I understand what this title mean but it is a bit weird to read dynamics constrain structure. I feel we don't need to highlight ecological dynamics, as this may be misleading. Because we focus on food webs at steady state equilibrium, which means there is no temporal dynamics. Perhaps we can highlight that feasible food webs share topological structures independent of generative models??

does this mean these "failed" food webs are mainly due to the topological properties of richness and ChLen? accroding to Figure 1a.

a steady state

Do we need to mention somewhere that the food web cases analysed in Figure 1b are feasible food webs at steady state, corresponding to the equilibrium case in Figure 2

Then we know the convergence in structure is partly due to the pruning from the initial networks to feasible networks and partly due to excluding some fodo web cases, e.g. non-steady state, collapse.

explain S4?

I am thinking about some terminology in the text. 1. Equilibrium. I don't think this is a good term. Our simulation set-up actually pushes the simulation to steady state. The equilibrium case in Figure 2 actually means steady state. The non-equilibrium case in Figure 2 is just non-steady state, but they are at equilibrium; I am looking into whether they are all limit cycles. 2. How do we name the food web at equilibrium. Currently, we use terms like realised post-simulation network, post-dynamics networks. I am wondering whether we name it as feasible food web/networks at equilibrium (thereafter feasible food web/networks), considering the definition of a feasible food web is an ecological network model where the interaction strengths between species, their growth rates, and their mortality rates allow all species in the web to coexist with strictly positive biomasses at equilibrium.

This is generative model.

merge these two together? a bit redundant?

this makes me wonder whether other models allow cannibalism?

if there is a brief explanation of what the generative model mean would be great. Iam not sure whether generative models is a standardised term and widely used?

For me, this is not very clearly understandable. It is the realised networks across generative models that converge in structure. I am not sure whether the food web reconstructive frameworks include dynamic simulations.

It would be more appropriate to use the term approximation error here, since, in the context of PDEs, the residual usually refers to the imbalance obtained by substituting an approximate solution into the governing equation, e.g., \(R(x)=L(U)-f\).

This set of basis functions spans the same space as {1 x}.

Strings are immutable, not changeable, and there are a sequential collection of characters.

This is just like sets.

Virtue lens: When AI is becoming as powerful as it is, at the speed that it is, following safeguards are the only ways we can make sure it is somewhat safe. It isn't even just about being responsible or honest. If a program gaining this kind of traction has no sort of training, safeguards, guidelines, etc., how will it continue to function without falling apart or harming someone. So having some kind of base level of safety and operational practices should, honestly, already be something that was thought of and put in place.

反反复复

Strings are made of smaller strings: one character.

Is it so?

eLife Assessment

This important study links blood-derived dietary content to sustained increases in sleep in the mosquito Aedes aegypti. Using multiple independent approaches, the authors provide convincing evidence for blood-induced changes in sleep. These findings have broad implications for understanding how specialized diets regulate sleep across species and for mosquito vector biology.

Reviewer #1 (Public review):

Summary:

The presented investigation aims to expand the sleep definition and its relationship with blood meal and/or circadian clock in the mosquito, Aedes aegypti. The authors exhausted the established sleep analytical paradigm and three behaviour toolkits: LAM10, EthoVision, and DART. They also investigated the potential underlying molecular mechanism by using dsRNA injection (LkR) and KO mosquito (Cyc-/-).

Strengths:

The authors presented a very solid dataset showing posture changes and increase in the arousal threshold of mosquito after 10 minutes of immobility. This is major clarification and extension to our understanding in insect sleep beyond Drosophila. Inclusion of analytical parameters such as bout length, waking activity and pDoze/Wake provide critical reminder for other investigators of the steps needed for defining sleep in a new species. The investigation, with its technical span in behaviour assays, therefore, establish a good standard for mosquito sleep analysis to the same quality seen in the landmark studies (Shaw et al 2000 and Hendricks et al 2000) for Drosophila sleep. The pioneering data showing clear effect of blood meal and LkR reduction on locomotion and sleep provides an entry point for further investigations. The author has addressed previous concern on coincidence of sleep increase and locomotion reduction by using their two high-res. video tracking velocity or pDoze/Wake, showing that the "sleepy" mosquitos remain capable to reach high speed locomotion albeit less frequently. The authors also discuss the possibility of ATP and alternative explanation regarding sugar content in diet.

Reviewer #2 (Public review):

Summary:

Zhang et al. investigate how blood feeding and dietary protein influence sleep in the mosquito Aedes aegypti. The authors first establish a behavioural definition of sleep using postural analysis and arousal threshold measurements, then demonstrate that both blood meals and a bovine serum albumin (BSA)-based protein diet increase sleep for several days. They further show that RNAi-mediated knockdown of the leucokinin receptor (Lkr) enhances sleep, implicating neuropeptide signalling in the regulation of postprandial sleep.

Strengths:

The central question is well-motivated, and the experimental approach is systematic. The use of multiple independent methods to characterise sleep - postural analysis, infrared activity monitoring, videography, and arousal threshold - provides converging evidence. The 10-minute immobility criterion is grounded in the arousal threshold data, bouts exceeding 10 minutes corresponding to the first bin at which a significant effect emerges. The demonstration that the sleep increase is already detectable before oviposition establishes that the phenotype begins with feeding rather than with the completion of the reproductive cycle. The BSA feeding experiment is a particularly effective demonstration that dietary protein, rather than other blood components, is a key regulator of the sleep increase. The conservation of leucokinin signalling in sleep regulation between Drosophila and Ae. aegypti is a noteworthy finding that adds comparative depth. The "opportunistic versus determined" host-seeking distinction is appropriately framed as a hypothesis for future testing rather than as a conclusion drawn from the present data, and the limits of the design with respect to reproductive physiology are stated explicitly.

Weaknesses:

(1) Confound of reproduction and sleep. Blood and BSA both support egg development, so neither condition isolates nutrient sensing from reproductive physiology. The relative contributions of diet, egg development and post-reproductive recovery remain undetermined.

(2) Sleep versus reduced locomotion. The pDoze and pWake measures are defined here as proportions of time above or below a velocity threshold, rather than as the per-minute transition probabilities of the established definition (Wiggin et al. 2020, PNAS). So defined, they are equivalent to percent sleep and percent wake and cannot distinguish a sleep-like state from the mechanical consequences of engorgement.

(3) Data availability. Raw data are stated to be available on request rather than deposited in a public repository, which makes independent reanalysis less straightforward than it need be.

Author response:

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

We thank both reviewers for their thoughtful and constructive evaluations of our manuscript. We are grateful that both reviewers found the study to provide a strong behavioral framework for defining sleep in Aedes aegypti and appreciated the breadth of the behavioral and genetic approaches used. We also appreciate the reviewers’ careful identification of several issues requiring clarification, particularly regarding the interpretation of post-blood-meal sleep, the support for the 10-min sleep threshold, possible nutritional confounds in the BSA experiments, the framing of the host-seeking model, and the description of statistical analyses. In the revised manuscript, we have addressed these concerns by clarifying our rationale, tempering several conclusions, revising the statistical reporting and methods, explicitly stating sample sizes, and expanding the Discussion to better acknowledge limitations and alternative interpretations. Where appropriate, we have also revised the text to distinguish more clearly between increased sleep and reduced locomotion, and to frame mechanistic conclusions more cautiously.

Public Reviews:

Reviewer #1 (Public review):

(1) Conventionally, a coincidence of sleep increase and locomotion reduction would weaken the certainty of a sleep increase assessment. The authors implied this concurrence observed after blood meal is derived from internal "drowsy" neural state instead of physical "cripple", but they did not use their two high-resolution video tracking velocity or pDoze/Wake to clarify this.

Thank you for addressing this point. We understand the need to validate locomotion when used as a readout of sleep. We note that analysis of waking activity is normalized to time spent awake, and therefore should be separate from the time spent inactive that is classified as sleep. Based on the reviewers’ suggestions we have reanalyzed some data and revised the relevant sections in include this analysis.: In brief we performed pDoze/pWake analyses on the two high-resolution tracking video from EthoVision XT system. A velocity threshold of 0.4 mm/s was used, with velocities above 0.4 mm/s defined as wake/activity and velocities below 0.4 mm/s defined as doze/sleep state. pWake and pDoze were defined as proportional time metrics of wake/active (velocity > 0.4 mm/s) and doze/sleep (velocity > 0.4 mm/s) within each LD cycle. The conclusion that sleep is increased following blood feeding is supported by these data. We also note (as described in response to Reviewer 2, that this paper represents a step towards describing sleep in mosquitoes. We hope that future application of approaches used in Drosophila, such as brain imaging and indirect calorimetry will further refine our understanding. Along these lines, we have also included a section in the Discussion about how additional measures, including systems like FlyVista might be applied in the future.

(2) The major molecular component underlying blood meal effect on sleep/locomotion is less certain, because the BSA solution used for feeding contains ATP, which itself is able to enter haemolymph and potentially exerts sleep/locomotion effect. Additionally, the basal or control sleep recording is done after sucrose feeding. It is, however, unclear from the method if this is 10% too? And if the observed sleep level increase after a blood meal is a result of sugar level reduction in the blood (~0.1%).

We thank the reviewer for raising this important issue. We think it is unlikely that the small amount of ATP used for feeding is driving the sleep phenotype. We have now included this point as a caveat within the discussion, and explained its inclusion.

(2) Sucrose concentration in controls

We apologize that this was not clearly stated. Yes, the control mosquitoes were maintained on 10% sucrose, and we have now clarified this explicitly in the Methods and figure legends where relevant.

(3) Could the effect reflect reduced sugar intake rather than blood/protein?

We think this is unlikely, however it cannot be ruled out based on the experiments we have run. We have added discussion of this point. However, we note in fruit flies, this has been studied extensively, and loss of sugar under certain contexts reduces sleep. The points above highlight the need for systematic analysis of the dietary components that contribute to sleep in mosquitoes. While we regret being unable to include them in this manuscript, we note that many of these experiments are challenging (with many controls) and have been ongoing for over a decade (with contributions from many labs) in Drosophila.

Reviewer #2 (Public review):

(1) The authors settle on a 10-minute immobility threshold, but their own data do not convincingly support this choice… A 15-minute threshold would be better supported by the data as presented.

We appreciate this evaluation of the sleep threshold. We chose 10 minutes because the first significance in arousal threshold is at the time-point of 10-15 minutes. Therefore, we believe that sleep bouts longer than 10 minutes should be qualified as sleep. We are particularly interested in why arousal threshold continues to increas at 15 minutes. This is either incomplete sleep between minutes 10 and 15 or the presence of multiple sleep states. We have established a new system in the lab using Zantiks that we believe will allow for simultaneous recording of posture and arousal threshold. We now explicitly comment on this in the discussion, and the need for further analysis of the timeframe for which sleep is defined. Nevertheless, we believe we have honed in on a period of 10-15 minutes that serves as a good proxy for sleep regulation. We hope that this initial description of sleep in mosquitoes provides an initial step towards defining sleep, and that future studies that include techniques applied in Drosophila including brain imaging, indirect calorimetry and additional videography will define more nuanced changes in sleep. We have written in limitations and future opportunities to better define sleep throughout the manuscript.

(2) The primary experimental paradigm measures sleep beginning at Day 4 post-blood feeding, immediately after oviposition... what is being measured as ‘sleep’ could reflect post-reproductive quiescence or recovery rather than diet-induced sleep per se. The BSA experiment partially addresses this, but since BSA also triggers vitellogenesis and egg production, the confound persists.

We agree this is an important concern. Our intent in measuring sleep after oviposition was to isolate prolonged post-feeding effects from the well-established transient suppression of host-seeking that occurs during the first ~72 h after blood feeding. However, as the reviewer notes, this design does not by itself distinguish post-feeding sleep from other physiological processes associated with reproduction, including vitellogenesis, oviposition, or post-reproductive recovery. To address this issue, we included the experiment measuring sleep immediately after blood feeding, before oviposition. We agree, however, that this rationale should have been stated more clearly and that the limitation remains relevant, particularly because BSA can also support egg development. In the revised manuscript, we have therefore: In the current version we have clarified more explicitly that the immediate post-blood-meal recording was included to show that the sleep increase begins before oviposition; We have also tempered our interpretation of the Day 4–5 phenotype to avoid implying that it is purely diet-driven and fully independent of reproductive state; and expanded the Discussion to acknowledge that blood feeding, protein feeding, and reproductive physiology are closely linked in female mosquitoes and that our current experiments do not fully disentangle these processes. These changes frame the data more cautiously: blood/protein feeding is sufficient to induce a sleep-promoting state that begins immediately after feeding and persists into the post-oviposition period, but the relative contributions of nutrient sensing, egg development, and reproductive recovery remain to be determined.

(3) The opportunistic vs. determined host-seeking hypothesis… requires actual measurement of host-seeking alongside sleep to be substantiated, or at least the caveats need to be discussed more explicitly.

We agree with the reviewer. Our intention was to present this as a conceptual model motivated by the temporal dissociation between published host-seeking recovery and the prolonged sleep phenotype observed here, not as a demonstrated behavioral framework directly tested in this study. In the revised manuscript, we have substantially softened this section by clarifying that we did not directly measure host-seeking behavior in the current study; adding explicit caveats that the proposed framework remains speculative until sleep and hostseeking are measured simultaneously in the same animals across the same post-feeding time course. We appreciate this comment and agree that the distinction should be presented as a model for future testing rather than as a central conclusion established by the current data.

(4) The methods describe ‘one-way ANOVA, followed by Mann-Whitney tests with Welch’s correction,’ which is an internally inconsistent combination…

We thank the reviewer for catching this lack of clarity. We apologize for this inconsistency. We have fixed this error. In the revised manuscript, we have carefully rewritten the statistical analysis section to specify: which datasets were analyzed using parametric tests (e.g., ANOVA, with appropriate post hoc comparisons where assumptions were met), which datasets were analyzed using non-parametric tests (e.g., Mann-Whitney), and where Welch’s correction was applied, specifically for unequal-variance t-tests, not Mann-Whitney tests. We have also revised Methods, Figure legends and reporting throughout to ensure that the statistical test named in the text matches the reported test statistics. The changes include statistical methods rewritten for consistency and accuracy, and updated figure legends that include exact sample sizes. In addition, one summary spreadsheet of statistical analysis throughout this study is provided and will be submitted as a supplementary file.

eLife Assessment

This important study demonstrates how individual taste preferences change over time, how these changes are reflected in cortical activity, and how sensory experience contributes to reshaping both. The evidence is convincing and broadly supports the main conclusions. The findings should be of interest to neuroscientists studying sensory processing and cortical plasticity.

Reviewer #1 (Public review):

Summary:

Maigler et al. set out to test the hypothesis that individual differences in taste preferences are (in part) due to individual differences in central taste processing. They first tested rats' preferences for a variety of taste stimuli on multiple days. They then recorded responses of neurons in taste cortex to the same tastes on two consecutive days.

Strengths:

The authors collected high-resolution behavioral data from the same animals across multiple days, allowing for a detailed characterization of individual variation in taste preferences. They then performed recordings from the same set of animals in response to the same stimuli, allowing them to draw parallels between behavioral and neural responses.

Weaknesses:

(1) The authors collect extensive behavioral data and show that preference vary between animals and days, but little insight is provided into what underlies these changes and to what extent they reflect "preference". Two animals drank equal amounts of sucrose and quinine on day one of preference testing, suggesting that behavior does not reflect preference but (lack of) habituation to/proficiency with the testing environment.

(2) Recordings were performed only after multiple days of preference testing, and preferences were not tested in between/following recording sessions. This design precludes a direct comparison between neural and behavioral responses.

(3) Similarly, correlations between neural responses and behavioral measures are not analyzed/reported on an animal-by-animal basis.

Reviewer #2 (Public review):

Summary:

The study from Maigler et al investigates how between- and within-animal differences in taste preference relate to differences in neural responsiveness. The experiments rely on an elegant combination of behavioral assays to measure preference (e.g., repeated brief access testing, BAT) and electrophysiological recordings to monitor the activity of ensembles of neurons in the gustatory cortex (GC) of rats.

BAT with distinct batteries of tastants revealed pronounced variability in preference (measured as licking bout size) across individuals. This variability across individuals persisted after repeated testing. Repeated BAT also revealed that each individual rat's preference for different tastants changed across time.

Electrophysiological responses of GC neurons to batteries of tastants showed that firing in the "late epoch" of taste processing (i.e., 500ms post taste delivery) correlated more strongly with the individualized rat's BAT preference rather than with a canonical preference ranking. Importantly, this correlation was stronger for the last BAT session compared to the first. Finally, the authors show that the correlation disappeared in a second, consecutive recording session, indicating that exposure to tastants reconfigure preferences.

Strengths:

(1) The experimental design allows for an unprecedented look at the relationship between individual variability in taste preferences and neural processing.

(2) The study demonstrates that taste preference variability is not mere experimental noise but reflects the dynamic nature of taste. A key strength is the clear evidence that behavioral variability is reflected in neural activity patterns, establishing a strong correlation between brain and behavior.

(3) The evidence that simple exposure to familiar tastes can reconfigure preferences and taste representations is interesting.

Weaknesses:

The authors appropriately addressed the weaknesses in the revision process.

Reviewer #3 (Public review):

Summary:

Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.

Strengths:

(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.

(2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.

(3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).

(4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.<br /> The findings are novel and impactful, and the results are relatively complete.

Weaknesses:

(1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

(2) It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.

(3) A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

It is unclear whether there are any systematic changes in preferences over the course of testing that could explain the observed changes in correlation with neural responses, such as changes due to learning (e.g., flavor nutrient conditioning, relief of neophobia), changes in deprivation state, or habituation to/proficiency with the BAT setup.

For the revision, we have added analysis, including a new figure (Figure 3) between what are now Figures 2 & 4, testing the hypothesis that preference changes across testing days are non-random in direction (e.g., that they reflect attenuation of neophobia). This new analysis failed to reveal evidence supporting the hypotheses that: 1) preference for palatable tastes increases with experience (a result that would make sense given research on neophobia; 2) the preference for aversive tastes decrease with experience; or 3) absolute consumption of any particular taste changes in a reliable direction from session to session (lines 142-157 and new Figure 3).

A secondary point is whether any changes in preference are attributed to internal individual versus external contextual factors. Both types of variation (i.e., across individuals and across time within an individual) are mentioned in the introduction, but it is not clear what the authors believe about the nature or neural representation of these sources of variation.

While we assume that differences between rats are due to internal factors (given the controlled home-cage environment), we can’t be sure that some subtle, subthreshold (for us as observers) factor impacts taste preferences. Similarly, while changes across time within an individual is categorically within the individual, we cannot be sure whether some subtle facet of their experiences determines how preferences change (as opposed to it being purely internal). We have added prose to the Discussion session on this topic—including citation of Hilary Schiff’s recent work showing nurture-related preference changes as part of this new prose (lines 387-398).

With respect to neural data analysis, no individual animal/day data are shown, making it difficult to assess the extent to which differences in correlation match individual differences in preferences and/or changes in preference with time within individuals.

The revision now explicitly includes Figure panels (with analysis) showing the relationships between individual neural responses and consumption in the first and last BAT tests for a representative rat (lines 172-198; Figures 4A and 4D). As requested in the non-public comments, we have also added waveforms recorded for the representative neuron in an inset to Figure 4B.

The correlation analysis is also lacking control for the fact that there is a certain degree of "chance" associated with behavioral and neural measures having matching ranks.

Certainly chance cannot explain our results, which consist centrally of within-rat differences in match (that is, regardless of chance match levels, what we observed was specifically an enhancement of that match for the most recent behavioral assessment compared to an earlier assessment in the same rat)—a finding that is all the more surprising given that: 1) 2 weeks separate that behavior test and the electrophysiology session; and that 2) that gap between the ephys test and the (less well-matched) first behavioral test is only 1-3 days longer. Nonetheless, in appreciation of Reviewer 1’s concern, we have added an independent, convergent analysis to the revision, testing whether the observed pattern vanishes when we shuffle the preference ranks between tastes with neighboring ranks in the behavioral data (a more conservative test than complete shuffles among tastes). The results of this analysis, which are in the new Figure 5, provide further proof that our result is not based on chance—that they specifically reflect a match between neuronal activity and behavior (lines 242-251).

Finally, …it is unclear to what extent changes in correlation may be attributed to overall changes in responsiveness of the neural population.

We include several new analyses in the revision that test the hypothesis that the reduction in match between behavioral rankings and neural responses in the second electrophysiology sessions reflects spontaneous or taste-driven changes in neural excitability. These additional analyses reveal no clear between-session differences in baseline and/or taste-evoked responses, or in the percentages of neurons that are taste responsive and/or palatability-related (lines 292-309; Figure 7).

Reviewer #2 (Public review):

The manuscript could use additional corollary analyses to provide a more complete picture of the phenomenon. For instance, how many neurons (per animal and in total) have significant correlations with the final BAT patterns? And with the first BAT? Can a time course of such counts be provided? Can some decoding analyses be performed at a single session level to reconstruct a rat's behavioral preference pattern from its neural activity?

These are all really good ideas. As noted in our response to Reviewer 1, we have implemented all but the last of the suggested analyses, which did not produce evidence suggesting that our results can be explained by changes in neuronal properties between the two recording sessions (lines 292-309; Figure 7). We have also made attempts to apply the decoding analysis; unfortunately, we don’t have large enough samples to obtain stable results such a subtle decoding task (reflecting the last BAT session’s preference pattern is significantly better than the first session’s pattern).

The manuscript could benefit from additional polishing, both in the text as well as in the figures.

An extensive holistic edit has been done, starting with suggestions made by Reviewer 2 in the non-public comments.

Reviewer #3 (Public review):

Without a behavioral measure collected after recording day 1 intraoral exposure, it is not possible to determine whether taste preference was altered by that experience…The authors' conclusion would be strengthened by adding an intervening brief access test between recording days 1 and 2.

We very much appreciate Reviewer 3’s suggestion. Alas, the primary authors involved in data collection on this project have moved on, and we won’t be able to collect the additional dataset that would be required. Instead, we have softened the conclusion that we reached in the last section, and suggested the proposed experiment as a future direction (lines 366-374).

The current experimental design exposes animals to 3 distinct sets of substances … [that] differ in identity … and concentration. Because palatability is known to be comparative depending on the other substances available and concentration-dependent, this introduces challenges to interpretation, [and] without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli biases the main conclusions.”

This is an interesting point. Analyzing each set of batteries separately and performing between-battery comparisons would require a larger number of experimental subjects then we have in our current sample size. That said, while we acknowledge that taste preference ranking is relative, we believe the ranking system used here deviates little, if any, from the 'true' ranking (and is therefore significantly relevant to gustatory activity). This is supported by our newly obtained result in response to Reviewer 1 & Reviewer 2 (see above), where an ancillary shuffle analysis (Figure 5C) showed that swapping adjacent preference orders eliminated the experimental effects across all batteries.

Responses to sweet tastes are not reported in the electrophysiology data. This is seemingly the case because rats given set 1 received no sweet stimulus while rats given set 2 received to 2 distinct sweet tastes. Finally, rats given set 3 did not receive quinine, yet quinine is reported in electrophysiology data.

We are unsure of the source of this confusion—in every case, the rat received the same tastes in the electrophysiology sessions that were delivered in the BAT preference tests—but in appreciation of Reviewer 2’s concern, we have modified the text and table to ensure: 1) that panels reflecting data from single example rats (panels that therefore necessarily include only a subset of possible tastes) are clearly marked as such; and 2) that the nature of which taste batteries were delivered is more explicit (lines 104-112; 172-178).

The choice of reporting average lick cluster size is problematic because the authors use thirsty rats with 10-second-long trials. Thirsty rats are likely to lick in relatively long clusters, especially for neutral and palatable tastes. If the rat is mid-cluster when the trial ends, the final cluster would be cut off prematurely, resulting in shorter overall average lick cluster size, disproportionately affecting neutral and palatable tastes over aversive tastes.

We have ourselves been deeply concerned with this issue, and in fact have recently published a paper that includes within it a direct test demonstrating that calculations of lick bout lengths from 10-sec BAT trials result in taste palatability estimates that are identical to (and less noisy than) those generated from more classically-used 15-min ad lib licking. We now cite this paper (Stone, Lin, et al., 2026) in the Methods section, along with text clarifying how we calculated lick clusters. We also conducted an additional analysis that estimates taste preference after removing these “prematurely ended bouts” without changing the observed pattern of results (lines 494-510).

Of course, even if this last analysis had changed things, the result of clusters being cut short by the end of a trial would be an underestimation of the preference for the palatable tastes (which drive far more licking than aversive tastes and are therefore more likely to be mid-bout at the end of a trial). Such an underestimation would in turn be expected to reduce the observed neural-behavioral correlation. This fact highlights the robustness of our findings.

Canonical palatability rankings may not apply to the concentrations selected in every stimulus set. This is particularly true for set 1, which included two concentrations of citric acid and quinine for the behavior. It is also not clear which concentrations are reported in Figures 3A2 and 3B2. Meanwhile, the concentrations of quinine and citric acid used for electrophysiology are quite low.

In the revised Methods section, we explicitly motivate our reasoning (including citations) behind canonical rankings for each taste battery used (lines 513-522). Every taste used was of agreed-upon preference levels, and in the rare case that two concentrations of the same taste were used, both were known to have distinct palatabilities (e.g., 0.1M NaCl is preferred to 0.05M NaCl). This careful selection of tastes ensured that it was trivial to avoid misordering of canonical palatability rankings.

And even if mistakes in canonical rankings had been made, the impact of these inaccuracies in these rankings would have been minimal. Our findings are primarily driven by high levels of inter-individual (between different rats) and intra-individual (day-to-day fluctuations within the same rat) preference differences. Given this variability, the fact that the brain-behavior correlation were consistently worse using these rankings almost certainly means that the neural activity matches preference behavior—our thesis. This conclusion is further supported by our shuffle analysis, which demonstrated that randomizing the order did not yield superior correlations between taste ranking and GC activity.

Explain to someone who doesn't understanding: When you get injured, your body sends special cells, macrophages, to clean up the damage and repair the area. These cells call for other cells called, fibroblasts, which build a scaffolding, called the ECM, that helps rebuild the injury. The scientists in this paper are looking for if different types of macrophages instruct the fibroblasts to build the ECM differently.

this is still a gap. yes M1/M2 hybrids exists, but what do those macrophages look like and how are they actually involve in ECM assembly?

I would like to see them actually test whether changing the ECM to be more easily remodeled would actually improve the body's response to implants. Yes, this shows that the M1/M2 hybrids are involved, but how will that actually look when an implant come to play?

this was a strong experiment to add because you can visually see the decreased alignment and increased thickness in the IL4+IL13 group, which puts into context their PCA data.

reduces fibril alignment

This is the hypothesis: hybrid M1/M2 phenotypes play a role in normal wounding healing and the development of fibrosis during the foreign body response.

I think they did not do a good job of stating it, and may have strayed from this point by the end of the paper

I like that they included another test that wasn't just a PCA to support their results and looked into the structural biology

so as macrophages were changing from M1 to M2, the M1 genes were still present

geven aanleiding voor het uitvoeren van een onderzoek

The news report is from Albuquerque, New Mexico. Suggesting the audience would specifically be directed towards residents of New Mexico "landlord" also shows the intended audience is specifically the renters of New Mexico.

This use of Pathos was particularly persuasive in my opinion because it evoked emotion in me, my father is 65 and is bordering the same situation and it hurts me, It is hard to hear other elderly people being kicked out of their long term homes for quick and easy cash. It feels like more landlords need to watch It's a Wonderful Life.

The author uses Logos and Ethos here, quoting a UNM professor, Elizabeth Elia. She states many New Mexicans are either unaware of the resources available to them, are simply too busy to manage everything, or both.

The author uses Pathos here, appealing to the emotions of the reader showing the different excuses landlords craft up to illegally evict renters, invoking anger or sadness.

The authors intended purpose of the text is to inform New Mexico voters of landlords mistreating their tenants, illegally evicting them from their home at an astounding rate especially in New Mexico.

people need to protect themselves

important to know

people assume

not be accepted=suicide

people think being gay is just a rebellion against other people?

Showing that quantum computing is not only a threat. While that is the focus of the article, it will be used for good too

Where does he say that? references/Citation not found

Showing interest not only from scholars and technology specialists, but also from a more general/broad public

Are the studies cited?

eLife Assessment

This useful study introduces MULTI i2, a robust and high-throughput method to measure Plasmodium falciparum viability in the presence of drugs. This new assay has the premise to offer significant time and cost savings over the traditional Parasite Reduction Rate (PRR) assay and should enable faster screening of drug combinations, which is urgently needed in the field. The assay is well validated, with convincing data showing it can reproduce known drug interactions and identify new interaction patterns, but the study falls short of demonstrating broad applicability.

Reviewer #1 (Public review):

Summary:

The authors describe a clever genetic system based on rapamycin-inducible expression of a beta-galactose reporter. The authors compare this spectrophotometer-based readout to the parasite reduction rate version 2 (PRRv2) recently described by some of the same authors and based on incorporation of [3H]-hypoxanthine. The results are generally comparable, with some differences for slower-acting compounds. The authors report that this format is better suited for higher-throughput studies and requires less time to quantify time-dependent onset of parasiticidal action compared with the PRRv2.

This is a very well-executed and well-described body of work with a comprehensive set of analyses. The revised manuscript provided more context in comparing this method to other methods in the field, with a clearer explanation that the current MULT-i2 assay focuses on assessing viability, whereas other methods are more focused on assessing growth inhibition. The new assay is also faster than the prior PRR methodology. The current design will be useful to stay multi-drug combinations. The authors state that this parasite line will be available from BEI with an accompanying MTA. Other earlier comments and concerns were very well addressed by the authors.

Reviewer #2 (Public review):

Summary

Antimalarial combination therapy is the standard of care for malaria, a disease that impacts hundreds of millions of people annually. Combination therapy is crucial for effectively treating the disease and delaying the emergence of drug resistance. Despite the importance of choosing appropriate partner antimalarials for combination therapy, drug interactions are typically evaluated late in the course of drug development. Standard in vitro assays that determine synergistic, antagonistic or additive interactions between drug combinations rely on measuring inhibition of parasite proliferation, which is inadequate for translation to pharmacodynamic models for parasite clearance in the patient. Direct measurement of parasite viability under drug treatment has previously relied on methods that are labor and resource intensive, limiting applications to single compounds and single concentrations. Here, Hellingman et al make use of an inducible chemiluminescence reporter to measure cell viability and apply this novel approach to quantify drug interactions. The methodology is a significant improvement upon prior methods, requiring significantly fewer resources, half the time, and substantially less handling than the standard PRRv2 assay, whilst maintaining high resolution and sensitivity.

They assess the limit of detection for the improved method and cross-reference their results for single drugs at a single concentration with the currently standard PRRv2 assay. The authors next established analytical methods to characterize the impact of drug combinations on parasite viability using the GDPI pharmacodynamic model and compare their MULT-i2 assay to the prior cPRR approach. Their refined workflow allowed them to comprehensively evaluate the known synergistic combination between atovaquone and proguanil with greater resolution than the comparable cPRR assay and identified additional interaction parameters between the fast-acting antimalarials piperaquine and pyrimethamine. Overall, the authors demonstrate that their inducible lacZ system provides significant advantages compared with prior approaches to determine parasite viability. They convincingly demonstrate the strengths of their approach by characterizing two antimalarial combinations at much greater resolution than previously possible with prior methods. The system and methods established here will be particularly useful for evaluating novel antimalarial combinations with chemical series in preclinical evaluation and to optimize future antimalarial therapies.

Strengths:

The streamlined approach relies on induction of the lacZ enzyme only after drug washout. As opposed to when stably expressed, this allows the authors to estimate parasite viability without undergoing serial dilutions to estimate viable parasite titers. This innovation vastly reduced resource and time intensity, enabling greater throughput for parasite viability estimation. The established methodology and analysis pipeline enabled the testing of 49 drug combinations for parasite viability in the MULT-i2 assay compared to only 9 in the conventional cPRR assay. This provided improved resolution in the ability to estimate drug combination parameters in a pharmacodynamic model. The ability to comprehensively characterize combination pharmacodynamic properties in vitro will have important implications for downstream modelling of in vivo combinations, and for optimizing future antimalarial combination therapies.

The authors made good use of modelling and AICc for parametric estimation and model evaluation to demonstrate the advantages of the richer dataset afforded by the MULT-i2 assay.

Weaknesses:

The authors correctly identified a range of confounding effects that lead to artefacts in their assay results when compared to the cPRR assay. For instance, the authors observed reduced signal at high parasite density during recovery due to overgrowth and likely enzyme degradation, and suggested residual signal may remain from non-proliferating sexual stage parasites surviving drug treatment that would not be detected in the cPRR assay.<br /> Measurement of parasite viability in the MULT-i2 assay was achieved by extrapolating chemoluminescence signal to that of a serial dilution of parasites made at the initiation of drug treatment. How did the authors account for differing levels of enzyme expression at early (eg ring) vs late stage parasites (trophozoite or schizonts)? Were cultures synchronized prior to initiation of assays? Could differences in life-cycle progression following drug treatment be an additional confounding factor that may account for differences with the PRRv2 assay?

The addition of an inducible element is an improvement of their earlier lacZ/β-galSENSOR (PMID: 41575867), however, the authors fail to explain why this is an improvement and how this adds additional merit over the initial system. While the authors compare their new assay to the PRRv2, they fail to compare it to their own non-inducible lacZ/β-galSENSOR system. Their non-inducible system already showed superiority to the cPRR assays and it would be good to show how they compare and what the advantages of the new system are over the old. Eg how is the signal to noise improved? How does the sensitivity compare? How quickly does the can the signal be detected after induction? They show signal after 48h but it would be very useful to the community to look at earlier timepoints as well and compare it to the uninduced line and a line that has been induced 48h earlier to match the expression patterns throughout the lifecycle (something like 2h,4h,6h, 12h and 24h).

Is the chemiluminescence signal for the i-lacZ induced parasites comparable to the stably expressed lacZ parasites previously characterized by the group? If so, do the authors consider this inducible iteration a complete replacement for PRR assays?

Comments on revised version:

The authors adequately addressed our comments and the resulting manuscript describes a specialized resource for antimalarial drug development.

Reviewer #3 (Public review):

In this manuscript, the authors strived to develop a highly efficient drug survival assay for in vitro cultured human malaria parasites P. falciparum. This was done by generating a transgenic P. falciparum line using a creLox strategy that allows detection of (presumably) viable parasites by a β-lactamase assay. To estimate the Limit of quantification of the recombined P. falciparum NF54i-lacZ, the authors ultimately designed a protocol in which viable parasites are detected by the luminescence of β-D-galactoside generated by β-lactamase within the transgenic parasites. For this, the parasite must be incubated with rapamycin for 120 hours to induce CreLox recombinase, which places β-lactamase under an active promoter. Using this assay, termed MULTI-i2, the author shows interactions between two antimalarial drug pairs that were previously demonstrated by another assay. In the case of pyronaridine and piperaquine pair, the NULT-i2 assay generated some additional insights compared to the previous assay, presumably by virtue of including more concentration datapoints. In conclusion, the authors argue that the MULTI-i2 assay is much less resource-intensive and time-consuming and can be applied on a large scale at a much lower cost and with the highest efficiency.

Overall, the data generated in this manuscript are clear and well represented, and I am convinced that MULTI-i2 provides yet another of many drug assays for malaria parasites and could be put to good use. However, I struggle to fully appreciate the merit of his study, as the manuscript reads more like a technical document than a scientific study.

I particularly lack an understanding of the strengths and weaknesses/limitations of the MULTI-i2 methodology and, thus, its applicability. I also do not fully appreciate the need for such an elaborate luminescence-based experimental setup. It would be good if some of these issues were addressed.

Specifically:

(1) The whole assay is based on detecting parasites by luminescence after 120 hr (5 days) after drug exposure. During that time, presumably the parasites that survived the drug pressure regrow to a detectable level and, at the same time, perform efficacious CreLox-based recombination to produce β-D-galactoside for detection. Is this necessary? How superior is this detection method to other methods, such as Fluorescence-assisted Cell Sorting (FACS) e.t.c.? Moreover, the 5-day growth-CreLox-β-D-galactoside production could introduce a series of confounding effects. In my view, more studies (beyond comparisons with a single existing method) would be useful for understanding this entire process.

(2) Related to that above, how would MULTI-i2 perform in case of drugs that do not necessarily kill all parasites, such as artemisinin? In the case of artemisinin, it is becoming evident that at least a small fraction of the parasite revives after treatment via a temporary dormancy state. This has, in fact, also been shown for other drugs such as mefloquine, pyrimethamine, etc. Would such a situation produce a range of false readings? In general, in its current state, it is hard to see what the limitations of this method are, which makes it hard to decide whether to use it for a particular application.

(3) Given the stated cost and labor efficiency of MULTI-i2, it is disappointing to see only two applications for two drug pairs: atovaquone/proguanil and piperquine/pyronaridine, for both of which their interactions were already known. The manuscript would benefit greatly if the authors demonstrated more drug interactions and identified (and ultimately validated) new ones. This would certainly make MULT-i2 method more attractive. In particular, it would be nice to see if one could use MULTI-i2 for studies of triple combinations as enthusiastically suggested.

(4) Throughout the manuscript, the authors claim that MULTI-i2 is considerably less expensive and can be done much faster than previous methods. In my view, this is not exactly a scientific argument. The cost of an assay depends heavily on the cost of reagents and labor, which are subject to market price fluctuations. The efficiency and time consumption can very much depend on laboratory organization etc. Unless the author could specifically demonstrate where and how these assays are cheaper and faster, I suggest not discussing this.

Comments on revisions:

I have no more comments.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors describe a clever genetic system based on rapamycin-inducible expression of a beta-galactose reporter. The authors compare this spectrophotometer-based readout to the parasite reduction rate version 2 (PRR v2) recently described by some of the same authors and based on incorporation of [³H]-hypoxanthine. The results are generally comparable, with some differences for slower-acting compounds. The authors report that this format is better suited for higher-throughput studies and requires less time to quantify the time-dependent onset of parasiticidal action compared with the PRR v2.

Strengths:

This is a very well-executed and well-described body of work with a comprehensive set of analyses.

Weaknesses:

The authors should revise their text to also describe other methods used to quantify parasite growth. This method saves time compared to the PRR v2 but is too complex for simple screening of antiplasmodial activity of agents tested alone. Its value lies in assessing the speed of action of compounds tested in combination.

We thank reviewer 1 for the supportive feedback and for raising some important points.

Many antimalarials have quite specific times of action. Are these MULT-i² assays, and the comparator PRR v2 assays, conducted with asynchronous cultures? This should be described in the methods and referred to in the text (apologies if I missed some references).

We thank the reviewer for this important comment. Both, the MULT-i² and PRR v2 assays were performed using asynchronous parasite cultures. This information is included in the Methods section together with the relevant references. To improve clarity, we have also explicitly stated this in the main text.

The authors correctly state that flow cytometry-based readouts, such as with MitoTracker alone, can limit throughput and that MitoTracker alone can produce spurious results. The authors should cite work from other labs that combine MitoTracker with a nuclear dye, such as SYBR Green I. I think others have also been used, such as YoYo-1, which overcomes the limitations of using MitoTracker alone. Also, many labs use a nuclear dye such as SYBR Green I in a spectrophotometer-based format that enables rapid processing of plates at scale (96, 384, or even 1536 wells per plate). Luciferase-based screens have also been used in large-scale screening campaigns. The introduction should cite these various approaches, especially as the MULT-i² method is quite a complex screen with an initial period of drug exposure (up to 3 days) followed by a five-day phase initiated by rapamycin addition to induce expression of the beta-gal sensor.

We thank the reviewer for this helpful suggestion. In the Introduction we mention and describe alternative approaches for assessing parasite viability. This also includes the work by Maiga et al., which combines MitoTracker with a nuclear dye to improve the reliability of flow cytometry-based readouts. We have revised the text and now explicitly mention the use of dual staining to make this discussion more explicit.

We agree that several additional methods, such as luciferase-based reporter systems, have been successfully applied in antimalarial screening. However, these approaches are primarily designed to assess parasite growth inhibition rather than directly measuring parasite viability after drug exposure, which is the focus of the present study. Readout methods used to assess parasite viability in a PRR assay setup are so far based on HRP2-ELISA (de Carvalho et al.), MitoTracker and SYBR green staining (Maiga et al.) and [³H]-hypoxanthine incorporation (Sanz et al.; Walz et al.) as cited in the manuscript. Many other readout methods to assess parasite growth have other limitations as briefly discussed in Hellingman et al., 2024. A comprehensive comparison and review of all available readout methods would therefore be beyond the scope of this manuscript.

It would be helpful for authors to provide some indication of the cost comparison between the PPR v2 and MULT-i².

We thank the reviewer for this valuable suggestion. We agree that a comparison of the costs associated with the PRR v2 and MULT-i² assays would be informative, but while the consumable costs provide one measure of assay expense, we consider the reduction in hands-on time and the simplified workflow to be the main contributors to the overall cost advantage of the MULT-i² assay. These reductions in labor requirements are subject to large regional differences and impossible for us to access. Nevertheless, together with the increased throughput and the reduced labor, make the MULT-i² assay more cost-effective for larger-scale applications compared with the PRR v2 assay.

Also, the authors should indicate whether these reagents will be deposited in a repository such as BEI Resources. They should also indicate conditions for other groups to request these materials, such as whether an MTA is required.

We thank the reviewer for this important suggestion. The engineered parasite line will be made available for non-commercial use to other researchers upon request. An MTA will be required excluding commercial use of the provided strains. The detailed code used for data analysis is available upon request, and an example code file has already been included as a Supplementary File.

The pharmacological models are interesting, but likely well out of the range of expertise of many labs. Has code been deposited into public repositories that make it possible for other labs to implement these analyses?

We thank the reviewer for this valuable comment. We agree that implementation of pharmacological modeling approaches can represent a barrier for laboratories without prior experience in pharmacometric analysis, particularly due to the requirement for specialized software such as NONMEM. To facilitate implementation, an example code is provided in the Supplementary File. The final model was developed using a forward–backward selection approach for parameter estimation and model refinement as described in the Methods section. These additions should help other researchers adapt the approach to their own datasets.

Reviewer #2 (Public review):

Summary

Antimalarial combination therapy is the standard of care for malaria, a disease that impacts hundreds of millions of people annually. Combination therapy is crucial for effectively treating the disease and delaying the emergence of drug resistance. Despite the importance of choosing appropriate partner antimalarials for combination therapy, drug interactions are typically evaluated late in the course of drug development. Standard in vitro assays that determine synergistic, antagonistic, or additive interactions between drug combinations rely on measuring inhibition of parasite proliferation, which is inadequate for translation to pharmacodynamic models for parasite clearance in the patient. Direct measurement of parasite viability under drug treatment has previously relied on methods that are labor and resource-intensive, limiting applications to single compounds and single concentrations. Here, Hellingman et al make use of an inducible chemiluminescence reporter to measure cell viability and apply this novel approach to quantify drug interactions. The methodology is a significant improvement upon prior methods, requiring significantly fewer resources, half the time, and substantially less handling than the standard PRR v2 assay, whilst maintaining high resolution and sensitivity.

They assess the limit of detection for the improved method and cross-reference their results for single drugs at a single concentration with the currently standard PRRv2 assay. The authors next established analytical methods to characterize the impact of drug combinations on parasite viability using the GDPI pharmacodynamic model and compared their MULT-i² assay to the prior cPRR approach. Their refined workflow allowed them to comprehensively evaluate the known synergistic combination between atovaquone and proguanil with greater resolution than the comparable cPRR assay and identified additional interaction parameters between the fast-acting antimalarials piperaquine and pyrimethamine. Overall, the authors demonstrate that their inducible lacZ system provides significant advantages compared with prior approaches to determine parasite viability. They convincingly demonstrate the strengths of their approach by characterizing two antimalarial combinations at much greater resolution than previously possible with prior methods. The system and methods established here will be particularly useful for evaluating novel antimalarial combinations with chemical series in preclinical evaluation and to optimize future antimalarial therapies.

Strengths:

The streamlined approach relies on induction of the lacZ enzyme only after drug washout. As opposed to when stably expressed, this allows the authors to estimate parasite viability without undergoing serial dilutions to estimate viable parasite titers. This innovation vastly reduced resource and time intensity, enabling greater throughput for parasite viability estimation. The established methodology and analysis pipeline enabled the testing of 49 drug combinations for parasite viability in the MULT-i² assay compared to only 9 in the conventional cPRR assay. This provided improved resolution in the ability to estimate drug combination parameters in a pharmacodynamic model. The ability to comprehensively characterize combination pharmacodynamic properties in vitro will have important implications for downstream modelling of in vivo combinations, and for optimizing future antimalarial combination therapies.

The authors made good use of modelling and AICc for parametric estimation and model evaluation to demonstrate the advantages of the richer dataset afforded by the MULT-i² assay.

We thank reviewer 2 for her/his appreciation of our work.

Weaknesses:

The authors correctly identified a range of confounding effects that lead to artefacts in their assay results when compared to the cPRR assay. For instance, the authors observed reduced signal at high parasite density during recovery due to overgrowth and likely enzyme degradation, and suggested residual signal may remain from non-proliferating sexual stage parasites surviving drug treatment that would not be detected in the cPRR assay.

Measurement of parasite viability in the MULT-i² assay was achieved by extrapolating the chemoluminescence signal to that of a serial dilution of parasites made at the initiation of drug treatment. How did the authors account for differing levels of enzyme expression at early (e.g., ring) vs late stage parasites (trophozoite or schizonts)? Were cultures synchronized prior to initiation of assays? Could differences in life-cycle progression following drug treatment be an additional confounding factor that may account for differences with the PRR v2 assay?

We thank the reviewer for raising this important point. All, the MULT-i² and PRR v2 assay were performed using asynchronous parasite cultures. We have clarified this in the revised manuscript.

We agree that parasite developmental stages may influence the MULT-i² readout, as LacZ expression levels differ between parasite stages, with differences observed between ring stages and more mature trophozoite/schizont stages as published by Hellingman et al., 2024. This represents a potential source of variability, as the MULT-i² assay quantifies the amount of expressed reporter enzyme rather than directly measuring parasite numbers at the time of readout. The use of asynchronous cultures minimizes the impact of stage-specific effects by providing a mixed parasite population representative of the natural distribution of developmental stages. Nevertheless, we acknowledge that differences in parasite stage progression following drug exposure may contribute to variation in the extrapolated parasite numbers and may partially explain differences observed between the MULT-i² and PRR v2 assay measurements. We have added this consideration to the Discussion.

The addition of an inducible element is an improvement of their earlier lacZ/β-gal^SENSOR (PMID: 41575867); however, the authors fail to explain why this is an improvement and how this adds additional merit over the initial system. While the authors compare their new assay to the PRR v2, they fail to compare it to their own non-inducible lacZ/β-gal^SENSOR system. Their non-inducible system already showed superiority to the cPRR assays, and it would be good to show how they compare and what the advantages of the new system are over the old. e.g., how is the signal-to-noise improved?

We thank the reviewer for this important comment. The main improvement provided by the inducible system is the temporal separation of parasite growth/drug exposure from reporter expression. In the original non-inducible lacZ/β-gal^SENSOR system, reporter expression occurs continuously throughout the assay, resulting in accumulation of β-galactosidase during parasite growth/drug exposure and therefore an increasing background signal. Consequently, quantification relies on endpoint reporter levels and does not allow the reporter expression window to be standardized independently of parasite exposure history.

In contrast, in the MULT-i² system, reporter expression is initiated only after addition of rapamycin post-antimalarial drug washout. This prevents reporter accumulation during the drug exposure window and ensures a defined reporter enzyme accumulation window after drug exposure. Importantly, this allows parasite numbers to be extrapolated from a calibration curve generated at the time of induction, which would not be possible with the non-inducible system because reporter expression would continue after drug removal and would depend on the previous culture history.

We have revised the manuscript to more clearly describe these advantages and to emphasize that the key benefit of the inducible system is not simply an increase in signal intensity, but improved control of reporter expression, reduced background accumulation, and the ability to perform quantitative parasite reduction rate measurements.

How does the sensitivity compare? How quickly does the can the signal be detected after induction? They show signal after 48h, but it would be very useful to the community to look at earlier timepoints as well and compare them to the uninduced line and a line that has been induced 48h earlier to match the expression patterns throughout the lifecycle (something like 2h,4h,6h, 12h, and 24h).

We thank the reviewer for this important suggestion. We acknowledge that the sensitivity of the MULT-i² readout depends on both the initial parasite density and the duration of the induction period and that a detailed characterization of the induction kinetics, including earlier time points after rapamycin addition, would provide additional information on the sensitivity and temporal resolution of the MULT-i² system.

In the present study, we focused on the time window relevant for application of the assay in a PRR assay workflow and routine drug screening setting. Earlier time points (<24 h after induction) were therefore not systematically evaluated. The selected time points were chosen based on the expected kinetics of the loxP-DiCre recombination system, which has previously been reported to achieve high recombination efficiency within one asexual parasite cycle, (Collins et al., 2013) and shown with own data in this study, as well as on practical considerations for implementation in routine workflows.

Is the chemiluminescence signal for the i-lacZ induced parasites comparable to the stably expressed lacZ parasites previously characterized by the group? If so, do the authors consider this inducible iteration a complete replacement for PRR assays?

We thank the reviewer for this question. The chemiluminescence signal obtained with the inducible lacZ (i-lacZ) parasites is comparable to that observed with the previously characterized constitutively expressing lacZ parasites. However, the inducible system provides an important additional advantage by avoiding continuous β-galactosidase production and accumulation during parasite growth, thereby reducing background signal and enabling a controlled reporter expression window.

We do not consider the MULT-i² assay to be a replacement for classical PRR assays. Rather, we consider it a complementary approach that enables more efficient screening and characterization of drug combinations, particularly by providing information on the time-dependent onset of parasiticidal activity in a higher-throughput format. Promising combinations identified using MULT-i² assay can subsequently be investigated in more extensive PRR assays.

The authors observed differences between their i-lacZ assay and conventional PRR assays attributable to the accumulation of lacZ enzyme at higher levels of surviving parasites, followed by degradation. Have the authors tested how long lacZ remains stable in standard or overgrown parasite cultures?

We thank the reviewer for this important question. We assessed the stability of β-galactosidase activity in parasite lysates stored under different conditions and observed that the enzymatic activity remained stable for up to 21 days when lysates were stored at either -20°C or 37°C (Hellingman et al., 2024).

We have not systematically characterized β-galactosidase stability in standard or overgrown parasite cultures. However, in experiments involving overgrown cultures, we observed that the β-galactosidase-derived signal decreased rapidly in overgrown culture settings, suggesting that enzyme stability in overgrown cultures is lower than in standard cultures and parasite lysates.

At what parasitemia were the counts reported in Figure 1D conducted at?

We thank the reviewer for this clarification request. The measurements shown in Figure 1D were performed at approximately 3% parasitemia, assuming an erythrocyte infection rate of 10-fold within 48 hours as parasite cultures were initiated at 0.3% parasitemia and incubated for 48 hours under rapamycin before the measurements were performed.

Figure 2: Is the increasing background in DMSO-treated parasites attributable to leakage of the di-cre system contributing to a background level of LacZ induction? To what extent would this impact results in the PRR assay format?

We thank the reviewer for this important observation. We agree that low-level leakage of the loxP-DiCre system may contribute to the increased lacZ signal observed in DMSO-treated parasites under overgrowth conditions. However, this effect was only observed when parasites were allowed to proliferate extensively in the absence of effective drug pressure.

In the context of the MULT-i² assay, these conditions correspond to compound concentrations that do not affect parasite survival or replication. Such concentrations are outside the range of interest for evaluating antimalarial activity, as they represent inactive treatment conditions. Therefore, although reporter leakage may contribute to background signal under extreme overgrowth conditions, we expect this effect to have a negligible impact on the interpretation of MULT-i² assay results.

Please define the abbreviations used (e.g., NONMEM and DV).

We thank the reviewer for pointing this out. We have revised the manuscript to define all abbreviations at their first occurrence in the text and have added the relevant terms to the abbreviation list.

Line 297: cPRR assay - give citation.

We thank the reviewer for pointing this out. We have added the appropriate citation for the cPRR assay at the indicated location in the revised manuscript.

The 2 in MULT-i² is not always superscripted.

We thank the reviewer for pointing this out. We have corrected the formatting throughout the manuscript to ensure that the “2” in MULT-i² is consistently presented as a superscript where appropriate.

Reviewer #3 (Public review):

In this manuscript, the authors strived to develop a highly efficient drug survival assay for in vitro cultured human malaria parasites P. falciparum. This was done by generating a transgenic P. falciparum line using a creLox strategy that allows detection of (presumably) viable parasites by a β-lactamase assay. To estimate the Limit of quantification of the recombined P. falciparum NF54i-lacZ, the authors ultimately designed a protocol in which viable parasites are detected by the luminescence of β-D-galactoside generated by β-lactamase within the transgenic parasites. For this, the parasite must be incubated with rapamycin for 120 hours to induce CreLox recombinase, which places β-lactamase under an active promoter. Using this assay, termed MULT-i², the author shows interactions between two antimalarial drug pairs that were previously demonstrated by another assay. In the case of pyronaridine and piperaquine pair, the MULT-i² assay generated some additional insights compared to the previous assay, presumably by virtue of including more concentration datapoints. In conclusion, the authors argue that the MULT-i² assay is much less resource-intensive and time-consuming and can be applied on a large scale at a much lower cost and with the highest efficiency.

Overall, the data generated in this manuscript are clear and well represented, and I am convinced that MULT-i² provides yet another of many drug assays for malaria parasites and could be put to good use. However, I struggle to fully appreciate the merit of his study, as the manuscript reads more like a technical document than a scientific study.

We thank reviewer 3 for her/his appreciation of our work.

I particularly lack an understanding of the strengths and weaknesses/limitations of the MULT-i² methodology and, thus, its applicability. I also do not fully appreciate the need for such an elaborate luminescence-based experimental setup. It would be good if some of these issues were addressed.

Specifically:

(1) The whole assay is based on detecting parasites by luminescence after 120 hr (5 days) after drug exposure. During that time, presumably the parasites that survived the drug pressure regrow to a detectable level and, at the same time, perform efficacious CreLox-based recombination to produce β-D-galactoside for detection. Is this necessary? How superior is this detection method to other methods, such as Fluorescence-assisted Cell Sorting (FACS), etc? Moreover, the 5-day growth-CreLox-β-D-galactoside production could introduce a series of confounding effects. In my view, more studies (beyond comparisons with a single existing method) would be useful for understanding this entire process.

We thank the reviewer for raising this important point regarding the rationale, applicability, and limitations of the MULT-i² methodology.

Quantification of viable parasites after drug exposure remains challenging, particularly when surviving parasites are present at low frequencies or require extended recovery periods. Current approaches, such as the parasite reduction ratio (PRR) assay based on [³H]-hypoxanthine incorporation, provide sensitive measurements of replicating parasites but are labor-intensive, require specialized infrastructure, and are not easily scalable for large numbers of drug combinations. Alternative approaches based on HRP2 detection no longer rely on radioactive readouts but generally provide lower sensitivity, particularly when quantifying low levels of surviving parasites within a shorter time frame.

The MULT-i² assay was developed to address these limitations by combining a highly sensitive chemiluminescent β-galactosidase readout with an inducible reporter system. The 5-day induction period after drug exposure serves as a controlled gene expression step, allowing surviving parasites to recover and produce sufficient reporter signal for sensitive quantification using a standard plate reader. This approach enables higher-throughput assessment of parasiticidal activity while avoiding radioactive readouts and reducing the need for labor-intensive dilution-based approaches.

We acknowledge that the recovery and reporter expression period introduces additional biological steps compared with direct parasite detection methods and may therefore represent a potential source of variability. The MULT-i² assay is not intended to replace all existing viability measurements but rather to provide a complementary screening tool for investigating larger numbers of drug combinations. More detailed comparisons with additional detection platforms, including fluorescence-based approaches such as flow cytometry, would be valuable; however, a comprehensive comparison of all available parasite viability readouts was beyond the scope of this study. We have added more explanations to the Discussion including the strengths and limitations.

Related to that above, how would MULT-i² perform in case of drugs that do not necessarily kill all parasites, such as artemisinin? In the case of artemisinin, it is becoming evident that at least a small fraction of the parasite revives after treatment via a temporary dormancy state. This has, in fact, also been shown for other drugs such as mefloquine, pyrimethamine, etc. Would such a situation produce a range of false readings? In general, in its current state, it is hard to see what the limitations of this method are, which makes it hard to decide whether to use it for a particular application.

We thank the reviewer for raising this important point regarding the interpretation and applicability of the MULT-i² assay. We agree that distinguishing between growth inhibition assays and viability-based assays is essential when interpreting the response to drugs that induce temporary parasite dormancy or delayed recovery.

The MULT-i² assay was specifically developed as a viability-based approach and therefore differs fundamentally from conventional IC50 assays, which primarily measure inhibition of parasite growth during drug exposure and may not capture parasites that survive treatment through temporary growth arrest or dormancy. Similar to the PRR assay, the MULT-i² assay measures the ability of surviving parasites to recover and proliferate after drug exposure. Therefore, parasites that temporarily enter a dormant state but subsequently resume replication are expected to contribute to the measured signal rather than representing false-positive or false-negative results.

This is illustrated by the artemisinin experiments presented in this study, where the MULT-i² assay captures the recovery of surviving parasites following treatment as it does the PRR v2 assay.

Given the stated cost and labor efficiency of MULT-i², it is disappointing to see only two applications for two drug pairs: atovaquone/proguanil and piperquine/pyronaridine, for both of which their interactions were already known. The manuscript would benefit greatly if the authors demonstrated more drug interactions and identified (and ultimately validated) new ones. This would certainly make MULT-i² method more attractive. In particular, it would be nice to see if one could use MULT-i² for studies of triple combinations as enthusiastically suggested.

We thank the reviewer for this valuable suggestion. We agree that demonstrating additional applications, including triple-drug combinations, would further highlight the potential of the MULT-i² assay.

The primary aim of this study was to validate the MULT-i² methodology against the established PRR v2 assay and to demonstrate that the new platform can reproduce known parasiticidal interaction profiles while providing a more scalable workflow. For this reason, we selected well-characterized drug combinations, including atovaquone/proguanil and piperaquine/pyronaridine, which provide suitable benchmark systems for comparison with previous PRR data.

Although evaluation of a larger number of novel combinations and triple-drug regimens would be highly valuable, generating corresponding PRR datasets for direct comparison was beyond the scope of the current study.

Throughout the manuscript, the authors claim that MULT-i² is considerably less expensive and can be done much faster than previous methods. In my view, this is not exactly a scientific argument. The cost of an assay depends heavily on the cost of reagents and labor, which are subject to market price fluctuations. The efficiency and time consumption can very much depend on laboratory organization, etc. Unless the author could specifically demonstrate where and how these assays are cheaper and faster, I suggest not discussing this.

We thank the reviewer for this important comment. We agree that absolute assay costs can vary depending on local reagent prices, labor costs and laboratory infrastructure.

When comparing both methods under the same laboratory conditions, the total assay duration of the MULT-i² assay is shorter than that of the PRR assay (11 days (MULT-i²) compared with approximately 21–28 days (PRR) according to published protocols). In addition, the MULT-i² assay reduces labor-intensive processing steps and enables higher-throughput measurements using a plate reader for readout. These factors contribute to reduced workload and improved scalability, independent of fluctuations in individual reagent or personnel costs.

It is published in Applied and Environmental Microbiology doi: 10.1128/aem.00656-26

eLife Assessment

This study reports a novel function for syntaxin 11, a specialized SNARE protein critical for the immune system whose mutations cause familial hemophagocytic lymphohistiocytosis type 4. The data convincingly show that depletion of STX11 impairs store-operated calcium entry in Jurkat T cells and that this defect is recapitulated in primary cells from a patient suffering from the disease; the authors further show that the syntaxin interacts with the pore subunit of the ORAI1 channel and propose that it primes the channel by promoting the assembly of multimers before activation by its endogenous ligand, the ER Ca2+ sensing protein STIM1. This is a conceptually important claim that challenges the prevailing view that all structural transitions in ORAI1 are STIM-driven. The high-quality data strongly support the interpretations, but the discussion would be strengthened if the authors suggest alternative mechanisms and mention earlier studies reporting ORAI1 regulation by vesicular trafficking.

Reviewer #1 (Public review):

Summary:

Patients with STX11 mutations develop familial hemophagocytic lymphohistiocytosis Type 4, a fatal immune disorder marked by defective T and NK cell cytotoxicity and cytokine storm. The conventional explanation attributes this to impaired cytotoxic granule release, but this has never fully accounted for the broader disease picture. This study proposes an alternative mechanism. The authors show that STX11 is required for store-operated calcium entry through ORAI1 channels, which are essential for both cytotoxic killing and NFAT-driven gene expression in T cells. In STX11-deficient cells, ORAI1 currents drop, NFAT nuclear translocation fails, IL-2 expression is suppressed, and degranulation is impaired. These defects are largely rescued by ionomycin or a constitutively active ORAI1 mutant, placing the primary lesion at calcium signaling rather than the fusion machinery. Mechanistically, STX11 binds the C-terminal tail of ORAI1 via its Habc domain and maintains ORAI1 in a state competent for productive assembly prior to STIM1-dependent gating, a step the authors call "priming."

Strengths:

The paper identifies a novel and disease-relevant role for STX11 in calcium channel regulation and raises the possibility of using channel agonists as a therapeutic strategy in the disease. The biochemical and functional data are of high quality and generally consistent with the interpretation. The proposal that a non-conventional syntaxin directly interacts with ion channels to prime its activation is novel and interesting. Additional experiments now exclude the possibility that STX11 acts as a SNARE to sustain calcium fluxes by promoting the delivery of additional functional channels.

Weaknesses:

Previous studies reporting regulation of ORAI1 by vesicular trafficking are ignored and alternative mechanisms are not considered.

Reviewer #2 (Public review):

Summary:

Vig's lab delineates a critical role for STX11 in CRAC channel function, particularly in the context of the fatal immune disorder familial hemophagocytic lymphohistiocytosis type 4 (FHL4). They demonstrate that Syntaxin 11 directly binds and regulates Orai1, and that STX11 depletion abolishes CRAC currents and downstream signaling. Loss of STX11 reduces IL2 gene expression and impairs degranulation, both of which are rescued by the constitutively active Orai1 mutant H134S, whereas a gain‑of‑function mutant targeting the C‑terminus fails to restore these defects. The authors conclude that STX11 primes Orai1 for optimal local assembly that is independent of STIM1 yet required for CRAC channel gating.

Strengths:

This study is firmly grounded in disease biology and demonstrates that STX11 downregulation leads to profound functional defects. Using a comprehensive suite of methods and analyses, the authors interrogate the co-regulation of STX11 and Orai1 and present a near-complete view of STX11's modulatory role in CRAC channel function and downstream signaling pathways. The figures are clear, and the statistical analyses are rigorous and convincing.

Weaknesses:

The authors conclude that Syntaxin 11 directly binds Orai1. This conclusion is well supported by a multifaceted approach-including co-immunoprecipitation (co-IP), molecular dynamics simulations, co-localization/FRET assays, and targeted mutational analysis-all of which are thoroughly executed. While the interaction appears reasonably strong in co-IP experiments, the STX11-Orai1 interaction is comparatively weaker in pull-down assays, which the authors attribute to instability of the purified His-STX11 protein. A remaining gap is direct evidence of interaction in live cells; this is understandably challenging given that fluorescent tagging of STX11 is not feasible. Fully resolving this question lies beyond the scope of the present study and will require more advanced approaches to capture STX11 binding dynamics.

Comments on revised version.

The authors have addressed all my comments highly satisfactorily!

Author response:

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

eLife Assessment

This study reports a novel function for syntaxin 11, a specialized SNARE protein critical for the immune system whose mutations cause familial hemophagocytic lymphohistiocytosis type 4. The data convincingly show that depletion of STX11 impairs store-operated calcium entry in Jurkat T cells and that this defect is recapitulated in primary cells from a patient suffering from the disease; the authors further show that the syntaxin interacts with the pore subunit of the ORAI1 channel and propose that it primes the channel by promoting the assembly of multimers before activation by its endogenous ligand, the ER Ca2+ sensing protein STIM1. This is a conceptually important claim that challenges the prevailing view that all structural transitions in ORAI1 are STIM-driven. The data are high-quality and broadly consistent with the interpretation, but alternative mechanisms for the defects are not considered; additional work should rule out vesicular trafficking, discuss other mechanisms, and address methodological issues.

We thank the editor and reviewers for assessing our work. We have now included additional experiments in a new main Figure 2, which directly rule out any general or Orai1 plasma membrane trafficking defects in Syntaxin11-depleted cells. There are additional experiments and/or analysis in many other figures, throughout the paper. We have included new and missing methods, quantifications and calibrations, and provided response to each of the reviewer’s comments below.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Patients with STX11 mutations develop familial hemophagocytic lymphohistiocytosis Type 4, a fatal immune disorder marked by defective T and NK cell cytotoxicity and cytokine storm. The conventional explanation attributes this to impaired cytotoxic granule release, but this has never fully accounted for the broader disease picture. This study proposes an alternative mechanism. The authors show that STX11 is required for store-operated calcium entry through ORAI1 channels, which are essential for both cytotoxic killing and NFAT-driven gene expression in T cells. In STX11-deficient cells, ORAI1 currents drop, NFAT nuclear translocation fails, IL-2 expression is suppressed, and degranulation is impaired. These defects are largely rescued by ionomycin or a constitutively active ORAI1 mutant, placing the primary lesion at calcium signaling rather than the fusion machinery. Mechanistically, STX11 binds the C-terminal tail of ORAI1 via its Habc domain and maintains ORAI1 in a state competent for productive assembly prior to STIM1-dependent gating, a step the authors call "priming."

Strengths:

The paper identifies a novel and disease-relevant role for STX11 in calcium channel regulation and raises the possibility of using channel agonists as a therapeutic strategy in the disease. The biochemical and functional data are of high quality and generally consistent with the interpretation. The proposal that a non-conventional syntaxin directly interacts with ion channels to prime its activation is novel and interesting.

Weaknesses:

For readers to appreciate the value of patient experiments derived from a single individual, the authors should quote prior studies showing that STX11 protein levels are abolished in all known human STX11 mutations. The priming model, while functionally well-supported, rests on indirect structural evidence, and the precise conformational transition involved remains to be defined. These are acknowledged limitations, but alternate mechanisms have not been explored and formally excluded. More direct evidence should be provided to exclude the possibility that STX11 could act as a conventional SNARE and sustain calcium fluxes by promoting the delivery of additional ORAI1 channels from vesicles.

In the revised version, we have included references for all those prior STX11 human mutations that have been biochemically characterized till date. The reviewer has correctly pointed out that STX11 protein levels were almost abolished in almost all previously reported mutations. See line 168-173. Therefore, the prior STX11 patient mutations are essentially comparable to the frameshift mutation characterized in this study, in terms of STX11 protein depletion and, therefore, the mechanisms underlying the phenotypic defects reported here as well as earlier. We, therefore, believe that our data from even a single FHLH4 patient, with severely depleted STX11 levels, and additional knockdown studies across three different cell lines, are representative of majority of STX11 mutant FHLH4 patients that have been previously characterized.

Regarding the Reviewers’ concern that absence of STX11 as a conventional SNARE could affect Orai1 channel delivery from intracellular vesicles. We would like to point out the following:

(1) In Miao et al. 2013 (1), Figure 3C-D, we showed that expression of a dominant-negative mutant of NSF, a non-redundant protein in vesicle trafficking, impaired vesicle trafficking but did not affect SOCE. This experiment had essentially ruled out a role for vesicle trafficking in SOCE. In the same paper, we had also shown that Orai1 levels in the PM do not increase post-store depletion (Figure 3-figure supplement 2).

(2) SNAP23/25 form a four helical bundle with R- and Q-SNAREs in orchestrating vesicle fusion. In this paper, we have ruled out a direct role for SNAP23, SNAP25 and SNAP29 in SOCE (Figure 2-figure supplement 3).

(3) In v1 of this manuscript, we had shown that U2OS cells stably expressing Orai1-BBS-YFP have identical levels of Orai1 in the PM with and without STX11 depletion (Supplementary Figure 3B). This showed that the biosynthesis or delivery of Orai1 to the PM is not affected by STX11 depletion. The levels were also assessed in store-depleted U2OS cells but not included because in Miao et al. 2013 we had already established that levels of PM Orai1 remain essentially equal in resting versus store-depleted cells.

In the revised version, we have included the data from store-depleted cells in U2OS and also done quantification of PM Orai1 in HEK293 and Jurkat T cells. In addition, we have added three independent membrane trafficking/ vesicle secretion assays performed in STX11-depleted cells (new Figure 2 and associated supplements). In all cases, we find no evidence of Orai1 in intracellular vesicles or a general defect in membrane trafficking/ secretion in STX11-depleted cells. Orai1 is constitutively and stably expressed in the PM in resting as well as store-depleted cells in three different cell lines.

(4) Most importantly, in Figure 7I-J of this manuscript, we showed that calcium influx from a constitutively active mutant Orai1 (Orai H134S) is identical between STX11-depleted and scramble control cells. If wildtype Orai1 was indeed stuck in vesicles in STX11-depleted cells, then how would mutant H134S Orai1 be able to rescue the defect in SOCE? We have included the quantification of PM levels of Orai1 mutants w.r.t WT Orai1 in new Figure 8-figure supplement 3B and 3D.

In summary, we have now done several new experiments to directly measure Orai1 levels in the PM and general vesicle trafficking assays in HEK293 and Jurkat T cells and have found no defects in these upon STX11 depletion.

Regarding STX11 induced precise conformational transition, we are trying to setup collaborations with scientists who might be able to visualize this in situ. Please note that while purification of isolated pore subunits of ion channels followed by crystallization or expression in synthetic membranes for cryo-EM is currently considered a gold standard in the analysis of ion channel pore subunits, we have shown that ion channels are dynamic macromolecular complexes, in vivo (2), where synaptic proteins dynamically bind to induce conformational changes and affect their stoichiometry (2). Please also see (3) and (4). More advanced approaches, therefore, need to be developed to enable visualization of the dynamics of ion channel macromolecular complexes in their native environment in situ. In the absence of such approaches, the structural insights obtained from detergent-purified isolated subunits will remain incomplete.

Reviewer #2 (Public review):

Summary:

Vig's lab delineates a critical role for STX11 in CRAC channel function, particularly in the context of the fatal immune disorder familial hemophagocytic lymphohistiocytosis type 4 (FHL4). They demonstrate that Syntaxin 11 directly binds and regulates Orai1, and that STX11 depletion abolishes CRAC currents and downstream signaling. Loss of STX11 reduces IL2 gene expression and impairs degranulation, both of which are rescued by the constitutively active Orai1 mutant H134S, whereas a gain‑of‑function mutant targeting the C‑terminus fails to restore these defects. The authors conclude that STX11 primes Orai1 for optimal local assembly that is independent of STIM1 yet required for CRAC channel gating.

Strengths:

This study is firmly grounded in disease biology and demonstrates that STX11 downregulation leads to profound functional defects. Using a comprehensive suite of methods and analyses, the authors interrogate the co-regulation of STX11 and Orai1 and present a near-complete view of STX11's modulatory role in CRAC channel function and downstream signaling pathways. The figures are clear, and the statistical analyses are rigorous and convincing.

Weaknesses:

The authors conclude that Syntaxin 11 directly binds Orai1. This conclusion is well supported by a multifaceted approach, including co-immunoprecipitation (co-IP), molecular dynamics simulations, co-localization/FRET assays, and targeted mutational analysis-all of which are thoroughly executed. While the interaction appears reasonably strong in co-IP experiments, the STX11-Orai1 interaction is comparatively weaker in pull-down assays, which the authors attribute to instability of the purified His-STX11 protein. A remaining gap is direct evidence of interaction in live cells; this is understandably challenging given that fluorescent tagging of STX11 is not feasible. Fully resolving this question lies beyond the scope of the present study and will require more advanced approaches to capture STX11 binding dynamics.

We thank the reviewer for acknowledging that analysis of the dynamic binding of STX11 will require standardization of advanced techniques which are beyond the scope of the present study. We plan to continue developing methods that will allow us to visualize the binding and unbinding of STX11 to Orai1 in vivo.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Mechanistic issues:

(1) More direct evidence should be provided to exclude the possibility that STX11 could act as a conventional SNARE and sustain calcium fluxes by promoting the delivery of additional functional channels, stored in secretory vesicles or recycling endosomes, to the membrane. A significant fraction of the ORAI1 channel is in vesicles, and the mobilization of this intracellular pool regulates the rates of calcium fluxes in HEK-293 cells (PMID 26116575) and effector T cells (PMID: 35217583). Mobilization of this pool could account for part or all of the functional effects reported here. The only evidence that STX11 depletion does not impact the plasma membrane availability of the channel relies on one single flow cytometry profile (Supplementary Figure 3). This experiment is performed in U2OS cells stably expressing a fusion protein containing an extracellular bungarotoxin site. This cellular system is only used here; the other data are obtained either in HEK-293 cells or in Jurkat T cells. The level of endogenous STX11 in U2OS cells is unknown, and the efficiency of protein depletion has not been assessed. The efficiency of depletion should be shown, and the total number of channels assessed by comparing the expression levels of permeabilized and non-permeabilized cells. A flow cytometry profile of cells treated with thapsigargin should be included to match the experimental conditions of functional recordings. It would be valuable to repeat this experiment in Jurkat T cells by expressing ectopically the channel tagged on the extracellular side. To further exclude the involvement of vesicular trafficking, the authors should also evaluate the contribution of VAMP8, as this R-SNARE has been proposed to interact with STX11 to regulate the exocytosis of specialized granules in cytotoxic T cells (PMID: 26124288).

We have not observed significant levels of intracellular Orai1, as described in the PMID 26116575 paper. Multiple technical reasons could explain the artefactual appearance of intracellular Orai1. HEK293 is an embryonic kidney cell line, with most cells showing a distinct spindle shape and filopodia as shown on the ATCC website (https://www.atcc.org/products/crl-1573). The HEK cells shown throughout the Hodeify et al. 2015 paper (PMID 26116575) lack this typical morphology that majority of the HEK cells should show. Transfecting cells with high amounts of DNA and liposomes can severely affect the health and morphology of the cells leading to artifacts where PM proteins appear to be stuck intracellularly. The plasma membrane of the cell in movie 1 of PMID 26116575, for instance, also shows membrane blebs or membrane ruffles. The authors should have used a PM marker such as WGA (wheat germ agglutinin) to distinguish PM Orai1 from any intracellular Orai1 to establish whether what appear as intracellular Orai1 vesicles are not blebs of PM Orai1. Similarly, no endo/exocytotic vesicle marker is used to distinguish them from Orai1’s presence in apoptotic vesicles of unhealthy cells. In view of the overall abnormal morphology and any PM or intracellular vesicle marker, the claim that Orai1 resides in intracellular vesicles is unfounded.

Similarly, the PMID: 35217583, quoted by reviewer #1, is about in vitro differentiated primary mouse T cells. The study lacks any detail of the generation of the HA-tagged mouse Orai1 plasmid, used in the study, or even a back reference to show how this plasmid was validated for normal expression in primary T cells earlier. Ectopic expression of CMV promoter-driven plasmids in mouse primary T cells is extremely challenging and often results in poor cell health and incomplete and selective expression in only 5-10% of cells. It is unclear if the same HA-tagged human Orai1 plasmid that was used in PMID 26116575 is being used in this study to express in primary mouse cells. In the absence of all this information, it is unclear whether there was an issue with the generation of a new HA-tagged mouse Orai1 construct, which made the protein get stuck intracellularly. Or potentially the expression of a human protein in mouse primary T cells is the problem. The functional verification of the construct by showing rescue of SOCE in Orai1-deficient primary mouse cells is an absolutely essential control but is missing. In the absence of these controls, one cannot disregard decades of robust data on the localization of Orai1 in the PM from multiple labs and papers that have established its PM localization conclusively (5) (6).

Most importantly, in both the PMID 26116575 and PMID: 35217583, HA tagged-Orai1 is detected using a bivalent antibody, followed by secondary antibody. It is well known that cross-linking of cell surface receptors or proteins using bivalent antibodies has a major caveat that involves antibody-mediated clustering and capping, especially in lymphocytes, which typically induces rapid internalization of the entire antigen-antibody complex. The paper by Sekine-Aizawa et al., in 2004, showed that tagging of receptors/ channels with bungarotoxin binding site (BBS) followed by labelling with bungarotoxin (BTX) bypasses this confounding factor and, therefore, allows accurate estimation and localization of PM versus intracellular proteins. The artefactual bivalent antibody-induced endocytosis continues even when cells are incubated on ice as endocytosis is only slowed but not stopped on ice.

Due to this challenge, we have generated BBS tagged Orai1-YFP. We use flow cytometry to show PM Orai1 because it is an unbiased and quantitative way of showing surface Orai1 expression (estimated by measuring the intensity of surface-bound BTX), simultaneously, in thousands of cells with no potential for visual bias in the selection and imaging of cells. BTX labelling is done on ice and cells are washed and fixed right after labelling with BTX-A647 to stop endocytosis. In the revised version, we have used both complementary approaches of flow cytometry and microscopy, showing representative images of cells, alongside quantifications. All new experiments were performed in HEK293 and Jurkat T cells to estimate surface versus total expression in a new main Figure 2. The post-store-depletion data have been added to the existing U2OS experiment in new Figure 2-figure supplement 2A-E.

Our BBS-tagged Orai1 construct also has a YFP tag at the C-terminus. Since the flow cytometry experiment involves gating on YFP-positive cells, the total number of channels (biosynthesis) can be compared by looking at the YFP intensities in the scramble versus STX11-depleted groups. These data have now been included in the previous and new experiments. In none of the cases could we detect any difference in YFP or BTX-A647 intensities, pre- or post-store-depletion in scr or STX11-depleted cells which would indicate defects in biosynthesis or Orai1’s presence in vesicles in any group.

Regarding a role for VAMP8, in Miao et al. eLIFE 2013 (1), Figure 3C-D, we showed that expression of a dominant negative mutant of NSF, a non-redundant protein in the vesicle trafficking pathway, impaired Transferrin receptor recycling within 20 hours but did not affect SOCE at all. This experiment had conclusively ruled out any role for vesicle trafficking in SOCE and therefore assessment of the role of each of the individual proteins involved in membrane trafficking becomes redundant. We have additionally ruled out a role for SNAP23/ SNAP25/ SNAP29 (old supplementary Figure 12, new Figure 2-figure supplement 3A-C). SNAP23/25 form a four helical bundle with most R-SNARE and Q-SNAREs to orchestrate vesicle fusion. R-SNAREs, typically, need SNAP23/25 to interact with Q-SNAREs. Since a role for these non-redundant proteins has also been ruled out by us, it is unlikely that VAMP8 plays a role in modulating the effects of STX11 in SOCE.

(2) Both ORAI1 and STX11 are S-Acylated on cysteine residues, and this post-translational modification promotes their recruitment to the immune synapse (PMID: 24910990, 34913437). One possibility that should be discussed is that STX11 could enhance the recruitment of the ORAI1 channel into lipid domains rich in cholesterol, thereby favoring its activation. This type of priming would still require direct interaction between the two proteins but involve a different mechanism than the one discussed by the authors. This could be experimentally tested by expressing a STX11 mutant lacking the cysteine residues required for its S-Acylation. It would also be interesting to test whether depletion of STX11 impairs the recruitment of ORAI1 to the immune synapse forming between Jurkat T cells and antigen-presenting cells.

In our experiments reported in this paper, we have used soluble anti-CD3 as well as plate-coated anti-CD3 in combination with soluble anti-CD28 to stimulate Jurkat and primary T cells. These antibodies are routinely used to stimulate T cells and this type of stimulus doesn’t depend on the formation of a classical immune synapse with an antigen-presenting cell (APC). Despite the absence of synapse, the T cells get fully activated and functional, as seen by NFAT translocation and secretion of cytokines, such as IL-2 in new Figure 4. Therefore, whether there is a defect in the recruitment of Orai1, or STX11, to T cell synapse formed with an APC is not within the scope of this study. Furthermore, accurate analysis of protein localization within the immune synapse requires a dedicated study employing sub-diffraction resolution microscopy approaches.

Regarding PMID: 24910990, and the mechanism of recruitment of STX11 to the membranes. We believe this remains unknown. The frameshift mutant used in our study lacked all terminal cysteines, which have been earlier proposed to be crucial for membrane targeting, as well as a terminal part of the SNARE domain and yet it localized to the PM just as well as wild-type STX11 (See new Figure 5E). We have added this result in the text line 302-305 and removed the line stating that post-translational modifications of the terminal cysteines target STX11 to the PM as previously claimed in PMID: 24910990 and mentioned in v1 of this paper. In view of these new data, it currently remains unknown whether potential attachment to PIP2 in PM via various basic residues, spread throughout the sequence (7, 8), or binding to another protein targets STX11 to the PM (PMID: 26771955). There is no obvious poly-basic stretch in STX11 sequence, therefore, a systematic and focused deletion and mutagenesis study will be needed to individually assess the above possibilities which is outside the scope of the present study.

Methodological issues

(3) Since STX11 colocalize with Orai1 already in basal conditions, independently of STIM1, it could influence basal calcium levels. This cannot be appreciated from the data presented, because all the SOCE protocols start in calcium-free conditions, preventing baseline comparison between WT and STX11-deficient cells. A potential difference in basal calcium levels should be explored, and the impact of STX11 depletion on basal calcium fluxes should be documented by calcium shifts (2 mM → 0 mM → 2 mM) or manganese quenching approaches.

The cells are typically loaded with Fura2 in 2mM calcium containing Ringer’s buffer. We switch the cells to 0mM right at the start of the SOCE protocol and start imaging within 5-10 seconds. Therefore, in our experience, the baselines of scramble versus STX11-depleted cells should show a difference even in the SOCE protocol if the basal calcium levels are affected because Fura2 is already present and bound to basal calcium present in the cytosol at the start of the protocol. Still, we have done the experiment suggested by the reviewer as shown in Author response image 1. The assay started with cells in 2 mM extracellular Ca²⁺ followed by addition of 10 mM EGTA, which according to the following equation quenches the 2 mM extracellular Ca²⁺ (https://somapp.ucdmc.ucdavis.edu/pharmacology/bers/maxchelator/CaEGTA-TS.htm).

where, [Ca2+]_Free is the free/unbound Ca2+, [Ca2+]_Total is the total Ca2+, [EGTA]_Total is the total EGTA concentration and Kd is the dissociation constant between Ca2+ and EGTA at 37°C and pH 7.4.

To confirm complete sequestration of the extracellular Ca²⁺, we also repeated the assay with 20 mM EGTA but did not notice any difference between the 10 mM and 20 mM EGTA conditions. We do not see any differences in basal calcium, under any condition, between Scr and STX11 shRNA treated cells HEK or Jurkat T cells.

Author response image 1.

Representative Fura-2 traces of Scr (black) and STX11 (red) shRNA-treated HEK293 (A) and Jurkat (B) cells, where the cells were incubated with 2 mM Ca²⁺ followed by addition of 10 mM EGTA to quench the 2 mM Ca²⁺. Since we do not have access to a perfusion system, we could not test Fura2 response after re-addition of 2mM calcium to the existing EGTA and Ca²⁺ mixture. However, the transition from 0mM to 2mM is already shown in Figure 8.

(4) The quantification of the calcium imaging data is problematic and requires clarification. In most figures, the data are shown normalized to the control condition. According to the method section (lines 721-724), 100% is the maximum value of the scramble shRNA group (amongst the three experiments). But what was measured here? The slope during calcium readmission? The peak amplitude after calcium readmission? Expressed as a ratio or as calcium values? The recordings are presented in micromolar calcium concentration. This implies calibration, but a calibration procedure is not mentioned. Please clarify. For the recordings of constitutive calcium entry in Figure 7, this normalization is not performed, and the data are expressed as ratio values. Here, it looks like the parameter quantified and compared is the absolute ratio value after calcium readmission. This is inappropriate. The trace in Figure 7G shows that the basal levels differ by more than two ratio units between control and STX11-depleted cells. Normalizing the data in Figure 7G to the basal ratio value would show no difference in the peak response amplitude between the two conditions. These data should be re-analyzed, and both the slope and the amplitude of the response should be presented, with statistics performed on independent recordings, not on individual cells pooled from different experiments. Cells from the same recording are not experimentally independent samples but rather replicates of the same experiment.

We have updated the relevant method section with more details in lines 823-858. The peak amplitude after calcium readmission was measured and compared to the baseline as described in the updated methods. The Fura 2 calibration method has also been added to the revised version, we apologize for this omission earlier. Separate calibration was done for Fura 2 experiments in all figures except Figure 7 from version 1 (new Figure 8). The experiments in new figure 8 were done using a different objective (20X, water) and, therefore, although a separate calibration was done for these experiments it was not applied to the data. We apologise for this omission on our part and have now applied the respective calibration to these experiments.

The trace in Figure 7G of version 1 should look different even in 0mM calcium in our opinion. The reason is the same as that explained in point 3 above; CAD-mediated constitutive activation of Orai1 is one of the strongest. The cells, even when they are being loaded with Fura2 in Ringer’s buffer with 2mM calcium, are constitutively recruiting calcium ions. This should result in a shift in Fura2 excitation due to higher levels of basal calcium. When the cells are switched to 0mM calcium and imaged within 4-5 seconds, the intracellular Fura 2 is still bound to all this extra calcium in the cytosol and therefore the baselines should show a significant difference. If the cells were imaged for several minutes in 0mM calcium, we might have seen the difference in basal ratios slowly reducing. However, we switched to 2mM calcium within 120sec. At this point any free Fura2 would be expected to bind incoming calcium again. For the same reason, the cytosol of Scr cells which would still have relatively higher levels of intracellular calcium concentration compared to STX11-depleted cells will show a smaller further increase in 2mM due to calcium-dependent inhibition of CRAC currents within the time frames we have measured. We have now added the Fura-2-calibrated response in the new Figure 8G-H. We have shown below the baseline-subtracted (normalized) response for Figure 8G. As you can see, there is still a significant difference in 2mM calcium between Scr and STX11-depleted cells but in this representation the important difference at 0mM is masked, we have therefore chosen to retain the original figure with Fura-calibrated values at 0 as well as 2mM calcium in Figure 8G-H.

The cells shown in the old Figure 7G of version 1 were not from the same recording but from three different experiments. Because the cells were imaged with 20X objective in these experiments to allow selection of Orai1-CFP or mutant Orai1-CFP and CAD-YFP double-positive cells, the number of cells analyzed per experiment was less compared to other experiments. We have now shown Fura 2 calibrated values in the new Figure 8G-L. We have also re-done the statistical analysis on three independent experiments from each. As shown in Author response image 2, the difference is still statistically significant whether we show merged cells from all three experiments or single representative experiment out of three repeats. We believe merged cells have more information to offer and therefore have retained the same figures with the original analysis in the main Figure 8G-L.

Author response image 2.

Box plots representing quantification of individual repeats of constitutive calcium influx in Scr and STX11 shRNA-treated HEK293 cells expressing YFP-CAD and Orai1-CFP without (A) and with baseline subtraction (B). (C-D) Quantification of individual repeats of Scr and STX11 shRNA-treated HEK293 cells expressing Orai1-H134S (C) and Orai1-ANSGA (D) mutants.

(5) Quantification of pull-down experiments. The binding data in Figures 4F, 5F, and 5J are presented largely qualitatively. Densitometric quantification with statistical comparisons across wild-type and mutant conditions would make these results more convincing, particularly given that the authors themselves acknowledge the interaction appears relatively weak in vitro.

This has been done and included alongside the respective panels in the new Figure 6G and 6L (for old Figure 5F and 5J of version 1, where differences appeared relatively small in some experiments). The differences across lanes in both figures and their repeats were statistically significant.

Figure 4F showed a clear and visually significant difference in binding across lanes and repeats and therefore no quantification is needed for these experiments in our opinion.

Limitations of the study and mechanistic inferences.

(7) Interpretation of the ORAI:ORAI FRET and crosslinking data. STX11 depletion increases basal ORAI:ORAI FRET (Figure 7A-C) and shifts crosslinked species toward higher molecular weights (Figure 7D-E). The authors interpret this as ORAI1 being trapped in an unprimed state, but higher FRET and higher-order species would conventionally suggest increased rather than decreased assembly. The paper needs a clearer mechanistic explanation of what "unprimed" looks like structurally. Is this aberrant crowding, non-productive oligomerization, or something else? The distinction between a change in intermolecular distance within existing oligomers versus an increase in oligomer density matters here and should be addressed.

Higher ORAI: ORAI FRET and a shift in the size of crosslinked Orai1 oligomers, when analyzed together, suggests formation of ‘non-functional’ higher-order oligomers. Higher order does not necessarily translate to better function in the case of ion channels, it can also lead to non-selectivity or formation of ‘non-productive’ oligomers, as mentioned by the reviewer. It was shown by us earlier in Li et al. (2016) (2) that bigger oligomer size revealed by higher number of photobleaching steps of Orai1 did not translate to better function but led to non-selectivity.

In new Figure 8, crosslinking with BS3, which has a spacer arm and working distance of ~11 Å, very likely reflects a change in the number of subunits within individual oligomers and not crosslinking of independent existing oligomers. This is because we show that neither total Orai1 expression nor Orai1 expression in the PM change in any group in new Figure 2. FRET works best within 1 to 10 nm distance, and therefore, in theory, can lead to energy transfer between neighbouring Orai1 oligomers in high Orai1-expressing cells. However, because there was no change in Orai1 abundance in the PM (new Figure 2) or distribution within PM (new Figure 7E,F,J,L) of any group, FRET changes also likely reflect intra-oligomer changes rather than inter-oligomer interactions. FRET changes can also arise from a change in the respective orientation of fluorophore pairs but when analyzed together with crosslinking studies, changes in pore assembly likely coincide with conformational shifts in Orai1 protomers. Furthermore, FRET has been used earlier to show shifts in conformation of other ion channels (9). Therefore, we believe that, when used together, these two approaches strongly suggest an intermediate conformational state along with a change in number of Orai1 subunits per channel since there was no evidence of overcrowding in the PM or obvious segregation of Orai1 in specific regions of PM in new Figures 2 and 7E,F,J,L.

We could not assess whether the oligomers of Orai1 formed in the absence of STX11 possess an intact pore. The presence or absence of pore in STX11-depleted cells will require extraction of Orai1 oligomers from native membranes and performing systematic structural analysis using cryo-EM or related approaches which is outside the scope of this study.

(8) The ANSGA versus H134S discrepancy. H134S ORAI1 rescues calcium influx in STX11-depleted cells (Figure 7I-J), but the ANSGA mutant does not (Figure 7K-L). The authors conclude from this that STX11 induces molecular shifts within ORAI1 transmembrane helices, and not in its C-terminal tail. This is an important mechanistic inference that needs more discussion. What does this imply about the conformational state of primed ORAI1? And why is straightening of the tails not sufficient for full opening without the correct TM helix arrangement? This distinction has implications for how the STX11-ORAI1 interaction should be modelled and should be engaged with more thoroughly.

There is no discrepancy here, please also see our response to reviewer 2’s comment #4. The experiment implies that the conformational state of primed Orai1 involves shifts in the TM region of Orai1 and is different from unprimed state. The structural similarities between H134S and ANSGA Orai1 mutants have not been formally established. Unlike H134S, no structure exists for the ANSGA mutant. In the absence of this, it is impossible to comment on whether the two constitutively active mutants are structurally comparable or whether there are multiple ways to stabilize open states of CRAC channel pore, especially when using TM mutants of Orai1.

The goal of this experiment was to determine what kinds of structural shits STX11 potentially induces in native Orai1. Using previously characterized constitutively active mutants and fusion proteins from the CRAC field, we have ruled out a potential role for STX11 in simply changing the orientation of Orai1 C-terminal tails. A discussion on the topic of why tail straightening of Orai1 is insufficient to open Orai1 is outside the scope of this paper. As pointed by reviewer 2, it is possible that C-term tails already exist pointing towards the cytosol in native, resting Orai1, although this has not been shown in any study using structure of full-length WT Orai1 and is purely speculative at this point. We prefer to not engage in speculative structural insights.

Other points

(9) Figures 1G and 1H. The patient-derived mutant STX11 band runs at approximately 37 kDa rather than the predicted 39.5 kDa. The authors suggest instability or reduced antibody reactivity, but premature translation termination is also a possibility that should be acknowledged.

We have added this point in line 168.

(10) Figure 2B. The traces and current voltage relationships should be rescaled to show the rectification and inactivation profile of the current in cells depleted of STX11.

This has been done and modified in new Figure 3 (Figure 2 of version 1).

While preparing source data files for all figures, we noticed an error in the value of the SE in the STX11-depleted group of old Figure 2C, which has now been corrected. The SE value in the older version was erroneously pasted from an adjacent data column.

Similarly, in old Figure 3 (version 1), new Figure 4C, we noticed that some data points in the STX11 group were pasted twice in the same excel column. These cells were removed and additional cells were analyzed from the same experiment and added to this group. The overall result remains the same but the distribution of data points looks a bit different.

(11) Figure 4B: This experiment should be repeated in cells treated with thapsigargin to deplete intracellular calcium stores, and the extent of colocalization quantified by measuring the Pearson's correlation coefficient.

This has been done. Pearson’s correlation coefficient is included in new Figure 5D.

(12) Figure 4F. Why is there no detectable band in the input lane of the left blot?

Western blots show relative intensities of bands of proteins across lanes. A faint band in the input lane of old Figure 4F suggests that the IP/ co-IP/ pull down was robust. If we increase the exposure, the input band would become stronger but the pull-down band would become over-saturated and the difference in the intensities would not be linear. The faint non-specific bands in other lanes represent a fraction of soluble STX11 that tends to crash out of solution over time and gets spun down with the beads. See lines 535-541 explaining this.

(13) Figure 5. Immunofluorescence data showing the membrane staining of the mutated syntaxin and channel should be included, as well as calcium recordings of cells expressing YFP-CAD with WT and mutated ORAI1.

In version 2 Figure 6A, we have now also shown co-localization of mutant STX11 with Orai1-YFP in resting and store-depleted cells, in addition to WGA. Pearson’s correlation (not shown) did not show any significant difference in the localization of mutant synatxin 11 w.r.t Orai1. Calcium recordings of CAD-induced constitutive calcium influx from wild-type versus mutant Orai1 are now shown in new Figure 6O-P.

(14) Figure 6B. A clear colocalization of CFP-O1 and STIM1-YFP is visible on the images, yet the authors conclude from morphometric analysis that the channel is not recruited into ER-PM clusters. Please show the difference in colocalization quantified by measuring the Pearson's correlation coefficient. Pictures should also be provided with the C-terminally tagged construct.

The quantification of CFP-Orai1 localization inside Stim1-YFP puncta was already shown in old Figure 6E and F. The residence of Orai1 inside STIM1 puncta versus total Orai1 in the PM of STX11-depleted groups was clearly reduced. We have now also shown Pearson’s correlation coefficient for Stim Orai co-localization inside puncta in new Figure 7F.

TIRF microscopy images of C-terminally tagged Orai1 were already included in Supplementary Figure 10. No defect in co-clustering of C-terminally tagged Orai1-YFP and N-terminally tagged CFP-Stim1 was seen and yet SOCE was inhibited. Therefore, we never concluded from Figure 6 that Orai1 and Stim1 fail to co-localize. We said, they fail to form ‘functional’ clusters. We have now moved the representative TIRF images from supplementary figure 10 to the new main Figure 7G. The Pearson’s correlation coefficient for Stim Orai1 co-localization inside puncta is shown in new Figure 7L.

(15) Figure 6E and 6F show the same data.

Figure 6E showed fraction of Orai1 inside Stim1 puncta divided by total Orai1, and 6F showed fraction of Orai1 outside puncta divided by total Orai1. The plots are different but we agree that the data are coming from same cells. We have removed old panel 6F and replaced it with Pearson’s correlation coefficient of Stim1:Orai1 colocalization in puncta in new Figure 7F.

(16) Figure 7G-L. The difference in constitutive calcium fluxes should be confirmed by Manganese quench recordings. The surface expression of the Orai1 mutants should be shown.

We have now shown the quantification of surface expression of Orai1 mutants for each respective mutant in the new Figure 8-figure supplement 3B and 3D. The Orai1 mutants we have used in this paper are well established in the literature, they showed clear surface localization and the differences in calcium influx between Scr and STX11 treated cells upon overexpression of Orai1 mutants in HEK are robust. Therefore, we do not see any compelling reason for repeating all of the experiments from Figure 7G to 7L to also show manganese quench recordings, as suggested by the reviewer. We have applied Fura 2 calibration done for these experiments to calculate intracellular calcium. These have been shown in the revised and new Figure 8G-L, where F340/380 ratios of representative calcium assays have also been replaced with the calibrated intracellular calcium concentration.

(17) Supplementary Figure 12. The recordings show a very large variability between experiments. The different SNAREs that are depleted here could compensate for each other, accounting for this variability. It would be interesting to show the effect of the combined silencing of all the SNARES tested here. The efficiency of the protein depletion should also be documented.

Genome-wide high- or medium-throughput screens are inherently noisy. None of the genome-wide high- or medium-throughput screens show evidence of protein depletion for each gene in any of the published screens to our knowledge. We chose to only characterize the candidates that reproducibly showed > 70% inhibition of SOCE, others were ignored as noise.

Silencing of all SNAREs together will definitely lead to loss of morphology and early lethality as all membrane trafficking will be stopped. We never analyze cells that do not show normal morphology and have compromised viability for ablation of SOCE.

(18) Lines 236-238. The authors note that STX11 harbors a stretch of C-terminal cysteines proposed to be essential for its membrane localization, but do not elaborate on the underlying mechanism. It would strengthen the discussion to explicitly acknowledge that this membrane anchoring is mediated by S-acylation of these cysteines PMID: 24910990 and to connect this to the known enrichment of Orai1 in lipid rafts and the immune synapse PMID 34913437. Both observations are relevant to understanding how STX11 and Orai1 are brought into proximity at the plasma membrane, and their omission leaves an explanatory gap in the proposed interaction model.

Please see our response to point #2 above. We do not think C-terminal cysteines target STX11 to the PM. We have corrected this claim based on an earlier study, PMID: 24910990, in the revised version of this paper. Analysis of immune synapse and lipid rafts are outside the scope of this paper. The mechanism of PM targeting of STX11 is currently unestablished and will require a systematic and focused mutational analysis which is outside the scope and main focus of this paper.

(19) Line 351. The statement that syntaxin depletion does not alter the structure or proximity of junctional ER to the plasma membrane is not supported by data. Neither electron microscopy nor TIRF imaging has been performed, which would be required to back up this claim.

Because Stim1 itself can be used as a marker of ER-PM junctions, this statement was supported by data shown in Figure 6C, D, G, H of version 1 of this paper where the intensity and area of Stim1 clusters was assessed using TIRF microscopy and found to be indistinguishable between STX11 and scramble control cells. The imaging done in Figure 6G, H was TIRF imaging and this was already specified in the legend. We have now also done TIRF imaging of GFP-Mapper-expressing scr and STX11-depleted cells. Mapper is a genetically encoded fluorescent protein that was previously shown to mark ER-PM junctions (10). We found no significant difference in the area or intensity of GFP-Mapper puncta (new Figure 7O-Q), just like Stim1 puncta didn’t show any defect in STX11-depleted cells. Please see modified text from 416-423.

(20) The molecular dynamics methods need more detail: force field, simulation length, water box dimensions, and convergence criteria should all be specified to allow replication. The supplementary RMSD plots (Supplementary Figure 5B) should also show individual replicate trajectories rather than averages only.

We had already mentioned the force field (OPLS4) and simulation length (500ns) in the methods section. Also, the RMSD plots in Supplementary Figure 5B already showed individual replicates in version 1.

We have now updated the methods with following additions:

The OPLS4 force field was used for all 500 ns simulations in an orthorhombic water box with a buffer distance of 10 Å beyond the solute in each direction. Simulation stability was assessed based on the protein backbone RMSD over simulation time.

Trajectory clustering was performed using the trajectory clustering tool in Schrödinger, which applies affinity propagation to the pairwise backbone RMSD-based similarity matrix. Within each affinity propagation run, convergence was defined as no change in the set of exemplar frames for 15 consecutive iterations, with a maximum of 400 iterations per run. If convergence was not reached, the damping factor was increased from 0.5 in increments of 0.01 until convergence.

Reviewer #2 (Recommendations for the authors):

Overall, this is a timely and impactful study supported by a broad set of methods and cell types. Before publication, the manuscript should address the following points.

Major:

(1) The authors note that STX11 contains cysteine residues that enable membrane association. What is the specific mechanism of membrane attachment? Could it occur via S-acylation (palmitoylation)? Both Orai1 and STIM1 are known to undergo S-acylation, which raises the possibility that this modification might also facilitate STX11 membrane anchoring and/or co-residence with Orai1. Is STX11 constitutively membrane-associated, or does it show preferential localization to specific membrane subdomains, particularly in proximity to Orai1?

STX11 is constitutively membrane-associated and does not show any preferential localization to specific membrane subdomains in confocal images. Figure 4, panel A and B from version1 clearly showed this. In an earlier paper by Hellewell et al. 2014, PMID: 24910990, S-acylation of terminal cysteines of STX-11 was proposed to be crucial for membrane attachment of STX11 and its recruitment to the immune synapse. However, please see our response to reviewer 1’s comment #2 and a new Figure 5E for the localization of the frameshift FHLH4 mutant characterized in this paper. The frameshift mutant that we have characterized lacked all terminal cysteines as well as a short terminal part of the SNARE domain. Cloning and ectopic expression of this mutant still showed constitutive localization to PM and did not show preferential distribution to any specific regions. Therefore, we do not think that terminal cysteines of STX11 contribute to its membrane attachment, we have accordingly modified lines 291-293, 302-305, 540 in the revised version. Also see our response to your point#6 below.

(2) Is there a possibility to monitor a dynamic change in STX11 co-localization from before to after store-depletion?

We did not observe any change in the overall distribution of STX11 in cells expressing STX11 alone or co-expressing Orai1 with STX11, pre- or post-store-depletion (please see new figure 5B-C). In cells co-expressing ORAI1, STX11 and STIM1 (see new Figure 5M-N), we could not capture the dynamic segregation of STX11 into regions of PM devoid of STIM:ORAI puncta and therefore have only pre- or post-store-depletion images. Dynamic change in STX11 distribution would require live, multi-colour, high-resolution imaging of diffraction-limited ER-PM junctions and adjacent regions which is technically extremely challenging, and especially due to our inability to tag STX11 with a fluorescent tag without disrupting its localization. Also see our response to your point#6 below.

(3) The authors use CAD to prove that the interaction with the R289A_E272A_E275A_E278A mutant is normal as for the wild-type. Does this also hold for STIM1 wild-type full-length?

This is also true for full-length STIM1. The data have now been added to the new Figure 7-figure supplement1.

(4) The authors state that STIM1 binds both the N- and C-termini of Orai1. While STIM1 binding to the Orai1 C-terminus is well established, the nature of its interaction with the N-terminus remains debated. Fragment-based assays suggest direct binding to the N-terminus; however, direct interaction with full-length Orai1 has not been conclusively demonstrated. This point should be phrased more cautiously to reflect the current uncertainty.

We have re-phrased the sentence as follows in line 543: “The individual relevance of Orai1 N- versus C-terminus in the trapping versus gating of Orai1 remains unclear”

(4) In the discussion, the authors report: "Though crucial for trapping and gating, Orai1 tails were missing from early structures of Drosophila Orai [28]". A previous NMR structure suggested that the C-terminal tails of two adjacent Orai1 subunits bend and pair with each other in an antiparallel fashion, and sit closely apposed to PM [37]. However, in recent structures of constitutively active H134 mutant Orai, the C-terminal tails were found to orient away from the membrane [28]. In STX11-depleted cells, switching the CFP-tag from the Orai1 N- to the C-terminus could rescue its clustering but not gating by Stim1. Furthermore, STX11 depletion inhibited the constitutively active ANSGA mutant of Orai1 [29], where the tails of Orai1 are proposed to be constitutively unlatched. These data essentially reinforce our conclusions that STX11 induced molecular shifts encompass Orai1 transmembranes.' However, the information provided here is not fully correct. The early Drosophila Orai structure lacks the full N-terminus but retains most of the C-terminus. It was the X‑ray, not cryo‑EM, structure that suggested an antiparallel arrangement of the Orai1 C-termini. Although the "open" X‑ray structure shows unlatching and straightening of TM4-C-termini, it remains uncertain whether these features reflect physiological gating or crystallization artifacts. It is also unclear whether the Orai1 ANSGA gain‑of‑function mutant adopts a similar unlatching; however, prior work indicates that ANSGA impairs proper coupling to the C‑terminal binding interface (in contrast to Orai1 H134S, which maintains effective coupling). This raises the key question: why do H134S and ANSGA respond differently to STX11 depletion? One possibility is that these mutants stabilize distinct conformations of the TM4-C-termini ("latched" vs "unlatched" states) that differentially dictate the requirement for STX11 in channel assembly or gating. We recommend refining the discussion

We have changed the word ‘missing’ to ‘truncated’ in line 545 and 546.

We agree that there is no evidence in literature that establishes similarity between H134S and ANSGA mutation-induced conformations of Orai1. It is, however, implied in most previous studies of mutant Orai1s that there is only one possible open state/conformation. We have added the suggested point and modified the discussion in line 551-556.

(5) The authors highlight: 'A major problem with this interpretation is that even though amplification of CRAC currents was shown, none of the previous patch clamp studies established whether the higher currents resulted from a greater number of active channels or unchecked conductance per channel by performing single channel recordings.' It should be noted that CRAC channels have extremely low single‑channel conductance, making direct single‑channel recordings challenging. As a result, estimates of open probability and channel number typically rely on fluctuation (noise) analysis rather than direct measurements of single‑channel events (see https://doi.org/10.1085/jgp.200609588). We suggest acknowledging this limitation in the discussion to contextualize the interpretation of gating and channel density.

We acknowledge how challenging it is to record the single-channel conductance from CRAC channels. We have added this fact to the discussion and the reference that the reviewer has suggested in line 570-572.

(6) STX11 appears to shift Orai1 localization into puncta. Activated STIM1 is known to engage plasma membrane PIP2 to facilitate Orai1 coupling. How, if at all, is STX11 linked to PIP2 or PIP2-rich microdomains? Is there evidence for direct PIP2 binding by STX11, or for indirect recruitment via PIP2-binding partners? Any available data on STX11's lipid interactions or its enrichment within PIP2-enriched regions would help clarify this mechanism.

We have not claimed that STX11 shifts Orai1 into puncta. We already showed in old supplementary figure 10 and Figure 6G-J of version1 (v1) of this paper that the C-terminally tagged Orai1-CFP can very well form puncta and co-localize with STIM1 in STX11-depleted cells. To avoid this confusion, we have moved the old Supplementary Figure 10 from v1 to the main figure in revised version, see new Figure 7 panel G. Despite the presence of ORAI1-CFP in puncta with YFP-Stim1, the SOCE was inhibited in STX11-depleted cells. Please also see new Pearson’s correlation coefficient for Stim1 and Orai1 colocalization in Figure 7 panel L. Therefore we concluded that, Orai1 forms ‘nonfunctional’ clusters with Stim1 in STX11 depleted cells, please see modified lines 408-412, clearly explaining this.

Although syntaxin 1A has been shown to interact with cholesterol (11) as well as PIP2 (7, 8) using either a stretch of polybasic residues or basic residues spread throughout several domains. To our knowledge, these have not been proposed to recruit or segregate STX11 in membranes. STX11 doesn’t contain an obvious stretch of poly-basic residues in its sequence, either, to quickly mutate and address this question. Please also see our response to your point #1 and #2 above. Answering this question will require a systematic and dedicated mutagenesis study.

Minor:

(1) Please indicate in Figure 1 in the respective graphs in which cell type the Ca2+ imaging studies have been performed.

Done.

(2) Figure 4C, D: Why are the input bands so weak?

Please see our response to reviewer #1’s similar comment 12 above.

(3) Figure 5A: Please clarify what WGA is.

WGA is wheat germ agglutinin which is used to mark PM in imaging experiments. It binds to N-acetyl-D-glucosamine and sialic acid residues found in mammalian cell membranes and glycoproteins. We have added the explanation to the new Figure 6A legend.

The authors state that "the constitutively active ANSGA (261-265) mutant of Orai1 (Supplementary Figure 11G), which harbors 4 consecutive mutations in the Orai1 C-terminus ...". Please clearly state this is the nexus region connecting the C-terminus with TM4. The 5 aa stretch is not the C-terminus; it is just close to the C-terminus.

We have modified this, as suggested, in line 470-471.

References:

(1) Miao Y, Miner C, Zhang L, Hanson PI, Dani A, Vig M. An essential and NSF independent role for alpha-SNAP in store-operated calcium entry. Elife. 2013;2:e00802.

(2) Li P, Miao Y, Dani A, Vig M. alpha-SNAP regulates dynamic, on-site assembly and calcium selectivity of Orai1 channels. Mol Biol Cell. 2016;27(16):2542-53.

(3) Chorev DS, Baker LA, Wu D, Beilsten-Edmands V, Rouse SL, Zeev-Ben-Mordehai T, et al. Protein assemblies ejected directly from native membranes yield complexes for mass spectrometry. Science. 2018;362(6416):829-34.

(4) Dorwart MR, Wray R, Brautigam CA, Jiang Y, Blount P. S. aureus MscL is a pentamer in vivo but of variable stoichiometries in vitro: implications for detergent-solubilized membrane proteins. PLoS Biol. 2010;8(12):e1000555.

(5) Vig M, Peinelt C, Beck A, Koomoa DL, Rabah D, Koblan-Huberson M, et al. CRACM1 is a plasma membrane protein essential for store-operated Ca2+ entry. Science. 2006;312(5777):1220-3.

(6) Feske S, Gwack Y, Prakriya M, Srikanth S, Puppel SH, Tanasa B, et al. A mutation in Orai1 causes immune deficiency by abrogating CRAC channel function. Nature. 2006;441(7090):179-85.

(7) Murray DH, Tamm LK. Clustering of syntaxin-1A in model membranes is modulated by phosphatidylinositol 4,5-bisphosphate and cholesterol. Biochemistry. 2009;48(21):4617-25.

(8) van den Bogaart G, Meyenberg K, Risselada HJ, Amin H, Willig KI, Hubrich BE, et al. Membrane protein sequestering by ionic protein-lipid interactions. Nature. 2011;479(7374):552-5.

(9) Miranda P, Contreras JE, Plested AJ, Sigworth FJ, Holmgren M, Giraldez T. State-dependent FRET reports calcium- and voltage-dependent gating-ring motions in BK channels. Proc Natl Acad Sci U S A. 2013;110(13):5217-22.

(10) Chang CL, Chen YJ, Liou J. ER-plasma membrane junctions: Why and how do we study them? Biochim Biophys Acta Mol Cell Res. 2017;1864(9):1494-506.

(11) Lang T, Bruns D, Wenzel D, Riedel D, Holroyd P, Thiele C, et al. SNAREs are concentrated in cholesterol-dependent clusters that define docking and fusion sites for exocytosis. EMBO J. 2001;20(9):2202-13.

eLife Assessment

This valuable study provides insights into the role of steroid signaling during tumorigenesis in the adult male drosophila accessory gland (functional equivalent of the prostate gland in mammals), hinting at a possible counterintuitive anti-tumoral role of sex hormones during prostate cancer in certain patients. While the Drosophila model provides an elegant way to study the hypothesis derived from the Cancer Atlas analysis, the analyses of public prostate cancer expression data are incomplete and critical knowledge on patients' treatment modalities and normalization across different datasets is missing. This work would be of interest to prostate cancer researchers as it suggests that the absence of androgen receptor signaling in humans could constitute a mechanism promoting tumor escape.

Reviewer #1 (Public review):

Summary:

In this article, Vialat and his colleagues examine the early stages - which remain largely unknown - of the tumor escape process, particularly the basal extrusion of tumor cells following endocrine therapy for prostate cancer.

They first used the "Prostate Cancer Atlas" database, which provides access to a vast amount of transcriptomic data, to perform high-throughput analyses. Interestingly, analyzing a series of Androgen Receptor (AR) target genes in castration-resistant prostate cancers, they concluded that the loss of the canonical AR signaling pathway may contribute to tumor resistance.

Using a well-established model in Drosophila, they then replicated in vivo an endocrine therapy targeting the accessory gland by genetically inhibiting the expression of ecdysone, the only sex steroid present in Drosophila. These experiments induced basal extrusion similar to the mechanism observed in tumor escape in humans.<br /> These results suggest that the deprivation of sex steroids may play an important role in tumor progression.

However, although the data from the "Prostate Cancer Atlas" constitutes a powerful tool that serves as the basis for this new concept, clinical validation using carefully selected human tumor samples would help strengthen the authors' conclusions.

Strengths:

(1) The Prostate Cancer Atlas is a comprehensive collection of clinical data derived from RNA sequencing and serves as a powerful tool for conducting high-throughput analyses in this paper.

(2) The Drosophila model used in this article is well established and has already been the subject of publications by the team. In addition to being an in vivo model, Drosophila offers a threefold advantage for this study: i) the presence of an accessory gland, similar to the prostate, which allows for the simulation of tumor formation and, in particular, extrusion mechanisms; ii) its regulation by a single sex steroid, ecdysone; iii) the genetic ability to modulate or inactivate ecdysone expression, which allows for a parallel to be drawn with hormonal deprivation in humans.

(3) This study presents interesting and original findings. The data are, for the most part, of high quality.

Weaknesses:

(1) The Prostate Cancer Atlas, which is an essential tool in this study, was described only briefly - if at all - in both the introduction and the "Materials and Methods" section. The selection criteria used to distinguish CRPC or NEPC from adenocarcinoma in the Atlas or as determined by the authors, as well as the analytical methods, were not specified. It is therefore difficult to be convinced by the results, particularly those presented in Figures 1 and 2.

(2) Although the hypothesis put forward by the authors - that the deprivation of sex hormones contributes to tumor progression - is strongly supported by the Drosophila model and by the in silico analysis of transcriptomic data from the Atlas, this concept still needs to be clinically validated by analyzing a series of prostate cancer samples, either through transcriptomic analysis or by tracking gene expression in histological sections.

(3) With regard to the cells responsible for tumor escape, stem cells have been described as "candidates for the initiating resistant tumor growth" (lanes 50-55), but it is also essential to address the recent concept of "persistent cells". Indeed, these cells have been primarily associated with their tolerance to treatment (chemotherapy) and are referred to as "drug-tolerant cells". However, persistent cells could also correspond to cells that evade hormone therapy in the case of prostate cancer. This possibility should be discussed in the article.

Reviewer #2 (Public review):

Summary:

In this study, Vialat and collaborators study the role of steroid hormone signalling on the development of prostate cancer (patients) and of accessory gland tumours in Drosophila, a tissue functionally equivalent to the prostate. Mining publicly available prostate cancer expression data and using gene expression signatures, they uncover that androgen signalling is actually down-regulated in castration resistant prostate cancers (CRPC) compared to "primary" cancers, leading the authors to wonder whether down-regulation of canonical androgen signalling could represent an important event increasing tumour aggressiveness. They then take advantage of their recently published tumour model in the accessory gland of Drosophila adult males, in which cells are primed for tumorigenesis by the constitutive activation of the EGFR receptor, to test directly this hypothesis. They show that the genetic invalidation of ecdysone reception and signalling increases the aggressiveness of the "pre-cancerous" lesions, and that ecdysone-insensitive tumours present higher proliferation and initiate basal extrusion.

Strengths:

The authors bring original observations on the role of ecdysone signalling to prevent male accessory gland tumour development in Drosophila

Weaknesses:

(1) The link between the human data mining and Drosophila model is not straightforward.<br /> (2) Important information, in particular clinical information, is missing in the presentation of the cancer patients' data, making it complicated to grasp the solidity of the claims.<br /> (3) Data-mining insights should be validated by orthogonal approaches.<br /> (4) Ecdysone signalling activity should be monitored.

While the two parts of the study both investigate the role of steroid signalling on tumour growth, the link remains slightly artificial. I think starting with Drosophila and then opening with some patient data would be better suited to the level of proof reached here, implying that the anti-tumoral role of steroids observed experimentally in the fly might be conserved based on data mining in patients, rather than trying to prove in the fly the hints gained from public data mining. Indeed, there are many important differences between the mammalian prostate and the fly accessory gland, as well as between sex hormone androgen signalling and developmental timing ecdysone signalling.

The prostate cancer data mining and re-evaluation brings some interesting observations that appear to challenge the androgen driver, contrary to the vast amount of literature. Indeed, the authors observe an apparent decrease in androgen signalling in the more advanced states of the disease, in particular CRPC. In order to better evaluate its clinical relevance, more background on the tumours analysed should be provided.

What treatments were received by the patients? Hormonotherapy? LH/RH analogues? +/- anti-androgens? Are these treatments still given when CRPC emerge and tissues were banked? Metastatic disease? Are these only primary tumours in situ? Are there metastases included in the analyses?

Frequently, castration resistance is associated with alternatively spliced variants of the AR (AR-V7) that become constitutive and could bind to new AR-sensitive enhancers, even in the absence of androgen. Is the splice variant status of patients known, or could it be inferred from the expression data? Would there be different responses according to AR-V7 status?

Regarding the signature used. Why not monitor PSMA, one of the major prostate cancer markers, which is regulated by AR?

Finally, to consolidate the surprising observation that AR signalling is repressed in CRPCs, the authors should back these in silico predictions with orthogonal approaches such as histochemistry on patients' TMA or tissues from mouse models, monitoring AR activity.

Regarding the fly experiments, the observation that ecdysone signalling depletion cooperates with EGFR-lambda activation to generate big overgrowths that delaminate basally without passing through the muscular sheet is interesting. However, several important controls need to be provided in order to support the claims:<br /> a) The authors should use an ecdysone reporter (ERE-LacZ, ERE-GFP...) to monitor and show that Ecdysone signalling is indeed lower in the tumours after genetic manipulations, or that it is higher in EGFR-lambda small clones.<br /> b) EcR is normally a repressor, which is turned into an activator in the presence of 20-hydroxyecdysone. The removal of EcR could lead to de-repression of genes and thus slightly activate the pathway. Monitoring ecdysone signalling activity is thus critical.<br /> c) The authors should also monitor the expression of Phantom, Shadow, Shade, and EcR in the different accessory glands (wild-type, EGFR-lambda, EGFR-lambda & EcR-RNAi). It is extremely surprising that systemic ecdysone has so little role since Phantom, Shadow, or Shade RNAi appear as potent as EcR-RNAi. This quantification has actually been performed for Sad in Figure S5, which is not even mentioned in the text. It should be done for Phtm.

A UAS-yellow-RNAi (or similarly irrelevant RNAi) rather than UAS-GFP should be used as a control for the EcR, Phtm, Sad, Shd, and Tub RNAi. Indeed, loading the RNAi machinery could have some unexpected effects not controlled by the UAS-GFP.

The authors should not use the term "sex steroid" when referring to ecdysone. It is a steroid hormone important for developmental timing and rate of growth, but is not a sex hormone, as sex is cell autonomously genetically determined in the fly.

Author response:

eLife Assessment

This valuable study provides insights into the role of steroid signaling during tumorigenesis in the adult male drosophila accessory gland (functional equivalent of the prostate gland in mammals), hinting at a possible counterintuitive anti-tumoral role of sex hormones during prostate cancer in certain patients. While the Drosophila model provides an elegant way to study the hypothesis derived from the Cancer Atlas analysis, the analyses of public prostate cancer expression data are incomplete and critical knowledge on patients' treatment modalities and normalization across different datasets is missing. This work would be of interest to prostate cancer researchers as it suggests that the absence of androgen receptor signaling in humans could constitute a mechanism promoting tumor escape.

We thank the reviewers for the time they have spent on the manuscript, the production of a public review and their useful recommendations. As a general goal for the corrected version, we will try to provide more insight on the data (especially the human data), and more controls, to strengthen our conclusions. We are aware of the lack of a definitive proof of the role of the apparent decrease in AR signaling on tumour progression, but hope that this manuscript will encourage medical scientists to test/challenge its counterintuitive results in large cohorts of tissues and mouse/human models.

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this article, Vialat and his colleagues examine the early stages - which remain largely unknown - of the tumor escape process, particularly the basal extrusion of tumor cells following endocrine therapy for prostate cancer.

They first used the "Prostate Cancer Atlas" database, which provides access to a vast amount of transcriptomic data, to perform high-throughput analyses. Interestingly, analyzing a series of Androgen Receptor (AR) target genes in castration-resistant prostate cancers, they concluded that the loss of the canonical AR signaling pathway may contribute to tumor resistance.

Using a well-established model in Drosophila, they then replicated in vivo an endocrine therapy targeting the accessory gland by genetically inhibiting the expression of ecdysone, the only sex steroid present in Drosophila. These experiments induced basal extrusion similar to the mechanism observed in tumor escape in humans.

These results suggest that the deprivation of sex steroids may play an important role in tumor progression.

However, although the data from the "Prostate Cancer Atlas" constitutes a powerful tool that serves as the basis for this new concept, clinical validation using carefully selected human tumor samples would help strengthen the authors' conclusions.

Strengths:

(1) The Prostate Cancer Atlas is a comprehensive collection of clinical data derived from RNA sequencing and serves as a powerful tool for conducting high-throughput analyses in this paper.

(2) The Drosophila model used in this article is well established and has already been the subject of publications by the team. In addition to being an in vivo model, Drosophila offers a threefold advantage for this study: i) the presence of an accessory gland, similar to the prostate, which allows for the simulation of tumor formation and, in particular, extrusion mechanisms; ii) its regulation by a single sex steroid, ecdysone; iii) the genetic ability to modulate or inactivate ecdysone expression, which allows for a parallel to be drawn with hormonal deprivation in humans.

(3) This study presents interesting and original findings. The data are, for the most part, of high quality.

Weaknesses:

(1) The Prostate Cancer Atlas, which is an essential tool in this study, was described only briefly - if at all - in both the introduction and the "Materials and Methods" section. The selection criteria used to distinguish CRPC or NEPC from adenocarcinoma in the Atlas or as determined by the authors, as well as the analytical methods, were not specified. It is therefore difficult to be convinced by the results, particularly those presented in Figures 1 and 2.

As the tool has been published in different articles, we chose to limit its description. However, we agree that explanation are necessary, that will be added in the new version. First, we initially used here just basic categories, in order to avoid any possible bias; so mCRPC includes rare DNPC and NECP patients. We will also put the data with true ARPC, with essentially the same results.

For human data, we also expect to use transcriptomic data from an independent cohort to check whether the same loss of AR signaling occurs during progression. Furthermore, we consider to add the data showing that decrease in canonical AR signaling also (logically with the previous results) correlates with castration status or ADT exposure (these info are available on ProstateCancerAtlas). Interestingly, and this can be put in supplementary data, prostatecanceratlas detects changes in EMT genes or proliferation genes that are coherent with what is known about cancer progression, indicating that the apparent decrease in AR signaling should correspond to a real phenomenon.

(2) Although the hypothesis put forward by the authors - that the deprivation of sex hormones contributes to tumor progression - is strongly supported by the Drosophila model and by the in silico analysis of transcriptomic data from the Atlas, this concept still needs to be clinically validated by analyzing a series of prostate cancer samples, either through transcriptomic analysis or by tracking gene expression in histological sections.

There are many indirect evidences that loss of AR signaling induces tumor progression in mouse (as stated in the intro or the discussion of the manuscript). However, as suggested in the introduction of the letter, we believe that medical scientists are the most qualified to prove that sex steroid deprivation indeed induces tumor progression in human. We will add in any case data to at least reinforce this puzzling finding of a decrease in AR canonical signaling during progression.

(3) With regard to the cells responsible for tumor escape, stem cells have been described as "candidates for the initiating resistant tumor growth" (lanes 50-55), but it is also essential to address the recent concept of "persistent cells". Indeed, these cells have been primarily associated with their tolerance to treatment (chemotherapy) and are referred to as "drug-tolerant cells". However, persistent cells could also correspond to cells that evade hormone therapy in the case of prostate cancer. This possibility should be discussed in the article.

This is an interesting suggestion, which can be discussed: on the one hand, intrabasal cells may not have accumulated mutations to survive the loss of EcR signaling, as would do persistent cells. On the other hand, they strongly proliferate, and show no sign of senescence, behaving more like resistant cells. So, it does not look to us that we induced the appearance of persistent cells in the Drosophila accessory gland, except if these cells are quickly reactivating to give rise to intrabasal cells.

Reviewer #2 (Public review):

Summary:

In this study, Vialat and collaborators study the role of steroid hormone signalling on the development of prostate cancer (patients) and of accessory gland tumours in Drosophila, a tissue functionally equivalent to the prostate. Mining publicly available prostate cancer expression data and using gene expression signatures, they uncover that androgen signalling is actually down-regulated in castration resistant prostate cancers (CRPC) compared to "primary" cancers, leading the authors to wonder whether down-regulation of canonical androgen signalling could represent an important event increasing tumour aggressiveness. They then take advantage of their recently published tumour model in the accessory gland of Drosophila adult males, in which cells are primed for tumorigenesis by the constitutive activation of the EGFR receptor, to test directly this hypothesis. They show that the genetic invalidation of ecdysone reception and signalling increases the aggressiveness of the "pre-cancerous" lesions, and that ecdysone-insensitive tumours present higher proliferation and initiate basal extrusion.

Strengths:

The authors bring original observations on the role of ecdysone signalling to prevent male accessory gland tumour development in Drosophila

Weaknesses:

(1) The link between the human data mining and Drosophila model is not straightforward.

(2) Important information, in particular clinical information, is missing in the presentation of the cancer patients' data, making it complicated to grasp the solidity of the claims.

(3) Data-mining insights should be validated by orthogonal approaches.

(4) Ecdysone signalling activity should be monitored.

While the two parts of the study both investigate the role of steroid signalling on tumour growth, the link remains slightly artificial. I think starting with Drosophila and then opening with some patient data would be better suited to the level of proof reached here, implying that the anti-tumoral role of steroids observed experimentally in the fly might be conserved based on data mining in patients, rather than trying to prove in the fly the hints gained from public data mining. Indeed, there are many important differences between the mammalian prostate and the fly accessory gland, as well as between sex hormone androgen signalling and developmental timing ecdysone signalling.

This is an interesting suggestion. Actually, we first wrote the manuscript by starting with Drosophila data and then going to patients data, and previous reviewers said that this was not possible to directly go from Drosophila to human. So, we suppose that the real way to solve this will be by the validation or refutation of the data by other teams in different models.

The prostate cancer data mining and re-evaluation brings some interesting observations that appear to challenge the androgen driver, contrary to the vast amount of literature. Indeed, the authors observe an apparent decrease in androgen signalling in the more advanced states of the disease, in particular CRPC. In order to better evaluate its clinical relevance, more background on the tumours analysed should be provided.

This point is also important to Reviewer 1, and will be implemented.

What treatments were received by the patients? Hormonotherapy? LH/RH analogues? +/- anti-androgens? Are these treatments still given when CRPC emerge and tissues were banked? Metastatic disease? Are these only primary tumours in situ? Are there metastases included in the analyses?

Most CRPC come from metastatic sites. Most of the CRPC were treated by ADT. We chose to have an approach that included all the samples; but we will provide insight, whenever available, on these absolutely relevant questions.

Frequently, castration resistance is associated with alternatively spliced variants of the AR (AR-V7) that become constitutive and could bind to new AR-sensitive enhancers, even in the absence of androgen. Is the splice variant status of patients known, or could it be inferred from the expression data? Would there be different responses according to AR-V7 status?

As a first approach, from the cohorts that were used, it seems that in the PCA patients, there are around or less than 15% of patients harboring the AR-V7 driver. It could be of interest to test their behavior regarding the same set of genes, and it will be done if we can identify the patients.

Regarding the signature used. Why not monitor PSMA, one of the major prostate cancer markers, which is regulated by AR?

It will be done. PSMA behaves as the others, even though the drop between primary samples and ARPC samples is very limited and just statistically significant.

Finally, to consolidate the surprising observation that AR signalling is repressed in CRPCs, the authors should back these in silico predictions with orthogonal approaches such as histochemistry on patients' TMA or tissues from mouse models, monitoring AR activity.

As said previously, we believe that this specific work will be better done by medical scientists.

Regarding the fly experiments, the observation that ecdysone signalling depletion cooperates with EGFR-lambda activation to generate big overgrowths that delaminate basally without passing through the muscular sheet is interesting. However, several important controls need to be provided in order to support the claims:

Considering the comments regarding the fly experiments, we agree that, if experiences are taken individually, controls are lacking. However, we have to explain our strategy and why the results taken in their entirety have a significance. In our model of epithelial tumorigenesis, we have started to explore the EcR pathway after years of work on other pathways. At the first experiment (with the EcR RNAi line), we were struck by the intrabasal phenotype that did not occurred in our previous experiments, and especially for the 14 RNAi lines that we published in two independent articles on Ras/MAPK, Pi3K/Akt pathways and cholesterol metabolism. As justly said in the review, many unexpected effects can happen, so we decided to explore the role of five other genes of the same pathway to be sure of the reproducibility of the phenotype when we block the EcR pathway. The odds of having the same specific phenotype for 6 lines of the EcR pathway when there is always another phenotype for 14 lines targeting other pathways can be calculated: p = 0.00000494. So, the best control we offer, and it is largely significant, is the repetition of the experiments intended to downregulate the EcR pathway, that produce the same phenotypes independently of the target.

Furthermore, all lines we used were previously tested, validated and most of the time published in other scientific works. This is essential to us, as one complexity of doing rare clones in an otherwise normal tissue is that decreasing an mRNA in less than 5% of the cells of course difficultly leads to a detectable drop in overall expression in the whole gland. This is also the reason why we always tried pairs of fly lines to block the receptor activity itself (RNAi EcR, RNAi Shd), the receptor's downstream targets (RNAi HR3, RNAi HR4), and the production of ecdysone (RNAi Sad, RNAi Phtm). As we validated RNAi Sad, we can try anyway to validate at least another RNAi of another category. Furthermore, we did use a RNAi White control: it behaves in the same way as the GFP control. We will put the results comparing the two lines in supplementary data.

(a) The authors should use an ecdysone reporter (ERE-LacZ, ERE-GFP...) to monitor and show that Ecdysone signalling is indeed lower in the tumours after genetic manipulations, or that it is higher in EGFR-lambda small clones.

This would be of interest to validate that the 6 lines are behaving in the same way (at least, they give similar phenotypes). However, in the adult accessory gland, ERE activity is largely lower than during development (DOI: 10.1016/j.jinsphys.2011.03.027), and to be able to decrease it, authors had to express notoriously strong dominant-negative EcR-DN. We can try the experiment but are really not persuaded that we will be able to see a drop of activity with only a decrease of expression of the gene. If we can think of another solution that could be more efficient, we will try it as the idea is of course interesting.

(b) EcR is normally a repressor, which is turned into an activator in the presence of 20-hydroxyecdysone. The removal of EcR could lead to de-repression of genes and thus slightly activate the pathway. Monitoring ecdysone signalling activity is thus critical.

Actually, there are different EcR isoforms. EcR-B1 is generally considered as the main activator of the pathway, as EcR-A is a repressor of the pathway. The EcR RNAi line which was used does not target a specific isoform.

(c) The authors should also monitor the expression of Phantom, Shadow, Shade, and EcR in the different accessory glands (wild-type, EGFR-lambda, EGFR-lambda & EcR-RNAi). It is extremely surprising that systemic ecdysone has so little role since Phantom, Shadow, or Shade RNAi appear as potent as EcR-RNAi. This quantification has actually been performed for Sad in Figure S5, which is not even mentioned in the text. It should be done for Phtm.

The levels of ecdysone are tenths of times lower in adult compared to the peaks during embryogenesis or metamorphosis. And one source of production is the epithelial cells of the accessory glands themselves. Considering that EcR is expressed in all the cells of the accessory gland (epithelial cells and muscle cells), it seems plausible that there is only a very little amount of ecdysone that can in fact be available for the other epithelial cells.

A UAS-yellow-RNAi (or similarly irrelevant RNAi) rather than UAS-GFP should be used as a control for the EcR, Phtm, Sad, Shd, and Tub RNAi. Indeed, loading the RNAi machinery could have some unexpected effects not controlled by the UAS-GFP.

The NLSGFP line we used here is the one we already published twice (and we compared it to RNAi lines), and this is the reason why we used this already validated control. However, we tested a RNAi White line, and it behaves in the same way.

The authors should not use the term "sex steroid" when referring to ecdysone. It is a steroid hormone important for developmental timing and rate of growth, but is not a sex hormone, as sex is cell autonomously genetically determined in the fly.

In human, sex hormones control sexual differentiation (up to adult characteristics) and sexual reproduction. In Drosophila, ecdysone controls sexual reproduction in both sexes and sexual differentiation at least in the female (DOI: 10.1007/s004270050186). From these results, we do not think that saying it is a sex steroid (not a sex hormone) is ill suited. We intend to precise what we put in this term in the introduction to avoid overinterpretation from our part.

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DOI: 10.1111/ejn.70686

Resource: RRID:Addgene_162374

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_162374

What is this?

DOI: 10.1111/bph.15962

Resource: RRID:Addgene_1000000163

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000163

What is this?

DOI: 10.1105/tpc.19.00454

Resource: RRID:Addgene_1000000036

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000036

What is this?

DOI: 10.1093/plcell/koac207

Resource: RRID:Addgene_1000000047

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000047

What is this?

DOI: 10.1093/plcell/koac207

Resource: RRID:Addgene_1000000044

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000044

What is this?

DOI: 10.1083/jcb.202104128

Resource: RRID:Addgene_1000000094

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000094

What is this?

DOI: 10.1080/22221751.2026.2671469

Resource: (RRID:CVCL_0063)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0063

What is this?

DOI: 10.1080/22221751.2026.2671469

Resource: (CCLV Cat# CCLV-RIE 1035, RRID:CVCL_0023)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0023

What is this?

DOI: 10.1080/01635581.2026.2713789

Resource: FlowJo (RRID:SCR_008520)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_008520

What is this?

DOI: 10.1080/01635581.2026.2713789

Resource: FlowJo (RRID:SCR_008520)

Curator: @dhovakimyan1

SciCrunch record: RRID:SCR_008520

What is this?

DOI: 10.1038/s41590-026-02644-x

Resource: (RRID:CVCL_1926)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_1926

What is this?

DOI: 10.1038/s41590-026-02644-x

Resource: (BCRC Cat# 60005, RRID:CVCL_0030)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0030

What is this?

DOI: 10.1038/s41590-026-02644-x

Resource: (RRID:CVCL_0006)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0006

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: RRID:Addgene_174862

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_174862

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: RRID:Addgene_205559

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_205559

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: RRID:Addgene_205525

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_205525

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: RRID:Addgene_205555

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_205555

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: RRID:Addgene_174858

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_174858

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: RRID:Addgene_23935

Curator: @dhovakimyan1

SciCrunch record: RRID:Addgene_23935

What is this?

DOI: 10.1038/s41586-026-10952-3

Resource: (ATCC Cat# CRL-9855, RRID:CVCL_6444)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_6444

What is this?

DOI: 10.1038/s41392-026-02795-1

Resource: (DSMZ Cat# ACC-305, RRID:CVCL_0045)

Curator: @dhovakimyan1

SciCrunch record: RRID:CVCL_0045

What is this?

DOI: 10.1021/acssynbio.6b00358

Resource: RRID:Addgene_1000000055

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000055

What is this?

DOI: 10.1016/j.xcrm.2026.103051

Resource: R Project for Statistical Computing (RRID:SCR_001905)

Curator: @nmaralla

SciCrunch record: RRID:SCR_001905

What is this?

DOI: 10.1016/j.xcrm.2026.103051

Resource: scVelo (RRID:SCR_018168)

Curator: @nmaralla

SciCrunch record: RRID:SCR_018168

What is this?

DOI: 10.1016/j.xcrm.2026.103051

Resource: immunarch (RRID:SCR_023089)

Curator: @nmaralla

SciCrunch record: RRID:SCR_023089

What is this?

DOI: 10.1016/j.virol.2018.07.020

Resource: RRID:Addgene_1000000055

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000055

What is this?

DOI: 10.1016/j.omtn.2023.09.005

Resource: RRID:Addgene_205466

Curator: @sonofthor

SciCrunch record: RRID:Addgene_205466

What is this?

DOI: 10.1016/j.omtn.2023.09.005

Resource: RRID:Addgene_205465

Curator: @sonofthor

SciCrunch record: RRID:Addgene_205465

What is this?

DOI: 10.1016/j.omtn.2023.09.005

Resource: RRID:Addgene_205464

Curator: @sonofthor

SciCrunch record: RRID:Addgene_205464

What is this?

DOI: 10.1016/j.omtn.2023.09.005

Resource: RRID:Addgene_205463

Curator: @sonofthor

SciCrunch record: RRID:Addgene_205463

What is this?

DOI: 10.1016/j.omtn.2023.09.005

Resource: RRID:Addgene_1000000212

Curator: @sonofthor

SciCrunch record: RRID:Addgene_1000000212

What is this?

DOI: 10.1016/j.molcel.2026.08.018

Resource: (RCB Cat# RCB0806, RRID:CVCL_0065)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0065

What is this?

DOI: 10.1016/j.molcel.2026.08.018

Resource: (RRID:CVCL_0002)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0002

What is this?

DOI: 10.1016/j.molcel.2026.08.018

Resource: (JCRB Cat# JCRB0118, RRID:CVCL_0098)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0098

What is this?

DOI: 10.1016/j.molcel.2026.08.018

Resource: (CLS Cat# 300299/p638_NB-4, RRID:CVCL_0005)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0005

What is this?

DOI: 10.1016/j.molcel.2026.08.018

Resource: (BCRJ Cat# 0233, RRID:CVCL_0559)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0559

What is this?

DOI: 10.1016/j.molcel.2026.08.018

Resource: (DSMZ Cat# ACC-11, RRID:CVCL_0001)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0001

What is this?

DOI: 10.1016/j.isci.2026.117523

Resource: Pandas (RRID:SCR_018214)

Curator: @nmaralla

SciCrunch record: RRID:SCR_018214

What is this?

DOI: 10.1016/j.isci.2026.117523

Resource: SciPy (RRID:SCR_008058)

Curator: @nmaralla

SciCrunch record: RRID:SCR_008058

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00032360

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00032360

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00004165

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00004165

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00040431

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00040431

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00007494

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00007494

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: (WB Cat# WBStrain00024040,RRID:WB-STRAIN:WBStrain00024040)

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00024040

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00035480

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00035480

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00032169

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00032169

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00022373

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00022373

What is this?

DOI: 10.1016/j.isci.2026.117518

Resource: RRID:WB-STRAIN:WBStrain00007339

Curator: @nmaralla

SciCrunch record: RRID:WB-STRAIN:WBStrain00007339

What is this?

DOI: 10.1016/j.isci.2026.117508

Resource: (RRID:CVCL_0006)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0006

What is this?

DOI: 10.1016/j.cell.2026.08.031

Resource: (ATCC Cat# HTB-55, RRID:CVCL_0609)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0609

What is this?

DOI: 10.1016/j.cell.2026.08.031

Resource: (RRID:CVCL_0063)

Curator: @nmaralla

SciCrunch record: RRID:CVCL_0063

What is this?