that do not have the canonical (crystal) structure.”

The authors could explain more why they sometimes use conventional the 2D followed by 3D classification approach and sometimes "direct 3-D classification". Why, for example, do they do 2D followed by 3D in Figure S5A? This Figure could be considered a regular figure since it shows the main message of the paper.

Since the classification of subtomograms in situ is still a work in progress, we felt it would be better to show one instance of 2-D classification for lysates and one for lamellae. While it is true that we could have presented direct 3-D classification for the entire paper, we anticipate that readers will be interested to see what the in situ 2-D class averages look like.

The main message is that there are canonical nucleosomes in situ (at least in wild-type cells), but they are a minority. Therefore, the conventional classification for Figure S5A should not be a main figure because it does not show any canonical nucleosome class averages in situ.

Figure 1: Why is there a gap in the middle of the nucleosome in panel B? The authors write that this is a higher resolution structure (18Å), but in the even higher resolution crystallography structure (3Å resolution), there is no gap in the middle.

There is a lower concentration of amino acids at the middle in the disc view; unfortunately, the space-filling model in Figure 1A hides this feature. The gap exists in experimental cryo-EM density maps. See Author response image 1 for an example (pubmed.ncbi.nlm.nih.gov/29626188). The size of the gap depends on the contour level and probably the contrast mechanism, as the gap is less visible in the VPP subtomogram averages. To clarify this confusing phenomenon, we added the following lines to the figure legend:

“The gap in the disc view of the nuclear-lysate-based average is due to the lower concentration of amino acids there, which is not visible in panel A due to space-filling rendering. This gap’s visibility may also depend on the contrast mechanism because it is not visible in the VPP averages.”

Author response image 1.

Reviewer #2 (Public Review):

Nucleosome structures inside cells remain unclear. Tan et al. tackled this problem using cryo-ET and 3-D classification analysis of yeast cells. The authors found that the fraction of canonical nucleosomes in the cell could be less than 10% of total nucleosomes. The finding is consistent with the unstable property of yeast nucleosomes and the high proportion of the actively transcribed yeast genome. The authors made an important point in understanding chromatin structure in situ. Overall, the paper is well-written and informative to the chromatin/chromosome field.

We thank Reviewer 2 for their positive assessment.

Reviewer #3 (Public Review):

Several labs in the 1970s published fundamental work revealing that almost all eukaryotes organize their DNA into repeating units called nucleosomes, which form the chromatin fiber. Decades of elegant biochemical and structural work indicated a primarily octameric organization of the nucleosome with 2 copies of each histone H2A, H2B, H3 and H4, wrapping 147bp of DNA in a left handed toroid, to which linker histone would bind.

This was true for most species studied (except, yeast lack linker histone) and was recapitulated in stunning detail by in vitro reconstitutions by salt dialysis or chaperone-mediated assembly of nucleosomes. Thus, these landmark studies set the stage for an exploding number of papers on the topic of chromatin in the past 45 years.

An emerging counterpoint to the prevailing idea of static particles is that nucleosomes are much more dynamic and can undergo spontaneous transformation. Such dynamics could arise from intrinsic instability due to DNA structural deformation, specific histone variants or their mutations, post-translational histone modifications which weaken the main contacts, protein partners, and predominantly, from active processes like ATP-dependent chromatin remodeling, transcription, repair and replication.

This paper is important because it tests this idea whole-scale, applying novel cryo-EM tomography tools to examine the state of chromatin in yeast lysates or cryo-sections. The experimental work is meticulously performed, with vast amount of data collected. The main findings are interpreted by the authors to suggest that majority of yeast nucleosomes lack a stable octameric conformation. The findings are not surprising in that alternative conformations of nucleosomes might exist in vivo, but rather in the sheer scale of such particles reported, relative to the traditional form expected from decades of biochemical, biophysical and structural data. Thus, it is likely that this work will be perceived as controversial. Nonetheless, we believe these kinds of tools represent an important advance for in situ analysis of chromatin. We also think the field should have the opportunity to carefully evaluate the data and assess whether the claims are supported, or consider what additional experiments could be done to further test the conceptual claims made. It is our hope that such work will spark thought-provoking debate in a collegial fashion, and lead to the development of exciting new tools which can interrogate native chromatin shape in vivo. Most importantly, it will be critical to assess biological implications associated with more dynamic - or static forms- of nucleosomes, the associated chromatin fiber, and its three-dimensional organization, for nuclear or mitotic function.

Thank you for putting our work in the context of the field’s trajectory. We hope our EMPIAR entry, which includes all the raw data used in this paper, will be useful for the community. As more labs (hopefully) upload their raw data and as image-processing continues to advance, the field will be able to revisit the question of non-canonical nucleosomes in budding yeast and other organisms. 

Reviewer #1 (Recommendations For The Authors):

The manuscript sometimes reads like a part of a series rather than a stand-alone paper. Be sure to spell out what needs to be known from previous work to read this article. The introduction is very EM-technique focused but could do with more nucleosome information.

We have added a new paragraph that discusses the sources of structural variability to better prepare readers, as lines 50 – 59:

“In the context of chromatin, nucleosomes are not discrete particles because sequential nucleosomes are connected by short stretches of linker DNA. Variation in linker DNA structure is a source of chromatin conformational heterogeneity (Collepardo-Guevara and Schlick, 2014). Recent cryo-EM studies show that nucleosomes can deviate from the canonical form in vitro, primarily in the structure of DNA near the entry/exit site (Bilokapic et al., 2018; Fukushima et al., 2022; Sato et al., 2021; Zhou et al., 2021). In addition to DNA structural variability, nucleosomes in vitro have small changes in histone conformations (Bilokapic et al., 2018). Larger-scale variations of DNA and histone structure are not compatible with high-resolution analysis and may have been missed in single-particle cryo-EM studies.”

Line 165-6 "did not reveal a nucleosome class average in..". Add "canonical", since it otherwise suggests there were no nucleosomes.

Thank you for catching this error. Corrected.

Lines 177-182: Why are the disc views missed by the classification analysis? They should be there in the sample, as you say.

We suspect that RELION 3 is misclassifying the disc-view canonical nucleosomes into the other classes. The RELION developers suspect that view-dependent misclassification arises from RELION 3’s 3-D CTF model. RELION 4 is reported to be less biased by the particles’ views. We have started testing RELION 4 but do not have anything concrete to report yet.

Line 222: a GFP tag.

Fixed.

Line 382: "Note that the percentage .." I can't follow this sentence. Why would you need to know how many chromosome's worth of nucleosomes you are looking at to say the percentage of non-canonical nucleosomes?

Thank you for noticing this confusing wording. The sentence has been both simplified and clarified as follows in lines 396 – 398:

“Note that the percentage of canonical nucleosomes in lysates cannot be accurately estimated because we cannot determine how many nucleosomes in total are in each field of view.”

Line 397: "We're not implying that..." Please add a sentence clearly stating what you DO mean with mobility for H2A/H2B.

We have added the following clarifying sentence in lines 412 – 413:

“We mean that H2A-H2B is attached to the rest of the nucleosome and can have small differences in orientation.”

Line 428: repeated message from line 424. "in this figure, the blurring implies.."

Redundant phrase removed.

Line 439: "on a HeLa cell" - a single cell in the whole study?

Yes, that study was done on a single cell.

A general comment is that the authors could help the reader more by developing the figures and making them more pedagogical, a list of suggestions can be found below.

Thank you for the suggestions. We have applied all of them to the specific figure callouts and to the other figures that could use similar clarification.

Figure 2: Help the reader by avoiding abbreviations in the figure legend. VPP tomographic slice - spell out "Volta Phase Plate". Same with the term "remapped" (panel B) what does that mean?

We spelled out Volta phase plate in full and explained “remapped” the additional figure legend text:

“the class averages were oriented and positioned in the locations of their contributing subtomograms”.

Supplementary figures:

Figure S3: It is unclear what you mean with "two types of BY4741 nucleosomes". You then say that the canonical nucleosomes are shaded blue. So what color is then the non-canonical? All the greys? Some of them look just like random stuff, not nucleosomes.

“Two types” is a typo and has been removed and “nucleosomes” has been replaced with “candidate nucleosome template-matching hits” to accurately reflect the particles used in classification.

Figure S6: Top left says "3 tomograms (defocus)". I wonder if you meant to add the defocus range here. I have understood it like this is the same data as shown in Figure S5, which makes me wonder if this top cartoon should not be on top of that figure too (or exclusively there).

To make Figures S6 (and S5) clearer, we have copied the top cartoon from Figure S6 to S5.

Note that we corrected a typo for these figures (and the Table S7): the number of template-matched candidate nucleosomes should be 93,204, not 62,428.

The description in the parentheses (defocus) is shorthand for defocus phase contrast and was not intended to also display a defocus range. All of the revised figure legends now report the meaning of both this shorthand and of the Volta phase plate (VPP).

To help readers see the relationship between these two figures, we added the following clarifying text to the Figure S5 and S6 legends, respectively:

“This workflow uses the same template-matched candidate nucleosomes as in Figure S6; see below.”

“This workflow uses the same template-matched candidate nucleosomes as in Figure S5.”

Figure S7: In the first panel, it is unclear why the featureless cylinder is shown as it is not used as a reference here. Rather, it could be put throughout where it was used and then put the simulated EM-map alone here. If left in, it should be stated in the legend that it was not used here.

It would indeed be much clearer to show the featureless cylinder in all the other figures and leave the simulated nucleosome in this control figure. All figures are now updated. The figure legend was also updated as follows:

“(A) A simulated EM map from a crystal structure of the nucleosome was used as the template-matching and 3-D classification reference.”

Figure S18: Why are there classes where the GFP density is missing? Mention something about this in the figure legend.

We have appended the following speculations to explain the “missing” GFP densities:

“Some of the class averages are “missing” one or both expected GFP densities. The possible explanations include mobility of a subpopulation of GFPs or H2A-GFPs, incorrectly folded GFPs, or substitution of H2A for the variant histone H2A.Z.”

Reviewer #2 (Recommendations For The Authors):

My specific (rather minor) comments are the following:

1) Abstract:

yeast -> budding yeast.

All three instances in the abstract have been replaced with “budding yeast”.

It would be better to clarify what ex vivo means here.

We have appended “(in nuclear lysates)” to explain the meaning of ex vivo.

2) Some subtitles are unclear.

e.g., "in wild-type lysates" -> "wild-type yeast lysates"

Thank you for this suggestion. All unclear instances of subtitles and sample descriptions throughout the text have been corrected.

3) Page 6, Line 113. "...which detects more canonical nucleosomes." A similar thing was already mentioned in the same paragraph and seems redundant.

Thank you for noticing this redundant statement, which is now deleted.

4) Page 25, Line 525. "However, crowding is an unlikely explanation..." Please note that many macromolecules (proteins, RNAs, polysaccharides, etc.) were lost during the nuclei isolation process.

This is a good point. We have rewritten this paragraph to separate the discussion on technical versus biological effects of crowding, in lines 538 – 546:

“Another hypothesis for the low numbers of detected canonical nucleosomes is that the nucleoplasm is too crowded, making the image processing infeasible. However, crowding is an unlikely technical limitation because we were able to detect canonical nucleosome class averages in our most-crowded nuclear lysates, which are so crowded that most nucleosomes are butted against others (Figures S15 and S16). Crowding may instead have biological contributions to the different subtomogram-analysis outcomes in cell nuclei and nuclear lysates. For example, the crowding from other nuclear constituents (proteins, RNAs, polysaccharides, etc.) may contribute to in situ nucleosome structure, but is lost during nucleus isolation.”

5) Page 7, Line 126. "The subtomogram average..." Is there any explanation for this?

Presumably, the longer linker DNA length corresponds to the ordered portion of the ~22 bp linker between consecutive nucleosomes, given the ~168 bp nucleosome repeat length. We have appended the following explanation as the concluding sentence, lines 137 – 140:

“Because the nucleosome-repeat length of budding yeast chromatin is ~168 bp (Brogaard et al., 2012), this extra length of DNA may come from an ordered portion of the ~22 bp linker between adjacent nucleosomes.”

6) "Histone GFP-tagging strategy" subsection:

Since this subsection is a bit off the mainstream of the paper, it can be shortened and merged into the next one.

We have merged the “Histone GFP-tagging strategy” and “GFP is detectable on nucleosome subtomogram averages ex vivo” subsections and shortened the text as much as possible. The new subsection is entitled “Histone GFP-tagging and visualization ex vivo”

7) Page 16, Line 329. "Because all attempts to make H3- or H4-GFP "sole source" strains failed..." Is there a possible explanation here? Cytotoxic effect because of steric hindrance of nucleosomes?

Yes, it is possible that the GFP tag is interfering with the nucleosomes interactions with its numerous partners. It is also possible that the histone-GFP fusions do not import and/or assemble efficiently enough to support a bare-minimum number of functional nucleosomes. Given that the phenotypic consequences of fusion tags is an underexplored topic and that we don’t have any data on the (dead) transformants, we would prefer to leave out the speculation about the cause of death in the attempted creation of “sole source” strains.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Trebino et al. investigated the BRAF activation process by analysing the interactions of BRAF N-terminal regulatory regions (CRD, RBD, and BSR) with the C-terminal kinase domain and with the upstream regulators HRAS and KRAS. To this end, they generated four constructs comprising different combinations of N-terminal domains of BRAF and analysed their interaction with HRAS as well as conformational changes that occur. By HDX-MS they confirmed that the RBD is indeed the main mediator of interaction with HRAS. Moreover, they observed that HRAS binding leads to conformational changes exposing the BSR to the environment. Next, the authors used OpenSPR to determine the binding affinities of HRAS to the different BRAF constructs. While BSR+RBD, RBD+CRD, and RBD bound HRAS with nanomolar affinity, no binding was observed with the construct comprising all three domains. Based on these experiments, the authors concluded that BSR and CRD negatively regulate binding to HRAS and hypothesised that BSR may confer some RAS isoform specificity. They corroborated this notion by showing that KRAS bound to BRAF-NT1 (BSR+RBD+CRD) while HRAS did not. Next, the authors analysed the autoinhibitory interaction occurring between the N-terminal regions and the kinase domain. Through pulldown and OpenSPR experiments, they confirm that it is mainly the CRD that makes the necessary contacts with the kinase domain. In addition, they show that the BSR stabilizes these interactions and that the addition of HRAS abolishes them. Finally, the D594G mutation within the KD of BRAF is shown to destabilise these autoinhibitory interactions, which could explain its oncogenic potential.

Overall, the in vitro study provides new insights into the regulation of BRAF and its interactions with HRAS and KRAS through a comprehensive in vitro analysis of the BRAF N-terminal region. Also, the authors report the first KD values for the N- and C-terminal interactions of BRAF and show that the BSR might provide isoform specificity towards KRAS. While these findings could be useful for the development of a new generation of inhibitors, the overall impact of the manuscript could probably be enhanced if the authors were to investigate in more detail how the BSR-mediated specificity of BRAF towards certain RAS isoforms is achieved. Moreover, though the very "clean" in vitro approach is appreciated, it also seems useful to examine whether the observed interactions and conformational changes occur in the full-length BRAF molecule and in more physiological contexts. Some of the results could be compared with studies including full-length constructs.

Public Response: We would like to express our gratitude for your valuable feedback on our manuscript. Your insightful suggestions have significantly improved the quality and completeness of our research. In response to your comments, we have conducted additional experiments and incorporated new data into the revised manuscript.

To gain a deeper understanding of how the BSR-mediated specificity of BRAF towards certain RAS isoforms is achieved, we performed HDX-MS to investigate the impact of KRAS interactions on the BSR. Our findings indicate that when KRAS is bound to BRAF NT2, there is no significant difference in hydrogen-deuterium exchange rates in the BSR compared to the apo-NT2 state (Figure 4). This observation contrasts with the effect of HRAS binding, where peptides from the BRAF-BSR exhibit an increased rate change, suggesting that HRAS induces a conformationally more dynamic state (Figure 2).

Our results align with the conclusions of Terrell et al. in their 2019 publication, which propose that isoform preferences in the RAS-RAF interaction are driven by opposite charge attractions between BRAF-BSR and KRAS-HVR, promoting the interaction.1 Our data offers a potential mechanistic explanation, suggesting that HRAS disrupts the conformational stability of the BSR provided by the RBD, while KRAS-HVR restores stability and enhances interaction favorability. It is important to note that our results do not directly confirm a long-lasting interaction between the BRAF-BSR and KRAS-HVR, but they do not rule out the possibility of a transient, low-affinity interaction or close proximity between the two.

Furthermore, our binding kinetics measurements conducted using OpenSPR support these findings. Particularly, in the case of NT1, when the CRD accompanies the BSR and RBD, no interactions with HRAS were observed. Additionally, we quantified the binding affinities between NT3:KRAS and NT4:KRAS, demonstrating that they are equally strong and that the presence of the BSR or CRD does not singularly affect the primary RBD interaction, consistent with HRAS. The BSR appears to exert an inhibitory effect on HRAS when the entire N-terminal region (BSR+RBD+CRD) is present. The BSR-mediated specificity is achieved through a coordinated interplay with the CRD.

Moreover, we have addressed your concern regarding the physiological relevance of our conclusions. In response, we utilized active, full-length (FL) BRAF purified from HEK293F cells in OpenSPR experiments. Our findings indicate that FL-BRAF behaves similarly to BRAF-NT1, as it does not bind to HRAS but binds to KRAS with a deviation comparable to NT1. We have demonstrated that post-translational modifications or native intramolecular interactions do not alter our initial results. Several literature sources, employing cell systems or expressing proteins from insect or mammalian cells, further support the findings presented in our study.2–5

Thank you once again for your constructive feedback, which has contributed significantly to the refinement of our work.

For the author:

Major points:

1. Figure 1D: Negative control is missing.

Response: We have incorporated the negative control into this figure as suggested.

1. Figure 3F and G: negative controls (GST only) are missing.

Response: We have incorporated the negative control into this figure as suggested.

1. The authors demonstrate that BRAF NT1 (BSR+RBD+CRD) interacts with KRAS but not HRAS in SPR experiments (Figure 4). What about the conformational change that affects the positioning of BSR when NT2 (BSR+RBD) binds to HRAS (Figure 2)? Does it also occur with KRAS or not? When a rate change is observed between free protein and bound protein in HDX, particularly when this rate change results in a sigmoidal curve that closely parallels the reference curve, it signifies that all residues within the peptide share a uniform protection factor. This suggests that they collectively undergo conformational changes at the same rate, likely due to a concerted opening as a cohesive unit. In the context of our time plots, we observe this distinctive characteristic in the curves derived from the BSR peptides, indicating that HRAS binding perturbs this region, alters its flexibility, and induces a coordinated conformational shift. This compelling evidence strongly supports our assertion that HRAS instigates a reorientation of the BSR.

Response: In response to the reviewer's comments, we conducted additional experiments to explore whether KRAS elicits any comparable alterations in the H-D exchange of the BSR within BRAF-NT2. Our findings indicate that KRAS does not induce a similar conformational change in the BSR. We have detailed these results in the Results section under the heading "BSR Differentiates the BRAF-KRAS Interaction from the BRAF-HRAS Interaction" and have included corresponding panels in Figure 4 to visually illustrate these observations.

1. Related to point 3: The authors mention that the HVR domain is responsible for isoform-specific differences. Does the BSR interact with the HVR domain of KRAS (but not HRAS)?

Response: It has been suggested by Terrell and colleagues1 that the BRAF-BSR and KRASHVR are directly responsible for the isoform specific interactions. We have no direct evidence confirming an interaction between the HVR and BSR. However, we deduce the possibility of such interaction based on previous research findings. Our HDX-MS experiments have demonstrated that the BRAF-BSR does not engage with HRAS. In our new HDX-MS experiments involving KRAS, we observed that the presence of KRAS does not lead to any discernible increase or decrease in the rate of deuterium exchange within the BRAF-BSR. It is important to emphasize that the absence of a rate change does not necessarily negate the occurrence of binding; rather, it might indicate a transient interaction with an affinity level below the detection threshold of HDX-MS.

Given that the only major difference between H- and K-RAS isoforms is the HVR, we hypothesize that binding differences between BRAF and RAS isoforms can be attributed to the HVR. Notably, BRAF-NT3 resembles CRAF, which also behaves in line with the findings from Terrell et al. in which the BSR is not present to impact RAS-RAF association. We have updated some of the discussion section to include the new results and draw relevant conclusion.

We mention in the text in the results section, “The HVR is an important region for regulating RAS isoform differences, like membrane anchoring, localization, RAS dimerization, and RAF interactions6… These results, combined with HDX-MS results, which showed that the BSR is exposed when bound to HRAS, suggest that the electrostatic forces surrounding the BSR promote BRAF autoinhibition and the specificity of RAF-RAS interactions.”

We also write in the discussion, “However, BRET assays suggest that CRAF does not show preference for either H- or KRAS, while BRAF appears to prefer KRAS.1 This preference is suggested to result from the potential favorable interactions between the negatively charged BSR of BRAF and the positively charged, poly-lysine region of the HVR of KRAS1… Our binding data provide additional examples of isoform-specific activity. We speculate that diminished BRAF-NT1 binding to HRAS and increased BSR exposure upon HRAS binding may be due to electrostatic repulsion between HRAS and the BSR. Our full-length KRAS and its interaction with NT1 support the hypothesis that the BSR attenuates fast binding to HRAS but not to KRAS.”

1. The authors might consider including NRAS in their study to give more weight to this interesting aspect.

Response: While this suggestion is intriguing and could contribute to the expanding body of literature on RAS signaling, particularly in the context of NRAS-mutant tumors, we believe that delving into this topic would be beyond the scope of the present manuscript.

1. Figure 6A: In this pulldown experiment the authors wish to demonstrate that binding of HRAS abolishes the autoinhibitory binding between NT1 and the kinase domain. However, the experimental design (i.e., pulldown of RAS) does not allow us to assess whether NT1 and KD are bound to each other in these conditions at all. The authors should rather pull down the KD and show that the interaction with NT1 is abolished when RAS is added.

Response: We appreciate your suggestion. The experimental design for this study was intentionally structured to focus on the specific subset of NT1 that interacts with HRAS. The BRAF N-terminal region has the capacity to bind both HRAS and KD, resulting in two distinct populations within BRAF-NT1: NT1:KD and NT1:HRAS, although we believe the ratio between those two populations is not 1:1. If we were to design the experiment by isolating either the KD or NT1, it would lead to the observation of both populations simultaneously, making it challenging to distinguish between them. Our pulldown experiments are performed under the same conditions (i.e. all the proteins were maintained in a molar ratio of 1:1 and exposed to the same buffer components), and we rely on pulldown assays, such as those depicted in Figure 5, to clearly demonstrate the binding interactions between NT1 and KD.

1. The authors have chosen a purely in vitro approach for their interaction studies, which initially makes sense for the addressed questions. However, since the BRAF constructs studied are only fragments and neither BRAF nor K/HRAS has any posttranslational modifications, the question arises to what extent the findings obtained hold up in vivo. Therefore, the manuscript would greatly benefit from monitoring the described interactions in full-length proteins and in cells or at least with proteins purified from cells.

Response: Thank you for your valuable suggestion, which we take very seriously to enhance the quality of our manuscript. Upon carefully reviewing your comments, we conducted additional experiments involving full-length, wild-type BRAF (FL-BRAF) that was purified from mammalian cells, encompassing the post-translational modifications and scaffolding proteins such as 14-3-3 (Supplementary Fig 8A). We have incorporated the findings from these OpenSPR experiments into the revised manuscript within the Results Section titled "BSR Differentiates the BRAF-KRAS Interaction from the BRAFHRAS Interaction" and Figure 4. In summary, our results with FL-BRAF affirm the extension of our initial observations. Both NT1 and FL-BRAF interact with KRAS with comparable affinities, and neither NT1 nor FL-BRAF demonstrates an interaction with HRAS using OpenSPR. These results underscore that BRAF fragments accurately represent active, fully processed BRAF, lending support to our in vitro approach.

Moreover, the conserved interactions we report in this manuscript are supported by literature. The interaction between RAF-RBD and RAS has been extensively documented, spanning investigations conducted in both insect and mammalian cell lines. For instance, Tran et al. (2021) utilized mammalian expression systems to explore the role of RBD in mediating BRAF activation through RAS interaction, identifying the same binding surfaces that we highlighted using HDX-MS.2 They quantified the KRAS-CRAF interaction yielding binding affinities in the low nanomolar range, similar to our findings for BRAF-NT:KRAS OpenSPR.2 In the manuscript text, we compared the binding affinity of BRAF residues 1245 purified from insect cells3 to our BRAF 1-227 (NT2 from E. coli), noting that the published value falls within the standard deviation of our experimental value. Additionally, our results align with the autoinhibited FL-BRAF:MEK:14-3-3 structure, which was expressed in Sf9 insect cells and reveals the central role of the CRD in maintaining autoinhibition through interactions with KD.4 In 2005, Tran and colleagues revealed specific domains within the BRAF N-terminal region are involved in binding to KD through Co-IP experiments conducted in mammalian cells.5

While we are fully aware of the limitations of taking a purely in vitro approach to study the role of BRAF regulatory domains in RAS-RAF interactions and autoinhibition, as well as to quantify the affinity of these interactions, we emphasize that this approach enables us to dissect and examine the specific regions of RAF that are under investigation. As we write in the manuscript: “Our in vitro studies were conducted using proteins purified from E. coli, which lack the membrane, post-translational modifications, and regulatory, scaffolding, or chaperone proteins that are involved in BRAF regulation. Nonetheless, our study provides a direct characterization of the intra- and inter-molecular protein-protein interactions involved in BRAF regulation, without the complications that arise in cell-based assays.” We have added the following comment to clarify the advantages of our in vitro approach and the challenges associated with cell-based assays: “… without the complications and false-positives that can arise in cell-based assays, which often cannot distinguish between proximity and biochemical interactions.”

Once again, we appreciate your insight feedback, which has contributed significantly to the improvement of our manuscript.

Minor:

1. Page 7, paragraph 2, line 6: It should probably read "BRAF autoinhibition" not "BRAF autoinhibitory".

Response: Thank you for bringing this to our attention. We have fixed this typo.

1. Figure 3G: In the first lane (time point 0 min) there is no input band for His/MBP-NT1. Probably a mistake when cropping the image from the original photo.

Response: We sincerely appreciate your diligence in identifying cropping errors, and we have taken comprehensive measures to review the manuscript and correct any such errors. Regarding this specific figure, it is important to note that NT1 was not added at the "0" minute time point, which explains the absence of an input band at that stage. To avoid any confusion, we have revised the notation from "0" to "-" for clarity.

Reviewer #2 (Public Review):

In the manuscript entitled 'Unveiling the Domain-Specific and RAS Isoform-Specific Details of BRAF Regulation', the authors conduct a series of in vitro experiments using Nterminal and C-terminal BRAF fragments (SPR, HDX-MS, pull-down assays) to interrogate BRAF domain-specific autoinhibitory interactions and engagement by H- and KRAS GTPases. Of the three RAF isoforms, BRAF contains an extended N-terminal domain that has yet to be detected in X-ray and cryoEM reconstructions but has been proposed to interact with the KRAS hypervariable region. The investigators probe binding interactions between 4 N-terminal (NT) BRAF fragments (containing one more NT domain (BRS, RBD, and CRD)), with full-length bacterial expressed HRAS, KRAS as well as two BRAF C-terminal kinase fragments to tease out the underlying contribution of domainspecific binding events. They find, consistent with previous studies, that the BRAF BSR domain may negatively regulate RAS binding and propose that the presence of the BSR domain in BRAF provides an additional layer of autoinhibitory constraints that mediate BRAF activity in a RAS-isoform-specific manner. One of the fragments studied contains an oncogenic mutation in the kinase domain (BRAF-KDD594G). The investigators find that this mutant shows reduced interactions with an N-terminal regulatory fragment and postulate that this oncogenic BRAF mutant may promote BRAF activation by weakening autoinhibitory interactions between the N- and C-terminus.

While this manuscript sheds light on B-RAF specific autoinhibitory interactions and the identification and partial characterization of an oncogenic kinase domain (KD) mutant, several concerns exist with the vitro binding studies as they are performed using taggedisolated bacterial expressed fragments, 'dimerized' RAS constructs, lack of relevant citations, controls, comparisons and data/error analysis. Detailed concerns are listed below.

1. Bacterial-expressed truncated BRAF constructs are used to dissect the role of individual domains in BRAF autoinhibition. Concerns exist regarding the possibility that bacterial expression of isolated domains or regions of BRAF could miss important posttranslational modifications, intra-molecular interactions, or conformational changes that may occur in the context of the full-length protein in mammalian cells. This concern is not addressed in the manuscript.

Response: Reviewer 1 raised a similar concern, and we have duplicated our response below for your reference:

Thank you for your valuable suggestion, which we take very seriously to enhance the quality of our manuscript. Upon carefully reviewing your comments, we conducted additional experiments involving full-length, wild-type BRAF (FL-BRAF) that was purified from mammalian cells, encompassing the post-translational modifications and scaffolding proteins such as 14-3-3 (Supplementary Fig 8A). We have incorporated the findings from these OpenSPR experiments into the revised manuscript within the Results Section titled "BSR Differentiates the BRAF-KRAS Interaction from the BRAF-HRAS Interaction" and Figure 4. In summary, our results with FL-BRAF affirm the extension of our initial observations. Both NT1 and FL-BRAF interact with KRAS with comparable affinities, and neither NT1 nor FL-BRAF demonstrates an interaction with HRAS using OpenSPR. These results underscore that BRAF fragments accurately represent active, fully processed BRAF, lending support to our in vitro approach.

Moreover, the conserved interactions we report in this manuscript are supported by literature. The interaction between RAF-RBD and RAS has been extensively documented, spanning investigations conducted in both insect and mammalian cell lines. For instance, Tran et al. (2021) utilized mammalian expression systems to explore the role of RBD in mediating BRAF activation through RAS interaction, identifying the same binding surfaces that we highlighted using HDX-MS.2 They quantified the KRAS-CRAF interaction yielding binding affinities in the low nanomolar range, similar to our findings for BRAF-NT:KRAS OpenSPR.2 In the manuscript text, we compared the binding affinity of BRAF residues 1245 purified from insect cells3 to our BRAF 1-227 (NT2 from E. coli), noting that the published value falls within the standard deviation of our experimental value. Additionally, our results align with the autoinhibited FL-BRAF:MEK:14-3-3 structure, which was expressed in Sf9 insect cells and reveals the central role of the CRD in maintaining autoinhibition through interactions with KD.4 In 2005, Tran and colleagues revealed specific domains within the BRAF N-terminal region are involved in binding to KD through Co-IP experiments conducted in mammalian cells.5

While we are fully aware of the limitations of taking a purely in vitro approach to study the role of BRAF regulatory domains in RAS-RAF interactions and autoinhibition, as well as to quantify the affinity of these interactions, we emphasize that this approach enables us to dissect and examine the specific regions of RAF that are under investigation. As we write in the manuscript: “Our in vitro studies were conducted using proteins purified from E. coli, which lack the membrane, post-translational modifications, and regulatory, scaffolding, or chaperone proteins that are involved in BRAF regulation. Nonetheless, our study provides a direct characterization of the intra- and inter-molecular protein-protein interactions involved in BRAF regulation, without the complications that arise in cell-based assays.” We have added the following comment to clarify the advantages of our in vitro approach and the challenges associated with cell-based assays: “… without the complications and false-positives that can arise in cell-based assays, which often cannot distinguish between proximity and biochemical interactions.”

Once again, we appreciate your insight feedback, which has contributed significantly to the improvement of our manuscript.

1. The experiments employ BRAF NT constructs that retain an MBP tag and RAS proteins with a GST tag. Have the investigators conducted control experiments to verify that the tags do not induce or perturb native interactions?

Response: Thank you for highlighting this important issue. We have conducted control experiments whenever feasible, particularly in cases where tags were not required for visualization, immobilization, or where cleave sites were present. We have subsequently included these control experiments in the supplementary figures and accompanying text within the manuscript.

It is essential to note that many of the techniques employed in this manuscript rely on tags, such as immobilizing proteins onto NTA OpenSPR sensors and employing various resins/beads for pulldown assays. Utilizing tags for protein immobilization in OpenSPR applications offers distinct advantages, including homogeneous and site-specific immobilization of the protein, ensuring that binding sites remain accessible for the study of protein-protein interactions (PPIs) of interest. Furthermore, in all BRAF-RAS SPR experiments, the MBP protein serves as the reference channel "blocking" protein. This reference channel is instrumental in mitigating any potential false-positive signals resulting from binding interactions with the MBP protein. Any such signal is subsequently subtracted out during data analysis.

To provide a comprehensive understanding of these aspects, we have incorporated these details into the manuscript text for clarity:

“Maltose bind protein (MBP) is immobilized on the OpenSPR reference channel, which accounts for any non-specific binding or impacts to the native PPIs that may result from the presence of tags. Kinetic analysis is performed on the corrected binding curves, which subtracts any response in the reference channel.”

We describe the control experiment to examine whether His/MBP-tag affects NT1 binding with BRAF-KD: “Similarly, we removed the His/MBP-tag from BRAF-NT1 through a TEV protease cleavage reaction and flowed over untagged NT1. Kinetic analysis confirmed that the interaction is preserved with the KD=13 nM (Supplemental Figure 6F).”

We show that the GST-tag does not affect KRAS interactions with NTs in supplemental figure 6. We purified full-length, His/MBP-KRAS and subsequently removed the tag through TEV cleavage. BRAF-NT interactions are preserved with untagged KRAS. GST alone, also does not interact with BRAF-NTs. We updated the text in the results section “BSR differentiates the BRAF-KRAS interaction from the BRAF-HRAS interaction.”

Additionally, Vojtek and colleagues used the same fusion-protein combinations (GSTRAS and MBP-RAF) in pulldown experiments and also found no perturbations from these tags.8

1. The investigators state that the GST tag on the RAS constructs was used to promote RAS dimerization, as RAS dimerization is proposed to be key for RAF activation. However, recent findings argue against the role of RAS dimers in RAF dimerization and activation (Simanshu et al, Mol. Cell 2023). Moreover, while GST can dimerize, it is unclear whether this promotes RAS dimerization as suggested. In methods for the OpenSPR experiments probing NT BRAF:RAS interactions, it is stated that "monomeric KRAS was flowed...". This terminology is a bit confusing. How was the monomeric state of KRAS determined and what was the rationale behind the experiment? Is there a difference in binding interactions between "monomeric vs dimeric KRAS"?

Response: Thank you for conducting such a comprehensive review of our manuscript and for identifying the mention of "monomeric KRAS" in the experimental section, which was inadvertently included and should not have been present. This terminology originally referred to a series of experiments involving "monomeric" KRAS that were initially considered for inclusion in the main body of the manuscript but were subsequently removed before submission. Furthermore, we adjusted the terminology to prevent any confusion or unwarranted implications.

To clarify, this "monomeric" construct refers to the tagless, full-length KRAS variant that was confirmed to exist in a monomeric state through Size Exclusion Chromatography, eluting at a volume equivalent to 21 kDa. We have incorporated the findings from experiments involving this untagged KRAS variant into the supplementary figures to provide supporting evidence, particularly in response to comment #2, that the GST-tag does not interfere with native interactions. Supplementary Figure 1 illustrates that both GST-HRAS (45 kDa) and GST-KRAS (45 kDa) elute as dimers in solution, at approximately 90 kDa. It is important to note that the main text figures primarily feature the GST-tagged, "dimeric" RAS constructs. Our research results do not suggest any significant differences between "monomeric," untagged KRAS and "dimeric" GST-tagged KRAS, indicating that the binding kinetics between RAS and RAF are not influenced by oligomerization state (Supplementary Fig 6). To mitigate any potential confusion, we have made the necessary distinctions in the text and have revised the methods description to accurately reflect these aspects.

While the recent findings summarized by Simanshu and colleagues were published concurrently with our manuscript submission, we would like to address this comment in the following manner. The authors assert that RAS does not engage in dimerization through the G domain, a hypothesis that contrasts with certain prior research findings. Instead, they propose that the plasma membrane plays a pivotal role in the clustering of RAS. Furthermore, the authors mention the involvement of RAS "dimerization" in RAF dimerization and activation in the subsequent statements:

“Recruitment of two RAF proteins by RAS proteins in close proximity facilitate RAF activation but are not required for RAF dimerization.”

“However, the PM recruitment of two RAF proteins by two non-dimerized but co- localized RAS proteins would serve equally well to promote RAF dimerization. Moreover, recent work on the activation cycle of RAF dimers (ref 20–23) argues strongly against a role for RAS dimers while revealing regulation by the 14-3-3 and SHOC2-MRAS- PP1C complexes. (Ref 24)”

The primary focus of our study centers on elucidating the intricate details of the RAS-RAF interaction and the mechanisms underlying RAF autoinhibition, rather than emphasizing RAF dimerization as the sole pathway to RAF activation. It is important to recognize that RAF activation encompasses multiple steps, including RAS-mediated relief of RAF autoinhibition.

To mimic physiological conditions as closely as possible, we employed a GST-tag on RAS in our experiments. It's worth noting that GST has a dimerization property,9 which brings RAS molecules into close proximity to one another, effectively emulating conditions akin to the plasma membrane. Our primary objective is not solely to facilitate interactions by bringing RAS into close proximity. Instead, our aim is to replicate cellular conditions to the greatest extent feasible, especially within the predominantly in vitro framework of our studies. Furthermore, we have revised the sentence pertaining to HRAS as follows: “As verified by size exclusion chromatography (Supplementary Fig 1A), the GST-tag dimerizes and forces HRAS into close proximity to recapitulate physiological conditions. (ref. 35)”

1. The investigators determine binding affinities between GST-HRAS and NT BRAF domains (NT2 7.5 {plus minus} 3.5; NT3 22 {plus minus} 11 nM) by SPR, and propose that the BRS domain has an inhibitory role HRAS interactions with the RAF NT. However, it is unclear whether these differences are statistically meaningful given the error.

Response: Thank you for bringing up this matter for further discussion. We are fully aware that these distinctions (NT2 and NT3), considering the overlapping error, lack statistical significance. Our conclusion points toward the most notable differences occurring when comparing NT1 to either NT2 or NT3, highlighting that the presence of the BSR has an inhibitory effect, particularly when the CRD is also present. It's important to note that we did not directly compare NT2 and NT3 to each other. Our comparison primarily elucidates that BSR without the CRD, and conversely, CRD without the BSR, do not exhibit the inhibitory effect. This collective evidence leads to the conclusion that all three domains collaboratively play a role in negatively regulating BRAF against HRAS.

1. It is unclear why NT1 (BSR+RBD+CRD) was not included in the HDX experiments, which makes it challenging to directly compare and determine specific contributions of each domain in the presence of HRAS. Including NT1 in the experimental design could provide a more comprehensive understanding of the interplay between the domains and their respective roles in the HRAS-BRAF interaction. Further, excluding certain domains from the constructs, such as the BSR or CRD, may overlook potential domain-domain interactions and their influence on the conformational changes induced by HRAS binding.

Response: We acknowledge that incorporating NT1 into the HDX experiments would have provided clearer insights into the specific contributions of each domain. Originally, it was our intention to include NT1 in these experiments. Unfortunately, we encountered challenges with the HDX experiments when it came to BRAF-NT1, as it yielded a significantly low sequence coverage after MS/MS analysis. We made multiple attempts to address this issue, which included additional protein purifications involving reducing agents, increasing the concentration of reaction buffer components, and extending the incubation time with reducing agents before injection. Despite these efforts, we were unable to obtain the desired sequence coverage for NT1. Consequently, we switched our approach to analyze NT2 and NT3 as the next best alternative.

1. The authors perform pulldown experiments with BRAF constructs (NT1: BSR+RBD+CRD, NT2: BSR+RBD, NT3: RBD+CRD, NT4: RBD alone), in which biotinylated BRAF-KD was captured on streptavidin beads and probed for bound His/MBP-tagged BRAF NTs. Western blot results suggest that only NT1 and NT3 bind to the KD (Figure 5). However, performing a pulldown experiment with an additional construct, CRD alone, it would help to determine whether the CRD alone is sufficient for the interaction or if the presence of the RBD is required for higher affinity binding. This additional experiment would strengthen the authors' arguments and provide further insights into the mechanism of BRAF autoinhibition.

Response: We are grateful for this valuable suggestion, and in response, we have taken the initiative to clone and purify a CRD-only construct (NT5) to strengthen our arguments. Subsequently, we conducted OpenSPR experiments to measure the binding affinity between NT5 and KD. Our findings clearly indicate that the CRD alone is not sufficient to mediate the autoinhibitory interactions and that the presence of the RBD is indeed necessary. These results have been incorporated into Figure 5 and are described within the Results Section for enhanced clarity and support.

1. While the investigators state that their findings indicate that H- and KRAS differentially interact with BRAF, most of the experiments are focused on HRAS, with only a subset on KRAS. As SPR & pull-down experiments are only conducted on NT1 and NT2, evidence for RAS isoform-specific interactions is weak. It is unclear why parallel experiments were not conducted with KRAS using BRAF NT3 & NT4 constructs.

Response: We sincerely appreciate your suggestion, which has contributed to enhancing the overall robustness of the evidence regarding isoform-specific differences between H- and K-RAS. In response, we performed additional experiments involving NT3 and NT4. The outcomes of these experiments have been integrated into Figure 4, and we have provided a comprehensive description of these results within the Results section “BSR differentiates the BRAF-KRAS interaction from the BRAF-HRAS interaction” of the manuscript.

1. The investigators do not cite the AlphaFold prediction of full-length BRAF (AFP15056-F1) or the known X-ray structure of the BRAF BRS domain. Hence, it is unclear how Alpha-Fold is used to gain new structural information, and whether it was used to predict the structure of the N-terminal regulatory or the full-length protein.

Response: We greatly appreciate the reviewer’s commitment to upholding good scientific practices and ensuring the inclusion of relevant citations in publications. In our original manuscript, we employed the UniProt ID P15056 to reference the specific AlphaFold structure used in our study. This was clarified as follows: "Since the full-length structure of BRAF is still unresolved, we applied the AlphaFold Protein Structure Database for a model of BRAF to display the conformation of the N-terminal domains and the HDX-MS results.40,41” Additionally, we referenced AlphaFold using the two citations recommended on their website (references 35 and 36 in the original manuscript). To prevent any potential confusion in the future, we have incorporated "AF-P15056-F1," as suggested.

We are sorry for any misunderstanding that may have arisen regarding the use of AlphaFold for gaining new structural insights. Our sole intention was to utilize AlphaFold as a tool for modeling HDX, as a full-length structure of BRAF, encompassing the entire N-terminal domain, remains unavailable. We have taken steps to clarify our objectives in the manuscript to ensure the purpose of our AlphaFold utilization is unambiguous.

Furthermore, we wish to emphasize that our utilization of AlphaFold was never intended to exclude the known X-ray structure of the BRAF-BSR domain. In our revised text, we have added clarity to our purposes and cited the Lavoie et al. Nature publication from 2018, which provides alignment between the X-ray structure and the AlphaFold model, thereby enhancing the confidence in the latter.

1. In HDX-MS experiments, it is unclear how the authors determine whether small differences in deuterium uptake observed for some of the peptide fragments are statistically significant, and why for some of the labeling reaction times the investigators state " {plus minus} HRAS only" for only 3 time points?

Response: First, in reference to the question about " ‘{plus minus} HRAS only’ for only 3 time points,” we write:

“Both constructs were incubated with and without GMPPNP-HRAS in D2O buffer for set labeling reaction times (NT3: 2 sec [NT3 ± HRAS only], 6 sec [NT3 ± HRAS only], 20 sec, 30 sec [NT3 ± HRAS only], 60 sec, 5 min, 10 min, 30 min, 90 min, 4.5 h, 15 h, and 24 h)...”

We realize how this can be confusing. To avoid such confusion, we fixed the text to read instead:<br /> “Both constructs were incubated with and without GMPPNP-HRAS in D2O buffer for set labeling reaction times (NT3: 2 sec, 6 sec, 20 sec, 30 sec, 60 sec, 5 min, 10 min, 30 min, 90 min, 4.5 h, 15 h, 45 h and 24 h at RT; NT2: 20 sec, 60 sec, 5 min, 10 min, 30 min, 90 min, 4.5 h, 15 h, and 24 h at RT)...”

Next, with regard to assessing significance, we determine it by closely examining a consistent trend in smooth time course plots. To establish this trend, we rely on the presence of more than four overlapping peptides, each with multiple charge states, within a specific sequence range. When we observe multiple peptides showing even a small difference in rate exchange, we can confidently infer that structural changes have taken place. This confidence stems from the inherent reliability and redundancy in the data analysis approach we have employed.11,12 It is noteworthy that our focus is primarily on reporting the binding or no binding, rather than quantifying the magnitude of exchange. As such, conducting multiple replicates or statistical testing is not deemed necessary.13,14 This is true for multiple reasons:

1) Instead of small deuterium changes (y-axis), we are focusing on the x-axis changes, which provides a slowing factor and how much that H-D exchange rate has changed.

• In a publication investigating the ideal HDX-MS data set, the author explains, “with the availability of high resolution HDX-MS raw data, it may be the time to shift the data analysis paradigm from determination of centroid values and presentation of deuteration levels to deconvolution of isotope envelopes and presentation of exchange rates.” 15

• Presentation of data through rate changes provides a physical chemistry measurement, as opposed to a relative measurement with percent deuteration. For example, slowing with a factor of 10 equates to the energy in 1 kCal. By quick visual estimation, we see a slowing factor of about 2 when RAS is bound to the BRAF-RBD.

• We made some changes to the text to clear up any confusion about measuring D uptake vs rate.

2) Looking at sigmoidal curves only—the “smooth time course” shows that the timedependent deuterium changes are not random, artifacts, or false positives/negatives. When parallel sigmoidal curves are present, any x-axis change is a measure of H-D exchange. Only plots with a smooth time course are used to make conclusions about BRAF’s conformational changes or binding interfaces.

3) Wide time range- the extended time also confirms that any observed difference is reliable and accurate. This extended time frame provides coverage for deuteration levels from 0 to 100% for peptides. A smooth time course is present in complete coverage.

• A narrow time window is a common flaw in HDX-MS studies14,15

4) The rate change is observed at multiple time points (at least 4 for each peptide), which are all independent reactions, and show reproducibility of change

5) Many overlapping peptides show the same pattern- the exchange rate difference is observed in at least 4 peptide time plots without contradictory evidence within the sequence range.

• We included the complete set of peptide time plots in the supplemental materials.

6) The many other peptide time plots that do not show any difference with and without RAS is a form of reproducibility, that no difference means no difference.

1. The investigators find that KRAS binds NT1 in SPR experiments, whereas HRAS does not. However, the pull-down assays show NT1 binding to both KRAS and HRAS. SI Fig 5 attributes this to slow association, yet both SPR (on/off rates) and equilibrium binding measurements are conducted. This data should be able to 'tease' out differences in association.

Response: Thank you for bringing up this important point. It's crucial to note that the experiments conducted at slow flow rates generated low responses, making it challenging to perform kinetic analyses effectively. Consequently, we are unable to provide accurate equilibrium binding measurements (on/off rates) for NT1 and HRAS. Regrettably, comparing the association rates between KRAS and HRAS is not feasible due to the differing flow rates employed. We have addressed this limitation in the manuscript as follows:

“We therefore immobilized NT1 and flowed over HRAS at a much slower flow rate (5 µL/min), during which we saw minimal but consistent binding (Supplementary Fig 5A). The low response and long timeframe of each injection, however, makes the dissociation constant (KD) unmeasurable and incomparable to our other NT-HRAS OpenSPR results.”

1. The model in Figure 7B highlights BSR interactions with KRAS, however, BSR interactions with the KRAS HVR (proximal to the membrane) are not shown, as supported by Terrell et al. (2019).

Response: Thank you for the suggestion. We reoriented the BSR closer to HVR of KRAS rather than G-domain.

1. The investigators state that 'These findings demonstrate that HRAS binding to BRAF directly relieves BRAF autoinhibition by disrupting the NT1-KD interaction, providing the first in vitro evidence of RAS-mediated relief of RAF autoinhibition, the central dogma of RAS-RAF regulation. However, in Tran et al (2005) JBC, they report pulldown experiments using N-and C-terminal fragments of BRAF and state that 'BRAF also contains an N-terminal autoinhibitory domain and that the interaction of this domain with the catalytic domain was inhibited by binding to active HRAS'. This reference is not cited.

Response: We appreciate the concern raised regarding our statement. We want to clarify that it was never our intention to disregard this JBC publication, and we apologize for any misunderstanding caused by our phrasing. We recognize that our initial statement was contentious, and we have removed the word "first" from the phrase "first in vitro evidence." In the section of the discussion where we originally cited the Tran et al. (2005) publication, we have revised the language to eliminate "first" and have rephrased the sentence, as provided below:

“Our in vitro binding studies align with previous implications that RAS relieves RAF autoinhibition shown through cell-based coIP’s.5”

1. In Fig 2, panels A and C, it is unclear what the grey dotted line in is each plot.

Response: Thank you for drawing our attention to the additional explanation needed here. The gray dotted lines represent the maximum deuterium exchange. We added the following description to the figure 2 legend:

“Gray dotted lines represent the theoretical exchange behavior for specified peptide that is fully unstructured (top) or for specified peptide with a uniform protection factor (fraction of time the residue is involved in protecting the H-bond) of 100 (lower).”

1. In Fig 3, error analysis is not provided for panel E.

Response: We added the standard deviation values to this panel. We additionally added these for Fig 4C and Fig 5B.

1. How was RAS GMPPNP loading verified?

Response: Ras loading is a well-established protocol with a solid foundation in the literature.16– 21 We followed this accepted method for nucleotide exchange. Our controls, as evident in pulldown and OpenSPR experiments (fig 1C, 4E), unequivocally demonstrate that GMPPNPloaded RAS is active, while unloaded RAS is inactive, as evidenced by the absence of no binding. We also added supplemental figure 6E to show that inactive (unloaded) GST-KRAS does not bind to BRAF during OpenSPR analysis. To exemplify this, we included binding curves of 1 µM GST-KRAS- GMPPNP and -GDP flowed over NTA-immobilized BRAF-NT2 at a flow rate of 30 µl/min.

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Reviewer #2 (Public Review):

In this study, the investigators describe an unbiased phosphoproteomic analysis of cardiac-specific overexpression of adenylyl cyclase type 8 (TGAC8) mice that was then integrated with transcriptomic and proteomic data. The phosphoproteomic analysis was performed using tandem mass tag-labeling mass spectrometry of left ventricular (LV) tissue in TGAC8 and wild-type mice. The initial principal component analysis showed differences between the TGAC8 and WT groups. The integrated analysis demonstrated that many stress-response, immune, and metabolic signaling pathways were activated at transcriptional, translational, and/or post-translational levels.

The authors are to be commended for a well-conducted study with quality control steps described for the various analyses. The rationale for following up on prior transcriptomic and proteomic analyses is described. The analysis appears thorough and well-integrated with the group's prior work. Confirmational data using Western blot is provided to support their conclusions. Their findings have the potential of identifying novel pathways involved in cardiac performance and cardioprotection.

UN-Generalsekretär Guterres hat die Untätigkeit der politischen Führungen scharf angegriffen. Er spricht davon, dass man die vergifteten Wurzeln der Klimakrise, die fossilen Energien, endlich ausreißen muss. Der Emission Gap Report 2023 zeigt, dass keiner der G20-Staaten eine Dekarbonisierungspolitik betreibt, die mit den Zielen des Pariser Abkommens vereinbar ist. https://www.liberation.fr/environnement/climat/le-monde-va-faire-face-a-un-rechauffement-de-25-c-a-29-c-dici-2100-alerte-lonu-avant-la-cop-28-20231120_QXFYQM3CJNHALBWI4PV5KR4NIY/

Mehr zum Emissions Gap Report 2023: https://hypothes.is/search?q=tag%3A%22Emissions%20Gap%20Report%202023%22