Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

Chang and colleagues used tetrode recordings in behaving rats to study how learning an audiovisual discrimination task shapes multisensory interactions in the auditory cortex. They found that a significant fraction of neurons in the auditory cortex responded to visual (crossmodal) and audiovisual stimuli. Both auditory-responsive and visually-responsive neurons preferentially responded to the cue signaling the contralateral choice in the two-alternative forced choice task. Importantly, multisensory interactions were similarly specific for the congruent audiovisual pairing for the contralateral side.

Strengths:

The experiments were conducted in a rigorous manner. Particularly thorough are the comparisons across cohorts of rats trained in a control task, in a unisensory auditory discrimination task, and the multisensory task, while also varying the recording hemisphere and behavioral state (engaged vs. anesthesia). The resulting contrasts strengthen the authors' findings and rule out important alternative explanations. Through the comparisons, they show that the enhancements of multisensory responses in the auditory cortex are specific to the paired audiovisual stimulus and specific to contralateral choices in correct trials and thus dependent on learned associations in a task-engaged state.

We thank Reviewer #1 for the thorough review and valuable feedback.

Weaknesses:

The main result is that multisensory interactions are specific for contralateral paired audiovisual stimuli, which is consistent across experiments and interpretable as a learned task-dependent effect. However, the alternative interpretation of behavioral signals is crucial to rule out, which would also be specific to contralateral, correct trials in trained animals. Although the authors focus on the first 150 ms after cue onset, some of the temporal profiles of activity suggest that choice-related activity could confound some of the results.

We thank the reviewer for raising this important point regarding the potential influence of choice-related activity on our results. In our experimental setup, it is challenging to completely disentangle the effects of behavioral choice from multisensory interaction. However, we conducted relevant analyses to examine the influence of choice-related components on multisensory interaction.

First, we analyzed neural responses during incorrect trials and found a significant reduction in multisensory enhancement for the A^10k-V^vt pairing (Fig. 4). In contrast, for the A^3k-V^hz pairing, there was no strong multisensory interaction during either correct (right direction) or incorrect (left direction) choices. This finding suggests that the observed multisensory interactions are strongly associated with specific cue combinations during correct task performance.

Second, we conducted experiments with unisensory training, in which animals were trained separately on auditory and visual discriminations without explicit multisensory associations. The results demonstrated that unisensory training did not lead to the development of selective multisensory enhancement or congruent auditory-visual preferences, as observed in the multisensory training group. This indicates that the observed multisensory interactions in the auditory cortex are specific to multisensory training and cannot be attributed solely to behavioral signals or choice-related effects.

Finally, we specifically focused on the early 0-150 ms time window after cue onset in our main analyses to minimize contributions from motor-related or decision-related activity, which typically emerge later. This time window allowed us to capture early sensory processing while reducing potential confounds.

Together, these findings strongly suggest that the observed choice-dependent multisensory enhancement is a learned, task-dependent phenomenon that is specific to multisensory training.

The auditory stimuli appear to be encoded by short transient activity (in line with much of what we know about the auditory system), likely with onset latencies (not reported) of 15-30 ms. Stimulus identity can be decoded (Figure 2j) apparently with an onset latency around 50-75 ms (only the difference between A and AV groups is reported) and can be decoded near perfectly for an extended time window, without a dip in decoding performance that is observed in the mean activity Figure 2e. The dynamics of the response of the example neurons presented in Figures 2c and d and the average in 2e therefore do not entirely match the population decoding profile in 2j. Population decoding uses the population activity distribution, rather than the mean, so this is not inherently problematic. It suggests however that the stimulus identity can be decoded from later (choice-related?) activity. The dynamics of the population decoding accuracy are in line with the dynamics one could expect based on choice-related activity. Also the results in Figures S2e,f suggest differences between the two learned stimuli can be in the late phase of the response, not in the early phase.

We appreciate the reviewer’s detailed observations and questions regarding the dynamics of auditory responses and decoding profiles in our study. In our experiment, primary auditory cortex (A1) neurons exhibited short response latencies that meet the established criteria for auditory responses in A1, consistent with findings from many other studies conducted in both anesthetized and task-engaged animals. While the major responses typically occurred during the early period (0-150ms) after cue onset (see population response in Fig. 2e), individual neuronal responses in the whole population were generally dynamic, as illustrated in Figures 2c, 2d, and 3a–c. As the reviewer correctly noted, population decoding leverages the distribution of activity across neurons rather than the mean activity, which explains why the dynamics of population decoding accuracy align well with choice-related activity. This also accounts for the extended decoding window observed in Figure 2j, which does not entirely match the early population response profiles in Figure 2e.

To address the reviewer’s suggestion that differences between the two learned stimuli might arise in the late phase of the response, we conducted a cue selectivity analysis during the 151–300 ms period after cue onset. The results, shown below, indicate that neurons maintained cue selectivity in this late phase for each modality (Supplementary Fig. 5), though the selectivity was lower than in the early phase. However, interpreting this late-phase activity remains challenging. Since A^3k, V^hz, and A^3k-V^hz were associated with the right choice, and A^10k, V^vt, and A^10k-V^vt with the left choice, it is difficult to disentangle whether the responses reflect choice, sensory features, or a combination of both.

To further investigate, we examined multisensory interactions during the late phase, controlling for choice effects by calculating unisensory and multisensory responses within the same choice context. Our analysis revealed no evident multisensory enhancement for any auditory-visual pairing, nor significant differences between pairings—unlike the robust effects observed in the early phase (Supplementary Fig. 5). We hypothesize that early responses are predominantly sensory-driven and exhibit strong multisensory integration, whereas late responses likely reflect task-related, choice-related, or combined sensory-choice activity, where sensory-driven multisensory enhancement is less prominent. As the focus of this manuscript is on multisensory integration and cue selectivity, we prioritized a detailed analysis of the early phase, where these effects are most prominent. However, the complexity of interpreting late-phase activity remains a challenge and warrants further investigation. We cited Supplementary Fig. 5 in revised manuscript as the following:

“This resulted in a significantly higher mean MSI for the A^10k-V^vt pairing compared to the A^3k-V^hz pairing (0.047 ± 0.124 vs. 0.003 ± 0.096; paired t-test, p < 0.001). Among audiovisual neurons, this biasing is even more pronounced (enhanced vs. inhibited: 62 vs. 2 in A^10k-V^vt pairing, 6 vs. 13 in A^3k-V^hz pairing; mean MSI: 0.119±0.105 in A^10k-V^vt pairing vs. 0.020±0.083 A^3k-V^hz pairing, paired t-test, p<0.00001) (Fig. 3f). Unlike the early period (0-150ms after cue onset), no significant differences in multisensory integration were observed during the late period (151-300ms after cue onset) (Supplementary Fig. 5).”

First, it would help to have the same time axis across panels 2,c,d,e,j,k. Second, a careful temporal dissociation of when the central result of multisensory enhancements occurs in time would discriminate better early sensory processing-related effects versus later decision-related modulations.

Thank you for this valuable feedback. Regarding the first point, we used a shorter time axis in Fig. 2j-k to highlight how the presence of visual cues accelerates the decoding process. This visualization choice was intended to emphasize the early differences in processing speed. For the second point, we have carefully analyzed multisensory integration across different temporal windows. The results presented in the Supplementary Fig. 5 (also see above) already address the late phase, where our data show no evidence of multisensory enhancement for any auditory-visual pairings. This distinction helps clarify that the observed multisensory effects are primarily related to early sensory processing rather than later decision-related modulations. We hope this addresses the concerns raised and appreciate the opportunity to clarify these points.

In the abstract, the authors mention "a unique integration model", "selective multisensory enhancement for specific auditory-visual pairings", and "using this distinct integrative mechanisms". I would strongly recommend that the authors try to phrase their results more concretely, which I believe would benefit many readers, i.e. selective how (which neurons) and specific for which pairings?

We appreciate the reviewer’s suggestion to clarify our phrasing for better accessibility. To address this, we have revised the relevant sentence in the abstract as follows:

"This model employed selective multisensory enhancement for the auditory-visual pairing guiding the contralateral choice, which correlated with improved multisensory discrimination."

Reviewer #2 (Public review):

Summary

In this study, rats were trained to discriminate auditory frequency and visual form/orientation for both unisensory and coherently presented AV stimuli. Recordings were made in the auditory cortex during behaviour and compared to those obtained in various control animals/conditions. The central finding is that AC neurons preferentially represent the contralateral-conditioned stimulus - for the main animal cohort this was a 10k tone and a vertically oriented bar. Over 1/3rd of neurons in AC were either AV/V/A+V and while a variety of multisensory neurons were recorded, the dominant response was excitation by the correctly oriented visual stimulus (interestingly this preference was absent in the visual-only neurons). Animals performing a simple version of the task in which responses were contingent on the presence of a stimulus rather than its identity showed a smaller proportion of AV stimuli and did not exhibit a preference for contralateral conditioned stimuli. The contralateral conditioned dominance was substantially less under anesthesia in the trained animals and was present in a cohort of animals trained with the reverse left/right contingency. Population decoding showed that visual cues did not increase the performance of the decoder but accelerated the rate at which it saturated. Rats trained on auditory and then visual stimuli (rather than simultaneously with A/V/AV) showed many fewer integrative neurons.

Strengths

There is a lot that I like about this paper - the study is well-powered with multiple groups (free choice, reversed contingency, unisensory trained, anesthesia) which provides a lot of strength to their conclusions and there are many interesting details within the paper itself. Surprisingly few studies have attempted to address whether multisensory responses in the unisensory cortex contribute to behaviour - and the main one that attempted to address this question (Lemus et al., 2010, uncited by this study) showed that while present in AC, somatosensory responses did not appear to contribute to perception. The present manuscript suggests otherwise and critically does so in the context of a task in which animals exhibit a multisensory advantage (this was lacking in Lemus et al.,). The behaviour is robust, with AV stimuli eliciting superior performance to either auditory or visual unisensory stimuli (visual were slightly worse than auditory but both were well above chance).

We thank the reviewer for their positive evaluation of our study.

Weaknesses

I have a number of points that in my opinion require clarification and I have suggestions for ways in which the paper could be strengthened. In addition to these points, I admit to being slightly baffled by the response latencies; while I am not an expert in the rat, usually in the early sensory cortex auditory responses are significantly faster than visual ones (mirroring the relative first spike latencies of A1 and V1 and the different transduction mechanisms in the cochlea and retina). Yet here, the latencies look identical - if I draw a line down the pdf on the population level responses the peak of the visual and auditory is indistinguishable. This makes me wonder whether these are not sensory responses - yet, they look sensory (very tightly stimulus-locked). Are these latencies a consequence of this being AuD and not A1, or ... ? Have the authors performed movement-triggered analysis to illustrate that these responses are not related to movement out of the central port, or is it possible that both sounds and visual stimuli elicit characteristic whisking movements? Lastly, has the latency of the signals been measured (i.e. you generate and play them out synchronously, but is it possible that there is a delay on the audio channel introduced by the amp, which in turn makes it appear as if the neural signals are synchronous? If the latter were the case I wouldn't see it as a problem as many studies use a temporal offset in order to give the best chance of aligning signals in the brain, but this is such an obvious difference from what we would expect in other species that it requires some sort of explanation.

Thank you for your insightful comments. I appreciate the opportunity to clarify these points and strengthen our manuscript. Below, I address your concerns in detail:

We agree that auditory responses are typically faster than visual responses due to the distinct transduction mechanisms. However, in our experiment, we intentionally designed the stimulus setup to elicit auditory and visual responses within a similar time window to maximize the potential for multisensory integration. Specifically, we used pure tone sounds with a 15 ms ramp and visual stimuli generated by an LED array, which produce faster responses compared to mostly used light bars shown on a screen (see Supplementary Fig. 2a). The long ramp of the auditory stimulus slightly delayed auditory response onset, while the LED-generated bar (compared to the bar shown on the screen) elicited visual responses more quickly. This alignment likely facilitated the observed overlap in response latencies.

Neurons’ strong spontaneous activity in freely moving animals complicates the measurement of first spike latencies. Despite that, we still can infer the latency from robust cue-evoked responses. Supplementary Fig. 2b illustrates responses from an exemplar neuron (the same neuron as shown in Fig. 2c), where the auditory response begins 9 ms earlier than the visual response. Given the 28 ms auditory response latency observed here using 15 ms-ramp auditory stimulus, this value is consistent with prior studies in the primary auditory cortex usually using 5 ms ramp pure tones, where latencies typically range from 7 to 28 ms. Across the population (n=559), auditory responses consistently reached 0.5 of the mean Z-scored response 15 ms earlier than visual responses (Supplementary Fig. 2c). The use of Gaussian smoothing in PSTHs supports the reliability of using the 0.5 threshold as an onset latency marker. We cited Supplementary Fig. 2 in the revised manuscript within the Results section (also see the following):

“This suggests multisensory discrimination training enhances visual representation in the auditory cortex. To optimize the alignment of auditory and visual responses and reveal the greatest potential for multisensory integration, we used long-ramp pure tone auditory stimuli and quick LED-array-elicited visual stimuli (Supplementary Fig. 2). While auditory responses were still slightly earlier than visual responses, the temporal alignment was sufficient to support robust integration.”

We measured the time at which rats left the central port and confirmed that these times occur significantly later than the neuronal responses analyzed (see Fig. 1c-d). While we acknowledge the potential influence of movements such as whiskering, facial movements, head direction changes, or body movements on neuronal responses, precise monitoring of these behaviors in freely moving animals remains a technical challenge. However, given the tightly stimulus-locked nature of the neuronal responses observed, we believe they primarily reflect sensory processing rather than movement-related activity.

To ensure accurate synchronization of auditory and visual stimuli, we verified the latencies of our signals. The auditory and visual stimuli were generated and played out synchronously with no intentional delay introduced. The auditory amplifier used in our setup introduces minimal latency, and any such delay would have been accounted for during calibration. Importantly, even if a small delay existed, it would not undermine our findings, as many studies intentionally use temporal offsets to facilitate alignment of neural signals. Nonetheless, the temporal overlap observed here is primarily a result of our experimental design aimed at promoting multisensory integration.

We hope these clarifications address your concerns and highlight the robustness of our findings.

Reaction times were faster in the AV condition - it would be of interest to know whether this acceleration is sufficient to violate a race model, given the arbitrary pairing of these stimuli. This would give some insight into whether the animals are really integrating the sensory information. It would also be good to clarify whether the reaction time is the time taken to leave the center port or respond at the peripheral one.

We appreciate your request for clarification. In our analysis, reaction time (RT) is defined as the time taken for the animal to leave the center port after cue onset. This measure was chosen because it reflects the initial decision-making process and the integration of sensory information leading to action initiation. The time taken to respond at the peripheral port, commonly referred to as movement time, was not included in our RT measure. However, movement time data is available in our dataset, and we are open to further analysis if deemed necessary.

To determine whether the observed acceleration in RTs in the audiovisual (AV) condition reflects true multisensory integration rather than statistical facilitation, we tested for violations of the race model inequality (Miller, 1982). This approach establishes a bound for the probability of a response occurring within a given time interval under the assumption that the auditory (A) and visual (V) modalities operate independently. Specifically, we calculated cumulative distribution functions (CDFs) for the RTs in the A, V, and AV conditions (please see Author response image 1). In some rats, the AV_RTs exceeded the race model prediction at multiple time points, suggesting that the observed acceleration is not merely due to statistical facilitation but reflects true multisensory integration. Examples of these violations are shown in Panels a-b of the following figure. However, in other rats, the AV_RTs did not exceed the race model prediction, as illustrated in Author response image 1c-d.

This variability may be attributed to task-specific factors in our experimental design. For instance, the rats were not under time pressure to respond immediately after cue onset, as the task emphasized accuracy over speed. This lack of urgency may have influenced their behavioral responses and movement patterns. The race model is typically applied to assess multisensory integration in tasks where rapid responses are critical, often under conditions that incentivize speed (e.g., time-restricted tasks). In our study, the absence of strict temporal constraints may have reduced the likelihood of observing consistent violations of the race model. Furthermore, In our multisensory discrimination task, animals should discriminate multiple cues and make a behavioral choice have introduced additional variability in the degree of integration observed across individual animals. Additionally, factors such as a decline in thirst levels and physical performance as the task progressed may have significantly contributed to the variability in our results. These considerations are important for contextualizing the race model findings and interpreting the data within the framework of our experimental design.

Author response image 1.

Reaction time cumulative distribution functions (CDFs) and race model evaluation. (a) CDFs of reaction times (RTs) for auditory (blue), visual (green), and audiovisual stimuli (red) during the multisensory discrimination task. The summed CDF of the auditory and visual conditions (dashed purple, CDF_Miller) represents the race model prediction under independent sensory processing. The dashed yellow line represents the CDF of reaction times predicted by the race model. According to the race model inequality, the CDF for audiovisual stimuli (CDF_AV) should always lie below or to the right of the sum of CDF_A and CDF_V. In this example, the inequality is violated at nearly t = 200 ms, where CDF_AV is above CDF_Miller. (b) Data from another animal, showing similar results. (c, d) CDFs of reaction times for two other animals. In these cases, the CDFs follow the race model inequality, with CDF_AV consistently lying below or to the right of CDF_A + CDF_V.

The manuscript is very vague about the origin or responses - are these in AuD, A1, AuV... ? Some attempts to separate out responses if possible by laminar depth and certainly by field are necessary. It is known from other species that multisensory responses are more numerous, and show greater behavioural modulation in non-primary areas (e.g. Atilgan et al., 2018).

Thank you for highlighting the importance of specifying the origin of the recorded responses. In the manuscript, we have detailed the implantation process in both the Methods and Results sections, indicating that the tetrode array was targeted to the primary auditory cortex. Using a micromanipulator (RWD, Shenzhen, China), the tetrode array was precisely positioned at stereotaxic coordinates 3.5–5.5 mm posterior to bregma and 6.4 mm lateral to the midline, and advanced to a depth of approximately 2–2.8 mm from the brain surface, corresponding to the primary auditory cortex. Although our recordings were aimed at A1, it is likely that some neurons from AuD and/or AuV were also included due to the anatomical proximity.

In fact, in our unpublished data collected from AuD, we observed that over 50% of neurons responded to or were modulated by visual cues, consistent with findings from many other studies. This suggests that visual representations are more pronounced in AuD compared to A1. However, as noted in the manuscript, our primary focus was on A1, where we observed relatively fewer visual or audiovisual modulations in untrained rats.

Regarding laminar depth, we regret that we were unable to determine the specific laminar layers of the recorded neurons in this study, a limitation primarily due to the constraints of our recording setup.

Reviewer #3 (Public review):

Summary:

The manuscript by Chang et al. aims to investigate how the behavioral relevance of auditory and visual stimuli influences the way in which the primary auditory cortex encodes auditory, visual, and audiovisual information. The main result is that behavioral training induces an increase in the encoding of auditory and visual information and in multisensory enhancement that is mainly related to the choice located contralaterally with respect to the recorded hemisphere.

Strengths:

The manuscript reports the results of an elegant and well-planned experiment meant to investigate if the auditory cortex encodes visual information and how learning shapes visual responsiveness in the auditory cortex. Analyses are typically well done and properly address the questions raised.

We sincerely thank the reviewer for their thoughtful and positive evaluation of our study.

Weaknesses:

Major

(1) The authors apparently primarily focus their analyses of sensory-evoked responses in approximately the first 100 ms following stimulus onset. Even if I could not find an indication of which precise temporal range the authors used for analysis in the manuscript, this is the range where sensory-evoked responses are shown to occur in the manuscript figures. While this is a reasonable range for auditory evoked responses, the same cannot be said for visual responses, which commonly peak around 100-120 ms, in V1. In fact, the latency and overall shape of visual responses are quite different from typical visual responses, that are commonly shown to display a delay of up to 100 ms with respect to auditory responses. All traces that the authors show, instead, display visual responses strikingly overlapping with auditory ones, which is not in line with what one would expect based on our physiological understanding of cortical visually-evoked responses. Similarly, the fact that the onset of decoding accuracy (Figure 2j) anticipates during multisensory compared to auditory-only trials is hard to reconcile with the fact that visual responses have a later onset latency compared to auditory ones. The authors thus need to provide unequivocal evidence that the results they observe are truly visual in origin. This is especially important in view of the ever-growing literature showing that sensory cortices encode signals representing spontaneous motor actions, but also other forms of non-sensory information that can be taken prima facie to be of sensory origin. This is a problem that only now we realize has affected a lot of early literature, especially - but not only - in the field of multisensory processing. It is thus imperative that the authors provide evidence supporting the true visual nature of the activity reported during auditory and multisensory conditions, in both trained, free-choice, and anesthetized conditions. This could for example be achieved causally (e.g. via optogenetics) to provide the strongest evidence about the visual nature of the reported results, but it's up to the authors to identify a viable solution. This also applies to the enhancement of matched stimuli, that could potentially be explained in terms of spontaneous motor activity and/or pre-motor influences. In the absence of this evidence, I would discourage the author from drawing any conclusion about the visual nature of the observed activity in the auditory cortex.

We thank the reviewers for highlighting the critical issue of validating the sensory origin of the reported responses, particularly regarding the timing of visual responses and the potential confound of motor-related activity.

We analyzed neural responses within the first 150 ms following cue onset, as stated in the manuscript. This temporal window encompasses the peak of visual responses. The responses to visual stimuli occur predominantly within the first 100 ms after cue onset, preceding the initiation of body movements in behavioral tasks. This temporal dissociation aligns with previous studies, which demonstrate that motor-related activity in sensory cortices generally emerges later and is often associated with auditory rather than visual stimuli

We acknowledge that auditory responses are typically faster than visual responses due to distinct transduction mechanisms. However, in our experiment, we intentionally designed the stimulus setup to elicit auditory and visual responses within a similar time window to maximize the potential for multisensory integration. Specifically, we used pure tone sounds with a 15 ms ramp and visual stimuli generated by an LED array, which produce faster responses compared to commonly used light bars shown on a screen. The long ramp of the auditory stimulus slightly delayed auditory response onset, while the LED-generated bar elicited visual responses more quickly (Supplementary Fig. 2). This alignment facilitated the observed overlap in response latencies. As we measured in neurons with robust visual response, first spike latencies is approximately 40 ms, as exemplified by a neuron with a low spontaneous firing rate and a strong, stimulus-evoked response (Supplementary Fig. 4). Across the population (n = 559 neurons), auditory responses reached 0.5 of the mean Z-scored response 15 ms earlier than visual responses on average (Supplementary Fig. 2). We cited Supplementary Fig. 4 in the Results section as follows:

“Regarding the visual modality, 41% (80/196) of visually-responsive neurons showed a significant visual preference (Fig. 2f). The visual responses observed within the 0–150 ms window after cue onset were consistent and unlikely to result from visually evoked movement-related activity. This conclusion is supported by the early timing of the response (Fig. 2e) and exemplified by a neuron with a low spontaneous firing rate and a robust, stimulus-evoked response (Supplementary Fig. 4).”

We acknowledge the growing body of literature suggesting that sensory cortices can encode signals related to motor actions or non-sensory factors. To address this concern, we emphasize that visual responses were present not only during behavioral tasks but also in anesthetized conditions, where motor-related signals are absent. Additionally, movement-evoked responses tend to be stereotyped and non-discriminative. In contrast, the visual responses observed in our study were highly consistent and selective to visual cue properties, further supporting their sensory origin.

In summary, the combination of anesthetized and behavioral recordings, the temporal profile of responses, and their discriminative nature strongly support the sensory (visual) origin of the observed activity within the early response period. While the current study provides strong temporal and experimental evidence for the sensory origin of the visual responses, we agree that causal approaches, such as optogenetic silencing of visual input, could provide even stronger validation. Future work will explore these methods to further dissect the visual contributions to auditory cortical activity.

(2) The finding that AC neurons in trained mice preferentially respond - and enhance - auditory and visual responses pertaining to the contralateral choice is interesting, but the study does not show evidence for the functional relevance of this phenomenon. As has become more and more evident over the past few years (see e.g. the literature on mouse PPC), correlated neural activity is not an indication of functional role. Therefore, in the absence of causal evidence, the functional role of the reported AC correlates should not be overstated by the authors. My opinion is that, starting from the title, the authors need to much more carefully discuss the implications of their findings.

We fully agree that correlational data alone cannot establish causality. In light of your suggestion, we will revise the manuscript to more carefully discuss the implications of our findings, acknowledging that the preferred responses observed in AC neurons, particularly in relation to the contralateral choice, are correlational. We have updated several sentences in the manuscript to avoid overstating the functional relevance of these observations. Below are the revisions we have made:

Abstract section

"Importantly, many audiovisual neurons in the AC exhibited experience-dependent associations between their visual and auditory preferences, displaying a unique integration model. This model employed selective multisensory enhancement for the auditory-visual pairing guiding the contralateral choice, which correlated with improved multisensory discrimination."

(Page 8, fourth paragraph in Results Section)

"This aligns with findings that neurons in the AC and medial prefrontal cortex selectively preferred the tone associated with the behavioral choice contralateral to the recorded cortices during sound discrimination tasks, potentially reflecting the formation of sound-to-action associations. However, this preference represents a neural correlate, and further work is required to establish its causal link to behavioral choices."

(rewrite 3rd paragraph in Discussion Section)

"Consistent with prior research(10,31), most AC neurons exhibited a selective preference for cues associated with contralateral choices, regardless of the sensory modality. This suggests that AC neurons may contribute to linking sensory inputs with decision-making, although their causal role remains to be examined. "

"These results indicate that multisensory training could drive the formation of specialized neural circuits within the auditory cortex, facilitating integrated processing of related auditory and visual information. However, further causal studies are required to confirm this hypothesis and to determine whether the auditory cortex is the primary site of these circuit modifications."

MINOR:

(1) The manuscript is lacking what pertains to the revised interpretation of most studies about audiovisual interactions in primary sensory cortices following the recent studies revealing that most of what was considered to be crossmodal actually reflects motor aspects. In particular, recent evidence suggests that sensory-induced spontaneous motor responses may have a surprisingly fast latency (within 40 ms; Clayton et al. 2024). Such responses might also underlie the contralaterally-tuned responses observed by the authors if one assumes that mice learn a stereotypical response that is primed by the upcoming goal-directed, learned response. Given that a full exploration of this issue would require high-speed tracking of orofacial and body motions, the authors should at least revise the discussion and the possible interpretation of their results not just on the basis of the literature, but after carefully revising the literature in view of the most recent findings, that challenge earlier interpretations of experimental results.

Thank you for pointing out this important consideration. We have revised the discussion (paragraph 8-9) as follows:

“There is ongoing debate about whether cross-sensory responses in sensory cortices predominantly reflect sensory inputs or are influenced by behavioral factors, such as cue-induced body movements. A recent study shows that sound-clip evoked activity in visual cortex have a behavioral rather than sensory origin and is related to stereotyped movements(48). Several studies have demonstrated sensory neurons can encode signals associated with whisking(49), running(50), pupil dilation (510 and other movements(52). In our study, the responses to visual stimuli in the auditory cortex occurred primarily within a 100 ms window following cue onset. This early timing suggests that the observed responses likely reflect direct sensory inputs, rather than being modulated by visually-evoked body or orofacial movements, which typically occur with a delay relative to sensory cue onset(53).

A recent study by Clayton et al. (2024) demonstrated that sensory stimuli can evoke rapid motor responses, such as facial twitches, within 50 ms, mediated by subcortical pathways and modulated by descending corticofugal input(56). These motor responses provide a sensitive behavioral index of auditory processing. Although Clayton et al. did not observe visually evoked facial movements, it is plausible that visually driven motor activity occurs more frequently in freely moving animals compared to head-fixed conditions. In goal-directed tasks, such rapid motor responses might contribute to the contralaterally tuned responses observed in our study, potentially reflecting preparatory motor behaviors associated with learned responses. Consequently, some of the audiovisual integration observed in the auditory cortex may represent a combination of multisensory processing and preparatory motor activity. Comprehensive investigation of these motor influences would require high-speed tracking of orofacial and body movements. Therefore, our findings should be interpreted with this consideration in mind. Future studies should aim to systematically monitor and control eye, orofacial, and body movements to disentangle sensory-driven responses from motor-related contributions, enhancing our understanding of motor planning’s role in multisensory integration.”

(2) The methods section is a bit lacking in details. For instance, information about the temporal window of analysis for sensory-evoked responses is lacking. Another example: for the spike sorting procedure, limited details are given about inclusion/exclusion criteria. This makes it hard to navigate the manuscript and fully understand the experimental paradigm. I would recommend critically revising and expanding the methods section.

Thank you for raising this point. We clarified the temporal window by including additional details in the methods section, even though this information was already mentioned in the results section. Specifically, we now state:

(Neural recordings and Analysis in methods section)

“...These neural signals, along with trace signals representing the stimuli and session performance information, were transmitted to a PC for online observation and data storage. Neural responses were analyzed within a 0-150ms temporal window after cue onset, as this period was identified as containing the main cue-evoked responses for most neurons. This time window was selected based on the consistent and robust neural activity observed during this period.”

We appreciate your concern regarding spike sorting procedure. To address this, we have expanded the methods section to provide more detailed information about the quality of our single-unit recordings. we have added detailed information in the text, as shown below (Analysis of electrophysiological data in methods section):

“Initially, the recorded raw neural signals were band-pass filtered in the range of 300-6000 Hz to eliminate field potentials. A threshold criterion, set at no less than three times the standard deviation (SD) above the background noise, was applied to automatically identify spike peaks. The detected spike waveforms were then subjected to clustering using template-matching and built-in principal component analysis tool in a three-dimensional feature space. Manual curation was conducted to refine the sorting process. Each putative single unit was evaluated based on its waveform and firing patterns over time. Waveforms with inter-spike intervals of less than 2.0 ms were excluded from further analysis. Spike trains corresponding to an individual unit were aligned to the onset of the stimulus and grouped based on different cue and choice conditions. Units were included in further analysis only if their presence was stable throughout the session, and their mean firing rate exceeded 2 Hz. The reliability of auditory and visual responses for each unit was assessed, with well-isolated units typically showing the highest response reliability.”

Reviewer #1 (Recommendations for the authors):

(1) Some of the ordering of content in the introduction could be improved. E.g. line 49 reflects statements about the importance of sensory experience, which is the topic of the subsequent paragraph. In the discussion, line 436, there is a discussion of the same findings as line 442. These two paragraphs in general appear to discuss similar content. Similarly, the paragraph starting at line 424 and at line 451 both discuss the plasticity of multisensory responses through audiovisual experience, as well as the paragraph starting at line 475 (but now audiovisual pairing is dubbed semantic). In the discussion of how congruency/experience shapes multisensory interactions, the authors should relate their findings to those of Meijer et al. 2017 and Garner and Keller 2022 (visual cortex) about enhanced and suppressed responses and their potential role (as well as other literature such as Banks et al. 2011 in AC).

We thank the reviewer for their detailed observations and valuable recommendations to improve the manuscript's organization. Below, we address each point:

We deleted the sentence, "Sensory experience has been shown to shape cross-modal presentations in sensory cortices" (Line 49), as the subsequent paragraph discusses sensory experience in detail.

To avoid repetition, we removed the sentence, "This suggests that multisensory training enhances AC's ability to process visual information" (Lines 442–443).

Regarding the paragraph starting at Line 475, we believe its current form is appropriate, as it focuses on the influence of semantic congruence on multisensory integration, which differs from the topics discussed in the other paragraphs.

We have cited the three papers suggested by the reviewer in the appropriate sections of the manuscript.

(Paragraph 6 in discussion section)

“…A study conducted on the gustatory cortex of alert rats has shown that cross-modal associative learning was linked to a dramatic increase in the prevalence of neurons responding to nongustatory stimuli (24). Moreover, in the primary visual cortex, experience-dependent interactions can arise from learned sequential associations between auditory and visual stimuli, mediated by corticocortical connections rather than simultaneous audiovisual presentations (26).”

(Paragraph 2 in discussion section)

“...Meijer et al. reported that congruent audiovisual stimuli evoke balanced enhancement and suppression in V1, while incongruent stimuli predominantly lead to suppression(6), mirroring our findings in AC, where multisensory integration was dependent on stimulus feature…”

(Paragraph 2 in introduction section)

“...Anatomical investigations reveal reciprocal nerve projections between auditory and visual cortices(4,11-15), highlighting the interconnected nature of these sensory systems. Moreover, two-photon calcium imaging in awake mice has shown that audiovisual encoding in the primary visual cortex depends on the temporal congruency of stimuli, with temporally congruent audiovisual stimuli eliciting balanced enhancement and suppression, whereas incongruent stimuli predominantly result in suppression(6).”

(2) The finding of purely visually responsive neurons in the auditory cortex that moreover discriminate the stimuli is surprising given previous results (Iurilli et al. 2012, Morrill and Hasenstaub 2018 (only L6), Oude Lohuis et al. 2024, Atilgan et al. 2018, Chou et al. 2020). Reporting the latency of this response is interesting information about the potential pathways by which this information could reach the auditory system. Furthermore, spike isolation quality and histological verification are described in little detail. It is crucial for statements about the auditory, visual, or audiovisual response of individual neurons to substantiate the confidence level about the quality of single-unit recordings and where they were recorded. Do the authors have data to support that visual and audiovisual responses were not restricted to posteromedial tetrodes or clusters with poor quality? A discussion of finding V-responsive units in AC with respect to literature is warranted. Furthermore, the finding that also in visual trials behaviorally relevant information about the visual cue (with a bias for the contralateral choice cue) is sent to the AC is pivotal in the interpretation of the results, which as far as I note not really considered that much.

We appreciate the reviewer’s thoughtful comments and have addressed them as follows:

Discussion of finding choice-related V-responsive units in AC with respect to literature and potential pathways

3rd paragraph in the Discussion section

“Consistent with prior research(10,31), most AC neurons exhibited a selective preference for cues associated with contralateral choices, regardless of the sensory modality. This suggests that AC neurons may contribute to linking sensory inputs with decision-making, although their causal role remains to be examined. Associative learning may drive the formation of new connections between sensory and motor areas of the brain, such as cortico-cortical pathways(35). Notably, this cue-preference biasing was absent in the free-choice group. A similar bias was also reported in a previous study, where auditory discrimination learning selectively potentiated corticostriatal synapses from neurons representing either high or low frequencies associated with contralateral choices(32)…”

6th paragraph in the Discussion section

“Our results extend prior finding(4,47), showing that visual input not only reaches the AC but can also drive discriminative responses, particularly during task engagement. This task-specific plasticity enhances cross-modal integration, as demonstrated in other sensory systems. For example, calcium imaging studies in mice showed that a subset of multimodal neurons in visual cortex develops enhanced auditory responses to the paired auditory stimulus following coincident auditory–visual experience(25)…”

8th paragraph in the Discussion section

“…In our study, the responses to visual stimuli in the auditory cortex occurred primarily within a 100 ms window following cue onset, suggesting that visual information reaches the AC through rapid pathways. Potential candidates include direct or fast cross-modal inputs, such as pulvinar-mediated pathways(8) or corticocortical connections(5，54), rather than slower associative mechanisms. This early timing indicates that the observed responses were less likely modulated by visually-evoked body or orofacial movements, which typically occur with a delay relative to sensory cue onset(55).”

Response Latency

Regarding the latency of visually driven responses, we have included this information in our response to the second reviewer’s first weakness (please see the above). Briefly, we analyzed neural responses within a 0-150ms temporal window after cue onset, as this period captures the most consistent and robust cue-evoked responses across neurons.

Purely Visually Responsive Neurons in A1

We agree that the finding of visually responsive neurons in the auditory cortex may initially seem surprising. However, these neurons might not have been sensitive to target auditory cues in our task but could still respond to other sound types. Cortical neurons are known to exhibit significant plasticity during the cue discrimination tasks, as well as during passive sensory exposure. Thus, the presence of visually responsive neurons is not inconsistent with prior findings but highlights task-specific sensory tuning. We confirm that responses were not restricted to posteromedial tetrodes or low-quality clusters (see an example of a robust visually responsive neuron in supplementary Fig. 4). Histological analysis verified electrode placements across the auditory cortex.

For spike sorting, we have added detailed information in the text, as shown below:

“Initially, the recorded raw neural signals were band-pass filtered in the range of 300-6000 Hz to eliminate field potentials. A threshold criterion, set at no less than three times the standard deviation (SD) above the background noise, was applied to automatically identify spike peaks. The detected spike waveforms were then subjected to clustering using template-matching and built-in principal component analysis tool in a three-dimensional feature space. Manual curation was conducted to refine the sorting process. Each putative single unit was evaluated based on its waveform and firing patterns over time. Waveforms with inter-spike intervals of less than 2.0 ms were excluded from further analysis. Spike trains corresponding to an individual unit were aligned to the onset of the stimulus and grouped based on different cue and choice conditions. Units were included in further analysis only if their presence was stable throughout the session, and their mean firing rate exceeded 2 Hz. The reliability of auditory and visual responses for each unit was assessed, with well-isolated units typically showing the highest response reliability.”

(3) In the abstract it seems that in "Additionally, AC neurons..." the connective word 'additionally' is misleading as it is mainly a rephrasing of the previous statement.

Replaced "Additionally" with "Furthermore" to better signal elaboration and continuity.

(4) The experiments included multisensory conflict trials - incongruent audiovisual stimuli. What was the behavior for these trials given multiple interesting studies on the neural correlates of sensory dominance (Song et al. 2017, Coen et al. 2023, Oude Lohuis et al. 2024).

We appreciate your feedback and have addressed it by including a new figure (supplemental Fig. 8) that illustrates choice selection during incongruent audiovisual stimuli. Panel (a) shows that rats displayed confusion when exposed to mismatched stimuli, resulting in choice patterns that differed from those observed in panel (b), where consistent audiovisual stimuli were presented. To provide clarity and integrate this new figure effectively into the manuscript, we updated the results section as follows:

“...Rats received water rewards with a 50% chance in either port when an unmatched multisensory cue was triggered. Behavioral analysis revealed that Rats displayed notable confusion in response to unmatched multisensory cues, as evidenced by their inconsistent choice patterns (supplementary Fig. 8).”

(5) Line 47: The AC does not 'perceive' sound frequency, individual brain regions are not thought to perceive.

e appreciate the reviewer’s observation and have revised the sentence to ensure scientific accuracy. The updated sentence in the second paragraph of the Introduction now reads:

“Even irrelevant visual cues can affect sound discrimination in AC¹⁰.”

(6) Line 59-63: The three questions are not completely clear to me. Both what they mean exactly and how they are different. E.g. Line 60: without specification, it is hard to understand which 'strategies' are meant by the "same or different strategies"? And Line 61: What is meant by the quotation marks for match and mismatch? I assume this is referring to learned congruency and incongruency, which appears almost the same question as number 3 (how learning affects the cortical representation).

We have revised the three questions for improved clarity and distinction as follows:<br /> “This limits our understanding of multisensory integration in sensory cortices, particularly regarding: (1) Do neurons in sensory cortices adopt consistent integration strategies across different audiovisual pairings, or do these strategies vary depending on the pairing? (2) How does multisensory perceptual learning reshape cortical representations of audiovisual objects? (3) How does the congruence between auditory and visual features—whether they "match" or "mismatch" based on learned associations—impact neural integration?”

(7) Is the data in Figures 1c and d only hits?

Only correct trials are included. We add this information in the figure legend. Please see Fig. 1 legend. Also, please see below

“c Cumulative frequency distribution of reaction time (time from cue onset to leaving the central port) for one representative rat in auditory, visual and multisensory trials (correct only). d Comparison of average reaction times across rats in auditory, visual, and multisensory trials (correct only).”

(8) Figure S1b: Preferred frequency is binned in non-equidistant bins, neither linear nor logarithmic. It is unclear what the reason is.

The edges of the bins for the preferred frequency were determined based on a 0.5-octave increment, starting from the smallest boundary of 8 kHz. Specifically, the bin edges were calculated as follows:

8×2^0.5=11.3 kHz;

8×2¹=16 kHz;

8×2^1.5=22.6 kHz;

8×2²=32 kHz;

This approach reflects the common practice of using changes in octaves to define differences between pure tone frequencies, as it aligns with the logarithmic perception of sound frequency in auditory neuroscience.

(9) Figure S1d: why are the responses all most neurons very strongly correlated given the frequency tuning of A1 neurons? Further, the mean normalized response presented in Figure S2e does seem to indicate a stronger response for 10kHz tones than 3kHz, in conflict with the data from anesthetized rats presented in Figure S2e.

There is no discrepancy in the data. In Figure S1d, we compared neuronal responses to 10 kHz and 3 kHz tones, demonstrating that most neurons responded well to both frequencies. This panel does not aim to illustrate frequency selectivity but rather the overall responsiveness of neurons to these tones. For detailed information on sound selectivity, readers can refer to Figures S3a-b, which show that while more neurons preferred 10 kHz tones, the proportion is lower than in neurons recorded during the multisensory discrimination task. This distinction explains the observed differences and aligns with the results presented.

(10) Line 79: For clarity, it can be added that the multisensory trials presented are congruent trials (jointly indicated rewarded port), and perhaps that incongruent trials are discussed later in the paper.

We believe additional clarification is unnecessary, as the designations "A^3kV^hz" and "A^10kV^vt" clearly indicate the specific combinations of auditory and visual cues presented during congruent trials. Additionally, the discussion of incongruent trials is provided later in the manuscript, as noted by the reviewer.

(11) Line 111: the description leaves unclear that the 35% reflects the combination of units responsive to visual only and responsive to auditory or visual.

The information is clearly presented in Figure 2b, which shows the proportions of neurons responding to auditory-only (A), visual-only (V), both auditory and visual (A, V), and audiovisual-only (VA) stimuli in a pie chart. Readers can refer to this figure for a detailed breakdown of the neuronal response categories.

(12) Figure 2h: consider a colormap with diverging palette and equal positive and negative maximum (e.g. -0.6 to 0.6) and perhaps reiterate in the color bar legend which stimulus is preferred for which selectivity index.

We appreciate the suggestion; however, we believe that the current colormap effectively conveys the data and the intended interpretation. The existing color bar legend already provides clear information about the selectivity index, and the stimulus preference is adequately explained in the figure caption. As such, further adjustments are not necessary.

(13) Line 160: "a ratio of 60:20 for V^vt 160 preferred vs. V^hz preferred neurons." Is this supposed to add up to 100, or is this a ratio of 3:1?

We rewrite the sentence. Please see below:

“Similar to the auditory selectivity observed, a greater proportion of neurons favored the visual stimulus (V^vt) associated with the contralateral choice, with a 3:1 ratio of V^vt-preferred to V^hz-preferred neurons.”

(14) The statement in Figure 2g and line 166/167 could be supported by a statistical test (chi-square?).

Thank you for the suggestion. However, we believe that a statistical test is not required in this case, as the patterns observed are clearly represented in Figure 2g. The qualitative differences between the groups are evident and sufficiently supported by the data.

(15) Line 168, it is unclear in what sense 'dominant' is meant. Is audition perceived as a dominant sensory modality in a behavioral sense (e.g. Song et al. 2017), or are auditory signals the dominant sensory signal locally in the auditory cortex?

Thank you for the clarification. To address your question, by "dominant," we are referring to the fact that auditory inputs are the most prominent and influential among the sensory signals feeding into the auditory cortex. This reflects the local dominance of auditory signals within the auditory cortex, rather than a behavioral dominance of auditory perception. We have revised the sentence as follows:

“We propose that the auditory input, which dominates within the auditory cortex, acts as a 'teaching signal' that shapes visual processing through the selective reinforcement of specific visual pathways during associative learning.”

(16) Line 180: "we discriminated between auditory, visual, and multisensory cues." This phrasing indicated that the SVMs were trained to discriminate sensory modalities (as is done later in the manuscript), rather than what was done: discriminate stimuli within different categories of trials.

Thank you for your comment. We have revised the sentence for clarity. Please see the updated version below:

“Using cross-validated support vector machine (SVM) classifiers, we examined how this pseudo-population discriminates stimulus identity within the same modality (e.g., A^3k vs. A^10k for auditory stimuli, V^hz vs. V^vt for visual stimuli, A^3kV^hz vs. A^10kV^vt for multisensory stimuli).”

(17) Line 185: "a deeply accurate incorporation of visual processing in the auditory cortex." the phrasing is a bit excessive for a binary classification performance.

Thank you for pointing this out. We have revised the sentence to better reflect the findings without overstating them:

“Interestingly, AC neurons could discriminate between two visual targets with around 80% accuracy (Fig. 2j), demonstrating a meaningful incorporation of visual information into auditory cortical processing.”

(18) Figure 3, title. An article is missing (a,an/the).

Done. Please see below:

“Fig. 3 Auditory and visual integration in the multisensory discrimination task”

(19) Line 209, typo pvalue: p<-0.00001.

Done (p<0.00001).

(20) Line 209, the pattern is not weaker. The pattern is the same, but more weakly expressed.

Thank you for your valuable feedback. We appreciate your clarification and agree that our phrasing could be improved for accuracy. The observed pattern under anesthesia is indeed the same but less strongly expressed compared to the task engagement. We have revised the sentence to better reflect this distinction:

“A similar pattern, albeit less strongly expressed, was observed under anesthesia (Supplementary Fig. 3c-3f), suggesting that multisensory perceptual learning may induce plastic changes in AC.”

(21) Line 211: choice-free group → free-choice group.

Done.

(22) Line 261: wrong → incorrect (to maintain consistent terminology).

Done.

(23) Line 265: why 'likely'? Are incorrect choices on the A^3k-V^hz trials not by definition contralateral and vice versa? Or are there other ways to have incorrect trials?

We deleted the word of ‘likely’. Please see below:

“…, correct choices here correspond to ipsilateral behavioral selection, while incorrect choices correspond to contralateral behavioral selection.”

(24) Typo legend Fig 3a-c (tasks → task). (only one task performed).

Done.

(25) Line 400: typo: Like → like.

Done.

(26) Line 405: What is meant by a cohesive visual stimulus? Congruent? Rephrase.

Done. Please see the below:

“…layer 2/3 neurons of the primary visual cortex(7), and a congruent visual stimulus can enhance sound representation…”

(27) Line 412: Very general statement and obviously true: depending on the task, different sensory elements need to be combined to guide adaptive behavior.

We really appreciate the reviewer and used this sentence (see second paragraph in discussion section).

(28) Line 428: within → between (?).

Done.

(29) Figure 3L is not referenced in the main text. By going through the figures and legends my understanding is that this shows that most neurons have a multisensory response that lies between 2 z-scores of the predicted response in the case of 83% of the sum of the auditory and the visual response. However, how was the 0.83 found? Empirically? Figure S3 shows a neuron that does follow a 100% summation. Perhaps the authors could quantitatively support their estimate of 83% of the A + V sum, by varying the fraction of the sum (80%, 90%, 100% etc.) and showing the distribution of the preferred fraction of the sum across neurons, or by showing the percentage of neurons that fall within 2 z-scores for each of the fractions of the sum.

Thank you for your detailed feedback and suggestions regarding Figure 3L and the 83% multiplier.

(1) Referencing Figure 3L:

Figure 3L is referenced in the text. To enhance clarity, we have revised the text to explicitly highlight its relevance:

“Specifically, as illustrated in Fig. 3k, the observed multisensory response approximated 83% of the sum of the auditory and visual responses in most cases, as quantified in Fig. 3L.”

(2) Determination of the 0.83 Multiplier:

The 0.83 multiplier was determined empirically by comparing observed audiovisual responses with the predicted additive responses (i.e., the sum of auditory and visual responses). For each neuron, we calculated the auditory, visual, and audiovisual responses. We then compared the observed audiovisual response with scaled sums of auditory and visual responses (Fig. 3k), expressed as fractions of the additive prediction (e.g., 0.8, 0.83, 0.9, etc.). We found that when the scaling factor was 0.83, the population-wide difference between predicted and observed multisensory responses, expressed as z-scores, was minimized. Specifically, at this value, the mean z-score across the population was approximately zero (-0.0001±1.617), indicating the smallest deviation between predicted and observed responses.

(30) Figure 5e: how come the diagonal has 0.5 decoding accuracy within a category? Shouldn't this be high within-category accuracy? If these conditions were untested and it is an issue of the image display it would be informative to test the cross-validated performance within the category as well as a benchmark to compare the across-category performance to. Aside, it is unclear which conventions from Figure 2 are meant by the statement that conventions were the same.

The diagonal values (~0.5 decoding accuracy) within each category reflect chance-level performance. This occurs because the decoder was trained and tested on the same category conditions in a cross-validated manner, and within-category stimulus discrimination was not the primary focus of our analysis. Specifically, the stimuli within a category shared overlapping features, leading to reduced discriminability for the decoder when distinguishing between them. Our primary objective was to assess cross-category performance rather than within-category accuracy, which may explain the observed pattern in the diagonal values.

Regarding the reference to Figure 2, we appreciate the reviewer pointing out the ambiguity. To avoid any confusion, we have removed the sentence referencing "conventions from Figure 2" in the legend for Figure 5e, as it does not contribute meaningfully to the understanding of the results.

(31) Line 473: "movement evoked response", what is meant by this?

Thank the reviewer for highlighting this point. To clarify, by "movement-evoked response," we are referring to neural activity that is driven by the animal's movements, rather than by sensory inputs. This type of response is typically stereotyped, meaning that it has a consistent, repetitive pattern associated with specific movements, such as whisking, running, or other body or facial movements.

In our study, we propose that the visually-evoked responses observed within the 150 ms time window after cue onset primarily reflect sensory inputs from the visual stimulus rather than movement-related activity. This interpretation is supported by the response timing: visual-evoked activity occurs within 100 ms of the light flash onset, a timeframe too rapid to be attributed to body or orofacial movements. Additionally, unlike stereotyped movement-evoked responses, the visual responses we observed are discriminative, varying based on specific visual features—a hallmark of sensory processing rather than motor-driven activity.

We have revised the manuscript as follows (eighth paragraph in discussion section):

“There is ongoing debate about whether cross-sensory responses in sensory cortices predominantly reflect sensory inputs or are influenced by behavioral factors, such as cue-induced body movements. A recent study shows that sound-clip evoked activity in visual cortex have a behavioral rather than sensory origin and is related to stereotyped movements(49). Several studies have demonstrated sensory neurons can encode signals associated with whisking(50), running(51), pupil dilation(52) and other movements(53). In our study, the responses to visual stimuli in the auditory cortex occurred primarily within a 100 ms window following cue onset. suggests that visual information reaches the AC through rapid pathways. Potential candidates include direct or fast cross-modal inputs, such as pulvinar-mediated pathways(8) or corticocortical connections(5，54), rather than slower associative mechanisms. This early timing suggests that the observed responses were less likely modulated by visually-evoked body or orofacial movements, which typically occur with a delay relative to sensory cue onset(55). ”

(32) Line 638-642: It is stated that a two-tailed permutation test is done. The cue selectivity can be significantly positive and negative, relative to a shuffle distribution. This is excellent. But then it is stated that if the observed ROC value exceeds the top 5% of the distribution it is deemed significant, which corresponds to a one-tailed test. How were significantly negative ROC values detected with p<0.05?

Thank you for pointing this out. We confirm that a two-tailed permutation test was indeed used to evaluate cue selectivity. In this approach, significance is determined by comparing the observed ROC value to both tails of the shuffle distribution. Specifically, if the observed ROC value exceeds the top 2.5% or falls below the bottom 2.5% of the distribution, it is considered significant at p< 0.05. This two-tailed test ensures that both significantly positive and significantly negative cue selectivity values are identified.

To clarify this in the manuscript, we have revised the text as follows:

“This generated a distribution of values from which we calculated the probability of our observed result. If the observed ROC value exceeds the top 2.5% of the distribution or falls below the bottom 2.5%, it was deemed significant (i.e., p < 0.05).”

(33) Line 472: the cited paper (reference 52) actually claims that motor-related activity in the visual cortex has an onset before 100ms and thus does not support your claim that the time window precludes any confound of behaviorally mediated activity. Furthermore, that study and reference 47 show that sensory stimuli could be discriminated based on the cue-evoked body movements and are discriminative. A stronger counterargument would be that both studies show very fast auditory-evoked body movements, but only later visually-evoked body movements.

We appreciate the reviewer’s comments. As Lohuis et al. (reference 55) demonstrated, activity in the visual cortex (V1) can reflect distinct visual, auditory, and motor-related responses, with the latter often dissociable in timing. In their findings, visually-evoked movement-related activity arises substantially later than the sensory visual response, generally beginning around 200 ms post-stimulus onset. In contrast, auditory-evoked activity in A1 occurs relatively early.

We have revised the manuscript as follows (eighth paragraph in discussion section):

“A recent study shows that sound-clip evoked activity in visual cortex have a behavioral rather than sensory origin and is related to stereotyped movements(49). ...This early timing suggests that the observed responses were less likely modulated by visually-evoked body or orofacial movements, which typically occur with a delay relative to sensory cue onset(55). ”

(34) The training order (multisensory cue first) is important to briefly mention in the main text.

We appreciate the reviewer’s suggestion and have added this information to the main text. The revised text now reads:

“The training proceeded in two stages. In the first stage, which typically lasted 3-5 weeks, rats were trained to discriminate between two audiovisual cues. In the second stage, an additional four unisensory cues were introduced, training the rats to discriminate a total of six cues.”

(35) Line 542: As I understand the multisensory rats were trained using the multisensory cue first, so different from the training procedure in the unisensory task rats where auditory trials were learned first.

Thank you for pointing this out. You are correct that, in the unisensory task, rats were first trained to discriminate auditory cues, followed by visual cues. To improve clarity and avoid any confusion, we have removed the sentence "Similar to the multisensory discrimination task" from the revised text.

(36) Line 546: Can you note on how the rats were motivated to choose both ports, or whether they did so spontaneously?

Thank you for your insightful comment. The rats' port choice was spontaneous in this task, as there was no explicit motivation required for choosing between the ports. We have clarified this point in the text to address your concern. The revised sentence now reads:

“They received a water reward at either port following the onset of the cue, and their port choice was spontaneous.”

(37) It is important to mention in the main text that the population decoding is actually pseudopopulation decoding. The interpretation is sufficiently important for interpreting the results.

Thank you for this valuable suggestion. We have revised the text to specify "pseudo-population" instead of "population" to clarify the nature of our decoding analysis. The revised text now reads:

“Our multichannel recordings enabled us to decode sensory information from a pseudo-population of AC neurons on a single-trial basis. Using cross-validated support vector machine (SVM) classifiers, we examined how this pseudo-population discriminates between stimuli.”

(38) The term modality selectivity for the description of the multisensory interaction is somewhat confusing. Modality selectivity suggests different responses to the visual or auditory trials. The authors could consider a different terminology emphasizing the multisensory interaction effect.

Thank you for your insightful comment. We have replaced " modality selectivity " with " multisensory interactive index " (MSI). This term more accurately conveys a tendency for neurons to favor multisensory stimuli over individual sensory modalities (visual or auditory alone).

(39) In Figures 3 e and g the color code is different from adjacent panels b and c and is to be deciphered from the legend. Consider changing the color coding, or highlight to the reader that the coloring in Figures 3b and c is different from the color code in panels 3 e and g.

We appreciate the reviewer’s observation. However, we believe that a change in the color coding is not necessary. Figures 3e and 3g differentiate symbols by both shape and color, ensuring accessibility and clarity. This is clearly explained in the figure legend to guide readers effectively.

(40) Figure S2b: was significance tested here?

Yes, we did it.

(41) Figure S2d: test used?

Yes, test used.

(42) Line 676: "as appropriate", was a normality test performed prior to statistical test selection?

In our analysis, we assessed normality before choosing between parametric (paired t-test) and non-parametric (Wilcoxon signed-rank test) methods. We used the Shapiro-Wilk test to evaluate the normality of the data distributions. When data met the assumption of normality, we applied the paired t-test; otherwise, we used the Wilcoxon signed-rank test.

Thank you for pointing this out. We confirm that a normality test was performed prior to the selection of the statistical test. Specifically, we used the Shapiro-Wilk test to assess whether the data distributions met the assumption of normality. Based on this assessment, we applied the paired t-test for normally distributed data and the Wilcoxon signed-rank test for non-normal data.

To ensure clarity, we update the "Statistical Analysis" section of the manuscript with the following revised text:

“For behavioral data, such as mean reaction time differences between unisensory and multisensory trials, cue selectivity and mean modality selectivity across different auditory-visual conditions, comparisons were performed using either the paired t-test or the Wilcoxon signed-rank test. The Shapiro-Wilk test was conducted to assess normality, with the paired t-test used for normally distributed data and the Wilcoxon signed-rank test for non-normal data.”

(43) Line 679: incorrect, most data is actually represented as mean +- SEM.

Thank you for pointing this out. In the Results section, we report data as mean ± SD for descriptive statistics, while in the figures, the error bars typically represent the standard error of the mean (SEM) to visually indicate variability around the mean. We have specified in each figure legend whether the error bars represent SD or SEM.

Reviewer #2 (Recommendations for the authors):

(1) Line 182 - here it sounds like you mean your classifier was trained to decode the modality of the stimulus, when I think what you mean is that you decoded the stimulus contingencies using A/V/AV cues?

Thank you for pointing out this potential misunderstanding. We would like to clarify that the classifier was trained to decode the stimulus identity (e.g., A^3k vs. A^10k for auditory stimuli, V^hz vs. V^vt for visual stimuli, and A^3kV^hz vs. A^10kV^vt for multisensory stimuli) rather than the modality of the stimulus. The goal of the analysis was to determine how well the pseudo-population of AC neurons could distinguish between individual stimuli within the same modality. We have revised the relevant text in the revised manuscript to ensure this distinction is clear. Please see the following:

“Our multichannel recordings enabled us to decode sensory information from a pseudo-population of AC neurons on a single-trial basis. Using cross-validated support vector machine (SVM) classifiers, we examined how this pseudo-population discriminates stimulus identity (e.g.,  A^3k vs. A^10k for auditory stimuli, V^hz vs. V^vt for visual stimuli,  A^3kV^hz vs. A^10kV^vt for multisensory stimuli).”

(2) Lines 256 - here the authors look to see whether incorrect trials diminish audiovisual integration. I would probably seek to turn the causal direction around and ask are AV neurons critical for behaviour - nevertheless, since this is only correlational the causal direction cannot be unpicked. However, the finding that contralateral responses per se do not result in enhancement is a key control. Showing that multisensory enhancement is less on error trials is a good first step to linking neural activity and perception, but I wonder if the authors could take this further however by seeking to decode choice probabilities as well as stimulus features in an attempt to get a little closer to addressing the question of whether the animals are using these responses for behaviour.

Thank you for your comment and for highlighting the importance of understanding whether audiovisual (AV) neurons are critical for behavior. As you noted, the causal relationship between AV neural activity and behavioral outcomes cannot be directly determined in our current study due to its correlational nature. We agree that this is an important topic for future exploration. In our study, we examined how incorrect trials influence multisensory enhancement. Our findings show that multisensory enhancement is less pronounced during error trials, providing an initial link between neural activity and behavioral performance. To address your suggestion, we conducted an additional analysis comparing auditory and multisensory selectivity between correct and incorrect choice trials. As shown in Supplementary Fig. 7, both auditory and multisensory selectivity were significantly lower during incorrect trials. This result highlights the potential role of these neural responses in decision-making, suggesting they may extend beyond sensory processing to influence choice selection. We have cited this figure in the Results section as follows: ( the paragraph regarding Impact of incorrect choices on audiovisual integration):

“Overall, these findings suggest that the multisensory perception reflected by behavioral choices (correct vs. incorrect) might be shaped by the underlying integration strength. Furthermore, our analysis revealed that incorrect choices were associated with a decline in cue selectivity, as shown in Supplementary Fig. 7.”

We acknowledge your suggestion to decode choice probabilities alongside stimulus features as a more direct approach to exploring whether animals actively use these neural responses for behavior. Unfortunately, in the current study, the low number of incorrect trials limited our ability to perform such analyses reliably. Nonetheless, we are committed to pursuing this direction in subsequent work. We plan to use techniques such as optogenetics in future studies to causally test the role of AV neurons in driving behavior.

(3) Figure 5E - the purple and red are indistinguishable - could you make one a solid line and keep one dashed?

We thank the reviewer for pointing out that the purple and red lines in Figure 5E were difficult to distinguish. To address this concern, we modified the figure by making two lines solid and changing the color of one square, as suggested. These adjustments enhance visual clarity and improve the distinction between them.

(4) The unisensory control training is a really nice addition. I'm interested to know whether behaviourally these animals experienced an advantage for audiovisual stimuli in the testing phase? This is important information to include as if they don't it is one step closer to linking audiovisual responses in AC to improved behavioural performance (and if they do, we must be suitably cautious in interpretation!).

Thank you for raising this important point. To address this, we have plotted the behavioral results for each animal (see Author response image 2). The data indicate that performance with multisensory cues is slightly better than with the corresponding unisensory cues. However, given the small sample size (n=3) and the considerable variation in behavioral performance across individuals, we remain cautious about drawing definitive conclusions on this matter. We recognize the need for further investigation to establish a robust link between audiovisual responses in the auditory cortex and improved behavioral performance. In future studies, we plan to include a larger number of animals and more thoroughly explore this relationship to provide a comprehensive understanding.

Author response image 2.

(5) Line 339 - I don't think you can say this leads to binding with your current behaviour or neural responses. I would agree there is a memory trace established and a preferential linking in AC neurons.

We thank the reviewer for raising this important point. In the revised manuscript, we have clarified that our data suggest the formation of a memory trace and preferential linking in AC neurons. The text has been updated to emphasize this distinction. Please see the revised section below (first paragraph in Discussion section).

“Interestingly, a subset of auditory neurons not only developed visual responses but also exhibited congruence between auditory and visual selectivity. These findings suggest that multisensory perceptual training establishes a memory trace of the trained audiovisual experiences within the AC and enhances the preferential linking of auditory and visual inputs. Sensory cortices, like AC, may act as a vital bridge for communicating sensory information across different modalities.”

Author Response

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

eLife assessment

This valuable manuscript attempts to identify the brain regions and cell types involved in habituation to dark flash stimuli in larval zebrafish. Habituation being a form of learning widespread in the animal kingdom, the investigation of neural mechanisms underlying it is an important endeavor. The authors use a combination of behavioral analysis, neural activity imaging, and pharmacological manipulation to investigate brain-wide mechanisms of habituation. However, the data presented are incomplete and do not show a convincing causative link between pharmacological manipulations, neural activity patterns, and behavioral outcomes.

We thank the reviewers and editors for their careful reading and reviews of our work. We are grateful that they appreciate the value in our experimental approach and results. We acknowledge what we interpret as the major criticism, that in our original manuscript we focused too heavily on the hypothesized role of GABAergic neurons in driving habituation. This hypothesis will remain only indirectly supported until we can identify a GABAergic population of neurons that drives habituation. Therefore, we have revised our manuscript, decreasing the focus on GABA, and rather emphasizing the following three points:

1) By performing the first Ca2+ imaging experiments during dark flash habituation, we identify multiple distinct functional classes of neurons which have different adaptation profiles, including non-adapting and potentiating classes. These neurons are spread throughout the brain, indicating that habituation is a complex and distributed process.

2) By performing a pharmacological screen for dark flash habituation modifiers, we confirm habituation behaviour manifests from multiple distinct molecular mechanisms that independently modulate different behavioural outputs. We also implicate multiple novel pathways in habituation plasticity, some of which we have validated through dose-response studies.

3) By combining pharmacology and Ca2+ imaging, we did not observe a simple relationship between the behavioural effects of a drug treatment and functional alterations in neurons. This observation further supports our model that habituation is a multidimensional process, for which a simple circuit model will be insufficient.

We would like to point out that, in our opinion, there appears to be a factual error in the final sentence of the eLife assessment:

“However, the data presented are incomplete and do not show a convincing causative link between pharmacological manipulations, neural activity patterns, and behavioral outcomes”.

We believe that a “convincing causative link” between pharmacological manipulations and behavioural outcomes has been clearly demonstrated for PTX, Melatonin, Estradiol and Hexestrol through our dose response experiments. Similarly a link between pharmacology and neural activity patterns has also been directly demonstrated. As mentioned in (3), we acknowledge that our data linking neural activity and behaviour is more tenuous, as will be more explicitly reflected in our revised manuscript.

Nevertheless, we maintain that one of the primary strengths of our study is our attempt to integrate analyses that span the behavioural, pharmacological, and neural activity-levels.

In our revised manuscript, we have substantially altered the Abstract and Discussion, removed the Model figure (previously Figure 8), and changed the title from :

“Inhibition drives habituation of a larval zebrafish visual response”

to:

“Functional and pharmacological analyses of visual habituation learning in larval zebrafish”

Text changes from the initial version are visible as track changes in the word document: “LamireEtAl_2022_eLifeRevisions.docx”

Reviewer #1 (Public Review):

This manuscript addresses the important and understudied issue of circuit-level mechanisms supporting habituation, particularly in pursuit of the possible role of increases in the activity of inhibitory neurons in suppressing behavioral output during long-term habituation. The authors make use of many of the striking advantages of the larval zebrafish to perform whole brain, single neuronal calcium imaging during repeated sensory exposure, and high throughput screening of pharmacological agents in freely moving, habituating larvae. Notably, several blockers/antagonists of GABAA(C) receptors completely suppress habituation of the O-bend escape response to dark flashes, suggesting a key role for GABAergic transmission in this form of habituation. Other substances are identified that strikingly enhance habituation, including melatonin, although here the suggested mechanistic insight is less specific. To add to these findings, a number of functional clusters of neurons are identified in the larval brain that has divergent activity through habituation, with many clusters exhibiting suppression of different degrees, in line with adaptive filtration during habituation, and a single cluster that potentiates during habituation. Further assessment reveals that all of these clusters include GABAergic inhibitory neurons and excitatory neurons, so we cannot take away the simple interpretation that the potentiating cluster of neurons is inhibitory and therefore exerts an influence on the other adapting (depressing) clusters to produce habituation. Rather, a variety of interpretations remain in play.

Overall, there is great potential in the approach that has been used here to gain insight into circuit-level mechanisms of habituation. There are many experiments performed by the authors that cannot be achieved currently in other vertebrate systems, so the manuscript serves as a potential methodological platform that can be used to support a rich array of future work. While there are several key observations that one can take away from this manuscript, a clear interpretation of the role of GABAergic inhibitory neurons in habituation has not been established. This potential feature of habituation is emphasized throughout, particularly in the introduction and discussion sections, meaning that one is obliged as a reader to interrogate whether the results as they currently stand really do demonstrate a role for GABAergic inhibition in habituation. Currently, the key piece of evidence that may support this conclusion is that picrotoxin, which acts to block some classes of GABA receptors, prevents habituation. However, there are interpretations of this finding that do not specifically require a role for modified GABAergic inhibition. For instance, by lowering GABAergic inhibition, an overall increase in neural activity will occur within the brain, in this case below a level that could cause a seizure. That increase in activity may simply prevent learning by massively increasing neural noise and therefore either preventing synaptic plasticity or, more likely, causing indiscriminate synaptic strengthening and weakening that occludes information storage. Sensory processing itself could also be disrupted, for instance by altering the selectivity of receptive fields. Alternatively, it could be that the increase in neural activity produced by the blockade of inhibition simply drives more behavioral output, meaning that more excitatory synaptic adaptation is required to suppress that output. The authors propose two specific working models of the ways in which GABAergic inhibition could be implemented in habituation. An alternative model, in which GABAergic neurons are not themselves modified but act as a key intermediary between Hebbian assemblies of excitatory neurons that are modified to support memory and output neurons, is not explored. As yet, these or other models in which inhibition is not required for habituation, have not been fully tested.

This manuscript describes a really substantial body of work that provides evidence of functional clusters of neurons with divergent responses to repeated sensory input and an array of pharmacological agents that can influence the rate of a fundamentally important form of learning.

We thank the reviewer for their careful consideration of our work, and we agree that multiple models of how habituation occurs remain plausible. As discussed above and below in more detail, we have revised our manuscript to better reflect this. We hope the reviewer will agree that this has improved the manuscript.

Reviewer #2 (Public Review):

In this study, Lamire et al. use a calcium imaging approach, behavioural tests, and pharmacological manipulations to identify the molecular mechanisms behind visual habituation. Overall, the manuscript is well-written but difficult to follow at times. They show a valuable new drug screen paradigm to assess the impact of pharmacological compounds on the behaviour of larval zebrafish, the results are convincing, but the description of the work is sometimes confusing and lacking details.

We thank the reviewer for identifying areas where our description lacked details. We apologize for these omissions and have attempted to add relevant details as described below. We note that all of the analysis code is available online, though we appreciate that navigating and extracting data from these files is not straightforward.

The volumetric calcium imaging of habituation to dark flashes is valuable, but the mix of responses to visual cues that are not relevant to the dark flash escape, such as the slow increase back to baseline luminosity, lowers the clarity of the results. The link between the calcium imaging results and free-swimming behaviour is not especially convincing, however, that is a common issue of head-restrained imaging with larval zebrafish.

We agree with the reviewer that the design of our stimulus, and specifically the slow increase back to baseline luminosity, is perhaps confusing for the interpretation of some of the response profiles of neurons. We originally chose this stimulus type (rather than a square wave of 1s of darkness, for example) in order to better highlight the responses of the larvae to the onset of darkness (rather than the response to abruptly returning to full brightness). We therefore believe that the slow return to baseline is an important feature of the stimulus,, which better separates activity related to the fast offset from activity related to light onset. And since all of the foundational behavioural data (Randlett et al., Current Biology 2019), and pharmacological data, used this stimulus type, we did not change it for the Ca2+ imaging experiments. Our use of relatively slow nuclear-targeted GCaMP indicators also means that the temporal resolution of our imaging experiments is relatively poor, and therefore we felt that using a stimulus that highlighted light offset might be best.

We also fully acknowledge in the Results section that the behaviour of the head embedded fish is not the same as that of free-swimming fish, and that therefore establishing a direct link between these types of experiments is complicated. This is an unavoidable caveat in the head-embedded style experiments. To further emphasize this, we have also added a paragraph to the discussion where this is acknowledged explicitly.

“We also found that the same pharmacological treatments that result in strong alterations to habituation behaviour in freely swimming larvae ([fig:5]), resulted in relatively subtle and complex functional alterations in the circuit ([fig:6]). Making direct comparisons between freely-swimming behaviour and head-fixed Ca2+ imaging is always challenging due to the differences in behaviour observed in the two contexts, and therefore our failure to identify a clear logic in these experiments may have technical explanations that will require approaches to measure neural activity from unrestrained and freely-behaving animals to resolve . Alternatively, these results are again consistent with the idea that habituation is a multidimensional and perhaps highly non-linear phenomenon in the circuit, which cannot be captured by a simple model.”

The strong focus on GABA seems unwarranted based on the pharmacological results, as only Picrotoxinin gives clear results, but the other antagonists do not give a consistent results. On the other hand, the melatonin receptor agonists, and oestrogen receptor agonists give more consistent results, including more convincing dose effects.

We agree that our manuscript focused too strongly on GABA and have toned this down. We are currently performing genetic experiments aimed at identifying the Melatonin, Estrogen and GABA receptors that function during habituation, which we think will be necessary to move beyond pharmacology and the necessary caveats that such experiments bring.

The pharmacological manipulation of the habituation circuits mapped in the first part does not arrive at any satisfying conclusion, which is acknowledged by the authors. These results do reinforce the disconnect between the calcium imaging and the behavioural experiments and undercut somewhat the proposed circuit-level model.

We agree with this criticism and have toned down the focus on GABA specifically in the circuit, and have removed the speculative model previously in Figure 8.

Overall, the authors did identify interesting new molecular pathways that may be involved in habituation to dark flashes. Their screening approach, while not novel, will be a powerful way to interrogate other behavioural profiles. The authors identified circuit loci apparently involved in habituation to dark flashes, and the potentiation and no adaptation clusters have not been previously observed as far as I know.

The data will be useful to guide follow-up experiments by the community on the new pathway candidates that this screen has uncovered, including behaviours beyond dark flash habituation.

We again thank the reviewer for both their support of our approach, and in pointing out where our conclusions were not well supported by our data.

Reviewer #3 (Public Review):

To analyze the circuit mechanisms leading to the habituation of the O-bed responses upon repeated dark flashes (DFs), the authors performed 2-photon Ca2+ imaging in larvae expressing nuclear-targeted GCaMP7f pan-neuronally panning the majority of the midbrain, hindbrain, pretectum, and thalamus. They found that while the majority of neurons across the brain depress their responsiveness during habituation, a smaller population of neurons in the dorsal regions of the brain, including the torus longitudinalis, cerebellum, and dorsal hindbrain, showed the opposite pattern, suggesting that motor-related brain regions contain non-depressed signals, and therefore likely contribute to habituation plasticity.

Further analysis using affinity propagation clustering identified 12 clusters that differed both in their adaptation to repeated DFs, as well as the shape of their response to the DF.

Next by the pharmacological screening of 1953 small molecule compounds with known targets in conjunction with the high-throughput assay, they found that 176 compounds significantly altered some aspects of measured behavior. Among them, they sought to identify the compounds that 1) have minimal effects on the naive response to DFs, but strong effects during the training and/or memory retention periods, 2) have minimal effects on other aspects of behaviors, 3) show similar behavioral effects to other compounds tested in the same molecular pathway, and identified the GABAA/C Receptor antagonists Bicuculline, Amoxapine, and Picrotoxinin (PTX). As partial antagonism of GABAAR and/or GABACR is sufficient to strongly suppress habituation but not generalized behavioral excitability, they concluded that GABA plays a very prominent role in habituation. They also identified multiple agonists of both Melatonin and Estrogen receptors, indicating that hormonal signaling may also play a prominent role in habituation response.

To integrate the results of the Ca2+ imaging experiments with the pharmacological screening results, the authors compared the Ca2+ activity patterns after treatment with vehicle, PTX, or Melatonin in the tethered larvae. The behavioral effects of PTX and Melatonin were much smaller compared with the very strong behavioral effects in freely-swimming animals, but the authors assumed that the difference was significant enough to continue further experiments. Based on the hypothesis that Melatonin and GABA cooperate during habituation, they expected PTX and Melatonin to have opposite effects. This was not the case in their results: for example, the size of the 12(Pot, M) neuron population was increased by both PTX and Melatonin, suggesting that pharmacological manipulations that affect habituation behavior manifest in complex functional alterations in the circuit, making capturing these effects by a simple difficult.

Since the 12(𝑃𝑜𝑡, 𝑀) neurons potentiate their responses and thus could act to progressively depress the responses of other neuronal classes, they examined the identity of these neurons with GABA neurons. However, GABAergic neurons in the habituating circuit are not characterized by their Adaptation Profile, suggesting that global manipulations of GABAergic signaling through PTX have complex manifestations in the functional properties of neurons.

Overall, the authors have performed an admirably large amount of work both in whole-brain neural activity imaging and pharmacological screening. However, they are not successful in integrating the results of both experiments into an acceptably consistent interpretation due to the incongruency of the results of different experiments. Although the authors present some models for interpretation, it is not easy for me to believe that this model would help the readers of this journal to deepen the understanding of the mechanisms for habituation in DF responses at the neural circuit level.

This reviewer would rather recommend the authors divide this manuscript into two and publish two papers by adding some more strengthening data for each part such as cellular manipulations, e.g. ablation to prove the critical involvement of 12(Pot, M) neurons in habituation.

We thank the reviewer for their careful consideration of our manuscript, and we agree that our emphasis on a particular model of DF habituation, namely the potentiation of GABAergic synapses, was overly speculative. We hope they will agree that our revised manuscript better reflect the results from our experiments, and we have tried to more specifically emphasize the incongruency in our behavioural and Ca2+ imaging data after pharmacological treatment, which we agree shows that a simple model is insufficient to capture both of these sets of observations.

We have opted not to split the paper into two, since we feel that the collective message of this paper and approach combining molecular and functional analysis will be of interest. Moreover, we feel that the molecular and functional analyses feed off of each other and provide a level of complementarity that would be lost if the manuscript would be split, even if the message in this particular case is rather complex

Reviewer #1 (Recommendations For The Authors):

There is much to commend about this manuscript. The advantages of studying habituation in the zebrafish larva are very clearly demonstrated, including the wonderful calcium imaging across the brain and the relatively high throughput screening of large numbers of different pharmacological agents. The habituation to dark flashes in freely moving larvae is also striking and the very large effect size serves the screening beautifully. Thus, if we take the really substantial amount of work of a very high standard that has been done here, there is clearly potential for an important new contribution to the literature. However, as you will see from my public review, I am of the opinion that a specific role for the modification of GABAergic inhibitory systems has not yet been established through this work. While the potential role for GABAergic inhibitory neurons in habituation, either as the key modifiable element or as an intermediary between memory and motor output, is an attractive theory with many strengths, your study as it currently stands does not categorically demonstrate that one of those two options holds. For instance, the more traditional view, that adaptive filtration is mediated by weakened synaptic connectivity between excitatory sensory systems and excitatory motor output or reduced intrinsic excitability in those same neurons, could still be in operation here. By lowering GABAergic influence over post-synaptic targets with picrotoxin, it is possible that motor output remains highly active, and even lower activity or synaptic drive from those excitatory sensory systems that feed into the output may still reliably produce behavioral output. Alternatively, it could be the formation of a memory of the familiar stimulus is disrupted by reduced inhibition that alters sensory coding either by introducing noise or reducing the selectivity of receptive fields. I believe that there are several options to address these concerns:

1) You could change the emphasis of the manuscript so that it is less focused on inhibition and instead emphasizes the categorization of clusters of neurons that have divergent responses during habituation, including either strong suppression to potentiation. To this, you add a high throughput screening system with a wide range of different agents being tested, several of which produce a significant effect on habituation in either direction. These observations in themselves provide powerful building blocks for future work.

2) If GABAergic neurons play a key role in habituation in this paradigm, then picrotoxin is having its effect by blocking receptors on excitatory neurons. Thus, it seems that selectively imaging GABAergic neurons before and after the application of these drugs is not likely to reveal the contribution of GABAergic synaptic influence on excitatory targets. More important is to get a stronger sense of how the GABAergic neurons change their activity throughout habituation and then influence the downstream target neurons of those GABAergic neurons (some of which may themselves be inhibitory and participating in disinhibition). For instance, you could interrogate whether anti-correlations in activity levels exist between presynaptic inhibitory neurons and putative post-synaptic targets. This analysis could be further bolstered by removing that relationship in the presence of Picrotoxin, thereby demonstrating a direct influence of inhibition from a GABAergic presynaptic partner on a postsynaptic target. While this would constitute a lot more work, it is likely to yield greater insight into a specific role for GABAergic neurons in habituation, and I suspect much of that information is in the existing datasets.

3) To really reveal causal roles for inhibition in this form of habituation, it seems to me that there needs to be some selective intervention in GABAergic neuronal activity, ideally bidirectionally, to transiently interrupt or enhance habituation. Optogenetic or chemogenetic stimulation/inactivation is one option in this regard, which I imagine would be challenging to implement and certainly involves a lot of further work, particularly if you are then going to target specific subpopulations of GABAergic neurons. I appreciate that this option seems way beyond the scope of a review process and would probably constitute a follow-up study.

We agree with the reviewer that we have not “categorically demonstrated” that GABAergic inhibitory neurons drive habituation by increasing their influence on the circuit, and appreciate the suggestions for how to reformulate our manuscript to better reflect this. We have opted to follow suggestion (1), and have considerably changed the focus of the manuscript.

The additional analysis suggested in (2) is very interesting, but since we can not identify which cells are inhibitory in our imaging experiments with picrotoxinin treatment, nor which are pre- or post-synaptic, we feel that this analysis will be very unconstrained. Also, if GABA is acting as an inhibitory neurotransmitter, it therefore is expected to act to drive anticorrelations among pre and postsynaptic neurons through inhibition. Therefore, blockage of GABA through PTX would be expected to result in increased correlations, regardless of our hypothesized role of neurons during habituation. Our current efforts are aimed at identifying critical neurons driving habituation plasticity, and we will perform such analysis once we have mechanisms for identifying these neurons.

Finally, we agree that (3) is the obvious and only way to demonstrate causation here, and this is where we are working towards. However, since we currently have no means of genetically targeting these neurons, we are not able to perform these suggested experiments today.

I have some additional concerns that I would really appreciate you addressing:

1) The behavioral habituation is striking in the freely moving larvae, but very hard to monitor in the larvae that are immobilized for calcium imaging. Are there steps that could be taken in the long run to improve direct observation of the habituation effect in these semi-stationary fish? For instance, is it possible to observe eye movements or some more subtle behavioral readout than the O-bend reflex? I apologize if this is a naïve question, but I am not entirely familiar with this specific experimental paradigm.

In the Dark Flash paradigm, we do not have readouts beyond the “O-bend” response itself, which is characterized by a large-angle bend of the tail and turning maneuver. We have not observed other, more subtle behavioural responses, such as eye or fin movements, for example. If we would be able to identify alternative behavioural outputs that were more robustly performed during head-embedded preparations, this would indeed be an advantage allowing us to more directly interpret the Ca2+ imaging results with respect to behaviour.

2) The dark flash as a stimulus to which the larvae habituate is obviously used as a powerful and ethologically relevant stimulus. However, it does leave an element of traditional habituation paradigms out, which is a novel stimulus that can be used to immediately re-instate the habituated response (otherwise known as dishabituation). Is there a way that you can imagine implementing that with zebrafish larvae, for instance through systematically altering a visual feature, such as spatial frequency or orientation? This would be a powerful development in my view as it would not only allow you to rule out motor or sensory fatigue as an underlying cause of reduced behavior but also it would provide an extra feature that strengthens your assessment of neuronal response profiles in candidate populations of inhibitory and excitatory neurons.

We agree that identifying a dishabituating stimulus would be very powerful for our experiments. For short-term habituation of the acoustic startle response, Wolman et al demonstrated that dishabituation occurs after a touch stimulus (Wolman et al., PNAS, 2011; https://doi.org/10.1073/pnas.1107156108). We attempted to dishabituate the O-Bend response with tap and touch stimuli, and this unfortunately did not occur. Our understanding of dishabituation is that this generally requires a second stimulus that elicits the same behaviour as the habituated stimulus (e.g. both acoustic and touch-stimuli elicit the Mauthner-dependent C-bend response). In zebrafish the only stimulus that has been identified that elicits the O-bend is a dark-flash. This lack of an appropriate alternative stimulus is perhaps why we have been unsuccessful in identifying a dishabituating stimulus.

3) You have written about the concept of 'short' and 'long' response shapes when using calcium imaging as a proxy for neural activity, surmising that the short response shape may reflect transient bursting. Although calcium imaging obviously has many advantages, this feature reveals one notable limitation of calcium imaging in contrast to electrophysiology, in that the time course of the signal is considerably longer and does not allow you with confidence to fully detect the response profile of neurons. Is there some kind of further deconvolution process that you could implement to improve the fidelity of your calcium imaging to the occurrence of action potentials? The burstiness of neurons is obviously important as it can indicate a particular type of neuron (for instance fast-spiking inhibitory neurons) or it might reveal a changing influence on post-synaptic neurons. For instance, bursting can be a response to inhibition due to the triggering of T-type calcium channels in response to hyperpolarization.

One of the major limitations to Ca2+ imaging is the lack of temporal resolution. In our particular approach, using nuclear-targeted H2B-GCaMP indicators, further reduces our temporal resolution. Deconvolution approaches can be used in some instances to approximate spike rate, since the rise-time of Ca2+ indicators can be relatively fast. However, in our imaging we chose to image larger volumes at the expense of scan rate, where our imaging is performed at only 2hz. Therefore, deconvolution and spike-rate estimation is not appropriate. Considering these limitations, we would argue that the fact that we can observe differences in kinetics of the 'short' and 'long' response shapes indicates that they likely show very different response kinetics, which we hope to confirm by electrophysiology once we have established ways of targeting these neurons for recordings.

4) I note that among the many substances you screened with is MK801. An obvious candidate mechanism in habituation is the NMDA receptor, given the importance of this receptor for so many forms of learning and bidirectional synaptic plasticity. If I am to understand correctly, this NMDA receptor blocker actually enhances habituation in the zebrafish larvae, similar to melatonin. That is a very surprising observation, which is worth looking into further or at least discussed in the manuscript. The finding would, at least, be consistent with the idea that plasticity is not occurring at excitatory synapses and could potentially bolster the argument that plasticity of inhibitory synapses is at play in this particular form of habituation.

This is a very important point. We were also particularly interested in MK801, which has been shown to inhibit other forms of habituation, like short-term acoustic habituation (Wolman et al., PNAS, 2011; https://doi.org/10.1073/pnas.1107156108). In our experiments we did see that fish become even less responsive to dark flashes when treated with MK-801 (SSMD fingerprint data: Prob-Train = -0.39, Prob-Test = -1.58) which would indicate that MK-801 promotes dark flash habituation, similar to Melatonin. However, we also observed that MK-801 caused a decrease in the performance in the other visual assay we tested: the optomotor response (OMR-Perf = -0.93), indicating that MK-801 causes a generalized decrease in visual responses, perhaps by acting on circuits within the retina. Therefore, based on these experiments with global drug applications, we cannot determine if MK-801 influences the plasticity process in dark-flash habituation, and this is why we did not pursue it further in this project.

Anyway, I hope that you take these suggestions as constructive and, in the spirit that they are intended, as possible routes for improving an already very interesting manuscript.

We are very grateful for your suggestions, which we feel has helped us to improve our manuscript substantially.

Reviewer #2 (Recommendations For The Authors):

Overall, the manuscript is well-written, but confusing at times. The results are not always presented in a consistent way, and I found myself having to dig in the raw data or code to find answers. There is a certain disconnect between the free-swimming results, and the calcium imaging, which is somewhat inevitable based on other published work. But I am unsure of what they each bring to the other, as the results from Fig.6 do not match at all the changes observed in the behavioural assays, it almost feels like two separate studies and the inconsistencies make the model appear unlikely.

We agree that there is a disconnect at the behavioural level in our free-swimming and head-embedded imaging experiments. However, this does not necessarily mean that the activity we observe during the imaging experiments cannot be informative about processes that are also occurring in freely-swimming fish. For example, it is possible that the dark-flash circuit is responding and habitating similarly in the head-embedded and freely-swimming preparations, but that in the latter context there is an additional blockade on motor output that massively decreases the propensity of the fish to initiate any movements. In such a case, the “disconnect between the free-swimming results, and the calcium imaging” would indicate that the relationship between neural activity and habituation behaviour is rather complex.

Without a method to record activity from freely swimming fish at our disposal, we can not determine this, one way or the other.

We hope that we now acknowledge these concerns appropriately in the discussion:

“We also found that the same pharmacological treatments that result in strong alterations to habituation behaviour in freely swimming larvae ([fig:5]), resulted in relatively subtle and complex functional alterations in the circuit ([fig:6]). Making direct comparisons between freely-swimming behaviour and head-fixed Ca2+ imaging is always challenging due to the differences in behaviour observed in the two contexts, and therefore our failure to identify a clear logic in these experiments may have technical explanations that will require approaches to measure neural activity from unrestrained and freely-behaving animals to resolve . Alternatively, these results are again consistent with the idea that habituation is a multidimensional and perhaps highly non-linear phenomenon in the circuit, which cannot be captured by a simple model. “

I am not convinced by the results surrounding GABA, from the inconsistent GABA receptor antagonist profile to the post hoc identification of GABAergic neurons as it is currently done in the manuscript. I think that the current focus on GABA does a disservice to the manuscript. However, the novel findings surrounding the potential role of Melatonin, and Estrogen, in habituation are quite interesting.

We agree that we focused too heavily on our hypothesized role for GABA in our original manuscript, and we hope that the reviewer agrees that our updated manuscript is an improvement. We also thank the reviewer for their interest in our Melatonin and Estrogen results, for which follow up studies are ongoing to characterize the effects of these hormones and their receptors on habituation.

There is an assumption that all the adaptation profiles are related to the DF (although that is somewhat alleviated in the discussions of the ON responses) and not to the luminosity changes. But there is no easy way to deconvolve those two in the current experiments. I would like the timing of the fluorescence rise to be quantified compared to the dark flash stimulus onset, potentially spike inference methods could help with giving a better idea of the timing of those responses. Based on the behavioural responses that were <500ms in Randlet O et al, eLife, 2019; we would expect only the fastest DF responses to be linked to the behaviour.

We agree that we are unable to disambiguate responses to the dark flash that initiate the O-bend response, and those that are related to only changes in luminosity. As discussed above, our Ca2+ imaging approach is severely limited in temporal resolution and therefore spike inference methods are not appropriate.

Major comments

Fig.1: There seems to be a very variable lag between the motor events and DF responses, furthermore, it does not seem that the motor responses follow a similar habituation rate as in 1Bi. Although this only shows the smoothed 'movement cluster' from the rastermap, it could hide individual variability. It would be important to know what the 'escape' rate was in the embedded experiment, as

Fig.1 sup.1 seems to indicate there was little to no habituation. It would also be needed to know which motor events are considered linked to the DF stimulus, and how that was decided. Was there a movement intensity threshold and lag limit in the response?

We interpret this concern as relating to the data presented in Figure 6A, where we quantify the habituation rate in the head-embedded experiments. As we have discussed, both above and in the manuscript, we saw very strongly muted responses to DFs in the head-embedded preparation, but we neglected to describe our method of quantifying the responses. We have added the following description to the methods:

“To quantify responses to the dark flash stimuli we used motion artifacts in the imaging data to identify frames associated with movements ([fig:1]-[fig:S1]). Motion artifact was quantified using the “corrXY” parameter from suite2p, which reflects the peak of phase correlation comparing each acquired frame and reference image used for motion correction. The “motion power” was quantified as the standard deviation of a 3-frame rolling window, which was smoothed in time using a Savitzky-Golay filter (window length = 15 frames, polyorder = 2). A response to a dark flash was defined as a “motion power” signal greater than 3 (z-score) occurring within 10-seconds of the dark-flash onset, and was used to quantify habituation in the head-embedded preparation ([fig:6]A).“

Line 94: This seems to be a strong claim based on the sparse presence of non-habituating, or potentiating, neurons in downstream regions. However, these neurons appear to be extremely rare, and as mentioned in my comment above, the behavioural habituation appears minimal. These neurons could encode the luminosity and be part of other responses, such as light-seeking in Karpenko S et al, eLife, 2020 or escape directionality in Heap et al, Neuron, 2018. Furthermore, dimming information has been shown to have parallel processing pathways in Robles E et al, JCN, 2020; so it would make sense that not all the observed responses in this manuscript would be involved in behavioural habituation to dark flashes.

We agree that without functional interventions, we do not know which of the neurons we have categorized are specifically involved in the dark flash response habituation. It is possible that the non-adapting and potentiating neurons are involved in other behaviours. We have therefore removed this statement.

Line 103: It appears that several of those responses are to the changes in luminosity and not the DF itself, especially the ON and sustained responses. Based on the previous DF habituation study from Randlet O et al, eLife, 2019; the latency of the response is below 0.5s. So the behaviour-relevant responses must only include the shortest latency one, as discussed above.

We appreciate the point that the reviewer is making here, but we are less clear about what the difference between “changes in luminosity” and a “dark flash” response are, since a dark flash consists of a change in luminosity. We take it that the reviewer means the difference between a luminance stimulus that elicits an O-bend, from one that does not. In order to disambiguate the two, one would likely need to use stimuli where the luminosity changes, but do not elicit O-bends.

Perhaps due to the limited temporal resolution of our Ca2+ imaging data, we do not see a clear difference in the onset of the stimulus response for any of the functional clusters that would help us to determine which neurons are more relevant to the acute DF response.

Fig.2B. It is very difficult to make out the actual average z-scored fluorescence, a supplementary figure would help by making these bigger. A plot to quantify the maximum response would also be useful to judge how it changes between the first few and few last DF. Another plot to give the time between the onset of the responses and the onset of the DF stimulus is also needed to judge which cluster may be relevant to the DF escapes observed in the free-swimming experiments.

We agree with the reviewer that interpreting these datasets are challenging. We did include the actual average z-scored fluorescence in Figure 6—figure supplement 1, panel D. This figure also includes a comparison between the predicted Ca2+ response to the dark flash (the stimulus convolved with the approximate GCaMP response kernel), which shows that all OFF-responding neuronal classes show very similar rise time response kinetics, and thus this analysis does not help to judge whether a cluster is more or less relevant to O-bend responses in the free-swimming experiments. We appreciate that there are differences in opinion about the best way to present the data, but we have opted to leave our original presentation.

Line 130: Is a correlation below 0.1 meaningful or significant? It does not seem like this cluster would be a motor or decision cluster.

Our goal with this correlational analysis to motor signals was to identify if certain clusters of DF responsive neurons were more associated with motor output, and therefore may be more downstream in the sensori-motor cascade. Cluster 4 showed the highest median correlation across the population of cells. Whether a median correlation of ~0.1 is “meaningful” is impossible for us to answer, but it is highly “significant” in the statistical sense, as is evident by the 99.99999% confidence intervals plotted. We note that these cells were not selected based on their correlation to the motor stimulus, but only to the dark flash stimulus. There are “motor” clusters that show much higher correlations to the motors signals, as is evident in Figure 1G.

Line 165: Did the changes observed for Pimozide fall below the significance threshold, were lethal, or were the results not repeated? It does not appear in source data 2.

Pimozide was lethal in our screen and therefore does not appear in the source data file. Indeed, in our previous experiments with Pimozide we had already established that a 10uM dose is lethal, and that the maximal effective dose we tried was 1uM as reported in (Randlett et al., Current Biology, 2019).

We have clarified this in the text:

“While the false negative rate is difficult to determine since so little is known about the pharmacology of the system, we note that of the three small molecules we previously established to alter dark flash habituation that were included in the screen, Clozapine, Haloperidol and Pimozide , the first two were identified among our hits while Pimozide was lethal at the 10\muM screening concentration.”

Fig.1B and Fig.3B are the same data, which is awkward and should be explicitly stated. But the legends do not match in terms of the rest period. Which is correct? It is also important to note the other behavioural assays in the 'rest' period.

We thank the reviewer for pointing out this discrepancy in the legend. We have corrected the typo in the figure legend of Figure 3B :

“Habituation results in a progressive decrease in responsiveness to dark flashes repeated at 1-minute intervals, delivered in 4 training blocks of 60 stimuli, separated by 1hr of rest (from 0:00-7:00).”

We have also added a statement that the data is the same as that in Figure 1B.

Figure 3-4: SSMD fingerprint, there is no description of the different behavioural parameters. What they represent is left to the reader's inference. There is no mention of SpontDisp in the GitHub for example, so it is hard to know how these different parameters were measured. Even referring to the previous manuscript on habituation (Randlet O et al, eLife, 2019) does not shed light on most of them, for example, I suppose TwoMvmt represents the 'double responses' from the previous manuscript. Furthermore, there are inconsistencies between 3C and 4B, some minor (SpontDisp becomes SpntDisp), but Curve-Tap has disappeared for example, and I suspect became BendAmp-Tap. A more thorough description of these measures, and making the naming scheme consistent, are essential for readers to know what they are looking at.

We again thank the reviewer for their careful assessment of our data, and we apologize for this sloppiness. We have gone through and made the naming of these parameters consistent in both figures, and have added another supplementary table that describes in more detail what each parameter is, and how it relates to the analysis code (Figure3_sourcedata3_SSMDFingerprintParameters.xls). This was an essential missing piece of information from our original manuscript.

Line 206: While this prioritization makes sense, how was it implemented, how was the threshold decided and which were they? A table, or supplementary figure, would help to clarify the reason behind the choices. Fig.4C being cropped only around the response probability makes it impossible to judge if the criteria were respected, as the main heatmap is too small. For example, the choice of GABA receptor antagonists is somewhat puzzling, as besides PTX it does not seem that the other compounds had strong effects, with Amoxapine for example having seemingly as much effect on Naive and Train, with little in Test. And Bicuculline gave negative SSMD for prob in the three cases. The dose-response for PTX does lend credence to its effect, but I would have liked the other compounds, especially bicuculline. The melatonin results, for example, are much more convincing and interesting in our opinion.

While in hindsight it may have been possible to do the hit prioritization in a systematic way using thresholding and ranking, we did this manually by inspecting the clustered fingerprints. We have clarified this in the text: “This manual prioritization led to the identification of the GABAA/C Receptor antagonists…”

While we agree that it is not possible to judge how well we performed this prioritization based on the images presented, we note that we do provide the full fingerprint data in the supplementary data, for which the reader is welcome to draw their own conclusions.

We have not performed further experiments with amoxapine, so we can not comment further on this. We did perform additional experiments with bicuculline, for which we did see effects similar to those of PTX, were habituation was inhibited. However, the effects are weaker and more variable than what we observe with PTX, and bicuculline also inhibits the initial responses of the larvae, causing their Naive response to be lower. Therefore we did not include it in our manuscript. We include these data here in Author response image 1 to reassure the Reviewer that picrotoxinin is not the only GABA Receptor antagonist for which we see inhibitory effects on habituation.

Author response image 1.

Fig.6: Why was the melatonin concentration used only 1um instead of 10um on the screen?

Based on dose response experiments (Figure 5B, and others not shown), we found that the effect of Melatonin on habituation saturates at about 1uM, and therefore we used this dose.

Line 277: As the correlation with motor output is marginal at best, and the authors recognize the lack of behaviour in tethered animals, I would be careful about such speculation. Especially since the other changes are complex and go in all directions.

While we appreciate the reviewer's caution, we feel that our statement is appropriately hedged using “might be”. We have also removed the statement “and thus is most closely associated with behavioural initiation”.

We now state:

“However, opposite effects of PTX and Melatonin were observed for 4_L^{strgD} neurons ([fig:6]C), which we found to be most strongly correlated with motor output ([fig:2]F). Therefore, this class might be most critical for habituation of response Probability.”

Fig.7: I am not sure how convincing these results are. 7F may have been more convincing, but to be thorough the authors would need to register the Gad1b identity to the calcium imaging and use their outline to extract the neuron's fluorescence. As it is, in the tectum, it is hard to be sure that all the identified neurons are indeed Gad1b positive, as that population is intermingled with other neuronal populations. The authors should consider the approach of Lovett-Barron M et al, Nat Neuro, 2020. Alternatively, the authors can tone down the language used in this section to match the confidence level of the association they propose.

Figure 7A-E are what can be considered “virtual colocalization” analyses, where we are comparing the localization of data acquired in different experiments using image registration to common atlas coordinates. We agree that these results alone will never be very strong evidence for the identification of individual cells. The MultiMAP approach of Lovett-Barron is a powerful approach, though it makes the assumption that registration accuracy will be subcellular, which in practice may often not be the case. We believe that a better approach is to label the cells of interest during the Ca2+ imaging experiment itself, as we did 7F and G. The challenge in this experiment is binarizing the ROIs and thus deciding what is and is not a Gad1b-positive cell. In our opinion, the fact that these two independent experiments came to the same conclusion regarding Cluster 10 and 11 is good evidence that these cell types are likely predominantly GABAergic.

As discussed above, we have re-written the manuscript to tone down our claims about the role of GABA and GABAergic neurons in habituation, which we hope the reviewer will agree better reflects the limitations of the data in Figure 6 and 7.

Line 317: Based on the somewhat inconsistent results of the other GABA antagonists, I would be careful. Picrotoxin has been reported to antagonize other receptors besides GABA, see Das P et al, Neuropharma, 2003. So the results may be explained by a complex set of effects on multiple pathways with PTX.

Off target effects are an important concern with any pharmacological experiment, and perhaps especially in zebrafish where receptors and targets can be quite divergent from those in mammals where most drug targets have been characterized. We have added this sentiment to the discussion:

“We cannot rule out the possibility that off-targets of PTX, or subtle non-specific changes in excitatory/inhibitory balance alter habituation behaviour.”

Line 400-403, 430: There are some conflicting statements regarding the potential role of clusters 1 and 2 in DF habituation. Do the authors think they play a role in the behaviour measured in this manuscript? Could they clarify what they mean?

We see how our original statement in line 429 about the presence of cluster 1 and 2 neurons in the TL implied a role in dark flash habituation. This was not our intent, and we have removed “which also contains high concentrations of on-responding neurons”.

Our thoughts on these neurons are now stated in the discussion as:

“We also observed classes exhibiting an On-response profile ( and ). These neurons fire at the ramping increase in luminance after the DF, making it unlikely that they play a role in aspects of acute DF behaviour we measured here. These neurons exist in both non-adapting and depressing forms suggesting a yet unidentified role in behavioural adaptation to repeated DFs.“

Minor comments

Line 73 (and elsewhere): Why use adaptation instead of habituation (also in the adaptation profile)? Do you suspect your observations do not reflect habituation, but a sensory adaptation mechanism?

We have used the convention that “habituation” refers to observations at the behavioural level, while “depression” and “potentiation” refer to observations at the neuronal level. We use the term “adaptation” to refer to neuronal adaptations of either sign (depression or potentiation), as in line 73.

We believe that our observations reflect neuronal adaptations that underlie habituation behaviour.

Line 71: It is debatable that the strongest learning happens in the first block, the difference between the first and last response seems to grow larger with each successive block. What do the authors mean by 'strongest'

We agree that “strongest” was ambiguous. We have changed this to “initial”:

“We focused on a single training block of 60 DFs to identify neuronal adaptations that occur during the initial phase of learning ”

Fig.1F: there is no rastermap call in the GitHub repository, was the embedding done in the GUI? If so, it should also be shared for reproducibility's sake.

Yes, Fig.1F was created using the suite2p GUI, as we have now clarified in the methods:

“The clustered heatmap image of neural activity (([fig:3]F) was generated using the suite2p GUI using the “Visualize selected cells” function, and sorting the neurons using the rastermap algorithm ”

The image is available in the “Figure1 - Ca2Imaging.svg” file available here: https://github.com/owenrandlett/lamire_2022/tree/main/LamireEtAl_2022

Line 101: while true that AffinityPropagation does not require input on the number of clusters, preference can influence the number of clusters. It seems that at least two values were tested in the search for the clusters, can the authors comment on how many clusters the other preference value converged (or failed to converge) on?

Indeed, as with any clustering approach, the resultant clusters are highly dependent on the input parameters, in this case the “preference”, as well as “damping” and the choice of affinity metric. By varying these parameters one can arrive at anywhere between 2 and hundreds of clusters.

It is for this reason that we feel that the anatomical analyses of these clusters is very important, making the assumption that neurons of differing functional types will have different localizations in the brain, as we explained in the Results:

“While these results indicate the presence of a dozen functionally distinct neuron types, such clustering analyses will force categories upon the data irrespective of if such categories actually exist. To determine if our cluster analyses identified genuine neuron types, we analyzed their anatomical localization ([fig:2]C-E). Since our clustering was based purely on functional responses, we reasoned that anatomical segregation of these clusters would be consistent with the presence of truly distinct types of neurons.”

We also acknowledge in the Results that the clustering approach has limitations:

“These results highlight a diversity of functional neuronal classes active during DF habituation. Whether there are indeed 12 classes of neurons, or if this is an over- or under-estimate, awaits a full molecular characterization. Independent of the precise number of neuronal classes, we proceed under the hypothesis that these clusters define neurons that play distinct roles in the DF response and/or its modulation during habituation learning“

Fig.2. My understanding is that the cluster numbers are arbitrary unless there is a meaning to them, which then should be explained. I would recommend grouping the clusters per functional category as in Fig.6 to make it easier for the reader.

Cluster number reflects the ordering in the hierarchical clustering tree shown in Figure 2B. We feel that this is the most logical representation of their functional similarity. We have clarified this in the Methods:

“ We then used the Affinity Propagation clustering from scikit-learn , with “affinity” computed as the Pearson product-moment correlation coefficients (corrcoef in NumPy ), preference=-9, and damping=0.9, and clustered using Hierarchical clustering (cluster.hierarchy in SciPy ). Cluster number was assigned based on the ordering of the hierarchical clustering tree. ”

Fig.3 SSMD fingerprint, it would be much easier for the readers if the list of parameters was clearer and rotated 90 degrees. Maybe in a supplementary figure to show what each represents.

We agree that the SSMD fingerprint is very difficult to interpret. As discussed above, we have now included a supplementary table (Figure3_sourcedata2_SSMDFingerprintParameters.xlsx) where we have clarified what each parameter represents.

Fig.4: The use of the same colours across the clustering methods is confusing, especially after the use of colours for the SSMD fingerprint in Fig.3. and at the bottom of 4A. Fig.4A for example could have been colour coded according to the most affected behaviour in the fingerprint at the bottom.

Fig.4B the coloured text is difficult to read, especially for the lighter colours.

We agree that our use of color is not perfect, but we have attempted to use them consistently: for example when referring to a functional cluster, or a drug manipulation. We don’t think that there is a sufficient number of distinguishable colors for us to never use the same color twice.

Fig.4C if the goal is to show similarity, the relevant drugs could be placed adjacent to each other. One could also report the Euclidean distance, or compute how correlated the different fingerprints are within one pharmacological target space.

The goal of Fig 4C is to highlight where Bicuculline, Amoxapine, Picrotoxinin, Melatonin, Ethinyl Estradiol and Hexestrol lie within the clustered heatmap of the behavioural fingerprints (Fig 4A), and<br /> demonstrate how the probability of response to dark flashes is modulated by these drugs. In our analyses, “similarity” is a function of the clustering distance.

Fig.6D 'Same data as M, ...' I assume should be 'Same data as C,...'

Indeed, thank you for pointing out this error that we have corrected.

Fig. 7 How many GCaMP6s double transgenic larvae were imaged?

6 fish were imaged, as is stated in the legend to Fig 7G

Line 407: all is repeated.

We apologize, but we do not see what is repeated at line 407. Can you please clarify?

Line 481: Would testing spontaneous activity after training for 7h be unbiased, could there be fatigue effects?

We tested for fatigue effects in our previous study, comparing larvae that received the training for 7hrs and those that did not, and we saw no deficits in spontaneous activity, tap response, or OMR performance (Figure S1, Randlett et al., Current Biology, 2019).

Line 610: There are some inconsistencies between the authors' contributions in the manuscript and the one provided to eLife.

Thank you, we will double check this in the resubmission forms. The authors' contributions in the manuscript are correct.

Reviewer #3 (Recommendations For The Authors):

I would rather recommend the authors divide this manuscript into two and publish two papers by adding some more strengthening data for each part such as cellular manipulations, e.g. ablation to prove the critical involvement of 12(Pot, M) neurons in habituation.

We thank the reviewer for their suggestion, but have opted not to split the paper into two. We feel that the collective message of this paper and approach combining molecular and functional analysis will be of interest, and we believe the incongruencies in our results reflects the complexity inherent within the system.

Author response:

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

Public Reviews:

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant issues regarding the experimental design and potential misinterpretations of key findings. Consequently, the manuscript contributes little to our understanding of SynGap1 loss mechanisms.

Major issues in the second version of the manuscript:

In the review of the first version there were major issues and contradictions with the sEPSC and mEPSC data, and were not resolved after the revision, and the new control experiments rather confirmed the contradiction.

In the original review I stated: "One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity.‎ The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar."<br /> Contradictions remained after the revision of the manuscript. On one hand, the authors claimed in the revised version that "We found no difference in mEPSC amplitude between the two genotypes (Fig. 1g), indicating that the observed difference in sEPSC amplitude (Figure 1b) could arise from decreased network excitability". On the other hand, later they show "no significative difference in either amplitude or inter-event intervals between sEPSC and mEPSC, suggesting that in acute slices from adult A1, most sEPSCs may actually be AP independent." The latter means that sEPSCs and mEPSCs are the same type of events, which should have the same sensitivity to manipulations.

We understand that the data are confusing. Our results suggest a diverse population of PV+ cells, with varying reliance on action potential-dependent and -independent release. Several PV+ cells indeed show TTX sensitivity (reduced EPSC event amplitudes following TTX application: See Fig.1c-f, at the end of this document), but their individual responses are diluted when all cells are pooled together. To account for this variability, we are currently recording sEPSC followed by mEPSC from more mice of both genotypes. We will rephrase the text to reflect the updated data accordingly, keeping with the editors and reviewers’ suggestions.

Concerns about the quality of the synapse counting experiments were addressed by showing additional images in a different and explaining quantification. However, the admitted restriction of the analysis of excitatory synapses to the somatic region represent a limitation, as they include only a small fraction of the total excitation - even if, the slightly larger amplitudes of their EPSPs are considered.

We agree with the reviewer that restricting the anatomical analysis of excitatory synapses to PV cell somatic region is a limitation, which is what we have already highlighted in the discussion of the revised manuscript. Recent studies, based on serial block-face scanning electron microscopy, suggest that cortical PV+ interneurons receive more robust excitatory inputs to their perisomatic region as compared to pyramidal neurons (see for example, Hwang et al. 2021, Cerebral Cortex, http://doi.org/10.1093/cercor/bhaa378). It is thus possible that putative glutamatergic synapses, analysed by vGlut1/PSD95 colocalisation around PV+ cell somata, may be representative of a substantially major excitatory input population. Similar immunolabeling and quantification approach coupled with mEPSC analysis have been reported in several publications by other labs (for example Bernard et al 2022, Science 378, doi: 10.1126/science.abm7466; Exposito-Alonso et al, 2020 eLife, doi: 10.7554/eLife.57000). Since analysing putative excitatory synapses onto PV+ dendrites would be difficult and require a much longer time, we will re-phrase the text to more clearly highlight the rationale and limitation of this approach.

New experiments using paired-pulse stimulation provided an answer to issues 3 and 4. Note that the numbering of the Figures in the responses and manuscript are not consistent.

We are glad that the reviewer found that the new paired-pulse experiments answered previously raised concerns. We will correct the discrepancy in figure numbers in the manuscript.

I agree that low sampling rate of the APs does not change the observed large differences in AP threshold, however, the phase plots are still inconsistent in a sense that there appears to be an offset, as all values are shifted to more depolarized membrane potentials, including threshold, AP peak, AHP peak. This consistent shift may be due to a non-biological differences in the two sets of recordings, and, importantly, it may negate the interpretation of the I/f curves results (Fig. 5e).

We agree with the reviewers that higher sampling rate would allow to more accurately assess different parameters, such as AP height, half-width, rise time, etc., while it would not affect the large differences in AP threshold we observed between control and mutant mice. Since the phase plots to not add to our result analysis, we will remove them. The offset shown in Fig.5 was due to the unfortunate choice of two random neurons; this offset is not present in the different examples shown in Fig.7. We apologize for the confusion.

Additional issues:

The first paragraph of the Results mentioned that the recorded cells were identified by immunolabelling and axonal localization. However, neither the Results nor the Methods mention the criteria and levels of measurements of axonal arborization.

As suggested, we will add this information in the revised manuscript.

The other issues of the first review were adequately addressed by the Authors and the manuscript improved by these changes.

Reviewer #3 (Public review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences between control and mutants in both interneuron populations, although they claim a predominance in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunctions observed in Syngap1 haploinsufficiency-related intellectual disability.

The subject of the work is interesting, and most of the approach is rather direct and straightforward, which are strengths. There are also some methodological weaknesses and interpretative issues that reduce the impact of the paper.

(1) Supplementary Figure 3: recording and data analysis. The data of Supplementary Figure 3 show no differences either in the frequency or amplitude of synaptic events recorded from the same cell in control (sEPSCs) vs TTX (mEPSCs). This suggests that, under the experimental conditions of the paper, sEPSCs are AP-independent quantal events. However, I am concerned by the high variability of the individual results included in the Figure. Indeed, several datapoints show dramatically different frequencies in control vs TTX, which may be explained by unstable recording conditions. It would be important to present these data as time course plots, so that stability can be evaluated. Also, the claim of lack of effect of TTX should be corroborated by positive control experiments verifying that TTX is working (block of action potentials, for example). Lastly, it is not clear whether the application of TTX was consistent in time and duration in all the experiments and the paper does not clarify what time window was used for quantification.

We understand the reviewer’s concern about high variability. To account for this variability, we are currently recording sEPSC followed by mEPSC from more mice of both genotypes.

Indeed, we confirmed that TTX was working several times through the time course of this study, in different aliquots prepared from the same TTX vial used for all experiments. The results of the last test we performed, showing that TTX application blocks action potentials (2 recordings, one from a SST+ and one from a PV+ interneuron), are shown in Fig.1a,b at the end of this document. TTX was applied using the same protocol for all recorded neurons. In particular, sEPSCs were first sampled over a 2 min period. TTX (1μM; Alomone Labs) was then perfused into the recording chamber at a flow rate of 2 mL/min. We then waited for 5 min before sampling mEPSCs over a 2 min period. We will add this information in the revised manuscript methods. Finally, Fig.1g-j shows series resistance (Rs) over time for 4 different PV+ interneurons, indicating recording stability. These results are representative of the entire population of recorded neurons, which we have meticulously analysed one by one.

(2) Figure 1 and Supplementary Figure 3: apparent inconsistency. If, as the authors claim, TTX does not affect sEPSCs (either in the control or mutant genotype, Supplementary Figure 3 and point 1 above), then comparing sEPSC and mEPSC in control vs mutants should yield identical results. In contrast, Figure 1 reports a _selective_ reduction of sEPSCs amplitude (not in mEPSCs) in mutants, which is difficult to understand. The proposed explanation relying on different pools of synaptic vesicles mediating sEPSCs and mEPSCs does not clarify things. If this was the case, wouldn't it also imply a decrease of event frequency following TTX addition? However, this is not observed in Supplementary Figure 3. My understanding is that, according to this explanation, recordings in control solution would reflect the impact of two separate pools of vesicles, whereas, in the presence of TTX, only one pool would be available for release. Therefore, TTX should cause a decrease in the frequency of the recorded events, which is not what is observed in Supplementary Figure 3.

Our results suggest a diverse population of PV+ cells, with varying reliance on action potential-dependent and -independent release. Several PV+ cells indeed show TTX sensitivity (reduced EPSC event amplitudes following TTX application: See Fig.1c-f, at the end of this document), but their individual responses are diluted when all cells are pooled together. As mentioned above, we are currently recording sEPSCs followed by mEPSCs from more mice of both genotypes, to account for the large variability. We will rephrase the text in the revised manuscript according to the updated data and reviewers’ suggestions.

(3) Figure 1: statistical analysis. Although I do appreciate the efforts of the authors to illustrate both cumulative distributions and plunger plots with individual data, I am confused by how the cumulative distributions of Figure 1b (sEPSC amplitude) may support statistically significant differences between genotypes, but this is not the case for the cumulative distributions of Figure 1g (inter mEPSC interval), where the curves appear even more separated. A difference in mEPSC frequency would also be consistent with the data of Supplementary Fig 2b, which otherwise are difficult to reconciliate. I would encourage the authors to use the Kolmogorov-Smirnov rather than a t-test for the comparison of cumulative distributions.

We thank the reviewer for this suggestion. We used both cumulative distribution and plunger plots with individual data because they convey 2 different kinds of information. Cumulative distributions highlight where the differences lie (the deltas between the groups), while plunger plots with individual data show the variability between data points. In histogram 1g, the variability is greater than in 1b (due to the smaller sample size in 1g), which leads to larger error bars and directly impacts the statistical outcome. So, while the delta is larger in 1g, the variability is also greater. In contrast, the delta in 1b is smaller, as is the variability, which in turn affects the statistical outcome. To address this issue, we are currently increasing N of recordings.

We will include Kolmogorov-Smirnov analysis in the revision, as suggested; nevertheless, we will base our conclusions on statistical results generated by the linear mixed model (LMM), modelling animal as a random effect and genotype as the fixed effect. We used this statistical analysis since we considered the number of mice as independent replicates and the number of cells in each mouse as repeated/correlated measures. The reason we decided to use LMM for our statistical analyses is based on the growing concern over reproducibility in biomedical research and the ongoing discussion on how data are analysed (see for example, Yu et al (2022), Neuron 110:21-35 https://doi: 10.1016/j.neuron.2021.10.030; Aarts et al. (2014). Nat Neurosci 17, 491–496. https://doi.org/10.1038/nn.3648). We acknowledge that patch-clamp data has been historically analysed using t-test and analysis of variance (ANOVA), or equivalent non-parametric tests. However, these tests assume that individual observations (recorded neurons in this case) are independent of each other. Whether neurons from the same mouse are independent or correlated variables is an unresolved question, but does not appear to be likely from a biological point of view. Statisticians have developed effective methods to analyze correlated data, including LMM. In parallel, we also tested the data by using the standard parametric and non-parametric analyses and reported these results as well (Tables 1-9, and S1-S2).

(4) Methods. I still maintain that a threshold at around -20/-15 mV for the first action potential of a train seems too depolarized (see some datapoints of Fig 5c and Fig7c) for a healthy spike. This suggest that some cells were either in precarious conditions or that the capacitance of the electrode was not compensated properly.

As suggested by the reviewer, we will exclude the neurons with threshold at -20/-15 mV. In addition, we performed statistical analysis with and without these cells (data reported below) and found that whether these cells are included or excluded, the statistical significance of the results does not change.

Fig.5c: including the 2 outliers from cHet group with values of -16.5 and 20.6 mV: -42.6±1.01 mV in control, n=33 cells from 15 mice vs -35.3±1.2 mV in cHet, n=40 cells from 17 mice, ***p<0.001, LMM; excluding the 2 outliers from cHet group -42.6±1.01 mV in control, n=33 cells from 15 mice vs -36.2±1.1 mV in cHet, n=38 cells from 17 mice, ***p<0.001, LMM.

Fig.7c: including the 2 outliers from cHet group with values of -16.5 and 20.6 mV: -43.4±1.6 mV in control, n=12 cells from 9 mice vs -33.9±1.8 mV in cHet, n=24 cells from 13 mice, **p=0.002, LMM; excluding the 2 outliers from cHet group -43.4±1.6 mV in control, n=12 cells from 9 mice vs -35.4±1.7 mV in cHet, n=22 cells from 13 mice, *p=0.037, LMM.

(5) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties (Figure 8d,e); however, their evoked firing properties were affected with fewer AP generated in response to the same depolarizing current injection".<br /> This sentence is intrinsically contradictory. Action potentials triggered by current injections are dependent on the integration of passive and active properties. If the curves of Figure 8f are different between genotypes, then some passive and/or active property MUST have changed. It is an unescapable conclusion. The general _blanket_ statement of the authors that there are no significant changes in active and passive properties is in direct contradiction with the current/#AP plot.

We shall rephrase the text according to the reviewer’s suggestion to better represent the data. As discussed in the first revision, it's possible that other intrinsic factors, not assessed in this study, may have contributed to the effect shown in the current/#AP plot.

(6) The phase plots of Figs 5c, 7c, and 7h suggest that the frequency of acquisition/filtering of current-clamp signals was not appropriate for fast waveforms such as spikes. The first two papers indicated by the authors in their rebuttal (Golomb et al., 2007; Stevens et al., 2021) did not perform a phase plot analysis (like those included in the manuscript). The last work quoted in the rebuttal (Zhang et al., 2023) did perform phase plot analysis, but data were digitized at a frequency of 20KHz (not 10KHz as incorrectly indicated by the authors) and filtered at 10 kHz (not 2-3 kHz as by the authors in the manuscript). To me, this remains a concern.

We agree with the reviewer that higher sampling rate would allow to more accurately assess different AP parameters, such as AP height, half-width, rise time, etc. The papers were cited in context of determining AP threshold, not performing phase plot analysis. We apologize for the confusion and error. Further, as mentioned above, we will remove the phase plots since they do not add relevant information.

(7) The general logical flow of the manuscript could be improved. For example, Fig 4 seems to indicate no morphological differences in the dendritic trees of control vs mutant PV cells, but this conclusion is then rejected by Fig 6. Maybe Fig 4 is not necessary. Regarding Fig 6, did the authors check the integrity of the entire dendritic structure of the cells analyzed (i.e. no dendrites were cut in the slice)? This is critical as the dendritic geometry may affect the firing properties of neurons (Mainen and Sejnowski, Nature, 1996).

As suggested by the reviewer, we will remove Fig.4. All the reconstructions used for dendritic analysis contained intact cells with no evidently cut dendrites.

Author response image 1.

(a, b) Representative voltage responses of a SST+ cell (a) and a PV+ cell (b) in absence (left) and presence (right) of TTX in response to depolarizing current injections corresponding to threshold current and 2x threshold current. (c-f) Cumulative histograms of sEPSCs/mEPSCs amplitude (bin width 0.5 pA) and frequency (bin width 10 ms) recorded from four PV+ cells.  sEPSC were recorded for 2 minutes, then TTX (1μM; Alomone Labs) was perfused into the recording chamber. After 5 minutes, mEPSC were recorded for 2 minutes. (g, h, i, j) Time course plots of series resistance (Rs) of the four representative PV+ cells shown in c-f before (sEPSC) and during the application of TTX (mEPSC).

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

Public Reviews:

Reviewer #1 (Public Review):

The study is designed to assess the role of Syngap1 in regulating the physiology of the MGE-derived PV+ and SST+ interneurons. Syngap1 is associated with some mental health disorders, and PV+ and SST+ cells are the focus of many previous and likely future reports from studies of interneuron biology, highlighting the translational and basic neuroscience relevance of the authors' work.

Strengths of the study are using well-established electrophysiology methods and the highly controlled conditions of ex vivo brain slice experiments combined with a novel intersectional mouse line, to assess the role of Syngap1 in regulating PV+ and SST+ cell properties. The findings revealed that in the mature auditory cortex, Syngap1 haploinsufficiency decreases both the intrinsic excitability and the excitatory synaptic drive onto PV+ neurons from Layer 4. In contrast, SST+ interneurons were mostly unaffected by Syngap1 haploinsufficiency. Pharmacologically manipulating the activity of voltagegated potassium channels of the Kv1 family suggested that these channels contributed to the decreased PV+ neuron excitability by Syngap insufficiency. These results therefore suggest that normal Syngap1 expression levels are necessary to produce normal PV+ cell intrinsic properties and excitatory synaptic drive, albeit, perhaps surprisingly, inhibitory synaptic        transmission was not affected by Syngap1 haploinsufficiency.

Since the electrophysiology experiments were performed in the adult auditory cortex, while Syngap1 expression was potentially affected since embryonic stages in the MGE, future studies should address two important points that were not tackled in the present study. First, what is the developmental time window in which Syngap1 insufficiency disrupted PV+ neuron properties? Albeit the embryonic Syngap1 deletion most likely affected PV+ neuron maturation, the properties of Syngap-insufficient PV+ neurons do not resemble those of immature PV+ neurons. Second, whereas the observation that Syngap1 haploinsufficiency affected PV+ neurons in auditory cortex layer 4 suggests auditory processing alterations, MGE-derived PV+ neurons populate every cortical area. Therefore, without information on whether Syngap1 expression levels are cortical area-specific, the data in this study would predict that by regulating PV+ neuron electrophysiology, Syngap1 normally controls circuit function in a wide range of cortical areas, and therefore a range of sensory, motor and cognitive functions. These are relatively minor weaknesses regarding interpretation of the data in the present study that the authors could discuss.

We agree with the reviewer on the proposed open questions, which we now discuss in the revised manuscript. We do have experimental evidence suggesting that Syngap1 mRNA is expressed by PV+ and SST+ neurons in different cortical areas, during early postnatal development and in adulthood (Jadhav et al., 2024); therefore, we agree that it will be important, in future experiments, to tackle the question of when the observed phenotypes arise.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors investigated how partial loss of SynGap1 affects inhibitory neurons derived from the MGE in the auditory cortex, focusing on their synaptic inputs and excitability. While haplo-insufficiently of SynGap1 is known to lead to intellectual disabilities, the underlying mechanisms remain unclear.

Strengths:

The questions are novel

Weaknesses:

Despite the interesting and novel questions, there are significant concerns regarding the experimental design and data quality, as well as potential misinterpretations of key findings. Consequently, the current manuscript fails to contribute substantially to our understanding of SynGap1 loss mechanisms and may even provoke unnecessary controversies.

Major issues:

(1) One major concern is the inconsistency and confusion in the intermediate conclusions drawn from the results. For instance, while the sEPSC data indicates decreased amplitude in PV+ and SOM+ cells in cHet animals, the frequency of events remains unchanged. In contrast, the mEPSC data shows no change in amplitudes in PV+ cells, but a significant decrease in event frequency. The authors conclude that the former observation implies decreased excitability. However, traditionally, such observations on mEPSC parameters are considered indicative of presynaptic mechanisms rather than changes of network activity. The subsequent synapse counting experiments align more closely with the traditional conclusions. This issue can be resolved by rephrasing the text. However, it would remain unexplained why the sEPSC frequency shows no significant difference. If the majority of sEPSC events were indeed mediated by spiking (which is blocked by TTX), the average amplitudes and frequency of mEPSCs should be substantially lower than those of sEPSCs. Yet, they fall within a very similar range, suggesting that most sEPSCs may actually be independent of action potentials. But if that was indeed the case, the changes of purported sEPSC and mEPSC results should have been similar.

We understand the reviewer’s perspective; indeed, we asked ourselves the very same question regarding why the sEPSC and mEPSC frequency fall within a similar range when we analysed neuron means (bar graphs). We thus recorded sEPSCs followed by mEPSCs from several PV neurons (control and cHet) and included this data to the revised version of the manuscript (new Supplementary Figure 3). We found that the average amplitudes and frequency of mEPSCs together with their respective cumulative probability curves were not significantly different than those of sEPSCs. We rephrased the manuscript to present potential interpretations of the data.

We hope that we have correctly interpreted the reviewer's concern. If the question is why we do not observe a significant difference in the average frequency when comparing sEPSC and mEPSC in control mice, this could be explained by the fact that increased mean amplitude of sEPSCs was primarily driven by alterations in large sEPSCs (>9-10pA, as shown in cumulative probability in Fig. 1b right), with smaller ones being relatively unaffected. Consequently, a reduction in sEPSC amplitude may not necessarily result in a significant decrease in frequency since their values likely remain above the detection threshold of 3 pA. 

If the question is whether we should see the same parameters affected by the genetic manipulation in both sEPSC and mEPSC, then another critical consideration is the involvement of the releasable pool in mEPSCs versus sEPSCs. Current knowledge suggests that activity-dependent and -independent release may not necessarily engage the same pool of vesicles or target the same postsynaptic sites. This concept has been extensively explored (Sara et al., 2005; Sara et al., 2011; reviewed in Ramirez and Kavalali, 2011; Kavalali, 2015). Consequently, while we may have traditionally interpreted activitydependent and -independent data assuming they utilize the same pool, this is no longer accurate. The current discussion in the field revolves around understanding the mechanisms underlying such phenomena. Therefore, comparisons between sEPSCs and mEPSCs may not yield conclusive data but rather speculative interpretations. 

(2) Another significant concern is the quality of synapse counting experiments. The authors attempted to colocalize pre- and postsynaptic markers Vglut1 and PSD95 with PV labelling. However, several issues arise. Firstly, the PV labelling seems confined to soma regions, with no visible dendrites. Given that the perisomatic region only receives a minor fraction of excitatory synapses, this labeling might not accurately represent the input coverage of PV cells. Secondly, the resolution of the images is insufficient to support clear colocalization of the synaptic markers. Thirdly, the staining patterns are peculiar, with PSD95 puncta appearing within regions clearly identified as somas by Vglut1, hinting at possible intracellular signals. Furthermore, PSD95 seems to delineate potential apical dendrites of pyramidal cells passing through the region, yet Vglut1+ partners are absent in these segments, which are expected to be the marker of these synapses here. Additionally, the cumulative density of Vglut2 and Vglut1 puncta exceeds expectations, and it's surprising that subcortical fibers labeled by Vglut2 are comparable in number to intracortical Vglut1+ axon terminals. Ideally, N(Vglut1)+N(Vglut2) should be equal or less than N(PSD95), but this is not the case here. Consequently, these results cannot be considered reliable due to these issues.

We apologize, as it appears that the images we provided in the first submission have caused confusion. The selected images represent a single focal plane of a confocal stack, which was visually centered on the PV cell somata. We chose just one confocal plane because we thought it showed more clearly the apposition of presynaptic and postsynaptic immunolabeling around the somata. In the revised version of the manuscript, we now provide higher magnification images, which will clearly show how we identified and selected the region of interest for the quantification of colocalized synaptic markers (Supplemental Figure 2). In our confocal stacks, we can also identify PV immunolabeled dendrites and colocalized vGlut1/PSD95 or vGlut2/PSD95 puncta on them; but these do not appear in the selected images because, as explained, only one focal plane, centered on the PV cell somata, was shown. 

We acknowledge the reviewer's point that in PV+ cells the majority of excitatory inputs are formed onto dendrites; however, we focused on the somatic excitatory inputs to PV cells, because despite their lower number, they produce much stronger depolarization in PV neurons than dendritic excitatory inputs (Hu et al., 2010; Norenberg et al., 2010). Further, quantification of perisomatic putative excitatory synapses is more reliable since by using PV immunostaining, we can visualize the soma and larger primary dendrites, but smaller, higher order dendrites are not be always detectable. Of note, PV positive somata receive more excitatory synapses than SST positive and pyramidal neuron somata as found by electron microscopy studies in the visual cortex (Hwang et al., 2021; Elabbady et al., 2024).

Regarding the comment on the density of vGlut1 and vGlut2 puncta, the reason that the numbers appear high and similar between the two markers is because we present normalized data (cHet normalized to their control values for each set of immunolabelling) to clearly represent the differences between genotypes. We now provide a more detailed explanation of our methods in the revised manuscript.  Briefly, immunostained sections were imaged using a Leica SP8-STED confocal microscope, with an oil immersion 63x (NA 1.4) at 1024 X 1024, z-step =0.3 μm, stack size of ~15 μm. Images were acquired from the auditory cortex from at least 3 coronal sections per animal. All the confocal parameters were maintained constant throughout the acquisition of an experiment. All images shown in the figures are from a single confocal plane. To quantify the number of vGlut1/PSD95 or vGlut2/PSD95 putative synapses, images were exported as TIFF files and analyzed using Fiji (Image J) software. We first manually outlined the profile of each PV cell soma (identified by PV immunolabeling). At least 4 innervated somata were selected in each confocal stack. We then used a series of custom-made macros in Fiji as previously described (Chehrazi et al, 2023). After subtracting background (rolling value = 10) and Gaussian blur (σ value = 2) filters, the stacks were binarized and vGlut1/PSD95 or vGlut2/PSD95 puncta were independently identified around the perimeter of a targeted soma in the focal plane with the highest soma circumference. Puncta were quantified after filtering particles for size (included between 0-2μm2) and circularity (included between 01). Data quantification was done by investigators blind to the genotype, and presented as normalized data over control values for each experiment.

(3) One observation from the minimal stimulation experiment was concluded by an unsupported statement. Namely, the change in the onset delay cannot be attributed to a deficit in the recruitment of PV+ cells, but it may suggest a change in the excitability of TC axons.

We agree with the reviewer, please see answer to point below.

(4) The conclusions drawn from the stimulation experiments are also disconnected from the actual data. To make conclusions about TC release, the authors should have tested release probability using established methods, such as paired-pulse changes. Instead, the only observation here is a change in the AMPA components, which remained unexplained.

As suggested, we performed additional paired-pulse ratio experiments at different intervals. We found that, in contrast with Control mice, evoked excitatory inputs to layer IV PV+ cells showed paired-pulse facilitation in cHet mice (Figure 3g, h), suggesting that thalamocortical presynaptic sites likely have decreased release probability in mutant compared to control mice.  We rephrased the text according to the data obtained from this new experiment.

(5) The sampling rate of CC recordings is insufficient to resolve the temporal properties of the APs. Therefore, the phase-plots cannot be interpreted (e.g. axonal and somatic AP components are not clearly separated), raising questions about how AP threshold and peak were measured. The low sampling rate also masks the real derivative of the AP signals, making them apparently faster.

We acknowledge that a higher sampling rate would provide a more detailed and smoother phase-plot. However, in the context of action potential parameters analysis here, it is acceptable to use sampling rates ranging from 10 kHz to 20 kHz (Golomb et al., 2007; Stevens et al., 2021; Zhang et al., 2023), which are considered adequate in the context of the present study. Indeed, our study aims to evaluate "relative" differences in the electrophysiological phenotype when comparing groups following a specific genetic manipulation. A sampling rate of 10 kHz is commonly employed in similar studies, including those conducted by our collaborator and co-author S. Kourrich (e.g., Kourrich and Thomas 2009, Kourrich et al., 2013), as well as others (Russo et al., 2013; Ünal et al., 2020; Chamberland et al., 2023). Despite being acquired at a lower sampling rate than potentially preferred by the reviewer, our data clearly demonstrate significant differences between the experimental groups, especially for parameters that are negligibly or not affected by the sampling rate used here (e.g., #spikes/input, RMP, Rin, Cm, Tm, AP amplitude, AP latency, AP rheobase).

Regarding the phase-plots, a higher sampling rate would indeed have resulted in smoother curves. However, the differences were sufficiently pronounced to discern the relative variations in action potential waveforms between the experimental groups.

A related issue is that the Methods section lacks essential details about the recording conditions, such as bridge balance and capacitance neutralization.

We indeed performed bridge balance and neutralized the capacitance before starting every recording. We added the information in the methods.

(6) Interpretation issue: One of the most fundamental measures of cellular excitability, the rheobase, was differentially affected by cHet in BCshort and BCbroad. Yet, the authors concluded that the cHet-induced changes in the two subpopulations are common.

We are uncertain if we have correctly interpreted the reviewer's comment. While we observed distinct impacts on the rheobase (Fig. 7d and 7i), there seems to be a common effect on the AP threshold (Fig. 7c and 7h), as interpreted and indicated in the final sentence of the results section for Figure 7. If our response does not address the reviewer's comment adequately, we would greatly appreciate it if the reviewer could rephrase their feedback.

(7) Design issue:

The Kv1 blockade experiments are disconnected from the main manuscript. There is no experiment that shows the causal relationship between changes in DTX and cHet cells. It is only an interesting observation on AP halfwidth and threshold. However, how they affect rheobase, EPSCs, and other topics of the manuscript are not addressed in DTX experiments.

Furthermore, Kv1 currents were never measured in this work, nor was the channel density tested. Thus, the DTX effects are not necessarily related to changes in PV cells, which can potentially generate controversies.

While we acknowledge the reviewer's point that Kv1 currents and density weren't specifically tested, an important insight provided by Fig. 5 is the prolonged action potential latency. This delay is significantly influenced by slowly inactivating subthreshold potassium currents, namely the D-type K+ current. It's worth noting that D-type current is primarily mediated by members of the Kv1 family. The literature supports a role for Kv1.1containing channels in modulating responses to near-threshold stimuli in PV cells (Wang et al., 1994; Goldberg et al., 2008; Zurita et al., 2018). However, we recognize that besides the Kv1 family, other families may also contribute to the observed changes.

To address this concern, we revised the manuscript by referring to the more accurate term "D-type K+ current", and rephrased the discussion to clarify the limit of our approach. It is not our intention to open unnecessary controversy, but present the data we obtained. We believe this approach and rephrasing the discussion as proposed will prevent unnecessary controversy and instead foster fruitful discussions.

(8) Writing issues:

Abstract:

The auditory system is not mentioned in the abstract.

One statement in the abstract is unclear. What is meant by "targeting Kv1 family of voltagegated potassium channels was sufficient..."? "Targeting" could refer to altered subcellular targeting of the channels, simple overexpression/deletion in the target cell population, or targeted mutation of the channel, etc. Only the final part of the Results revealed that none of the above, but these channels were blocked selectively.

We agree with the reviewer and we will rephrase the abstract accordingly.

Introduction:

There is a contradiction in the introduction. The second paragraph describes in detail the distinct contribution of PV and SST neurons to auditory processing. But at the end, the authors state that "relatively few reports on PV+ and SST+ cell-intrinsic and synaptic properties in adult auditory cortex". Please be more specific about the unknown properties.

We agree with the reviewer and we will rephrase more specifically.

(9) The introduction emphasizes the heterogeneity of PV neurons, which certainly influences the interpretation of the results of the current manuscript. However, the initial experiments did not consider this and handled all PV cell data as a pooled population.

In the initial experiments, we handled all PV cell data together because we wanted to be rigorous and not make assumptions on the different PV cells, which in later experiments we distinguished based on the intrinsic properties alone. Nevertheless, based on this and other reviewers’ comments, we completely rewrote the introduction in the revised manuscript to increase both focus and clarity.

(10) The interpretation of the results strongly depends on unpublished work, which potentially provide the physiological and behavioral contexts about the role of GABAergic neurons in SynGap-haploinsufficiency. The authors cite their own unpublished work, without explaining the specific findings and relation to this manuscript.

We agree with the reviewer and provided more information and updated references in the revised version of this manuscript. Our work is now in press in Journal of Neuroscience.

(11) The introduction of Scholl analysis experiments mentions SOM staining, however, there is no such data about this cell type in the manuscript.

We thank the reviewer for noticing the error; we changed SOM with SST (SOM and SST are two commonly used acronyms for Somatostatin expressing interneurons).

Reviewer #3 (Public Review):

This paper compares the synaptic and membrane properties of two main subtypes of interneurons (PV+, SST+) in the auditory cortex of control mice vs mutants with Syngap1 haploinsufficiency. The authors find differences at both levels, although predominantly in PV+ cells. These results suggest that altered PV-interneuron functions in the auditory cortex may contribute to the network dysfunction observed in Syngap1 haploinsufficiencyrelated intellectual disability. The subject of the work is interesting, and most of the approach is direct and quantitative, which are major strengths. There are also some weaknesses that reduce its impact for a broader field.

(1) The choice of mice with conditional (rather than global) haploinsufficiency makes the link between the findings and Syngap1 relatively easy to interpret, which is a strength. However, it also remains unclear whether an entire network with the same mutation at a global level (affecting also excitatory neurons) would react similarly.

We agree with the reviewer and now discuss this important caveat in the revised manuscript.

(2) There are some (apparent?) inconsistencies between the text and the figures. Although the authors appear to have used a sophisticated statistical analysis, some datasets in the illustrations do not seem to match the statistical results. For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences. 

We respectfully disagree, we do not think the text and figures are inconsistent. In the cited example, large apparent difference in mean values does not show significance due to the large variability in the data; further, we did not exclude any data points, because we wanted to be rigorous. In particular, for Fig.1g, statistical analysis shows a significant increase in the inter-mEPSC interval (*p=0.027, LMM) when all events are considered (cumulative probability plots), while there is no significant difference in the inter-mEPSCs interval for inter-cell mean comparison (inset, p=0.354, LMM).  Inter-cell mean comparison does not show difference with Mann-Whitney test either (p=0.101, the data are not normally distributed, hence the choice of the Mann-Whitney test). For Fig. 3f (eNMDA), the higher mean value for the cHet versus the control is driven by two data points which are particularly high, while the other data points overlap with the control values. The MannWhitney test show also no statistical difference (p=0.174).

In the manuscript, discussion of the data is based on the results of the LMM analysis, which takes in account both the number of cells and the numbers of mice from which these cells are recorded. We chose this statistical approach because it does not rely on the assumption that cells recorded from same mouse are independent variables. In the supplemental tables, we provided the results of the statistical analysis done with both LMM and the most commonly used Mann Whitney (for not normally distributed) or t-test (for normally distributed), for each data set.

Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not seem to show that.

We apologize for our lack of clarity. In legend 9, we reported the statistical comparisons between 1) vehicle-treated cHET vs control PV+ cells and 2) a-DTX-treated cHET vs control PV+ cells. We rephrased the legend of the figure to avoid confusion.

(3) The authors mention that the lack of differences in synaptic current kinetics is evidence against a change in subunit composition. However, in some Figures, for example, 3a, the kinetics of the recorded currents appear dramatically different. It would be important to know and compare the values of the series resistance between control and mutant animals.

We agree with the reviewer that there appears to be a qualitative difference in eNMDA decay between conditions, although quantified eNMDA decay itself is similar between groups. We have used a cutoff of 15 % for the series resistance (Rs), which is significantly more stringent as compared to the cutoff typically used in electrophysiology, which are for the vast majority between 20 and 30%. To answer this concern, we re-examined the Rs, we compared Rs between groups and found no difference for Rs in eAMPA (Control mice: 13.2±0.5, n=16 cells from 7 mice vs cHet mice: 13.7±0.3, n=14 cells from 7 mice; LMM, p=0.432) and eNMDA (Control mice: 12.7±0.7, n=6 cells from 3 mice vs cHet mice: 13.8±0.7 in cHet n=6 cells from 5 mice: LMM, p=0.231). Thus, the apparent qualitative difference in eNMDA decay stems from inter-cell variability rather than inter-group differences. Notably, this discrepancy between the trace (Fig. 3a) and the data (Fig. 3f, right) is largely due to inter-cell variability, particularly in eNMDA, where a higher but non-significant decay rate is driven by a couple of very high values (Fig. 3f, right). In the revised manuscript, we now show traces that better represent our findings.

(4) A significant unexplained variability is present in several datasets. For example, the AP threshold for PV+ includes points between -50-40 mV, but also values at around -20/-15 mV, which seems too depolarized to generate healthy APs (Fig 5c, Fig7c).

We acknowledge the variability in AP threshold data, with some APs appearing too depolarized to generate healthy spikes. However, we meticulously examined each AP that spiked at these depolarized thresholds and found that other intrinsic properties (such as Rin, Vrest, AP overshoot, etc.) all indicate that these cells are healthy. Therefore, to maintain objectivity and provide unbiased data to the community, we opted to include them in our analysis. It's worth noting that similar variability has been observed in other studies (Bengtsson Gonzales et al., 2020; Bertero et al., 2020).

Further, we conducted a significance test on AP threshold excluding these potentially unhealthy cells and found that the significant differences persist. After removing two outliers from the cHet group with values of -16.5 and 20.6 mV, we obtain: -42.6±1.01 mV in control, n=33, 15 mice vs -36.2±1.1 mV in cHet, n=38 cells, 17 mice (LMM, ***p<0.001). Thus, whether these cells are included or excluded, our interpretations and conclusions remain unchanged.

We would like to clarify that these data have not been corrected with the junction potential, as described in the revised version.

(5) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2.

We apologize for our lack of clarity. Although the analysis was done at high resolution, the figures were focused on showing multiple PV somata receiving excitatory inputs. We added higher magnification figures and more detailed information in the methods of the revised version. Please also see our response to reviewer #2.

(6) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

While we acknowledge the theoretical expectation that changes in intrinsic parameters should correlate with alterations in neuronal firing, the absence of differences in the parameters analyzed in this study is not incompatible with the clear and significant decrease in firing rate observed in cHet SST+ cells. It's indeed possible that other intrinsic factors, not assessed in this study, may have contributed to this effect. However, exploring these mechanisms is beyond the scope of our current investigation. We rephrased the discussion and added this limitation of our study in the revised version.

(7) The plots used for the determination of AP threshold (Figs 5c, 7c, and 7h) suggest that the frequency of acquisition of current-clamp signals may not have been sufficient, this value is not included in the Methods section.

This study utilized a sampling rate of 10 kHz, which is a standard rate for action potential analysis in the present context. While we acknowledge that a higher sampling rate could have enhanced the clarity of the phase plot, our recording conditions, as detailed in our response to Rev#2/comment#5, were suitable for the objectives of this study.

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Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major points:

(1) The introduction nicely summarizes multiple aspects of cortical auditory physiology and auditory stimulus processing, but the experiments in this study are performed ex vivo in acute slices. I wonder if it would be beneficial to shorten the initial parts of the introduction and consider a more focused approach highlighting, for example, to what extent Syngap1 expression levels change during development and/or vary across cortical areas. What cortical cell types express Syngap1 in addition to PV+ and SST+ cells? If multiple cell types normally express Syngap1, the introduction could clarify that the present study investigated Syngap1 insufficiency by isolating its effects in PV+ and SST+ neurons, a condition that may not reflect the situation in mental health disorders, but that would allow to better understand the global effects of Syngap1 deficiency.

We thank the reviewer for this very helpful suggestion. We have changed the introduction as suggested.

(2) Because mEPSCs are not affected in Syngap+/- interneurons, the authors conclude that the lower sEPSC amplitude is due to decreased network activity. However, it is likely that the absence of significant difference (Fig 1g), is due to lack of statistical power (control: 18 cells from 7 mice, cHet: 8 cells from 4 mice). By contrast, the number of experiments recording sIPSCs and mIPSCs (Fig 2) is much larger. Hence, it seems that adding mEPSC data would allow the authors to more to convincingly support their conclusions. To more directly test whether Syngap insufficiency affects excitatory inputs by reducing network activity, ideally the authors would want to record sEPSCs followed by mEPSCs from each PV+ neuron (control or cHet). Spontaneous event frequency and amplitude should be higher for sEPSCs than mEPSCs, and Syngap1 deficiency should affect only sEPSCs, since network activity is abolished following tetrodotoxin application for mEPSC recordings.

We agreed with the reviewer’s suggestion, and recorded sEPSCs followed by mEPSCs from PV+ neurons in control and cHet mice (Figure supplement 3). In both genotypes, we found no significative difference in either amplitude or inter-event intervals between sEPSC and mEPSC, suggesting that in acute slices from adult A1, most sEPSCs may actually be action potentialindependent. While perhaps surprisingly at first glance, this result can be explained by recent published work suggesting that action potentials-dependent (sEPSC) and -independent (mEPSC) release may not necessarily engage the same pool of vesicles or target the same postsynaptic sites (Sara et al., 2005; Sara et al., 2011; reviewed in Ramirez and Kavalali, 2011; Kavalali, 2015). Consequently, while we may have traditionally interpreted activity-dependent and -independent data assuming they utilize the same pool, this is no longer accurate; and indeed, the current discussion in the field revolves around understanding the mechanisms underlying such phenomena.

Therefore, comparisons between sEPSCs and mEPSCs may not yield conclusive data but rather speculative interpretations. We have added this caveat in the result section.

(3) The interpretation of the data of experiments studying thalamic inputs and single synapses should be clarified and/or rewritten. First, it is not clear why the authors assume they are selectively activating thalamic fibers with electrical stimulation. Presumably the authors applied electrical stimulation to the white matter, but the methods not clearly explained? Furthermore, the authors could clarify how stimulation of a single axon was verified and how could they distinguish release failures from stimulation failures, since the latter are inherent to using minimal stimulation conditions. Interpretations of changes in potency, quantal content, failure rate, etc, depend on the ability to distinguish release failures from stimulation failures. In addition, can the authors provide information on how many synapses a thalamic axon does establish with each postsynaptic PV+ cell from control or Syngap-deficient mice? Even if stimulating a single thalamic axon would be possible, if the connections from single thalamic axons onto single PV+ or SST+ cells are multisynaptic, this would make the interpretation of minimal stimulation experiments in terms of single synapses very difficult or unfeasible. In the end, changes in EPSCs evoked by electrical stimulation may support the idea that Syngap1 insufficiency decreases action potential evoked release, that in part mediates sEPSC, but without indicating the anatomical identity of the stimulated inputs (thalamic, other subcortical or cortico-cortical?

We agree with the reviewer, our protocol does not allow the stimulation of single synapses/axons, but rather bulk stimulation of multiple axons. We thank the reviewer for bringing up this important point.  In our experiment, we reduced the stimulus intensity until no EPSC was observed, then increased it until we reached the minimum intensity at which we could observe an EPSC. We now explain this approach more clearly in the method and changed the results section by removing any reference to “minimal” stimulation.

Electrical stimulation of thalamic radiation could indeed activate not only monosynaptic thalamic fibers but also polysynaptic (corticothalamic and/or corticocortical) EPSC component. To identify monosynaptic thalamocortical connections, we used as criteria the onset latencies of EPSC and the variability jitter obtained from the standard deviation of onset latencies, as previously published by other studies (Richardson et al., 2009; Blundon et al., 2011; Chun et al., 2013). Onset latencies were defined as the time interval between the beginning of the stimulation artifact and the onset of the EPSC. Monosynaptic connections are characterized by short onset latencies and low jitter variability (Richardson et al., 2009; Blundon et al., 2011; Chun et al., 2013). In our experiments, the initial slopes of EPSCs evoked by white matter stimulation had short onset latencies (mean onset latency, 4.27 ± 0.11 ms, N=16 neurons in controls, and 5.07 ± 0.07 ms, N=14 neurons in cHet mice) and low onset latency variability jitter (0.24 ± 0.03 ms in controls vs 0.31 ± 0.03 ms in cHet mice), suggestive of activation of monosynaptic thalamocortical monosynaptic connections (Richardson et al., 2009; Blundon et al., 2011; Chun et al., 2013). Of note, a previous study in adult mice (Krause et al., 2014) showed that local field potentials evoked by electrical stimulation of medial geniculate nucleus or thalamic radiation were comparable. The information is included in the revised manuscript, in the methods section.

(4) The data presentation in Fig 6 is a bit confusing and could be clarified. First, in cluster analysis (Fig 6a), the authors may want to clarify why a correlation between Fmax and half width is indicative of the presence of subgroups. Second, performing cluster analysis based on two variables alone (Fmax and half-width) might not be very informative, but perhaps the authors could better explain why they chose two variables and particularly these two variables? For reference, see the study by Helm et al. 2013 (cited by the authors) using multivariate cluster analysis. Additionally, the authors may want to clarify, for non-expert readers, whether or not finding correlations between variables (heatmap in the left panel of Fig 6b) is a necessary condition to perform PCA (Fig 6b right panel).

We apologize for the confusion and thank the reviewer for the comment. The choice of Fmax and half width to cluster PV+ subtypes was based on past observation of atypical PV+ cells characterized by a slower AP half-width and lower maximal AP firing frequency (Nassar et al., 2015; Bengtsson Gonzales et al., 2018; Ekins et al., 2020; Helm et al., 2013). Based on these previous studies we performed hierarchical clustering of AP half-width and Fmax-initial values based on Euclidean distance. However, in our case some control PV+ cells showed no correlation between these parameters (as it appears in Fig 6a left, right, and 6b left), requiring the use of additional 11 parameters to perform Principal Component Analysis (PCA). PCA takes a large data set with many variables per observation and reduces them to a smaller set of summary indices (Murtagh and Heck 1987).  We choose in total 13 parameters that are largely unrelated, while excluding others that are highly correlated and represent similar features of membrane properties (e.g., AP rise time and AP half-width). PCA applies a multiexponential fit to the data, and each new uncorrelated variable [principal component (PC)] can describe more than one original parameter (Helm et al., 2013). We added information in the methods section as suggested.

Minor points:

(1) In Fig 3a, the traces illustrating the effects of syngap haplo-insufficiency on AMPA and NMDA EPSCs do not seem to be the best examples? For instance, the EPSCs in syngap-deficient neurons show quite different kinetics compared with control EPSCs, however Fig 3f suggests similar kinetics.

We changed the traces as suggested.

(2) In the first paragraph of results, it would be helpful to clarify that the experiments are performed in acute brain slices and state the age of animals.

Done as suggested.

(3) The following two sentences are partly redundant and could be synthesized or merged to shorten the text: "Recorded MGE-derived interneurons, identified by GFP expression, were filled with biocytin, followed by posthoc immunolabeling with anti-PV and anti-SST antibodies. PV+ and SST+ interneuron identity was confirmed using neurochemical marker (PV or SST) expression and anatomical properties (axonal arborisation location, presence of dendritic spines)."

We rewrote the paragraph to avoid redundancy, as suggested.

(4) In the following sentence, the mention of dendritic spines is not sufficiently clear, does it mean that spine density or spine morphology differ between PV and SST neurons?: "PV+ and SST+ interneuron identity was confirmed using neurochemical marker (PV or SST) expression and anatomical properties (axonal arborisation location, presence of dendritic spines)."

We meant absence or presence of spines. PV+ cells typically do not have spines, while SST+ interneurons do. We corrected the sentence to improve clarity.

(5) The first sentence of the discussion might be a bit of an overinterpretation of the data? Dissecting the circuit mechanisms of abnormal auditory function with Syngap insufficiency requires experiments very different from those reported in this paper. Moreover, that PV+ neurons from auditory cortex are particularly vulnerable to Syngap deficiency is possible, but this question is not addressed directly in this study because the effects on auditory cortex PV+ neurons were not thoroughly compared with those on PV+ cells from other cortical areas.

We agreed with the reviewer and changed this sentence accordingly.

Reviewer #2 (Recommendations For The Authors):

Minor issues:

"glutamatergic synaptic inputs to Nkx2.1+ interneurons from adult layer IV (LIV) auditory cortex" it would be more correct if this sentence used "in adult layer IV" instead of "from".

We made the suggested changes.

It would be useful information to provide whether the slice quality and cellular health was affected in the cHet animals.

We did not observe any difference between control and cHet mice in terms of slices quality, success rate of recordings and cellular health. We added this sentence in the methods.

Were BCshort and BCbroad observed within the same slice, same animals? This information is important to exclude the possibility of experimental origin of the distint AP width.

We have indeed found both type of BCs in the same animal, and often in the same slice.

Reviewer #3 (Recommendations For The Authors):

(1) The introduction is rather diffuse but should be more focused on Syngap1, cellular mechanisms and interneurons. For example, the authors do not even define what Syngap1 is.

We thank the reviewer for this very helpful suggestion. We have changed the introduction as suggested.

(2) Some of the figures appear very busy with small fonts that are difficult to read. Also, it is very hard to appreciate the individual datapoints in the blue bars. Could a lighter color please be used?

We thank the reviewer for this helpful suggestion. We made the suggested changes.

(3)     The strength/limit of using a conditional knockout should be discussed.

Done as suggested, in the revised Discussion.

(4) Statistical Methods should be described more in depth and probably some references should be added. Also, do (apparent?) inconsistencies between the text and the figures depend on the analysis used? For example, neither Fig 1g nor Fig 3f (eNMDA) reach significance despite large differences in the illustration. Maybe the authors could acknowledge this trend and discuss potential reasons for not reaching significance. Also, the legend to Fig 9 indicates the presence of "a significant decrease in AP half-width from cHet in absence or presence of a-DTX", but the bar graph does not show that.

The interpretation of the data is based on the results of the LMM analysis, which takes in account both the number of cells and the numbers of mice from which these cells are recorded. We chose this statistical approach because it does not rely on the assumption that cells recorded from same mouse are independent variables. We further provided detailed information about statistical analysis done in the tables associated to each figure where we show both LMM and the most commonly used Mann Whitney (for not normally distributed) or t-test (for normally distributed), for each data set.  As suggested, we added reference about LMM in Methods section.

(5) Were overall control and mutant mice of the same average postnatal age? Is there a reason for the use of very young animals? Was any measured parameter correlated with age?

Control and mutant mice were of the same postnatal age. In particular, the age range was 75.5 ± 1.8 postnatal days for control group and 72.1 ± 1.7 postnatal days in cHet group (mean ± S.E.M.). We did not use any young mice. We have added this information in the methods.

(6) Figure 6. First, was the dendritic arborization of all cells fully intact? Second, if Figure 7 uses the same data of Figure 5 after a reclassification of PV+ cells into the two defined subpopulations, then Figure 5 should probably be eliminated as redundant. Also, if the observed changes impact predominantly one PV+ subpopulation, maybe one could argue that the synaptic changes could be (at least partially) explained by the more limited dendritic surface of BC-short (higher proportion in mutant animals) rather than only cellular mechanisms.

All the reconstructions used for dendritic analysis contained intact cells with no evidently cut dendrites. We added this information in the methods section.

Regarding Figure 5 we recognize the reviewer’s point of view; however, we think both figures are informative. In particular, Figure 5 shows the full data set, avoiding assumptions on the different PV cells subtype classification, and can be more readily compared with several previously published studies.

We apologize for our lack of clarity, which may have led to a misunderstanding. In Figure 6i our data show that BC-short from cHet mice have a larger dendritic surface and a higher number of branching points compared to BC-short from control mice. 

(7) I am rather surprised by the AP threshold of ~-20/-15 mV observed in the datapoints of some figures. Did the authors use capacitance neutralization for their current-clamp recordings? What was the sampling rate used? Some of the phase plots (Vm vs dV/dT) suggests that it may have been too low.

See responses to public review.

(8) Please add the values of the series resistance of the recordings and a comparison between control and mutant animals.

As suggested, we re-examined the series resistance values (Rs), comparing Rs between groups and found no difference for Rs in eAMPA (Control mice: 13.2±0.5,  n=16 cells from 7 mice; cHet mice: 13.7±0.3, n=14 cells from 7 mice; LMM, p=0.432) and eNMDA (Control mice: 12.7±0.7, n=6 cells from 3 mice; cHet mice: 13.8±0.7, n=6 cells from 5 mice;  LMM, p=0.231).

(9) I am unclear as to how the authors quantified colocalization between VGluts and PSD95 at the low magnification shown in Supplementary Figure 2. Could they please show images at higher magnification?

Quantification was done on high resolution images. Immunostained sections were imaged using a Leica SP8-STED confocal microscope, with an oil immersion 63x (NA 1.4) at 1024 X 1024, zoom=1, z-step =0.3 μm, stack size of ~15 μm. As suggested by the reviewer, we changed the figure by including images at higher magnification.

(10) The authors claim that "cHet SST+ cells showed no significant changes in active and passive membrane properties", but this claim would seem to be directly refused by the data of Fig 8f. In the absence of changes in either active or passive membrane properties shouldn't the current/#AP plot remain unchanged?

The reduction in intrinsic excitability observed in SST+ cells from cHet mice could be due to intrinsic factors not assessed in this study. However, exploring these mechanisms is beyond the scope of our current investigation. We rephrased the discussion and added this limitation of our study in the revised version.

(11) Please check references as some are missing from the list.

Thank you for noticing this issue, which is now corrected.

References  

Bengtsson Gonzales C, Hunt S, Munoz-Manchado AB, McBain CJ, Hjerling-Leffler J (2020) Intrinsic electrophysiological properties predict variability in morphology and connectivity among striatal Parvalbumin-expressing Pthlh-cells Scientific Reports 10:15680 https://doi.org/10.1038/s41598-020-72588-1

Blundon JA, Bayazitov IT, Zakharenko SS (2011) Presynaptic gating of postsynaptically expressed plasticity at mature thalamocortical synapses The Journal of Neuroscience 31:1601225 https://doi.org/10.1523/JNEUROSCI.3281-11.2011

Chun S, Bayazitov IT, Blundon JA, Zakharenko SS (2013) Thalamocortical long-term potentiation becomes gated after the early critical period in the auditory cortex The journal of Neuroscience 33:7345-57 https://doi.org/10.1523/JNEUROSCI.4500-12.2013.

Ekins TG, Mahadevan V, Zhang Y, D’Amour JA, Akgül G, Petros TJ, McBain CJ (2020) Emergence of non-canonical parvalbumin-containing interneurons in hippocampus of a murine model of type I lissencephaly eLife 9:e62373 https://doi.org/10.7554/eLife.62373

Helm J, Akgul G, Wollmuth LP (2013) Subgroups of parvalbumin-expressing interneurons in layers 2/3 of the visual cortex Journal of Neurophysiology 109:1600–1613 https://doi.org/10.1152/jn.00782.2012

Kavalali E (2015) The mechanisms and functions of spontaneous neurotransmitter release Nature Reviews Neuroscience 16:5–16 https://doi.org/10.1038/nrn3875

Krause BM, Raz A, Uhlrich DJ, Smith PH, Banks MI (2014) Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity Frontiers in Systemic Neuroscience 8:170. https://doi.org/10.3389/fnsys.2014.00170

Murtagh F, Heck A (1987) Multivariate Data Analysis. Dordrecht, The Netherlands: Kluwer Academic.

Nassar M, Simonnet J, Lofredi R, Cohen I, Savary E, Yanagawa Y, Miles R, Fricker D (2015) Diversity and overlap of Parvalbumin and Somatostatin expressing interneurons in mouse presubiculum Frontiers in Neural Circuits 9:20. https://doi.org/10.3389/fncir.2015.00020

Ramirez DM, Kavalali ET (2011) Differential regulation of spontaneous and evoked neurotransmitter release at central synapses Current Opinion in Neurobiology 21:275-282 https://doi.org/10.1016/j.conb.2011.01.007

Richardson RJ, Blundon JA, Bayazitov IT, Zakharenko SS (2009) Connectivity patterns revealed by mapping of active inputs on dendrites of thalamorecipient neurons in the auditory cortex. The Journal of Neuroscience 29:6406-17 https://doi.org/10.1523/JNEUROSCI.3028-09.2009

Sara Y, Virmani T, Deák F, Liu X, Kavalali ET (2005) An isolated pool of vesicles recycles at rest and drives spontaneous neurotransmission Neuron 45:563-573 https://doi.org/10.1016/j.neuron.2004.12.056

Sara Y, Bal M, Adachi M, Monteggia LM, Kavalali ET (2011) Use-dependent AMPA receptor block reveals segregation of spontaneous and evoked glutamatergic neurotransmission Journal of Neuroscience 14:5378-5382 https://doi.org/10.1523/JNEUROSCI.5234-10.2011

Author response:

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

Reviewer #1 (Public Review):

In this study, the authors examined the role of IBTK, a substrate-binding adaptor of the CRL3 ubiquitin ligase complex, in modulating the activity of the eiF4F translation initiation complex. They find that IBTK mediates the non-degradative ubiquitination of eiF4A1, promotes cap-dependent translational initiation, nascent protein synthesis, oncogene expression, and tumor cell growth. Correspondingly, phosphorylation of IBTK by mTORC1/ S6K1 increases eIF4A1 ubiquitination and sustains oncogenic translation.

Strengths:

This study utilizes multiple biochemical, proteomic, functional, and cell biology assays to substantiate their results. Importantly, the work nominates IBTK as a unique substrate of mTORC1, and further validates eiF4A1 (a crucial subunit of the ei44F complex) as a promising therapeutic target in cancer. Since IBTK interacts broadly with multiple members of the translational initial complex - it will be interesting to examine its role in eiF2alpha-mediated ER stress as well as eiF3-mediated translation. Additionally, since IBTK exerts pro-survival effects in multiple cell types, it will be of relevance to characterize the role of IBTK in mediating increased mTORC1 mediated translation in other tumor types, thus potentially impacting their treatment with eiF4F inhibitors.

Limitations/Weaknesses:

The findings are mostly well supported by data, but some areas need clarification and could potentially be enhanced with further experiments:

(1) Since eiF4A1 appears to function downstream of IBTK1, can the effects of IBTK1 KO/KD in reducing puromycin incorporation (in Fig 3A), cap-dependent luciferase reporter activity (Fig 3G), reduced oncogene expression (Fig 4A) or 2D growth/ invasion assays (Fig 4) be overcome or bypassed by overexpressing eiF4A1? These could potentially be tested in future studies.

We appreciate the reviewer for bringing up this crucial point. As per the reviewer's suggestion, we conducted experiments where we overexpressed Myc-eIF4A1 in IBTK-KO SiHa cells. Our findings indicate that increasing levels of eIF4A1 through ectopic overexpression is unable to reverse the decrease in puromycin incorporation (Fig. S3C) and protein expression of eIF4A1 targets caused by IBTK ablation (Fig. S4E). These results clearly demonstrate that IBTK ablation-induced eIF4A1 dysfunctions cannot be rescued by simply elevating eIF4A1 protein levels. Given the above results are negative, the impacts of eIF4A1 overexpression on the 2D growth/invasion capacities of IBTK-KO cells were not further examined. We sincerely appreciate the reviewer's understanding regarding this matter.

(2) The decrease in nascent protein synthesis in puromycin incorporation assays in Figure 3A suggest that the effects of IBTK KO are comparable to and additive with silvesterol. It would be of interest to examine whether silvesterol decreases nascent protein synthesis or increases stress granules in the IBTK KO cells stably expressing IBTK as well.

We appreciate the reviewer for bringing up this crucial point. We have showed that silvestrol treatment still decreased nascent protein synthesis in IBTK-KO cells overexpressing FLAG-IBTK as well (Fig. S3B).

(3) The data presented in Figure 5 regarding the role of mTORC1 in IBTK- mediated eiF4A1 ubiquitination needs further clarification on several points:

• It is not clear if the experiments in Figure 5F with Phos-tag gels are using the FLAG-IBTK deletion mutant or the peptide containing the mTOR sites as it is mentioned on line 517, page 19 "To do so, we generated an IBTK deletion mutant (900-1150 aa) spanning the potential mTORC1-regulated phosphorylation sites" This needs further clarification.

We appreciate the reviewer for bringing up this crucial point. The IBTK deletion mutant used in Fig. 5F is FLAG-IBTK900-1150aa. We have annotated it with smaller font size in the panel (red box) in Author response image 1.

Author response image 1.

• It may be of benefit to repeat the Phos tag experiments with full-length FLAG- IBTK and/or endogenous IBTK with molecular weight markers indicating the size of migrated bands.

We appreciate the reviewer for bringing up this crucial point. We attempted to perform Phos-tag assays to detect the overexpressed full-length FLAG-IBTK or endogenous IBTK. However, we encountered difficulties in successfully transferring the full-length FLAG-IBTK or endogenous IBTK onto the nitrocellulose membrane during Phos-tag WB analysis. This is likely due to the limitations of this technique. Based on our experience, phos-tag gel is less efficient in detecting protein motility shifts with large molecular weights. As the molecular weight of IBTK protein is approximately 160 kDa, it falls within this category. Considering these technical constraints, we did not include Phos-tag assay results for full-length IBTK in our study. We sincerely appreciate the reviewer's understanding regarding this matter.

The binding of Phos-tag to phosphorylated proteins induces a mobility shift during gel electrophoresis or protein separation techniques. This shift allows for the visualization and quantification of phosphorylated proteins separately from non-phosphorylated proteins. It's important to note that these mobility shifts indicate phosphorylation status, rather than actual molecular weights. pre- stained protein markers are typically used as a reference to assess the efficiency of protein transfer onto the membrane [Ref: 1]. Considering the aforementioned reasons, we did not add molecular weights to the WB images.

Reference [1]. FUJIFILM Wako Pure Chemical Corporation, https://www.wako- chemicals.de/media/pdf/c7/5e/20/FUJIFILM-Wako_Phos-tag-R.pdf

• Additionally, torin or Lambda phosphatase treatment may be used to confirm the specificity of the band in separate experiments.

We appreciate the reviewer for bringing up this crucial point. Torin1 is a synthetic mTOR inhibitor by preventing the binding of ATP to mTOR, leading to the inactivation of both mTORC1 and mTORC2, whereas rapamycin primarily targets mTORC1 activity and may inhibit mTORC2 in certain cell types after a prolonged treatment. We have identified that the predominant mediator of IBTK phosphorylation is the mTORC1/S6K1 complex. Therefore, in this context, we think that rapamycin is sufficient to inactivate the mTORC1/S6K1 pathway. As shown in Fig. 5F, the phosphorylated IBTK900-1150aa was markedly decreased while the non-phosphorylated form was simultaneously increased in rapamycin- treated cells. As per the reviewer's suggestion, we treated FLAG-IBTK900-1150aa overexpressed cells with lambda phosphatase. As shown in Fig. 5G, lambda phosphatase treatment completely abolished the mobility shifts of phosphorylated FLAG-IBTK900-1150aa. Additionally, the lowest band displayed an abundant accumulation of the non-phosphorylated form of FLAG-IBTK900-1150aa. These findings confirm that the mobility shifts observed in WB analysis correspond to the phosphorylated forms of FLAG-IBTK900-1150aa.

• Phos-tag gels with the IBTK CRISPR KO line would also help confirm that the non-phosphorylated band is indeed IBTK.

We appreciate the reviewer for bringing up this crucial point. As we state above, we performed Phos-tag assays to detect the mobility shifts of phosphorylated FLAG-IBTK900-1150aa. Anti-FLAG antibody, but not the anti-IBTK antibody was used for WB detection. This antibody does not exhibit cross-reactivity with endogenous IBTK.

• It is unclear why the lower, phosphorylated bands seem to be increasing (rather than decreasing) with AA starvation/ Rapa in Fig 5H.

We appreciate the reviewer for bringing up this crucial point. We think the panel the reviewer mentioned is Fig. 5F. According to the principle of Phos-tag assays, proteins with higher phosphorylation levels have slower migration rates on SDS-PAGE, while proteins with lower phosphorylation levels have faster migration rates.

As shown in Author response image 2, the green box indicates the most phosphorylated forms of FLAG-IBTK900-1150aa, the red box indicates the moderately phosphorylated forms of FLAG-IBTK900-1150aa, and the yellow box indicates the non-phosphorylated forms of FLAG-IBTK900-1150aa. AA starvation or Rapamycin treatment reduced the hyperphosphorylated forms of FLAG-IBTK900-1150aa (green box), while simultaneously increasing the hypophosphorylated (red box) and non- phosphorylated (yellow box) forms of FLAG-IBTK900-1150aa. Thus, we conclude that AA starvation or Rapamycin treatment leads to a marked decrease in the phosphorylation levels of FLAG-IBTK900-1150aa.

Author response image 2.

Reviewer #2 (Public Review):

Summary:

This study by Sun et al. identifies a novel role for IBTK in promoting cancer protein translation, through regulation of the translational helicase eIF4A1. Using a multifaceted approach, the authors demonstrate that IBTK interacts with and ubiquitinates eIF4A1 in a non-degradative manner, enhancing its activation downstream of mTORC1/S6K1 signaling. This represents a significant advance in elucidating the complex layers of dysregulated translational control in cancer.

Strengths:

A major strength of this work is the convincing biochemical evidence for a direct regulatory relationship between IBTK and eIF4A1. The authors utilize affinity purification and proximity labeling methods to comprehensively map the IBTK interactome, identifying eIF4A1 as a top hit. Importantly, they validate this interaction and the specificity for eIF4A1 over other eIF4 isoforms by co- immunoprecipitation in multiple cell lines. Building on this, they demonstrate that IBTK catalyzes non-degradative ubiquitination of eIF4A1 both in cells and in vitro through the E3 ligase activity of the CRL3-IBTK complex. Mapping IBTK phosphorylation sites and showing mTORC1/S6K1-dependent regulation provides mechanistic insight. The reduction in global translation and eIF4A1- dependent oncoproteins upon IBTK loss, along with clinical data linking IBTK to poor prognosis, support the functional importance.

Weaknesses:

While these data compellingly establish IBTK as a binding partner and modifier of eIF4A1, a remaining weakness is the lack of direct measurements showing IBTK regulates eIF4A1 helicase activity and translation of target mRNAs. While the effects of IBTK knockout/overexpression on bulk protein synthesis are shown, the expression of multiple eIF4A1 target oncogenes remains unchanged.

Summary:

Overall, this study significantly advances our understanding of how aberrant mTORC1/S6K1 signaling promotes cancer pathogenic translation via IBTK and eIF4A1. The proteomic, biochemical, and phosphorylation mapping approaches established here provide a blueprint for interrogating IBTK function. These data should galvanize future efforts to target the mTORC1/S6K1-IBTK-eIF4A1 axis as an avenue for cancer therapy, particularly in combination with eIF4A inhibitors.

Reviewer #1 (Recommendations For The Authors):

(1) Certain references should be provided for clarity. For e.g.,: Page 15, line 418 " The C-terminal glycine glycine (GG) amino acid residues are essential for Ub conjugation to targeted proteins".

We appreciate the reviewer for bringing up this crucial point. We have taken two fundamental review papers (PMID: 22524316, 9759494) on the ubiquitin system as references in this sentence.

(2) Please describe the properties of the ΔBTB mutant on page 15 when first describing it. What motifs does it lack and has it been described before in functional studies?

We appreciate the reviewer for bringing up this crucial point. We added a sentence to describe the properties of the ΔBTB mutant. This mutant lacks the BTB1 and BTB2 domains (deletion of aa 554–871), which have been previously demonstrated to be essential for binding to CUL3. The original reference has been added to the revised manuscript.

(3) In Figure 2G how do the authors explain the fact that co-expression of the Ub K-ALLR mutant, which is unable to form polyubiquitin chains, formed only a moderate reduction in IBTK-mediated eIF4A1 ubiquitination?

We appreciate the reviewer for bringing up this crucial point. The Ub K-ALLR mutant can indeed conjugate to substrate proteins, but it cannot form chains due to its absence of lysine residues, resulting in mono-ubiquitination. Multi- mono-ubiquitination refers to the attachment of single ubiquitin molecules to multiple lysine residues on a substrate protein. It's worth noting that a poly- ubiquitinated protein and a multi-mono-ubiquitinated protein appear strikingly similar in Western blot. Our findings demonstrated that the co-expression of the Ub K-ALL-R mutant resulted in only a modest reduction in IBTK-mediated eIF4A1 ubiquitination (Fig. 2G), and that eIF4A1 was ubiquitinated at twelve lysine residues when co-expressed with IBTK (Fig. S2F). As such, we conclude that the CRL3IBTK complex primarily catalyzes multi-mono-ubiquitination on eIF4A1. .

(4) In Figure 5, The identity of the seven sites in the IBTK 7ST A mutants should be specified.

We appreciate the reviewer for bringing up this crucial point. We have specified the seven mutation sites in the IBTK-7ST A mutant (Fig. 6A).

(5) In Figure 5, the rationale for generating antibodies only to S990/992/993, as opposed to the other mTORC1/S6K motifs should be specified.

We appreciate the reviewer for bringing up this crucial point. Upon demonstrating that IBTK can be phosphorylated—with evidence from positive Phos-tag and in vitro phosphorylation assays—we sought to directly detect changes in the phosphorylation levels using an antibody specific to IBTK phosphorylation. However, the expense of generating seven phosphorylation- specific antibodies for each site is significant. Recognizing that S990/992/993 are three adjacent sites, we deemed it appropriate to generate a single antibody to recognize the phospho-S990/992/993 epitope. Moreover, out of the seven phosphorylation sites, S992 perfectly matches the consensus motif for S6K1 phosphorylation (RXRXXS). Utilizing this antibody allowed us to observe a substantial decrease in the phosphorylation levels of these three adjacent Ser residues in IBTK following either AA deprivation or Rapamycin treatment (Fig. 5L). We have specified these points in the manuscript.

Reviewer #2 (Recommendations For The Authors):

The following suggestions would strengthen the study:

(1) Directly examine the effects of IBTK modulation (knockdown/knockout/ overexpression) on eIF4A1 helicase activity.

We appreciate the reviewer for bringing up this crucial point. We agree with the reviewer's suggestion that evaluating IBTK's influence on eIF4A1 helicase activity directly would enhance the strength of our conclusion. However, the current eIF4A1 helicase assays, as described in previous publications [Ref: 1, 2], can only be conducted using in vitro purified recombinant proteins. For instance, it is feasible to assess the varying levels of helicase activity exhibited by recombinant wild-type or mutant EIF4A1 proteins [Ref: 2]. Importantly, there is currently no reported methodology for evaluating the helicase activity of EIF4A1 in vivo, as mentioned by the reviewer in gene knockdown, knockout, or overexpression cellular contexts. Therefore, we have not performed these assays and we sincerely appreciate the reviewer's understanding in this regard. We sincerely appreciate the reviewer's understanding regarding this matter.

Reference:

[1] Chu J, Galicia-Vázquez G, Cencic R, Mills JR, Katigbak A, Porco JA, Pelletier J. CRISPR-mediated drug-target validation reveals selective pharmacological inhibition of the RNA helicase, eIF4A. Cell reports. 2016 Jun 14;15(11):2340-7.

[2] Chu J, Galicia-Vázquez G, Cencic R, Mills JR, Katigbak A, Porco JA, Pelletier J. CRISPR-mediated drug-target validation reveals selective pharmacological inhibition of the RNA helicase, eIF4A. Cell reports. 2016 Jun 14;15(11):2340-7.

(2) Justify why the expression of some but not all eIF4A1 target oncogenes is affected in IBTK-depleted/overexpressing cells. This is important if IBTK should be considered as a therapeutic target. The authors should consider which of the eIF4A1 targets are most impacted by IBTK KO. This would provide a more focused therapeutic approach in the future.

We appreciate the reviewer for bringing up this crucial point. As the reviewer has pointed out, we assessed the protein levels of ten reported eIF4A1 target genes across three cancer cell lines (Fig.4, Fig. S4A, C）. We observed that IBTK depletion led to a substantial reduction in the protein levels of most eIF4A1- regulated oncogenes upon IBTK depletion, although there were some exceptions. For instance, IBTK KO in H1299 cells exerted minimal influence on the protein levels of ROCK1 (Fig. S4A). Several possible explanations might account for this observation: firstly, given that our list of eIF4A1 target genes collected from previous studies conducted using distinct cell lines, it is not unexpected for different lines to exhibit subtle differences in regulation of eIF4A1 target genes. Secondly, as a CRL3 adaptor, IBTK potentially performs other biological functions via ubiquitination of specific substrates; dysregulation of these could buffer the impact of IBTK KO on the protein expression of some eIF4A1 target genes. We added these comments to the Discussion section of the revised manuscript.

(3) Expand mTOR manipulation experiments (inhibition, Raptor knockout, activation) and evaluate impacts on IBTK phosphorylation, eIF4A1 ubiquitination, and translation.

The mTORC1 signaling pathway is constitutively active under normal culture conditions. In order to inhibit mTORC1 activation, we employed several approaches including AA starvation, Rapamycin treatment, or Raptor knockout. Our results have demonstrated that both AA starvation and rapamycin treatment led to a reduction in eIF4A1 ubiquitination (Fig. 5M). Moreover, we have included new findings in the revised manuscript, which highlight that Raptor knockout specifically decreases eIF4A1 ubiquitination (Fig. 5N). It is worth mentioning that the impacts of mTOR inhibition or activation on protein translation have been extensively investigated and documented in numerous studies. Therefore, in our study, we did not feel it necessary to examine these treatments further.

(4) Although not absolutely necessary, it would be nice to see if some of these findings are true in other cancer cell types.

We appreciate the reviewer for bringing up this crucial point. We concur with the reviewer's suggestion that including data from other cancer cell types would enhance the strength of our conclusion. While the majority of our data is derived from two cervical cancer cell lines, we have corroborated certain key findings— such as the impact of IBTK on eIF4A1 and its target gene expression—in H1299 cells (human lung cancer) (Fig. 2C, Fig. S4A, B) and in CT26 cells (murine colon adenocarcinoma) (Fig. S4C, D). Additionally, we demonstrated that IBTK promotes IFN-γ-induced PD-L1 expression and tumor immune escape in both the H1299 and CT26 cells (Fig. S6A-K).

Author Response:

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

General response

(1) Evaluation of mitochondrial activity in mox-YG overexpression cells

To determine whether the observed “mitochondrial development” seen in transcriptomic, proteomic, and microscopic analyses corresponds to an actual phenotypic shift toward respiration, we measured oxygen consumption in mox-YG overexpression cells. The results showed that oxygen consumption rates were indeed elevated in these cells, suggesting a metabolic shift from fermentation toward respiration. These findings have been incorporated into the revised manuscript as new Figure 4E and Figure 4—figure supplement 9, along with the corresponding descriptions in the Results section.

(2) Evaluation of TORC1 Pathway Inactivation in mox-YG Overexpression Cells

While the proteomic response in mox-YG overexpression cells overlapped with known responses to TORC1 pathway inactivation, we had not obtained direct evidence that TORC1 activity was indeed reduced. To address this, we assessed TORC1 activity by testing the effect of rapamycin, a TORC1 inhibitor, and by attempting to detect the phosphorylation state of known TORC1 targets. Our results showed that mox-YG overexpressing cells exhibited reduced sensitivity to rapamycin compared to vector control cells, supporting the idea that TORC1 is already inactivated in the mox-YG overexpression condition.

In parallel, we attempted to detect phosphorylation of TORC1 targets Sch9 and Atg13 by Western blotting. Specifically, we tested several approaches: detecting phospho-Sch9 using a phospho-specific antibody, assessing the band shift of HA-tagged Sch9, and monitoring Atg13 band shift using an anti-Atg13 antibody. While we were unable to detect Sch9 phosphorylation, likely due to technical limitations, we finally succeeded in detecting Atg13 with the help of our new co-author, Dr. Kamada. However, we observed a marked reduction in Atg13 protein levels in mox-YG overexpression cells, making it difficult to interpret the biological significance of any apparent decrease in phosphorylation. Therefore, we decided not to pursue further experiments on TORC1 phosphorylation within the current revision period.

These findings have been summarized in new Figure 4—figure supplement 7, and the relevant description has been added to the Results section.

(3) Phenotypes of Gpm1-CCmut

We focused our initial analysis on the phenotypes of cells overexpressing mox-YG, the protein with the lowest Neutrality Index (NI) in our dataset, as a model of protein burden. However, it remained unclear to what extent the phenotypes observed in mox-YG overexpression cells are generalizable to protein burden as a whole. We agree with the reviewers’ suggestion that it is important to examine whether similar phenotypes are also observed in cells overexpressing Gpm1-CCmut, which was newly identified in this study as having a similarly low NI. We therefore performed validation experiments using Gpm1-CCmut overexpression cells to assess whether they exhibit the characteristic phenotypes observed in mox-YG overexpression cells. These phenotypes included: transcriptional responses, mitochondrial development, metabolic shift toward respiration, and nucleolar shrinkage.

As a result, mitochondrial development and nucleolar shrinkage were also observed in Gpm1-CCmut overexpression cells, consistent with mox-YG. In contrast, the transcriptional response associated with amino acid starvation and the metabolic shift toward respiration were not observed. Furthermore, an abnormal rounding of cell morphology—absent in mox-YG overexpression cells—was uniquely observed in Gpm1-CCmut cells. These results suggest that the phenotypes observed under mox-YG overexpression may comprise both general effects of protein burden and effects specific to the mox-YG protein. Alternatively, it is possible that Gpm1-CCmut imposes a different kind of constraint or toxicity not shared with mox-YG. In any case, these findings highlight that the full range of phenotypes associated with protein burden cannot yet be clearly defined and underscore the need for future analyses using a variety of “non-toxic” proteins.

Given that these results form a coherent set, we have relocated original Figure 3—which previously presented the NI values of Gpm1 and Tdh3 in the original version—to new Figure 6, which now includes all related phenotypic analyses. Correspondingly, we have added new Figures 6—figure supplement 1 through 6—figure supplement 7. The associated results have been incorporated into the Results section, and we have expanded the Discussion to address this point

As a result of these revisions, the order of figures has changed from the original version. The correspondence between the original and revised versions is as follows:

original→ Revised

Figure 1 → Figure 1<br />  Figure 2 → Figure 2<br />  Figure 3 → Figure 6<br />  Figure 4 → Figure 3<br />  Figure 5 → Figure 4<br />  Figure 6 → Figure 5

Public Reviews:

Reviewer #1 (Public Review):

Weaknesses:

While the introduction of the neutrality index seems useful to differentiate between cytotoxicity and protein burden, the biological relevance of the effects of overexpression of the model proteins is unclear.

Thank you for your comment. This point is in fact the core message we wished to convey in this study. We believe that every protein possesses some degree of what can be described as “cytotoxicity,” and that this should be defined by the expression limit—specifically, the threshold level at which growth inhibition occurs. This index corresponds to what we term the neutrality index. We further argue that protein cytotoxicity arises from a variety of constraints inherent to each protein. These constraints act in a stepwise manner to determine the expression limit (i.e., the neutrality) of a given protein (Figure 1A). To demonstrate the real existence of such constraints, there are two complementary approaches: an inductive one that involves large-scale, systematic investigation of naturally occurring proteins, and a deductive one that tests hypotheses using selected model proteins. Our current study follows the latter approach. In addition, we define protein burden as a phenomenon that can only be elicited by proteins that are ultimately harmless (Figure 1B). We assume that such burden results in a shared physiological state, such as depletion of cellular resources. Through continued efforts to identify a protein suitable for investigating this phenomenon, we eventually arrived at mox-YG. As the reviewer rightly pointed out, examining only mox-YG does not reveal the full picture of protein burden. In fact, in response to the reviewer’s suggestion, we investigated the physiological consequences of overexpressing a mutant glycolytic protein, Gpm1-CCmut (General Response 3). We found that the resulting phenotype was notably different from that observed in cells overexpressing mox-YG. Going forward, we believe that our study provides a foundation for further systematic exploration of “harmless proteins” and the cellular impacts of their overexpression.

Reviewer #2 (Public Review):

Weaknesses:

The authors concluded from their RNA-seq and proteomics results that cells with excess mox-YG expression showed increased respiration and TORC1 inactivation. I think it will be more convincing if the authors can show some characterization of mitochondrial respiration/membrane potential and the TOR responses to further verify their -omic results.

These points are addressed in General Response 1 and 2.

In addition, the authors only investigated how overexpression of mox-YG affects cells. It would be interesting to see whether overexpressing other non-toxic proteins causes similar effects, or if there are protein-specific effects. It would be good if the authors could at least discuss this point considering the workload of doing another RNA-seq or mass-spectrum analysis might be too heavy.

These points are addressed in General Response 3.

Reviewer #3 (Public Review):

Weaknesses:

The data are generally convincing, however in order to back up the major claim of this work - that the observed changes are due to general protein burden and not to the specific protein or condition - a broader analysis of different conditions would be highly beneficial.

These points are addressed in General Response 3.

Major points:

(1) The authors identify several proteins with high neutrality scores but only analyze the effects of mox/mox-YG overexpression in depth. Hence, it remains unclear which molecular phenotypes they observe are general effects of protein burden or more specific effects of these specific proteins. To address this point, a proteome (and/or transcriptome) of at least a Gpm1-CCmut expressing strain should be obtained and compared to the mox-YG proteome. Ideally, this analysis should be done simultaneously on all strains to achieve a good comparability of samples, e.g. using TMT multiplexing (for a proteome) or multiplexed sequencing (for a transcriptome). If feasible, the more strains that can be included in this comparison, the more powerful this analysis will be and can be prioritized over depth of sequencing/proteome coverage.

This comment has been addressed in General Response 3. Gpm1-CCmut overexpression cells exhibited both phenotypes that were shared with, and distinct from, those observed in mox-YG overexpression cells. To define a unified set of phenotypes associated with "protein burden," we believe that extensive omics analyses targeting multiple "non-toxic" protein overexpression strains will be necessary. However, such an effort goes beyond the scope of the current study, and we would like to leave it as an important subject for future investigation.

(2) The genetic tug-of-war system is elegant but comes at the cost of requiring specific media conditions (synthetic minimal media lacking uracil and leucine), which could be a potential confound, given that metabolic rewiring, and especially nitrogen starvation are among the observed phenotypes. I wonder if some of the changes might be specific to these conditions. The authors should corroborate their findings under different conditions. Ideally, this would be done using an orthogonal expression system that does not rely on auxotrophy (e.g. using antibiotic resistance instead) and can be used in rich, complex mediums like YPD. Minimally, using different conditions (media with excess or more limited nitrogen source, amino acids, different carbon source, etc.) would be useful to test the robustness of the findings towards changes in media composition.

We appreciate the reviewer’s clear understanding of both the advantages and limitations of the gTOW system. As rightly pointed out, since our system relies on leucine depletion, it is essential to carefully consider the potential impact this may have on cellular metabolism. Another limitation—though it also serves as one of the strengths—of the gTOW system is its reliance on copy number variation to achieve protein overexpression. This feature limits the possibility of observing rapid responses, as immediate induction is not feasible. To address this issue, we have recently developed a strong and inducible promoter that minimizes effects on other metabolic systems (Higuchi et al., 2024), and we believe this tool will be essential in future experiments.

In response to the reviewer’s comments, we conducted two additional sets of experiments. First, we established a new overexpression system in nutrient-rich conditions (YPD medium) that is conceptually similar to gTOW but uses aureobasidin A and the AUR1d resistance gene to promote gene amplification (new Figure 4—figure supplement 2). Using this system, we observed that non-fluorescent YG mutants led to increased expression of mox. Total protein levels appeared to rise correspondingly, suggesting that the overall synthetic capacity of cells might be higher in YPD compared to SC medium. However, the degree of overexpression achieved in this system was insufficient to strongly inhibit growth, meaning we could not replicate the stress conditions observed with the original gTOW system. Further studies will be needed to determine whether stronger induction under these nutrient-rich conditions will yield comparable responses.

Second, we performed a control experiment to examine whether the amino acid starvation response observed in mox-YG overexpressing cells could be attributed to leucine depletion from the medium (new Figure 3—figure supplement 3). By titrating leucine concentrations in SC medium, we confirmed that lower leucine levels reduced the growth rate of vector control cells, indicating leucine limitation. However, GAP1 induction was not observed under these conditions. In contrast, mox-YG overexpression led to strong GAP1 induction under similar growth-inhibitory conditions, suggesting that the amino acid starvation response is not simply due to environmental leucine depletion, but rather a consequence of the cellular burden imposed by mox-YG overexpression.

These findings have been incorporated into the manuscript, along with the corresponding figures (new Figure 4—figure supplement 2, Figure 3—figure supplement 3), and relevant descriptions have been added to the Results and Discussion sections.

(3) The authors suggest that the TORC1 pathway is involved in regulating some of the changes they observed. This is likely true, but it would be great if the hypothesis could be directly tested using an established TORC1 assay.

This comment has been addressed in General Response 2. We assessed the rapamycin sensitivity of mox-YG overexpression cells—which was found to be reduced—and attempted to detect phosphorylation of the TORC1 target Atg13, although the latter was only partially successful. These findings have been incorporated into the Results section.

(4) The finding that the nucleolus appears to be virtually missing in mox-YG-expressing cells (Figure 6B) is surprising and interesting. The authors suggest possible mechanisms to explain this and partially rescue the phenotype by a reduction-of-function mutation in an exosome subunit. I wonder if this is specific to the mox-YG protein or a general protein burden effect, which the experiments suggested in point 1 should address. Additionally, could a mox-YG variant with a nuclear export signal be expressed that stays exclusively in the cytosol to rule out that mox-YG itself interferes with phase separation in the nucleus?

As also described in our General Response 3, we observed nucleolar shrinkage upon Gpm1-CCmut overexpression as well (new Figure 6E and 6—figure supplement 7), suggesting that this phenomenon may represent a general feature of protein burden. The reviewer’s suggestion to test whether this effect persists when mox-YG is excluded from the nucleus is indeed intriguing. However, based on our previous work, we have shown that overexpression of NES-tagged proteins (e.g., NES-EGFP) causes severe growth inhibition due to depletion of nuclear export factors (Kintaka et al., 2020). Unfortunately, this technical limitation makes it difficult for us to carry out the proposed experiment as suggested.

Minor points:

(5) It would be great if the authors could directly compare the changes they observed at the transcriptome and proteome levels. This can help distinguish between changes that are transcriptionally regulated versus more downstream processes (like protein degradation, as proposed for ribosome components).

We also considered this point to be important, and therefore compared the transcriptomic and proteomic changes associated with mox-YG overexpression. However, somewhat unexpectedly, we found little correlation between these two layers of response. As shown in new Figure 3 and 4 (original Figures 4 and 5), while genes related to oxidative phosphorylation were consistently upregulated at both the mRNA and protein levels in mox-YG overexpressing cells, ribosomal proteins showed a discordant pattern: their mRNA levels were significantly increased, whereas their protein levels were significantly decreased.

Several factors may explain this discrepancy: (1) differences in analytical methods between transcriptomics and proteomics; (2) temporal mismatches arising from the dynamic changes in mRNA and protein expression during batch culture; and (3) the possibility that, under protein burden conditions, specific regulatory mechanisms may govern the selective translation or targeted degradation of certain proteins. However, at this point, we were unable to clearly determine which of these factors account for the observed differences.

For this reason, we did not originally include a global transcriptome–proteome comparison in the manuscript. In response to the reviewer’s comment, however, we have now included the comparison data (new Figure 4—figure supplement 3D).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Major points:

(1) While the study provides a detailed description of physiological changes, the underlying mechanisms remain speculative. For example, the exact reasons for nitrogen source depletion or increased respiration are unclear. The transcriptomic and proteomic data should be complemented by basic growth assay tests on rapamycin or glycerol to strengthen these observations.

This comment has been addressed in General Responses 1 and 2. We conducted oxygen consumption assays and growth assays in the presence of rapamycin, and incorporated these results into the revised version of the manuscript.

We also performed culture experiments using glycerol as a carbon source. However, both the vector control and mox-YG overexpression cells showed extremely poor growth. Although there was a slight difference between the two, we judged that it would be difficult to draw any meaningful conclusions from these results. Therefore, we have chosen not to include them in the main text (the data are attached below for reference).

Author response image 1.

(2) The study mainly focuses on two proteins, mox-YG/ FP proteins and Gpm1-CCmut. Did the authors look also at a broader range of proteins with varying degrees of cytotoxicity to validate the neutrality index and generalize their findings? Such as known cytotoxic proteins.

In our calculation of the Neutrality Index (NI), we use two parameters: the maximum growth rate (expressed as %MGR relative to the control) and the protein expression level. For the latter, we measure the abundance of the overexpressed protein as a percentage of total cellular protein, based on the assumption that the protein is expressed at a sufficiently high level to be detectable by SDS-PAGE. In our view, proteins typically regarded as “cytotoxic” cannot be overexpressed to levels detectable by SDS-PAGE without the use of more sensitive techniques such as Western blotting. This limitation in expression itself is an indication of their high cytotoxicity. Consequently, for such proteins, NI is determined solely by the MGR value, and will inherently fall below 100.

To test whether this interpretation is valid, we re-evaluated a group of EGFP variants previously reported by us to exhibit higher cytotoxicity than EGFP (Kintaka et al., 2016), due to overloading of specific cellular transport pathways. These include EGFPs tagged with localization signals. At the time of the original study, we had not calculated their NI values. Upon re-analysis, we found that all of these localization-tagged EGFP variants indeed have NI values below 100.

This result has been included as a new Figure 2—figure supplement 3, and the relevant descriptions have been added to the Results section.

(3) The partial rescue of ribosomal biosynthesis defects by a mutation in the nuclear exosome is intriguing but not fully explored. The specific role of the nuclear exosome in managing protein burden remains unclear. This result could be supported by alternative experiments. For example, would tom1 deletion or proteasome inhibition (degradation of ribosomal proteins in the nucleus) partially rescue the nuclear formation?

As described in the main text, our interest in exosome mutants was prompted by our previous SGA (Synthetic Genetic Array) analysis, in which these mutants exhibited positive genetic interactions with GFP overexpression—namely, they acted in a rescuing manner (Kintaka et al., 2020). In contrast, proteasome mutants did not show such positive interactions in the same screening. On the contrary, proteasome mutants that displayed negative genetic interactions have been identified, such as the pre7ts mutant. Furthermore, the proteasome is involved in various aspects of proteostasis beyond just orphan ribosomal proteins, making the interpretation of its effects potentially quite complex.

Regarding the TOM1 mutant raised by the reviewer, we attempted to observe nucleolar morphology using the NSR1-mScarlet-I marker in the tom1Δ deletion strain. However, we were unsuccessful in constructing the strain. This failure may be due to the strong detrimental effects of this perturbation in the tom1Δ background. As we were unable to complete this experiment within the revision period, we would like to address this issue in future work.

Minor comments:

(1) It would be interesting to include long-term cellular and evolutionary responses to protein overexpression to understand how cells adapt to chronic protein burden.

Thank you for the suggestion. We are currently conducting experiments related to these points. However, as they fall outside the scope of the present study, we would like to refrain from including the data in this manuscript.

(2) The microscopy of Nsr1 in Figure 6G does not clearly demonstrate the restored formation of the nucleolus in the mrt4-1 mutant. Electron microscopy images would be a better demonstration.

The restoration of nucleolar size in the mtr4-1 mutant, as shown in Figure 5—figure supplement 5 (original Figure 6_S5), is statistically significant. However, as described in the main text, the degree of rescue by the mutation is partial, and, as the reviewer notes, not clearly distinguishable by eye. It becomes apparent only when analyzing a large number of cells, allowing for detection as a statistically significant difference. Given that electron microscopy images are inherently limited in the number of cells that can be analyzed and pose challenges for statistical evaluation, we believe it would be difficult to detect such a subtle difference using this method. Therefore, we respectfully ask for your understanding that we will not include additional EM experiments in this revision.

(3) On page 24, line 451 it says that of the 84 ribosomal proteins... latest reviews and structures described/ identified 79 ribosomal proteins in budding yeast of which the majority are incorporated into the pre-ribosomal particles in the nucleolus. We could not find this information in the provided reference. Please align with the literature.

Thank you for the comment. In S. cerevisiae, many ribosomal protein genes are duplicated due to gene duplication events, resulting in a total of 136 ribosomal proteins (http://ribosome.med.miyazaki-u.ac.jp/rpg.cgi?mode=genetable). However, not all of them are duplicated, and among the duplicated pairs, some can be distinguished by proteomic analysis based on differences in amino acid sequences, while others cannot. As a result, we report that 84 ribosomal proteins were “detected” in our proteomic analysis. To avoid confusion, we have added the following explanation to the legend of Figure 5—figure supplement 1 (original Figure 6_S1), as follows.

“Note that when the amino acid sequences of paralogs are identical, they cannot be distinguished by proteomic analysis, and the protein abundance of both members of the paralog pair is represented under the name of only one.”

Reviewer #2 (Recommendations for the authors):

(1) The authors mentioned that based on their proteomics results, overexpressing mox-YG appears to increase respiration. I think it is worth doing some quick verification, such as oxygen consumption experiments or mitochondrial membrane potential staining to provide some verification on that.

This comment has been addressed in General Response 1. We measured oxygen consumption in mox-YG overexpression cells and found that it was indeed elevated, suggesting a metabolic shift from fermentation toward aerobic respiration.

(2) Similar to point 1, the authors concluded from their proteomics data that the mox-YG overexpression induced responses that are similar to TORC1 inactivation. It might be worth testing whether there is any actual TORC1 inactivation, e.g. by detecting whether there is reduced Sch9 phosphorylation by western blot.

This comment has been addressed in General Response 2. We assessed the rapamycin sensitivity of mox-YG overexpression cells—which was found to be reduced—and attempted to detect phosphorylation of the TORC1 target Atg13, although the latter was only partially successful. These findings have been incorporated into the Results section.

(3) The authors showed that overexpressing excess mox-YG caused downregulated glycolysis pathways. It is worth discussing whether overexpressing glycolysis-related non-toxic proteins such as Gpm1-CCmut will also lead to similar results.

This comment has been addressed in General Response 3. Gpm1-CCmut overexpression cells exhibited both phenotypes shared with mox-YG overexpression and distinct ones. These findings suggest that a unified set of phenotypes associated with "protein burden" has yet to be clearly defined, and further investigation will be necessary to elucidate this.

Reviewer #3 (Recommendations for the authors):

(1) The authors identify several proteins with high neutrality scores but only analyze the effects of mox/mox-YG overexpression in depth. Hence, it remains unclear which molecular phenotypes they observe are general effects of protein burden or more specific effects of these specific proteins. To address this point, a proteome (and/or transcriptome) of at least a Gpm1-CCmut expressing strain should be obtained and compared to the mox-YG proteome. Ideally, this analysis should be done simultaneously on all strains to achieve a good comparability of samples, e.g. using TMT multiplexing (for a proteome) or multiplexed sequencing (for a transcriptome). If feasible, the more strains that can be included in this comparison, the more powerful this analysis will be and can be prioritized over depth of sequencing/proteome coverage.

This comment has been addressed in General Response 3. Gpm1-CCmut overexpression cells exhibited both phenotypes that were shared with, and distinct from, those observed in mox-YG overexpression cells. To define a unified set of phenotypes associated with "protein burden," we believe that extensive omics analyses targeting multiple "non-toxic" protein overexpression strains will be necessary. However, such an effort goes beyond the scope of the current study, and we would like to leave it as an important subject for future investigation.

(2) The genetic tug-of-war system is elegant but comes at the cost of requiring specific media conditions (synthetic minimal media lacking uracil and leucine), which could be a potential confound, given that metabolic rewiring, and especially nitrogen starvation are among the observed phenotypes. I wonder if some of the changes might be specific to these conditions. The authors should corroborate their findings under different conditions. Ideally, this would be done using an orthogonal expression system that does not rely on auxotrophy (e.g. using antibiotic resistance instead) and can be used in rich, complex mediums like YPD. Minimally, using different conditions (media with excess or more limited nitrogen source, amino acids, different carbon source, etc.) would be useful to test the robustness of the findings towards changes in media composition.

We appreciate the reviewer’s clear understanding of both the advantages and limitations of the gTOW system. As rightly pointed out, since our system relies on leucine depletion, it is essential to carefully consider the potential impact this may have on cellular metabolism. Another limitation—though it also serves as one of the strengths—of the gTOW system is its reliance on copy number variation to achieve protein overexpression. This feature limits the possibility of observing rapid responses, as immediate induction is not feasible. To address this issue, we have recently developed a strong and inducible promoter that minimizes effects on other metabolic systems (Higuchi et al., 2024), and we believe this tool will be essential in future experiments.

In response to the reviewer’s comments, we conducted two additional sets of experiments. First, we established a new overexpression system in nutrient-rich conditions (YPD medium) that is conceptually similar to gTOW but uses aureobasidin A and the AUR1d resistance gene to promote gene amplification (new Figure 4—figure supplement 2). Using this system, we observed that non-fluorescent YG mutants led to increased expression of mox. Total protein levels appeared to rise correspondingly, suggesting that the overall synthetic capacity of cells might be higher in YPD compared to SC medium. However, the degree of overexpression achieved in this system was insufficient to strongly inhibit growth, meaning we could not replicate the stress conditions observed with the original gTOW system. Further studies will be needed to determine whether stronger induction under these nutrient-rich conditions will yield comparable responses.

Second, we performed a control experiment to examine whether the amino acid starvation response observed in mox-YG overexpressing cells could be attributed to leucine depletion from the medium (new Figure 3—figure supplement 3). By titrating leucine concentrations in SC medium, we confirmed that lower leucine levels reduced the growth rate of vector control cells, indicating leucine limitation. However, GAP1 induction was not observed under these conditions. In contrast, mox-YG overexpression led to strong GAP1 induction under similar growth-inhibitory conditions, suggesting that the amino acid starvation response is not simply due to environmental leucine depletion, but rather a consequence of the cellular burden imposed by mox-YG overexpression.

These findings have been incorporated into the manuscript, along with the corresponding figures (new Figure 4—figure supplement 2, Figure 3—figure supplement 3), and relevant descriptions have been added to the Results and Discussion sections.

(3) The authors suggest that the TORC1 pathway is involved in regulating some of the changes they observed. This is likely true, but it would be great if the hypothesis could be directly tested using an established TORC1 assay.

This comment has been addressed in General Response 2. We assessed the rapamycin sensitivity of mox-YG overexpression cells—which was found to be reduced—and attempted to detect phosphorylation of the TORC1 target Atg13, although the latter was only partially successful. These findings have been incorporated into the Results section.

(4) The finding that the nucleolus appears to be virtually missing in mox-YG-expressing cells (Figure 6B) is surprising and interesting. The authors suggest possible mechanisms to explain this and partially rescue the phenotype by a reduction-of-function mutation in an exosome subunit. I wonder if this is specific to the mox-YG protein or a general protein burden effect, which the experiments suggested in point 1 should address. Additionally, could a mox-YG variant with a nuclear export signal be expressed that stays exclusively in the cytosol to rule out that mox-YG itself interferes with phase separation in the nucleus?

As also described in our General Response 3, we observed nucleolar shrinkage upon Gpm1-CCmut overexpression as well (new Figure 6E and 6—figure supplement 7), suggesting that this phenomenon may represent a general feature of protein burden. The reviewer’s suggestion to test whether this effect persists when mox-YG is excluded from the nucleus is indeed intriguing. However, based on our previous work, we have shown that overexpression of NES-tagged proteins (e.g., NES-EGFP) causes severe growth inhibition due to depletion of nuclear export factors (Kintaka et al., 2020). Unfortunately, this technical limitation makes it difficult for us to carry out the proposed experiment as suggested.

(5) It would be great if the authors could directly compare the changes they observed at the transcriptome and proteome levels. This can help distinguish between changes that are transcriptionally regulated versus more downstream processes (like protein degradation, as proposed for ribosome components).

We also considered this point to be important, and therefore compared the transcriptomic and proteomic changes associated with mox-YG overexpression. However, somewhat unexpectedly, we found little correlation between these two layers of response. As shown in new Figure 3 and 4 (original Figures 4 and 5), while genes related to oxidative phosphorylation were consistently upregulated at both the mRNA and protein levels in mox-YG overexpressing cells, ribosomal proteins showed a discordant pattern: their mRNA levels were significantly increased, whereas their protein levels were significantly decreased.

Several factors may explain this discrepancy: (1) differences in analytical methods between transcriptomics and proteomics; (2) temporal mismatches arising from the dynamic changes in mRNA and protein expression during batch culture; and (3) the possibility that, under protein burden conditions, specific regulatory mechanisms may govern the selective translation or targeted degradation of certain proteins. However, at this point, we were unable to clearly determine which of these factors account for the observed differences.

For this reason, we did not originally include a global transcriptome–proteome comparison in the manuscript. In response to the reviewer’s comment, however, we have now included the comparison data (new Figure 4—figure supplement 3D).

Minor points:

(1) The authors repeatedly state that 'mitochondrial function' is increased. This is inaccurate in two ways: first, mitochondria have multiple functions, and it should be specified which one is referred to (probably mitochondrial respiration); second, the claim is based solely on the abundance of transcripts/proteins, which may or may not reflect increased activity.

The authors should either perform functional tests (e.g. measure oxygen consumption or extracellular acidification), or change their wording to more accurately reflect the findings.

To more directly reflect our findings, we revised two instances of the phrase “mitochondrial function” to “mitochondrial proteins” in the manuscript. Furthermore, as described in General Response 1, we confirmed that oxygen consumption is elevated in mox-YG overexpression cells. This observation suggests that mitochondrial respiratory activity is indeed enhanced under these conditions.

(2) Similarly, the authors state that FPs are 'not localized' (e.g. line 137). This should be specified (e.g. 'not actively sorted into cellular compartments other than the cytosol').

As pointed out by the reviewer, we have revised the relevant sections accordingly.

(3) In Figure 4D, some of the reporter assays don't fully recapitulate the RNAseq findings (e.g. for PHO84 and ZPS1, where mox-FS and mox-YG behave differently in the reporter assay, but not in the RNAseq data). This may stem from technical limitations given that the reporter assay relies on RFP expression which could generally be affected by protein overexpression (cf. ACT1pro in mox-FS), but it should be mentioned in the text.

We apologize for the confusion caused by our insufficient explanation of "moxFS" in new Figure 3D (original Figure 4D). As clarified here, "moxFS" refers to a frameshift mutant in which the mRNA is transcribed but the protein is not translated due to an early frameshift mutation. This is not a functional mox protein. The behavior of this mutant is nearly identical to that of the vector control, indicating that the transcriptional response observed in this assay is not triggered by mRNA expression itself, but rather by events occurring after protein synthesis begins. Importantly, the transcriptional responses identified by RNA-seq in mox-YG overexpression cells are largely recapitulated by this reporter assay, supporting the reliability of our experimental design.

We appreciate the reviewer’s comment, which helped us recognize the lack of clarity in our original description. In response, we have added an explanation of the FS mutation to the figure legend (new Figure 3D), and we have also expanded the description of the moxFS experimental results in the Results section.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

All comments made in the public section.

We would like to thank the reviewer for their assessment of our study and for suggestions for additional experiments to follow up our studies.

Reviewer #2 (Recommendations For The Authors):

‐ Preparation of spike proteins and VLPs. Although Triton‐X114 extraction was done to remove endotoxin from the recombinant spike protein preparations, its removal efficiency depends on the levels of endotoxin in the samples. Therefore, the residual endotoxin levels in each of the test samples and batches should be measured. Even very low but varying levels of residual endotoxin would substantially impact the reported results, as they create inconsistent data that are not interpretable.

Certainly, endotoxin contamination in instilled materials is always an issue. Established protocols for inducing acute inflammatory responses using endotoxin outline specific ranges of endotoxin levels in the instillation materials. To induce acute lung inflammation in mice at least 2 µg of endotoxin must be instilled. We have endeavored to reduce the possibility of endotoxin contamination in our recombinant proteins by using a mammalian expression system; careful aseptic culture and protein purification techniques; and a final Triton-X114 partitioning protocol. We assessed the possibility of endotoxin contamination using the Pierce™ Chromogenic Endotoxin Quant Kit, which is based on the amebocyte lysate assay. Our analysis revealed that the endotoxin level in the purified recombinant protein preparation is below 1.0 EU/ml, which closely aligns with the levels specified for recombinant proteins. An endotoxin concentration of 1.0 EU/ml is equivalent to approximately 0.1 ng/ml. Throughout all mouse nasal instillation experiments, the total volume of recombinant protein administered did not exceed 6 µl. The amount of contaminant endotoxin instilled did not exceed 1 pg (50 µl of 0.02 ng/ml of endotoxin). Consequently, we can confirm that the extent of endotoxin contamination is at trace levels. Moreover, our study reveals multiple results indicating that the level of endotoxin contamination in the recombinant protein was inadequate to independently induce neutrophil recruitment in the cremaster muscle, lymph nodes, and liver. For further insights, refer to Figure 5.

‐ Doses of spike and VLPs: The amount of spike protein incorporated into HIV Gag‐based VLPs should be determined and compared to that found in the native SARS‐CoV‐2 virus particles. This should provide more physiologic doses (or dose ranges/titration) of spike than the arbitrary doses (3 ug or 5 ug) used in the mouse experiments.

To visualize the acquisition of spike protein and track cells that have acquired the spike protein, we conducted a series of tests and optimizations using different concentrations of Alexa 488 labeled spike protein, ranging from 0.5 to 5 µg. During the processing of lung tissue for microscopic imaging, it was of utmost importance to preserve the integrity of the labeled spike protein in the tissue samples. We determined that instillation of 3 µg of Alexa 488 labeled spike protein yielded the optimal signal strength across the lung sections. Notably, in many mouse models employing intra-nasal instillation protocols for SARS-CoV2 spike protein or RBD domain-only recombinant proteins, a dosage of approximately 3 µg or higher were commonly used. Regarding the titer of spike-incorporated VLPs, it is important to highlight that we did not directly compare the quantity of spike protein present in NL4.3 VLPs to that of the naïve SARS-CoV-2 virus. HIV-1 and SARS-CoV-2 viruses typically carry around 70 gp120 spikes and 30 spikes, respectively. We estimated that SARS-CoV-2 spike-incorporated NL4.3 VLPs may display twice the number of spikes compared to naïve SARS-CoV-2. Notably, our measurements of SARS-CoV-2 spike on NL4.3 VLPs demonstrated similar behavior to SARS-CoV-2 in terms of specific binding to ACE2-expressing 293T cells, indicating their functional similarity in this context.

Author response image 1.

Spike protein-incorporated NL4.3 VLPs test with human ACE2-transfected HEK293 cells. The wild-type spike protein-incorporated VLPs and delta envelope NL4.3 VLPs were analyzed using human ACE2-transfected HEK293 cells. The first plot shows ACE2 expression levels in HEK293 cells. The second plot displays the binding pattern of Delta Env NL4.3 VLPs on ACE2-expressing HEK293 cells. The third plot illustrates the binding pattern of wild-type spike protein-incorporated NL4.3 VLPs on ACE2expressing HEK293 cells. The histogram provides a comparison of VLP binding strength to ACE2expressing HEK293 cells.

‐ The PNGase F‐treated protein was not studied in Fig 1. In Fig 2, glycan‐removal by PNGaseF has little effects on cell uptake and cell recruitment in the lung. If binding to one of the Siglec lectins is a critical initial step, experiments should be designed to evaluate this aspect of the spike‐cell interaction in a greater depth.

As the reviewer states results with the PNGase F-treated protein were not shown in Fig. 1 although we showed results in Figs. 2 & 3. See discussion below about our preparation of the PNGase F-treated protein. Perhaps because we elected to use a purified fraction that retained ACE2 binding, the protein we used likely retained some complex glycans. As the reviewer notes the PNGase F treated protein had similar overall cellular recruitment and uptake profiles compared to the untreated spike protein. The PNGase Ftreated fraction we used no longer bound Siglec-F in the flow-based assay, shown in Fig. 7. This argues that the initial uptake and cellular recruitment following intranasal instillation of the Spike protein did not depend upon the engagement of Siglec-F. While Siglec-F on the murine alveolar macrophage can likely efficiently capture the spike proteins other cellular receptors contribute and the overall impact of the spike protein on alveolar macrophages likely reflects its engagement of multiple receptors.

• Enzymatic removal of sialic acids from spike may be one parameter to explore. The efficiency of enzymatic removal should also be verified prior to experiments. Finally, the authors need to assess whether the proteins remained functional, folded properly, and did not aggregate.

To obtain the de-glycosylated form of the SARS-CoV-2 spike protein, we employed PNGase F enzymatic digestion to remove glycans. Subsequently, the spike protein was purified using a size exclusion column. During this purification process, the PNGase F-treated spike protein segregated into two distinct fractions, specifically fraction 6 to 8 and fraction 9 to 11 (see revised Figure 1- figure supplement 1).

Author response image 2.

Size exclusion chromatography. The peak lines represent the absorbance at 280 nm. PNGase F-treated spike proteins were loaded onto a Superdex 26/60 column, resolved at a flow rate of 1.0 ml/min, and collected in 1 ml fractions.

The Coomassie blue staining of an SDS-PAGE gel revealed that fractions 6 to 8 likely underwent a more pronounced de-glycosylation by PNGase F compared to fractions 9 to 11. Additionally, during the size column purification, we noticed that fraction 6 to 8 exhibited a faster mobility than the untreated spike protein, implying a potentially substantial modification of the protein's conformation. To probe the functional characteristics of the de-glycosylated spike protein in fraction 6 to 8, we conducted binding tests with human ACE2. Strikingly, the spike protein in fraction 6 to 8 completely lost its binding affinity to ACE2, indicating a loss of its ACE2-binding capability. Conversely, the protein in fraction 9 to 11 showed partial de-glycosylation but still retained its original functionality to bind to ACE2 and its antibody.

Author response image 3.

FACS analysis of various spike protein-bound beads. Protein bound beads were detected with labeled spike antibody, recombinant human ACE2, and recombinant mouse Siglec-F.

Based on these results, we concluded that fraction 9 to 11 would be the most suitable choice for further studies as the de-glycosylated spike protein, considering its retained functional properties relevant for ligating ACE2 and antibody motifs yet had lost Siglec-F binding. In the revised manuscript we have describe in more detail the purification of the PNGase F treated Trimer and its functional assessment.

‐ Increases in macrophages and alveolar macrophages by Kifunensine Tx spike in Fig 2A suggest effects that are not related to Siglec lectins. These effects are not seen with the wild type or D614 spike trimers, so the relevance of high‐ mannose spike is unclear. On the other hand, there were clear differences between Wuhan and D614 trimers seen in Fig 2A and 2B, but there was no verification to ascertain whether these differences were indeed due to strain differences and not due to batch‐to‐batch variability of the recombinant protein production. The overall glycan contents of the Wuhan and D614 spike protein samples should be measured. If Siglec interaction is the main interest in this study, the terminal sialic acid contents should be determined and compared to those in the corresponding strains in the context of native SARS‐CoV‐2 virions.

Our initial observation that Siglec-F positive alveolar macrophages (AMs) avidly acquired spike proteins followed by a rapid leukocyte recruitment provided the rational for us to examine the impact of modifying the glycosylation pattern on the spike protein (de-glycosylated and spike variants) on their binding tropism and their cellular recruitment profiles in the lung. In this context, we examined the influence of several glycan modification on spike proteins, hypothesizing that these modifications would alter the acquisition of the spike protein by mouse AMs compared to the wild-type trimer. While we did not conduct an indepth analysis of the glycan composition and terminal sialic acid contents of the SARS-CoV-2 spike proteins we used we did verify that the different proteins behaved as expected. Most of the biochemical studies were performed in Jim Arthos’ laboratory, which has a long interest in the glycosylation of the HIV envelope protein. On SDS-PAGE the SARS-CoV-2 spike protein purified from the Kifunesine treated CHO cells exhibited a 12 kDa reduction. It bound much better to L-Sign, DC-Sign, and maltose binding lectin, and poorly to Siglec-F. In the cellular studies it bound less well to most of the cellular subsets examined including murine alveolar macrophages. In studies with human blood leukocytes, it relied on cations for binding. However, it retained its toxicity directed at mouse and human neutrophils and it elicited a similar cytokine profile when added to human macrophages. The D614G mutation increased the spike protein binding to P-Selectin, CD163, and snowdrop lectin (mannose binding) suggesting that the mutation had altered the glycan content of the protein. We used the D614G spike protein in a limited number of experiments as it behaved like the wild-type protein except for a slightly altered cellular retention pattern 18 hrs after intranasal instillation. In the revised manuscript we have included its binding to peripheral blood leukocytes. The D614G mutation conferred stronger binding to human monocytes than the original Spike protein. As discussed above, we recovered two fractions following the PNGase F treatment, one with a 40 kDa reduction on SDS-PAGE and the other a 60 kDa decrease and we chose to evaluate the fraction with a 40 kDa reduction in subsequent experiments. Consistent with a loss of N-linked glycans the PNGase F treatment reduced the binding to the lectin PHA, which recognizes complex carbohydrates, and it resulted in a sharp reduction in Siglec-F binding. The lower molecular weight fraction recovered after PNGase F treatment no longer bound ACE2. While our studies showed that alveolar macrophages likely employ Siglec-F as a capturing receptor they possess other receptors that also can capture the spike protein. The downstream consequences of engaging SiglecF and other Siglecs by the SARS-CoV-2 spike protein will require additional studies.

While acknowledging the possibility of some batch-batch variation in recombinant protein preparation, we don’t think this was a major issue. We have noted some batch-batch variations in yield- efficiency, however the purified proteins consistently gave similar results in the various experiments.

‐ Fig 3: The same concern described above applies to the hCoV‐HKU1 spike protein. In Panel D, the PNGase and Kifunensine treatment did not appear to abrogate the neutrophil recruitment. Panel A did not include PNGase and Kif Tx spike proteins. Quantification of images in panel D is missing and should be done on many randomly selected areas.

We analyzed the neutrophil count of images in panel D and the results are presented. (Figure 3-figure supplement 1C). The Kifunensine treatment reduced the neutrophil recruitment at 3 hours, while the PNGase F treated Spike protein recruited as well or slightly more neutrophils. The hCoV-HKU1 S1 domain did not differ much from the saline control.

‐ Fig 4: Kifunensine Tx spike caused more increase in neutrophil damage after intrascrotal injections. PNGase Tx spike was not tested. Connection between Siglec‐spike binding and neutrophil recruitment/damage is lacking.

Exteriorized cremaster muscle imaging functions as a model system for monitoring neutrophil behavior recruited by spike proteins within the local tissue, distinct from Siglec F-positive alveolar macrophages residing in lung tissue. Hence, our primary focus was not on investigating the Siglec/Spike protein interaction. Consequently, we did not utilize PNGase F-treated spike protein in these experiments. To clarify this issue, we added a sentence in main text ‘Although this model lacks Siglec F-positive macrophages, it is worth monitoring the effect of the SARS-CoV-2 Spike protein on neutrophils recruited in the inflammatory local tissue.’

‐ Fig 5. Neutrophil injury was also seen after inhalation (intranasal) of spike protein in mice and in vitro with human neutrophils. Panel B shows no titrating effects of spike (from 0.1 to 2) on Netosis of murine neutrophils. Panel C: Netosis was seen with human neutrophils at 1 but not 0.1. Is this species difference important?

Given the observation of neutrophil NETosis in the mouse imaging experiment, our objective was to characterize the direct impact of the spike protein on human and murine neutrophils. The origins of the neutrophils are different as the murine neutrophils were purified from mouse bone marrow while the human neutrophils were purified from human blood. Both purification protocols led to greater than 98% neutrophils. However, the murine neutrophils contain many more immature cells (50-60%) because the bone marrow served as their source. Furthermore, the murine neutrophils are from 6–8-week-old mice while the human neutrophils are from 30-50 year-old humans. More work would be needed to sort out whether there is any difference between human and mouse neutrophils in their propensity to undergo netosis in response to Spike protein.

‐ Kifunensine Tx again did not cause any reduction, indicating the lack of involvement of sialic acid. How was this related to Siglec participation directly or indirectly? There was no quantification for Panel D.

We do not think that Siglecs play a role in the induction of neutrophil netosis as the Spike proteins lacking Siglec interactions induced similar levels of netosis. Likely other neutrophil receptors are important. As noted in the text,

"human neutrophils express several C-type lectin receptors including CLEC5A, which has been implicated in SARS-CoV-2 triggered neutrophil NETosis." Our goal with the data in Panel D was to visualize human neutrophil NETosis on trimer-bearing A549 cells we relied on the flow cytometry assays for quantification.

‐ The rationale for testing cation dependence is unclear and should be described. What is the significance of "cations enhanced leukocyte binding particularly so with the high mannose protein"? Are there cationdependent receptors for spike independent of glycans and huACE‐2? If so, how is this relevant to the main topic of this paper?

It is well known that many glycan bindings by C-type lectins are calcium-dependent, involving specific amino acid residues that coordinate with calcium ions and bind to the hydroxyl groups of sugars. As discussed in our previous draft, the C-type lectin receptor L-SIGN has been suggested as a calciumdependent receptor for SARS-CoV-2, specifically interacting with high-mannose-type N-glycans on the SARS-CoV-2 spike protein. Therefore, it was worthwhile to investigate the calcium-dependent manner of spike protein binding to various types of immune cells. We added some data to this figure. It now includes the binding profile of the D614G protein. In addition, we corrected the binding data by subtracting the fluorescent signal from the unstained control cells.

‐ Fig 7: human Siglec 5 and 8 were studied in comparison with mouse Siglec F. Recombinant protein data are not congruent with transfected 293 cell data. Panel A, the best binding to hSiglec 5 and 8 are the PNGase F Tx spike protein; how to interpret these data? Panel B: only the WT and D614G spike proteins binding to Siglec 5 and 8 on transfected cells. It made sense that kif Tx (high‐mannose) and PNGaseF Tx (no glycan) spike would not bind to the Siglecs, but they did not bind to ACE2 either, indicative of nonfunctional spike proteins.

We discussed this as follows: ‘The closest human paralog of mouse Siglec-F is hSiglec-8 (reference 40). While expressed on human eosinophils and mast cells, human AMs apparently lack it. In contrast, human AMs do express Siglec-5 (reference 37). Along with its paired receptor, hSiglec-14, Siglec-5 can modulate innate immune responses (reference 41). When tested in a bead binding assay, in contrast to Siglec-F, neither hSiglec-5 or -8 bound the recombinant spike protein, yet their expression in a cellular context allowed binding. The in vitro bead binding assay we established demonstrated the specific binding of the bait molecule to target molecules. However, it does have limitations in replicating the complexities of the actual cellular environment. As discussed previously the PNGase Tx fraction we used in these experiments retained ACE2 binding, but loss binding to Siglec-F in the bead assay. In a biacore assay, not shown, the PNGase Tx fraction bound L-Sign and DC-Sign better than the untreated trimer, and it retained human ACE2 binding although it bound less well than wild type-trimer. Why the PNGase Tx fractions bound poorly to the human ACE2 transfected HEK293 cells is unclear. A higher density of recombinant ACE2 on the beads compared to that expressed on the surface of HEK293 may explain the difference. Alternatively in the bead assay we used a recombinant human ACE2-Fc fragment fusion protein purified from HEK293 cells, while in the transfection assay, we expressed human full length ACE2. The biacore, the bead binding, and the functional assays we performed all suggest that we had used intact recombinant proteins.

‐ Fig 8: This last set of experiment was to measure cytokine release by different types of macrophage cultures treated with spike from different cells with vs without Kifunensine Tx. The connection of these experiments to the rest is tenuous and is not explained. This is one of the examples where bits of data are presented without tying them together.

Dysregulated cytokine production significantly contributes to the pathogenesis of severe COVID-19 infection. Since we had observed strong binding of the spike protein to human monocytes and murine alveolar macrophages, we tested whether the spike protein altered cytokine production by human monocyte-derived macrophages. Depending on the culture conditions human monocytes can be differentiated M0, M1, or M2 phenotypes. Each type of macrophage responds differently to stimulants, often leading to distinct patterns of cytokine secretion. These patterns offer valuable insights into the immune response. The cytokine profiling conducted in this study enhances our understanding of how distinct macrophage types react to the spike protein.

‐ Discussion section did not describe how the various experiments and data are tied together. The authors explained the interactions of spike with different cell types in each paragraph separately, leaving this reviewer really confused as to what the authors want to convey as the main message of the paper.

We have modified discussion to address this issue.

Reviewer #3 (Recommendations For The Authors):

‐ The authors may want to refer to "intranasal instillation" to distinguish it from inhalation of an aerosolised liquid. How was the dose of the spike protein selected? There is some dose information in different settings, but usually between 0.1‐1 µg/ml or 0.1 µg‐5 µg range for in vivo injection, but the rationale for these ranges should be discussed. Is this mimicking a real situation during infections or a condition that might be used for vaccines?

While inhalation of aerosolized liquid closely mimics the natural route of human exposure to respiratory infectious materials, intranasal instillation with a liquid inoculum remains a widely accepted standard approach for virus or vaccine inoculation across various laboratory species. To clearly define our mouse model, we are changing the term 'inhalation' to 'instillation'. We previously answered to Reviewer #2 as following: To visualize the acquisition of spike protein and track cells that have acquired the spike protein, we conducted a series of tests and optimizations using different concentrations of Alexa Fluor 488 labeled spike protein, ranging from 0.5 to 5 µg. During the processing of lung tissue for microscopic imaging, it was of utmost importance to preserve the integrity of the labeled spike protein on the tissue samples. Through our investigations, we determined that an instillation of 3 µg of Alexa Fluor 488 labeled spike protein yielded the most optimal signal strength across the lung sections. Notably, in many mouse models employing intra-nasal instillation protocols for SARS-CoV-2 spike protein or RBD domain-only recombinant proteins, a dosage of approximately 3 µg or higher was commonly used. Hence, based on these references and our preliminary studies, we selected 3 µg as the optimal concentration of instilled spike protein per mouse.

‐ Controls are not evenly applied. In some cases, the control for the large and complex SARS‐CoV2 spiker trimer is PBS. This seems insufficient to control against effects of injecting such complex proteins that can undergo significant conformational changes after uptake by a cell. In some cases, human coronavirus spike proteins from different viruses are used, but not much is said about these proteins and the different glycoforms are not explored. Are these prepared in the same way and do they have similar glycoforms. For example, if the Siglecs bind sialic acid on N‐linked glycans, then why do the purified Siglecs or Siglecs expressed in cells not bind the HKU‐1 spike, which would have such sialic acids if expressed in the same way as the CoV2 spike?

We have taken careful consideration to select an appropriate control material for these experiments. Initially, we opted to employ Saline or PBS for intranasal instillation as a vehicle control, a choice aligned with the approach taken in numerous previous studies involving lung inflammation mouse models. However, as the reviewer pointed out, we share the concern for achieving more meaningful and comparable control materials, particularly considering the size and complexity of the recombinant protein. In accordance with this perspective, we introduced glycan-modified spike proteins and the HCoV-HKU1 S1 subunit. Figure 3 illustrates our comprehensive evaluation of various spike proteins in terms of their impact on neutrophil recruitment. The diversity of sialic acid structures observed on recombinant proteins expressed within the same cell emerges from the intricate interplay of multiple factors within the cellular glycosylation machinery. This complex enzymatic process empowers cells to finely modulate glycan structures and sialic acid patterns, tailoring them to suit the diverse biological functions of distinct proteins. Despite structural similarities between the HCoV-HKU1 and SARS-CoV-2 spike proteins, their glycan modifications vary, thereby leading to distinct binding properties with various Siglec subtypes. All recombinant proteins used in this study except for the S1 subunits were generated within our laboratory. These include the wild-type spike protein, the D614G Spike protein, the Kifunensine-treated high mannose spike proteins, and the PNGase F-treated deglycosylated spike proteins. All the proteins were produced using the same protocol using CHO cells or on occasion HEK293F cells. We have indicated in the manuscript where we used HEK293F cells for the protein production otherwise they were produced in CHO cells.

‐ Figure 1 F‐I, there should be a control for VLP without SARS‐CoV2 spike as the VLP will contain other components that may be active in the system.

We tested the delta Env VLP for alveolar macrophage acquisition and neutrophil recruitment. We found a similar alveolar macrophage acquisition of the VLPs, but significantly less neutrophil recruitment compared to the free Spike protein. Since the uptake pattern with the VLPs matched that of the spike protein we did not consider adding a non-spike bearing VLP as a control. The rapid VLPs clearance into the lymphatics shortly after instillation may account for the reduced neutrophil recruitment following their instillation (Figure 1 figure supplement 2B, C).

‐ In Figure 1H, that do they mean by autofluorescence? Is this the cyan signal?

Is the green signal also autofluorescence as this is identified as the VLP?

We appreciate reviewer pointing out the typo regarding autofluorescence in the figure image. To provide clarity regarding the background in all lung section images, we have included additional supplemental data. During the fixation process of lung tissue, various endogenous elements in the tissue sample contribute to autofluorescence when exposed to lasers in the confocal microscope. Specifically, collagen and elastin present in the lung vasculature, including airways and blood vessels, are dominant structures that generate autofluorescence. To address this issue, we have implemented optimizations to distinguish between real signals and the noise caused by autofluorescence. We inadvertently failed to indicate the source of the strong cyan signal. The signal is due to Evans Blue dye delineating lung airway structures, which contain collagen and elastin—known binding materials for Evans Blue dye. This explains the strong fluorescence signals observed in the airways. We conjugated the recombinant spike protein with Alexa Fluor 488, and viral-like particles (VLPs) were visualized with gag-GFP. (Figure 1 figure supplement 2A, D)

‐ The control for SARS‐CoV2 spike trimer is PBS, but how can the authors distinguish patterns specific to the spike trimer from any other protein delivered by intranasal instillation. Could they use another channel with a control glycoprotein to determine if there is anything unique about the pattern for spike trimer?

Alveolar macrophages employ numerous receptors to capture glycoproteins that have mannose, Nacetylglucosamine, or glucose exposed. Galactose-terminal glycoproteins are typically not bound. We do not think that the Spike protein is unique in its propensity to target alveolar macrophages.

‐ What is the parameter measured in Figure S2B?

The percentage of the different cell types that have retained the instilled Spike protein at the three-hour time point. .

‐ The Spike trimer with high mannose oligosaccharides may gain binding to the mannose receptor. It may be helpful to state the distribution of this receptor and comment is it could be responsible for this having the largest effect size for some cell types.

We agree that the spike trimer with high mannose should target cells bearing the mannose receptor. We have modified the discussion to address this point and have mentioned some of the cell types likely to bind the high mannose bearing spike protein.

‐ A key experiment is the Evans Blue measure of lung injury in Figure 3A. A control with the HKU‐1 spike is also performed, but more details on the matching of this proteins production to the SARS‐CoV2 spike trimer and the quantification of these comparative result should be provided. To show that the SARSCoV2 spike trimer can cause tissue injury on its own seems like a very important result, but the impact is currently reduced by the inconsistent application of controls and quantification of key results. Furthermore, if these results can be repeated in the B6 and B6 K18‐hACE2 mouse model it might further increase the impact by demonstrating whether or not hACE2 contributes to this effect.

We repeated the lung permeability assay using the S1 subunit from the original SARS-CoV-2 and the S1 subunit from HCoV-HKU1. Both proteins were made by the same company using a similar expression system and purification protocol. Consistent with our original data, the instillation of the SARS-CoV-2 S1 subunit led to an increase in lung vasculature permeability, whereas the HCoV-HKU-1 S1 subunit had a minimal impact. (Figure 3 figure supplement 1A). This experiment suggests that it the S1 subunit that leads to the increase in vascular permeability. To address the contribution of hACE2 in this phenomenon, we conducted a lung permeability assay using K18-hACE2 transgenic mice. The K18-hACE2 transgenic mice exhibited a slight increase in lung vasculature permeability upon SARS-CoV-2 trimer instillation compared to the non-transgenic mice. This suggests that the hACE2-Spike protein interaction may contribute to an increase in lung vascular permeability during SARS-CoV-2 lung infection (Figure 3 figure supplement 1B).

‐ For Figure 4A, could they provide quantification. The neutrophil extravasation with Trimer appears quite robust, but the authors seem to down‐play this and it's not clear without quantification.

To address this issue, we analyzed and graphed the neutrophil numbers in each image. Injection of the trimer along with IL-1β significantly increased neutrophil infiltration. (Figure 4 figure supplement 1)

‐ In Figure 4B, there are no neutrophils at all in the BSA condition. Is this correct? Intravascular neutrophils were detected with PBS injection in Figure 4A.

We demonstrated that the neutrophil behaviors occur within the infiltrated tissue rather than within the blood vessels. Even when examining the blood vessels in all other images, it is challenging to identify neutrophils adhering to the endothelium of the blood vessels. Neutrophils observed in the PBS 3-hour control group are likely acute responders to the local injection, as a smaller number of neutrophils were observed in the 6-hour image.

‐ In Figure 5A the observation of neutrophil response in lung slices seems to be presented an anecdotal account. The neutrophil appears to polarize, but is this a consistent observation? How many such observations were made?

We have consistent observations across three different experiments. In addition, highly polarized and fragmented neutrophils were consistently observed in the fixed lung section images.

‐ The statement: "human Siglec‐5 and Siglec‐8 bound poorly despite being the structural and functional equivalents of Siglec F, respectively (37)". How can one Siglec be the structural and the other the functional equivalent of Siglec‐F? It might help to provide a little more detail as to how these should be seen.

Mouse Siglec-F has two distinct counterparts in the human Siglec system, both in terms of structure and function. In the context of domain structure, human Siglec-5 serves as the counterpart to mouse Siglec-F. However, it's important to note that while human Siglec-8 is not a genetic ortholog of mouse Siglec-F, it is expressed on similar cellular populations and functions as a functional paralog.

‐ The assay using purified proteins and proteins expressed in cells don't fully agree. For example, it's very surprising that recombinant Siglec 5 and 8 bind better to the non‐glycosylated form than to the glycosylated trimer. It appears from Figure S1 that the PNGaseF treated Spike contains at least partly glycosylated monomers and it also appears that the Kifunesine effect may be partial. PNGaseF may have a hard time removing some glycans from a native protein.

We were also surprised by the results using the PNGase F treated Spike protein in that it lost binding to Siglec-F and retained binding to human Siglec-5 and 8 in the bead assay, shown in Figure 7A. As explained above we used a purified fraction of the PNGase F treated protein that retained some functional activity as assessed in the ACE2 binding assay and in biacore assays not shown. The persistent binding of Siglec-5 and Siglec-8 suggests that removal of some of the complex glycans had revealed sites capable of binding Siglec-5 and 8. We would agree with the reviewer that the PNGase treatment we used only removed some of the glycans from the native protein. In data not shown the high mannose spike protein behaved as predicted in biacore assays, binding better to DC-SIGN and maltose binding lectin, but less well to PHA and less well to ACE2. The high mannose trimer also bound less to the HEK293 cells expressing ACE2, Siglec-5, or Siglec-8 as well as peripheral blood leukocytes.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

There is a long-standing idea that choices influence evaluation: options we choose are re-evaluated to be better than they were before the choice. There has been some debate about this finding, and the authors developed several novel methods for detecting these re-evaluations in task designs where options are repeatedly presented against several alternatives. Using these novel methods the authors clearly demonstrate this re-evaluation phenomenon in several existing datasets.

Strengths:

The paper is well-written and the figures are clear. The authors provided evidence for the behaviour effect using several techniques and generated surrogate data (where the ground truth is known) to demonstrate the robustness of their methods.

Weaknesses:

The description of the results of the fMRI analysis in the text is not complete: weakening the claim that their re-evaluation algorithm better reveals neural valuation processes.

We appreciate the reviewer’s comment regarding the incomplete account of the fMRI results. In response, we implemented Reviewer #2's suggestion to run additional GLM models for a clearer interpretation of our findings. We also took this opportunity to apply updated preprocessing to the fMRI data and revise the GLM models, making them both simpler and more comprehensive. The results section is thus substantially revised, now including a new main figure and several supplemental figures that more clearly present our fMRI findings. Additionally, we have uploaded the statistical maps to NeuroVault, allowing readers to explore the full maps interactively rather than relying solely on the static images in the paper. The new analyses strengthen our original conclusion: dynamic values (previously referred to as revalued values, following the reviewer’s suggestion) better explain BOLD activity in the ventromedial prefrontal cortex, a region consistently associated with valuation, than static values (values reported prior to the choice phase in the auction procedure).

Reviewer #2 (Public Review):

Summary:

Zylberberg and colleagues show that food choice outcomes and BOLD signal in the vmPFC are better explained by algorithms that update subjective values during the sequence of choices compared to algorithms based on static values acquired before the decision phase. This study presents a valuable means of reducing the apparent stochasticity of choices in common laboratory experiment designs. The evidence supporting the claims of the authors is solid, although currently limited to choices between food items because no other goods were examined. The work will be of interest to researchers examining decision-making across various social and biological sciences.

Strengths:

The paper analyses multiple food choice datasets to check the robustness of its findings in that domain.

The paper presents simulations and robustness checks to back up its core claims.

Weaknesses:

To avoid potential misunderstandings of their work, I think it would be useful for the authors to clarify their statements and implications regarding the utility of item ratings/bids (e-values) in explaining choice behavior. Currently, the paper emphasizes that e-values have limited power to predict choices without explicitly stating the likely reason for this limitation given its own results or pointing out that this limitation is not unique to e-values and would apply to choice outcomes or any other preference elicitation measure too. The core of the paper rests on the argument that the subjective values of the food items are not stored as a relatively constant value, but instead are constructed at the time of choice based on the individual's current state. That is, a food's subjective value is a dynamic creation, and any measure of subjective value will become less accurate with time or new inputs (see Figure 3 regarding choice outcomes, for example). The e-values will change with time, choice deliberation, or other experiences to reflect the change in subjective value. Indeed, most previous studies of choice-induced preference change, including those cited in this manuscript, use multiple elicitations of e-values to detect these changes. It is important to clearly state that this paper provides no data on whether e-values are more or less limited than any other measure of eliciting subjective value. Rather, the paper shows that a static estimate of a food's subjective value at a single point in time has limited power to predict future choices. Thus, a more accurate label for the e-values would be static values because stationarity is the key assumption rather than the means by which the values are elicited or inferred.

Thank you for this helpful comment. We changed the terminology following the reviewer’s suggestion. The “explicit” values (e-values or ve) are now called “static” values (s-values or vs). Accordingly, we also changed the “Reval” values (r-values or vr) to “dynamic” values (d-values or vd).

We also address the reviewer's more general point about the utility of item ratings/bids (s-values) and whether our results are likely to hold with other ways of eliciting subjective values. We added a new sub-section in Discussion addressing this and other limitations of our study. To address the reviewer’s point, we write:

“One limitation of our study is that we only examined tasks in which static values were elicited from explicit reports of the value of food items. It remains to be determined if other ways of eliciting subjective values (e.g., Jensen and Miller, 2010) would lead to similar results. We think so, as the analysis of trials with identical item pairs (Fig. 3) and the difference between forward and backward Reval (Fig. 7) are inconsistent with the notion that values are static, regardless of their precise value. It also remains to be determined if our results will generalize to non-food items whose value is less sensitive to satiety and other dynamic bodily states. Perceptual decisions also exhibit sequential dependencies, and it remains to be explored whether these can be explained as a process of value construction, similar to what we propose here for the food-choice task (Gupta et al., 2024; Cho et al., 2002; Zylberberg et al., 2018; Abrahamyan et al., 2016).”

There is a puzzling discrepancy between the fits of a DDM using e-values in Figure 1 versus Figure 5. In Figure 1, the DDM using e-values provides a rather good fit to the empirical data, while in Figure 5 its match to the same empirical data appears to be substantially worse. I suspect that this is because the value difference on the x-axis in Figure 1 is based on the e-values, while in Figure 5 it is based on the r-values from the Reval algorithm. However, the computation of the value difference measure on the two x-axes is not explicitly described in the figures or methods section and these details should be added to the manuscript. If my guess is correct, then I think it is misleading to plot the DDM fit to e-values against choice and RT curves derived from r-values. Comparing Figures 1 and 5, it seems that changing the axes creates an artificial impression that the DDM using e-values is much worse than the one fit using r-values.

We agree with the reviewer that this way of presenting the DDM fits could be misleading. In the previous version of the manuscript, we included the two fits in the same figure panel to make it clear that the sensitivity (slope) of the choice function is greater when we fit the data using the r-values (now d-values) than when we fit them using the e-values (now s-values). In the revised version of Figure 5, we include the data points already shown in Figure 1, so that each DDM fit is shown with their corresponding data points. Thus we avoid giving the false impression that the DDM model fit using the s-values is much worse than the one fit using the d-values. This said, the fit is indeed worse, as we now show with the formal model comparison suggested by the reviewer (next comment).

Relatedly, do model comparison metrics favor a DDM using r-values over one using e-values in any of the datasets tested? Such tests, which use the full distribution of response times without dividing the continuum of decision difficulty into arbitrary hard and easy bins, would be more convincing than the tests of RT differences between the categorical divisions of hard versus easy.

We now include the model comparison suggested by the reviewer. The comparison shows that the DDM model using dynamic values explains the choice and response time data better than one using static values. One potential caveat of this comparison, which explains why we did not include it in the original version of the manuscript, is that the d-values are obtained from a fit to the choice data, which could bias the subsequent DDM comparison. We control for this in three ways: (1) by calculating the difference in Bayesian Information Criterion (BIC) between the models, penalizing the DDM model that uses the d-values for the additional parameter (δ); (2) by comparing the difference in BIC against simulations of a model in which the choice and RT data were obtained assuming static values; this analysis shows that if values were static, the DDM using static values would be favored in the comparison despite having one fewer parameter; (3) ignoring the DDM fit to the choices in the model comparison, and just comparing how well the two models explain the RTs; this comparison is unbiased because the δ values are fit only to the choice data, not the RTs. These analyses are now included in Figure 5 and Figure 5–Figure supplement 2.

Revaluation and reduction in the imprecision of subjective value representations during (or after) a choice are not mutually exclusive. The fact that applying Reval in the forward trial order leads to lower deviance than applying it in the backwards order (Figure 7) suggests that revaluation does occur. It doesn't tell us if there is also a reduction in imprecision. A comparison of backwards Reval versus no Reval would indicate whether there is a reduction in imprecision in addition to revaluation. Model comparison metrics and plots of the deviance from the logistic regression fit using e-values against backward and forward Reval models would be useful to show the relative improvement for both forms of Reval.

We agree with the reviewer that the occurrence of revaluation does not preclude other factors from affecting valuation. Following the reviewer’s suggestion we added a panel to Figure 6 (new panel B), in which we show the change in the deviance from the logistic regression fits between Reval (forward direction) and no-Reval. The figure clearly shows that the difference in deviance for the data is much larger than that obtained from simulations of choice data generated from the logistic fits to the static values (shown in red).

Interestingly, we also observe that the deviance obtained after applying Reval in the backward direction is lower than that obtained using the s-values. We added a panel to figure 7 showing this (Fig. 7B). This observation, however, does not imply that there are factors affecting valuation besides revaluation (e.g.,”reduction in imprecision”). Indeed, as we now show in a new panel in Figure 11 (panel F), the same effect (lower deviance for backward Reval than no-Reval) is observed in simulations of the ceDDM.

Besides the new figure panels (Fig. 6B, 7B, 11F), we mention in Discussion (new subsection, “Limitations...”, paragraph #2) the possibility that there are other non-dynamic contributions to the reduction in deviance for Backward Reval compared to no-Reval:

“Another limitation of our study is that, in one of the datasets we analyzed (Sepulveda et al. 2020), applying Reval in the forward direction was no better than applying it in the backward direction (Fig. 10). We speculate that this failure is related to idiosyncrasies of the experimental design, in particular, the use of alternating blocks of trials with different instructions (select preferred vs. select non-preferred). More importantly, Reval applied in the backward direction led to a significant reduction in deviance relative to that obtained using the static values. This reduction was also observed in the ceDDM, suggesting that the effect may be explained by the changes in valuation during deliberation. However, we cannot discard a contribution from other, non-dynamic changes in valuation between the rating and choice phase including contextual effects (Lichtenstein and Slovic, 2006), stochastic variability in explicit value reporting (Polania et al., 2019), and the limited range of numerical scales used to report value.”

Did the analyses of BOLD activity shown in Figure 9 orthogonalize between the various e-valueand r-value-based regressors? I assume they were not because the idea was to let the two types of regressors compete for variance, but orthogonalization is common in fMRI analyses so it would be good to clarify that this was not used in this case. Assuming no orthogonalization, the unique variance for the r-value of the chosen option in a model that also includes the e-value of the chosen option is the delta term that distinguishes the r and e-values. The delta term is a scaled count of how often the food item was chosen and rejected in previous trials. It would be useful to know if the vmPFC BOLD activity correlates directly with this count or the entire r-value (e-value + delta). That is easily tested using two additional models that include only the r-value or only the delta term for each trial.

We did not orthogonalize the static value and dynamic value regressors. We have included this detail in the revised methods. We thank the reviewer for the suggestion to run additional models to improve our ability to interpret our findings. We have substantially revised all fMRI-related sections of the paper. We took this opportunity to apply standardized and reproducible preprocessing steps implemented in fmriprep, present whole-brain corrected maps on a reconstructed surface of a template brain, and include links to the full statistical maps for the reader to navigate the full map, rather than rely on the static image in the figures. We implemented four models in total: model 1 includes both static value (Vs) obtained during the auction procedure prior to the choice phase and dynamic value (Vd) output by the revaluation algorithm (similar to the model presented in the first submission); model 2 includes only delta = Vd - Vs; model 3 includes only Vs; model 4 includes only Vd. All models included the same confound and nuisance regressors. We found that Vd was positively related to BOLD in vmPFC when accounting for Vs, correcting for familywise error rate at the whole brain level. Interestingly, the relationship between delta and vmPFC BOLD did not survive whole-brain correction and the effect size of the relationship between Vd and vmPFC bold in model 4 was larger than the effect size of the relationship between Vs and vmPFC bold in model 3 and survived correction at the whole brain level encompassing more of the vmPFC. Together, these findings bolster our claim that Vd better accounts for BOLD variability in vmPFC, a brain region reliably linked to valuation.

Please confirm that the correlation coefficients shown in Figure 11 B are autocorrelations in the MCMC chains at various lags. If this interpretation is incorrect, please give more detail on how these coefficients were computed and what they represent.

We added a paragraph in Methods explaining how we compute the correlations in Figure 11B (last paragraph of the sub-section “Correlated-evidence DDM” in Methods):

“The correlations in Fig. 11B were generated using the best-fitting parameters for each participant to simulate 100,000 Markov chains. We generate Markov chain samples independently for the left and right items over a 1-second period. To illustrate noise correlations, the simulations assume that the static value of both the left and right items is zero. We then and for each of the Markov chains (𝑥). Pearson's𝑥 correlation is computed between these 𝑡 calculate the difference in dynamic value ( ) between the left and right items at each time (𝑡) differences at time zero, 𝑥𝑖(𝑡 = 0), and at time 𝑥𝑖(𝑡 = τ), for different time lags τ. Correlations were calculated independently for each participant. Each trace in Fig. 11B represents a different participant.”

The paper presents the ceDDM as a proof-of-principle type model that can reproduce certain features of the empirical data. There are other plausible modifications to bounded evidence accumulation (BEA) models that may also reproduce these features as well or better than the ceDDM. For example, a DDM in which the starting point bias is a function of how often the two items were chosen or rejected in previous trials. My point is not that I think other BEA models would be better than the ceDDM, but rather that we don't know because the tests have not been run. Naturally, no paper can test all potential models and I am not suggesting that this paper should compare the ceDDM to other BEA processes. However, it should clearly state what we can and cannot conclude from the results it presents.

Indeed, the ceDDM should be interpreted as a proof-of-principle model, which shows that drifting values can explain many of our results. It is definitely wrong in the details, and we are open to the possibility that a different way of introducing sequential dependencies between decisions may lead to a better match to the experimental data. We now mention this in a new subsection of Discussion, “Limitations...” paragraph #3:

“Finally, we emphasize that the ceDDM should be interpreted as a proof-of-principle model used to illustrate how stochastic fluctuations in item desirability can explain many of our results. We chose to model value changes following an MCMC process. However, other stochastic processes or other ways of introducing sequential dependencies (e.g., variability in the starting point of evidence accumulation) may also explain the behavioral observations. Furthermore, there likely are other ways to induce changes in the value of items other than through past decisions. For example, attentional manipulations or other experiences (e.g., actual food consumption) may change one's preference for an item. The current version of the ceDDM does not allow for these influences on value, but we see no fundamental limitation to incorporating them in future instantiations of the model.”

This work has important practical implications for many studies in the decision sciences that seek to understand how various factors influence choice outcomes. By better accounting for the context-specific nature of value construction, studies can gain more precise estimates of the effects of treatments of interest on decision processes.

Thank you!

That said, there are limitations to the generalizability of these findings that should be noted.

These limitations stem from the fact that the paper only analyzes choices between food items and the outcomes of the choices are not realized until the end of the study (i.e., participants do not eat the chosen item before making the next choice). This creates at least two important limitations. First, preferences over food items may be particularly sensitive to mindsets/bodily states. We don't yet know how large the choice deltas may be for other types of goods whose value is less sensitive to satiety and other dynamic bodily states. Second, the somewhat artificial situation of making numerous choices between different pairs of items without receiving or consuming anything may eliminate potential decreases in the preference for the chosen item that would occur in the wild outside the lab setting. It seems quite probable that in many real-world decisions, the value of a chosen good is reduced in future choices because the individual does not need or want multiples of that item. Naturally, this depends on the durability of the good and the time between choices. A decrease in the value of chosen goods is still an example of dynamic value construction, but I don't see how such a decrease could be produced by the ceDDM.

These are all great points. The question of how generalizable our results are to other domains is wide open. We do have preliminary evidence suggesting that in a perceptual decision-making task with two relevant dimensions (motion and color; Kang, Loffler et al. eLife 2021), the dimension that was most informative to resolve preference in the past is prioritized in future decisions. We believe that a similar process underlies the apparent change in value in value-based decisions. We decided not to include this experiment in the manuscript, as it would make the paper much longer and the experimental designs are very different. Exploring the question of generality is a matter for future studies.

We also agree that food consumption is likely to change the value of the items. For example, after eating something salty we are likely to want something to drink. We mention in the revised manuscript that time, choice deliberation, attentional allocation and other experiences (including food consumption) are likely to change the value of the alternatives and thus affect future choices and valuations.

The ceDDM captures only sequential dependencies that can be attributed to values that undergo diffusion-type changes during deliberation. While the ceDDM captures many of the experimental observations, the value of an item may change for reasons not captured by the ceDDM. For example, food consumption is likely to change the value of items (e.g., wanting something to drink after eating something salty). The reviewer is correct that the current version of ceDDM could not account for these changes in value. However, we see no fundamental limitation to extending the ceDDM to account for them.

We discuss these issues in a new subsection in Discussion (“Limitations...” paragraph #3).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Summary

The authors address assumptions of bounded accumulation of evidence for value-based decision-making. They provide convincing evidence that subjects drift in their subjective preferences across time and demonstrate valuable methods to detect these drifts in certain task designs.

My specific comments are intended to assist the authors with making the paper as clear as possible. My only major concern is with the reporting of the fMRI results.

Thank you, please see our responses above for a description of the changes we made to the fMRI analyses.

Specific comments

- In the intro, I would ask the authors to consider the idea that things like slow drift in vigilance/motivation or faster drifts in spatial attention could also generate serial dependencies in perceptual tasks. I think the argument that these effects are larger in value-based tasks is reasonable, but the authors go a bit too far (in my opinion) arguing that similar effects do not exist *at all* in perceptual decision-making.

We added a sentence in the Discussion (new section on Limitations, paragraph #1) mentioning some of the literature on sequential dependencies in perceptual tasks and asking whether there might be a common explanation for such dependencies for perceptual and value-based decisions. We tried including this in the Introduction, but we thought it disrupted the flow too much.

- Figure 1: would it not be more clear to swap the order of panels A and B? Since B comes first in the task?

We agree, we swapped the order of panels A and B.

- Figure 2: the label 'simulations' might be better as 'e-value simulations'

Yes, we changed the label ‘simulations’ to ‘simulations with s-values’ (we changed the term explicit value to static value, following a suggestion by Reviewer #2).

- For the results related to Figure 2, some citations related to gaps between "stated versus revealed preferences" seem appropriate.

We added a few relevant citations where we explain the results related to Figure 2.

- Figure 3: in addition to a decrease in match preferences over the session, it would be nice to look at other features of the task which might have varied over the session. e.g. were earlier trials more likely to be predicted by e-value?

We do see a trend in this direction, but the effect is not significant. The following figure shows the consistency of the choices with the stated values, as a function of the |∆value|, for the first half (blue) and the second half (red) of the trials. The x-axis discretizes the absolute value of the difference in static value between the left and right items, binned in 17 bins of approximately equal number of trials.

Author response image 1.

The slope is shallower for the second half, but a logistic regression model revealed that the difference is not significant:

,

where Ilate is an indicator variable that takes a value of 1 for the second half of the trials and zero otherwise.

As expected from the figure β2 was negative (-0.15) but the effect was not significant (p-value =0.32, likelihood ratio test).

We feel we do not have much to say about this result, which may be due to lack of statistical power, so we would rather not include this analysis in the revised manuscript.

It is worth noting that if we repeat the analysis using the dynamic values obtained from Reval instead of the static values, the consistency is overall much greater and little difference is observed between the first and second halves of the experiment:

Author response image 2.

- The e-value DDM fit in Figure 1C/D goes through the points pretty well, but the e-value fits in 5A do not because of a mismatch with the axis. The x-axis needs to say whether the value difference is the e-value or the r-value. Also, it seems only fair to plot the DDM for the r-value on a plot with the x-axis being the e-value.

Thank you for this comment, we have now changed Figure 5A, such that both sets of data points are shown (data grouped by both e-values and by r-values). We agree that the previous version made it seem as if the fits were worse for the DDM fit to the e-values. The fits are indeed worse, as revealed by a new DDM model comparison (Figure 5–Figure supplement 2), but the effect is more subtle than the previous version of the figure implied.

- How is Figure 5B "model free" empirical support? The fact that the r-value model gives better separation of the RTs on easy and hard trials doesn't seem "model-free" and also it isn't clear how this directly relates to being a better model. It seems that just showing a box-plot of the R2 for the RT of the two models would be better?

We agree that “model free” may not be the best expression, since the r-values (now d-values) are derived from a model (Reval). Our intention was to make clear that because Reval only depends on the choices, the relationship between RT and ∆vdynamic is a prediction. We no longer use the term, model free, in the caption. We tried to clarify the point in Results, where we explain this figure panel. We have also included a new model comparison (Figure 5–Figure supplement 2), showing that the DDM model fit to the d-values explains choice and RT better than one fit to the s-values.

This said, we do consider the separation in RTs between easy and hard trials to be a valid metric to compare the accuracy of the static and dynamic values. The key assumption is that there is a monotonically decreasing relationship between value difference, ∆v, and response time. The monotonic relationship does not need to hold for individual trials (due to the noisiness of the RTs) but should hold if one were to average a large enough number of trials for each value of ∆v.

Under this assumption, the more truthful a value representation is (i.e., the closer the value we infer is to the true subjective value of the item on a given trial, assuming one exists), the greater the difference in RTs between trials judged to be difficult and those considered easy. To illustrate this with an extreme case, if an experimenter’s valuation of the items is very inaccurate (e.g., done randomly), then on average there will be no difference between easy and difficult RTs as determined by this scoring.

- Line 189: Are the stats associated with Eq 7, was the model fit subject by subject? Combining subjects? A mixed-effects model? Why not show a scatter plot of the coefficients of Δvₑ and Δvᵣ (1 point/subject).

The model was not fit separately for each subject. Instead, we concatenated trials from all subjects, allowing each subject to have a different bias term (β0,i ).

We have now replaced it with the analysis suggested by the reviewer. We fit the logistic regression model independently for each participant. The scatter plot suggested by the reviewer is shown in Figure 5–Figure supplement 1. Error bars indicate the s.e. of the regression coefficients:

It can be seen that the result is consistent with what we reported before: βd is significantly positive for all participants, while βs is not.

- I think Figure S1 should be a main figure.

Thank you for this suggestion, we have now included the former Figure S1 as an additional panel in Figure 5.

- Fig 9 figure and text (line 259) don't exactly match. In the text it says that the BOLD correlated with vᵣ and not vₑ, but the caption says there were correlations with vᵣ after controlling for vₑ. Is there really nothing in the brain that correlated with vₑ? This seems hard to believe given how correlated the two estimates are. In the methods, 8 regressors are described. A more detailed description of the results is needed.

Thank you for pointing out the inconsistency in our portrayal of the results in the main text and in the figure caption. We have substantially revised all fMRI methods, re-ran fMRI data preprocessing and implemented new, simpler, and more comprehensive GLM models following Reviewer #2's suggestion. Consequently, we have replaced Figure 9, added Figure 9 — Figure Supplement 1, and uploaded all maps to NeuroVault. These new models and maps allow for a clearer interpretation of our findings. More details about the fMRI analyses in the methods and results are included in the revision. We took care to use similar language in the main text and in the figure captions to convey the results and interpretation. The new analyses strengthen our original conclusion: dynamic values better explain BOLD activity in the ventromedial prefrontal cortex, a region consistently associated with valuation, than static values.

- It's great that the authors reanalyzed existing datasets (fig 10). I think the ΔRT plots are the least clear way to show that _reval_ is better. Why not a figure like Figure 6a and Figure 7 for the existing datasets?

We agree with the reviewer. We have replaced Fig. 10 with a more detailed version. For each dataset, we show the ΔRT plots, but we also show figures equivalent to Fig. 6a, Fig. 7a, and the new Fig. 6b (Deviance with and without Reval).

Reviewer #2 (Recommendations For The Authors):

I assume that the data and analysis code will be made publicly and openly available once the version of record is established.

Yes, the data and analysis code is now available at: https://github.com/arielzylberberg/Reval_eLife_2024

We added a Data Availability statement to the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Previous studies have used a randomly induced label to estimate the number of hematopoietic precursors that contribute to hematopoiesis. In particular, the McKinneyFreeman lab established a measurable range of precursors of 50-2500 cells using random induction of one of the 4 fluorescent proteins (FPs) of a Confetti reporter in the fetal liver to show that hundreds of precursors establish lifelong hematopoiesis. In the presented work, Liu and colleagues aim to extend the measurable range of precursor numbers previously established and enable measurement in a variety of contexts beyond embryonic development. To this end, the authors investigated whether the random induction of a given Confetti FP follows the principles of binomial distribution such that the variance inversely correlates with the precursor number. They tested their hypothesis using a simplified 2-color in vitro system, paying particular attention to minimizing sources of experimental error (elimination of outliers, sample size, events recorded, etc.) that may obscure the measurement of variance. As a result, the data generated are robust and show that the measurable range of precursors can be extended up to 105 cells. They use tamoxifen-inducible Scl-CreER, which is active in hematopoietic stem and progenitor cells (HSPCs) to induce Confetti labeling, and investigated whether they could extend their model to cell numbers below 50 with in vivo transplantation of high versus low numbers of Confetti total bone marrow (BM) cells. The premise of binomial distribution requires that the number of precursors remains constant within a group of mice. The rare frequency of HSPCs in the BM means that the experimentally generated "low" number recipient animals showed some small variability of seeding number, which does not follow the requirement for binomial distribution. While variance due to differences in precursor numbers still dominates, it is unclear how accurate estimated numbers are when precursor numbers are low (<10).

According to our simulation, the differences between estimated numbers and the corresponding expected numbers are more profound at numbers below 10, but they are still relatively small. Since Figure S4A is in log-scale, it might be difficult for readers to appreciate the magnitude in difference from the graph. We plan to add a linear scale figure to Figure S4A for better visualization of the absolute value differences (left). We also plan to provide an additional graph quantifying the value differences between estimated and expected values for numbers below 15 (right). From both graphs, the maximum difference between estimated n and expected n occurs at 10 precursor numbers (estimated as 7.6). We admit that these numbers are not numerically the same, and some minor correction of the formula may be needed if a very accurate absolute number is warrant. However, we also want to emphasize that 1. most estimated n values are within 25% range of the expected n; 2. despite the minor discrepancy, the estimated n is still highly correlated with the expected n, so the comparison between different precursor numbers was not affected.

Author response image 1.

The authors then apply their model to estimate the number of hematopoietic precursors that contribute to hematopoiesis in a variety of contexts including adult steady state, fetal liver, following myeloablation, and a genetic model of Fanconi anemia. Their modeling shows:

- thousands of precursors (~2400-2600) contribute to adult myelopoiesis, which is in line with results from a previous study (Sun et al, 2014).

- myeloablation (single dose 5-FU), while reducing precursor numbers of myeloid progenitors and HSPCs, was not associated with a reduction in precursor numbers of LTHSCs.

- no major expansion of precursor number in the fetal liver derived from labeling at E11.5 versus E14.5, consistent with recent findings from Ganuza et al, 2022.

- normal precursor numbers in Fancc-/- mice at steady state and from competitive transplantation of young Fancc-/- BM cells, suggesting that reduced Fancc-/- cell proliferation may underlie the reduced chimerism upon transplantation.

- reduced number of lymphoid precursors following transplantation of BM cells from 9month-old Fancc-/- animals (beyond this age animals have decreased survival).

Although this system does not permit the tracing of individual clones, the modeling presented allows measurements of clonal activity covering nearly the entire HSPC population (as recently estimated by Cosgrove et al, 2021) and can be applied to a wide range of in vivo contexts with relative ease. The conclusions are generally sound and based on high-quality data. Nevertheless, some results could benefit from further explanation or discussion:

- The estimated number of LT-HSCs that contribute to myelopoiesis is not specifically provided, but from the text, it would be calculated to be 1958/5 = ~391. Data from Busch et al, 2015 suggest that the number of differentiation-active HSCs is 5.2x103, which is considered the maximum limit. There is nevertheless a more than 10-fold difference between these two estimates, and it is unclear how this discrepancy arises.

First, we would like to clarify a sentence in the manuscript. 

“The average myeloid precursor number at the time of BM analysis (1958) matched the average precursor number calculated from BM myeloid progenitors (MP, Lin-Sca-1-cKit+) and HSPCs (1773 and 1917), but it was five-fold higher than that of LT-HSC (Figure 3E).”

In this sentence, we compared the number of precursors calculated from peripheral blood myeloid cells to the those calculated from BM myeloid progenitor, HSPC and LT-HSC. However, we did not intend to imply that those precursors numbers calculated from HSPC and LT-HSC specifically contribute to myelopoiesis. To avoid misunderstanding, we propose to change this sentence to read:

“The average precursor number calculated from PB myeloid cells at the time of BM analysis (1958) matched those calculated from BM myeloid progenitors (MP, Lin-Sca-1-cKit+) and HSPCs (1773 and 1917), but it was fivefold higher than that of LT-HSC (Figure 3E).”

Nonetheless, we appreciate the reviewers’ comment on the gap between the precursor numbers of LT-HSC and the number of differentiation-active HSCs reported in Busch et al, 2015. We propose the following explanation: 

First of all, precursor numbers reflect LT-HSC self-renewal by symmetric division and maintenance by asymmetric division but not differentiation. To compare the number of differentiation-active LT-HSC, precursor numbers measured from differentiated progeny (progenitors) is a better choice. As our system does not differentiate the origin of a precursor, measuring the precursor number of differentiation-active LT-HSC is difficult, since progenitors may also derive from other long-lived MPPs. However, if we assume that most divisions of LT-HSC are asymmetric division, generating one LT-HSC and one progenitor, then we can approximate the number of differentiation-active HSCs with the precursor numbers of LT-HSC.

Second, when Busch et al, 2015 calculated the number of differentiation-active HSC, they measured the cumulative activity of stem cells by following the mice up to 36 weeks postinduction. Our method measured the recent but not accumulative activity of HSC, thus the number of differentiation-active HSC in Busch et al 2015 is predicted to be higher. 

Third, Busch et al, 2015 used Tie2MCM Cre to trace HSC. It has been shown that Tie2+ HSC have a higher reconstitution capacity (Ito et al 2016, Science), but no one has compared the in situ activity of Tie2+ and Tie2- HSC in a native environment. Since the behavior of HSCs in situ may be very different from their behavior in a transplantation setting, it is possible that Tie2+ HSC are more prone to differentiation than Tie2- HSC in a native environment, leading to an overestimation of differentiation-active HSC in the HSC pool. 

- Similarly, in Figure 3E, the estimated number of precursors is highest in MPP4, a population typically associated with lymphoid potential and transient myeloid potential, whereas the numbers of MPP3, traditionally associated with myeloid potential, tend to be higher but are not significantly different than those found in HSCs.

We believe this question results from similar confusion of the nomenclature of myeloid precursors in the previous question. As explained previously, the precursors quantified reflect a variety of possible differentiation routes, not just myelopoiesis. Thus, Figure 3E did not suggest that the lymphoid-biased MPP4 has more myeloid precursors than LTHSC. Instead, it simply means more precursors contribute to MPP4 population than the LT-HSC pool. We apologize for the confusion.

- The requirement for estimating precursor numbers at stable levels of Confetti labeling is not well explained. As a result, it is unclear how accurate the estimates of B cell precursors upon transplantation of Fancc-/- cells are. In previous experiments on normal Confetti mice (Figure 3B), the authors do not estimate precursors of lymphopoiesis because Confetti labeling of B cells is not saturated, and this appears to be the case in Fanc-/- animals as well (Fig. 5B).

We appreciate the request for clarification. Our approach required the labeling level to be stable in peripheral blood because we calculate the total number of precursors by normalizing precursor numbers in Confetti+ population with the labeling level (precursor numbers in Confetti+ population divided by labeling efficiency). If the labeling level is not saturated, then the calculation of total precursors will be overestimated. This requirement is more important in native hematopoiesis, since it takes a long time for the mature population, especially the lymphoid population, to be fully replaced by the progenies from the labeled HSPC population (as suggested by Busch et al 2015 and Säwen et al 2018). In transplantation, since lethal irradiation was performed, mature blood cells were rapidly generated by HSPCs, thus saturation of labeling level is not a major concern for precursor quantification. We plan to add Author response image 2 as evidence that Confetti labeling level was stable in mice transplanted with Fancc-/- cells.  

Author response image 2.

- Do 9-month-old Fanc-/- animals have reduced lymphoid precursors as well?

Because of the non-saturated labeling in peripheral blood B cells and extra-HSPC induction of Confetti in T cells, we cannot accurately measure lymphoid precursor numbers in 9-month-old Fancc-/- animals. As an alternative, the precursor number of lymphoid biased MPP4 population were comparable between Fancc+/+ and Fancc-/- animals (Figure 5D).   We plan to add the frequency of common lymphoid progenitors (defined by Lin-IL-7Ra+Sca-1midcKitmid) add a supplementary figure to show were CLP frequencies between these two genotypes.

Author response image 3.

Reviewer #2 (Public Review):

Summary:

This manuscript by Liu et al. uses Confetti labeling of hematopoietic stem and progenitor cells in situ to infer the clonal dynamics of adult hematopoiesis. The authors apply a new mathematical framework to analyze the data, allowing them to increase the range of applicability of this tool up to tens of thousands of precursors. With this tool, they (1) provide evidence for the large polyclonality of adult hematopoiesis, (2) offer insights on the expansion dynamics in the fetal liver stage, (3) assess the clonal dynamics in a Fanconi anemia model (Fancc), which has engraftment defects during transplantation.

Strengths:

The manuscript is well written, with beautiful and clear figures, and both methods and mathematical models are clear and easy to understand.

Since 2017, Mikel Ganuza and Shannon McKinney-Freeman have been using these Confetti approaches that rely on calculating the variance across independent biological replicates as a way to infer clonal dynamics. This is a powerful tool and it is a pleasure to see it being implemented in more labs around the world. One of the cool novelties of the current manuscript is using a mathematical model (based on a binomial distribution) to avoid directly regressing the Confetti labeling variance with the number of clones (which only has linearity for a small range of clone numbers). As a result, this current manuscript of Liu et al. methodologically extends the usability of the Confetti approach, allowing them more precise and robust quantification.

They then use this model to revisit some questions from various Ganuza et al. papers, validating most of their conclusions. The application to the clonal dynamics of hematopoiesis in a model of Fanconi anemia (Fancc mice) is very much another novel aspect, and shows the surprising result that clonal dynamics are remarkably similar to the wild-type (in spite of the defect that these Fancc HSCs have during engraftment).

Overall, the manuscript succeeds at what it proposes to do, stretching out the possibilities of this Confetti model, which I believe will be useful for the entire community of stem cell biologists, and possibly make these assays available to other stem cell regenerating systems.

Weaknesses:

My main concern with this work is the choice of CreER driver line, which then relates to some of the conclusions made. Scl-CreER succeeds at being as homogenous as possible in labeling HSC/MPPs... however it is clear that it also labels a subcompartment of HSC clones that become dominant with time... This is seen as the percentage of Confettirecombined cells never ceases to increase during the 9-month chase of labeled cells, suggesting that non-labeled cells are being replaced by labeled cells. The reason why this is important is that then one cannot really make conclusions about the clonal dynamics of the unlabeled cells (e.g. for estimating the total number of clones, etc.).

We appreciate the reviewers’ comments. We also agree that this is especially a concern for measuring B cell precursors in native hematopoiesis. For myeloid cells, the increase was much less profound (0.5% per month) after month four post-induction. One way to investigate the dynamics of unlabeled cells is to induce different groups of mice with different doses of tamoxifen so that labeling efficiency varies among different groups. With 14 days of tamoxifen treatment, maximum 60% of HSPC can be labeled (RFP+CFP+YFP). If the unlabeled cells behave similarly with labeled cells, then varying the labeling efficiency shouldn’t affect the total number of precursors calculated (if excluding the potential effect of longer tamoxifen treatment on HSC). While we haven’t extensively performed such lengthy experiment, we have performed one measurement (5 mice) with 14-days of tamoxifen treatment and showed that peripheral blood myeloid precursor numbers calculated from this experiment were comparable to the ones from Figure 3 (2-day tamoxifen).

Author response image 4.

It's possible that those HSPC that are never labeled with Confetti even during longer tamoxifen treatment could behave differently. In this case, a different Cre driver may provide insight into the total precursor numbers.

I am not sure about the claims that the data shows little precursor expansion from E11 to E14. First, these experiments are done with fewer than 5 replicates, and thus they have much higher error, which is particularly concerning for distinguishing differences of such a small number of clones. Second, the authors do see a ~0.5-1 log difference between E11 and E14 (when looking at months 2-3). When looking at months 5+, there is already a clear decline in the total number of clones in both adult-labeled and embryonic-labeled, so these time points are not as good for estimating the embryonic expansion. In any case, the number of precursors at E11 (which in the end defines the degree of expansion) is always overestimated (and thus, the expansion underestimated) due to the effects of lingering tamoxifen after injection (which continues to cause Confetti allele recombination as stem cell divide). Thus, I think these results are still compatible with expansion in the fetal liver (the degree of which still remains uncertain to me).

We agreed adding additional replicates will reducing any error and boost confidence in our conclusions. The dilemma of comparing fetal- and adult-labeled cohorts is that HSPC activities could not be synchronized among different developmental stages. At fetal to neonatal stage, HSPC proliferate faster to generate new blood cells and support developmental need, while at adult stage HSPC proliferate much slower. Thus, it takes long time for the mature myeloid cells in the adult-labeled cohort to reach a stable Confetti labeling and provide an accurate quantification of precursor. While we agree that it might be better to compare precursor numbers in earlier months, we preferred to compare precursor numbers at later time points for the aforementioned reasons. The other option is to compare the number of HSPC precursors in the BM at earlier time points, as no equilibration of labeling level is required in HSPC, but this requires earlier sacrifice, compromising long term assessment.    

We did not revisit questions about the lingering effect of tamoxifen, as this has been studied by Ganuza et al 2017. They showed that tamoxifen was not able to induce additional Confetti recombination if given one day ahead, suggesting the effective window for tamoxifen is less than 24h.

Based on our data, the expansion of lifelong precursors range anywhere from 1.4 to 7.0 (Figure 4G). It’s possible that we might observe a higher level of expansion if the comparison was done in earlier time points. Nonetheless, the assertion that the expansion of life-long HSPC is not as profound as evidenced by transplantation, emphasizes value of HSPC activity analysis in situ.

Reviewer #3 (Public Review):

Summary:  

Liu et al. focus on a mathematical method to quantify active hematopoietic precursors in mice using Confetti reporter mice combined with Cre-lox technology. The paper explores the hematopoietic dynamics in various scenarios, including homeostasis, myeloablation with 5-fluorouracil, Fanconi anemia (FA), and post-transplant environments. The key findings and strengths of the paper include (1) precursor quantification: The study develops a method based on the binomial distribution of fluorescent protein expression to estimate precursor numbers. This method is validated across a wide dynamic range, proving more reliable than previous approaches that suffered from limited range and high variance outside this range; (2) dynamic response analysis: The paper examines how hematopoietic precursors respond to myeloablation and transplantation; (3) application in disease models: The method is applied to the FA mouse model, revealing that these mice maintain normal precursor numbers under steady-state conditions and posttransplantation, which challenges some assumptions about FA pathology. Despite the normal precursor count, a diminished repopulation capability suggests other factors at play, possibly related to cell proliferation or other cellular dysfunctions. In addition, the FA mouse model showed a reduction in active lymphoid precursors post-transplantation, contributing to decreased repopulation capacity as the mice aged. The authors are aware of the limitation of the assumption of uniform expansion. The paper assumes a uniform expansion from active precursor to progenies for quantifying precursor numbers. This assumption may not hold in all biological scenarios, especially in disease states where hematopoietic dynamics can be significantly altered. If non-uniformity is high, this could affect the accuracy of the quantification. Overall, the study underscores the importance of precise quantification of hematopoietic precursors in understanding both normal and pathological states in hematopoiesis, presenting a robust tool that could significantly enhance research in hematopoietic disorders and therapy development. The following concerns should be addressed.

Major Points:

• The authors have shown a wide range of seeded cells (1 to 1e5) (Figure 1D) that follow the linear binomial rule. As the standard deviation converges eventually with more seeded cells, the authors need to address this limitation by seeding the number of cells at which the assumption fails.

While number range above 105 is not required for our measurement of hematopoietic precursors in mice, we agree that it will be valuable to understand the upper limit of experimental measurement. we plan to seed 106-107 cells per replicate to address reviewer’s comments. 

• Line 276: This suggests myelopoiesis is preferred when very few precursors are available after irradiation-mediated injury. Did the authors see more myeloid progenitors at 1 month post-transplantation with low precursor number? The authors need to show this data in a supplement.

While we appreciate the concern, we did not generate this dataset because this requires take down of a substantial number of animals at one-month post-transplantation. 

Minor Points:

• Please cite a reference for line 40: a rare case where a single HSPC clone supports hematopoiesis.

• Line 262-263: "This discrepancy may reflect uneven seeding of precursors to the BM throughout the body after transplantation and the fact that we only sampled a part of the BM (femur, tibia, and pelvis)." Consider citing this paper (https://doi.org/10.1016/j.cell.2023.09.019) that explores the HSPCs migration across different bones.

• Lines 299 and 304. Misspellings of RFP.

We appreciate reviewer’s suggestions and will modify as suggested. 

• The title is misleading as the paper's main focus is the precursor number estimator using the binomial nature of fluorescent tagging. Using a single-copy cassette of Confetti mice cannot be used to measure clonality.

We appreciate reviewer’s suggestions and plan to modify the title of the manuscript to read: “Dynamic Tracking of Native Precursors in Adult Mice”.

Author response:

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

Reviewer #1:

Summary:

In this study, Nishi et al. claim that the ratio of long-term hematopoietic stem cell (LT-HSC) versus short-term HSC (ST-HSC) determines the lineage output of HSCs and reduced ratio of ST-HSC in aged mice causes myeloid-biased hematopoiesis. The authors used Hoxb5 reporter mice to isolate LT-HSC and ST-HSC and performed molecular analyses and transplantation assays to support their arguments. How the hematopoietic system becomes myeloid-biased upon aging is an important question with many implications in the disease context as well. However, their study is descriptive with remaining questions.

Weaknesses:

Comment #1-1: The authors may need conceptual re-framing of their main argument because whether the ST-HSCs used in this study are functionally indeed short-term "HSCs" is questionable. The data presented in this study and their immunophenotypic definition of ST-HSCs (Lineage negative/Sca-1+/c-Kit+/Flk2-/CD34-/CD150+/Hoxb5-) suggest that authors may find hematopoietic stem cell-like lymphoid progenitors as previously shown for megakaryocyte lineage (Haas et al., Cell stem cell. 2015) or, as the authors briefly mentioned in the discussion, Hoxb5- HSCs could be lymphoid-biased HSCs.

The authors disputed the idea that Hoxb5- HSCs as lymphoid-biased HSCs based on their previous 4 weeks post-transplantation data (Chen et al., 2016). However, they overlooked the possibility of myeloid reprogramming of lymphoid-biased population during regenerative conditions (Pietras et al., Cell stem cell., 2015). In other words, early post-transplant STHSCs (Hoxb5- HSCs) can be seen as lacking the phenotypic lymphoid-biased HSCs.

Thinking of their ST-HSCs as hematopoietic stem cell-like lymphoid progenitors or lymphoidbiased HSCs makes more sense conceptually as well.

Response #1-1: We appreciate this important suggestion and recognize the significance of the debate on whether Hoxb5- HSCs are ST-HSCs or lymphoid-biased HSCs.

HSCs are defined by their ability to retain hematopoietic potential after a secondary transplantation1-2. If Hoxb5- HSCs were indeed lymphoid-biased HSCs, they would exhibit predominantly lymphoid hematopoiesis even after secondary transplantation. However, functional experiments demonstrate that these cells lose their hematopoietic output after secondary transplantation3 (see Fig. 2 in this paper). Based on the established definition of HSCs in this filed, it is appropriate to classify Hoxb5- HSCs as ST-HSCs rather than lymphoid-biased HSCs.

Additionally, it has been reported that myeloid reprogramming may occur in the early posttransplant period, around 2-4 weeks after transplantation, even in lymphoid-biased populations within the MPP fraction, due to high inflammatory conditions4. However, when considering the post-transplant hematopoiesis of Hoxb5- HSC fractions as ST-HSCs, they exhibit almost the same myeloid hematopoietic potential as LT-HSCs not only during the early 4 weeks after transplantation but also at 8 weeks post-transplantation3, when the acute inflammatory response has largely subsided. Therefore, it is difficult to attribute the myeloid production by ST-HSCs post-transplant solely to myeloid reprogramming.

References

(1) Morrison, S. J. & Weissman, I. L. The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661–673 (1994).

(2) Challen, G. A., Boles, N., Lin, K. K. Y. & Goodell, M. A. Mouse hematopoietic stem cell identification and analysis. Cytom. Part A 75, 14–24 (2009).

(3) Chen, J. Y. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530, 223–227 (2016).

(4) Pietras, E. M. et al. Functionally Distinct Subsets of Lineage-Biased Multipotent Progenitors Control Blood Production in Normal and Regenerative Conditions. Cell Stem Cell 17, 35–46 (2015).

Comment #1-2: ST-HSCs come from LT-HSCs and further differentiate into lineage-biased multipotent progenitor (MPP) populations including myeloid-biased MPP2 and MPP3. Based on the authors' claim, LT-HSCs (Hoxb5- HSCs) have no lineage bias even in aged mice. Then these LT-HSCs make ST-HSCs, which produce mostly memory T cells. These memory T cell-producing ST-HSCs then produce MPPs including myeloid-biased MPP2 and MPP3.

This differentiation trajectory is hard to accept. If we think Hoxb5- HSCs (ST-HSCs by authors) as a sub-population of immunophenotypic HSCs with lymphoid lineage bias or hematopoietic stem cell-like lymphoid progenitors, the differentiation trajectory has no flaw.

Response #1-2: Thank you for this comment, and we apologize for the misunderstanding regarding the predominance of memory T cells in ST-HSCs after transplantation. 

Our data show that ST-HSCs are not biased HSCs that predominantly produce memory T cells, but rather, ST-HSCs are multipotent hematopoietic cells. ST-HSCs lose their ability to self-renew within a short period, resulting in the cessation of ST-HSC-derived hematopoiesis. As a result, myeloid lineage with a short half-life disappears from the peripheral blood, and memory lymphocytes with a long half-life remain (see Figure 5 in this paper). 

Comment #1-3: Authors' experimental designs have some caveats to support their claims. Authors claimed that aged LT-HSCs have no myeloid-biased clone expansion using transplantation assays. In these experiments, authors used 10 HSCs and young mice as recipients. Given the huge expansion of old HSC by number and known heterogeneity in immunophenotypically defined HSC populations, it is questionable how 10 out of so many old HSCs can faithfully represent the old HSC population. The Hoxb5+ old HSC primary and secondary recipient mice data (Figure 2C and D) support this concern. In addition, they only used young recipients. Considering the importance of the inflammatory aged niche in the myeloid-biased lineage output, transplanting young vs old LT-HSCs into aged mice will complete the whole picture.

Response #1-3: We appreciate the reviewer for the comments. We acknowledge that using ten HSCs may not capture the heterogeneity of aging HSCs.

However, although most of our experiments have used a small number of transplanted cells (e.g., 10 cells), we have conducted functional experiments across Figures 2, 3, 5, 6, S3, and S6, totaling n = 126, equivalent to over 1260 cells. Previous studies have reported that myeloid-biased HSCs constitute more than 50% of the aged HSC population1-2. If myeloidbiased HSCs increase with age, they should be detectable in our experiments. Our functional experiments have consistently shown that Hoxb5+ HSCs exhibit unchanged lineage output throughout life. In contrast, the data presented in this paper indicate that changes in the ratio of LT-HSCs and ST-HSCs may contribute to myeloid-biased hematopoiesis.

We believe that transplanting aged HSCs into aged recipient mice is crucial to analyzing not only the differentiation potential of aged HSCs but also the changes in their engraftment and self-renewal abilities. We aim to clarify further findings through these experiments in the future.

References

(1) Dykstra B, Olthof S, Schreuder J, Ritsema M, Haan G De. Clonal analysis reveals multiple functional defects of aged murine hematopoietic stem cells. J Exp Med. 2011 Dec 19;208(13):2691–703. 

(2) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

Comment #1-4: The authors' molecular data analyses need more rigor with unbiased approaches. They claimed that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid or lymphoid gene set enrichment but aged bulk HSCs, which are just a sum of LT-HSCs and ST-HSCs by their gating scheme (Figure 4A), showed the "tendency" of enrichment of myeloid-related genes based on the selected gene set (Figure 4D). Although the proportion of ST-HSCs is reduced in bulk HSCs upon aging, since ST-HSCs do not exhibit lymphoid gene set enrichment based on their data, it is hard to understand how aged bulk HSCs have more myeloid gene set enrichment compared to young bulk HSCs. This bulk HSC data rather suggests that there could be a trend toward certain lineage bias (although not significant) in aged LT-HSCs or ST-HSCs. The authors need to verify the molecular lineage priming of LT-HSCs and ST-HSCs using another comprehensive dataset.

Response #1-4: Thank you for pointing out that neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid

or lymphoid gene set enrichment, although aged bulk HSCs showed a tendency towards enrichment of myeloid-related genes.

The actual GSEA result had an FDR > 0.05. Therefore, we cannot claim that bulk HSCs showed significant enrichment of myeloid-related genes with age. Consequently, we have revised the following sentences:

[P11, L251] Neither aged LT-HSCs nor aged ST-HSCs exhibited myeloid/lymphoid gene set enrichment, while shared myeloid-related genes tended to be enriched in aged bulk-HSCs, although this enrichment was not statistically significant (Fig. 4, F and G).

In addition to the above, we also found that the GSEA results differ among myeloid gene sets (Fig. 4, D-F; Fig. 4S, C-D). These findings suggest that discussing lineage bias in HSCs using GSEA is challenging. We believe that functional experimental data is crucial. From our functional experiments, when the ratio of LT-HSC to ST-HSC was reconstituted to match the ratio in young Bulk-HSCs (LT= 2:8) or aged bulk-HSCs (LT= 5:5), myeloid-biased hematopoiesis was observed with the aged bulk-HSC ratio. Based on this data, the authors concluded that age-related changes in the ratio between LT-HSCs and ST-HSCs in bulkHSCs cause myeloid-biased hematopoiesis rather than an increase in myeloid gene expression in the aged bulk-HSCs.

Comment #1-5: Some data are too weak to fully support their claims. The authors claimed that age-associated extramedullary changes are the main driver of myeloid-biased hematopoiesis based on no major differences in progenitor populations upon transplantation of 10 young HSCs into young or old recipient mice (Figure 7F) and relatively low donor-derived cells in thymus and spleen in aged recipient mice (Figure 7G-J). However, they used selected mice to calculate the progenitor populations in recipient mice (8 out of 17 from young recipients denoted by * and 8 out of 10 from aged recipients denoted by * in Figure 7C). In addition, they calculated the progenitor populations as frequency in c-kit positive cells. Given that they transplanted 10 LT-HSCs into "sub-lethally" irradiated mice and 8.7 Gy irradiation can have different effects on bone marrow clearance in young vs old mice, it is not clear whether this data is reliable enough to support their claims. The same concern applies to the data Figure 7G-J. Authors need to provide alternative data to support their claims.

Response #1-5: Thank you for useful comments. Our claim regarding Fig. 7 is that age-associated extramedullary changes are merely additional drivers for myeloid-biased hematopoiesis are not the main drivers. But we will address the issues pointed out.

Regarding the reason for analyzing the asterisk mice

We performed two independent experiments for Fig. 7. In the first experiment, we planned to analyze the BM of recipients 16 weeks after transplantation. However, as shown in Fig. 7B, many of the aged mice died before 16 weeks. Therefore, we decided to examine the BM of the recipient mice at 12 weeks in the second experiment. Below are the peripheral blood results 11-12 weeks after transplantation for the mice used in the second experiment.

Author response image 1.

For the second experiment, we analyzed the BM of all eight all eight aged recipients. Then, we selected the same number of young recipients for analysis to ensure that the donor myeloid output would be comparable to that of the entire young group. Indeed, the donor myeloid lineage output of the selected mice was 28.1 ± 22.9%, closely matching the 23.5 ± 23.3% (p = 0.68) observed in the entire young recipient population. 

That being said, as the reviewer pointed out, it is considerable that the BM, thymus, and spleen of all mice were not analyzed. Hence, we have added the following sentences:

[P14, L327] We performed BM analysis for the mice denoted by † in Figure 7C because many of the aged mice had died before the analysis.

[P15, L338] The thymus and spleen analyses were also performed on the mice denoted by † in Figure 7C.

Regarding the reason for 8.7 Gy.

Thank you for your question about whether 8.7 Gy is myeloablative. In our previous report1, we demonstrated that none of the mice subjected to pre-treatment with 8.7 Gy could survive when non-LKS cells were transplanted, suggesting that 8.7 Gy is enough to be myeloablative with the radiation equipment at our facility.

Author response image 2.

Reference

(1)  Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

Regarding the normalization of c-Kit in Figure 7F.  

Firstly, as shown in Supplemental Figures S1B and S1C, we analyze the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in different panels. Therefore, normalization is required to assess the differentiation of HSCs from upstream to downstream. Additionally, the reason for normalizing by c-Kit+ is that the bone marrow analysis was performed after enrichment using the Anti-c-Kit antibody for both upstream and downstream fractions. Based on this, we calculated the progenitor populations as a frequency within the c-Kit positive cells.

Next, the results of normalizing the whole bone marrow cells (live cells) are shown below. 

Author response image 3.

Similar to the results of normalizing c-Kit+ cells, myeloid progenitors remained unchanged, including a statistically significant decrease in CMP in aged mice. Additionally, there were no significant differences in CLP. In conclusion, we obtained similar results between the normalization with c-Kit and the normalization with whole bone marrow cells (live cells).

However, as the reviewer pointed out, it is necessary to explain the reason for normalization with c-Kit. Therefore, we will add the following description.

[P21, L502] For the combined analysis of the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in Figures 1B and 7F, we normalized by c-Kit+ cells because we performed a c-Kit enrichment for the bone marrow analysis.

Reviewer #2:

Summary:  

Nishi et al, investigate the well-known and previously described phenomenon of ageassociated myeloid-biased hematopoiesis. Using a previously established HoxB5mCherry mouse model, they used HoxB5+ and HoxB5- HSCs to discriminate cells with long-term (LTHSCs) and short-term (ST-HSCs) reconstitution potential and compared these populations to immunophenotypically defined 'bulk HSCs' that consists of a mixture of LT-HSC and STHSCs. They then isolated these HSC populations from young and aged mice to test their function and myeloid bias in non-competitive and competitive transplants into young and aged recipients. Based on quantification of hematopoietic cell frequencies in the bone marrow, peripheral blood, and in some experiments the spleen and thymus, the authors argue against the currently held belief that myeloid-biased HSCs expand with age. 

Comment #2-1: While aspects of their work are fascinating and might have merit, several issues weaken the overall strength of the arguments and interpretation. Multiple experiments were done with a very low number of recipient mice, showed very large standard deviations, and had no statistically detectable difference between experimental groups. While the authors conclude that these experimental groups are not different, the displayed results seem too variable to conclude anything with certainty. The sensitivity of the performed experiments (e.g. Figure 3; Figure 6C, D) is too low to detect even reasonably strong differences between experimental groups and is thus inadequate to support the author's claims. This weakness of the study is not acknowledged in the text and is also not discussed. To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

Response #2-1: Thank you for your important remarks. The power analysis for this experiment shows that power = 0.319, suggesting that more number may be needed. On the other hand, our method for determining the sample size in Figure 3 is as follows:

(1) First, we checked whether myeloid biased change is detected in the bulk-HSC fraction (Figure S3). The results showed that the difference in myeloid output at 16 weeks after transplantation was statistically significant (young vs. aged = 7.2 ± 8.9 vs. 42.1 ± 35.5%, p = 0.01), even though n = 10.

(2) Next, myeloid biased HSCs have been reported to be a fraction with high self-renewal ability (2004, Blood). If myeloid biased HSCs increase with aging, the increase in myeloid biased HSCs in LT-HSC fraction would be detected with higher sensitivity than in the bulk-HSC fraction used in Figure S3.

(3) However, there was no difference not only in p-values but also in the mean itself, young vs aged = 51.4±31.5% vs 47.4±39.0%, p = 0.82, even though n = 8 in Figure 3. Since there was no difference in the mean itself, it is highly likely that no difference will be detected even if n is further increased.

Regarding Figure 6, we obtained a statistically significant difference and consider the sample size to be sufficient. 

In addition, we have performed various functional experiments (Figures 2, 5, 6 and S6), and have obtained consistent results that expansion of myeloid biased HSCs does not occur with aging in Hoxb5+HSCs fraction. Based on the above, we conclude that the LT-HSC fraction does not differ in myeloid differentiation potential with aging.

Comment #2-2: As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

Response #2-2: Thank you for the comments. As the reviewer pointed out, we hope we could reconfirm our results using single-cell level technology in the future.

On the other hand, we have reported that the ratio of myeloid to lymphoid cells in the peripheral blood changes when the number of HSCs transplanted, or the number of supporting cells transplanted with HSCs, is varied1-2. Therefore, single-cell transplant data need to be interpreted very carefully to determine differentiation potential.

From this viewpoint, future experiments will combine the Hoxb5 reporter system with a lineage tracing system that can track HSCs at the single-cell level over time. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. We have reflected this comment by adding the following sentences in the manuscript.

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty cell transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system3-4. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. 

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Sakamaki T, Kao KS, Nishi K, Chen JY, Sadaoka K, Fujii M, et al. Hoxb5 defines the heterogeneity of self-renewal capacity in the hematopoietic stem cell compartment. Biochem Biophys Res Commun [Internet]. 2021;539:34–41. Available from: https://doi.org/10.1016/j.bbrc.2020.12.077

(3) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(4) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

Comment #2-3: It is also unclear why the authors believe that the observed reduction of ST-HSCs relative to LT-HSCs explains the myeloid-biased phenotype observed in the peripheral blood. This point seems counterintuitive and requires further explanation.

Response #2-3: Thank you for your comment. We apologize for the insufficient explanation. Our data, as shown in Figures 3 and 4, demonstrate that the differentiation potential of LT-HSCs remains unchanged with age. Therefore, rather than suggesting that an increase in LT-HSCs with a consistent differentiation capacity leads to myeloid-biased hematopoiesis, it seems more accurate to highlight that the relative decrease in the proportion of ST-HSCs, which remain in peripheral blood as lymphocytes, leads to a relative increase in myeloid cells in peripheral blood and thus causes myeloid-biased hematopoiesis.

However, if we focus on the increase in the ratio of LT-HSCs, it is also plausible to explain that “with aging, the proportion of LT-HSCs capable of long-term myeloid hematopoiesis increases. As a result, from 16 weeks after transplantation, the influence of LT-HSCs maintaining the long-term ability to produce myeloid cells becomes relatively more significant, leading to an increase in the ratio of myeloid cells in the peripheral blood and causing myeloid-biased hematopoiesis.”

Comment #2-4: Based on my understanding of the presented data, the authors argue that myeloid-biased HSCs do not exist, as<br /> a) they detect no difference between young/aged HSCs after transplant (mind low n-numbers and large std!); b) myeloid progenitors downstream of HSCs only show minor or no changes in frequency and c) aged LT-HSCs do not outperform young LT-HSC in myeloid output LT-HScs in competitive transplants (mind low n-numbers and large std!).

Response #2-4: We appreciate the comments. As mentioned above, we will correct the manuscript regarding the sample size.

Regarding the interpreting of the lack of increase in the percentage of myeloid progenitor cells in the bone marrow with age, it is instead possible that various confounding factors, such as differentiation shortcuts or changes in the microenviroment, are involved.

However, even when aged LT-HSCs and young LT-HSCs are transplanted into the same recipient mice, the timing of the appearance of different cell fractions in peripheral blood is similar (Figure 3 of this paper). Therefore, we have not obtained data suggesting that clear shortcuts exist in the differentiation process of aged HSCs into neutrophils or monocytes. Additionally, it is currently consensually accepted that myeloid cells, including neutrophils and monocytes, differentiate from GMPs1. Since there is no changes in the proportion of GMPs in the bone marrow with age, we concluded that the differentiation potential into myeloid cells remains consistent with aging.

Reference

(1) Akashi K and others, ‘A Clonogenic Common Myeloid Progenitor That Gives Rise to All Myeloid Lineages’, Nature, 404.6774 (2000), 193–97.

Strengths: 

The authors present an interesting observation and offer an alternative explanation of the origins of aged-associated myeloid-biased hematopoiesis. Their data regarding the role of the microenvironment in the spleen and thymus appears to be convincing. 

Weaknesses: 

Comment #2-5: "Then, we found that the myeloid lineage proportions from young and aged LT-HSCs were nearly comparable during the observation period after transplantation (Figure 3, B and C)."<br /> Given the large standard deviation and low n-numbers, the power of the analysis to detect differences between experimental groups is very low. Experimental groups with too large standard deviations (as displayed here) are difficult to interpret and might be inconclusive. The absence of clearly detectable differences between young and aged transplanted HSCs could thus simply be a false-negative result. The shown experimental results hence do not provide strong evidence for the author's interpretation of the data. The authors should add additional transplants and include a detailed power analysis to be able to detect differences between experimental groups with reasonable sensitivity.

Response #2-5: Thank you for providing these insights. Regarding the sample size, we have addressed this in Response #2-1.

Comment #2-6: Line 293: "Based on these findings, we concluded that myeloid-biased hematopoiesis observed following transplantation of aged HSCs was caused by a relative decrease in ST-HSC in the bulk-HSC compartment in aged mice rather than the selective expansion of myeloid-biased HSC clones."<br /> Couldn't that also be explained by an increase in myeloid-biased HSCs, as repeatedly reported and seen in the expansion of CD150+ HSCs? It is not intuitively clear why a reduction of ST-HSCs clones would lead to a myeloid bias. The author should try to explain more clearly where they believe the increased number of myeloid cells comes from. What is the source of myeloid cells if the authors believe they are not derived from the expanded population of myeloid-biased HSCs?

Response #2-6: Thank you for pointing this out. We apologize for the insufficient explanation. We will explain using Figure 8 from the paper.

First, our data show that LT-HSCs maintain their differentiation capacity with age, while ST-HSCs lose their self-renewal capacity earlier, so that only long-lived memory lymphocytes remain in the peripheral blood after the loss of self-renewal capacity in ST-HSCs (Figure 8, upper panel). In mouse bone marrow, the proportion of LT-HSCs increases with age, while the proportion of STHSCs relatively decreases (Figure 8, lower panel and Figure S5). 

Our data show that merely reproducing the ratio of LT-HSCs to ST-HSCs observed in aged mice using young LT-HSCs and ST-HSCs can replicate myeloid-biased hematopoiesis. This suggests that the increase in LT-HSC and the relative decrease in ST-HSC within the HSC compartment with aging are likely to contribute to myeloid-biased hematopoiesis.

As mentioned earlier, since the differentiation capacity of LT-HSCs remain unchaged with age, it seems more accurate to describe that the relative decrease in the proportion of STHSCs, which retain long-lived memory lymphocytes in peripheral blood, leads to a relative increase in myeloid cells in peripheral blood and thus causes myeloid-biased hematopoiesis.

However, focusing on the increase in the proportion of LT-HSCs, it is also possible to explain that “with aging, the proportion of LT-HSCs capable of long-term myeloid hematopoiesis increases. As a result, from 16 weeks after transplantation, the influence of LT-HSCs maintaining the long-term ability to produce myeloid cells becomes relatively more significant, leading to an increase in the ratio of myeloid cells in the peripheral blood and causing myeloid-biased hematopoiesis.”

Reviewer #3:

Summary:

In this manuscript, Nishi et al. propose a new model to explain the previously reported myeloid-biased hematopoiesis associated with aging. Traditionally, this phenotype has been explained by the expansion of myeloid-biased hematopoietic stem cell (HSC) clones during aging. Here, the authors question this idea and show how their Hoxb5 reporter model can discriminate long-term (LT) and short-term (ST) HSC and characterized their lineage output after transplant. From these analyses, the authors conclude that changes during aging in the LT/ST HSC proportion explain the myeloid bias observed. 

Although the topic is appropriate and the new model provides a new way to think about lineage-biased output observed in multiple hematopoietic contexts, some of the experimental design choices, as well as some of the conclusions drawn from the results could be substantially improved. Also, they do not propose any potential mechanism to explain this process, which reduces the potential impact and novelty of the study. Specific concerns are outlined below. 

Major 

Comment #3-1: As a general comment, there are experimental details that are either missing or not clear. The main one is related to transplantation assays. What is the irradiation dose? The Methods sections indicates "recipient mice were lethally irradiated with single doses of 8.7 or 9.1 Gy". The only experimental schematic indicating the irradiation dose is Figure 7A, which uses 8.7 Gy. Also, although there is not a "standard", 11 Gy split in two doses is typically considered lethal irradiation, while 9.5 Gy is considered sublethal.

Response #3-1: We agree with reviewer’s assessment about whether 8.7 Gy is myeloablative. To confirm this, it would typically be necessary to irradiate mice with different dose and observe if they do not survive. However, such an experiment is not ethically permissible at our facility. Instead, in our previous report1, we demonstrated that none of the mice subjected to pretreatment with 8.7 Gy could survive when non-LKS cells were transplanted, suggesting that

8.7 Gy is enough to be myeloablative with the radiation equipment at our facility.

Reference

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

Comment #3-2:  Is there any reason for these lower doses? Same question for giving a single dose and for performing irradiation a day before transplant. 

Response #3-2: We appreciate the reviewer for these important comments. Although the 8.7 Gy dose used at our facility is lower than in other reports, we selected this dose to maintain consistency with our previous experiments. For the same reason, we used a single irradiation, not split.  Regarding the timing of irradiation, the method section specifies that irradiation timing is 12-24 hours prior to transplantation. In most experiments, irradiation is performed at 12 hours. However, due to experimental progress, there were occasional instances where nearly 24 hours elapsed between irradiation and transplantation. We provide this information to ensure accuracy.

Comment #3-3: The manuscript would benefit from the inclusion of references to recent studies discussing hematopoietic biases and differentiation dynamics at a single-cell level (e.g., Yamamoto et. al 2018; Rodriguez-Fraticelli et al., 2020). Also, when discussing the discrepancy between studies claiming different biases within the HSC pool, the authors mentioned that Montecino-Rodriguez et al. 2019 showed preserved lymphoid potential with age. It would be good to acknowledge that this study used busulfan as the conditioning method instead of irradiation.

Response #3-3: We agree with this comment and have incorporated this suggestion into the manuscript

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty cell transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system1-2. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. Additionally, in this report we purified LT-HSCs by Hoxb5 reporter system. In contrast, various LT-HSC markers have been previously reported2-3.  Therefore, it is ideal to validate our findings using other LT-HSC makers.

[P16, L368] Other studies suggest that blockage of lymphoid hematopoiesis in aged mice results in myeloid-skewed hematopoiesis through alternative mechanisms. However, this result should be interpreted carefully, since Busulfan was used for myeloablative treatment in this study4.   

References

(1) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(2) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

(3) Sanjuan-Pla A, Macaulay IC, Jensen CT, Woll PS, Luis TC, Mead A, et al. Plateletbiased stem cells reside at the apex of the haematopoietic stem-cell hierarchy. Nature. 2013;502(7470):232–6. 

(4) Montecino-Rodriguez E, Kong Y, Casero D, Rouault A, Dorshkind K, Pioli PD. Lymphoid-Biased Hematopoietic Stem Cells Are Maintained with Age and Efficiently Generate Lymphoid Progeny. Stem Cell Reports. 2019 Mar 5;12(3):584–96. 

Comment #3-4: When representing the contribution to PB from transplanted cells, the authors show the % of each lineage within the donor-derived cells (Figures 3B-C, 5B, 6B-D, 7C-E, and S3 B-C). To have a better picture of total donor contribution, total PB and BM chimerism should be included for each transplantation assay. Also, for Figures 2C-D and Figures S2A-B, do the graphs represent 100% of the PB cells? Are there any radioresistant cells?

Response #3-4: Thank you for highlighting this point. Indeed, donor contribution to total peripheral blood (PB) is important information. We have included the donor contribution data for each figure above mentioned.

Author response image 4.

In Figure 2C-D and Figure S2A-B, the percentage of donor chimerism in PB was defined as the percentage of CD45.1-CD45.2+ cells among total CD45.1-CD45.2+ and CD45.1+CD45.2+ cells as described in method section.

Comment #3-5: For BM progenitor frequencies, the authors present the data as the frequency of cKit+ cells. This normalization might be misleading as changes in the proportion of cKit+ between the different experimental conditions could mask differences in these BM subpopulations. Representing this data as the frequency of BM single cells or as absolute numbers (e.g., per femur) would be valuable.

Response #3-5: We appreciate the reviewer's comment on this point. 

Firstly, as shown in Supplemental Figures S1B and S1C, we analyze the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in different panels. Therefore, normalization is required to assess the differentiation of HSCs from upstream to downstream. Additionally, the reason for normalizing by c-Kit+ is that the bone marrow analysis was performed after enrichment using the Anti-c-Kit antibody for both upstream and downstream fractions. Based on this, we calculated the progenitor populations as a frequency within the c-Kit positive cells. Next, the results of normalizing the whole bone marrow cells (live cells) are shown in Author response image 2. 

Similar to the results of normalizing c-Kit+ cells, myeloid progenitors remained unchanged, including a statistically significant decrease in CMP in aged mice. Additionally, there were no significant differences in CLP. In conclusion, similar results were obtained between the normalization with c-Kit and the normalization with whole bone marrow cells (live cells).

However, as the reviewer pointed out, it is necessary to explain the reason for normalization with c-Kit. Therefore, we will add the following description.

[P21, L502] For the combined analysis of the upstream (HSC, MPP, Flk2+) and downstream (CLP, MEP, CMP, GMP) fractions in Figures 1B and 7F, we normalized by c-Kit+ cells because we performed a c-Kit enrichment for the bone marrow analysis.

Comment #3-6: Regarding Figure 1B, the authors argue that if myeloid-biased HSC clones increase with age, they should see increased frequency of all components of the myeloid differentiation pathway (CMP, GMP, MEP). This would imply that their results (no changes or reduction in these myeloid subpopulations) suggest the absence of myeloid-biased HSC clones expansion with age. This reviewer believes that differentiation dynamics within the hematopoietic hierarchy can be more complex than a cascade of sequential and compartmentalized events (e.g., accelerated differentiation at the CMP level could cause exhaustion of this compartment and explain its reduction with age and why GMP and MEP are unchanged) and these conclusions should be considered more carefully.

Response #3-6: We wish to thank the reviewer for this comment. We agree with that the differentiation pathway may not be a cascade of sequential events but could be influenced by various factors such as extrinsic factors.

In Figure 1B, we hypothesized that there may be other mechanisms causing myeloidbiased hematopoiesis besides the age-related increase in myeloid-biased HSCs, given that the percentage of myeloid progenitor cells in the bone marrow did not change with age. However, we do not discuss the presence or absence of myeloid-biased HSCs based on the data in Figure 1B. 

Our newly proposed theories—that the differentiation capacity of LT-HSCs remains unchanged with age and that age-related myeloid-biased hematopoiesis is due to changes in the ratio of LT-HSCs to ST-HSCs—are based on functional experiment results. As the reviewer pointed out, to discuss the presence or absence of myeloid-biased HSCs based on the data in Figure 1B, it is necessary to apply a system that can track HSC differentiation at single-cell level. The technology would clarify changes in the self-renewal capacity of individual HSCs and their differentiation into progenitor cells and peripheral blood cells. The authors believe that those single-cell technologies will be beneficial in understanding the differentiation of HSCs. Based on the above, the following statement has been added to the text.

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty cell transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system1-2. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. 

References

(1) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(2) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

Comment #3-7: Within the few recipients showing good donor engraftment in Figure 2C, there is a big proportion of T cells that are "amplified" upon secondary transplantation (Figure 2D). Is this expected?

Response #3-7: We wish to express our deep appreciation to the reviewer for insightful comment on this point. As the reviewers pointed out, in Figure 2D, a few recipients show a very high percentage of T cells. The authors had the same question and considered this phenomenon as follows:

(1) One reason for the very high percentage of T cells is that we used 1 x 107 whole bone marrow cells in the secondary transplantation. Consequently, the donor cells in the secondary transplantation contained more T-cell progenitor cells, leading to a greater increase in T cells compared to the primary transplantation.

(2) We also consider that this phenomenon may be influenced by the reduced selfrenewal capacity of aged LT-HSCs, resulting in decreased sustained production of myeloid cells in the secondary recipient mice. As a result, long-lived memory-type lymphocytes may preferentially remain in the peripheral blood, increasing the percentage of T cells in the secondary recipient mice.

We have discussed our hypothesis regarding this interesting phenomenon. To further clarify the characteristics of the increased T-cell count in the secondary recipient mice, we will analyze TCR clonality and diversity in the future.

Comment #3-8: Do the authors have any explanation for the high level of variability within the recipients of Hoxb5+ cells in Figure 2C?

Response #3-8: We appreciate the reviewer's comment on this point. As noted in our previous report, transplantation of a sufficient number of HSCs results in stable donor chimerism, whereas a small number of HSCs leads to increased variability in donor chimerism1. Additionally, other studies have observed high variability when fewer than 10 HSCs are transplanted2-3. Based on this evidence, we consider that the transplantation of a small number of cells (10 cells) is the primary cause of the high level of variability observed.

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Dykstra B, Olthof S, Schreuder J, Ritsema M, Haan G De. Clonal analysis reveals multiple functional defects of aged murine hematopoietic stem cells. J Exp Med. 2011 Dec 19;208(13):2691–703. 

(3) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

Comment #3-9: Can the results from Figure 2E be interpreted as Hoxb5+ cells having a myeloid bias? (differences are more obvious/significant in neutrophils and monocytes).

Response #3-9: Thank you for your insightful comments. Firstly, we have not obtained any data indicating that young LT-HSCs are myeloid biased HSCs so far. Therefore, we classify young LT-HSCs as balanced HSCs1. Secondly, our current data demonstrate no significant difference in differentiation capacity between young and aged LT-HSCs (see Figure 3 in this paper). Based on these findings, we interpret that aged LT-HSCs are balanced HSCs, similar to young LT-HSCs.

Reference

(1)  Chen JY, Miyanishi M, Wang SK, Yamazaki S, Sinha R, Kao KS, et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature. 2016 Feb 10;530(7589):223–7. 

Comment #3-10: Is Figure 2G considering all primary recipients or only the ones that were used for secondary transplants? The second option would be a fairer comparison.

Response #3-10: We appreciate the reviewer's comment on this point. We considered all primary recipients in Figure 2G to ensure a fair comparison, given the influence of various factors such as the radiosensitivity of individual recipient mice1. Comparing only the primary recipients used in the secondary transplantation would result in n = 3 (primary recipient) vs. n = 12 (secondary recipient). Including all primary recipients yields n = 11 vs. n = 12, providing a more balanced comparison. Therefore, we analyzed all primary recipient mice to ensure the reliability of our results.

Reference

(1) Duran-Struuck R, Dysko RC. Principles of bone marrow transplantation (BMT): providing optimal veterinary and husbandry care to irradiated mice in BMT studies. J Am Assoc Lab Anim Sci. 2009; 48:11–22

Comment #3-11: When discussing the transcriptional profile of young and aged HSCs, the authors claim that genes linked to myeloid differentiation remain unchanged in the LT-HSC fraction while there are significant changes in the ST-HSCs. However, 2 out of the 4 genes shown in Figure S4B show ratios higher than 1 in LT-HSCs.

Response #3-11: Thank you for highlighting this important point. As the reviewer pointed out, when we analyze the expression of myeloid-related genes, some genes are elevated in aged LT-HSCs compared to young LT-HSCs. However, the GSEA analysis using myeloid-related gene sets, which include several hundred genes, shows no significant difference between young and aged LT-HSCs (see Figure S4C in this paper). Furthermore, functional experiments using the co-transplantation system show no difference in differentiation capacity between young and aged LT-HSCs (see Figure 3 in this paper). Based on these results, we conclude that LT-HSCs do not exhibit any change in differentiation capacity with aging.

Comment #3-12: When determining the lymphoid bias in ST-HSCs, the authors focus on the T-cell subtype, not considering any other any other lymphoid population. Could the authors explain this?

Response #3-12: We thank the reviewer for this comment. We conducted the experiments in Figure 5 to demonstrate that the hematopoiesis observed 16 weeks post-transplantation—when STHSCs are believed to lose their self-renewal capacity—is not due to de novo production of T cells from ST-HSCs. Instead, it is attributed to long-lived memory cells which can persistently remain in the peripheral blood.

As noted by the reviewer, various memory cell types are present in peripheral blood. Our analysis focused on memory T cells due to the broad consensus on memory T cell markers1. 

Our findings show that transplanted Hoxb5- HSCs do not continuously produce lymphoid cells, unlike lymphoid-biased HSCs. Rather, the loss of self-renewal capacity in Hoxb5- HSCs makes the presence of long-lived memory cells in the peripheral blood more apparent.

Reference

(1)  Yenyuwadee S, Sanchez-Trincado Lopez JL, Shah R, Rosato PC, Boussiotis VA. The evolving role of tissue-resident memory T cells in infections and cancer. Sci Adv. 2022;8(33). 

Comment #3-13: Based on the reduced frequency of donor cells in the spleen and thymus, the authors conclude "the process of lymphoid lineage differentiation was impaired in the spleens and thymi of aged mice compared to young mice". An alternative explanation could be that differentiated cells do not successfully migrate from the bone marrow to these secondary lymphoid organs. Please consider this possibility when discussing the data.

Response #3-13: We strongly appreciate the reviewer's comment on this point. In accordance with the reviewer's comment, we have incorporated this suggestion into our manuscript.

[P15, L343] These results indicate that the process of lymphoid lineage differentiation is impaired in the spleens and thymi of aged mice compared to young mice, or that differentiating cells in the bone marrow do not successfully migrate into these secondary lymphoid organs. These factors contribute to the enhanced myeloid-biased hematopoiesis in peripheral blood due to a decrease in de novo lymphocyte production.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Recommendation #2-1: To support their conclusions the authors need to provide higher n-numbers and provide a detailed power analysis of the transplants in the methods section.

Response to Recommendation #2-1: Thank you for your important remarks. The power analysis for this experiment shows that power = 0.319, suggesting that more number may be needed. On the other hand, our method for determining the sample size in Figure 3 is as follows:

(1) First, we checked whether myeloid biased change is detected in the bulk-HSC fraction (Figure S3). The results showed that the difference in myeloid output at 16 weeks after transplantation was statistically significant (young vs. aged = 7.2 ± 8.9 vs. 42.1 ± 35.5%, p = 0.01), even though n = 10.

(2) Next, myeloid biased HSCs have been reported to be a fraction with high self-renewal ability (2004, Blood). If myeloid biased HSCs increase with aging, the increase in myeloid biased HSCs in LT-HSC fraction would be detected with higher sensitivity than in the bulk-HSC fraction used in Figure S3.

(3) However, there was no difference not only in p-values but also in the mean itself, young vs aged = 51.4±31.5% vs 47.4±39.0%, p = 0.82, even though n = 8 in Figure 3. Since there was no difference in the mean itself, it is highly likely that no difference will be detected even if n is further increased.

Regarding Figure S3, 5, 6, S6 and 7, we obtained a statistically significant difference and consider the sample size to be sufficient. 

Recommendation #2-2: As the authors attempt to challenge the current model of the age-associated expansion of myeloid-biased HSCs (which has been observed and reproduced by many different groups), ideally additional strong evidence in the form of single-cell transplants is provided.

Response to Recommendation #2-2: Thank you for the comments. As the reviewer pointed out, we hope we could reconfirm our results using single-cell level technology in the future.

On the other hand, we have reported that the ratio of myeloid to lymphoid cells in the peripheral blood changes when the number of HSCs transplanted, or the number of supporting cells transplanted with HSCs, is varied1-2. Therefore, single-cell transplant data need to be interpreted very carefully to determine differentiation potential.

From this viewpoint, future experiments will combine the Hoxb5 reporter system with a lineage tracing system that can track HSCs at the single-cell level over time. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. We have reflected this comment by adding the following sentences in the manuscript.

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty transplantation assays. Therefore, the current theory should be revalidated using single-cell technology. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells.

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Sakamaki T, Kao KS, Nishi K, Chen JY, Sadaoka K, Fujii M, et al. Hoxb5 defines the heterogeneity of self-renewal capacity in the hematopoietic stem cell compartment. Biochem Biophys Res Commun [Internet]. 2021;539:34–41. Available from: https://doi.org/10.1016/j.bbrc.2020.12.077

Minor points:

Recommendation #2-3: Figure 1: "Comprehensive analysis of hematopoietic alternations with age shows a discrepancy of age-associated changes between peripheral blood and bone marrow"

[Comment to the authors]: For clarity, the nature of the discrepancy should be stated clearly.

Response to Recommendation #2-3: Thank you for this important comment. Following the reviewer’s recommendation, we have revised the manuscript as follows

[P7, L139] Our analysis of hematopoietic alternations with age revealed that age-associated transition patterns of immunophenotypically defined HSC and CMP in BM were not paralleled with myeloid cell in PB (Fig. 1 C).

Recommendation #2-4: Figure 1B "(B) Average frequency of immunophenotypically defined HSC and progenitor cells in BM of 2-3-month mice (n = 6), 6-month mice (n = 6), 12-13-month mice (n = 6), {greater than or equal to} 23-month mice (n = 7).

[Comment to the authors]: It should be stated in the figure and legend that the values are normalized to the 2-3-month-old mice.

Response to Recommendation #2-4: Thank you for this comment. Figure 1B presents the actual measured values of each fraction in c-Kit positive cells in the bone marrow, without any normalization.

Recommendation #2-5: "We 127 found that the frequency of immunophenotypically defined HSC in BM rapidly increased 128 up to the age of 12 months. After the age, they remained plateaued throughout the 129 observation period (Fig. 1 B)."

[Comment to the authors]: The evidence for a 'plateau', where HSC numbers don't change after 12 months is weak. It appears that the numbers increase continuously (although less steep) after 12 months. I thus recommend adjusting the wording to better reflect the data.

Response to Recommendation #2-5: We thank the reviewer for the comments above and have incorporated these suggestions in our revision as follows. 

[P6, L126] We found that the frequency of immunophenotypically defined HSC in BM rapidly increased up to the age of 12 months. After the age, the rate of increase in their frequency appeared to slow down.

Recommendation #2-6: Figure 2G: [Comment to the authors]: Please add the required statistics, please check carefully all figures for missing statistical tests.

Response to Recommendation #2-6: Thank you for these important comments. In response, we have added the results of the significance tests for Figures 1A, 1C, 4C, and S5.

Recommendation #2-7: "If bulk-HSCs isolated from aged mice are already enriched by myeloid-biased HSC clones, we should see more myeloid-biased phenotypes 16 weeks after primary and the secondary transplantation. However, we found that kinetics of the proportion of myeloid cells in PB were similar across primary and the secondary transplantation and that the proportion of myeloid cells gradually decreased over time (Fig. 2 G). These results suggest the following two possibilities: either myeloid-biased HSCs do not expand in the LT-HSC fraction, or the expansion of myeloid-biased clones in 2-year-old mice has already peaked."

[Comment to the authors]: Other possible explanations include that the observed reduction in myeloid reconstitution over 16 weeks reflects the time required to return to homeostasis. In other words, it takes time until the blood system approaches a balanced output.

Response to Recommendation #2-7: We agree with the reviewer's comment. As the reviewer pointed out, the gradual decrease in the proportion of myeloid cells over time is not related to our two hypotheses in this part of the manuscript but rather to the hematopoietic system's process of returning to a homeostatic state after transplantation. Therefore, the original sentence could be misleading, as it is part of the section discussing whether age-associated expansion of myeloid-biased HSCs is observed. Based on the above, we have revised the sentence as follows.

[P8, L179] However, we found that kinetics of the proportion of myeloid cells in PB were similar across the primary and the secondary transplantation (Fig. 2 G). These results suggest the following two possibilities: either myeloid-biased HSCs do not expand in the LTHSC fraction, or the expansion of myeloid-biased clones in 2-year-old mice has already peaked.

Recommendation #2-8: It is also important to consider that the transplant results are highly variable (see large standard deviation), therefore the sensitivity to detect smaller but relevant changes is low in the shown experiments. As the statistical analysis of these experiments is missing and the power seems low these results should be interpreted with caution. For instance, it appears that the secondary transplants on average produce more myeloid cells as expected and predicted by the classical clonal expansion model.

Regarding "expansion of myeloid-biased clones in 2-year-old mice has already peaked". This is what the author suggested above. It might thus not be surprising that HSCs from 2-year-old mice show little to no increased myeloid expansion.

Response to Recommendation #2-8: Thank you for providing these insights. The primary findings of our study are based on functional experiments presented in Figures 2, 3, 5, 6, and 7. In Figure 3, there was no significant difference between young and aged LT-HSCs, with mean values of 51.4±31.5% and 47.4±39.0%, respectively (p = 0.82). Given the lack of difference in the mean values, it is unlikely that increasing the sample size would reveal a significant change. For ethical reasons, to minimize the use of additional animals, we conclude that LT-HSCs exhibit no change in lineage output throughout life based on the data in Figure 3. Statistically significant differences observed in Figures 2, 5, 6, and 7 further support our conclusions.

Additionally, because whole bone marrow cells were transplanted in the secondary transplantation, there may be various confounding factors beyond the differentiation potential of HSCs. Therefore, we consider that caution is necessary when evaluating the differentiation capacity of HSCs in the context of the second transplantation.

Recommendation #2-9: Figure 7C: [Comment to the authors]: The star * indicates with analyzed BM. As stars are typically used as indicators of significance, this can be confusing for the reader. I thus suggest using another symbol.

Response to Recommendation #2-9: We appreciate the reviewer for this comment and have incorporated the suggestion in the revised manuscript. We have decided to use † instead of the star*.

Reviewer #3 (Recommendations For The Authors):

Recommendation #3.1: In Figure 1A, the authors show the frequency of PB lineages (lymphoid vs myeloid) in mice of different ages. It would be great if they could show the same data for each subpopulation including these two main categories individually (granulocytes, monocytes, B cells, T cells...).

Response to Recommendation #3-1: We thank for this suggestion. We provide the frequency of PB lineages (granulocytes, monocytes, B cells, T cells, and NK cells) in mice of different ages.

Author response image 5.

Average frequency of neutrophils, monocytes, B cells, T cells, and NK cells in PB analyzed in Figure 1A. Dots show all individual mice. *P < 0.05. **P < 0.01. Data and error bars represent means ± standard deviation. 

Recommendation #3.2: It would be great if data from young mice could be shown in parallel to the graphs in Figure 2A.

Response to Recommendation #3-2: We thank the reviewer for the comments above and have incorporated these suggestions in Figure 2A. 

[P34, L916] (A) Hoxb5 reporter expression in bulk-HSC, MPP, Flk2+, and Lin-Sca1-c-Kit+ populations in the 2-year-old Hoxb5-tri-mCherry mice (Upper panel) and 3-month-old Hoxb5_tri-mCherry mice (Lower panel). Values indicate the percentage of mCherry+ cells ± standard deviation in each fraction (_n = 3). 

Recommendation #3.3: Do the authors have any explanation for the high level of variability within the recipients of Hoxb5+ cells in Figure 2C?

Response to Recommendation #3-3: Thank you for providing these insights. As noted in our previous report, transplantation of a sufficient number of HSCs results in stable donor chimerism, whereas a small number of HSCs leads to increased variability in donor chimerism1. Additionally, other studies have observed high variability when fewer than 10 HSCs are transplanted2-3. Based on this evidence, we consider that the transplantation of a small number of cells (10 cells) is the primary cause of the high level of variability observed.

References

(1) Nishi K, Sakamaki T, Sadaoka K, Fujii M, Takaori-Kondo A, Chen JY, et al. Identification of the minimum requirements for successful haematopoietic stem cell transplantation. Br J Haematol. 2022;196(3):711–23. 

(2) Dykstra B, Olthof S, Schreuder J, Ritsema M, Haan G De. Clonal analysis reveals multiple functional defects of aged murine hematopoietic stem cells. J Exp Med. 2011 Dec 19;208(13):2691–703. 

(3) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

Recommendation #3.4: Are the differences in Figure 3D statistically significant? If yes, please add statistics. Same for Figure 4C.

Response to Recommendation #3-4: Thank you for providing these insights. For Figure 3D, we performed an ANOVA analysis for each fraction; however, the results were not statistically significant. In contrast, for Figure 4C, we have added the results of significance tests for comparisons between Young LT-HSC vs. Young Bulk-HSC.

Recommendation #3.5: As a general comment, although the results in this study are interesting, the use of a Hoxb5 lineage tracing mouse model would be more valuable for this purpose than the Hoxb5 reporter used here. The lineage tracing model would allow for the assessment of lineage bias without the caveats introduced by the transplantation assays.

Response to Recommendation #3-5: We appreciate the reviewer for the important comments. Following the reviewer’s recommendation, we have revised the manuscript as follows

[P19, L451] In contrast, our findings should be considered in light of some limitations. In this report, we primarily performed ten to twenty transplantation assays. Therefore, the current theory should be revalidated using single-cell technology with lineage tracing system1-2. This approach will investigate changes in the self-renewal capacity of individual HSCs and their subsequent differentiation into progenitor cells and peripheral blood cells. 

References

(1) Yamamoto R, Wilkinson AC, Ooehara J, Lan X, Lai CY, Nakauchi Y, et al. LargeScale Clonal Analysis Resolves Aging of the Mouse Hematopoietic Stem Cell Compartment. Cell Stem Cell [Internet]. 2018;22(4):600-607.e4. Available from: https://doi.org/10.1016/j.stem.2018.03.013

(2) Rodriguez-Fraticelli AE, Weinreb C, Wang SW, Migueles RP, Jankovic M, Usart M, et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature [Internet]. 2020;583(7817):585–9. Available from: http://dx.doi.org/10.1038/s41586-020-2503-6

Author response:

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

We would like to thank the reviewers and editors for their careful assessment and review of our article. The many detailed comments, questions and suggestions were very helpful in improving our analyses and presentation of data. In particular, our Discussion benefited enormously from the comments. 

Below we respond in detail to every point raised. 

We especially note that Reviewer #3’s small query on “trial where learning is defined to have occurred, we were not given the quantitative criterion operationalizing "learning" - please provide” led to deeper analyses and insights and a lengthy response.

This analysis prompted the addition of a sentence (red) to the Abstract. 

“Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed “target estimation vector” (TEV) closely approximated the mice’s route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map. ”

Note: we added, at the end of the manuscript, the legends for the Shortcut video (Video 1) and the main text figure legends; these are with a larger font and so easier to read. 

Reviewer #1 (Public Review):

Assessment:

This important work advances our understanding of navigation and path integration in mammals by using a clever behavioral paradigm. The paper provides compelling evidence that mice are able to create and use a cognitive map to find "short cuts" in an environment, using only the location of rewards relative to the point of entry to the environment and path integration, and need not rely on visual landmarks.

Thank you.

Summary:

The authors have designed a novel experimental apparatus called the 'Hidden Food Maze (HFM)' and a beautiful suite of behavioral experiments using this apparatus to investigate the interplay between allothetic and idiothetic cues in navigation. The results presented provide a clear demonstration of the central claim of the paper, namely that mice only need a fixed start location and path integration to develop a cognitive map. The experiments and analyses conducted to test the main claim of the paper -- that the animals have formed a cognitive map -- are conclusive. While I think the results are quite interesting and sound, one issue that needs to be addressed is the framing of how landmarks are used (or not), as discussed below, although I believe this will be a straightforward issue for the authors to address.

We have now added detailed discussion on this important point. See below.

Strengths:

The 90-degree rotationally symmetric design and use of 4 distal landmarks and 4 quadrants with their corresponding rotationally equivalent locations (REL) lends itself to teasing apart the influence of path integration and landmark-based navigation in a clever way. The authors use a really complete set of experiments and associated controls to show that mice can use a start location and path integration to develop a cognitive map and generate shortcut routes to new locations.

Weaknesses:

I have two comments. The second comment is perhaps major and would require rephrasing multiple sentences/paragraphs throughout the paper.

(1) The data clearly indicate that in the hidden food maze (HFM) task mice did not use external visual "cue cards" to navigate, as this is clearly shown in the errors mice make when they start trials from a different start location when trained in the static entrance condition. The absence of visual landmark-guided behavior is indeed surprising, given the previous literature showing the use of distal landmarks to navigate and neural correlates of visual landmarks in hippocampal formation. While the authors briefly mention that the mice might not be using distal landmarks because of their pretraining procedure - I think it is worth highlighting this point (about the importance of landmark stability and citing relevant papers) and elaborating on it in greater detail. It is very likely that mice do not use the distal visual landmarks in this task because the pretraining of animals leads to them not identifying them as stable landmarks. For example, if they thought that each time they were introduced to the arena, it was "through the same door", then the landmarks would appear to be in arbitrary locations compared to the last time. In the same way, we as humans wouldn't use clouds or the location of people or other animate objects as trusted navigational beacons. In addition, the animals are introduced to the environment without any extra-maze landmarks that could help them resolve this ambiguity. Previous work (and what we see in our dome experiments) has shown that in environments with 'unreliable' landmarks, place cells are not controlled by landmarks - https://www.sciencedirect.com/science/article/pii/S0028390898000537, https://pubmed.ncbi.nlm.nih.gov/7891125/. This makes it likely that the absence of these distal visual landmarks when the animal first entered the maze ensured that the animal does not 'trust' these visual features as landmarks.

Thank you. We have added many references and discussion exactly on this point including both direct behavioral experiments as well as discussion on the effects of landmark (in)stability of place cell encoding of “place”.  See Page 18 third paragraph.

“An alternate factor might be the lack of reliability of distal spatial cues in predicting the food location. The mice, during pretraining trials, learned to find multiple food locations without landmarks. In the random trials, the continuous change of relative landmark location may lead the mice to not identifying them as “stable landmarks”. This view is supported by behavioral experiments that showed the importance of landmark stability for spatial learning (32-34) and that place cells are not controlled by “unreliable landmarks” (35-38). Control experiments without landmarks (Fig. S6A,B) or in the dark (Fig. S6C-F) confirmed that the mice did not need landmarks for spatial learning of the food location.”

(2) I don't agree with the statement that 'Exogenous cues are not required for learning the food location'. There are many cues that the animal is likely using to help reduce errors in path integration. For example, the start location of the rat could act as a landmark/exogenous cue in the sense of partially correcting path integration errors. The maze has four identical entrances (90-degree rotationally symmetric). Despite this, it is entirely plausible that the animal can correct path integration errors by identifying the correct start entrance for a given trial, and indeed the distance/bearing to the others would also help triangulate one's location. Further, the overall arena geometry could help reduce PI error. For example, with a food source learned to be "near the middle" of the arena, the animal would surely not estimate the position to be near the far wall (and an interesting follow-on experiment would be to have two different-sized, but otherwise nearly identical arenas). As the rat travels away from the start location, small path integration errors are bound to accumulate, these errors could be at least partially corrected based on entrance and distal wall locations. If this process of periodically checking the location of the entrance to correct path integration errors is done every few seconds, path integration would be aided 'exogenously' to build a cognitive map. While the original claim of the paper still stands, i.e. mice can learn the location of a hidden food size when their starting point in the environment remains constant across trials. I would advise rewording portions of the paper, including the discussion throughout the paper that states claims such as "Exogenous cues are not required for learning the food location" to account for the possibility that the start and the overall arena geometry could be used as helpful exogenous cues to correct for path integration errors.

We agree with the referee that our claim was ill-phrased. Surely the behavior of the mouse must be constrained by the arena size to some extent. To minimize potential geometric cues from the arena, we carefully analyzed many preliminary experiments (each with a unique batch of 4 mice) having the target positioned at different locations. We added a paragraph to the section “Further controls” where we explain our choice for the target position. Page 12 last paragraph; Page 13 “Arena geometry” paragraph.

Also, following the suggestion from the reviewer, we probed whether the hole checks accumulated near the center of the arena for the random entrance mice, as a potential sign that some spatial learning is going on. In fact, neither the density of hole checks, nor the distance of the hole checks to the center of the arena change with learning: panel A below shows the probability density of finding a hole check at a given distance from the center of the arena; both trial 1 and trial 14 have very similar profiles. Panel B shows the density of hole checks near (<20cm) and far (>20cm) from the arena’s center.

Author response image 1.

It also doesn’t show any significant differences between trials 1 and 14.

So even though there’s some trend (in panel A, the peak goes from 60cm to a double peak, one at 30cm away from the center, and the other still at 60cm), the distance from the center is still way too large compared to the mouse’s body size and to the average inter-hole distance (<10cm). These panels are now in the Supplementary Figure S8B.

Finally, we enhanced the wording in our claim. We now have a new section entitled: “What cues are required for learning the food location?”. There, we systematically cover all possible cues and how they might be affected by their stability under the perturbation of maze floor rotation. 

Reviewer #2 (Public Review):

Summary:

This manuscript reports interesting findings about the navigational behavior of mice. The authors have dissected this behavior in various components using a sophisticated behavioral maze and statistical analysis of the data.

Strengths:

The results are solid and they support the main conclusions, which will be of considerable value to many scientists.

Thank you.

Weaknesses:

Figure 1: In some trials the mice seem to be doing thigmotaxis, walking along the perimeter of the maze. This is perhaps due to the fear of the open arena. But, these paths along the perimeter would significantly influence all metrics of navigation, e.g. the distance or time to reward.

Perhaps analysis can be done that treats such behavior separately and the factors it out from the paths that are away from the perimeter.

In Page 4, we added a small section entitled: “Pretraining trials”. Our reference was suggested by Reviewer #3 (noted as “Golani” with first author “Fonio”). Our preliminary experiments used naïve mice and they typically took greater than 2 days before they ventured into the arena center and found the single filled hole. This added unacceptable delays and the Pretraining trials greatly diminished the extensive thigmotaxis (not quantified). The “near the walls” trajectories did continue in the first learning trial (Fig. 2A, 3A) but then diminished in subsequent trials. We found no evidence that thigmotaxis (trajectories adjacent to the wall) were a separate category of trajectory. 

Figure 1c: the color axis seems unusual. Red colors indicate less frequently visited regions (less than 25%) and white corresponds to more frequently visited places (>25%)? Why use such a binary measure instead of a graded map as commonly done?

Thank you; you are completely correct. We have completely changed the color coding. 

Some figures use linear scale and others use logarithmic scale. Is there a scientific justification? For example, average latency is on a log scale and average speed is on a linear scale, but both quantify the same behavior. The y-axis in panel 1-I is much wider than the data. Is there a reason for this? Or can the authors zoom into the y-axis so that the reader can discern any pattern?

We use logarithmic scale with the purpose of displaying variables that have a wide range of variation (mainly, distance, latency, and number of hole checks, since it linearly and positively correlates with both distance and latency – see new Fig. S4B,C). For example, Latency goes from hundreds of seconds (trial 1) to just a few seconds (trial 14). Similarly, the total distance goes from hundreds of centimeters (trial 1, sometimes more than 1000cm, see answer about the 10-fold variation of distance below) to just the start-target distance (which is ~100cm). These variables vary over a few orders of magnitude. We display speed in a linear axis because it does not increase for more than one order of magnitude.

Moreover, fitting the wide-ranged data (distance, latency, nchecks) yields smaller error in logscale [i.e., fitting log(y) vs. trial, instead of y vs. trial]. In these cases, the log-scale also helps visualizing how well the data was fitted by the curve. Thus, presenting wide-ranged data in linear scale could be misleading regarding goodness of fit.

We now zoomed into the Y axis scale in Panels I of Fig. 2 and Fig. 3. We kept it in log-scale, but linear Y scale produces Author response image 2 for Figs. 3I and 2I, respectively.

Author response image 2.

Thus, we believe that the loglog-scale in these panels won’t compromise the interpretation of the phenomenon. In fact, the loglog of the static case suggests that the probability of hole checking distance increases according to a power law as the mouse approaches the target (however, we did not check this thoroughly, so we did not include this point in the discussion). Power law behavior is observed in other animals (e.g, ants: DOI: 10.1371/journal.pone.0009621) and is sometimes associated with a stochastic process with memory.

1F shows no significant reduction in distance to reward. Does that mean there is no improvement with experience and all the improvement in the latency is due to increasing running speed with experience?

Correct and in the section “Random Entrance experiments” under “Results” (Page 5) we explicitly note this point.

“We hypothesize that the mice did not significantly reduce their distance travelled (Fig. 2A,B,F) because they had not learned the food location - the decrease in latency (Fig. 2D) was due to its increased running speed and familiarity with non-spatial task parameters.”

Figure 3: The distance traveled was reduced by nearly 10-fold and speed increased by by about 3fold. So, the time to reach the reward should decrease by only 3 fold (t=d/v) but that too reduced by 10fold. How does one reconcile the 3fold difference between the expected and observed values?

The traveled distance is obtained by linearly interpolating the sampled trajectory points. In other words, the software samples a discrete set of positions, for each recorded instant 𝑡. The total distance is 

where is the Euclidean distance between two consecutively sampled points. However, the same result (within a fraction of cm error) can be obtained by integrating the sampled speed over time 𝑣! using the Simpson method

Since Latency varies by 10-fold, it is just expected that, given 𝑑 = 𝑣𝑡, the total distance will also vary by 10-fold (since 𝑣 is constant in each time interval Δ𝑡; replacing 𝑣! in the integral yields the discrete sum above).

The correctness of our kinetic measurements can be simply verified by multiplying the data from the Latency panel with the data from the Velocity panel. If this results in the Distance plot, then there is no discrepancy. 

In Author response image 3, we show the actual measured distance, 𝑑_total_, for both conditions (random and static entrance), calculated with the discrete sum above (black filled circles). 

Author response image 3.

We compare this with two quantities: (a) average speed multiplied by average latency (red squares); and (b) average of the product of speed by latency (blue inverted triangles). The averages are taken over mice. Notice that if the multiplication is taken before the average (as it should be done), then the product 〈𝑣𝑡〉45*( is indistinguishable from the total distance obtained by linear interpolation. Even taking the averages prior to the multiplication (which is physically incorrect, since speed and latency and properties of each individual mouse), yields almost exactly the same result (well within 1 standard deviation).

The only thing to keep in mind here is that the Distance panel in the paper presents the normalized distance according to the target distance to the starting point. This is necessary because in the random entrance experiments, each mouse can go to 1 of 4 possible targets (each of which has a different distance to the starting point).

Figure 4: The reader is confused about the use of a binary color scheme here for the checking behavior: gray for a large amount of checking, and pink for small. But, there is a large ellipse that is gray and there are smaller circles that are also gray, but these two gray areas mean very different things as far as the reader can tell. Is that so? Why not show the entire graded colormap of checking probability instead of such a seemingly arbitrary binary depiction?

Thank you. Our coloring scheme was indeed poorly thought out and we have changed it. Hopefully the reviewer now finds it easier to interpret. The frequency of hole checks is now encoded into only filled circles of varying sizes and shades of pink. Small empty circles represent the arena holes (empty because they have no food); The large transparent gray ellipse is the variance of the unrestricted spatial distribution of hole checks.

Figure 4C: What would explain the large amount of checking behavior at the perimeter? Does that occur predominantly during thigmotaxis?

Yes. As mentioned above, thigmotaxis still occurs in the first trial of training. The point to note is that the hole checking shown in Fig. 4C is over all the mice so that, per mice, it does not appear so overwhelming. 

Was there a correlation between the amount of time spent by the animals in a part of the maze and the amount of reward checking? Previous studies have shown that the two behaviors are often positively correlated, e.g. reference 20 in the manuscript. How does this fit with the path integration hypothesis?

We thank the reviewer for pointing this out. Indeed, the time spent searching & the hole checking behavior are correlated. We added a new panel C to Fig. S4 showing a raw correlation plot between Latency and number of checks. 

Also, in the last paragraph of the “Revealing the mouse estimate of target position from behavior” section under “Results”), we now added a sentence relating the findings in Fig. 4H and 4K (spatial distribution of hole checks, and density of checks near the target, respectively) to note that these findings are in agreement with Fig 3C (time spent searching in each quadrant).

“The mean position of hole checks near (20cm) the target is interpreted as the mouse estimated target (Fig. 4C,D,G,H; green + sign=mean position; green ellipses = covariance of spatial hole check distribution restricted to 20cm near the target). This finding together with the displacement and spatial hole check maps (Figs. 4F and 4H, respectively) corroborates the heatmap of time spent in the target quadrant (Fig. 3C), suggesting a positive correlation between hole checks and time searching (see also Fig. S4C).”

"Scratches and odor trails were eliminated by washing and rotating the maze floor between trials." Can one eliminate scratches by just washing the maze floor? Rotation of the maze floor between trials can make these cues unreliable or variable but will not eliminate them. Ditto for odor cues.

The upper arena floor is rotated between trials so that any scratches will not be stable cues. We clarified this in the Discussion about potential cues. 

See “What cues are required for learning the food location?”

"Possible odor gradient cues were eliminated by experiments where such gradients were prevented with vacuum fans (Fig. S6E)" What tests were done to ensure that these were *eliminated* versus just diminished?

"Probe trials of fully trained mice resulted in trajectories and initial hole checking identical to that of regular trials thereby demonstrating that local odor cues are not essential for spatial learning." As far as the reader can tell, probe trials only eliminated the food odor cues but did not eliminate all other odors. If so, this conclusion can be modified accordingly.

We were most worried about odor cues guiding the mice and as now described at great length, we tried to mitigate this problem in many ways. As the reviewer notes, it is not possible to have absolute certainty that there are no odor cues remaining. The most difficult odor to eliminate was the potential odor gradient emanating from the mouse’s home cage. However, the 2 vacuum fans per cage were very powerful in first evacuating the cage air (150x in 5 minutes) and then drawing air from the arena, through the cage and out its top for the duration of each trial. We believe that we did at least vastly reduce any odor cues and perhaps completely eliminated them.

The interpretation of direction selectivity is a bit tricky. At different places in this manuscript, this is interpreted as a path integration signal that encodes goal location, including the Consync cells. However, studies show that (e.g. Acharya et al. 2016) direction selectivity in virtual reality is comparable to that during natural mazes, despite large differences in vestibular cues and spatial selectivity. How would one reconcile these observations with path integration interpretation?

Thank you. We had not been serious enough in considering the VR studies and their implications for optic flow as a cue for spatial learning. We now have a section (Optic flow cues) in the Discussion that acknowledges the potential role of such cues in spatial learning in our maze. 

However, spatial learning in our maze can also occur in the dark. The next small section (Vestibular and proprioceptive cues) addresses this point. We cannot be certain about the precise cues used by the mouse to effectively learn to locate food in our maze, but it will take further behavioral and electrophysiological studies to go deeper into these questions. 

An extended discussion is found in the sections entitled “What cues are required for learning the food location” and “A fixed start location and self-motion cues are required for spatial learning”.  We may have missed some references or ideas regarding VR maze learning with optic flow signals – the Acharya et al reference was an excellent starting point, and we would be grateful for additional pointers that would improve our discussion of this point.

The manuscript would be improved if the speculations about place cells, grid cells, BTSP, etc. were pared down. I could easily imagine the outcome of these speculations to go the other way and some claims are not supported by data. "We note that the cited experiments were done with virtual movement constrained to 1D and in the presence of landmarks. It remains to be shown whether similar results are obtained in our unconstrained 2D maze and with only self-motion cues available." There are many studies that have measured the evolution of place cells in non- virtual mazes, look up papers from the 1990s. Reference 43 reports such results in a 2D virtual maze.

We understand the reviewer’s concerns with the length of the manuscript. However, both the first and third reviewer did find this extensive section useful. We did not add the many papers on the evolution of place fields in real world mazes simply to prevent even greater expansion of the discussion, but relied on the very thorough review of Knierim and Hamilton instead. 

Reviewer #3 (Public Review):

Summary:

How is it that animals find learned food locations in their daily life? Do they use landmarks to home in on these learned locations or do they learn a path based on self-motion (turn left, take ten steps forward, turn right, etc.). This study carefully examines this question in a well-designed behavioral apparatus. A key finding is that to support the observed behavior in the hidden food arena, mice appear to not use the distal cues that are present in the environment for performing this task. Removal of such cues did not change the learning rate, for example. In a clever analysis of whether the resulting cognitive map based on self-motion cues could allow a mouse to take a shortcut, it was found that indeed they are. The work nicely shows the evolution of the rodent's learning of the task, and the role of active sensing in the targeted reduction of uncertainty of food location proximal to its expected location.

Strengths:

A convincing demonstration that mice can synthesize a cognitive map for the finding of a static reward using body frame-based cues. This shows that the uncertainty of the final target location is resolved by an active sensing process of probing holes proximal to the expected location. Showing that changing the position of entry into the arena rotates the anticipated location of the reward in a manner consistent with failure to use distal cues.

Thank you.

Weaknesses:

The task is low stakes, and thus the failure to use distal cues at most costs the animal a delay in finding the food; this delay is likely unimportant to the animal. Thus, it is unclear whether this result would generalize to a situation where the animal may be under some time pressure, urgency due to food (or water) restriction, or due to predatory threat. In such cases, the use of distal cues to make locating the reward robust to changing start locations may be more likely to be observed.

We have added “Combining trajectory direction and hole check locations yields a Target Estimation Vector” a section summarizing our main hypotheses and this section includes noting exactly this point + including the reference to the excellent MacIver paper on “robot aggression”.

The main point here follows the Knierim and Hamilton review and assumes that learning “heading direction” and “distance from start to food” require different cues and extraction mechanisms.  “Here we follow a review by Knierim and Hamilton (12) suggesting independent mechanisms for extraction of target direction versus target distance information. Averaging across trajectories gave a mean displacement direction, an estimate of the average heading direction as the mouse ran from start to food. The heading direction must be continuously updated as the mice runs towards the food, given that the mean displacement direction remains straight despite the variation across individual trajectories. Heading direction might be extracted from optic flow and/or vestibular system and be encoded by head direction cells. However, the distance from home to food is not encoded by head direction signals.”

And

“We hypothesize that path integration over trajectories is used to estimate the distance from start to food. The stimuli used for integration might include proprioception or acceleration (vestibular) signals as neither depends on visual input. Our conclusion is in accord with a literature survey that concluded that the distance of a target from a start location was based on path integration and separate from the coding of target heading direction (12). Our “in the dark” experiments reveal the minimal stimuli required for spatial learning – an anchoring starting point and directional information based on vestibular and perhaps proprioceptive signals. This view is in accord with recent studies using VR (47, 48). Under more naturalistic conditions, animals have many additional cues available that can be used for flexible control of navigation under time or predation pressure (51).”.

Furthermore, we added panel G do Fig S4, where we show the evolution of the heading angle along the trajectory, plotted as a function of the trials. We see that the mouse only steer towards the target in the last segment of the trajectory, consistent with having the head direction being continuously updated along the path to the food.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

All three reviewers agreed during the consultation that the context in which distal cues are described in the manuscript would benefit significantly from refinement. The distal cues may be made completely useless from an ethological perspective e.g. if they are seen as "moving" relative to the entrance point (i.e. if the animal were to think it were entering the same location), then the cues would appear as unstable in the random entrance. As such, they may be so unlike natural experiences as to be potentially confusing to the animal. Moreover, as reported in some of the reviews, the animals may be using the entrances and boundaries as cues to help refine path integration. The results are still very interesting, but more refinement in the text on the interpretation of cues would greatly improve the manuscript. Thus, we recommend that you revise your manuscript to address the reviews.

Thank you. We agree with this recommendation of the reviewers have greatly expanded our discussion on cue stability as already indicated above. 

Should you choose to revise your manuscript, pleasse ensure the manuscript include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.

Done

Lastly, I want to personally apologize for the long delay in editing this manuscript. All three reviews were unfortunately quite delayed, including my own review. I want to thank you for submitting your work to eLife and hope that we can be more efficient in editing your work in the future.

It was a long review process, but we also appreciate that our article was dense and difficult to read. We tried to be comprehensive in our controls and analyses and we appreciate the considerable effort it must have taken to carefully review our paper.

Reviewer #3 (Recommendations For The Authors):

I quite enjoyed this paper and have some suggestions for further improvement.

First, while I appreciate that the format of the journal has Methods at the end, there are some key details that need to be moved forward in the study for proper appreciation of the results. These include:

(1) Location and size of distal cues.

Done

(2) Use of floor washing between mice.  

Done

(3) Use of food across the subfloor to provide some masking of the location of the food reward.

Done

(4) A scale bar on one of the early figures showing the apparatus would be beneficial.

Done for Figure 1 where we also provide arena diameter and area.

(5) Motivational state of the mouse with respect to the food reward (in this case, not food restricted, correct?).

Done

Although we are told the trial where learning is defined to have occurred, we were not given the quantitative criterion operationalizing "learning" - please provide (unless I missed it!).

Thank you.  This question turned out to be of importance and led to more detailed analyses and related Discussion. We therefore answer in depth.

We now realize that learning the distance to food versus learning the direction to food must be analyzed separately.

On Page 5 second paragraph we provide a definition of “learning distance to food”.

“Fitting the function dtotal \= B*exp(-Trial/K) reveals the characteristic timescale of learning, K, in trial units (Fig. 2F). We obtained K= 26±24 giving a coefficient of variation (CV) of 0.92. The mean, K=26, is therefore very uncertain and far greater than the actual number of trials. Thus, we hypothesize that the mice did not significantly reduce their distance travelled (Fig. 2A,B,F) because they had not learned the food location – the decrease in latency (Fig. 2D) was due to its increased running speed and familiarity with non-spatial task parameters. ”

On Page 7 second paragraph the same analysis gives:

“Now the fitting of the function dtotal\=B exp(-Trial/K) yielded K\=5.6±0.5 with a CV = 0.08; the mean is therefore a reliable estimate of total distance travelled. We interpret this to indicate that it takes a minimum number of K= 6 trials for learning the distance to the target (see also Fig. S4D,E,F,G).

Learning is still not complete because it takes 14 trials before the trajectories become near optimal.”

Learning of distance to food is evident by Trial 6 but is not complete.

On Page 9 third paragraph we give a very precise answer to time taken to learn the direction from start to food. This was already very clear from Fig. 4I but we had missed the significance of this result. 

“We compared the deviation between the TEV and the true target vector (that points from start directly to the food hole; Fig. 4I). While the random entrance mice had a persistent deviation between TEV and target of more than 70o, the static entrance mice were able to learn the direction of the target almost perfectly by trial 6 (TEV-target deviation in first trial mean±SD = 57.27o ± 41.61o; last trial mean±SD = 5.16o ± 0.20o; P=0.0166). A minimum of 6 trials is sufficient for learning both the direction and distance to food (Fig. 4I) (Fig. 3F) (see Discussion). The kinetics of learning direction to food are clearly different from learning distance to food since the direction to food remains stable after Trial 6 while the distance to food continues to approach the optimal value.”

Learning the direction from start to food is completely learned by Trial 6. 

These analyses led to an addition to the Discussion on Page 20 (following the Heading).

“Here we follow a review by Knierim and Hamilton (12) that hypothesized independent mechanisms for extraction of target direction versus target distance information. Our data strongly supports their hypothesis. Target direction is nearly perfectly estimated at trial 6 (Fig. 4I and Results). The deviation of the TEV from the start to food vector is rapidly reduced to its minimal value (5.16o) and with minimal variability (SD=0.20o). Learning the distance from start to food is also evident at trial 6 but only reaches an asymptotic near optimal value at trial 14 (Fig. 3F). The learning dynamics are therefore very different for target direction versus target distance. As noted below, the food direction is likely estimated from the activity of head direction cells. The neural mechanisms by which distance from start to food is estimated are not known (but see (49)).”

We believe that this small addition summarizes the complicated answer to the reviewer’s question and is helpful in better connecting the Knierim and Hamilton paper to our data. However, if the reviewers and editors feel that we have gone too far or that this discussion is not clear, we can remove or alter the extra sentences as per any comments. 

Reference #49 is to a review paper on spatial learning in weakly electric fish in the dark (https://doi.org/10.1016/j.conb.2021.07.002). The review summarizes data on a neural “time stamp” mechanism for estimating distance from start to food. In this review article, we explicitly hypothesized that rodents might utilize such a time stamp mechanism for finding food. We did not include this in the discussion because it was too distracting and would likely confuse readers but put in the reference in case some readers did want to access the “time stamp” hypothesis for spatial learning in the dark. 

Second, the discussion was thoughtful and rich. I particularly enjoyed the segment describing the likely computations of the hippocampus. There are a few thoughts I have for the authors to think about that might be useful to potentially add to the discussion:

"The remaining one, mouse 34, went from B to the start location and then, to A."

This out-and-back pattern has been seen in the literature, such as multiple papers by Golani (here's one: https://www.pnas.org/doi/full/10.1073/pnas.0812513106). Would the authors speculate, given their suggested algorithm, what the significance of out and back may be? Is there something about the cell's encoding of direction and distance that requires a return to the start location, and would this be different if representation is based on self-motion versus based on distal cues in an allocentric representation?

We do discuss this for pretraining trials but have no idea what this mouse is doing in this case.

In a low-stakes task environment, for an animal that has a low acuity visual system, where the penalty for not using distal cues is at most some additional (likely enriching in itself to these mice who live a fairly unenriched life in small cages) search/learning/exploration time, perhaps it is not so surprising that body-frame cues are used. Considering the ethology of the animal, if it had multiple exits of an underground burrow, it might need to use distal cues to avoid confusion. The scenario you provide to the animal is essentially a deceptive one where it has no way of telling it is coming out to the arena from a different burrow hole, modulo some small landmarks on an otherwise uniform cylinder of space. This might be asking too much of an animal where the space it would enter normally would not be a uniform cylinder.

What happens with a higher-stakes case? This is clearly a different study, but you may find some recent work with a mobile predatory robot of interest (https://www.sciencedirect.com/science/article/pii/S2211124723016820). Visual cues are crucial in the avoidance of threats in this case. Re-routing, as shown by multiple videos of that study, is after a brief pause, and seemingly takes into account the likely future position of the threat.

Done. A fascinating paper that illustrates the unexpected “high level” behavior a rodent is capable of when placed in more naturalistic situations. I think our “two food location” experiments are along the same direction – unexpected rich behavior when the mouse are challenged.

Connected to the low-stakes vs high-stakes point, it might be nice for the paper to discuss situations in which cognitive-map-based spatial problem solutions make sense versus not.

Here is an example of such a discussion, around page 496:

https://www.dropbox.com/scl/fi/ayoo5w4jgnkblgfu7mpad/MacI09a_situated_cog.pdf?

rlkey=2qhh89ii7jbkavt6ivevarvdk&dl=0.

Right a very relevant discussion by MacIver. However, when I tried to write it in it took nearly half a page of dense writing to connect to the themes of our article. I figured that the already long discussion will try the patience of most readers and so decided to not include this extra discussion.

Minor points/ queries

Why the increase in sample density at about the 1/4 radius of arena distance? Static, trial 14, Figure 3I, shown also maybe Figure 4 H.

We were also puzzled when this occurred but have no explanation. And there are, in our figures, many other examples of the mice hole checking near their exit site. See next answer.

Why was the hole proximal to start so often probed in 7B?

We were also puzzled when this occurred but have no explanation.

Check Video 1 to exactly see this behavior. The mouse exits its home and immediately checks a nearby hole. It proceeds to Site B (empty) and then Site A (empty) with many hole checks along the way. After leaving Site A, the mouse proceeds to the wall located far from an entrance and does another hole check. The near the wall holes that are checked are in no way remarkable: a) they have never contained food; b) they are rotated between trials, and we wash the floor carefully, so they do not “smell” any particular hole; c) the food on the lower level floor is in no way “clumped” under that hole, etc.

We have discussed this phenomenon quite a lot and LM was able to come up with only one hypothesis for this behavior. In analogy to the electric fish work (responses of diencephalic neurons to “leaving or encountering a landmark”), the “near the entrance” hole check might be an active sensing probe to “time stamp” the exit from home while finding food would “time stamp” the end of a successful trajectory. Path integration between time stamps would then provide the estimate for time/distance from start to food – exactly our hypothesis for weakly electric fish spatial learning in the dark. This hypothesis is exceedingly speculative and so we do not want to include it.  

Normally I would cite a line number. Since I do not see line numbers, I will leave it to you to do a search:

"A than the expected by chance" -> "than expected"

Done. I apologize for the lack of line numbers. I have, so far, been unable to get Word to confine line numbers to selected text and not run over onto the Figure Legends. I have put in page numbers and hope this helps.

RW, VR, MWM, etc - please expand the acronym on first use.

Done

It might be interesting to see differences in demand/reliance on active sensing in the individuals who learn the task less well than the animals who learn the task well. If the point is to expunge uncertainty, then does the need for such expunging increase with the poverty of internal representation resolution / fewer decimal places on the internal TEV calculation?

We do have variation in the mice learning time but the numbers are not sufficient for this interesting extension. This is just one of many follow up studies we hope to carry out.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

The crystal structure of the Sld3CBD-Cdc45 complex presented by Li et al. is a novel contribution that significantly advances our understanding of CMG formation during the rate-limiting step of DNA replication initiation. This structure provides insights into the intermediate steps of CMG formation. The study builds upon previously known structures of Sld3 and Cdc45 and offers new perspectives into how Cdc45 is loaded onto MCM DH through Sld3-Sld7. The most notable finding is the structural difference in Sld3CBD when bound to Cdc45, particularly the arrangement of the α8-helix, which is essential for Cdc45 binding and may also pertain to its metazoan counterpart, Treslin. Additionally, the conformational shift in the DHHA1 domain of Cdc45 suggests a possible mechanism for its binding to MCM2NTD.

Strengths:

The manuscript is generally well-written, with a precise structural analysis and a solid methodological section that will significantly advance future studies in the field. The predictions based on structural alignments are intriguing and provide a new direction for exploring CMG formation, potentially shaping the future of DNA replication research.

Weaknesses:

The main weakness of the manuscript lies in the lack of experimental validation for the proposed Sld3-Sld7-Cdc45 model. Specifically, the claim that Sld3 binding to Cdc45-MCM does not inhibit GINS binding, a finding that contradicts previous research, is not sufficiently substantiated with experimental evidence. To strengthen their model, the authors must provide additional experimental data to support this mechanism. Also, the authors have not compared the recently published Cryo-EM structures of the metazoan CMG helicases with their predicted models to see if Sld3/Treslin does not cause any clash with the GINS when bound to the CMG. Still, the work holds great potential in its current form but requires further experiments to confirm the authors' conclusions.

We appreciate the reviewers’ careful reading and the comments.

Our structural analysis of Sld3CBD-Cdc45 showed the detailed interaction map between Sld3CBD and Cdc45 at 2.6 Å resolution. The Sld3, MCM and GINS binding sites of Cdc45 completely differed, suggesting that the Sld3CBD, Cdc45 and GINS could bind to MCM together. The SCMG-DNA model confirmed such a binding manner, although our study does not show how this binding manner affects the GINS loading by other initiation factors (Dpb11, Sld2, et. al). Regarding the previous studies, competition of Sld3 and GINS for binding to Cdc45 or Cdc45-MCM (Bruck et. al), which may be caused by the conformation change of Cdc45 DHHA1 between Sld3CBD-Cdc45 and CMG. We modified our manuscript and discussed (P7/L168-173, and P10/L282-286). Following the comment, we checked the recently published Cryo-EM structure (PDBID:8Q6O) with their predicted models of the metazoan CMG helicases (P7/L198-P8/L202) and added the Cdc45 mutation experiments to confirm our conclusion ([Recommendations for the authors] Q18).

Reviewer #2 (Public review):

Summary

The manuscript presents valuable findings, particularly in the crystal structure of the Sld3CBD-Cdc45 interaction and the identification of additional sequences involved in their binding. The modeling of the Sld7-Sld3CBD-CDC45 subcomplex is novel, and the results provide insights into potential conformational changes that occur upon interaction. However, the work remains incomplete as several main claims are only partially supported by experimental data, particularly the proposed model for Sld3 interaction with GINS on the CMG. Additionally, the single-stranded DNA binding data from different species do not convincingly advance the manuscript's central arguments.

Strengths

(1) The Sld3CBD-Cdc45 structure is a novel contribution, revealing critical residues involved in the interaction.

(2) The model structures generated from the crystal data are well presented and provide valuable insights into the interaction sequences between Sld3 and Cdc45.

(3) The experiments testing the requirements for interaction sequences are thorough and conducted well, with clear figures supporting the conclusions.

(4) The conformational changes observed in Sld3 and Cdc45 upon binding are interesting and enhance our understanding of the interaction.

(5) The modeling of the Sld7-Sld3CBD-CDC45 subcomplex is a new and valuable addition to the field.

Weaknesses

(1) The proposed model for Sld3 interacting with GINS on the CMG needs more experimental validation and conflicts with published findings. These discrepancies need more detailed discussion and exploration.

Our structural analysis experiment of Sld3CBD-Cdc45 showed the detailed interaction information between Sld3CBD and Cdc45 at 2.6 Å resolution. The Sld3CBD-binding site of Cdc45 is completely different from that of GINS and MCM binding to Cdc45, suggesting that the Sld3CBD, Cdc45, and GINS could bind to MCM together. The SCMG-DNA model confirmed such a binding manner. Following the comment, we added a Cdc45 mutant analysis, disrupting the binding to MCM and GINS but not affecting the Sld3CBD binding (Supplementary Figure 9). Our model is consistent with the GINS-loading requirement (the phosphorylation of Sld3 on Cdc45-MCM) and has no discrepancies with the stepwise loading fashion (Please see the responses to [Recommendations for the authors] Reviewer#1-Q14-15]). Regarding the previous studies, competition of Sld3 and GINS for binding to Cdc45 or Cdc45-MCM (Bruck et. al), by in vitro binding experiments, please see the responses to [Recommendations for the authors] Q6.

(2) The section on the binding of Sld3 complexes to origin single-stranded DNA needs significant improvement. The comparisons between Sld3-CBD, Sld3CBD-Cdc45, and Sld7-Sld3CBD-Cdc45 involve complexes from different species, limiting the comparisons' value.

As suggested, we tried to improve the ssDNA-binding section (Please see the responses to [Recommendations for the authors]: Q4 and Q5). We used Sld7-Sld3CBD-Cdc45 from different sources due to limitations in protein expression. These two sources belong to the same family and the proteins Sld7, Sld3 and Cdc45 have sequence conservation with similar structures predicted by the alphafold3 (RMSD = 0.356, 1.392, and 0.891 for Ca atoms of Sld7CTD, Sld7NTD-Sld3NTD, and Sld3CBD-Cdc45). Such similarity in source and protein lever allows us to do the comparison.

(3) The authors' model proposing the release of Sld3 from CMG based on its binding to single-stranded DNA is unclear and needs more elaboration.

Considering that ssDNA (ssARS1) is produced by CMG, the ssDNA-binding of Sld3 should happen after forming an active CMG. Therefore, the results of ssDNA binding experiments implied that the Sld3 release could be with the binding to ssDNA produced by CMG. We tried to present more elaborations in the revised version. (Please see the responses to [Recommendations for the authors] Q4, Q5).

Reviewer #3 (Public review):

Summary:

The paper by Li et al. describes the crystal structure of a complex of Sld3-Cdc45-binding domain (CBD) with Cdc45 and a model of the dimer of an Sld3-binding protein, Sld7, with two Sld3-CBD-Cdc45 for the tethering. In addition, the authors showed the genetic analysis of the amino acid substitution of residues of Sld3 in the interface with Cdc45 and biochemical analysis of the protein interaction between Sld3 and Cdc45 as well as DNA binding activity of Sld3 to the single-strand DNAs of the ARS sequence.

Strengths:

The authors provided a nice model of an intermediate step in the assembly of an active Cdc45-MCM-GINS (CMG) double hexamers at the replication origin, which is mediated by the Sld3-Sld7 complex. The dimer of the Sld3-Sld7 complexes tethers two MCM hexamers together for the recruitment of GINS-Pol epsilon on the replication origin.

Weaknesses:

The biochemical analysis should be carefully evaluated with more quantitative ways to strengthen the authors' conclusion.

We thank your positive assessment. We provided more quantitative information and tried to quantify the experiments as suggested (Please see the responses to [Recommendations for the authors]).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have several concerns that I will outline below, accompanied by my suggestions.

(1) "The title of the paper- "Structural and functional insights into Cdc45 recruitment by Sld7-Sld3 for CMG complex Formation," appears misleading because it appears that authors present a structure of Sld3-Sld7 in complex with Cdc45, which is not the case here. If authors can provide additional structures proving the function of this complex, then this title justifies it. Otherwise, I recommend making a title that justifies the presented work in its current form.

Following the comment, we change the title to “Sld3CBD-Cdc45 structural insights into Cdc45 recruitment for CMG complex formation”.

(2) In lines 70-72, where the authors mention the known structures of different proteins, intermediates, and complexes, I recommend including PDB IDs of the described structures and reference citations. This will help the readers to analyze what is missing in the pathway and why this structure is essential.

Following the comment, we added PBDIDs and references (P3/L72-74).

(3) The representation of Figure 1A is unclear and looks clumsy. If the structure were rotated in another orientation, where α8 and α9 would be displayed on the forward side, it would be more helpful to understand the complex forming regions by looking at the structure. Also, I recommend highlighting the α8 and α9 in a contrasting color to be easily visible and attract readers' attention. Similarly, it would also be helpful if DHAA1 would be shown in a different color.

Following the comment, we modified the Figure1 to show α8 and α9 of Sld3CBD and DHAA1 of Cdc45 clearly in revised version.

(4) Can authors add a supplementary figure showing the probability of disorderness of the α8 helix region in the Sld3? Also, highlight what region became ordered in their structure.

Yes, we have showed the disordered α8 helix region and highlight ordered α8 in the Sld3 in Figure S4 A.

(5) Can you compare the Cdc45 long distorted helix (Supplementary Figure 3B) in the Sld3-Cdc45 complex with the Xenoupus and drosophila Cdc45 from their CMG structures? Also, can the authors explain why this helix is destabilized in their structure but is relatively stable in another Cdc45 structure (in CMG and HuCdc45)?

We have checked all Cdc45 from published cryo-EM CMG structures, including Xenopus CMG-donson (8Q6O) and Drosophila CMG (6RAW), and all of them ordered the long helix in the CMG complex, whereas this long helix was disordered in the crystal structure of Sld3CBD-Cdc45 and Entamoeba histolytica Cdc45. The crystal packing around the long helix showed that it looks to be stabilized by crystal packing only in huCdc45, therefore we suggested that this long helix is detestable for crystallization.

(6) I recommend adding the following parameters to Supplementary Table 2: 1. Rmerge values, 2. Wilson B factor, 3. Average B factor, and 4. Total number of molecules in ASU.

We are sorry to make a mistake about Rmerge in Table 2. We correct it. We added the Wilson B factor, the average B factor, and the total number of Sld3CBD-Cde45 in ASU.

(7) Can authors provide the B factor values of the α8 helix of Sld3?

We checked the B factor values of the helix α8CTP of Sld3 in Sld3CBD-Cdc45. Since this helix binds to Cdc45 stably, the average B factor of the main chain is 45 Ų less than that of the whole structure. We added the average B factor of helix α8CTP into the Supplementary Figure 4A legend.

(8) Can authors explain why higher Ramachandran outliers exist in their structure? Can it be reduced below 1% during refinement?

There are 13 outliers (1.67%) in different places: four are close to the disorder regions (poor electron map), four are in a loop with poor map and the remains are turn parts or a loop. For the residues with poor electron maps, we could not modify them to the allow Ramachandran region with low Rfree value, so we could not reduce them to below 1% during refinement while keeping the current Rfree value.

(9) In Supplementary Figure 8, please show the CD spectra of the Sld3WT. Why is the Sld3-3S peak relatively flat? Was the sample precipitating while doing the measurements, or does it have less concentration than others?

To check the folding of the mutants, we did CD experiments with the estimated secondary structure elements. Because WT Sld3CBD was prepared in a complex with Cdc45, while the mutants of Sld3CBD existed along, we calculated the elements of secondary structure from the crystal structure of Sld3CBD-Cdc45. The concentration of samples was controlled to the same level for CD measurement. The relative plat of the Sld3-3S peak may be caused by precipitating while doing the measurement.

(10) Can authors generate the alpha fold three models of the Sld3CBD-Cdc45-MCM-dsDNA and SCMG-dsDNA and compare them with the models they have generated?

We tried to predict the Sld3CBD-Cdc45-MCM-dsDNA and SCMG-dsDNA using Alphafold3. Although the results showed similar structures to our models, many parts were disordered. So, we did not use the predicted structures.

(11) The authors say that the overall molecular mass of the Sld7-Sld3ΔC-Cdc45 was >400kDa on the SEC column. However, the column used for purifying this complex and the standards that were run on it for molecular weight calculations have not been written anywhere. If the Superdex 200 column was used, then the sample of more than 400kDa should not elute at the position shown in Supplementary Figure 2B. I recommend showing the standard MW plot and where the elution volume of the Sld7-Sld3ΔC-Cdc45 lies on the standard curve. Also, add how molecular weight calculations were done and the calculated molecular mass.

Following the comment, we added a measurement of Superdex 200 16/60 column (SEC) using a standard sample kit into Supplementary Figure 2 to show that the molecular weight of the peak at the position was estimated to be > 400 k Da.

(12) I also recommend using at least one of the techniques, either SEC-MALS or AUC, to calculate the actual molecular mass of the Sld7-Sld3ΔC-Cdc45 complex and to find its oligomeric state. If the authors want to prove their hypothesis that a dimer of this complex binds to MCMDH, it is essential to show that it exists as a dimer. Based on the current SEC profile, it appears as a monomer peak if the S200 SEC column is being used.

As the response to (11), we added the standard MW plot (measurement using Superdex 200 16/60 column) using a standard sample kit. The molecular weight at the peak elution position of Sld7-Sld3ΔC-Cdc45 was estimated to be 429k Da. Considering that the Sld7-Sld3ΔC-Cdc45 dimer should be a flexible long-shaped molecule, the elution position could be at a larger molecular weight position than the real one (158 x 2 k Da). We also tried to confirm the particle size using SEC-SAXS, as the response to the next question (13).

(13) Dynamic light scattering is not the most accurate method for calculating intermolecular distance. I recommend using another technique that calculates the accurate molecular distances between two Cdc45 if Sld7-Sld3ΔC-Cdc45 is forming a dimer. Techniques such as FRET could be used. Otherwise, some complementary methods, such as SAXS, could also be used to generate a low-resolution envelope and fit the speculated dimer model inside, or authors could try negative staining the purified Sld7-Sld3ΔC-Cdc45 and generate 2D class averages and low-resolution ab initio models to see how the structure of this complex appears and whether it satisfies the speculated model of the dimeric complex.

We have tried both negative staining TEM and SEC-SAXS experiments. We could not obtain images good enough of negative staining of TEM to generate 2D class averages and low-resolution ab initio models. The results of SEC-SAXS provided a molecular weight of 370 - 420 kDa, and an Rg > 85 Å, which are consistent with our conclusion from SEC and DLS results but with large error due to the measurement temperature at 10-15°C (measuring equipment limitation). The peak of SCE-SAXS under measurement conditions was not as sharp as purification at 4°C and SAXS data is not good enough to make a molecular model, so we did not add them to our manuscript.

(14) Authors mentioned in the introduction section (lines 72-73) that based on the single-molecule experiments, Cdc45 is recruited in a stepwise manner to MCMDH. If this is true and if Sld7-Sld3ΔC-Cdc45 forms a dimer, this is also true, then for stepwise recruitment, the dimer will have to break into monomers, and this will be an energy-expensive process for the cell. So, would such a process occur physiologically? Can the authors explain how this would physiologically happen inside the cell?

Sld7-Sld3-Cdc45 consists of domains linked by long loops, so the dimer Cdc45-Sld3-[Sld7]2-Sld3-Cdc45 is flexible long-sharp. Such a flexible dimer does not mean that two Cdc45 molecules must bind to MCM DH simultaneously and may bind to MCM DH by stepwise manner. The dimer formation of Sld7-Sld3-Cdc45 is advantageous for recruiting efficiently and saving energy. Moreover, our proposal of Cdc45-Sld3-[Sld7]2-Sld3-Cdc45 on MCM DH could be a stage during CMG formation in the cell. Following the comment, we added such descriptions (P7/L194, and P10/L276-279).

(15) Can authors show experimentally that a dimer of Sld7-Sld3ΔC-Cdc45 is binding to MCMDH and not a monomer in a stepwise fashion?

In our study, we provided experiments of particle size to show the dimer of Sld7-Sld3-Cdc45 off MCM DH and a model of SCMG to indicate the dimer of Sld7-Sld3ΔC-Cdc45 on MCM DH. This question should be addressed future by the Cryo-EM of Sld7-Sld3-Cdc45-MCM DH or Sld7-Sld3-CMG. As the response to Q14, the flexible dimer of Sld7-Sld3ΔC-Cdc45 binding on MCMDH does not contradict the stepwise-loading fashion. The dimer of Sld7-Sld3ΔC-Cdc45 binding on MCM DH shows a stage.

(16) Can authors highlight where Sld7 will lie on their model shown in Figures 3A and 3C, considering their model shown in 3B is true?

We predict that the Sld7-Sld3-Cdc45 should be in a dimer form of Cdc45-Sld3-[Sld7]2-Sld3-Cdc45 based on the structures and the particle size analysis. The Sld7 dimer could be across MCM DH on the top of Figure 3A right and 3C right. However, we could not add the Sld7 molecule to the models because there is no interaction data between Sld7 and MCM.

(17) In Supplementary Figure 10, can authors show the residues between the loop region highlighted in the dotted circle to show that there is no steric clash between the residues in that region of their predicted model?

Following the comment, we added the residues in Supplementary Figure 10 (Supplementary Figure 11 in the revised version) to show no steric clash in our predicted model.

(18) It is essential to show experimentally that Sld3CBD neighbors MCM2 and binds Cdc45 on the opposite side of the GINS binding site. I recommend that the authors design an experiment that proves this statement. Mutagenesis experiments for the predicted residues that could be involved in interaction with proper controls might help to prove this point. Since this is the overall crux of the paper, it has to be demonstrated experimentally.

We thank the reviewer’s recommendation. Our structural analysis experiment shows the interaction information between Sld3CBD and Cdc45 at 2.6 Å resolution. The Sld3CBD-binding site, GINS-binding site, and MCM-binding site of Cdc45 are completely different, indicating that the Sld3CBD, Cdc45 and GINS could bind to MCM together. The SCMG model confirmed such a binding manner. Following the recommendation, we added mutant analysis of Cdc45 G367D and W481R, which was reported to disrupt the binding to MCM and GINS, respectively. Both mutants do not affect the binging to Sld3CBD as we predicted (Supplementary Figure 9B). We modified our manuscript and discussed this point more clearly (P7/L170-173).

(19) I recommend rewriting the sentence in lines 208-210. During EMSA experiments, new bands do not appear; instead, there is no shift at lower ratios, so you see a band similar to the control for Sld3CBD-Cdc45. So, re-write the sentence correctly to avoid confusion when interpreting the result.

Following the comment, we rewrote this sentence to "The ssDNA band remained (Figure 4B) and new bands corresponding to the ssDNA–protein complex appeared in CBB staining PAGE (Supplementary Figures 13) when the Sld3CBD–Cdc45 complex was mixed with ssDNA at the same ratio, indicating that the binding affinity of Sld3CBD–Cdc45 for ssDNA was lower than that of Sld3CBD alone” (P8/L226-229)

(20) Since CDK-mediated phosphorylation of Sld3 is known to be required for GINS loading, the ssDNA binding affinity of phosphorylated Sld3 remains the same. I wonder what would happen if phosphorylated Sld3 were used for the experiment shown in Figure 4B.

The CDK phosphorylation site is located at Sld3CTD and our ssDNA-binding experiment did not include the Sld3CTD, so phosphorylated Sld3 does not affect the results shown in Figure 4B.

(21) Sld3CBD-Cdc45 has a reduced binding affinity for ss DNA, and Sld7-Sld3ΔC-Cdc45 and Sl7-Sld3ΔC have a similar binding affinity to Sld3CBD based on figure 4B. It appears that Sld3CBD reduces the DNA binding affinity of CDC45 or vice versa. Is it correct to say so?

Our opinion is “vice versa”. Cdc45 reduces the ssDNA-binding affinity of Sld3CBD. Although we could not point out the ssDNA-binding sites of Sld3CBD, the surface charge of Sld3CBD implies that α8CTP could contribute to ssDNA-binding (Supplementary Figures 15).

(22) Cdc45 binds to the ssDNA by itself, but in the case of Sld3CBD-Cdc45, the binding affinity is reduced for Sld3CBD and Cdc45. Based on their structure, can authors explain what leads to this complex's reduced binding affinity to the ssDNA? Including a figure showing how Sld7-Sld3CBD-Cdc45 interacts with the DNA would be a nice idea.

Previous studies showed that Cdc45 binds tighter to long ssDNA (> 60 bases) and the C-terminus of Cdc45 is responsible for the ssDNA binding activity. The structure of Sld3CBD-Cdc45 shows the C-terminal domain DHHA1 of Cdc45 binds to Sld3CBD, which may lead to Sld3CBD-Cdc45 complex reduced ssDNA-binding affinity of Cdc45. We agree that showing a figure of how Sld7-Sld3CBD-Cdc45 interacts with ssDNA is a nice idea. However, there is no detailed interaction information between Sld7-Sld3Δ-Cdc45 and ssDNA, so we could not give a figure to show the ssDNA-binding manner. We added a figure to show the surface charges of Sld3CBD of Sld3CBD-Cdc45, and Sld3NTD-Sld7NTD, respectively (Supplemental Figure 15).

(23) Based on the predicted model of Sld7-Sld3 and Cdc45 complex, can authors explain how Sld7 would restore the DNA binding ability of the Sld3CBD?

It can be considered that Sld7 and Sld3NTD could bind ssDNA. Although we did not perform the ssDNA-binding assay of Sld7, the Sld3NTD-Sld7NTD surface shows a large positive charge area which may contribute to ssDNA-binding (Supplemental Figure 15). We added the explanation (P9/L245-248).

(24) It would be important to show binding measurements and Kd values of all the different complexes shown in Figure 4B with ssDNA to explain the dissociation of Cdc45 from Sld7-Sld3 after the CMG formation. I also recommend describing the statement from lines 224-227 more clearly how Sld7-Sld3-Cdc45 is loading Cdc45 on CMG.

As the reviewer mentioned, the binding measurements and Kd of values of all the different complexes are important to explain the dissociation of Sld7-Sld3 from CMG. The pull-down assay using chromatography may be affected by balancing the binding affinity and chromatography conditions. Therefore, we used EMSA with native-PAGE, which is closest to the natural state. However, the disadvantage is that the Kd values could not be estimated. For lines 224-227, the ssARS1-binding affinity of Sld3 and its complex should relate to the dissociation of Sld7–Sld3 from the CMG complex but not Cdc45 loading, because ssARS1 is unwound from dsDNA by the CMG complex after Cdc45 and GINS loading. We modified the description (P9/L248-251).

(25) Can authors explain why SDS-PAGE was used to assess the ssDNA (See line 420)?

We are sorry for making this mistake and corrected it to “polyacrylamide gel electrophoresis”.

(26) In line 421, can the authors elaborate on a TMK buffer?

We are sorry for this omission and added the content of the TMK buffer (P16/L453).

(27) I am curious to know if the authors also attempted to Crystallize the Sld7-Sld3CBD-Cdc45 complex. This complex structure would support the authors' hypothesis in this article.

We tried to crystallize Sld7-Sld3Δ-Cdc45 but could not get crystals. We also tried using cryo-EM but failed to obtain data.

Reviewer #2 (Recommendations for the authors):

(1) The manuscript would be strengthened if the authors acknowledged in greater detail how their work agrees with or disagrees with Itou et al. (PMID: 25126958 DOI: 10.1016/j.str.2014.07.001). The introduction insufficiently described the findings of that previous work in lines 63-64.

We compared Sld3CBD in Sld3CBD-Cdc45 to the monomer reported by Itou et al. (PMID: 25126958 DOI: 10.1016/j.str.2014.07.001) in the section of [The overall structure of Sld3CBD-Cdc45] and point out the structural similarity and difference (P5/L105-106), especially, conformation change of Sld3CBD α8 for binding to Cdcd45, which agrees to the mutant experiments of Itou et al., (P3/L126-127). Another Cdc45-binding site of Sld3CBD in the Sld3CBD-Cdc45 complex is α9 not residues predicted in previous studies.

(2) Figure 2. Could you please perform and present data from multiple biological replicates (e.g., at least two independent experiments) for each mutant strain? This would help ensure that the observed pull-downs (2A-B) and growth patterns (2C) are consistent and reproducible.

We have done pull-downs three times from co-expression to purification and pull-down assay. We added descriptions to the method of [Mutant analysis of Sld3 and Cdc45]. The growth patterns are two times in Figure 2C.

(3) Figure 3B. The match between the predicted complex length and particle size measured by dynamic light scattering (DLS) is striking. Did the authors run the analysis with vehicle controls and particle size standards? There is no mention of these controls.

Following the comment, we added the control data of buffer and standard protein lysozyme, and the descriptions to the method of [Dynamic light scattering].

(4) Figure 4. In lines 216-217, the authors write that the binding of the K. marxianus complex "demonstrates that the presence of Sld7 could restore the single-stranded DNA binding capacity of Sld3." Another explanation is that complexes from each species bind differently. If the authors want to make a strong claim, they should compare the binding of complexes containing the same proteins.

Agree with the comment, to make a strong claim using samples from the same source is better. Due to limitations in protein overexpression, we used Sld7-Sld3ΔC-Cdc45 from different sources two sources belong to the identical family (Saccharomycetaceae) and the proteins Sld7, Sld3 and Cdc45 have sequence conservation with similar structures (RMSD = 0.356, 1.392, and 0.891 for Ca atoms of Sld7CTD, Sld7NTD-Sld3NTD, and Sld3CBD-Cdc45) predicted by the alphafold3. Such similarity in source and protein level allows us to do the comparison. Moreover, we modified the description to “indicates that the presence of Sld7 and Sld3NTD could increase the ssDNA-binding affinity to a level comparable to that of Sld3CBD.

(5) The logic of the following is unclear: "Considering that ssDNA is unwound from dsDNA by the helicase CMG complex, Sld7-Sld3ΔC-Cdc45, and Sld7-Sld3C having a stronger ssDNA-binding capacity than Sld3CBD-Cdc45 may imply a relationship between the dissociation of Sld7-Sld3 from the CMG complex and binding to ssDNA unwound by CMG." (Lines 224-227). How do the authors imagine that the binding affinity difference due to Sld7 contributes to the release of Sld3? Please explain.

Considering that ssARS1 is unwound from dsARS1 by the activated helicase CMG complex formed after loading Cdc45 and GINS, Sld3–Sld7 having a stronger ssARS1-binding affinity may provide an advantage for the dissociation of Sld7–Sld3 from the CMG complex. We modified the sentence of Lines 224-227 (P9/L248-251).

(6) The authors suggest that the release of Sld3 from the helicase is related to its association with single-stranded ARS1 DNA. They refer to the work of Bruck et al. (doi: 10.1074/jbc.M111.226332), which demonstrates that single-stranded origin DNA inhibits the interaction between Sld3 and MCM2-7 in vitro. The authors selectively choose data from this previous work, only including data that supports their model while disregarding other data. This approach hinders progress in the field. Specifically, Bruck proposed a model in which the association of Sld3 and GINS with MCM2-7 is mutually exclusive, explaining how Sld3 is released upon CMG assembly. In Figure 3 of the authors' model, they suggest that Sld3 can associate with MCM2-7 through CDC45, even when GINS is bound. Furthermore, Bruck's work showed that ssARS1-2 does not disrupt the Sld3-Cdc45 interaction. Instead, Bruck's data demonstrated that ssARS1-2 disrupts the interaction between MCM2-7 and Sld3 without Cdc45. While we do not expect the authors to consider all data in the literature when formulating a model, we urge them to acknowledge and discuss other critical data that challenges their model. Additionally, it would be beneficial for the field if the authors include both modes of Sld3 interaction with MCM2-7 (i.e., directly with MCM or through CDC45) when proposing a model for how CMG assembly and Sld3 release occurs.

In our discussion, we referred to the studies of Bruck’s data (doi: 10.1074/jbc.M111.226332) but did not discuss more because we didn’t perform similar experiments in vitro, and we do not think that no discussion hinders progress in the field. Promoting research progress, the new experiment should provide a new proposal and updated knowledge. Although we do not know exactly the positional relationship between Sld3 and Dpb11-Sld2 on MCM during GINS recruiting, the Sld3CBD-Cdc45 structure shows clearly that the Sld3CBD-binding site of Cdc45 is completely different from that of GINS and MCM binding to Cdc45. The model SCMG confirmed such a binding manner, Sld3, Cdc45 and GINS could bind together. The competition of Sld3 and GINS for binding to Cdc45 or Cdc45-MCM reported by Bruck et. al, may be caused by the conformation change of Cdc45 DHHA1 between Sld3CBD-Cdc45 and CMG, or without other initiation factors (CMG formation is regulated by the initial factors). We modified the discussion (P10/L282-286). Regarding ssARS1-binding, we did not discuss with Bruck's data that ARS1-2 does not disrupt the Sld3-Cdc45 interaction, because the data does not conflict with our proposal, although the data does not have an advantage. We propose that the release of Sld3 and Sld7 from CMG could be associated with the binding of ssARS1 unwound by CMG, but the dissociation event of Sl3-Sld7 doesn’t only ssARS1-binding. The exploration of unwound-ssARS1 causes the conformation change of CMG, which may be another event for Sld3-Sld7 dissociation. However, we do not have more experiments to confirm this and Bruck’s ssDNA-binding experiment did not use all of Sld3, Cdc45 and MCM, so we do not discuss more with Bruck’ data in the revised version (P11/L303-305).,

Reviewer #3 (Recommendations for the authors):

Major points:

(1) Figure 1, Sld3CBD-Cdc45 complex: Please indicate the number of critical residues and those of alpha-helixes and beta-sheets in this Figure or Supplemental Figure to confirm the authors' claim.

Following the comment, we added the number of alpha-helixes and beta-sheets with residue numbers in Figure 1, and Supplemental Figures 4 and 5. We also added a topology diagram (Supplemental Figure 3).

(2) Figure 2A and B: Please quantify the interaction here with a proper statistical comparison.

In the experiments of Figures 2A and 2B, we used a co-expression system to co-purify the complexes and check their binding. For quantifying, we added the concentrations of the samples used in the Method of [Mutant analysis of Sld3 and Cdc45].

(3) Figure 3B, EMSA: If these are from the EMSA assay, at least free DNAs and protein-bound DNAs are present on the gel. However, the authors showed one band, which seems to be free DNA in Figure 3B and separately the smear band of the protein complex in Supplementary Figure 12, and judged the DNA binding by the disappearance of the band (line 207). Interestingly, in the case of Sld3CBD, there are few smear bands (Supplementary Figure 12). Where is DNA in this case? The disappearance could be due to the contaminated nucleases (need a control non-specific DNA). Without showing the Sld3CBD-DNA complex in the gel, the conclusion that the DNA binding activity of Sld3CBD-Cdc45 to DNA is lower than Sld3CBD alone (line 210) is very much speculative. The same is true for Sld7-Sld3dC-Cdc45.

Please explain the method (EMSA) briefly in the main text and show a whole gel in both Figures. If the authors insist that the Sld3 DNA-binding activity is altered with Cdc43 (and MCM), it is better to perform a more quantitative DNA binding assay such as BIAcore (surface plasmon), etc.

In the EMSA, we use SYBR (Figure 4B) and CBB (Supplementary Figure 13) staining to show bands of ssDNA and protein, respectively. As the reviewer mentioned, the disappearance of the bands could be due to the contaminated nucleases, we did experiments with non-specific ssDNA-binding as a control using the same proteins shown in Supplementary Figure 14. So, we are convinced that the disappearance of the ssDNA bands or not disappearance could occur when binding to protein or not. We added such explanations in the text (P9/L242-244). As we mentioned in the legend of Supplementary Figure 13, the Sld3CBD could not enter the gel, even when bound to ssDNA, because the pI values exceeded the pH of the running buffer.

Following the reviewer's comments, we attempted a pull-down experiment using Histag (C-terminal histag of Sld3CBD/Sld3ΔC). Unfortunately, we encountered difficulties in achieving the balance between binding and chromatography conditions.

(4) Figure 3B: Please quantify the DNA binding here with a proper statistical comparison with triplicate.

For EMSA (Figure 3B), we used samples of ssDNA:protein= 1:0. 1:1, 1:2, 1:4 and 0:1 molecular ratios with 10 pM as a 1 unit. We added concentrations of the samples in the Method of [Electrophoretic mobility shift assay for ssDNA binding].

Following the comment, we tried to quantify the binding strength by integrating the grayscale of the bands in gel photos. However, we are concerned because this quantitative calculation through grayscale could not provide an accurate representation of results. Many sample groups cannot be run on one gel. Therefore, the gel differences in parameters cause large errors in the calculation as shown in Author response image 1. Although the calculated integral grayscale chart is consistent with our conclusion, we do not want to add this to our manuscript.

Author response image 1.

(5) Because of poor writing, the authors need to ask for English editing.

We are very sorry for the language. We asked a company (Editag, https:www.editage.jp) to do a native speaker revision and used AI to recheck English.

Minor points:

(1) Lines 47-58, Supplementary Figure 1: Although the sentences describe well how CMG assembles on the replication origin, the figure does not reflect what is written, but rather shows a simple schematic figure related to the work. However, for the general readers, it is very useful to see a general model of the CMG assembly. Then, the authors need to emphasize the steps focused in this study.

Thank you for your thoughtful comments. We optimized Figure 1 and hope it will be more understandable to general readers.

(2) Line 50, DDK[6F0L](superscript): what is 5F0L?

We are sorry for this mistake, that is a PDBID of the DDK structure. we deleted 6F0L.

(3) Lines 68 and 69, ssDNA and dsDNA: should be "single-stranded DNA (ssDNA)" and double-stranded DNA (dsDNA) when these words appear for the first time.

Following the comment, we modified it to “single-stranded DNA (ssDNA)” and “double-stranded DNA (dsDNA)” (P3/L68,70).

(4) Line 84, Cdc45s: What "s" means here?

We are sorry for this mistake, we modified it to “Cdc45”.

(5) Line 87, Sld3deltaC: What is Sld3deltaC? This is the deletion of either the Cdc45-binding domain or the C-terminal domain.

Sld3ΔC is a deletion of the C-terminal domain of Sld3. We added the residue range and explanation (P4/L91).

(6) Line 103: Although the authors mentioned beta-sheets 1-14 in the text, there is no indication in Figures. It is impossible to see the authors' conclusion.

The secondary structure elements of Sld3CBD-Cdc45 are shown in Supplementary Figures 4 and 5. Following the comment, we added a topology diagram of Sld3CBD and Cdc45 in the Sld3CBD-Cdc45 complex as Supplementary Figure 3 and added citations when describing structural elements.

(7) Line 106, huCdc45: Does this mean human Cdc45? If so, it should be "human CDC45 (huCDC45). CMG form is from budding yeast? Please specify the species.

Yes, huCdc45 is human Cdc45. We modified it into “human CDC45 (huCdc45)”.

(8) Line 107, Supplemental Figure 3B, black ovals: Please add "alpha7" in the Figure.

Following the comment, we added a label of Cdc45 α7 to Supplemental Figure 3B and 3C (Supplemental Figure 4B and 4C in revised version).

(9) Line 128, DHHA1: What is this? Please explain it in the text.

Following the comment, we added the information on DHHA1 (P3/L75-77).

(10) Line 130, beta13, and beta14: If the authors would like to point out these structures, please indicate where these sheets are in Figures.

We added a topology diagram as Supplementary Figure 3 to show the β-sheet in DHH and added a citation in the text.

(11) Line 133: Please add (Figure 1B) after the a8CTP.

Following the comment, we added “(Figure 1C)” (1B is 1C in revised version) after the α8CTP (P6/L133).

(12) Line 140: After DHHA1, please add (Figure 1C).

Following the comment, we added the figure citation after the DHHA1 (P6/L140).

(13) Line 142: After DHHA1, please add (Figure 1D).

Following the comment, we added the figure citation after the DHHA1 (P6/L142).

(14) Line 149, Sld3-Y seemed to retain a faint interaction with Cdc45. The Cdc45 band is too faint here. Moreover, as shown above, without the quantification with proper statistics, it is hard to draw this kind of conclusion.

We agree that the Cdc45 band corresponding to Sld3-Y in the pull-down assay was very faint, so we performed an in vivo experiment (Fig2C) to confirm this result.

(15) Line 149, Figure 2A and B: What kind of interaction assay was used here? Simple pull-down. It seems to eluate from the column. If so, how do the authors evaluate the presence of the proteins in different fractions? Please explain the method briefly in the main text.

Figure 2 shows a co-express pull-down binding assay. To describe the co-express pull-down experiments clearly, we added more explanations in the Methods [Mutation analysis of Sld3 and Cdc45].

(16) Line 154-155: Please show the quantification to see if the reduced binding is statistically significant.

Here, we explain why Cdc45-A remained Sld3CBD-bind ability. Although mutant Cdc45-A has reduced three hydrogen bonds with D344 of Sld3CBD, the remaining hydrogen-bond network keeps contact between Sld3CBD and Cdc45.

(17) Line 158, cell death: "No growth" does not mean cell death. Please rephrase here.

Following the comment, we modified it to “no growth” (P6/L158).

(18) Line 166: After CMG dimer, please add "respectively".

Following the comment, we added the word “, respectively” after CMG dimer (P7/L178).

(19) Line 194-195: I can not catch the meaning. Please rephrase here to clarify the claim. What are ssARS1-2 and ARS1-5?

Following the comment, we added more information about ssDNA fragments at the beginning of this section (P8/L210-214).

(20) Figure 4A and Supplemental Figure 12 top, schematic figure of ARS region. It is hard to catch. More explanation of the nature of the DNA substrates and much better schematic presentations would be appreciated.

Following the comment, we added more information about ARS1 to the figure legend.

(21) Figure 1A, dotted ovals should be dotted squares as shown in the enlarged images on the bottom.

Following the comment, we modified Figure 1A and the legend to change the dotted ovals into dotted squares.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors investigate the functional difference between the most commonly expressed form of PTH, and a novel point mutation in PTH identified in a patient with chronic hypocalcemia and hyperphosphatemia. The value of this mutant form of PTH as a potential anabolic agent for bone is investigated alongside PTH(1-84), which is a previously used anabolic therapy. The authors have achieved the aims of the study. Their conclusion, however, that this suggests a "new path of therapeutic PTH analog development" seems unfounded; the benefit of this PTH variant is not clear, but the work is still interesting.

The work does not identify why the patient with this mutation has hypocalcemia and hyperphosphatemia; this was not the goal of the study, but the data are useful for helping to understand that.

Strengths:

The work is novel, as it describes the function of a novel, naturally occurring, variant of PTH in terms of its ability to dimerise, to lead to cAMP activation, to increase serum calcium, and its pharmacological action compared to normal PTH.

Weaknesses:

(1) The use of very young, 8-10 week old, mice as a model of postmenopausal osteoporosis is a major limitation of this study. At 8 weeks, the effect of ovariectomy leads to lack of new trabecular bone formation, rather than trabecular bone loss due to a defect in bone remodelling. Although the findings here provide a comparison between two forms of PTH, it is unlikely to be of direct relevance to the patient population. For example, the authors find an inhibitory effect of PTH on osteoclast surface, which is very unusual. Adding to this concern is that the authors have not described the regions used for histomorphometry, and from their figures (particularly the TRAP stain), it seems that the primary spongiosa (which is a region of growth) has been used for histomorphometry, rather than the secondary spongiosa (which more accurately reflects bone remodelling). Much further detail is needed to justify the use of this very young model, and a section on the limitations of this model is needed. Please provide that section in the revised manuscript.

Thank you for your crucial comment. We obtained 8-week-old female mice and stabilized them in our facility for 2 weeks. Then, we performed OVX using 10-week-old mice and determined the effects of dimeric ^R25CPTH(1-34) on bone after 8 weeks because of 4 weeks for recovery and 4 weeks for PTH or ^R25CPTH(1-34). Therefore, we sacrificed the mice at 18-week-old mice. We revised the method section on page 18, line 436-441 and page 18, line 442-448 as follows.

- ‘Eight-week-old C57BL/6N female mice were purchased from KOATECH (Gyeonggi-do, Republic of Korea), and stabilized mice for 2 weeks. All animal care and experimental procedures were conducted under the guidelines set by the Institutional Animal Care and Use Committees of Kyungpook National University (KNU-2021-0101). The mice were housed in a specific pathogen-free environment, with 4-5 mice per cage, under a 12-h light cycle at 22 ± 2°C. They were provided with standard rodent chow and water ad libitum.’

- ‘An ovariectomized (OVX) mouse model was established using 10-week-old C57BL/6N female mice. Following surgery, mice were divided into the following four groups (n = 6 mice/group) as follows: sham, OVX control group, OVX + PTH (1–34) treated group (40 µg/kg/day), and OVX + dimeric ^R25CPTH treated group (40-80 µg/kg/day). OVX mice were allowed to recover for 4 weeks after surgery. Afterward, PTH (1–34) or ^R25CPTH was injected subcutaneously 5 times a week for 4 weeks. Micro-computed tomography (μ-CT) and histological analyses were performed on 4 groups at 18 weeks of age.’

We also appreciate the reviewer's helpful comment on histology analysis. We agree with the reviewer’s comment that the primary spongiosa does not fully reflect bone remodeling. For histomorphometry analysis in young or male mice, we commonly use the secondary spongiosa, which more accurately reflects bone remodeling. However, in aged or OVX-induced osteoporosis mouse models, we use the primary and secondary spongiosa for histomorphometry analysis because of the barely detectable bone in the secondary spongiosa. In the TRAP staining, we observed an inhibitory effect of PTH on the osteoclast surface/bone surface, which was due to an increased bone surface in the PTH treatment group and less bone in the OVX-vehicle group. Serum CTX1 levels showed no significant difference between the OVX+vehicle and OVX+PTH(1-34) groups. We revised the Materials and Methods (page 21, line 502) and Discussion (page 14, line 330) sections as follows.

- ‘In the histomorphometry analysis for TRAP staining, we used the secondary and primary spongiosa for the trabecular ROI because of the barely detectable in the secondary spongiosa of OVX model.’

- ‘This study has several limitations. First, it is urgently necessary to determine whether dimeric ^R25CPTH is present in human patient serum. Second, TRAP staining showed an inhibitory effect of PTH treatment on the primary spongiosa area. However, the secondary spongiosa, which more accurately reflects bone remodeling (55), was not examined due to the barely detectable bone in this area in OVX-induced osteoporosis mouse models. Third, it is unclear whether similar bone phenotypes exist between human ^R25CPTH patients and dimeric ^R25CPTH-treated mice, particularly regarding low bone strength. Although the dimeric ^R25CPTH-treated group showed higher cortical BMD compared to WT-Sham or PTH groups, there was no difference in bone strength compared to the osteoporotic mouse model. Fourth, our study showed that PTH or ^R25CPTH treatment decreased circumferential length; it is uncertain if this phenotype is also present in PTH-treated or ^R25CPTH patients. Finally, we did not analyze the ^R25CPTH mutant mouse model, which would allow us to compare phenotypes that most closely resemble those of human patients.’

(2) It is also somewhat concerning that the age range is from 8-10 weeks, increasing the variability within the model. Did the age of mice differ between the groups analysed?

We utilized mice of the same age (10 weeks) across all experiments involving the surgically induced ovariectomy (OVX) model described as above.

(3) Methods are not sufficiently detailed. For example, the regions used for histomorphometry are not described, there is no information on micro-CT thresholds, no detail on the force used for mechanical testing. Please address this request.

Thank you for your comment. Let me address your points step by step.

(1) Thresholds for analysis were determined manually based on grayscale values for each experimental group as follows: trabecular bone: 3000; cortical bone: 5000 for all samples. We utilized an HA (calcium hydroxyapatite) phantom with HA content ranging from 0 to 1200 mg CaHA/cm³ to measure the grayscale values via µ-CT. These measurements were then used to generate a standard curve.

Author response image 1.

(2) Bone parameters and density were analyzed in the region between 0.3–1.755 mm (Voxel size: 9.7um, 150 slices) from the bottom of the growth plate. Analysis of bone structure was performed using adaptive thresholding in a CT Analyser.

Author response image 2.

(3) Three‐point bending test, the left femur of the mouse was immersed in 0.9 % NaCl solution, wrapped in gauze, and stored at −20°C until ready for a three-point bending test. In this test, we placed the mouse femurs positioned horizontally with the anterior surface facing upwards, centered on the supports, and the compressive force was applied vertically to the mid-shaft. The pressure sensor was positioned at a distance that allowed for the maximum allowable pressure (200N) without interfering with the test (20.0 mm for the femur). A miniature material testing machine (Instron, MA, USA) was used for this test. The crosshead speed was decreased to 1 mm/min until failure. During the test, force-displacement data were collected to determine the maximum load and slope of the bones.

(4)  As the reviewer’s suggestion, we revised the methods on page 20, line 477 and line 482-486 as follows.

- ‘Bone parameters and density were analyzed in the region between 0.3–1.755 mm (150 slices) from the bottom of the growth plate. Analysis of bone structure was performed using adaptive thresholding in a µ-CT Analyser. Thresholds for analysis were determined manually based on grayscale values for each experimental group: trabecular bone: 3000; cortical bone: 5000 for all samples.’

-  ‘The left femur of the mouse was immersed in 0.9 % NaCl solution, wrapped in gauze, and stored at −20°C until ready for a three-point bending test. In this test, we placed the mouse femurs horizontally with the anterior surface facing upwards, centered on the supports, and the compressive force was applied vertically to the mid-shaft. The pressure sensor was positioned at a distance that allowed maximum allowable pressure (1000N) without interfering with the test (20.0 mm for the femur). A miniature material testing machine (Instron, MA, U.S.A.) was used for this test. The crosshead speed was decreased to 1 mm/min until failure. During the test, force-displacement data were collected to determine the maximum load and slope of the bones.’

(4) There are three things unclear about the calvarial injection mouse model. Firstly, were the mice injected over the calvariae or with a standard subcutaneous injection (e.g. at the back of the neck)? If they were injected over the calvaria, why were both surfaces measured? Secondly, why was the dose of the R25C-PTH double that of PTH(1-34)? Thirdly, there is no justification for the use of "more intense coloration" as a marker of new bone; this requires calcein labelling to prove it new bone. It would be more reliable to measure and report the thickness of the calvaria. Please address these technical questions.

Thank you for your valuable feedback on the calvarial injection mouse model. Below are our responses to the specific points mentioned:

(1) Injection method and measurement sites: The injections were administered subcutaneously above the calvaria, rather than at the standard subcutaneous site such as the back of the neck. This approach was chosen to ensure direct delivery of the peptide to the target area, enhancing the localized effects on bone formation. Measurements were taken at two different parts of the calvaria to account for any variation in the spread and absorption of the administered substance following injection. By analyzing both surfaces, we aimed to provide a comprehensive assessment of the impact on calvarial bone thickness.

(2) Dose of ^R25CPTH compared to PTH(1-34): The dose of ^R25CPTH used in our study was determined based on molecular weight calculations. The molecular weight of the dimeric ^R25CPTH(1-34) is approximately twice that of the monomeric PTH(1-34). Therefore, to maintain a consistent molar concentration and ensure comparable biological effects, the dose of ^R25CPTH was adjusted accordingly.

(3) Use of "more intense coloration" as a marker of new bone: We acknowledge that calcein labeling would provide a more reliable and quantifiable way to identify new bone formation. The use of “more intense coloration” was intended as a qualitative indicator in this study, and we recognize the technical limitations of this approach.

(5) The presentation of mechanical testing data is not sufficient. Example curves should be shown, and data corrected for bone size needs to be shown. The difference in mechanical behaviour is interesting, but does it stem from a difference in the amount of bone, or two a difference in the quality of the bone? Please explain this matter better in the manuscript.

Thank you for your comment.

As a reviewer's comment, we provided example curves for the rat femur three-point bending test as shown below.

Author response image 3.

(1) The cortical bone area was decreased in the OVX-Vehicle and OVX-^R25CPTH(1-34) groups but not in the OVX-PTH(1-34) group compared to the Sham group. However, the total bone area was decreased in the PTH(1-34) and ^R25CPTH(1-34) treated groups, with no significant difference in the OVX-Vehicle group compared to the Sham group. Collectively, there was an increase in cortical thickness which resulted in a narrowing of the bone marrow space in OVX-^R25CPTH(1-34) groups. Accordingly, we revised Fig 5B with the addition of Tt.Ar and Ct.Ar.

(2) As the reviewer’s suggestion, we revised the results on page 10, line 220-228 s follows.

- ‘Quantitative micro-computed tomography (μ-CT) analysis of the femurs obtained from each group revealed that, as compared to OVX + vehicle controls, treatment with PTH(1–34) increased femoral trabecular bone volume fraction (Tb.BV/TV) by 121%, cortical bone volume fraction (Ct.BV/TV) by 128%, cortical thickness (Ct.Th) by 115%, cortical area (Ct.Ar) by 110%, and cortical area fraction (Ct.Ar/Tt.Ar) by 118% while decreased total tissue area (Tt.Ar) by 93% (Figure 5A and 5B). Treatment with dimeric ^R25CPTH(1-34) had similar effects on the femoral cortical bone parameters, as it increased Ct.BMD by 104%, Ct.BV/TV by 125%, Ct.Th by 107%, and Ct.Ar/Tt.Ar by 116%, while decreased Tt.Ar 86% (Figure 5). Considering the reduction of Tt.Ar and no change of Ct.Ar compared to the OVX+vehicle controls, the increase of Ct.Ar/Tt.Ar indicates a decrease in bone marrow space. The increase in cortical bone BMD was significant with dimeric ^R25CPTH(1-34) but not with PTH(1-34), whereas an increase in femoral trabecular bone was only observed with PTH(1-34).’

(6) The micro-CT analysis of the cortical bone in the OVX model is insufficient. Please indicate whether cross-sectional area has increased. Is there an increase in the size of the bones, or is the increase in cortical thickness due to a narrowing of the marrow space? This may help resolve the apparent contradiction between the cortical thickness data (where there is no difference between the two PTH formulations) and the mechanical testing data (where there is a difference). Please explain this matter better in the manuscript.

Thank you for your comment.

(1) The cortical bone area was decreased in the OVX-Vehicle and OVX-^R25CPTH(1-34) groups but not in the OVX-PTH(1-34) group compared to the Sham group. However, the total bone area was decreased in the PTH(1-34) and ^R25CPTH(1-34) treated groups, with no significant difference in the OVX-vehicle group compared to the Sham group. Taken together, there was an increase in cortical thickness due to a narrowing of the bone marrow space in OVX-^R25CPTH(1-34) groups. Therefore, we revised as above.

(2) As the reviewer’s suggestion, we revised the results on page 10, line 220-228 as follows.

- ‘Quantitative micro-computed tomography (μ-CT) analysis of the femurs obtained from each group revealed that, as compared to OVX + vehicle controls, treatment with PTH(1–34) increased femoral trabecular bone volume fraction (Tb.BV/TV) by 121%, cortical bone volume fraction (Ct.BV/TV) by 128%, cortical thickness (Ct.Th) by 115%, cortical area (Ct.Ar) by 110%, and cortical area fraction (Ct.Ar/Tt.Ar) by 118% while decreased total tissue area (Tt.Ar) by 93% (Figure 5A and 5B). Treatment with dimeric ^R25CPTH(1-34) had similar effects on the femoral cortical bone parameters, as it increased Ct.BMD by 104%, Ct.BV/TV by 125%, Ct.Th by 107%, and Ct.Ar/Tt.Ar by 116%, while decreased Tt.Ar 86% (Figure 5B). Considering the reduction of Tt.Ar and no change of Ct.Ar compared to the OVX+vehicle controls, the increase of Ct.Ar/Tt.Ar indicates a decrease in bone marrow space. The increase in cortical bone BMD was significant with dimeric ^R25CPTH(1-34) but not with PTH(1-34), whereas an increase in femoral trabecular bone was only observed with PTH(1-34).’

(7) The evidence that dimeric PTH has a different effect to monomeric PTH is very slim; I am not sure this is a real effect. Such differences take a long time to sort out (e.g. the field is still trying to determine whether teriparatide and abaloparatide are different). I think the authors need to look more carefully at their data - almost all effects are the same. Ultimately, the statement that dimeric PTH may be a more effective anabolic therapy than monomeric PTH are not supported by the data, and this should be removed. There is little to no difference found between normal PTH and the variant in their effects on calcium and phosphate homeostasis or on bone mass. However, the analysis has been somewhat cursory, with insufficient mechanical testing or cortical data presented. Many of the effects seem to be the same (e.g. cortical thickness, P1NP, ALP, vertebral BV/TV and MAR), but the way it is written it sounds like there is a difference. Please remove some of the unfounded claims that you have made in this manuscript.

Thank you for your insightful comments. We strongly agree with your conclusion that PTH and dimeric ^R25CPTH indeed exhibit similar activities. We have toned-down our statement, however, there are still some elements showing statistical significance that need to be clearly stated. Specifically, when we changed the statistical method from t-test to one-way ANOVA, the significance of bone formation markers were only observed in dimeric PTH treated samples, and we have revised the manuscript of Results section on page 9, line 206-212 as follows to reflect the change.

- ‘These analyses revealed that both PTH(1-34) and dimeric ^R25CPTH(1-34) significantly increased the width of the new bone area by approximately four-fold, as compared to the vehicle group (Figure 4B). These findings thus support a capacity of dimeric ^R25CPTH(1-34) to induce new bone formation in vivo, similar to PTH, despite molecular and structural changes.’

Although it is unclear whether ^R25CPTH circulate as dimeric form or mutant monomeric form, the absence of bone resorption associated with long-term PTH exposure in the patients suggests the potential for a bone anabolic drug without side effects. Also, continued observation of the recently reported young patient in Denmark is expected to clarify this effect further. However, we acknowledge that our current data alone are insufficient to claim that ^R25CPTH may be a more effective anabolic therapy than wild type PTH, and we have adjusted our tone accordingly.

(8) Statistical analysis used multiple t-tests. ANOVA would be more appropriate.

We agree with your suggestion. To compare the means among three or more groups, ANOVA is more appropriate than the t-test. Accordingly, we performed new statistical analyses using one-way and two-way ANOVA. One-way ANOVA was applied to figure 4, 5, and 6 (In previous, figure 5, 6, and 7), and two-way ANOVA was applied to Figure 3, considering both time and treatment variables. We revised some of the figures and descriptions to reflect the changes in significance.

Thank you for Reviewer #1’s thorough and thoughtful review. We greatly appreciate the suggestions and will incorporate them to enhance the quality of our paper.

Reviewer #2 (Public Review):

Summary:

The study conducted by Noh et al. investigated the effects of parathyroid hormone (PTH) and a dimeric PTH peptide on bone formation and serum biochemistry in ovariectomized mice as a model for postmenopausal osteoporosis. The authors claimed that the dimeric PTH peptide has pharmacological benefits over PTH in promoting bone formation, despite both molecules having similar effects on bone formation and serum Ca2+. However, after careful evaluation, I am not convinced that this manuscript adds a significant contribution to the literature on bone and mineral research.

Strengths:

Experiments are well performed, but strengths are limited to the methodology used to evaluate bone formation and serum biochemical analysis.

Weaknesses:

(1) Limited significance of this study:

• This study follows a previous study (not cited) reporting the effect of the dimeric R25CPTH(1-34) on bone regeneration in an osteoporotic dog (Beagle) model (Jeong-Oh Shin et al., eLife 13:RP93830, 2024). It's unclear why the authors tested the dimeric R25C-PTH peptide on a rodent animal model, which has limitations because the healing mechanism of human bone is more similar in dogs than in mice.

Thank you for your interest in our research. To address the paper by Shin et al. (2024, DOI:10.7554/eLife.93830.1), we would like to clarify that our research on dimeric ^R25CPTH(1-34) was conducted first. Initially, we confirmed dimerization under in vitro conditions and observed its effects in a mouse model. Recognizing the need for additional animal models, we collaborated with Shin et al.'s team. Due to delays during the submission process, our paper was submitted later, which seems to have led to this misunderstanding. However, Shin et al. (2024) cited our pre-print article on bioRxiv (Noh, M., Che, X., Jin, X., Lee, D. K., Kim, H. J., Park, D. R., ... & Lee, S. (2024). Dimeric R25CPTH (1-34) Activates the Parathyroid Hormone-1 Receptor in vitro and Stimulates Bone Formation in Osteoporotic Female Mice. bioRxiv, 2024-03.DOI: 10.1101/2024.03.13.584815). Both Shin et al., and our mouse work supports the action of dimeric R25CPTH(1-34) on regulating bone metabolism.

• The authors should clarify why they tested the effects of dimeric ^R25CPTH(1-34) and not dimeric ^R25CPTH(1-84)?

Thank you for your valid comments. Here are several reasons why we used the 1-34 fragment peptide in our experiment. Currently, PTH analog peptides for medical purposes include human parathyroid hormone fragment 1-34 (PTH(1-34)) and full-length recombinant human parathyroid hormone (rhPTH(1-84)). PTH(1-34) is used as a bone anabolic agent, while rhPTH(1-84) is used for PTH replacement therapy in hypoparathyroid patients with hypocalcemia. We aimed to compare the bone formation effects of R25CPTH with wild-type PTH, for which PTH(1-34) was deemed more appropriate. Additionally, previous studies have shown that both PTH(1-34) and PTH(1-84) possess equal ligand binding affinity for the PTH1 receptor. Key sites within the first 34 N-terminal amino acids of PTH are critical for high-affinity interactions and receptor activation. Alterations in the N-terminal sequence of PTH(1-84) significantly reduce receptor binding, while truncations at the C-terminal end do not affect receptor affinity. The peptide used in our experiment was synthetic, and if the length does not affect affinity to its receptor affinity, the shorter length of PTH(1-34) made its synthesis more reasonable. Consequently, we tested the effects of PTH(1-34) and dimeric R25CPTH(1-34) due to its known efficacy on bone anabolic effect and relevance in receptor interactions. However, we aim to conduct functional analysis of the dimeric R25CPTH(1-84) in further study.

• The study is descriptive with no mechanism.

We recognize that your concern is legitimate. While our study includes descriptive elements, it extends beyond mere observation. The R25CPTH research, which began with a case report, has evolved to utilize molecular techniques to better understand the unique physiological phenomena observed in patients. We have validated the peptide’s dimerization caused by mutations in vitro and assessed their effects in both in vitro cell line models and in vivo mouse models. Although we have not yet confirmed whether ^R25CPTH exists as a dimer or monomer in patient blood, we anticipate it may exist in dimeric form at least some fractions and are currently conducting mass spectrometry on patient blood samples to determine this. Therefore, this paper serves as the first report on this PTH mutant suggesting that it may form a homodimer. Importantly, we are actively investigating the molecular mechanisms and downstream signaling pathways that differentiate normal PTH from dimeric ^R25CPTH. This includes analyzing differences in proteome and transcriptome induced by PTH and dimeric ^R25CPTH and examining the direct molecular characteristics and structural changes responsible for these mutations. Through this comprehensive approach, we aim to provide a detailed mechanistic understanding of ^R25CPTH in the subsequent publication.

(2) Statistics are inadequately described or performed for the experimental design:

• The statistical analysis in Figure 5 needs to be written in a way that makes it clearer how statistics were done; t-test or one-way ANOVA?

Sorry for the inconvenience and thank you for your thorough review. Initially, we conducted the statistical analysis using a t-test. However, during the revision process, we performed a new statistical analysis using one-way ANOVA, as it is more appropriate for comparing the means among three or more groups. Despite this change, there were no differences in statistical significance, so the descriptions remained unchanged.

• Statistics in Figures 6 and 7 should be performed by one-way ANOVA to compare the mean values of one variable among three or more groups, and not t-test.

Thank you for your thorough review, and I apologize for any inconvenience. I agree with your suggestion that ANOVA is more appropriate than the t-test for comparing means among three or more groups. Accordingly, we performed new statistical analyses using one-way ANOVA. When we changed the statistical method from t-test to one-way ANOVA, the significance of bone formation markers, P1NP and ALP, appeared only in dimeric R25CPTH and not in wild-type PTH. We have reflected these findings in the text.

(3) Misleading and confused discussion:

• The first paragraph lacks clarity in the PTH nomenclature and the authors should provide a clear statement that the PTH mutant found in patients is likely a monomeric R25CPTH(1-84), considering that there has been no proof of a dimeric form.

Thank you for your insightful comments. I agree that there was some ambiguity in the nomenclature used in the first paragraph of the Discussion section. However, we do not believe that no proof of a dimeric form of the ^R25CPTH(1-84) mutant necessarily indicates that the PTH mutant in the blood is solely monomeric. Identifying the in vivo structure of ^R25CPTH(1-84) is one of the goals of our ongoing project. While the exact form of ^R25CPTH(1-84) in patients is still elusive, we are investigating the possibility that some fraction may exist as a dimer. On page 12, line 274-276, we have revised the content to address this issue and improve clarity as follows.

- ‘In this study, we show the introduction of a cysteine mutation at the 25th amino acid position of mature parathyroid hormone (^R25CPTH) facilitates the formation of homodimers comprised of the resulting dimeric R25CPTH peptide in vitro.’

• Moreover, the authors should discuss the study by White et al. (PNAS 2019), which shows that there are defective PTH1R signaling responses to monomeric R25CPTH(1-34). This results in faster ligand dissociation, rapid receptor recycling, a short cAMP time course, and a loss of calcium ion allosteric effect.

Sorry for the inconvenience and thank you for your thorough review. The authors were aware of the referenced paper and deeply apologize for its omission during the writing and editing process. Citing this paper will enhance the credibility of our findings. We have now included this citation and made the necessary adjustments to the manuscript of Discussion section on page 12, line 295-296 as follows.

- ‘We also observed that the potency of cAMP production in cells was lower for dimeric ^R25CPTH as compared to the monomeric ^R25CPTH, in accordance with a lower PTH1R-binding affinity. Previous reports indicated that a mutation at the 25th position of PTH results in the loss of calcium ion allosteric effects on monomeric ^R25CPTH, leading to faster ligand dissociation, rapid receptor recycling, and a shorter cAMP time course (50). Correspondingly, the weaker receptor affinity and reduced cAMP production observed in dimeric ^R25CPTH suggest a possibility that the formation of a disulfide bond at the 25th position significantly alters the function of PTH as a PTH1R ligand. These structural effects are not yet fully understood and need to be investigated further.’

• The authors should also clarify what they mean by "the dimeric form of R25CPTH can serve as a new peptide ...(lines 328-329)" The dimeric R25CPTH(1-34) induces similar bone anabolic effects and calcemic responses to PTH(1-34), so it is unclear what the new benefit of the dimeric PTH is.

We apologize for any confusion in our previous description. We concur that, as you mentioned, PTH and dimeric ^R25CPTH indeed exhibit similar activities. We have toned-down our statement, however, there are still some elements showing statistical significance that need to be clearly stated. Specifically, when we changed the statistical method from t-test to one-way ANOVA, the significance of bone formation markers was only observed in dimeric PTH treated samples, and we have revised the manuscript of Results section on page 9, line 206-212 as follows to reflect the change.

- ‘These analyses revealed that both PTH(1-34) and dimeric ^R25CPTH(1-34) significantly increased the width of the new bone area by approximately four-fold, as compared to the vehicle group (Figure 4B). These findings thus support a capacity of dimeric ^R25CPTH(1-34) to induce new bone formation in vivo, similar to PTH, despite molecular and structural changes.’

Although it is unclear whether ^R25CPTH circulate as dimeric form or mutant monomeric form, the absence of bone resorption associated with long-term PTH exposure in the patients suggests the potential for a bone anabolic drug without side effects. Also, continued observation of the recently reported young patient in Denmark is expected to clarify this effect further. However, we acknowledge that our current data alone are insufficient to claim that ^R25CPTH may be a more effective anabolic therapy than wild type PTH, and we have adjusted our tone accordingly.

Thank you for Reviewer #2’s comprehensive and considerate review. We are grateful for the ideas, and we have revised our manuscript accordingly them to improve our paper.

Reviewer #1 (Recommendations For The Authors):

(1) Figure 1D lacks molecular weight markers.

Thank you for your thorough review. We added protein molecular weight markers in the figure.

(2) The lack of change in plasma cAMP is very surprising, particularly given that there is no difference in the effect of the two forms of PTH on serum calcium or phosphate, or urinary phosphate. This data is somewhat of a distraction since no effort has been made to assess the difference in the effects of these PTH forms on kidney function. I suggest removing this data and spending time working on the origin of this difference.

Thank you for your insightful comments and valuable suggestions on our manuscript. We also could not precisely explain the discrepancy between the cell line and animal model experiments. However, since the results were consistently observed, we included them in the paper as they may be significant. We acknowledge that in the context of our current research, these data lack sufficient correlation with other findings. Therefore, we have removed the data about the lack of change in plasma cAMP by PTH injection (Figure 4. Effect of cAMP production by PTH injection in CD1 female mice) and revised the manuscript accordingly (Page 8, line 188-194; page 12, line 301-306; page 19, line 454-456). We are currently conducting further research with multiomics data analysis to elucidate potential differences in the sub-signaling pathways between PTH and dimeric R25CPTH, to identify the specific functions affected by these variations, and to understand the underlying mechanisms. The lack of changes in plasma cAMP levels in vivo will be addressed in a subsequent publication detailing our findings.

(3) Introduction, line 61. The authors state that "most" anti-resorptive therapies cannot stimulate new bone formation. I don't believe that ANY anti-resorptive therapies stimulate new bone formation! If there is one, this should be referenced.

Thank you for pointing out important aspects. Romosozumab, a humanized monoclonal anti-sclerostin antibody, has a dual effect by enhancing bone formation and inhibiting bone resorption. Sclerostin, a protein produced by osteocytes, plays a role in the regulation of bone metabolism. It promotes osteoclast differentiation, which is associated with bone resorption, and suppresses osteoblast activity, which is crucial for bone formation. By binding to sclerostin, Romosozumab prevents it from blocking the signaling pathways necessary for osteogenesis. Consequently, Romosozumab therapy not only regulates bone resorption but also affects new bone formation. We added the references to that information.

(4) The authors tend to include a lot of methods in the results section (e.g. describing the number of replicates, and details of histological analysis). This should be minimized.

Thank you for your thorough review, and sorry for the inconvenience. We have minimized the methodological details in the results section, ensuring that only essential information for understanding the findings and the procedures remain.

(5) Lines 302-305: If retaining the blood cAMP data, please provide references for the assertion that renal PTH receptors mediate this response.

PTH exerts its effects primarily through the PTH1 receptor (PTH1R), a G protein-coupled receptor present in various tissues, including bone and kidney (Chase et al., 1968, Chase et al., 1970). When activated by PTH, this receptor stimulates the production of cyclic AMP (cAMP), with the kidneys playing a significant role in this process (Maeda et al., 2013). In the initial manuscript, the importance of renal PTH receptors in mediating the blood cAMP response may have been overemphasized. We appreciate your feedback on this point, and we have provided references to support this assertion. However, by process following the former ‘Recommendations for the Authors’, we removed the data about the lack of change in plasma cAMP by PTH injection, the description of the renal PTH receptors mediate this response of blood cAMP also removed.

- Chase, Lewis R., and G. D. Aurbach. "Renal adenyl cyclase: anatomically separate sites for parathyroid hormone and vasopressin." Science 159.3814 (1968): 545-547.DOI:10.1126/science.159.3814.545

- Chase, Lewis R., and G. D. Aurbach. "The effect of parathyroid hormone on the concentration of adenosine 3', 5'-monophosphate in skeletal tissue in vitro." Journal of Biological Chemistry 245.7 (1970): 1520-1526.DOI:10.1016/S0021-9258(19)77126-9

- Maeda, Akira, et al. "Critical role of parathyroid hormone (PTH) receptor-1 phosphorylation in regulating acute responses to PTH." Proceedings of the National Academy of Sciences 110.15 (2013): 5864-5869.DOI: 10.1073/pnas.1301674110

(6) Eosin stains bone pink and haematoxylin stains cells purple. This has been incorrectly described in the manuscript.

Thank you for your thorough review, and I apologize for any confusion caused by the poor description. It appears that the terms were used interchangeably during the editing process. We have corrected the description in the manuscript and will ensure such mistakes do not occur again in the future.

(7) Sodium thiosulphate is a fixative for Von Kossa staining, not an agent that removes nonspecific binding.

Thank you for your careful review. However, there seems to be a misunderstanding of sodium formaldehyde as sodium thiosulfate. A 5% sodium thiosulfate solution is a critical in vitro diagnostic agent used in various staining kits. As a reducing agent, it effectively removes excess silver ions in staining kits based on silver impregnation techniques. In our experiment, sodium thiosulfate was specifically used to remove residual silver ions in Von Kossa staining. For more details, please refer to the following link: https://www.morphisto.de/en/shop/detail/d/Natriumthiosulfat_5//12825/.

Reviewer #2 (Recommendations For The Authors):

Moderate-to-Minor points:

• Line 73: it's either class B GPCR or secretin receptor family but not class B GPCR family.

Thank you for your thorough review, and I apologize for any confusion in our previous description. We corrected the description in the manuscript as class B GPCR.

• Line 79: correct "adenylate cyclase" to "transmembrane adenylate cyclases"

Thank you for your thorough review, and I apologize for any confusion in our previous description. We corrected the description in the manuscript as transmembrane adenylate cyclases.

• Line 89: should "hypothyroidism" be "hypoparathyroidism"?

Thank you for your thorough review, and I apologize for any confusion in our previous description. We corrected the description in the manuscript as hypoparathyroidism.

• Line 159: all agonists display higher binding affinities when their receptors are coupled to G proteins, so it's unclear why the higher affinity of the dimeric ^R25CPTH(1-34) for the RG state seems to be important for the authors.

Thank you for your insightful comments. First of all, comparing the binding affinities of the R0 (G protein-uncoupled) and RG (G protein-coupled) conformations of the receptor is inappropriate. This is because the form and size of the radio-label ligand bound to each conformation differ, which consequently affects their binding affinities and, in turn, influences the binding strength of target ligands such as PTH, monomeric ^R25CPTH, and dimeric ^R25CPTH. Therefore, it is preferable to compare how the binding strengths of test ligands differ for each conformation. Additionally, the fact that significant binding affinity is lost for R⁰ while remaining high for the RG conformation of PTH1R is important because typical PTH exhibits high binding affinity for R0, whereas PTHrP shows higher affinity for the RG conformation. This suggests that dimeric ^R25CPTH may possess distinct molecular characteristics and potentially induce different downstream signaling pathways compared to typical PTH.

• Line 169-170 and Fig. 2: According to the theory of receptor pharmacology established in the 60s' for native receptors (Arch. Int. Pharmacodyn. 127:459-478 (1960); Arch. Int. Pharmacodyn. 136:385-413 (1962)) and verified later in the 80-90's for recombinant GPCRs, the activity constant (Kact or EC50) value of hormone actions in various tissues or cells is equal to the dissociation constant (Kd) of the hormone when receptors are not overexpressed (EC50 = Kd). When receptors are overexpressed (presence of spare receptors), then EC50 < Kd. Assuming that after Cheng-Prussof correction for data in Fig. 2, IC50 < Ki = Kd, how do the authors explain that IC50 values for RG are about 1-Log lower than EC50s (i.e., EC50 > Kd)?

We appreciate your insightful comment and fully acknowledge the established theory of receptor pharmacology, which states that Kd equals EC50, and when the receptor is overexpressed, EC50 is less than Kd. After having read your comments, we have revisited this paper Okazaki et al, PNAS, 2008 to better understand the PTH interaction with PTH1R. While our data might appear to contradict this theory, we believe that a direct comparison between the IC50 of RG and the EC50 in Figure 2 may not be entirely appropriate for the following reasons. First, the IC50 was determined from membrane preparations of a receptor-overexpressing cell line (GP-2.3), whereas the EC50 was calculated based on the cAMP response in SaOS-2 cells. These different experimental conditions contribute to the observed discrepancies. Second, the peptides used in the competition assays differ. R⁰ utilized radiolabeled PTH(1-34), while RG employed M-PTH(1-15) with several amino acid substitutions and a shorter length. This further complicates a direct comparison between the EC50 and IC50 values in our study.

Thank you for all the reviewers’ thorough and thoughtful reviews. We greatly appreciate your suggestions and have addressed all the issues to enhance the quality of our paper.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In "Changes in wing morphology..." Roy et al investigate the potential allometric scaling in wing morphology and wing kinematics in 8 different hoverfly species. Their study nicely combines different new and classic techniques, investigating flight in an important, yet understudied alternative pollinator. I want to emphasize that I have been asked to review this from a hoverfly biology perspective, as I do not work on flight kinematics. I will thus not review that part of the work.

Strengths:

The paper is well-written and the figures are well laid out. The methods are easy to follow, and the rationale and logic for each experiment are easy to follow. The introduction sets the scene well, and the discussion is appropriate. The summary sentences throughout the text help the reader.

We thank the reviewer for these positive comments on our study.

Weaknesses:

The ability to hover is described as useful for either feeding or mating. However, several of the North European species studied here would not use hovering for feeding, as they tend to land on the flowers that they feed from. I would therefore argue that the main selection pressure for hovering ability could be courtship and mating. If the authors disagree with this, they could back up their claims with the literature.

We thank the reviewer for this insight on potential selection pressures on hovering flight. As suggested, we now put the main emphasize on selection related to mating flight (lines 106–111).

On that note, a weakness of this paper is that the data for both sexes are merged. If we agree that hovering may be a sexually dimorphic behaviour, then merging flight dynamics from males and females could be an issue in the interpretation. I understand that separating males from females in the movies is difficult, but this could be addressed in the Discussion, to explain why you do not (or do) think that this could cause an issue in the interpretation.

We acknowledge that not distinguishing sexes in the flight experiment prevents investigating the hypothesis that selection may act especially on male’s flight. This weakness was not addressed in our first manuscript and is now discussed in the revised Discussion section. We nuanced the interpretation and suggested further investigation on flight dimorphism (lines 726–729).

The flight arena is not very big. In my experience, it is very difficult to get hoverflies to fly properly in smaller spaces, and definitely almost impossible to get proper hovering. Do you have evidence that they were flying "normally" and not just bouncing between the walls? How long was each 'flight sequence'? You selected the parts with the slowest flight speed, presumably to get as close to hovering as possible, but how sure are you that this represented proper hovering and not a brief slowdown of thrust?

We very much agree with the reviewer that flight studied in laboratory conditions does not perfectly reflects natural flight behavior. Moreover, having individual hoverflies performing stable hovering in the flight arena, in the intersecting field of view of all three cameras, is quite challenging. Therefore, we do not claim that we studied “true” hovering (i.e. flight speed = 0 m/s), but that we attempted to get as close as possible to true hovering by selecting the flight sections with the lowest flight speeds for our analysis.

In most animal flight studies, hovering is defined as flight with advance ratios J<0.1, i.e. when the forward flight speed is less than 10% of the wingbeat-induced speed of the wingtip (Ellington, 1984a; Fry et al., 2005; Liu and Sun, 2008). By selecting the low flight-speed wingbeats for our analysis, the mean advance ratio in our experiment was 0.08±0.02 (mean±sd), providing evidence that the hoverflies were operating close to a hovering flight mode. This is explained in both the methods and results sections (lines 228–231 and 467–469, respectively).

We however acknowledge that this definition of hovering, although generally accepted, is not perfect. We edited the manuscript to clarify that our experiment does not quantify perfect hovering (lines 186–188). We moreover added the mean±sd duration of the recorded flight sequence from which the slowest wingbeat was selected (line 179), as this info was missing, and we further describe the behaviour of the hoverflies during the experiment (lines 168–169).

Your 8 species are evolutionarily well-spaced, but as they were all selected from a similar habitat (your campus), their ecology is presumably very similar. Can this affect your interpretation of your data? I don't think all 6000 species of hoverflies could be said to have similar ecology - they live across too many different habitats. For example, on line 541 you say that wingbeat kinematics were stable across hoverfly species. Could this be caused by their similar habitat?

We agree with the reviewer that similarity in habitat and ecology might partially explain the similarity in the wingbeat kinematics that we observe. But this similarity in ecology between the eight studied species is in fact a design feature of our study. Here, we aim to study the effect of size on hoverfly flight, and so we designed our study such that we maximize size differences and phylogenetic spread among the eight species, while minimizing variations in habitat, ecology and flight behavior (~hovering). This allows us to best test for the effect of differences in size on the morphology, kinematics and aerodynamics of hovering flight.

Despite this, we agree with the reviewer that it would be interesting to test whether the observed allometric morphological scaling and kinematic similarity is also present beyond the species that we studied. In our revision, we therefore extended our analysis to address this question. Performing additional flight experiments and fluid mechanics simulations was beyond the scope of our current study, but extending the morphological scaling analyses was certainly possible.

In our revised study, we therefore extended our morphological scaling analysis by including the morphology of twenty additional hoverfly species. This extended dataset includes wing morphology data of 74 museum specimens from Naturalis Biodiversity Centre (Leiden, the Netherlands), including two males and two females per species, whenever possible (4.2±1.7 individuals per species (mean±sd)). This extended analysis shows that the allometric scaling of wing morphology with size is robust along the larger sample of species, from a wider range of habitats and ecologies. Nevertheless, we advocate for additional flight measurement in species from different habitats to ascertain the generality of our results (lines 729–732).

Reviewer #2 (Public review):

Summary

Le Roy et al quantify wing morphology and wing kinematics across eight hoverfly species that differ in body mass; the aim is to identify how weight support during hovering is ensured. Wing shape and relative wing size vary significantly with body mass, but wing kinematics are reported to be size-invariant. On the basis of these results, it is concluded that weight support is achieved solely through size-specific variations in wing morphology and that these changes enabled hoverflies to decrease in size throughout their phylogenetic history. Adjusting wing morphology may be preferable compared to the alternative strategy of altering wing kinematics, because kinematics may be under strong evolutionary and ecological constraints, dictated by the highly specialised flight and ecology of the hoverflies.

Strengths

The study deploys a vast array of challenging techniques, including flight experiments, morphometrics, phylogenetic analysis, and numerical simulations; it so illustrates both the power and beauty of an integrative approach to animal biomechanics. The question is well motivated, the methods appropriately designed, and the discussion elegantly and convincingly places the results in broad biomechanical, ecological, evolutionary, and comparative contexts.

We thank the reviewer for appreciating the strengths of our study.

Weaknesses

(1) In assessing evolutionary allometry, it is key to identify the variation expected from changes in size alone. The null hypothesis for wing morphology is well-defined (isometry), but the equivalent predictions for kinematic parameters remain unclear. Explicit and well-justified null hypotheses for the expected size-specific variation in angular velocity, angle-of-attack, stroke amplitude, and wingbeat frequency would substantially strengthen the paper, and clarify its evolutionary implications.

We agree with the reviewer that the expected scaling of wingbeat kinematics with size was indeed unclear in our initial version of the manuscript. In our revised manuscript (and supplement), we now explicitly define how all kinematic parameters should scale with size under kinematic similarity, and how they should scale for maintaining weight support across various sizes. These are explained in the introduction (lines 46–78), method section (lines 316–327), and dedicated supplementary text (see Supplementary Info section “Geometric and kinematic similarity and scaling for weight support”). Here, we now also provide a thorough description of the isometric scaling of morphology, and scaling of the kinematics parameters under kinematic similarity.

(2) By relating the aerodynamic output force to wing morphology and kinematics, it is concluded that smaller hoverflies will find it more challenging to support their body mass - a scaling argument that provides the framework for this work. This hypothesis appears to stand in direct contrast to classic scaling theory, where the gravitational force is thought to present a bigger challenge for larger animals, due to their disadvantageous surface-to-volume ratios. The same problem ought to occur in hoverflies, for wing kinematics must ultimately be the result of the energy injected by the flight engine: muscle. Much like in terrestrial animals, equivalent weight support in flying animals thus requires a positive allometry of muscle force output. In other words, if a large hoverfly is able to generate the wing kinematics that suffice to support body weight, an isometrically smaller hoverfly should be, too (but not vice versa). Clarifying the relation between the scaling of muscle force input, wing kinematics, and weight support would resolve the conflict between these two contrasting hypotheses, and considerably strengthen the biomechanical motivation and interpretation.

The reviewer highlights a crucial aspect of our study: our perspective on the aerodynamic challenges associated with becoming smaller or larger. This comment made us realize that our viewpoint might be unconventional regarding general scaling literature and requires further clarification.

Our approach is focused on the disadvantage of a reduction in size, in contrast with classic scaling theory focusing on the disadvantage of increasing in size. As correctly stated by the reviewer, producing an upward directed force to maintain weight support is often considered as the main challenge, constrained by size. Hereby, researchers often focus on the limitations on the motor system, and specifically muscle force: as animals increase in size, the ability to achieve weight support is limited by muscle force availability. An isometric growth in muscle cannot sustained the increased weight, due to the disadvantageous surface-to-volume ratio.

In animal flight, this detrimental effect of size on the muscular motor system is also present, particularly for large flying birds. But for natural flyers, there is also a detrimental effect of size on the propulsion system, being the flapping wings. The aerodynamic forces produced by a beating wing scales linearly with the second-moment-of-area of the wing. Under isometry, this second-moment-of-area decreases at higher rate than body mass, and thus producing enough lift for weight support becomes more challenging with reducing size. Because we study tiny insects, our study focuses precisely on this constraint on the wing-based propulsion system, and not on the muscular motor system.

We revised the manuscript to better explain how physical scaling laws differentially affect force production by the muscular flight motor system and the wingbeat-induced propulsion system (lines 46–78).

(3) The main conclusion - that evolutionary miniaturization is enabled by changes in wing morphology - is only weakly supported by the evidence. First, although wing morphology deviates from the null hypothesis of isometry, the difference is small, and hoverflies about an order of magnitude lighter than the smallest species included in the study exist. Including morphological data on these species, likely accessible through museum collections, would substantially enhance the confidence that size-specific variation in wing morphology occurs not only within medium-sized but also in the smallest hoverflies, and has thus indeed played a key role in evolutionary miniaturization.

We thank the reviewer for the suggestion to add additional specimens from museum collections to strengthen the conclusions of our work. In our revised study, we did so by adding the morphology of 20 additional hoverfly species, from the Naturalis Biodiversity Centre (Leiden, the Netherlands). This extended dataset includes wing morphology data of 74 museum specimens, and whenever possible we sampled at least two males and two females (4.2±1.7 individuals per species (mean±sd)). This extended analysis shows that the allometric scaling of wing morphology with size is robust along the larger sample of species, including smaller ones. We discuss these additional results now explicitly in the revised manuscript (see Discussion).

Second, although wing kinematics do not vary significantly with size, clear trends are visible; indeed, the numerical simulations revealed that weight support is only achieved if variations in wing beat frequency across species are included. A more critical discussion of both observations may render the main conclusions less clear-cut, but would provide a more balanced representation of the experimental and computational results.

We agree with the reviewer that variations in wingbeat kinematics between species, and specifically wingbeat frequency, are important and non-negligible. As mentioned by the reviewer, this is most apparent for the fact that weight support is only achieved with the species-specific wingbeat frequency. To address this in a more balanced and thorough way, we revised the final section of our analysis approach, by including changes in wingbeat kinematics to that analysis. By doing so, we now explicitly show that allometric changes in wingbeat frequency are important for maintaining weight support across the sampled size range, but that allometric scaling of morphology has a stronger effect. In fact, the relative contributions of morphology and kinematics to maintaining weight-support across sizes is 81% and 22%, respectively (Figure 7). We discuss this new analysis and results now thoroughly in the revised manuscript (lines 621–629, 650–664), resulting in a more balanced discussion and conclusion about the outcome of our study. We sincerely thank the reviewer for suggesting to look closer into the effect of variations in wingbeat kinematics on aerodynamic force production, as the revised analysis strengthened the study and its results.

In many ways, this work provides a blueprint for work in evolutionary biomechanics; the breadth of both the methods and the discussion reflects outstanding scholarship. It also illustrates a key difficulty for the field: comparative data is challenging and time-consuming to procure, and behavioural parameters are characteristically noisy. Major methodological advances are needed to obtain data across large numbers of species that vary drastically in size with reasonable effort, so that statistically robust conclusions are possible.

We thank the reviewer for their encouraging words about the scholarship of our work. We will continue to improve our methods and techniques for performing comparative evolutionary biomechanics research, and are happy to jointly develop this emerging field of research.

Reviewer #3 (Public review):

The paper by Le Roy and colleagues seeks to ask whether wing morphology or wing kinematics enable miniaturization in an interesting clade of agile flying insects. Isometry argues that insects cannot maintain both the same kinematics and the same wing morphology as body size changes. This raises a long-standing question of which varies allometrically. The authors do a deep dive into the morphology and kinematics of eight specific species across the hoverfly phylogeny. They show broadly that wing kinematics do not scale strongly with body size, but several parameters of wing morphology do in a manner different from isometry leading to the conclusion that these species have changed wing shape and size more than kinematics. The authors find no phylogenetic signal in the specific traits they analyze and conclude that they can therefore ignore phylogeny in the later analyses. They use both a quasi-steady simplification of flight aerodynamics and a series of CFD analyses to attribute specific components of wing shape and size to the variation in body size observed. However, the link to specific correlated evolution, and especially the suggestion of enabling or promoting miniaturization, is fraught and not as strongly supported by the available evidence.

We thank the reviewer for the accurate description of our work, and the time and energy put into reviewing our paper. We regret that the reviewer found our conclusions with respect to miniaturization fraught and not strongly supported by the evidence. In our revision, we addressed this by no longer focusing primarily on miniaturization, by extending our morphology analysis to 20 additional species (Figures 4 and 5), improving our analysis of both the kinematics and morphology data (Figure 7), and by discussing our results in a more balanced way (see Discussion). We hope that the reviewer finds the revised manuscript of sufficient quality for publication in eLife.

The aerodynamic and morphological data collection, modeling, and interpretation are very strong. The authors do an excellent job combining a highly interpretable quasi-steady model with CFD and geometric morphometrics. This allows them to directly parse out the effects of size, shape, and kinematics.

We thank the reviewer for assessing our experimental and modelling approach as very strong.

Despite the lack of a relationship between wing kinematics and size, there is a large amount of kinematic variation across the species and individual wing strokes. The absolute differences in Figure 3F - I could have a very large impact on force production but they do indeed not seem to change with body size. This is quite interesting and is supported by aerodynamic analyses.

We agree with the reviewer that there are important and non-negligible variations in wingbeat kinematics between species. As mentioned by the reviewer, although these kinematics do not significant scale with body mass, the interspecific variations are important for maintaining weight support during hovering flight. We thus also agree with the reviewer that these kinematics variations are interesting and deserve further investigations.

In our revised study, we did so by including these wingbeat kinematic variations in our analysis on the effect of variations in morphology and kinematics on aerodynamic force production for maintaining in-flight weight support across the sampled size range (lines 422–444, Figure 7). By doing so, we now explicitly show that variations in wingbeat kinematics are important for maintaining weight across sizes, but that allometric scaling of morphology has a stronger effect. In fact, the relative contributions of adaptations in morphology and kinematics to maintaining weight support across sizes is 81% and 22%, respectively (Figure 7). We discuss these new analysis and results now in the revised manuscript (lines 621–629, 650–664), resulting in a more balanced discussion about the relative importance of adaptations in morphology and kinematics. We hope the reviewer appreciates this newly added analysis.

The authors switch between analyzing their data based on individuals and based on species. This creates some pseudoreplication concerns in Figures 4 and S2 and it is confusing why the analysis approach is not consistent between Figures 4 and 5. In general, the trends appear to be robust to this, although the presence of one much larger species weighs the regressions heavily. Care should be taken in interpreting the statistical results that mix intra- and inter-specific variation in the same trend.

We agree that it was sometimes unclear whether our analysis is performed at the individual or species level. To improve clarity and avoid pseudoreplication, we now analyze all data at the species level, using phylogenetically informed analyses. Because we think that showing within-species variation is nonetheless informative, we included dedicated figures to the supplement (Figures S3 and S5) in which we show data at the individual level, as equivalent to figures 4 and 5 with data at the species level. Note that this cannot be done for flight data due to our experimental procedure. Indeed, we performed flight experiments with multiple individuals in a single experimental setup, pseudoreplication is thus possible for these flight data. This is explained in the manuscript (lines 167–175). All morphological measurements were however done on a carefully organized series of specimens and thus pseudoreplication is hereby not possible.

The authors based much of their analyses on the lack of a statistically significant phylogenetic signal. The statistical power for detecting such a signal is likely very weak with 8 species. Even if there is no phylogenetic signal in specific traits, that does not necessarily mean that there is no phylogenetic impact on the covariation between traits. Many comparative methods can test the association of two traits across a phylogeny (e.g. a phylogenetic GLM) and a phylogenetic PCA would test if the patterns of variation in shape are robust to phylogeny.

After extending our morphological dataset from 8 to 28 species, by including 20 additional species from a museum collection, we increased statistical power and found a significant phylogenetic signal on all morphological traits, except for the second moment of area (lines 458–460, Table S2). Although we do not detect an effect of phylogeny on flight traits, likely due to the limited number of species for which flight was quantified (n=8), we agree with the reviewer’s observation that the absence of a phylogenetic signal does not rule out the potential influence of phylogeny on the covariation between traits. This is now explicitly discussed in the manuscript (lines 599–608). As mentioned in the previous comment, we now test all relationships between body mass and other traits using phylogenetic generalized least squares (PGLS) regressions, therefore accounting for the impact of phylogeny everywhere. The revised analyses produce sensibly similar results as for our initial study, and so the main conclusions remain valid. We sincerely thank the reviewer for their suggestion for revising our statistical analysis, because the revised phylogenetic analysis strengthens our study as a whole.

The analysis of miniaturization on the broader phylogeny is incomplete. The conclusion that hoverflies tend towards smaller sizes is based on an ancestral state reconstruction. This is difficult to assess because of some important missing information. Specifically, such reconstructions depend on branch lengths and the model of evolution used, which were not specified. It was unclear how the tree was time-calibrated. Most often ancestral state reconstructions utilize a maximum likelihood estimate based on a Brownian motion model of evolution but this would be at odds with the hypothesis that the clade is miniaturizing over time. Indeed such an analysis will be biased to look like it produces a lot of changes towards smaller body size if there is one very large taxa because this will heavily weight the internal nodes. Even within this analysis, there is little quantitative support for the conclusion of miniaturization, and the discussion is restricted to a general statement about more recently diverged species. Such analyses are better supported by phylogenetic tests of directedness in the trait over time, such as fitting a model with an adaptive peak or others.

We thank the reviewer for their expert insight in our ancestral state estimate of body size. We agree that the accuracy of this estimate is rather low. Based on the comments by the reviewer we have now revised our main analysis and results, by no longer basing it on the apparent evolutionary miniaturization of hoverflies, but instead on the observed variations in size in our studied hoverfly species. As a result, we removed the figure mapping ancestral state estimates (called figure S1 in the first version) from the manuscript. We now explicitly mention that ascertaining the evolutionary directedness of body size is beyond the scope of our work, but that we nonetheless focus on the aerodynamic challenge of size reduction (lines 609–615).

Setting aside whether the clade as a whole tends towards smaller size, there is a further concern about the correlation of variation in wing morphology and changes in size (and the corresponding conclusion about lack of co-evolution in wing kinematics). Showing that there is a trend towards smaller size and a change in wing morphology does not test explicitly that these two are correlated with the phylogeny. Moreover, the subsample of species considered does not appear to recapitulate the miniaturization result of the larger ancestral state reconstruction.

As also mentioned above, we agree with the reviewer that we cannot ascertain the trajectory of body size evolution in the diversification of hoverflies. We therefore revised our manuscript such that we do no longer focus explicitly on miniaturization; instead, we discuss how morphology and kinematics scale with size, independently of potential trends over the phylogeny. To do so, we revised the title, abstract results and discussion accordingly.

Given the limitations of the phylogenetic comparative methods presented, the authors did not fully support the general conclusion that changes in wing morphology, rather than kinematics, correlate with or enable miniaturization. The aerodynamic analysis across the 8 species does however hold significant value and the data support the conclusion as far as it extends to these 8 species. This is suggestive but not conclusive that the analysis of consistent kinematics and allometric morphology will extend across the group and extend to miniaturization. Nonetheless, hoverflies face many shared ecological pressures on performance and the authors summarize these well. The conclusions of morphological allometry and conserved kinematics are supported in this subset and point to a clade-wide pattern without having to support an explicit hypothesis about miniaturization.

The reviewer argues here fully correct that we should be careful about extending our analysis based on eight species to hoverflies in general, and especially to extend it to miniaturization in this family of insects. As mentioned above, we therefore do no longer specifically focus on miniaturization. Moreover, we extended our analysis by including the morphology of 20 additional species of hoverflies, sampled from a museum collection. We hope that the reviewer agrees with this more balanced and focused discussion of our study.

The data and analyses on these 8 species provide an important piece of work on a group of insects that are receiving growing attention for their interesting behaviors, accessibility, and ecologies. The conclusions about morphology vs. kinematics provide an important piece to a growing discussion of the different ways in which insects fly. Sometimes morphology varies, and sometimes kinematics depending on the clade, but it is clear that morphology plays a large role in this group. The discussion also relates to similar themes being investigated in other flying organisms. Given the limitations of the miniaturization analyses, the impact of this study will be limited to the general question of what promotes or at least correlates with evolutionary trends towards smaller body size and at what phylogenetic scale body size is systematically decreasing.

We thank the reviewer for their encouraging words about the importance of our work on hoverfly flight. As suggested by the reviewer, we narrowed down the main question of our study by no longer focusing on apparent miniaturization, but instead on the correlation between wing morphology, wingbeat kinematics and variations in size.

In general, there is an important place for work that combines broad phylogenetic comparison of traits with more detailed mechanistic studies on a subset of species, but a lot of care has to be taken about how the conclusions generalize. In this case, since the miniaturization trend does not extend to the 8 species subsample of the phylogeny and is only minimally supported in the broader phylogeny, the paper warrants a narrower conclusion about the connection between conserved kinematics and shared life history/ecology.

We truly appreciated the reviewer’s positive assessment of the importance of our work and study. We also thank the reviewer for their advice to generalize the outcome of our work in a more balanced way. Based on the above comments and suggestions of the reviewer, we did so by revising several aspects of our study, including adding additional species to our study, amending the analysis, and revising the title, abstract, results and discussion sections. We hope that the reviewer warrants the revised manuscript of sufficient quality for final publication in eLife.

Recommendations For The Authors:

Reviewer #1 (Recommendations for the authors):

Figure S1 is lovely. I would recommend merging it with Figure 1 so that it does not disappear.

We appreciate the reviewer comment. However, reviewer 3 had several points of concern about the underlying analysis, which made us realize that our ancestral state estimation analysis does not conclusively support a miniaturization trend. We therefore are no longer focusing on miniaturization when interpreting our results.

Figure 4 is beautiful. The consistent color coding throughout is very helpful.

We thank the reviewer for this comment.

Sometimes spaces are missing before brackets, and sometimes there are double brackets, or random line break.

We did our best to remove these typos.

Should line 367 refer to Table S2?

Table S2 is now referred to when mentioning the result of phylogenetic signal (line 460 in the revised manuscript)

Can you also refer to Figure 2 on line 377?

Good suggestion, and so we now do so (line 462 in the revised manuscript).

Lines 497-512: Please refer to relevant figures.

We now refer to figure 4, and its panels (lines 621–629 in the revised manuscript).

Figure legend 1: Do you need to say that the second author took the photos?

We removed this reference.

Figure legend 4: "(see top of A and B)" is not aligned with the figure layout.

We corrected this.

Figure 5 seems to have a double legend, A, B then A, B. Panel A says it's color-coded for body mass, but the figure seems to be color-coded for species.

Thank you for noting this. We corrected this in the figure legend.

Figure 6 legend: Can you confidently say that they were hovering, or do you need to modify this to flying?

The CFD simulations were performed in full hovering (U_¥=0 m/s), but any true flying hoverflies will per definition never hover perfectly. But as explained in our manuscript, we define a hovering flight mode as flying with advance ratios smaller than 0.1 (Ellington, 1984a). Based on this we can state that our hoverflies were flying in a hovering mode. We hope that the reviewer agrees with this approach.

Reviewer #2 (Recommendations for the authors):

Below, I provide more details on the arguments made in the public review, as well as a few additional comments and observations; further detailed comments are provided in the word document of the manuscript file, which was shared with the authors via email (I am not expecting a point-by-point reply to all comments in the word document!).

We thank the reviewer for this detailed list of additional comments, here and in the manuscript. As suggested by the reviewer, we did not provide a point-by-point respond to all comments in the manuscript file, but did take them into account when improving our revised manuscript. Most importantly, we now define explicitly kinematic similarity as the equivalent from morphological similarity (isometry), we added a null hypothesis and the proposed references, and we revised the figures based on the reviewer suggestions.

Null hypotheses for kinematic parameters.

Angular amplitudes should be size-invariant under isometry. The angular velocity is more challenging to predict, and two reasonable options exist. Conservation of energy implies:

W = 1/2 I ω2

where I is the mass moment of inertia and W is the muscle work output (I note that this result is approximate, for it ignores external forces; this is likely not a bad assumption to first order. See the reference provided below for a more detailed discussion and more complicated calculations). From this expression, two reasonable hypotheses may be derived.

First, in line with classic scaling theory (Hill, Borelli, etc), it may be assumed that W∝m; isometry implies that I∝m5/3 from which ω ∝m-1/3 follows at once. Note well the implication with respect to eq. 1: isometry now implies F∝m2/3, so that weight support presents a bigger challenge for larger animals; this result is completely analogous to the same problem in terrestrial animals, which has received much attention, but in strong contrast to the argument made by the authors: weight support is more challenging for larger animals, not for smaller animals.

Second, in line with recent arguments, one may surmise that the work output is limited by the muscle shortening speed instead, which, assuming isometry and isophysiology, implies ω ∝m0 = constant; smaller animals would then indeed be at a seeming disadvantage, as suggested by the authors (but see below).

The following references contain a more detailed discussion of the arguments for and against these two possibilities:

Labonte, D. A theory of physiological similarity for muscle-driven motion. PNAS, 2023, 120, e2221217120

Labonte, D.; Bishop, P.; Dick, T. & Clemente, C. J. Dynamics similarity and the peculiar allometry of maximum running speed. Nat Comms., 2024, 15, 2181

Labonte, D. & Holt, N. Beyond power limits: the kinetic energy capacity of skeletal muscle. bioRxiv doi: 10.1101/2024.03.02.583090, 2024

Polet, D. & Labonte, D. Optimising the flow of mechanical energy in musculoskeletal systems through gearing. bioRxiv doi: 10.1101/2024.04.05.588347, 2024

Labonte et al 2024 also highlight that, due to force-velocity effects, the scaling of the velocity that muscle can impart will fall somewhere in between the extremes presented by the two hypotheses introduced above, so that, in general, the angular velocity should decrease with size with a slope of around -1/6 to -2/9 --- very close to the slope estimated in this manuscript, and to data on other flying animals.

We greatly appreciate the reviewer's detailed insights on null hypotheses for kinematics, along with the accompanying references. As noted in the Public Review section (comment/reply 2.3), our study primarily explores how small-sized insects adapt to constraints imposed by the wing-based propulsion system, rather than by the muscular motor system.

In this context, we chose to contrast the observed scaling of morphology and flight traits with a hypothetical scenario of geometric similarity (isometry) and kinematic similarity, where all size-independent kinematic parameters remain constant with body mass. While isometric expectations for morphological traits are well-defined (i.e., ), those for kinematic traits are more debatable (as pointed out by the reviewer). For this reason, we believe that adopting a simple approach based on kinematic similarity across sizes (f~m0, etcetera) enhances the interpretability of our results and strengthens the overall narrative.

Size range

The study would significantly benefit from a larger size range; it is unreasonable to ask for kinematic measurements, as these experiments become insanely challenging as animals get smaller; but it should be quite straightforward for wing shape and size, as this can be measured with reasonable effort from museum specimens. In particular, if a strong point on miniaturization is to be made, I believe it is imperative to include data points for or close to the smallest species.

We appreciate that the reviewer recognizes the difficulty of performing additional kinematic measurements. Collecting additional morphological data to extend the size range was however feasible. In our revised study, we therefore extended our morphological scaling analysis by including the morphology of twenty additional hoverfly species. This extended dataset includes wing morphology data of 74 museum specimens (4.2±1.7 individuals per species (mean±sd)) from Naturalis Biodiversity Centre (Leiden, the Netherlands). This increased the studied mass range of our hoverfly species from 5 100 mg to 3 132 mg, and strengthened our results and conclusions on the morphological scaling in hoverflies.

Is weight support the main problem?

Phrasing scaling arguments in terms of weight support is consistent with the classic literature, but I am not convinced this is appropriate (neither here nor in the classic scaling literature): animals must be able to move, and so, by strict physical necessity, muscle forces must exceed weight forces; balancing weight is thus never really a concern for the vast majority of animals. The only impact of the differential scaling may be a variation in peak locomotor speed (this is unpacked in more detail in the reference provided above). In other words, the very fact that these hoverfly species exist implies that their muscle force output is sufficient to balance weight, and the arguably more pertinent scaling question is how the differential scaling of muscle and weight force influences peak locomotor performance. I appreciate that this is beyond the scope of this study, but it may well be worth it to hedge the language around the presentation of the scaling problem to reflect this observation, and to, perhaps, motivate future work.

We agree with the reviewer that a question focused on muscle force would be inappropriate for this study, as muscle force and power availability is not under selection in the context of hovering flight, but instead in situation where producing increased output is advantageous (for example during take-off or rapid evasive maneuvers). But as explained in our revised manuscript (lines 81-85), we here do not focus on the scaling of the muscular motor with size and throughout phylogeny, but instead we focus on scaling of the flapping wing-based propulsion system. For this system there are known physical scaling laws that predict how this propulsion system should scale with size (in morphology and kinematics) for maintaining weight-support across sizes. In our study, we test in what way hoverflies achieve this weight support in hovering flight.

Of course, it would be interesting to also test how peak thrust is produced by the propulsion system, for example during evasive maneuvers. In the revised manuscript, we now explicitly mention this as potential future research (lines 733–735).

Other relevant literature

Taylor, G. & Thomas, A. Evolutionary biomechanics: selection, phylogeny, and constraint, Oxford University Press, 2014

This book has quite detailed analyses of the allometry of wing size and shape in birds in an explicit phylogenetic context. It was a while ago that I read it, but I think it may provide much relevant information for the discussion in this work.

Schilder, R. J. & Marden, J. H. A hierarchical analysis of the scaling of force and power production by dragonfly flight motors J. Exp. Biol., 2004, 207, 767

This paper also addresses the question of allometry of flight forces (if in dragonflies). I believe it is relevant for this study, as it argues that positive allometry of forces is partially achieved through variation of the mechanical advantage, in remarkable resemblance to Biewener's classic work on EMA in terrestrial animals (this is discussed and unpacked in more detail also in Polet and Labonte, cited above). Of course, the authors should not measure the mechanical advantage of this work, but perhaps this is an interesting avenue for future work.

We thank the reviewer for these valuable literature suggestions and the insights they offer for future work.

More generally, I thought the introduction misses an opportunity to broaden the perspective even further, by making explicit that running and flying animals face an analogous problem (with swimming likely being a curious exception!); some other references related to the role of phylogeny in biomechanical scaling analyses are provided in the comments in the word file.

The introduction has been revised to better emphasize the generality of the scaling question addressed in our study. Specifically, we now explicitly highlight the similar constraints associated with increasing or decreasing size in both terrestrial and flying animals (lines 53–59). We thank the reviewer for this suggestion, which has improved our manuscript.

Numerical results vs measurements

I felt that the paper did not make the strongest possible use of the very nice numerical simulations. Part of the motivation, as I understood it, was to conduct more complex simulations to also probe the validity of the quasi-steady aerodynamics assumption on which eq. 1 is based. All parameters in eq. 1 are known (or can be approximated within reasonable bounds) - if the force output is evaluated analytically, what is the result? Is it comparable to the numerical simulations in magnitude? Is it way off? Is it sufficient to support body mass? The interplay between experiments and numerics is a main potential strength of the paper, which in my opinion is currently sold short.

We agree with the reviewer that we did not make full use of the numerical simulations results. In fact, we did so deliberately because we aim to focus more on the fluid mechanics of hoverfly flight in a future study. That said, we thank the reviewer for suggesting to use the CFD for validating our quasi-steady model. We now do so by correlating the vertical aerodynamic force with variations in morphology and kinematics (revised Figure 7A). The striking similarity between the predicted and empirical fit shows that the quasi-steady model captures the aerodynamic force production during hovering flight surprisingly well.

Statistics

There are errors in the Confidence Intervals in Tab 2 (and perhaps elsewhere). Please inspect all tables carefully, and correct these mistakes. The disagreement between confidence intervals and p-values suggests a significant problem with the statistics; after a brief consultation with the authors, it appears that this result arises because Standard Major Axis regression was used (and not Reduced Major Axis regression, as stated in the manuscript). This is problematic because SMA confidence intervals become unreliable if the variables are uncorrelated, as appears to be the case for some parameters here (see https://cran.r-project.org/web/packages/lmodel2/vignettes/mod2user.pdf for more details on this point). I strongly recommend that the authors avoid SMA, and use MA, RMA or OLS instead. My recommendation would be to use RMA and OLS to inspect if the conclusions are consistent, in which case one can be shown in the SI; this is what I usually do in scaling papers, as there are some colleagues who have very strong and diverging opinions about which technique is appropriate. If the results differ, further critical analysis may be required.

The reviewer correctly identified an error in the statistical approach: a Standard Major Axis was indeed used under inappropriate conditions. Following Reviewer #3’s comments, the expanded sample size and the resulting increase in statistical power to detect phylogenetic signal, our revised analysis now accounts for phylogenetic effects in these regressions. We therefore now report the results from Phylogenetic Least Square (PGLS) regressions (the phylogenetic equivalent of an OLS).

Figures

Please plot 3E-F in log space, add trendlines, and the expectation from isometry/isophysiology, to make the presentation consistent, and comparison of effect strengths across results more straightforward.

The reviewer probably mentioned Figure 3F-I and not E-F (the four panels depicting the relationships between kinematics variables and body mass). As requested, we added the expectation for kinematic similarity to the revised figure, but prefer to not show the non-significant PGLS fits, as they are not used in any analysis. For completeness, we did add the requested figure in log-space with all trendlines to the supplement (Figure S2), and refer to it in the figure legend.

The visual impression of the effect strength in D is a bit misleading, due to the very narrow y-axis range; it took me a moment to figure this out. I suggest either increasing the y-range to avoid this incorrect impression or to notify the reader explicitly in the caption.

We believe the reviewer is referring to Figure 4D. As rightly pointed out, variation in non-dimensional second moment of area() is very low among species, which is consistent with literature (Ellington, 1984b). We agree that the small range on the y-axis might be confusing, and thus we increased it somewhat. More importantly, we now show, next to the trend line, the scaling for isometry (~m⁰) and for single-metric weight support. Especially the steepness of the last trend line shows the relatively small effect of on aerodynamic force production. This is even further highlighted by the newly added pie charts of the relative allometric scaling factor, where variations in contribute only 5% to maintaining weight support across sizes.

Despite this small variation, these adaptations in wing shape are still significant and are highly interesting in the context of our work. We now discuss this in more detail in the revised manuscript (lines 645–649).

In Figure 7b, one species appears as a very strong outlier, driving the regression result. Data of the same species seems to be consistent with the other species in 7a, c, and d - where does this strong departure come from? Is this data point flagged as an outlier by any typical regression metric (Cook's distance etc) for the analysis in 7b?

We agree with the reviewer: the species in dark green (Eristalis tenax) appears as an outlier on the in Figure 7B ( vs. vertical force) in our original manuscript. This is most likely due to the narrow range of variation in ( — as the reviewer pointed out in the previous comment — which amplifies differences among species. We expanded the y-axis range in the revised Figure 7, so that the point no longer appears as an outlier (see updated graph, now on Figure 7F).

In Figure 1, second species from the top, it reads "Eristalix tenax" when it is "Eristalis tenax" (relayed info by the Editor).

Corrected.

Reviewer #3 (Recommendations for the authors):

I really like the biomechanical and aerodynamic analyses and think that these alone make for a strong paper, albeit with narrower conclusions. I think it is perfectly valid and interesting to analyze these questions within the scope of the species studied and even to say that these patterns may therefore extend to the hoverflies as a whole group given the great discussion about the shared ecology and behavior of much of the clade. However, the extension to miniaturization is too tenuous. This would need much more support, especially from the phylogenetic methods which are not rigorously presented and likely need additional tests.

We thank the reviewer for the positive words about our study. We agree that our attempt to infer the directedness of size evolution was too simplistic, and thus the miniaturization aspect of our study would need more support. As suggested by the reviewer, we therefore do no longer focus on miniaturization, and thus removed these aspects from the title, abstract and main conclusion of our revised manuscript.

There is a lot of missing data about the tree and the parameters used for the phylogenetic methods that should be added (especially branch lengths and models of evolution). Phylogenetic tests for the relationships of traits should go beyond the analysis of phylogenetic signals in the specific traits. My understanding is also that phylogenetic signal is not properly interpreted as a "control" on the effect of phylogeny. The PCA should probably be a phylogenetic PCA with a corresponding morphospace reconstruction.

We agree with the reviewer that our phylogenetic approach based on phylogenetic signal only was incomplete. In our revised manuscript, we not only test for phylogenetic signal but also account for phylogeny in all regressions between traits and body mass using Phylogenetic Generalized Least Squares (PGLS) regressions. Additionally, we have provided more details about the model of evolution and the parameter estimation method in the Methods section (275–278).

Following the reviewer suggestion, in our revised study we now also performed a phylogenetic PCA instead of a traditional PCA on the superimposed wing shape coordinates. The resulting morphospace was however almost identical to the traditional PCA (Figure S4). We nonetheless included it in the revised manuscript for completion. We thank the reviewer for this suggestion, as the revised phylogenetic analysis strengthens our study as a whole.

For the miniaturization conclusion, my suggestion is a more rigorous phylogenetic analysis of directionality in the change in size across the larger phylogeny. However, even given this, I think the conclusion will be limited because it appears this trend does not hold up under the 8 species subsample. To support that morphology is evolutionarily correlated with miniaturization would for me require an analysis of how the change in body size relates to the change in wing shape and kinematics which is beyond what a scaling relationship does. In other words, you would need to test if the changes in body morphology occur in the same location phylogenetically with a shrinking of body size. I think even more would be required to use the words "enable" or "promote" when referring to the relationship of morphology to miniaturization because those imply evolutionary causality to me. To me, this wording would at least require an analysis that shows something like an increase in the ability of the wing morphological traits preceding the reduction in body size. Even that would likely be controversial. Both seem to be beyond the scope of what you could analyze with the given dataset.

As mentioned in reply 3.1, we agree with the reviewer that the miniaturization aspect of our study would need more support. And thus, as suggested by the reviewer, we therefore do no longer focus primarily on miniaturization, by removing these aspects from the title, abstract and main conclusion of our revised manuscript.

The pseudoreplication should be corrected. You can certainly report the data with all individuals, but you should also indicate in all cases if the analysis is consistent if only species are considered.

As mentioned in the Public Review section, our revised approach avoids pseudoreplication by analyzing all data at the species level. Nonetheless, we have included supplementary figures (Figures S3 and S5) to visualize within-species variation.

My overall suggestion is to remove the analysis of miniaturization and cast the conclusions with respect to the sampling you have. Add a basic phylogenetic test for the correlated trait analysis (like a phylogenetic GLM) which will likely still support your conclusions over the eight species and emphasize the specific conclusion about hoverflies' scaling relationships. I think that is still a very good study better supported by the extent of the data.

We thank the reviewer for the positive assessment of our study, and their detailed and constructive feedback. As suggested by the reviewer, miniaturization is no longer the primary focus of our study, and we revised our analysis by extending the morphology dataset to more species, and by using phylogenetic regressions.

References

Ellington C. 1984a. The aerodynamics of hovering insect flight. III. Kinematics. Philosophical Transactions of the Royal Society of London B: Biological Sciences 305:41–78.

Ellington C. 1984b. The aerodynamics of insect flight. II. Morphological parameters. Phil Trans R Soc Lond B 305:17–40.

Fry SN, Sayaman R, Dickinson MH. 2005. The aerodynamics of hovering flight in Drosophila. Journal of Experimental Biology 208:2303–2318. doi:10.1242/jeb.01612

Liu Y, Sun M. 2008. Wing kinematics measurement and aerodynamics of hovering droneflies. Journal of Experimental Biology 211:2014–2025. doi:10.1242/jeb.016931

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

Detecting unexpected epistatic interactions among multiple mutations requires a robust null expectation - or neutral function - that predicts the combined effects of multiple mutations on phenotype, based on the effects of individual mutations. This study assessed the validity of the product neutrality function, where the fitness of double mutants is represented as the multiplicative combination of the fitness of single mutants, in the absence of epistatic interactions. The authors utilized a comprehensive dataset on fitness, specifically measuring yeast colony size, to analyze epistatic interactions.

The study confirmed that the product function outperformed other neutral functions in predicting the fitness of double mutants, showing no bias between negative and positive epistatic interactions. Additionally, in the theoretical portion of the study, the authors applied a wellestablished theoretical model of bacterial cell growth to simulate the growth rates of both single and double mutants under various parameters. The simulations further demonstrated that the product function was superior to other functions in predicting the fitness of hypothetical double mutants. Based on these findings, the authors concluded that the product function is a robust tool for analyzing epistatic interactions in growth fitness and effectively reflects how growth rates depend on the combination of multiple biochemical pathways.

Strengths:

By leveraging a previously published extensive dataset of yeast colony sizes for single- and double-knockout mutants, this study validated the relevance of the product function, commonly used in genetics to analyze epistatic interactions. The finding that the product function provides a more reliable prediction of double-mutant fitness compared to other neutral functions offers significant value for researchers studying epistatic interactions, particularly those using the same dataset.

Notably, this dataset has previously been employed in studies investigating epistatic interactions using the product neutrality function. The current study's findings affirm the validity of the product function, potentially enhancing confidence in the conclusions drawn from those earlier studies. Consequently, both researchers utilizing this dataset and readers of previous research will benefit from the confirmation provided by this study's results.

Weaknesses:

This study exhibits several significant logical flaws, primarily arising from the following issues: a failure to differentiate between distinct phenotypes, instead treating them as identical; an oversight of the substantial differences in the mechanisms regulating cell growth between prokaryotes and eukaryotes; and the adoption of an overly specific and unrealistic set of assumptions in the mutation model. Additionally, the study fails to clearly address its stated objective-investigating the mechanistic origin of the multiplicative model. Although it discusses conditions under which deviations occur, it falls short of achieving its primary goal. Moreover, the paper includes misleading descriptions and unsubstantiated reasoning, presented without proper citations, as if they were widely accepted facts. Readers should consider these issues when evaluating this paper. Further details are discussed below.

(1) Misrepresentation of the dataset and phenotypes

The authors analyze a dataset on the fitness of yeast mutants, describing it as representative of the Malthusian parameter of an exponential growth model. However, they provide no evidence to support this claim. They assert that the growth of colony size in the dataset adheres to exponential growth kinetics; in contrast, it is known to exhibit linear growth over time, as indicated in [Supplementary Note 1 of https://doi.org/10.1038/nmeth.1534]. Consequently, fitness derived from colony size should be recognized as a different metric and phenotype from the Malthusian parameter. Equating these distinct phenotypes and fitness measures constitutes a fundamental error, which significantly compromises the theoretical discussions based on the Malthusian parameter in the study.

The reviewer is correct in pointing out that colony-size measurements are distinct from exponential growth kinetics. We acknowledge that our original text implied that the dataset directly measured the exponential growth rate (Malthusian parameter), when in fact it was measuring yeast colony expansion rates on solid media. Colony growth under these conditions often follows a biphasic pattern in that there is typically an initial microscopic phase where cells can grow exponentially, but as the colony expands further then the growth dynamics become more linear (Meunier and Choder 1999). We have revised our text to state clearly what the experiment measured.

However, while colony size does not exhibit exponential growth kinetics, several studies have argued that the rate of colony expansion is related to the exponential growth rate of cells growing in non-limiting nutrient conditions in liquid culture. This is because colony growth is dominated by cells at the colony boundaries that have access to nutrients and are in exponential growth. Cells in the colony interior lack nutrients and therefore contribute little to colony growth. This has been shown both in theoretical and experimental studies, finding that the linear growth rate of the colony is directly linked to the single-cell exponential growth rate (Pirt 1967; Gray and Kirwan 1974; Korolev et al. 2012; Gandhi et al. 2016; Meunier and Choder 1999). In particular, the above studies suggest that the linear colony growth rate is directly proportional to the square root of the exponential growth rate. Therefore, one would expect that the validity of the product model for one fitness measure implies its validity for the other measure. In addition, colony size was found to be highly correlated with the exponential growth rate of cells in non-limiting nutrients in liquid culture (Baryshnikova et al. 2010; Zackrisson et al. 2016; Miller et al. 2022). For these reasons, we treated the colony size and exponential growth rate as interchangeable in our original manuscript. 

To address the important point raised by the reviewer, we now explain more clearly in the text what the analyzed data on colony size show and why we believe it is reflective of the exponential growth rate. Finally, we note that our results supporting the product neutrality function are consistent with the work of (Mani et al. 2008), which used smaller datasets based on liquid culture growth rates (Jasnos and Korona 2007; Onge et al. 2007).

The text in Section 2.3 now reads:

“Having verified empirically that the Product neutrality function is supported by the latest data for cell proliferation, we now turn our attention to its origins. Addressing this question requires some mechanistic model of biosynthesis. However, most mechanistic models of growth apply directly to single cells in rich nutrient conditions, which may not directly apply to the SGA measurements of colony expansion rates. In particular, colony growth has been shown to follow a biphasic pattern (Meunier et al. 1999). A first exponential phase is followed by a slower linear phase as the colony expands. Previous modeling and empirical work indicates that this second linear expansion rate reflects the underlying exponential growth of cells in the periphery of the colony (Pirt 1967; Gray et al. 1974; Gandhi et al. 2016; Baryshnikova, Costanzo, S. Dixon, et al. 2010; Zackrisson et al. 2016; Miller et al. 2022). More precisely, mathematical models show the linear colony-size expansion rate is directly proportional to the square root of the exponential growth rate under non-limiting conditions. Intuitively, this relationship arises because colony growth is dominated by the expansion of the population of cells in an annulus at the colony border that are exposed to rich nutrient conditions. These cells expand at a rate similar to the exponential rate of cells growing in a rich nutrient liquid culture. In contrast, the cells in the interior of the colony experience poor nutrient conditions, grow very slowly, and do not contribute to colony growth.

This intimate relationship between both proliferation rates allows us to explore the origin of the Product neutrality function in mechanistic models of cell growth. Indeed, if colony-based fitnesses follow a Product model, then

where the superscript c indicates colony-based values for the fitness W and the growth rate λ. Taking into account the relationship between single-cell exponential growth rates and colony growth rates, we can write

where the superscript l denotes liquid cultures. Combining these expressions, we obtain

In other words, from the perspective of the Product neutrality function, fitnesses based on colony expansion rates are equivalent to fitnesses based on single-cell exponential growth rates. The prevalence of the Product neutrality model—both in the SGA data and in previous studies on datasets from liquid cultures (Jasnos et al. 2007; Onge et al. 2007; Mani et al. 2008)—encourages the exploration of its origin in mechanistic models of cell growth.”

(2) Misapplication of prokaryotic growth models

The study attempts to explain the mechanistic origin of the multiplicative model observed in yeast colony fitness using a bacterial cell growth model, particularly the Scott-Hwa model. However, the application of this bacterial model to yeast systems lacks valid justification. The Scott-Hwa model is heavily dependent on specific molecular mechanisms such as ppGppmediated regulation, which plays a crucial role in adjusting ribosome expression and activity during translation. This mechanism is pivotal for ensuring the growth-dependency of the ribosome fraction in the proteome, as described in [https://doi.org/10.1073/pnas.2201585119]. Unlike bacteria, yeast cells do not possess this regulatory mechanism, rendering the direct application of bacterial growth models to yeast inappropriate and potentially misleading. This fundamental difference in regulatory mechanisms undermines the relevance and accuracy of using bacterial models to infer yeast colony growth dynamics.

If the authors intend to apply a growth model with macroscopic variables to yeast double-mutant experimental data, they should avoid simply repurposing a bacterial growth model. Instead, they should develop and rigorously validate a yeast-specific growth model before incorporating it into their study.

There is nothing that is prokaryote specific in the Scott-Hwa model. It does not include the specific ppGpp mechanism to regulate ribosome fraction that does not exist in eukaryotes.  The general features of the model, like how the ribosome fraction is proportional to the growth rate have indeed been validated in yeast (Metzl-Raz et al. 2017; Elsemman et al. 2022; Xia et al. 2022). Performing a detailed physiological analysis of budding yeast across varying growth conditions in order to build a more extensive model is beyond the scope of this work. Finally, we note that the Weiße model, which we also analyzed, is also generic and has replicated empirical measurements both from bacteria and yeast (Weiße et al. 2015).

To clarify this point in the text, we have added the following to Section 2.3: 

“Experimental measurements in other organisms suggest that the observations leading to this model, including that the cellular ribosome fraction increases with growth rate, are in fact generic and also seen in the yeast S. cerevisiae (Metzl-Raz et al. 2017; Elsemman et al. 2022; Xia et al. 2022).”

(3) Overly specific assumptions in the theoretical model

he theoretical model in question assumes that two mutations affect only independent parameters of specific biochemical processes, an overly restrictive premise that undermines its ability to broadly explain the occurrence of the multiplicative model in mutations. Additionally, experimental evidence highlights significant limitations to this approach. For example, in most viable yeast deletion mutants with reduced growth rates, the expression of ribosomal proteins remains largely unchanged, in direct contradiction to the predictions of the Scott-Hwa model, as indicated in [https://doi.org/10.7554/eLife.28034]. This discrepancy emphasizes that the ScottHwa model and its derivatives do not reliably explain the growth rates of mutants based on current experimental data, suggesting that these models may need to be reevaluated or alternative theories developed to more accurately reflect the complex dynamics of mutant growth.

In the data from the Barkai lab referenced by the reviewer (reproduced below), we see that the ribosomal transcript fraction is in fact proportional to growth rate in response to gene deletions in contradiction to the reviewer’s interpretation. However, it is notable that the ribosomal transcript fraction is a bit higher for a given growth rate if that growth rate is generated by a mutation rather than generated by a suboptimal nutrient condition. We know that the very simple Scott-Hwa model is not a perfect representation of the cell. Nevertheless, it does recapitulate important aspects of growth physiology and therefore we thought it is useful to analyze its response to mutations and compare those responses to the different neutrality functions.  We never claimed the Scott-Hwa model was a perfect model and fully agree with the referee’s statement above that “... these models may need to be reevaluated, or alternative theories developed to more accurately reflect the complex dynamics of mutant growth.” Indeed, we say as much in our discussion where we wrote: 

“While we focused on coarse-grained models for their simplicity and mechanistic interpretability, they might be too simple to effectively model large double-mutant datasets and the resulting double-mutant fitness distributions. We therefore expect the combination of high throughput genetic data with the analysis of larger-scale models, for instance based on Flux Balance Analysis, Metabolic Control Analysis, or whole-cell modeling, to lead to important complementary insights regarding the regulation of cell growth and proliferation.”

To further clarify this point, we discuss and cite the Barkai lab data for gene deletions see Figure 2 from Metzl-Raz et al. 2017.

(4) Lack of clarity on the mechanistic origin of the multiplicative model

The study falls short of providing a definitive explanation for its primary objective: elucidating the "mechanistic origin" of the multiplicative model. Notably, even in the simplest case involving the Scott-Hwa model, the underlying mechanistic basis remains unexplained, leaving the central research question unresolved. Furthermore, the study does not clearly specify what types of data or models would be required to advance the understanding of the mechanistic origin of the multiplicative model. This omission limits the study's contribution to uncovering the biological principles underlying the observed fitness patterns.”

We appreciate the reviewer’s interest in a more complete mechanistic explanation for the product model of fitness. The primary goal of this study was to explore the validity of the Product model from the perspective of coarse-grained models of cell growth, and to extract mechanistic insights where possible. We view our work as a first step toward a deeper understanding of how double-mutant fitnesses combine, rather than a final, all-encompassing theory. As the referee notes, we are limited by the current state of the field, which has an incomplete understanding of cell growth. 

Nonetheless, our analysis does propose concrete, mechanistically informed explanations. For example, we highlight how growth-optimizing feedback—such as cells’ ability to reallocate ribosomes or adjust proteome composition—naturally leads to multiplicative rather than additive or minimal fitness effects. We also link the empirical deviations from pure multiplicative behavior to differences in how specific pathways re-balance under perturbation, and we suggest that a product-like rule emerges when multiple interconnected processes each partially limit cell growth.

In the discussion, we clarify what additional data and models we think will be required to advance this question. Namely, we propose extending our approach through larger-scale, more detailed modeling frameworks – that may include explicit modeling of ppGpp or TOR activities in bacteria or eukaryotic cells, respectively. We also emphasize the importance of refining the measurement of cell growth rates to uncover subtle deviations from the product rule that could yield greater mechanistic insight. By integrating high-throughput genetic data with nextgeneration computational models, it should be possible to hone in on the specific biological principles (e.g., metabolic bottlenecks, resource reallocation) that underlie the multiplicative neutrality function.

Reviewer #2 (Public review):

The paper deals with the important question of gene epistasis, focusing on asking what is the correct null model for which we should declare no epistasis.

In the first part, they use the Synthetic Genetic Array dataset to claim that the effects of a double mutation on growth rate are well predicted by the product of the individual effects (much more than e.g. the additive model). The second (main) part shows this is also the prediction of two simple, coarse-grained models for cell growth.

I find the topic interesting, the paper well-written, and the approach innovative.

One concern I have with the first part is that they claim that:

"In these experiments, the colony area on the plate, a proxy for colony size, followed exponential growth kinetics. The fitness of a mutant strain was determined as the rate of exponential growth normalized to the rate in wild type cells."

There are many works on "range expansions" showing that colonies expand at a constant velocity, the speed of which scales as the square root of the growth rate (these are called "Fisher waves", predicted in the 1940', and there are many experimental works on them, e.g. https://www.pnas.org/doi/epdf/10.1073/pnas.0710150104) If that's the case, the area of the colony should be proportional to growth_rate X time^2 , rather than exp(growth_rate*time), so the fitness they might be using here could be the log(growth_rate) rather than growth_rate itself? That could potentially have a big effect on the results.

We thank the reviewer for their thoughtful remarks. As they rightly pointed out, a large body of literature supports that colonies expand at constant velocity both from a theoretical and experimental standpoint. 

As discussed in the answer to the first question of Reviewer 1, this body of work also suggests that the linear expansion rate of the colony front is directly related to the single-cell exponential growth rate of the cells at the periphery. Hence, although the macroscopic colony growth may not be exponential in time, measuring colony size (or radial expansion) across different genotypes still provides a consistent and meaningful proxy for comparing their underlying growth capabilities. 

In particular, these studies suggest (consistently with Fisher-wave theory) that the linear growth rate of the colony 𝐾 is proportional to the square root of the exponential growth rate 𝜆. Under the assumption that the product model is valid for a given double mutant and for the exponential growth rate, we would have that

The associated wave-front velocities would then be predicted to be

In other words, if the product model is valid for fitness measures based on exponential growth rates, it should also be valid for fitness measures based on linear colony growth rates. 

We now include this discussion in the revised version of Section 2.3.

Additional comments/questions:

(1) What is the motivation for the model where the effect of two genes is the minimum of the two?

The motivation for the minimal model is the notion that there might be a particular process that is rate-limiting for growth due to a mutation. In this case, a mutation in process X makes it really slow and process Y proceeds in parallel and has plenty of time to finish its job before cell division takes place. In this case, even a mutation to process Y might not slow down growth because there is an excess amount of time for it to be completed. Thus, the double mutant might then be anticipated to have the growth rate associated with the single mutation to process X. We now add a similar description when we introduce the different neutrality functions in Section 2.1.

(2) How seriously should we take the Scott-Hwa model? Should we view it as a toy model to explain the phenomenon or more than that? If the latter, then since the number of categories in the GO analysis is much more than two (47?) in many cases the analysis of the experimental data would take pairs of genes that both affect one process in the Scott-Hwa model - and then the product prediction should presumably fail? The same comment applies to the other coarse-grained model.

From our perspective, models like the Scott-Hwa model constitute the simplest representation of growth based on data that is not trivial. Moreover, the Scott-Hwa model is able to incorporate interactions between two different biological processes. We believe models, like the Scott-Hwa and Weiße models, should be viewed as more than mere toy models because they have been backed up by some empirical data, such as that showing the ribosome fraction increases with growth rate. However, the Scott-Hwa model is inherently limited by its low dimensionality and relative simplicity. We do not claim that such models can provide a full picture of the cell. As argued in the main text, we have chosen to focus on such models because of their tractability and in the hope of extracting general principles. We nonetheless agree with the reviewer that they do not have the capacity to represent interactions between genes in the same biological process. We now note this limitation in the text. 

(3) There are many works in the literature discussing additive fitness contributions, including Kaufmann's famous NK model as well as spin-glass-type models (e.g. Guo and Amir, Science Advances 2019, Reddy and Desai, eLife 2021, Boffi et al., eLife 2023) These should be addressed in this context.

We thank the reviewer for pointing out this part of the literature. We do believe these works constitute a relevant body of work tackling the emergence of epistasis patterns from a theoretical grounding, and now reference and discuss them in the text. 

(4) The experimental data is for deletions, but it would be interesting to know the theoretical model's prediction for the expected effects of beneficial mutations and how they interact since that's relevant (as mentioned in the paper) for evolutionary experiments. Perhaps in this case the question of additive vs. multiplicative matters less since the fitness effects are much smaller.

This is an interesting question. Since mutations increasing the growth rate generated by gene deletions or other systematic perturbations are rare, we did not focus on them. Of course, as the reviewer notes, in the case of evolution experiments, these fitness enhancing mutations are selected for. To address the reviewer's question, we can first consider the Scott-Hwa model. In this case, the analytical solution remains valid in the case of fitness enhancing mutations so that the fitness of the double mutant will be the product neutrality function multiplied by an additional interaction term (see Figure 3). The mathematical derivation predicts that the double mutant fitness can potentially grow indefinitely. Indeed, the denominator can be equal to zero in some cases. In simulations, we see that the observation for deleterious mutations does not seem to hold for beneficial mutations (new supplementary Figure S5 shown below). Indeed, no model seems to replicate double mutant fitnesses much better than any other. This suggests that the growth-optimizing feedback we discuss in section 2.3 may have compound effects that ultimately make double-mutant fitnesses much larger than any model predicts.

We recognize this may be an important point, and discuss it in detail in the revised section 2.3 as well as in the discussion.

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Author response:

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

We thank you for the time you took to review our work and for your feedback! We have made only minor changes in this submission and primarily wanted to respond to the concerns raised by reviewer 1.

Reviewer #1 (Public review): 

Summary: 

Fluorescence imaging has become an increasingly popular technique for monitoring neuronal activity and neurotransmitter concentrations in the living brain. However, factors such as brain motion and changes in blood flow and oxygenation can introduce significant artifacts, particularly when activitydependent signals are small. Yogesh et al. quantified these effects using GFP, an activity-independent marker, under two-photon and wide-field imaging conditions in awake behaving mice. They report significant GFP responses across various brain regions, layers, and behavioral contexts, with magnitudes comparable to those of commonly used activity sensors. These data highlight the need for robust control strategies and careful interpretation of fluorescence functional imaging data. 

Strengths: 

The effect of hemodynamic occlusion in two-photon imaging has been previously demonstrated in sparsely labeled neurons in V1 of anesthetized animals (see Shen and Kara et al., Nature Methods, 2012). The present study builds on these findings by imaging a substantially larger population of neurons in awake, behaving mice across multiple cortical regions, layers, and stimulus conditions. The experiments are extensive, the statistical analyses are rigorous, and the results convincingly demonstrate significant GFP responses that must be accounted for in functional imaging experiments. 

In the revised version, the authors have provided further methodological details that were lacking in the previous version, expanded discussions regarding alternative explanations of these GFP responses as well as potential mitigation strategies. They also added a quantification of brain motion (Fig. S5) and the fraction of responsive neurons when conducting the same experiment using GCaMP6f (Fig. 3D-3F), among other additional information. 

Weaknesses: 

(1) The authors have now included a detailed methodology for blood vessel area quantification, where they detect blood vessels as dark holes in GFP images and measure vessel area by counting pixels below a given intensity threshold (line 437-443). However, this approach has a critical caveat: any unspecific decrease in image fluorescence will increase the number of pixels below the threshold, leading to an apparent increase in blood vessel area, even when the actual vessel size remains unchanged. As a result, this method inherently introduces a positive correlation between fluorescence decrease and vessel dilation, regardless of whether such a relationship truly exists. 

To address this issue, I recommend labelling blood vessels with an independent marker, such as a red fluorescence dye injected into the bloodstream. This approach would allow vessel dilation to be assessed independently of GFP fluorescence -- dilation would cause opposite fluorescence changes in the green and red channels (i.e., a decrease in green due to hemodynamic occlusion and an increase in red due to the expanding vessel area). In my opinion, only when such ani-correlation is observed can one reliably infer a relationship between GFP signal changes and blood vessel dynamics. 

Because this relationship is central to the author's conclusion regarding the nature of the observed GFP signals, including this experiment would greatly strengthen the paper's conclusion. 

This is correct – a more convincing demonstration that blood vessels dilate or constrict anticorrelated with apparent GFP fluorescence would be a separate blood vessel marker. However, we don’t think this experiment is worth doing, as it is also not conclusive in the sense the reviewer may have in mind. The anticorrelation does not mean that occlusion drives all of the observed effect. Our main argument is instead that there is no other potential source than hemodynamic occlusion with sufficient strength that we can think of. The experiment one would want to do is block hemodynamic changes and demonstrate that the occlusion explains all of the observed changes. 

(2) Regarding mitigation strategy, the authors advocate repeating key functional imaging experiments using GFP, and state that their aim here is to provide a control for their 2012 study (Keller et al., Neuron). Given this goal, I find it important to discuss how these new findings impact the interpretation of their 2012 results, particularly given the large GFP responses observed. 

We are happy to discuss how the conclusions of our own work are influenced by this (see more details below), but the important response of the field should probably be to revisit the conclusions of a variety of papers published in the last two decades. This goes far beyond what we can do here. 

For example, Keller et al. (2012) concluded that visuomotor mismatch strongly drives V1 activity (Fig. 3A in that study). However, in the present study, mismatch fails to produce any hemodynamic/GFP response (Fig. 3A, 3B, rightmost bar), and the corresponding calcium response is also the weakest among the three tested conditions (Fig. 3D). How do these findings affect their 2012 conclusions? 

The average calcium response of L2/3 neurons to visuomotor mismatch is probably roughly similar to the average calcium response at locomotion onset (both are on the order of 1% to 5%, depending on indicator, dataset, etc.). In the Keller et al. (2012) paper, locomotion onset was about 1.5% and mismatch about 3% (see Figure 3A in that paper). What we quantify in Figure 3 of the paper here is the fraction of responsive neurons. Thus, mismatch drives strong responses in a small subset of neurons (approx. 10%), while locomotion drives a combination of a weak responses in a large fraction of the neurons (roughly 70%) and also large responses in a subset of neurons. A strong signal in a subset of neurons is what one would expect from a neuronal response, a weak signal from many neurons would be indicative of a contaminating signal. This all appears consistent. 

Regarding influencing the conclusions of earlier work, the movement related signals described in the Keller et al. (2012) paper are probably overestimated, but are also apparent in electrophysiological recordings (Saleem et al., 2013). Thus, the locomotion responses reported in the Keller et al. (2012) paper are likely too high, but locomotion related responses in V1 are very likely real. The only conclusion we draw in the Keller et al. 2012 paper on the strength of the locomotion related responses is that they are smaller than mismatch responses (this conclusion is unaffected by hemodynamic contamination). In addition, the primary findings of the Keller et al. (2012) paper are all related to mismatch, and these conclusions are unaffected. 

Similarly, the present study shows that GFP reveals twice as many responsive neurons as GCaMP during locomotion (Fig. 3A vs. Fig. 3D, "running"). Does this mean that their 2012 conclusions regarding locomotion-induced calcium activity need reconsideration? Given that more neurons responded with GFP than with GCaMP, the authors should clarify whether they still consider GCaMP a reliable tool for measuring brain activity during locomotion. 

Comparisons of the fraction of significantly responsive neurons between GFP and GCaMP are not straightforward to interpret. One needs to factor in the difference in signal to noise between the two sensors. (Please note, we added the GCaMP responses here upon request of the reviewers). Note, there is nothing inherently wrong with the data, and comparisons within dataset are easily made (e.g. more grating responsive neurons than running responsive neurons in GCaMP, and vice versa with GFP). The comparison across datasets is not as straightforward as we define “responsive neurons” using a statistical test that compares response to baseline activity for each neuron. GFP labelled neurons are very bright and occlusion can easily be detected. Baseline fluorescence in GCaMP recordings is much lower and often close to or below the noise floor of the data (i.e. we only see the cells when they are active). Thus occlusion in GCaMP recordings is preferentially visible for cells that have high baseline fluorescence. Thus, in the GCaMP data we are likely underestimating the fraction of responsive neurons. 

Regarding whether GCaMP (or any other fluorescence indicator used in vivo) is a reliable tool, we are not sure we understand. Whenever possible, fluorescence-sensor based measurements should be corrected for hemodynamic contamination – to quantify locomotion related signals this will be more difficult than e.g. for mismatch, but that does not mean it is not reliable. 

(3) More generally, the author should discuss how functional imaging data should be interpreted going forward, given the large GFP responses reported here. Even when key experiments are repeated using GFP, it is not entirely clear how one could reliably estimate underlying neuronal activity from the observed GFP and GCaMP responses. 

We are not sure we have a good answer to this question. The strategy for addressing this problem will depend on the specifics of the experiment, and the claims. Take the case of mismatch. Here we have strong calcium responses and no evidence of GFP responses. We would argue that this is reasonable evidence that the majority of the mismatch driven GCaMP signal is likely neuronal. For locomotion onsets, both GFP and GCaMP signals go in the same direction on average. Then one could use a response amplitude distribution comparison to conservatively exclude all neurons with a GCaMP amplitude lower than e.g. the 99th percentile of the GFP response. Etc. But we don’t think there is an easy generalizable fix for this problem.  

For example, consider the results in Fig. 3A vs. 3D: how should one assess the relative strength of neuronal activity elicited by running, grating, or visuomotor mismatch? Does mismatch produce the strongest neuronal activity, since it is least affected by the hemodynamic/GFP confounds (Fig. 3A)? Or does mismatch actually produce the weakest neuronal activity, given that both its hemodynamic and calcium responses are the smallest? 

See above, the reviewer may be confounding “response strength” with “fraction of responsive neurons” here. Regarding the relationship between neuronal activity and hemodynamics, it is very likely not just the average activity of all neurons, but a specific subset that drives blood vessel constriction and dilation. This would of course be a very interesting question to answer for the interpretation of hemodynamic based measurements of brain activity, like fMRI, but goes beyond the aim of the current paper.  

In my opinion, such uncertainty makes it difficult to robustly interpret functional imaging results. Simply repeating experiments with GFP does not fully resolve this issue, as it does not provide a clear framework for quantifying the underlying neuronal activity. Does this suggest a need for a better mitigation strategy? What could these strategies be? 

If the reviewer has a good idea - we would be all ears. We don’t have a better idea currently.  

In my opinion, addressing these questions is critical not only for the authors' own work but also for the broader field to ensure a robust and reliable interpretation of functional imaging data. 

We agree, having a solution to this problem would be important – we just don’t have one.  

(4) The authors now discuss various alternative sources of the observed GFP signals. However, I feel that they often appear to dismiss these possibilities too quickly, rather than appreciating their true potential impacts (see below). 

For example, the authors argue that brain movement cannot explain their data, as movement should only result in a decrease in observed fluorescence. However, while this might hold for x-y motion, movement in the axial (z) direction can easily lead to both fluorescence increase and decrease. Neurons are not always precisely located at the focal plane -- some are slightly above or below. Axial movement in a given direction will bring some cells into focus while moving others out of focus, leading to fluorescence changes in both directions, exactly as observed in the data (see Fig. S2). 

The reviewer is correct that z-motion can result in an increase of apparent fluorescence (just like x-y motion can as well). On average however, just like with x-y motion, z-motion will always result in a decrease. This assumes that the user selecting regions of interest (the outlines of cells used to quantify fluorescence), will select these such that the distribution of cells selected centers on the zplane of the image. Thus, the distribution of z-location of the cell relative to the imaging plane will be some Gaussian like distribution centered on the z-plane of the image (with half the cell above the zplane and half below). Because the peak of the distribution is located on the z-plane at rest, any zmovement, up or down, will move away from the peak of the distribution (i.e. most cells will decrease in fluorescence). This is the same argument as for why x-y motion always results in decreases (assuming the user selects regions of interest centered on the location of the cells at rest).  

Furthermore, the authors state that they discard data with 'visible' z-motion. However, subtle axial movements that escape visual detection could still cause fluorescence fluctuations on the order of a few percent, comparable to the reported signal amplitudes. 

Correct, but as explained above, z-motion will always result in average decreases of average fluorescence as explained above.  

Finally, the authors state that "brain movement kinematics are different in shape than the GFP responses we observe". However, this appears to contradict what they show in Fig. 2A. Specifically, the first example neuron exhibits fast GFP transients locked to running onset, with rapid kinematics closely matching the movement speed signals in Fig. S5A. These fast transients are incompatible with slower blood vessel area signals (Fig. 4), suggesting that alternative sources could contribute significantly. 

We meant population average responses here. We have clarified this. Some of the signals we observed do indeed look like they could be driven by movement artifacts (whole brain motion, or probably more likely blood vessel dilation driven tissue distortion). We show this neuron to illustrate that this can also happen. However, to illustrate that this is a rare event we also show the entire distribution of peak amplitudes and the position in the distribution this neuron is from.  

In sum, the possibility that alternative signal sources could significantly contribute should be taken seriously and more thoroughly discussed. 

All possible sources (we could think of) are explicitly discussed (in roughly equal proportion). Nevertheless, the reviewer is correct that our focus here is almost exclusively on the what we think is the primary source of the problem. Given that – in my experience – this is also the one least frequently considered, I think the emphasis on – what we think is – the primary contributor is warranted.  

(5) The authors added a quantification of brain movement (Fig. S5) and claim that they "only find detectable brain motion during locomotion onsets and not the other stimuli." However, Fig. S5 presents brain 'velocity' rather than 'displacement'. A constant (non-zero) velocity in Fig. S5 B-D indicates that the brain continues to move over time, potentially leading to significant displacement from its initial position across all conditions. While displacement in the x-y plane are corrected, similar displacement in the z direction likely occurs concurrently and cannot be easily accounted for. To assess this possibility, the authors should present absolute displacement relative to pre-stimulus frames, as displacement -- not velocity -- determines the size of movement-related fluorescence changes. 

We use brain velocity here as a natural measure when using frame times as time bins. The problem with using a signed displacement is that if different running onsets move the brain in opposing directions, this can average out to zero. To counteract this, one can take the absolute displacement in a response window away from the position in a baseline time window. If this is done with time bins that correspond to frame times, this just becomes displacement per frame, i.e. velocity. Using absolute changes in displacement (i.e. velocity) is more sensitive than signed displacement. The responses for signed displacement are shown below (Author response image 1), but given that we are averaging signed quantities here, the average is not interpretable. 

Author response image 1.

Average signed brain displacement. 

Regarding a constant drift, the reviewer might be misled by the fact that the baseline brain velocity is roughly 1 pixel per frame. The registration algorithm works in integer number of pixels only. 1 pixel per frame corresponds roughly to the noise floor of the registration algorithm. Registrations are done independently for each frame. As a consequence, the registration oscillates between a shift of 17 and 18 pixels – frame by frame – if the actual shift is somewhere between 17 and 18 pixels. This “jitter” results in a baseline brain velocity of about 1 pixel per frame. 

(6) In line 132-133, the authors draw an analogy between the effect of hemodynamic occlusion and liquid crystal display (LCD) function. However, there are fundamental differences between the two. LCDs modulate light transmission by rotating the polarization of light, which then passes through a crossed polarizer. In contrast, hemodynamic occlusion alters light transmission by changing the number and absorbance properties of hemoglobin. Additionally, LCDs do not involve 'emission' light - backillumination travels through the liquid crystal layer only once, whereas hemodynamic occlusion affects both incoming excitation light and the emitted fluorescence. Given these fundamental differences, the LCD analogy may not be entirely appropriate. 

The mechanism of occlusion is, as the reviewer correctly points out, different for an LCD. In both cases however, there is a variable occluder between a light source and an observer. The fact that with hemodynamic occlusion the light passes through the occluder twice (excitation and emission) does not appear to hamper the analogy to us. We have rephrased to highlight the time varying occlusion part. 

Reviewer #2 (Public review):

-  Approach 

In this study, Yogesh et al. aimed at characterizing hemodynamic occlusion in two photon imaging, where its effects on signal fluctuations are underappreciated compared to that in wide field imaging and fiber photometry. The authors used activity-independent GFP fluorescence, GCaMP and GRAB sensors for various neuromodulators in two-photon and widefield imaging during a visuomotor context to evaluate the extent of hemodynamic occlusion in V1 and ACC. They found that the GFP responses were comparable in amplitude to smaller GCaMP responses, though exhibiting context-, cortical region-, and depth-specific effects. After quantifying blood vessel diameter change and surrounding GFP responses, they argued that GFP responses were highly correlated with changes in local blood vessel size. Furthermore, when imaging with GRAB sensors for different neuromodulators, they found that sensors with lower dynamic ranges such as GRAB-DA1m, GRAB-5HT1.0, and GRAB-NE1m exhibited responses most likely masked by the hemodynamic occlusion, while a sensor with larger SNR, GRAB-ACh3.0, showed much more distinguishable responses from blood vessel change. They thoroughly investigate other factors that could contribute to these signals and demonstrate hemodynamic occlusion is the primary cause. 

-  Impact of revision 

This is an important update to the initial submission, adding much supplemental imaging and population data that provide greater detail to the analyses and increase the confidence in the authors conclusions. 

Specifically, inclusion of the supplemental figures 1 and 2 showing GFP expression across multiple regions and the fluorescence changes of thousands of individual neurons provides a clearer picture of how these effects are distributed across the population. Characterization of brain motion across stimulation conditions in supplemental figure 5 provides strong evidence that the fluorescence changes observed in many of the conditions are unlikely to be primarily due to brain motion associated imaging artifacts. The role of vascular area on fluorescence is further supported by addition of new analyses on vasoconstriction leading to increased fluorescence in Figures 4C1-4, complementing the prior analyses of vasodilation. 

The expansion of the discussion on other factors that could lead to these changes is thorough and welcome. The arguments against pH playing a factor in fluorescence changes of GFP, due to insensitivity to changes in the expected pH range are reasonable, as are the other discussed potential factors. 

With respect to the author's responses to prior critique, we agree that activity dependent hemodynamic occlusion is best investigated under awake conditions. Measurement of these dynamics under anesthesia could lead to an underestimation of their effects. Isoflurane anesthesia causes significant vasodilation and a large reduction in fluorescence intensity in non-functional mutant GRABs. This could saturate or occlude activity dependent effects. 

- Strengths 

This work is of broad interest to two photon imaging users and GRAB developers and users. It thoroughly quantifies the hemodynamic driven GFP response and compares it to previously published GCaMP data in a similar context, and illustrates the contribution of hemodynamic occlusion to GFP and GRAB responses by characterizing the local blood vessel diameter and fluorescence change. These findings provide important considerations for the imaging community and a sobering look at the utility of these sensors for cortical imaging. 

Importantly, they draw clear distinctions between the temporal dynamics and amplitude of hemodynamic artifacts across cortical regions and layers. Moreover, they show context dependent (Dark versus during visual stimuli) effects on locomotion and optogenetic light-triggered hemodynamic signals. 

The authors suggest that signal to noise ratio of an indicator likely affects the ability to separate hemodynamic response from the underlying fluorescence signal. With a new analysis (Supplemental Figure 4) They show that the relative degree of background fluorescence does not affect the size of the artifact. 

Most of the first generation neuromodulator GRAB sensors showed relatively small responses, comparable to blood vessel changes in two photon imaging, which emphasizes a need for improved the dynamic range and response magnitude for future sensors and encourages the sensor users to consider removing hemodynamic artifacts when analyzing GRAB imaging data. 

- Weaknesses 

The largest weakness of the paper remains that, while they convincingly quantify hemodynamic artifacts across a range of conditions, they provide limited means of correcting for them. However they now discuss the relative utility of some hemodynamic correction methods (e.g. from Ocana-Santero et al., 2024). 

The paper attributes the source of 'hemodynamic occlusion' primarily to blood vessel dilation, but leaves unanswered how much may be due to shifts in blood oxygenation. Figure 4 directly addresses the question of how much of the signal can be attributed to occlusion by measuring the blood vessel dilation, and has been improved by now showing positive fluorescence effects with vasoconstriction. They now also discuss the potential impact of oxygenation. 

Along these lines, the authors carefully quantified the correlation between local blood vessel diameter and GFP response (or neuropil fluorescence vs blood vessel fluorescence with GRAB sensors). We are left to wonder to what extent does this effect depend on proximity to the vessels? Do GFP/ GRAB responses decorrelate from blood vessel activity in neurons further from vessels (refer to Figure 5A and B in Neyhart et al., Cell Reports 2024)? The authors argue that the primary impact of occlusion is from blood vessels above the plane of imaging, but without a vascular reconstruction, their evidence for this is anecdotal. 

The choice of ACC as the frontal region provides a substantial contrast in location, brain movement, and vascular architecture as compared to V1. As the authors note, ACC is close to the superior sagittal sinus and thus is the region where the largest vascular effects are likely to occur. A less medial portion of M2 may have been a more appropriate comparison. The authors now include example imaging fields for ACC and interesting out-of-plane vascular examples in the supplementary figures that help assess these impacts. 

-Overall Assessment 

This paper is an important contribution to our understanding of how hemodynamic artifacts may corrupt GRAB and calcium imaging, even in two-photon imaging modes. While it would be wonderful if the authors were able to demonstrate a reliable way to correct for hemodynamic occlusion which did not rely on doing the experiments over with a non-functional sensor or fluorescent protein, the careful measurement and reporting of the effects here is, by itself, a substantial contribution to the field of neural activity imaging. It's results are of importance to anyone conducting two-photon or widefield imaging with calcium and GRAB sensors and deserves the attention of the broader neuroscience and invivo imaging community. 

We agree with this assessment.

Reviewer #3 (Public review):

Summary:

In this study, the authors aimed to investigate if hemodynamic occlusion contributes to fluorescent signals measured with two-photon microscopy. For this, they image the activity-independent fluorophore GFP in 2 different cortical areas, at different cortical depths and in different behavioral conditions. They compare the evoked fluorescent signals with those obtained with calcium sensors and neuromodulator sensors and evaluate their relationship to vessel diameter as a readout of blood flow.

They find that GFP fluorescence transients are comparable to GCaMP6f stimuli-evoked signals in amplitude, although they are generally smaller. Yet, they are significant even at the single neuronal level. They show that GFP fluorescence transients resemble those measured with the dopamine sensor GRABDA1m and the serotonin sensor GRAB-5HT1.0 in amplitude an nature, suggesting that signals with these sensors are dominated by hemodynamic occlusion. Moreover, the authors perform similar experiments with wide-field microscopy which reveals the similarity between the two methods in generating the hemodynamic signals. Together the evidence presented calls for the development and use of high dynamic range sensors to avoid measuring signals that have another origin from the one intended to measure. In the meantime, the evidence highlights the need to control for those artifacts such as with the parallel use of activity independent fluorophores.

Strengths:

- Comprehensive study comparing different cortical regions in diverse behavioral settings in controlled conditions.

- Comparison to the state-of-the-art, i.e. what has been demonstrated with wide-field microscopy.

- Comparison to diverse activity-dependent sensors, including the widely used GCaMP.

Comments on revisions:

The authors have addressed my concerns well. I have no further comments.

We agree with this assessment.  

The following is the authors’ response to the original reviews

The major changes to the manuscript are:

(1) Re-wrote the discussion, going over all possible sources of the signals we describe.

(2) We added a quantification of brain motion as Figure S5.

(3) We added an example of blood vessel contraction as Figure 4C.

(4) We added data on the fraction of responsive neurons when measured with GCaMP as Figures 3D-3F.

(5) We added example imaging sites from all imaged regions as Figure S1.

(6) We added GFP response heatmaps of all neurons as Figure S2.

(7) We add a quantification of the relationship between GFP response amplitude and expression level Figure S4.

A detailed point-by-point response to all reviewer concerns is provided below.

Public Reviews:

Reviewer #1 (Public Review):

Fluorescence imaging has become an increasingly popular technique for monitoring neuronal activity and neurotransmitter concentrations in the living brain. However, factors such as brain motion and changes in blood flow and oxygenation can introduce significant artifacts, particularly when activity-dependent signals are small. Yogesh et al. quantified these effects using GFP, an activity-independent marker, under two-photon and wide-field imaging conditions in awake behaving mice. They report significant GFP responses across various brain regions, layers, and behavioral contexts, with magnitudes comparable to those of commonly used activity sensors. These data highlight the need for robust control strategies and careful interpretation of fluorescence functional imaging data.

Strengths:

The effect of hemodynamic occlusion in two-photon imaging has been previously demonstrated in sparsely labeled neurons in V1 of anesthetized animals (see Shen and Kara et al., Nature Methods, 2012). The present study builds on these findings by imaging a substantially larger population of neurons in awake, behaving mice across multiple cortical regions, layers, and stimulus conditions. The experiments are extensive, the statistical analyses are rigorous, and the results convincingly demonstrate significant GFP responses that must be accounted for in functional imaging experiments. However, whether these GFP responses are driven by hemodynamic occlusion remains less clear, given the complexities associated with awake imaging and GFP's properties (see below).

Weaknesses:

(1) The authors primarily attribute the observed GFP responses to hemodynamic occlusion. While this explanation is plausible, other factors may also contribute to the observed signals. These include uncompensated brain movement (e.g., axial-direction movements), leakage of visual stimulation light into the microscope, and GFP's sensitivity to changes in intracellular pH (see e.g., Kneen and Verkman, 1998, Biophysical Journal). Although the correlation between GFP signals and blood vessel diameters supports a hemodynamic contribution, it does not rule out significant contributions from these (or other) factors. Consequently, whether GFP fluorescence can reliably quantify hemodynamic occlusion in two-photon microscopy remains uncertain.

We concur; our data do not conclusively prove that the effect is only driven by hemodynamic occlusion. We have attempted to make this clearer in the text throughout the manuscript. In particular we have restructured the discussion to focus on this point. Regarding the specific alternatives the reviewer mentions here:

a) Uncompensated brain motion. While this can certainly contribute, we think the effect is negligible in our interpretation for the following reasons. First, just to point out the obvious, as with all two-photon data we acquire in the lab, we only keep data with no visible z-motion (axial). Second, and more importantly, uncompensated brain motion results in a net decrease of fluorescence. As regions of interest (ROI) are selected to be centered on neurons (as opposed to be randomly selected, or next to, or above or below), movement will – on average – result in a decrease in fluorescence, as neurons are moved out of the ROIs. In the early days of awake two-photon imaging (when preps were still less stable) – we used this movement onset decrease in fluorescence as a sign that running onsets were selected correctly (i.e. with low variance). See e.g. the dip in the running onset trace at time zero in figure 3A of (Keller et al., 2012). Third, we find no evidence for any brain motion in the case of visual stimulation, while the GFP responses during locomotion and visual stimulation are of similar magnitude. We have added a quantification of brain motion (Figure S5) and a discussion of this point to the manuscript.

b) Leakage of stimulation light. First, all light sources in the experimental room (the projector used for the mouse VR, the optogenetic stimulation light, as well as the computer monitors used to operate the microscope) are synchronized to the turnaround times of the resonant scanner of the two-photon microscope. Thus, light sources in the room are turned off for each line scan of the resonant scanner and turned on in the turnaround period. With a 12kHz scanner this results in a light cycle of 24 kHz (see Leinweber et al., 2014 for details). While the system is not perfect, we can occasionally get detectable light leak responses at the image edges (in the resonant axis as a result of the exponential off kinetics of many LEDs & lasers), these are typically 2 orders of magnitude smaller than what one would get without synchronizing, and far smaller than a single digit percentage change in GFP responses, and only detectable at the image edges. Second, while in visual cortex, dark running onsets are different from running onsets with the VR turned on (Figures 5A and B), they are indistinguishable in ACC (Figure 5C). Thus, stimulation light artefacts we can rule out.

c) GFP’s sensitivity to changes in pH. Activity results in a decrease in neuronal intracellular pH (https://pubmed.ncbi.nlm.nih.gov/14506304/, https://pubmed.ncbi.nlm.nih.gov/24312004/) – decreasing pH decreases GFP fluorescence (https://pubmed.ncbi.nlm.nih.gov/9512054/).

To reiterate, we don’t think hemodynamic occlusion is the only possible source to the effects we observe, but we do think it is most likely the largest.

(2) Regardless of the underlying mechanisms driving the GFP responses, these activity-independent signals must be accounted for in functional imaging experiments. However, the present manuscript does not explore potential strategies to mitigate these effects. Exploring and demonstrating even partial mitigation strategies could have significant implications for the field.

We concur – however, in brief, we think the only viable mitigation strategy (we are capable of), is to repeat functional imaging with GFP imaging. To unpack this: There have been numerous efforts to mitigate these hemodynamic effects using isosbestic illumination. When we started to use such strategies in the lab for widefield imaging, we thought we would calibrate the isosbestic correction using GFP recordings. The idea was that if performed correctly, an isosbestic response should look like a GFP response. Try as we may, we could not get the isosbestic responses to look like a GFP response. We suspect this is a result of the fact that none of the light sources we used were perfectly match to the isosbestic wavelength the GCaMP variants we used (not for a lack of trying, but neither lasers nor LEDs were available for purchase with exact wavelength matches). Complicating this was then also the fact that the similarity (or dissimilarity) between isosbestic and GFP responses was a function of brain region. Importantly however, just because we could not successfully apply isosbestic corrections, of course does not mean it cannot be done. Hence for the widefield experiments we then resorted to mitigating the problem by repeating the key experiments using GFP imaging (see e.g. (Heindorf and Keller, 2024)). Note, others have also argued that the best way to correct for hemodynamic artefacts is a GFP recording based correction (Valley et al., 2019). A second strategy we tried was using a second fluorophore (i.e. a red marker) in tandem with a GCaMP sensor. The problem here is that the absorption of the two differs markedly by blood and once again a correction of the GCaMP signal using the red channel was questionable at best. Thus, we think the only viable mitigation strategy we have found is GFP recordings and testing whether the postulated effects seen with calcium indicators are also present in GFP responses. This work is our attempt at a post-hoc mitigation of the problem of our own previous two-photon imaging studies.

(3) Several methodology details are missing from the Methods section. These include: (a) signal extraction methods for two-photon imaging data (b) neuropil subtraction methods (whether they are performed and, if so, how) (c) methods used to prevent visual stimulation light from being detected by the two-photon imaging system (d) methods to measure blood vessel diameter/area in each frame. The authors should provide more details in their revision.

Please excuse, this was an oversight. All details have been added to the methods.

Reviewer #2 (Public Review):

In this study, Yogesh et al. aimed at characterizing hemodynamic occlusion in two photon imaging, where its effects on signal fluctuations are underappreciated compared to that in wide field imaging and fiber photometry. The authors used activity-independent GFP fluorescence, GCaMP and GRAB sensors for various neuromodulators in two-photon and widefield imaging during a visuomotor context to evaluate the extent of hemodynamic occlusion in V1 and ACC. They found that the GFP responses were comparable in amplitude to smaller GCaMP responses, though exhibiting context-, cortical region-, and depth-specific effects. After quantifying blood vessel diameter change and surrounding GFP responses, they argued that GFP responses were highly correlated with changes in local blood vessel size. Furthermore, when imaging with GRAB sensors for different neuromodulators, they found that sensors with lower dynamic ranges such as GRAB-DA1m, GRAB5HT1.0, and GRAB-NE1m exhibited responses most likely masked by the hemodynamic occlusion, while a sensor with larger SNR, GRAB-ACh3.0, showed much more distinguishable responses from blood vessel change.

Strengths

This work is of broad interest to two photon imaging users and GRAB developers and users. It thoroughly quantifies the hemodynamic driven GFP response and compares it to previously published GCaMP data in a similar context, and illustrates the contribution of hemodynamic occlusion to GFP and GRAB responses by characterizing the local blood vessel diameter and fluorescence change. These findings provide important considerations for the imaging community and a sobering look at the utility of these sensors for cortical imaging.

Importantly, they draw clear distinctions between the temporal dynamics and amplitude of hemodynamic artifacts across cortical regions and layers. Moreover, they show context dependent (Dark versus during visual stimuli) effects on locomotion and optogenetic light-triggered hemodynamic signals.

Most of the first generation neuromodulator GRAB sensors showed relatively small responses, comparable to blood vessel changes in two photon imaging, which emphasizes a need for improved the dynamic range and response magnitude for future sensors and encourages the sensor users to consider removing hemodynamic artifacts when analyzing GRAB imaging data.

Weaknesses

(1) The largest weakness of the paper is that, while they convincingly quantify hemodynamic artifacts across a range of conditions, they do not quantify any methods of correcting for them. The utility of the paper could have been greatly enhanced had they tested hemodynamic correction methods (e.g. from Ocana-Santero et al., 2024) and applied them to their datasets. This would serve both to verify their findings-proving that hemodynamic correction removes the hemodynamic signal-and to act as a guide to the field for how to address the problem they highlight.

See also our response to reviewer 1 comment 2.

In the Ocana-Santero et al., 2024 paper they also first use GFP recordings to identify the problem. The mitigation strategy they then propose, and use, is to image a second fluorophore that emits at a different wavelength concurrently with the functional indicator. The authors then simply subtract (we think – the paper states “divisive”, but the data shown are more consistent with “subtractive” correction) the two signals to correct for hemodynamics. However, the paper does not demonstrate that the hemodynamic signals in the red channel match those in the green channel. The evidence presented that this works is at best anecdotal. In our hands this does not work (meaning the red channel does not match GFP recordings), we suspect this is a combination of crosstalk from the simultaneously recorded functional channel and the fact that hemodynamic absorption is strongly wavelength specific, or something we are doing wrong. Either way, we cannot contribute to this in the form of mitigation strategy.

Given that the GFP responses are a function of brain area and cortical depth – it is not a stretch to postulate that they also depend on genetic cell type labelled. Thus, any GFP calibration used for correction will need to be repeated for each cell type and brain area. Once experiments are repeated using GFP (the strategy we advocate for – we don’t think there is a simpler way to do this), the “correction” is just a subtraction (or a visual comparison).

(2) The paper attributes the source of 'hemodynamic occlusion' primarily to blood vessel dilation, but leaves unanswered how much may be due to shifts in blood oxygenation. Figure 4 directly addresses the question of how much of the signal can be attributed to occlusion by measuring the blood vessel dilation, but notably fails to reproduce any of the positive transients associated with locomotion in Figure 2. Thus, an investigation into or at least a discussion of what other factors (movement? Hb oxygenation?) may drive these distinct signals would be helpful.

See also our response to reviewer 1 comment 1.

We have added to Figure 4 an example of a positive transient. At running onset, superficial blood vessels in cortex tend to constrict and hence result in positive transients.

We now also mention changes in blood oxygenation as a potential source of hemodynamic occlusion. And just to be clear, blood oxygenation (or flow) changes in absence of any fluorophore, do not lead to a two-photon signal. Just in case the reviewer was concerned about intrinsic signals – these are not detectable in two photon imaging.

(3) Along these lines, the authors carefully quantified the correlation between local blood vessel diameter and GFP response (or neuropil fluorescence vs blood vessel fluorescence with GRAB sensors). To what extent does this effect depend on proximity to the vessels? Do GFP/ GRAB responses decorrelate from blood vessel activity in neurons further from vessels (refer to Figure 5A and B in Neyhart et al., Cell Reports 2024)?

We indeed thought about quantifying this, but to do this properly would require having a 3d reconstruction of the blood vessel plexus above (with respect to the optical axis) the neuron of interest, as well as some knowledge of how each vessel dilates as a function of stimulus. The prime effect is likely from blood vessels that are in the 45 degrees illumination cone above the neuron (Author response image 2). Lateral proximity to a blood vessel is likely only of secondary relevance. Thus, performing such a measurement is impractical and of little benefit for others.

Author response image 2.

A schematic representation of the cone of illumination.

While imaging a neuron (the spot on the imaging plane at the focus of the cone of illumination), the relevant blood vessels that primarily contribute to hemodynamic occlusion are those in the cone of illumination between the neuron and the objective lens. Blood vessels visible in the imaging plane (indicated by gray arrows), do not directly contribute to hemodynamic occlusion. Any distance dependence of hemodynamic occlusion in the observed response of a neuron to these blood vessels in the imaging plane is at best incidental.

(4) Raw traces are shown in Figure 2 but we are never presented with the unaveraged data for locomotion of stimulus presentation times, which limits the reader's ability to independently assess variability in the data. Inclusion of heatmaps comparing event aligned GFP to GCaMP6f may be of value to the reader.

We fear we are not sure what the reviewer means by “the unaveraged data for locomotion of stimulus presentation times”. We suspect this should read “locomotion or stimulus…”. We have added heat maps of the responses of all neurons of the data shown in Figure 1 – as Figure S2.

(5) More detailed analysis of differences between the kinds of dynamics observed in GFP vs GCaMP6f expressing neurons could aid in identifying artifacts in otherwise clean data. The example neurons in Figure 2A hint at this as each display unique waveforms and the question of whether certain properties of their dynamics can reveal the hemodynamic rather than indicator driven nature of the signal is left open. Eg. do the decay rate and rise times differ significantly from GCaMP6f signals?

The most informative distinction we have found is differences in peak responses (Figure 2B). Decay and rise time measurements critically depend on the identification of “events”. As a function of how selective one is with what one calls an event (e.g. easy in example 1 of Figure 2 – but more difficult in examples 2 and 3), one gets very different estimates of rise and decay times. Due to the fact that peak amplitudes are lower in GFP responses – rise and decay times will be either slower or noisier (depending on where the threshold for event detection is set).

(6) The authors suggest that signal to noise ratio of an indicator likely affects the ability to separate hemodynamic response from the underlying fluorescence signal. Does the degree of background fluorescence affect the size of the artifact? If there was variation in background and overall expression level in the data this could potentially be used to answer this question. Could lower (or higher!) expression levels increase the effects of hemodynamic occlusion?

There may be a misunderstanding (i.e. we might be misunderstanding the reviewer’s argument here). Our statement from the manuscript that the signal to noise ratio of an indicator matters is based on the simple consideration that hemodynamic occlusion is in the range of 0 to 2 % ΔF/F. The larger the dynamic range of the indicator, the less of a problem 2% ΔF/F are. Imagine an indicator with average responses in the 100’s of % ΔF/F - then this would be a non-problem. For indicators with a dynamic range less than 1%, a 2% artifact is a problem.

Regarding “background” fluorescence, we are not sure what is meant here. In case the reviewer means fluorescence that comes from indicator molecules in processes (as opposed to soma) that are typically ignored (or classified as neuropil) – we are not sure how this would help. The occlusion effects are identical for both somatic and axonal or dendritic GFP (the source of the GFP fluorescence is not relevant for the occlusion effect). In case the reviewer means “baseline” fluorescence – above a noise threshold ΔF/F₀ should be constant independent of F₀ (i.e. baseline fluorescence). This also holds in the data, see Figure S4. We might be stating the trivial - the normalization of fluorescence activity as ΔF/F₀ has the effect that the “occluder" effect is constant for all values of all F₀.

(7) The choice of the phrase 'hemodynamic occlusion' may cause some confusion as the authors address both positive and negative responses in the GFP expressing neurons, and there may be additional contributions from changes in blood oxygenation state.

Regarding the potential confusion with regards to terminology, occlusion can decrease or increase.

Only under the (incorrect) assumption that occlusion is zero at baseline would this be confusing – no? If the reviewer has a suggestion for a different term, we’d be open to changing it.

Regarding blood oxygenation – this is absolutely correct, we did not explicitly point this out in the previous version of the manuscript. Occlusion changes are driven by a combination of changes to volume and “opacity” of the blood. Oxygenation changes would be in the second category. We have clarified this in the manuscript.

(8) The choice of ACC as the frontal region provides a substantial contrast in location, brain movement, and vascular architecture as compared to V1. As the authors note, ACC is close to the superior sagittal sinus and thus is the region where the largest vascular effects are likely to occur. The reader is left to wonder how much of the ROI may or may not have included vasculature in the ACC vs V1 recordings as the only images of the recording sites provided are for V1. We are left unable to conclude whether the differences observed between these regions are due to the presence of visible vasculature, capillary blood flow or differences in neurovasculature coupling between regions. A less medial portion of M2 may have been a more appropriate comparison. At least, inclusion of more example imaging fields for ACC in the supplementary figures would be of value.

Both the choice of V1 and ACC were simply driven by previous experiments we had already done in these areas with calcium indicators. And we agree, the relevant axis is likely distance from midline, not AP – i.e. RSC and ACC are likely more similar, and V1 and lateral M2 more similar. We have made this point explicitly in the manuscript and have added sample fields of view as Figure S1.

(9) In Figure 3, How do the proportions of responsive GFP neurons compare to GCaMP6f neurons?

We have added the data for GCaMP responses.

(10) How is variance explained calculated in Figure 4? Is this from a linear model and R^2 value? Is this variance estimate for separate predictors by using single variable models? The methods should describe the construction of the model including the design matrix and how the model was fit and if and how cross validation was run.

This is simply a linear model (i.e. R^2) – we have added this to the methods.

(11) Cortical depth is coarsely defined as L2/3 or L5, without numerical ranges in depth from pia.

Layer 2/3 imaging was done at a depth of 100-250 μm from pia, and the same for layer 5 was 400-600 μm. This has been added to the methods.

Overall Assessment:

This paper is an important contribution to our understanding of how hemodynamic artifacts may corrupt GRAB and calcium imaging, even in two-photon imaging modes. Certain useful control experiments, such as intrinsic optical imaging in the same paradigms, were not reported, nor were any hemodynamic correction methods investigated. Thus, this limits both mechanistic conclusions and the overall utility with respect to immediate applications by end users. Nevertheless, the paper is of significant importance to anyone conducting two-photon or widefield imaging with calcium and GRAB sensors and deserves the attention of the broader neuroscience and in-vivo imaging community.

Reviewer #3 (Public review):

In this study, the authors aimed to investigate if hemodynamic occlusion contributes to fluorescent signals measured with two-photon microscopy. For this, they image the activity-independent fluorophore GFP in 2 different cortical areas, at different cortical depths and in different behavioral conditions. They compare the evoked fluorescent signals with those obtained with calcium sensors and neuromodulator sensors and evaluate their relationship to vessel diameter as a readout of blood flow.

They find that GFP fluorescence transients are comparable to GCaMP6f stimuli-evoked signals in amplitude, although they are generally smaller. Yet, they are significant even at the single neuronal level. They show that GFP fluorescence transients resemble those measured with the dopamine sensor GRABDA1m and the serotonin sensor GRAB-5HT1.0 in amplitude an nature, suggesting that signals with these sensors are dominated by hemodynamic occlusion. Moreover, the authors perform similar experiments with wide-field microscopy which reveals the similarity between the two methods in generating the hemodynamic signals. Together the evidence presented calls for the development and use of high dynamic range sensors to avoid measuring signals that have another origin from the one intended to measure. In the meantime, the evidence highlights the need to control for those artifacts such as with the parallel use of activity independent fluorophores.

Strengths:

- Comprehensive study comparing different cortical regions in diverse behavioral settings in controlled conditions.

- Comparison to the state-of-the-art, i.e. what has been demonstrated with wide-field microscopy.

- Comparison to diverse activity-dependent sensors, including the widely used GCaMP.

Weaknesses:

(1) The kinetics of GCaMP is stereotypic. An analysis/comment on if and how the kinetics of the signals could be used to distinguish the hemodynamic occlusion artefacts from calcium signals would be useful.

We might be misunderstanding what the reviewer means by “the kinetics of GCaMP are stereotypic”. The kinetics are clearly stereotypic if one has isolated single action potential responses in a genetically identified cell type. But data recorded in vivo looks very different, see e.g. example traces in figure 1g of (Keller et al., 2012). And these are selected example traces, the average GCaMP trace looks perhaps more like the three example traces shown in Figure 2 (this is not surprising if the GCaMP signals one records in vivo are a superposition of calcium responses and hemodynamic occlusion). All quantification of kinetics relies on identifying “events”. We cannot identify events in any meaningful way for most of the data (see e.g. examples 2 and 3 in Figure 2). The one feature we can reliably identify as differing between GCaMP and GFP responses is peak response amplitude (as quantified in Figure 2).

(2) Is it possible that motion is affecting the signals in a certain degree? This issue is not made clear.

See also our response to reviewer 1 comment 1. In brief, we have added a quantification of motion artefacts as Figure S5, and argue that motion artefacts could only account for locomotion onset responses (there is no detectable brain motion to visual responses) and would predict a decrease in fluorescence (not an increase).

(3) The causal relationship with blood flow remains open. Hemodynamic occlusion seems a good candidate causing changes in GFP fluorescence, but this remains to be well addressed in further research.

We agree – we have made this clearer in the manuscript.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 2A shows three neurons with convincing GFP responses, with amplitudes often exceeding 100%. However, after seeing these data, I actually feel less convinced that these responses are related to hemodynamic occlusion. Blood vessel diameter changes by at most a few percent during behavior -- how could such small changes lead to >100% changes in GFP fluorescence?

My guess is that these responses might instead be related to motion artifacts, particularly given the strong correlation between these responses and running speed (Figure 2A). One possible way to test this is by examining a pixelwise map of fluorescence changes (dF/F) during running vs. baseline. If hemodynamic effects are involved, one would likely see a shadow of the involved blood vessels in this map. Conversely, if motion artifacts are the primary factor, the map of dF/F should resemble the spatial gradients of the mean fluorescence image. Examining pixelwise maps of dF/F will likely provide insights regarding the nature of the GFP signals.

The underlying assumption (“blood vessel diameter changes by at most a few percent”) might be incorrect here. (Note also, relevant is likely the cross section, not diameter.) See Figure 4A1 and B1 for quantification of example blood vessel area changes - both example vessels change area by approximately 50%. Also note, example 1 in Figure 2 is an extreme example. The example was chosen to highlight that effects can be large. To try to illustrate that this is not typical however, we also show the distribution of all neurons in Figure 2B and mark all three example cells – example 1 is at the very tail of the distribution.

Regarding the analysis suggested, we have added examples of this for running onset to the manuscript (Figure S7). We have examples in which a blood vessel shadow is clearly visible. More typical however, is a general increase in fluorescence (on running onset) that we think is caused by blood vessels closer to the surface of the brain.

(2) Figure 3A shows strong GFP responses during running, while visuomotor mismatch elicit virtually no GFP-responsive neurons. This finding is puzzling, as visuomotor mismatch has been shown by the same group to activate L2/3 neurons more strongly than running (see Figure 3A, Keller et al., 2012, Neuron). Stronger neuronal activation should, in theory, result in more pronounced hemodynamic effects, and therefore, a higher proportion of GFP-responsive neurons. The absence of GFP responses during visuomotor mismatch raises questions about whether GFP signals are directly linked to hemodynamic occlusion.

An alternative explanation is that the strong GFP responses observed during running could instead be driven by motion artifacts, e.g., those associated with the increased head or body movements during running onsets. Such artifacts could explain the observed GFP responses, rather than hemodynamic occlusion.

This might be a misunderstanding. Mismatch responses are primarily observed in mismatch neurons. These are superficial L2/3 neurons (possibly the population that in higher mammals is L2 neurons). The fact that mismatch responses are primarily observed in this superficial population is likely the reason they were discovered using two-photon calcium imaging (which tends to have a bias towards superficial neurons as the image quality is best there), and seen in much fewer neurons when using electrophysiological techniques (Saleem et al., 2013) that are biased to deeper neurons. In response to Reviewer #2, we have now also added a quantification of the fraction of neurons responsive to these stimuli when using GCaMP (Figure 3D-F). The fraction of neurons responsive to visuomotor mismatch is smaller than those responsive on locomotion or to visual stimuli.

Thus, based on “average” responses across all cortical cell types (our L2/3 recordings here are as unbiased across all of L2/3 as possible) the response profiles (strong running onset and visual responses, and weak MM responses) are probably what one would expect in first approximation also in the blood vessel response profile. Complicating this is of course the fact that it is likely some cell type specific activity that contributes most to blood flow changes, not simply average neuronal activity.

See response to public review comment 1 for a discussion of alternative sources, including motion artefacts.

(3) Given the potential confound associated with brain motion, the authors might consider quantifying hemodynamic occlusion effects under more controlled conditions, such as in anesthetized animals, where brain movement is minimal. They could use drifting grating stimuli, which are known to produce wellcharacterized blood vessel and hemodynamic responses in V1. The effects of hemodynamic occlusion can then be quantified by imaging the fluorescence of an activity-independent marker. For maximal robustness, GFP should ideally be avoided, due to its known sensitivity to pH changes, as noted in the public review.

Brain motion is negligible to visual stimuli in the awake mouse as well (Figure S5). This is likely the better control than anesthetized recordings – anesthesia has strong effects on blood pressure, heart rate, breathing, etc. all of which would introduce more confounds.

(4) Regardless of the precise mechanism driving the observed GFP response, these activity-independent signals must be accounted for in functional imaging experiments. This applies not only to experiments using small dynamic range sensors but also to those employing 'high dynamic range' sensors like GCaMP6, which, according to the authors, exhibit responses only ~2-fold greater than those of GFP.

In this context, the extensive GFP imaging data are highly valuable, as they could serve as a benchmark for evaluating the effectiveness of correction methods. Ideally, effective correction methods should produce minimal responses when applied to GFP imaging data. With these data at hand, I strongly encourage the authors to explore potential correction methods, as such methods could have far-reaching impact on the field.

As discussed above, we have tested a number of such correction approaches for both widefield and two-photon imaging and could never recover a response profile that resembles the GFP response. The “correction method” we have come to favor, is repeating experiments using GFP (i.e. what we have done here).

(5) Several correction approaches could be considered: for instance, the strong correlation between GFP responses and blood vessel diameter (as shown in Figure 4) could potentially be leveraged to predict and compensate for the activity-independent signals. Alternatively, expressing an activity-independent marker alongside the activity sensor in orthogonal spectral channels could enable simultaneous monitoring and correction of activity-independent signals. Finally, computational procedure to remove common fluctuations, measured from background or 'neuropil' regions (see, e.g., Kerlin et al., 2010, Neuron; Giovannucci et al., 2019, eLife), may help reduce the contamination in cellular ROIs. The authors could try some or all of these methods, and benchmark their effectiveness by assessing, e.g., the number of GFP responsive neurons after correction.

Over the years we have tried many of these approaches. A correction using a second fluorophore of a different color likely fails because blood absorption is strongly wavelength dependent, making it challenging to calibrate the correction factor. Neuropil “correction” on GCaMP data, even with the best implementations, is just a common mode subtraction. The signal in the neuropil – as the name implies is just an average of many axons and dendrites in the vicinity – most of these processes are from nearby neurons making a neuropil response simply an average response of the neurons in some neighborhood. Adding the problem of hemodynamic responses (which on small scales will also influence nearby neurons and neuropil similarly) makes disentangling the two effects impossible (i.e. neuropil subtraction makes the problem worse, not better). However, just because we fail in implementing all of these methods, does not necessarily mean the method is faulty. Hence we have chosen not to comment on any such method, and simply provide the only mitigation strategy that works in our hands – record GFP responses.

(6) Given the potential usefulness of the GFP imaging data, I encourage the authors to share these data in a public repository to facilitate the development of correction methods.

Certainly – all of our data are always published. In the early years of the lab on an FMI repository here https://data.fmi.ch/ - more recently now on Zenodo.

(7) As noted in the public review, several methodology details are missing. Most importantly, I could not find the description in the Methods section explaining how fluorescence signals from individual neurons were extracted from two-photon imaging data. The existing section on 'Extraction of neuronal activity' appears to cover only the wide-field analysis, with details about two-photon analysis seemingly absent.

Please excuse the omission – this has all been added to the methods. In brief, to answer your questions:

Were regions of interest (ROIs) for individual cells identified manually or automatically?

We use a mixture of manual and automatic methods for our two-photon data. Based on a median filtered (spatially) version of the mean fluorescence image, we used a threshold based selection of ROIs. This was then visually inspected and manually corrected where necessary such that ROIs were at least 250 pixels and only labelled clearly identifiable neurons.

Was fluorescence within each ROI calculated by averaging signals across pixels, or were signal de-mixing algorithms (e.g., PCA, ICA, or NMF) applied?

We use the average fluorescence across pixels without any de-mixing algorithms here and in all our two-photon experiments. De-mixing algorithms can introduce a variety of artefacts.

Additionally, did the authors account for and correct the contribution of surrounding neuropil?

No neuropil correction was applied. It would also be difficult to see how this would help. If the model of hemodynamic occlusion is correct, one would expect occlusion effects to change on the length scale of blood vessels (i.e. tens to hundreds of microns). Thus, the effect of occlusion on neuropil and cells should be the similar. Neuropil “correction” is always based on the idea of removing signals that are common to both neuropil and somata, thereby complicating the interpretation of the resulting signal even further.

Without these methodological details, it is difficult to accurately interpret the two-photon signals reported in the manuscript.

(8) The rationale for using the average fluorescence of a ROI within the blood vessel as a proxy for blood vessel diameter is not entirely clear to me. The authors should provide a clearer justification for this approach in their revision.

Consider a ROI placed within a blood vessel at the focus of the illumination cone (Author response image 3). Given the axial point-spread-function of two-photon imaging is in the range of 0.5 μm laterally and 3 μm axially (indicated by the bicone), emitted photons from the fluorescent tissue outside of the blood vessel but within the two-photon volume will contribute to change in fluorescence in the ROI. A change in the blood vessel volume, say an increase on dilation, would decrease the amount of emission photons reaching the objective by, one, pushing more of the fluorescent tissue outside of the two-photon volume, and two, by presenting greater hemodynamic occlusion to the photons emitted by the fluorescent tissue immediately below the vessel. Conversely, on vasoconstriction there are more emission photons at the objective.

In line with this argument, as shown in Figure 4A1-A2, B1-B2 and C1-C2, we do find that the change in fluorescence of blood vessel ROI varies inversely with the area of the blood vessel. Of course, change in blood vessel ROI fluorescence is only a proxy for vessel size. Extracting blood vessel boundaries from individual two-photon frames was noisy and proved unreliable in the absence of specific dyes to label the vessel walls. We thus resorted to using blood vessel ROI fluorescence as a proxy for hemodynamic occlusion, and tested how much of the variance in GFP responses is explained by the change in blood vessel ROI response.

We have added an explanation to the manuscript, as suggested.

Author response image 3.

Average response of ROIs placed within blood vessels co-vary with hemodynamic occlusion.

(9) I find that the Shen et al., 2012, Nature Methods paper has gone quite far to demonstrate the effect of hemodynamic occlusion in two photon imaging. Therefore, I suggest the authors describe and cite this work not only in the discussion but also in the introduction, where they can highlight the key questions left unanswered by that study and explain how their manuscript aims to address them.

We have added the reference and point to the work in the introduction as suggested.

Reviewer #3 (Recommendations for the authors):

I appreciate very much that the study is presented in a very clear manner.

A few comments that could clarify it even further:

(1) Fig. 1: make clear on legend if it is an average of full FOVs.

The traces shown are the average over ROIs (neurons) – we have clarified in the figure legend as suggested.

(2) Give a more complete definition of hemodynamic occlusion to understand the hypothesis in the relationship between blood vessel dilation and GFP fluorescence (116-119). Maybe, move the phrase from conclusion "Since blood absorbs light, hemodynamic occlusion can affect fluorescence intensity measurements" (219-220).

Very good point – we expanded on the definition in the introduction.

(3) For clarity, mention in the main text the method used to assess how a parameter explains the variance (126-129).

Is implemented.

(4) Discuss the possible relationship of the signals to neuronal activity.

We have added this to the discussion.

(5) Discuss if the measurements could provide any functional insights, whether they could be used to learn something about the brain.

We have added this to the discussion.

Author Response

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

Reviewer #1 (Public Review):

The manuscript by Wagstyl et al. describes an extensive analysis of gene expression in the human cerebral cortex and the association with a large variety of maps capturing many of its microscopic and macroscopic properties. The core methodological contribution is the computation of continuous maps of gene expression for >20k genes, which are being shared with the community. The manuscript is a demonstration of several ways in which these maps can be used to relate gene expression with histological features of the human cortex, cytoarchitecture, folding, function, development and disease risk. The main scientific contribution is to provide data and tools to help substantiate the idea of the genetic regulation of multi-scale aspects of the organisation of the human brain. The manuscript is dense, but clearly written and beautifully illustrated.

Main comments

The starting point for the manuscript is the construction of continuous maps of gene expression for most human genes. These maps are based on the microarray data from 6 left human brain hemispheres made available by the Allen Brain Institute. By technological necessity, the microarray data is very sparse: only 1304 samples to map all the cortex after all subjects were combined (a single individual's hemisphere has ~400 samples). Sampling is also inhomogeneous due to the coronal slicing of the tissue. To obtain continuous maps on a mesh, the authors filled the gaps using nearest-neighbour interpolation followed by strong smoothing. This may have two potentially important consequences that the authors may want to discuss further: (a) the intrinsic geometry of the mesh used for smoothing will introduce structure in the expression map, and (b) strong smoothing will produce substantial, spatially heterogeneous, autocorrelations in the signal, which are known to lead to a significant increase in the false positive rate (FPR) in the spin tests they used.

Many thanks to the reviewer for their considered feedback. We have addressed these primary concerns into point-by-point responses below. The key conclusions from our new analyses are: (i) while the intrinsic geometry of the mesh had not originally been accounted for in sufficient detail, the findings presented in this manuscript paper are not driven by mesh-induced structure, (ii) that the spin test null models used in this manuscript [(including a modified version introduced in response to (i)] are currently the most appropriate way to mitigate against inflated false positive rates when making statistical inferences on smooth, surface-based data.

a. Structured smoothing

A brain surface has intrinsic curvature (Gaussian curvature, which cannot be flattened away without tearing). The size of the neighbourhood around each surface vertex will be determined by this curvature. During surface smoothing, this will make that the weight of each vertex will be also modulated by the local curvature, i.e., by large geometric structures such as poles, fissures and folds. The article by Ciantar et al (2022, https://doi.org/10.1007/s00429-022-02536-4) provides a clear illustration of this effect: even the mapping of a volume of pure noise into a brain mesh will produce a pattern over the surface strikingly similar to that obtained by mapping resting state functional data or functional data related to a motor task.

Comment 1

It may be important to make the readers aware of this possible limitation, which is in large part a consequence of the sparsity of the microarray sampling and the necessity to map that to a mesh. This may confound the assessments of reproducibility (results, p4). Reproducibility was assessed by comparing pairs of subgroups split from the total 6. But if the mesh is introducing structure into the data, and if the same mesh was used for both groups, then what's being reproduced could be a combination of signal from the expression data and signal induced by the mesh structure.

Response 1

The reviewer raises an important question regarding the potential for interpolation and smoothing on a cortical mesh to induce a common/correlated signal due to the intrinsic mesh structure. We have now generated a new null model to test this idea which indicates that intrinsic mesh structure is not inflating reproducibility in interpolated expression maps. This new null model spins the original samples prior to interpolation, smoothing and comparison between triplet splits of the six donors, with independent spins shared across the triplet. For computational tractability we took one pair of triplets and regenerated the dataset for each triplet using 10 independent spins. We used these to estimate gene-gene null reproducibility for 90 independent pairwise combinations of these 10 spins. Across these 90 permutations, the average median gene-gene correlation was R=0.03, whereas in the unspun triplet comparisons this was R=0.36. These results indicate that the primary source of the gene-level triplet reproducibility is the underlying shared gene expression pattern rather than interpolation-induced structure.

In Methods 2a: "An additional null dataset was generated to test whether intrinsic geometry of the cortical mesh and its impact on interpolation for benchmarking analyses of DEMs and gradients (Fig S1d, Fig S2d, Fig S3c). In these analyses, the original samples were rotated on the spherical surface prior to subsequent interpolation, smoothing and gradient calculation. Due to computational constraints the full dataset was recreated only for 10 independent spins. These are referred to as the “spun+interpolated null”.

Author response image 1.

Figure S1d, Gene predictability was higher across all triplet-triplet pairs than when compared to spun+interpolated null.

Comment 2

It's also possible that mesh-induced structure is responsible in part for the "signal boost" observed when comparing raw expression data and interpolated data (fig S1a). How do you explain the signal boost of the smooth data compared with the raw data otherwise?

Response 2

We thank the reviewer for highlighting this issue of mesh-induced structure. We first sought to quantify the impact of mesh-induced structure through the new null model, in which the data are spun prior to interpolation. New figure S1d, S2d and S3c all show that the main findings are not driven by interpolation over a common mesh structure, but rather originate in the underlying expression data.

Specifically, for the original Figure S1a, the reviewer highlights a limitation that we compared intersubject predictability of raw-sample to raw-sample and interpolated-to-interpolated. In this original formulation improved prediction scores for interpolated-to-interpolated (the “signal boost”) could be driven by mesh-induced structure being applied to both the input and predicted maps. We have updated this so that we are now comparing raw-to-raw and interpolated-to-raw, i.e. whether interpolated values are better estimations of the measured expression values. The new Fig S1a&b (see below) shows a signal boost in gene-level and vertex level prediction scores (delta R = +0.05) and we attribute this to the minimisation of location and measurement noise in the raw data, improving the intersubject predictability of expression levels.

In Methods 2b: "To assess the effect of data interpolation in DEM generation we compared gene-level and vertex-level reproducibility of DEMs against a “ground truth” estimate of these reproducibility metrics based on uninterpolated expression data. To achieve a strict comparison of gene expression values between different individuals at identical spatial locations we focused these analyses on the subset of AHBA samples where a sample from one subject was within 3 mm geodesic distance of another. This resulted in 1097 instances (spatial locations) with measures of raw gene expression of one donor, and predicted values from the second donor’s un-interpolated AHBA expression data and interpolated DEM. We computed gene-level and vertex-level reproducibility of expression using the paired donor data at each of these sample points for both DEM and uninterpolated AHBA expression values. By comparing DEM reproducibility estimates with those for uninterpolated AHBA expression data, we were able to quantify the combined effect of interpolation and smoothing steps in DEM generation. We used gene-level reproducibility values from DEMs and uninterpolated AHBA expression data to compute a gene-level difference in reproducibility, and we then visualized the distribution of these difference values across genes (Fig S1a). We used gene-rank correlation to compare vertex-level reproducibility values between DEMs and uninterpolated AHBA expression data (Fig S1b)."

Author response image 2.

Figure S1. Reproducibility of Dense Expression Maps (DEMs) interpolated from spatially sparse postmortem measures of cortical gene expression. a, Signal boost in the interpolated DEM dataset vs. spatially sparse expression data. Restricting to samples taken from approximately the same cortical location in pairs of individuals (within 3mm geodesic distance), there was an overall improvement in intersubject spatial predictability in the interpolated maps. Furthermore, genes with lower predictability in the interpolated maps were less predictable in the raw dataset, suggesting these regions exhibit higher underlying biological variability rather than methodologically introduced bias. b, Similarly at the paired sample locations, gene-rank predictability was generally improved in DEMs vs. sparse expression data (median change in R from sparse samples to interpolated for each pair of subjects, +0.5).

1. How do you explain that despite the difference in absolute value the combined expression maps of genes with and without cortical expression look similar? (fig S1e: in both cases there's high values in the dorsal part of the central sulcus, in the occipital pole, in the temporal pole, and low values in the precuneus and close to the angular gyrus). Could this also reflect mesh-smoothing-induced structure?

Response 3

As with comment 1, this is an interesting perspective that we had not fully considered. We would first like to clarify that non-cortical expression is defined from the independent datasets including the “cortex” tissue class of the human protein atlas and genes identified as markers for cortical layers or cortical cells in previous studies. This is still likely an underestimate of true cortically expressed genes as some of these “non-cortical genes” had high intersubject reproducibility scores. Nevertheless we think it appropriate to use a measure of brain expression independent of anything included in other analyses for this paper. These considerations are part of the reason we provide all gene maps with accompanying uncertainty scores for user discretion rather than simply filtering them out.

In terms of the spatially consistent pattern of the gene ranks of Fig S1f, this consistent spatial pattern mirrors Transcriptomic Distinctiveness (r=0.52 for non-cortical genes, r=0.75 for cortical genes), so we think that as the differences in expression signatures become more extreme, the relative ranks of genes in that region are more reproducible/easier to predict.

To assess whether mesh-smoothing-induced structure is playing a role, we carried out an additional the new null model introduced in response to comment 1, and asked if the per-vertex gene rank reproducibility of independently spun subgroup triplets showed a similar structure to that in our original analyses. Across the 90 permutations, the median correlation between vertex reproducibility and TD was R=0.10. We also recalculated the TD maps for the 10 spun datasets and the mean correlation with the original TD did not significantly differ from zero (mean R = 0.01, p=0.2, nspins =10). These results indicate that folding morphology is not the major driver of local or large scale patterning in the dataset. We have included this as a new Figure S3c.

We have updated the text as follows:

In Methods 3a: "Third, to assess whether the covariance in spatial patterning across genes could be a result of mesh-associated structure introduced through interpolation and smoothing, TD maps were recomputed for the spun+interpolated null datasets and compared to the original TD map (Fig S3c)."

In Results: "The TD map observed from the full DEMs library was highly stable between all disjoint triplets of donors (Methods, Fig S3a, median cross-vertex correlation in TD scores between triplets r=0.77) and across library subsets at all deciles of DEM reproducibility (Methods, Fig S3b, cross-vertex correlation in TD scores r>0.8 for the 3rd-10th deciles), but was not recapitulated in spun null datasets (Fig S3c)."

Author response image 3.

Figure S3c, Correlations between TD and TD maps regenerated on datasets spun using two independent nulls, one where the rotation is applied prior to interpolation and smoothing (spun+interpolated) and one where it is applied to the already-created DEMs. In each null, the same rotation matrix is applied to all genes.

Comment 4

Could you provide more information about the way in which the nearest-neighbours were identified (results p4). Were they nearest in Euclidean space? Geodesic? If geodesic, geodesic over the native brain surface? over the spherically deformed brain? (Methods cite Moresi & Mather's Stripy toolbox, which seems to be meant to be used on spheres). If the distance was geodesic over the sphere, could the distortions introduced by mapping (due to brain anatomy) influence the geometry of the expression maps?

Response 4

We have clarified in the Methods that the mapping is to nearest neighbors on the spherically-inflated surface.

The new null model we have introduced in response to comments 1 & 3 preserves any mesh-induced structure alongside any smoothing-induced spatial autocorrelations, and the additional analyses above indicate that main results are not induced by systematic mesh-related interpolation signal. In response to an additional suggestion from the reviewer (Comment 13), we also assessed whether local distortions due to the mesh could be creating apparent border effects in the data, for instance at the V1-V2 boundary. At the V1-V2 border, which coincides anatomically with the calcarine sulcus, we computed the 10 genes with the highest expression gradient along this boundary in the actual dataset and the spun-interpolated null. The median test expression gradients along this border was higher than in any of the spun datasets, indicating that these boundary effects are not explained by the interpolation and cortical geometry effects on the data (new Fig S2d). The text has been updated as follows:

In Methods 1: "For cortical vertices with no directly sampled expression, expression values were interpolated from their nearest sampled neighbor vertex on the spherical surface (Moresi and Mather, 2019) (Fig 1b)."

In Methods 2: "We used the spun+interpolated null to test whether high gene gradients could be driven by non-uniform interpolation across cortical folds. We quantified the average gradient for all genes along the V1-V2 border in the atlas, as well as for 10 iterations of the atlas where the samples were spun prior to interpolation. We computed the median gradient magnitude for the 20 top-ranked genes for each (Fig S2d)."

Author response image 4.

Figure S2d Mean of gradient magnitudes for 20 genes with largest gradients along V1-V2 border, compared to values along the same boundary on the spun+interpolated null atlas. Gradients were higher in the actual dataset than in all spun version indicating this high gradient feature is not primarily due to the effects of calcarine sulcus morphology on interpolation

Comment 5

Could you provide more information about the smoothing algorithm? Volumetric, geodesic over the native mesh, geodesic over the sphere, averaging of values in neighbouring vertices, cotangent-weighted laplacian smoothing, something else?

Response 5

We are using surface-based geodesic over the white surface smoothing described in Glasser et al., 2013 and used in the HCP workbench toolbox (https://www.humanconnectome.org/software/connectome-workbench). We have updated the methods to clarify this.

In Methods 1: "Surface expression maps were smoothed using the Connectome Workbench toolbox (Glasser et al. 2013) with a 20mm full-width at half maximum Gaussian kernel , selected to be consistent with this sampling density (Fig 1c)."

Comment 6

Could you provide more information about the method used for computing the gradient of the expression maps (p6)? The gradient and the laplacian operator are related (the laplacian is the divergence of the gradient), which could also be responsible in part for the relationships observed between expression transitions and brain geometry.

Response 6

We are using Connectome Workbench’s metric gradient command for this Glasser et al., 2013 and used in the HCP workbench pipeline. The source code for gradient calculation can be found here: https://github.com/Washington-University/workbench/blob/131e84f7b885d82af76e be21adf2fa97795e2484/src/Algorithms/AlgorithmMetricGradient.cxx

In Methods 2: >For each of the resulting 20,781 gene-level expression maps, the orientation and magnitude of gene expression change at each vertex (i.e. the gradient) was calculated for folded, inflated, spherical and flattened mesh representations of the cortical sheet using Connectome Workbench’s metric gradient command (Glasser et al. 2013).

b. Potentially inflated FPR for spin tests on autocorrelated data."

Spin tests are extensively used in this work and it would be useful to make the readers aware of their limitations, which may confound some of the results presented. Spin tests aim at establishing if two brain maps are similar by comparing a measure of their similarity over a spherical deformation of the brains against a distribution of similarities obtained by randomly spinning one of the spheres. It is not clear which specific variety of spin test was used, but the original spin test has well known limitations, such as the violation of the assumption of spatial stationarity of the covariance structure (not all positions of the spinning sphere are equivalent, some are contracted, some are expanded), or the treatment of the medial wall (a big hole with no data is introduced when hemispheres are isolated).

Another important limitation results from the comparison of maps showing autocorrelation. This problem has been extensively described by Markello & Misic (2021). The strong smoothing used to make a continuous map out of just ~1300 samples introduces large, geometry dependent autocorrelations. Indeed, the expression maps presented in the manuscript look similar to those with the highest degree of autocorrelation studied by Markello & Misic (alpha=3). In this case, naive permutations should lead to a false positive rate ~46% when comparing pairs of random maps, and even most sophisticated methods have FPR>10%.

Comment 7 There's currently several researchers working on testing spatial similarity, and the readers would benefit from being made aware of the problem of the spin test and potential solutions. There's also packages providing alternative implementations of spin tests, such as BrainSMASH and BrainSpace, which could be mentioned.

Response 7

We thank the reviewer for raising the issue of null models. First, with reference to the false positive rate of 46% when maps exhibit spatial autocorrelation, we absolutely agree that this is an issue that must be accounted for and we address this using the spin test. We acknowledge there has been other work on nulls such as BrainSMASH and BrainSpace. Nevertheless in the Markello and Misic paper to which the reviewer refers, the BrainSmash null models perform worse with smoother maps (with false positive rates approaching 30% in panel e below), whereas the spin test maintains false positives rates below 10%.

Author response image 5.

We have added a brief description of the challenge and our use of the spin test.

In Methods 2a: "Cortical maps exhibit spatial autocorrelation that can inflate the False Positive Rate, for which a number of methods have been proposed(Alexander-Bloch et al. 2018; Burt et al. 2020; Vos de Wael et al. 2020). At higher degrees of spatial smoothness, this high False Positive Rate is most effectively mitigated using the spin test(Alexander-Bloch et al. 2018; Markello and Misic 2021; Vos de Wael et al. 2020). In the following analyses when generating a test statistic comparing two spatial maps, to generate a null distribution, we computed 1000 independent spins of the cortical surface using https://netneurotools.readthedocs.io, and applied it to the first map whilst keeping the second map unchanged. The test statistic was then recomputed 1000 times to generate a null distribution for values one might observe by chance if the maps shared no common organizational features. This is referred to throughout as the “spin test” and the derived p-values as pspin."

Comment 8

Could it be possible to measure the degree of spatial autocorrelation?

Response 8

We agree this could be a useful metric to generate for spatial cortical maps. However, there are multiple potential metrics to choose from and each of the DEMs would have their own value. To address this properly would require the creation of a set of validated tools and it is not clear how we could summarize this variety of potential metrics for 20k genes. Moreover, as discussed above the spin method is an adequate null across a range of spatial autocorrelation degrees, thus while we agree that in general estimation of spatial smoothness could be a useful imaging metric to report, we consider that it is beyond the scope of the current manuscript.

Comment 9

Could you clarify which version of the spin test was used? Does the implementation come from a package or was it coded from scratch?

Response 9

As Markello & Misic note, at the vertex level, the various implementations of the spin test become roughly equivalent to the ‘original’ Alexander-Bloch et al., implementation. We used took the code for the ‘original’ version implemented in python here: https://netneurotools.readthedocs.io/en/latest/_modules/netneurotools/stats.html# gen_spinsamples.

This has been updated in the methods (see Response 7).

Comment 10

Cortex and non-cortex vertex-level gene rank predictability maps (fig S1e) are strikingly similar. Would the spin test come up statistically significant? What would be the meaning of that, if the cortical map of genes not expressed in the cortex appeared to be statistically significantly similar to that of genes expressed in the cortex?

Response 10

Please see response to comment 3, which also addresses this observation.

Reviewer #2 (Public Review):

The authors convert the AHBA dataset into a dense cortical map and conduct an impressively large number of analyses demonstrating the value of having such data.

I only have comments on the methodology.

Comment 1

First, the authors create dense maps by simply using nearest neighbour interpolation followed by smoothing. Since one of the main points of the paper is the use of a dense map, I find it quite light in assessing the validity of this dense map. The reproducibility values they calculate by taking subsets of subjects are hugely under-powered, given that there are only 6 brains, and they don't inform on local, vertex-wise uncertainties). I wonder if the authors would consider using Gaussian process interpolation. It is really tailored to this kind of problem and can give local estimates of uncertainty in the interpolated values. For hyperparameter tuning, they could use leave-one-brain-out for that.

I know it is a lot to ask to change the base method, as that means re-doing all the analyses. But I think it would strengthen the paper if the authors put as much effort in the dense mapping as they did in their downstream analyses of the data.

Response 1

We thank the reviewer for the suggestion to explore Gaussian process interpolation. We have implemented this for our dataset and attempted to compare this with our original method with the 3 following tests: i) intertriplet reproducibility of individual gene maps, ii) microscale validations: area markers, iii) macroscale validations: bio patterns.

Overall, compared to our original nearest-neighbor interpolation method, GP regression (i) did not substantially improve gene-level reproducibility of expression maps (median correlation increase of R=0.07 which was greater for genes without documented protein expression in cortex): ii) substantially worsened performance in predicting areal marker genes and iii) showed similar but slightly worse performance at predicting macroscale patterns from Figure 1.

Given the significantly poorer performance on one of our key tests (ii) we have opted not to replace our original database, but we do now include code for the alternative GP regression methodology in the github repository so others can reproduce/further develop these methods.

Author response image 6.

ii) Genes ranked by mean expression gradient from current DEMs (left) and Gaussian process-derived interpolation maps (right). Established Human and macaque markers are consistently higher-ranked in DEM maps. iii) Figure 1 Interpolated vs GP regression

Author response table 1.

Comment 2

It is nice that the authors share some code and a notebook, but I think it is rather light. It would be good if the code was better documented, and if the user could have access to the non-smoothed data, in case they was to produce their own dense maps. I was only wondering why the authors didn't share the code that reproduces the many analyses/results in the paper.

Response 2

We thank the reviewer for this suggestion. In response we have updated the shared github repository (https://github.com/kwagstyl/magicc). This now includes code and notebooks to reproduce the main analyses and figures.

Reviewer #1 (Recommendations For The Authors):

Minor comments

Comment 11

p4 mentions Fig S1h, but the supp figures only goes from S1a to S1g

Response 11

We thank the reviewer for capturing this error. It was in fact referring to what is now Fig S1h and has been updated.

Comment 12

It would be important that the authors share all the code used to produce the results in the paper in addition to the maps. The core methodological contribution of the work is a series of continuous maps of gene expression, which could become an important tool for annotation in neuroimaging research. Many arbitrary (reasonable) decisions were made, it would be important to enable users to evaluate their influence on the results.

Response 12

We thank both reviewers for this suggestion. We have updated the github to be able to reproduce the dense maps and key figures with our methods.

Comment 13

p5: Could the sharp border reflect the effect of the geometry of the calcarine sulcus on map smoothing? More generally, could there be an effect of folds on TD?

Response 13

Please see our response to Reviewer 1, Comment 1 above, where we introduce the new null models now analyzed to test for effects of mesh geometry on our findings. These new null models - where original source data were spun prior to interpolation suggest that neither the sharp V1/2 border or the TD map are effects of mesh geometry. Specifically: (i) , the magnitudes of gradients along the V1/2 boundary from null models were notably smaller than those in our original analyses (see new figure S2d), and (ii) TD maps computed from the new null models showed no correlation with TD maps from ur original analyses (new Figure S3c, mean R = 0.01, p=0.2, nspins =10).

Comment 14

p5: Similar for the matching with the areas in Glasser's parcellation: the definition of these areas involves alignment through folds (based on freesurfer 'sulc' map, see Glasser et al 2016). If folds influence the geometry of TDs, could that influence the match?

Response 14

We note that Fig S3c provided evidence that folding was not the primary driver of the TD patterning. However, it is true that Glasser et al. use both neuroanatomy (folding, thickness and myelin) and fMRI-derived maps to delineate their cortical areas. As such Figure 2 f & g aren’t fully independent assessments. Nevertheless the reason that these features are used is that many of the sulci in question have been shown to reliably delineate cytoarchitectonic boundaries (Fischl et al., 2008).

In Results: "A similar alignment was seen when comparing gradients of transcriptional change with the spatial orientation of putative cortical areas defined by multimodal functional and structural in vivo neuroimaging(Glasser et al., 2016) (expression change running perpendicular to area long-axis, pspin<0.01, Fig 2g, Methods)."

Comment 15

p6: TD peaks are said to overlap with functionally-specialised regions. A comment on why audition is not there, nor language, but ba 9-46d is? Would that suggest a lesser genetic regulation of those functions?

Response 15

The reviewer raises a valid point and this was a result that we were also surprised by. The finding that the auditory cortex is not as microstructurally distinctive as, say V1, is consistent with other studies applying dimensionality-reduction techniques to multimodal microstructural receptor data (e.g. Zilles et al., 2017, Goulas et al., 2020). These studies found that the auditory microstructure is not as extreme as either visual and somatomotor areas. From a methodological view point, the primary auditory cortex is significantly smaller than both visual and somatomotor areas, and therefore is captured by fewer independent samples, which could reduce the detail in which its structure is being mapped in our dataset.

For the frontal areas, we would note that i) the frontal peak is the smallest of all peaks found and was more strongly characterised by low z-score genes than high z-score. ii) the anatomical areas in the frontal cortex are much more highly variable with respect to folding morphology (e.g. Rajkowska 1995). The anatomical label of ba9-46d (and indeed all other labels) were automatically generated as localisers rather than strict area labels. We have clarified this in the text as follows:

In Methods 3a: "Automated labels to localize TD peaks were generated based on their intersection with a reference multimodal neuroimaging parcellation of the human cortex(Glasser et al., 2016). Each TD was given the label of the multimodal parcel that showed greatest overlap (Fig 2b)."

Comment 16.

p7: The proposition that "there is a tendency for cortical sulci to run perpendicular to the direction of fastest transcriptional change", could also be "there is a tendency for the direction of fastest transcriptional change to run perpendicular to cortical sulci"? More pragmatically, this result from the geometry of transcriptional maps being influenced by sulcal geometry in their construction.

Response 16

Please see our response to Reviewer 1, Comment 1 above, where we introduce the new null models now analyzed to test for effects of mesh geometry on our findings. These models indicate that the topography of interpolated gene expression maps do not reflect influences of sulcal geometry on their construction.

Comment 17

p7: TD transitions are indicated to precede folding. This is based on a consideration of folding development based on the article by Chi et al 1977, which is quite an old reference. In that paper, the authors estimated the tempo of human folding development based on the inspection of photographs, which may not be sufficient for detecting the first changes in curvature leading to folds. The work of the Developing Human Connectome consortium may provide a more recent indication for timing. In their data, by PCW 21 there's already central sulcus, pre-central, post-central, intra-parietal, superior temporal, superior frontal which can be detected by computing the mean curvature of the pial surface (I can only provide a tweet for reference: https://twitter.com/R3RT0/status/1617119196617261056). Even by PCW 9-13 the callosal sulcus, sylvian fissure, parieto-occipital fissure, olfactory sulcus, cingulate sulcus and calcarine fissure have been reported to be present (Kostovic & Vasung 2009).

Response 17

Our field lacks the data necessary to provide a comprehensive empirical test for the temporal ordering of regional transcriptional profiles and emergence of folding. Our results show that transcriptional identities of V1 and TGd are - at least - present at the very earliest stages of sulcation in these regions. In response to the reviewers comment we have updated with a similar fetal mapping project which similarly shows evidence of the folds between weeks 17-21 and made the language around directionality more cautious.

In Results: "The observed distribution of these angles across vertices was significantly skewed relative to a null based on random alignment between angles (pspin<0.01, Fig 2f, Methods) - indicating that there is indeed a tendency for cortical sulci and the direction of fastest transcriptional change to run perpendicular to each other (pspin<0.01, Fig 2f).

As a preliminary probe for causality, we examined the developmental ordering of regional folding and regional transcriptional identity. Mapping the expression of high-ranking TD genes in fetal cortical laser dissection microarray data(Miller et al., 2014) from 21 PCW (Post Conception Weeks) (Methods) showed that the localized transcriptional identity of V1 and TGd regions in adulthood is apparent during the fetal periods when folding topology begins to emerge (Chi et al. 1977; Xu et al. 2022) (Fig " S2d).

In Discussion: "By establishing that some of these cortical zones are evident at the time of cortical folding, we lend support to a “protomap”(Rakic 1988; O'Leary 1989; O'Leary et al. 2007; Rakic et al. 2009) like model where the placement of some cortical folds is set-up by rapid tangential changes in cyto-laminar composition of the developing cortex(Ronan et al., 2014; Toro and Burnod, 2005; Van Essen, 2020). The DEMs are derived from fully folded adult donors, and therefore some of the measured genetic-folding alignment might also be induced by mechanical distortion of the tissue during folding(Llinares-Benadero and Borrell 2019; Heuer and Toro 2019). However, no data currently exist to conclusively assess the directionality of this gene-folding relationship."

Comment 18

p7: In my supplemental figures (obtained from biorxiv, because I didn't find them among the files submitted to eLife) there's no S2j (only S2a-S2i).

Response 18

We apologize, this figure refers to S3k (formerly S3j), rather than S2j. We have updated the main text.

Comment 19 p7: It is not clear from the methods (section 3b) how the adult and fetal brains were compared. Maybe using MSM (Robinson et al 2014)?

Response 19

We have now clarified this in Methods text as reproduced below.

In Methods 3b: "We averaged scaled regional gene expression values between donors per gene, and filtered for genes in the fetal LDM dataset that were also represented in the adult DEM dataset - yielding a single final 20,476*235 gene-by-sample matrix of expression values for the human cortex at 21 PCW. Each TD peak region was then paired with the closest matching cortical label within the fetal regions. This matrix was then used to test if each TD expression signature discovered in the adult DEM dataset (Fig 2, Table 3) was already present in similar cortical regions at 21 PCW."

Comment 20

p7: WGCNA is used prominently, could you provide a brief introduction to its objectives? The gene coexpression networks are produced after adjusting the weight of the network edges to follow a scale-free topology, which is meant to reflect the nature of protein-protein interactions. Soft thresholding increases contrast, but doesn't this decrease a potential role of infinitesimal regulatory signals?

Response 20

We agree with the reviewer that the introduction to WGCNA needed additional details and have amended the Results (see below). One limitation of WGCNA-derived associations is that it will downweigh the role of smaller relationships including potentially important regulatory signals. WGCNA methods have been titrated to capture strong relationships. This is an inherent limitation of all co-expression driven methods which lead to an incomplete characterisation of the molecular biology. Nevertheless we feel these stronger relationships are still worth capturing and interrogating. We have updated the text to introduce WGCNA and acknowledge this potential weakness in the approach.

In Results: "Briefly, WGCNA constructs a constructs a connectivity matrix by quantifying pairwise co-expression between genes, raising the correlations to a power (here 6) to emphasize strong correlations while penalizing weaker ones, and creating a Topological Overlap Matrix (TOM) to capture both pairwise similarities expression and connectivity. Modules of highly interconnected genes are identified through hierarchical clustering. The resultant WGCNA modules enable topographic and genetic integration because they each exist as both (i) a single expression map (eigenmap) for spatial comparison with neuroimaging data (Fig 3a,b, Methods) and, (ii) a unique gene set for enrichment analysis against marker genes systematically capturing multiple scales of cortical organization, namely: cortical layers, cell types, cell compartments, protein-protein interactions (PPI) and GO terms (Methods, Table S2 and S4)."

Comment 21

WGCNA modules look even more smooth than the gene expression maps. Are these maps comparable to low frequency eigenvectors? Autocorrelation in that case should be very strong?

Response 21

These modules are smooth as they are indeed eigenvectors which likely smooth out some of the more detailed but less common features seen in individual gene maps. These do exhibit high degrees of autocorrelation, nevertheless we are applying the spin test which is currently the appropriate null model for spatially autocorrelated cortical maps (Response 7).

Comment 22

If the WGCNA modules provide an orthogonal basis for surface data, is it completely unexpected that some of them will correlate with low-frequency patterns? What would happen if random low frequency patterns were generated? Would they also show correlations with some of the 16 WGCNA modules?

Response 22

We agree with the reviewer that if we used a generative model like BrainSMASH, we would likely see similar low frequency patterns. However, the inserted figure in Response 7 from Makello & Misic provide evidence that is not as conservative a null as the spin test when data exhibit high spatial autocorrelation. The spatial enrichment tests carried out on the WGCNA modules are all carried out using the spin test.

Comment 23

In part (a) I commented on the possibility that brain anatomy may introduce artifactual structure into the data that's being mapped. But what if the relationship between brain geometry and brain organisation were deeper than just the introduction of artefacts? The work of Lefebre et al (2014, https://doi.org/10.1109/ICPR.2014.107; 2018, https://doi.org/10.3389/fnins.2018.00354) shows that clustering based on the 3 lowest frequency eigenvectors of the Laplacian of a brain hemisphere mesh produce an almost perfect parcellation into lobes, with remarkable coincidences between parcel boundaries and primary folds and fissures. The work of Pang et al (https://doi.org/10.1101/2022.10.04.510897) suggests that the geometry of the brain plays a critical role in constraining its dynamics: they analyse >10k task-evoked brain maps and show that the eigenvectors of the brain laplacian parsimoniously explain the activity patterns. Could brain anatomy have a downward effect on brain organisation?

Response 23

The reviewer raises a fascinating extension of our work identifying spatial modes of gene expression. We agree that these are low frequency in nature, but would first like to note that the newly introduced null model indicates that the overlaps with salient neuroanatomical features are inherent in the expression data and not purely driven by anatomy in a methodological sense.

Nevertheless we absolutely agree there is likely to be a complex multidirectional interplay between genetic expression patterns through development, developing morphology and the “final” adult topography of expression, neuroanatomical and functional patterns.

We think that the current manuscript currently contains a lot of in depth analyses of these expression data, but agree that a more extensive modeling analysis of how expression might pattern or explain functional activation would be a fascinating follow on, especially in light of these studies from Pang and Lefebre. Nevertheless we think that this must be left for a future modeling paper integrating these modes of microscale, macroscale and functional anatomy.

In Discussion: "Indeed, future work might find direct links between these module eigenvectors and similar low-frequency eigenvectors of cortical geometry have been used as basis functions to segment the cortex (Lefèvre et al. 2018) and explain complex functional activation patterns(Pang et al. 2023)."

Comment 24

On p11: ASD related to rare, deleterious mutations of strong effect is often associated with intellectual disability (where the social interaction component of ASD is more challenging to assess). Was there some indication of a relationship with that type of cognitive phenotype?

Response 24

Across the two ABIDE cohorts, the total number of those with ASD and IQ <70, which is the clinical threshold for intellectual disability was n=10, which unfortunately did not allow us to conduct a meaningful test of whether ID impacts the relationship between imaging changes in ASD and the expression maps of genes implicated in ASD by rare variants.

Comment 25

Could you clarify if the 6 donors were aligned using the folding-based method in freesurfer?

Response 25

The 6 donors were aligned using MSMsulc (Robinson et al., 2014), which is a folding based method from the HCP group. This is now clarified in the methods.

In Methods 1: "Cortical surfaces were reconstructed for each AHBA donor MRI using FreeSurfer(Fischl, 2012), and coregistered between donors using surface matching of individuals’ folding morphology (MSMSulc) (Robinson et al., 2018)."

Comment 26

The authors make available a rich resource and a series of tools to facilitate their use. They have paid attention to encode their data in standard formats, and their code was made in Python using freely accessible packages instead of proprietary alternatives such as matlab. All this should greatly facilitate the adoption of the approach. I think it would be important to state more explicitly the conceptual assumptions that the methodology brings. In the same way that a GWAS approach relies on a Mendelian idea that individual alleles encode for phenotypes, what is the idea about the organisation of the brain implied by the orthogonal gene expression modules? Is it that phenotypes - micro and macro - are encoded by linear combinations of a reduced number of gene expression patterns? What would be the role of the environment? The role of non-genic regulatory regions? Some modalities of functional organisation do not seem to be encoded by the expression of any module. Is it just for lack of data or should this be seen as the sign for a different organisational principle? Likewise, what about the aspects of disorders that are not captured by expression modules? Would that hint, for example, to stronger environmental effects? What about linear combinations of modules? Nonlinear? Overall, the authors adopt implicitly, en passant, a gene-centric conceptual standpoint, which would benefit from being more clearly identified and articulated. There are citations to Rakic's protomap idea (I would also cite the original 1988 paper, and O'Leary's 1989 "protocortex" paper stressing the role of plasticity), which proposes that a basic version of brain cytoarchitecture is genetically determined and transposed from the proliferative ventricular zone regions to the cortical plate through radial migration. In p13 the authors indicate that their results support Rakic's protomap. Additionally, in p7 the authors suggest that their results support a causal arrow going from gene expression to sulcal anatomy. The reviews by O'leary et al (2007), Ronan & Fletcher (2014, already cited), Llinares-Benadero & Borrell (2019) could be considered, which also advocate for a similar perspective. For nuances on the idea that molecular signals provide positional information for brain development, the article by Sharpe (2019, DOI: 10.1242/dev.185967) is interesting. For nuances on the gene-centric approach of the paper the articles by Rockmann (2012, DOI: 10.1111/j.1558-5646.2011.01486.x) but also from the ENCODE consortium showing the importance of non-genic regions of the genome ("Perspectives on ENCODE" 2020 DOI: 10.1038/s41586-021-04213-8) could be considered. I wouldn't ask to cite ideas from the extended evolutionary synthesis about different inheritance systems (as reviewed by Jablonka & Lamb, DOI: 10.1017/9781108685412) or the idea of inherency (Newman 2017, DOI: 10.1007/978-3-319-33038-9_78-1), but the authors may find them interesting. Same goes for our own work on mechanical morphogenesis which expands on the idea of a downward causality (Heuer and Toro 2019, DOI: 10.1016/j.plrev.2019.01.012)

Response 26

We thank the reviewer for recommending these papers, which we enjoyed reading and have deepened our thinking on the topic. In addition to toning down some of the language with respect to causality that our data cannot directly address, we have included additional discussion and references as follows:

In Discussion: "By establishing that some of these cortical zones are evident at the time of cortical folding, we lend support to a “protomap”(Rakic 1988; O'Leary 1989; O'Leary et al. 2007; Rakic et al. 2009) like model where the placement of some cortical folds is set-up by rapid tangential changes in cyto-laminar composition of the developing cortex(Ronan et al., 2014; Toro and Burnod, 2005; Van Essen, 2020). The DEMs are derived from fully folded adult donors, and therefore some of the measured genetic-folding alignment might also be induced by mechanical distortion of the tissue during folding(Llinares-Benadero and Borrell 2019; Heuer and Toro 2019). However, no data currently exist to conclusively assess the directionality of this gene-folding relationship.

Overall, the manuscript is very interesting and a great contribution. The amount of work involved is impressive, and the presentation of the results very clear. My comments indicate some aspects that could be made more clear, for example, providing additional methodological information in the supplemental material. Also, making aware the readers and future users of MAGICC of the methodological and conceptual challenges that remain to be addressed in the future for this field of research.

Reviewer #2 (Recommendations For The Authors):

Comment 1

The supplementary figures seem to be missing from the eLife submission (although I was able to find them on europepmc)

Response 1

We apologize that these were not included in the documents sent to reviewers. The up-to-date supplementary figures are included in this resubmission and again on biorxiv.

Author Response

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

Reviewer #1

Strengths:

This study uses a carefully constructed experiment design and decision-making task that allows separation of multiple electroencephalographic (EEG) signals thought to track different stages of decision-making. For example, the steady-state visual evoked potential measures can be cleanly dissociated from more anterior beta-band activity over the motor cortex. They also allow evaluation of how cued expectancy effects may unfold over a number of testing sessions. This is important because the most consistent evidence of expectation-related modulations of electrophysiological measures (using EEG, local field potentials, or single neuron firing rates) is from studies of nonhuman primates that involved many days of cue-stimulus contingency learning, and there is a lack of similar work using several testing sessions in humans. Although there were several experimental conditions included in the study, careful trial-balancing was conducted to minimise biases due to incidental differences in the number of trials included for analyses across each condition. Performance for each individual was also carefully calibrated to maximise the possibility of identifying subtle changes in task performance by expectation and avoid floor or ceiling effects.

We would like to thank Reviewer 1 for these very positive comments.

Weaknesses:

Although the experiment and analysis methods are cohesive and well-designed, there are some shortcomings that limit the inferences that can be drawn from the presented findings.

Comment #1

The first relates to the measures of SSVEPs and their relevance for decision-making in the task. In order to eliminate the influence of sporadic pulses of contrast changes that occurred during stimulus presentation, a time window of 680-975 ms post-stimulus onset was used to measure the SSVEPs. The mean response times for the valid and neutral cues were around 850-900 ms for correct responses, and within the same time window for errors in the invalid cue condition. In addition, a large portion of response times in perceptual decision-making tasks are substantially faster than the mean due to right-skewed response time distributions that are typically observed. As it has also been estimated to require 70-100 ms to execute a motor action (e.g., a keypress response) following the commitment to a decision. This raises some concerns about the proportion of trials in which the contrast-dependent visual responses (indexed by the SSVEPs) indexed visual input that was actually used to make the decision in a given trial. Additional analyses of SSVEPs that take the trial-varying pulses into account could be run to determine whether expectations influenced visual responses earlier in the trial.

The reviewer raises a very valid point and, indeed, it is an issue that we grappled with in our analyses. Actually, in this study, the RT distributions were not right-skewed, but appear to be relatively normal (RT distributions shown below). This is something that we have previously observed when using tasks that involve an initial zero-evidence lead in at the start of each trial which means that participants cannot start accumulating at stimulus onset and must rely on their knowledge of the lead-in duration to determine when the physical evidence has become available (e.g. Kelly et al 2021, Nat Hum Beh). We agree that it is important to establish whether the reported SSVEP modulations occur before or after choice commitment. In our original submission we had sought to address this question through our analysis of the response-locked ‘difference SSVEP’. Figure 4D clearly indicates that the cue modulations are evident before as well as after response.

However, we have decided to include an additional Bayesian analysis of the response-locked signal to offer more evidence that the cue effect is not a post-response phenomenon.

Manuscript Changes

To quantify the evidence that the cue effect was not driven by changes in the signal after the response, we ran Bayesian one-way ANOVAs on the SSVEP comparing the difference across cue conditions before and after the response. If the cue effect only emerged after the response, we would expect the difference between invalid and neutral or invalid and valid cues to increase in the post-response window. There was no compelling evidence of an increase in the effect when comparing invalid to neutral (BF10 = 1.58) or valid cues (BF10 = 0.32).

Comment #2

Presenting response time quantile plots may also help to determine the proportions of motor responses (used to report a decision) that occurred during or after the SSVEP measurement window.

We agree that it may be helpful for the reader to be able to determine the proportion of responses occurring at different phases of the trial, so we have included the requested response time quantile plot (shown below) as a supplementary figure.

Author response image 1.

Reaction time quantiles across cue conditions. The plot illustrates the proportion of trials where responses occurred at different stages of the trial. The SSVEP analysis window is highlighted in purple.

Comment #3

In addition, an argument is made for changes in the evidence accumulation rate (called the drift rate) by stimulus expectancy, corresponding to the observed changes in SSVEP measures and differences in the sensory encoding of the stimulus. This inference is limited by the fact that evidence accumulation models (such as the Diffusion Decision Model) were not used to test for drift rate changes as could be determined from the behavioural data (by modelling response time distributions). There appear to be ample numbers of trials per participant to test for drift rate changes in addition to the starting point bias captured in earlier models. Due to the very high number of trials, models could potentially be evaluated for each single participant. This would provide more direct evidence for drift rate changes than the findings based on the SSVEPs, particularly due to the issues with the measurement window relating to the response times as mentioned above.

The focus of the present study was on testing for sensory-level modulations by predictive cues, rather than testing any particular models. Given that the SSVEP bears all the characteristics of a sensory evidence encoding signal, we believe it is reasonable to point out that its modulation by the cues would very likely translate to a drift rate effect. But we do agree with the reviewer that any connection between our results and previously reported drift rate effects can only be confirmed with modelling and we have tried to make this clear in the revised text. We plan to comprehensively model the data from this study in a future project. While we do indeed have the benefit of plenty of trials, the modelling process will not be straightforward as it will require taking account of the pulse effects which could have potentially complicated, non-linear effects. In the meantime, we have made changes to the text to qualify the suggestion and stress that modelling would be necessary to determine if our hypothesis about a drift rate effect is correct.

Manuscript Changes

(Discussion): [...] We suggest that participants may have been able to stabilise their performance across task exposure, despite reductions in the available sensory evidence, by incorporating the small sensory modulation we detected in the SSVEP. This would suggest that the decision process may not operate precisely as the models used in theoretical work describe. Instead, our study tentatively supports a small number of modelling investigations that have challenged the solitary role of starting point bias, implicating a drift bias (i.e. a modulation of the evidence before or upon entry to the decision variable) as an additional source of prior probability effects in perceptual decisions (Dunovan et al., 2014; Hanks et al., 2011; Kelly et al., 2021; van Ravenzwaaij et al., 2012 Wyart et al., 2012) and indicates that these drift biases could, at least partly, originate at the sensory level. However, this link could only be firmly established with modelling in a future study.

Recommendations For The Authors:

Comment #4

The text for the axis labels and legends in the figures is quite small relative to the sizes of the accompanying plots. I would recommend to substantially increase the sizes of the text to aid readability.

Thank you for this suggestion. We have increased the size of the axis labels and made the text in the figure legends just 1pt smaller than the text in the main body of the manuscript.

Comment #5

It is unclear if the scalp maps for Figure 5 (showing the mu/beta distributions) are on the same scale or different scales. I assume they are on different scales (adjusted to the minimum/maximum within each colour map range), as a lack of consistent signals (in the neutral condition) would be expected to lead to a patchy pattern on the scalp as displayed in that figure (due to the colour range shrinking to the degree of noise across electrodes). I would recommend to include some sort of colour scale to show that, for example, in the neutral condition there are no large-amplitude mu/ beta fluctuations distributed somewhat randomly across the scalp.

Thank you to the reviewer for pointing this out. They were correct, the original topographies were plotted according to their own scale. The topographies in Figure 5 have now been updated to put them on a common scale and we have included a colour bar (as shown below). The caption for Figure 5 has also been updated to confirm that the topos are on a common scale.

Author response image 2.

Manuscript Changes

(Figure 5 Caption): [...] The topography of MB activity in the window - 200:0 ms before evidence onset is plotted on a common scale for neutral and cued conditions separately.

Comment #6

In Figure 2, the legend is split across the two panels, despite the valid/invalid/neutral legend also applying to the first panel. This gives an initial impression that the legend is incomplete for the first panel, which may confuse readers. I would suggest putting all of the legend entries in the first panel, so that all of this information is available to readers at once.

We are grateful to the reviewer for spotting this. Figure 2 has been updated so that the full legend is presented in the first panel, as shown below.

Author response image 3.

Comment #7

Although linear mixed-effects models (using Gaussian families) for response times are standard in the literature, they incorrectly specify the distributions of response times to be Gaussian instead of substantially right-skewed. Generalised linear mixed-effects models using gamma families and identity functions have been shown to more accurately model distributions of response times (see Lo and Andrews, 2015. Frontiers in Psychology). The authors may consider using these models in line with good practice, although it might not make a substantial difference relating to the patterns of response time differences.

We appreciate this thoughtful comment from Reviewer 1. Although RT distributions are often right skewed, we have previously observed that RT distributions can be closer to normal when the trial incorporates a lead-in phase with no evidence (e.g. Kelly et al 2021, Nat Hum Beh). Indeed, the distributions we observed in this study were markedly Gaussian (as shown in the plot below). Given the shape of these distributions and the reviewer’s suggestion that adopting alternative models may not lead to substantial differences to our results, we have decided to leave the mixed effects models as they are in the manuscript, but we will take note of this advice in future work.

Author response image 4.

Reviewer #2

Strengths:

The work is executed expertly and focuses cleverly on two features of the EEG signals that can be closely connected to specific loci of the perceptual decision-making process - the SSVEP which connects closely to sensory (visual) encoding, and Mu-Beta lateralisation which connects closely to movement preparation. This is a very appropriate design choice given the authors' research question.

Another advantage of the design is the use of an unusually long training regime (i.e., for humans) - which makes it possible to probe the emergence of different expectation biases in the brain over different timecourses, and in a way that may be more comparable to work with nonhuman animals (who are routinely trained for much longer than humans).

We are very grateful for these positive comments from Reviewer 2.

Weaknesses:

In my view, the principal shortcoming of this study is that the experimental task confounds expectations about stimulus identity with expectations about to-be-performed responses. That is, cues in the task don't just tell participants what they will (probably) see, but what they (probably) should do.

In many respects, this feature of the paradigm might seem inevitable, as if specific stimuli are not connected to specific responses, it is not possible to observe motor preparation of this kind (e.g., de Lange, Rahnev, Donner & Lau, 2013 - JoN).

However, the theoretical models that the authors focus on (e.g., drift-diffusion models) are models of decision (i.e., commitment to a proposition about the world) as much as they are models of choice (i.e., commitment to action). Expectation researchers interested in these models are often interested in asking whether predictions influence perceptual processing, perceptual decision, and/ or response selection stages (e.g., Feuerriegel, Blom & Hoogendorn, 2021 - Cortex), and other researchers have shown that parameters like drift bias and start point bias can be shifted in paradigms where observers cannot possibly prepare a response (e.g., Thomas, Yon, de Lange & Press, 2020 - Psych Sci).

The present paradigm used by Walsh et al makes it possible to disentangle sensory processing from later decisional processes, but it blurs together the processes of deciding about the stimulus and choosing/initiating the response. This ultimately limits the insights we can draw from this study - as it remains unclear whether rapid changes in motor preparation we see reflect rapid acquisition of new decision criterion or simple cue-action learning. I think this would be important for comprehensively testing the models the authors target - and a good avenue for future work.

Thank you to Reviewer 2 for these observations. We adopted this paradigm because it is typical of the perceptual decision making literature and our central focus in this study was to test for a sensory-level modulation as a source of a decision bias. We are pleased that the Reviewer agrees that the paradigm successfully disentangles sensory encoding from later decisional processes since this was our priority. However, we agree with Reviewer 2 that because the response mapping was known to the participants, the cues predicted both the outcome of the perceptual decision (“Is this a left- or right-tilted grating?”) and the motor response that the participant should anticipate making (“It’s probably going to be a left click on this trial”). They are correct that this makes it difficult to know whether the changes in motor preparation elicited by the predictive cues reflect action-specific preparation or a more general shift in the boundaries associated with the alternate perceptual interpretations. We fully agree that it remains an interesting and important question and in our future work we hope to conduct investigations that better dissect the distinct components of the decision process during prior-informed decisions. In the interim, we have made some changes to the manuscript to reflect the Reviewer’s concerns and better address this limitation of the study design (these are detailed in the response to the comment below).

Recommendations For The Authors:

Comment #8

As in my public review, my main recommendation to the authors is to think a bit more in the presentation of the Introduction and Discussion about the difference between 'perceiving', 'deciding', and 'responding'.

The paper is presently framed in terms of the debates around whether expectations bias decision or bias perception - and these debates are in turn mapped onto different aspects of the driftdiffusion model. Biases in sensory gain, for instance, are connected to biases in the drift rate parameter, while decisional shifts are connected to parameters like start points.

In line with this kind of typology, the authors map their particular EEG signals (SSVEP and MB lateralisation) onto perception and decision. I see the logic, but I think the reality of these models is more nuanced.

In particular, strictly speaking, the process of evidence accumulation to bound is the formation of a 'decision' (i.e., a commitment to having seen a particular stimulus). Indeed, the dynamics of this process have been beautifully described by other authors on this paper in the past. Since observers in this task simultaneously form decisions and prepare actions (because stimuli and responses are confounded) it is unclear whether changes in motor preparation are reflecting changes in what perceivers 'decide' (i.e., changes in what crosses the decision threshold) or what they 'do' (i.e., changes in the motor response threshold). This is particularly important for the debate around whether expectations change 'perception' or 'decision' because - in some accounts - is the accumulation of evidence to the bound that is hypothesised to cause the perceptual experience observers actually have (Pereira, Perrin & Faivre, 2022 - TiCS). The relevant 'bound' here though is not the bound to push the button, but the bound for the brain to decide what one is actually 'seeing'.

I completely understand the logic behind the authors' choices, but I would have liked more discussion of this issue. In particular, it seems strange to me to talk about the confounding of stimuli and responses as a particular 'strength' of this design in the manuscript - when really it is a 'necessary evil' for getting the motor preparation components to work. Here is one example from the Introduction:

"While some have reported expectation effects in humans using EEG/MEG, these studies either measured sensory signals whose relevance to the decision process is uncertain (e.g. Blom et al., 2020; Solomon et al., 2021; Tang et al., 2018) and/or used cues that were implicit or predicted a forthcoming stimulus but not the correct choice alternative (e.g. Aitken et al., 2020; Feuerriegel et al., 2021b; Kok et al., 2017). To assess whether prior probabilities modulate sensory-level signals directly related to participants' perceptual decisions, we implemented a contrast discrimination task in which the cues explicitly predicted the correct choice and where sensory signals that selectively trace the evidence feeding the decision process could be measured during the process of deliberation."

I would contend that this design allows you to pinpoint signals related to participant's 'choices' or 'actions' but not necessarily their 'decisions' in the sense outlined above.

As I say though, I don't think this is fatal and I think the paper is extremely interesting in any case. But I think it would be strengthened if some of these nuances were discussed a bit more explicitly, as a 'perceptual decision' is more than pushing a button. Indeed, the authors might want to consider discussing work that shows the neural overlap between deciding and acting breaks down when Ps cannot anticipate which actions to use to report their choices ahead of time (Filimon, Philiastides, Nelson, Kloosterman & Heekeren, 2013 - JoN) and/or work which has combined expectations with drift diffusion modelling to show how expectations change drift bias (Yon, Zainzinger, de Lange, Eimer & Press, 2020 - JEP:General) and/or start bias (Thomas, Yon, de Lange & Press, 2020 - Psych Sci) even when Ps cannot prepare a motor response ahead of time.

While our focus was on testing for sensory-level modulations, we think the question of whether the motor-level effects we observed are attributable to the task design or represents a more general perceptual bound adjustment is an important question for future research. In our previous work, we have examined this distinction between abstract, movement-independent evidence accumulation (indexed by the centro-parietal positivity, CPP) and response preparation in detail. The CPP has been shown to trace evidence accumulation irrespective of whether the sensory alternatives are associated with a specific response or not (Twomey et al 2016, J Neurosci). When speed pressure is manipulated in tasks with fixed stimulus-response mappings we have found that the CPP undergoes systematic adjustments in its pre-response amplitude that closely accord with the starting-level modulations observed in mu/beta, suggesting that motor-level adjustments do still translate to differences at the perceptual level under these task conditions (e.g. Kelly et al 2021, Nat Hum Beh; Steinemann et al., 2018, Nat Comms). We have also observed that the CPP and mu-beta exhibit corresponding adjustments in response to predictive cues (Kelly et al., 2021) that are consistent with both a starting-point shift and drift rate bias. However, the Kelly et al. study did not include a signature of sensory encoding and therefore could not test for sensory-level modulations.

We have added some remarks to the discussion to acknowledge this issue with the interpretation of the preparatory shifts in mu-beta activity we observed when the predictive cues were presented, and we have included references to the papers that the reviewer helpfully provided. We have also offered some additional consideration of the features of the task design that may have influenced the SSVEP results.

Manuscript Changes

An implication of using cues that predict not just the upcoming stimulus, but the most likely response, is that it becomes difficult to determine if preparatory shifts in mu-beta (MB) activity that we observed reflect adjustments directly influencing the perceptual interpretation of the stimulus or simply preparation of the more probable action. When perceptual decisions are explicitly tied to particular modes of response, the decision state can be read from activity in motor regions associated with the preparation of that kind of action (e.g. de Lafuente et al., 2015; Ding & Gold, 2012; Shadlen & Newsome, 2001; Romo et al., 2004), but these modules appear to be part of a constellation of decision-related areas that are flexibly recruited based on the response modality (e.g. Filimon et al., 2013). When the response mapping is withheld or no response is required, MB no longer traces decision formation (Twomey et al., 2015), but an abstract decision process is still readily detectable (e.g. O’Connell et al., 2012), and modelling work suggests that drift biases and starting point biases (Thomas et al., 2020; Yon et al., 2021) continue to influence prior-informed decision making. While the design of the present study does not allow us to offer further insight about whether the MB effects we observed were inherited from strategic adjustments at this abstract level of the decision process, we hope to conduct investigations in the future that better dissect the distinct components of prior-informed decisions to address this question.

Several other issues remain unaddressed by the present study. One, is that it is not clear to what extent the sensory effects may be influenced by features of the task design (e.g. speeded responses under a strict deadline) and if these sensory effects would generalise to many kinds of perceptual decision-making tasks or whether they are particular to contrast discrimination.

Comment #9

On a smaller, unrelated point - I thought the discussion in the Discussion section about expectation suppression was interesting, but I did not think it was completely logically sound. The authors suggest that they may see relative suppression (rather than enhancement) of their marginal SSVEP under a 'sharpening' account because these accounts suggest that there is a relative suppression of off-channel sensory units, and there are more off-channel sensory units than onchannel sensory units (i.e., there are usually more possibilities we don't expect than possibilities that we do, and suppressing the things we don't expect should therefore yield overall suppression).

However, this strikes me as a non-sequitur given that the marginal SSVEP only reflects featurespecific visual activity (i.e., activity tuned to one of the two grating stimuli used). The idea that there are more off-channel than on-channel units makes sense for explaining why we would see overall signal drops on expected trials e.g., in an entire visual ROI in an fMRI experiment. But surely this explanation cannot hold in this case, as there is presumably an equal number of units tuned to each particular grating?

My sense is that this possibility should probably be removed from the manuscript - and I suspect it is more likely that the absence of a difference in marginal SSVEP for Valid vs Neutral trials has more to do with the fact that participants appear to be especially attentive on Neutral trials (and so any relative enhancement of feature-specific activity for expected events is hard to detect against a baseline of generally high-precision sensory evidence on these highly attentive, neutral trials).

We thank the reviewer for flagging that we did not clearly articulate our thoughts in this section of the manuscript. Our primary purpose in mentioning this sharpening account was simply to point out that, where at first blush our results seem to conflict with expectation suppression effects in the fMRI literature, the sharpening account provides an explanation that can reconcile them. In the case of BOLD data, the sharpening account proposes that on-channel sensory units are boosted and off-channel units are suppressed and, due to the latter being more prevalent, this leads to an overall suppression of the global signal. In the case of the SSVEP, the signal isolates just the onunits and so the sharpening account would predict that when there is a valid cue, the SSVEP signal associated with the high-contrast, expected stimulus should be boosted and the SSVEP signal associated with the low-contrast, unexpected stimulus should be weakened; this would result in a larger difference between these signals and therefore, a larger ‘marginal SSVEP’. Conversely, when there is an invalid cue, the SSVEP signal associated with the, now unexpected, high-contrast stimulus should be relatively weakened and the SSVEP signal associated with the expected, but low-contrast stimulus should be relatively boosted; this would result in a smaller difference between these signals and therefore, a lower amplitude marginal SSVEP. We do not think that this account needs to make reference to any channels beyond those feature-specific channels driving the two SSVEP signals. Again our central point is simply that the sharpening account offers a means of reconciling our SSVEP findings with expectation suppression effects previously reported in the fMRI literature.

We suspect that this was not adequately explained in the discussion. We have adjusted the way this section is phrased to make it clear that we are not invoking off-channel activity to explain the SSVEP effect we observed and we thank the Reviewer for pointing out that this was unclear in the original text.

Manuscript Changes

An alternative account for expectation suppression effects, which is consistent with our SSVEP results, is that they arise, not from a suppression of expected activity, but from a ‘sharpening’ effect whereby the response of neurons that are tuned to the expected feature are enhanced while the responses of neurons tuned to unexpected features are suppressed (de Lange et al., 2018). On this account, the expectation suppression commonly reported in fMRI studies arises because voxels contain intermingled populations with diverse stimulus preferences and the populations tuned to the unexpected features outnumber those tuned to the expected feature. In contrast to these fMRI data, the SSVEP represents the activity of sensory units driven at the same frequency as the stimulus, and thus better isolates the feature-specific populations encoding the task-relevant sensory evidence. Therefore, according to the sharpening account, an invalid cue would have enhanced the SSVEP signal associated with the low contrast grating and weakened the SSVEP signal associated with the high contrast grating. As this would result in a smaller difference between these signals, and therefore, a lower amplitude marginal SSVEP compared to the neutral cue condition, this could explain the effect we observed. 

Reviewer #3

Observers make judgements about expected stimuli faster and more accurately. How expectations facilitate such perceptual decisions remains an ongoing area of investigation, however, as expectations may exert their effects in multiple ways. Expectations may directly influence the encoding of sensory signals. Alternatively (or additionally), expectations may influence later stages of decision-making, such as motor preparation, when they bear on the appropriate behavioral response.

In the present study, Walsh and colleagues directly measured the effect of expectations on sensory and motor signals by making clever use of the encephalogram (EEG) recorded from human observers performing a contrast discrimination task. On each trial, a predictive cue indicated which of two superimposed stimuli would likely be higher contrast and, therefore, whether a left or right button press was likely to yield a correct response. Deft design choices allowed the authors to extract both contrast-dependent sensory signals and motor preparation signals from the EEG. The authors provide compelling evidence that, when predictive cues provide information about both a forthcoming stimulus and the appropriate behavioral response, expectation effects are immediately manifest in motor preparation signals and only emerge in sensory signals after extensive training.

Future work should attempt to reconcile these results with related investigations in the field. As the authors note, several groups have reported expectation-induced modulation of sensory signals (using both fMRI and EEG/MEG) on shorter timescales (e.g. just one or two sessions of a few hundred trials, versus the intensive multi-session study reported here). One interesting possibility is that perceptual expectations are not automatic but demand the deployment of feature-based attention, while motor preparation is comparatively less effortful and so dominates when both sources of information are available, as in the present study. This hypothesis is consistent with the authors' thoughtful analysis showing decreased neural signatures of attention over posterior electrodes following predictive cues. Therefore, observing the timescale of sensory effects using the same design and methods (facilitating direct comparison with the present work), but altering task demands slightly such that cues are no longer predictive of the appropriate behavioral response, could be illuminating.

We would like to thank Reviewer 3 for their positive comments and thoughtful suggestions for future work.

Recommendations For The Authors:

Comment #10

In the methods, the term 'session' is used early on but only fleshed out at the end of the 'Procedure' subsection and never entirely explained (e.g., did sessions take place over multiple days?). A brief sentence laying this out early on, perhaps in 'Participants' after the (impressive) trial counts are reported, might be helpful.

Thank you to Reviewer 3 for pointing out that this was not clear in the original draft. We have amended the text in the Methods section to better explain the relationship between sessions, days, and trial bins.

Manuscript Changes

(Methods - Participants): [...] All procedures were approved by the Trinity College Dublin School of Psychology Ethics Committee and were in accordance with the Declaration of Helsinki. Participants completed between 4 and 6 testing sessions, each on a different day. While the sample size was small, on average, participants completed 5750 (SD = 1066) trials each.

(Methods - Data Analysis): [...] As there were two lengths of testing session and participants completed different numbers of sessions, we analysed the effect of task exposure by pooling trials within-subjects and dividing them into five ‘trial bins’. The first bin represents the participants’ earliest exposure to the task and the final bin represents trials at the end of their participation, when they had had substantial task exposure. All trials with valid responses and reaction times greater than 100 ms were included in the analyses of behavioural data and the SSVEP.

Comment #11

On a related note: participants completed a variable number of trials/sessions. To facilitate comparison across subjects, training effects are reported by dividing each subject's data into 5 exposure bins. This is entirely reasonable but does leave the reader wondering about whether you found any effects of rest or sleep between sessions.

We agree with the reviewer that this is an interesting question that absolutely merits further investigation. As different participants completed different numbers of sessions, different session lengths, and had variable gaps between their sessions, we do not think a per-session analysis would be informative. We think it may be better addressed in a future study, perhaps one with a larger sample where we could collect data specifically about sleep and more systematically control the intervals between testing sessions.

Comment #12

Fig 2B: the 'correct' and 'neutral' labels in the legend are switched

Thank you to the reviewer for spotting that error, the labels in Figure 2 have been corrected.

Comment #13

Fig 4B: it's a bit difficult to distinguish which lines are 'thick' and 'thin'

We have updated Figure 4.B to increase the difference in line thickness between the thick and thin lines (as shown below).

Author response image 5.

Comment #14

Fig 4C: missing (I believe?) the vertical lines indicating median reaction time

We have updated Figure 4.C to include the median reaction times.

Author response image 6.

Author response:

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

eLife Assessment

This important work presents a new methodology for the statistical analysis of fiber photometry data, improving statistical power while avoiding the bias inherent in the choices that are necessarily made when summarizing photometry data. The reanalysis of two recent photometry data sets, the simulations, and the mathematical detail provide convincing evidence for the utility of the method and the main conclusions, however, the discussion of the re-analyzed data is incomplete and would be improved by a deeper consideration of the limitations of the original data. In addition, consideration of other data sets and photometry methodologies including non-linear analysis tools, as well as a discussion of the importance of the data normalization are needed.

Thank you for reviewing our manuscript and giving us the opportunity to respond and improve our paper. In our revision, we have strived to address the points raised in the comments, and implement suggested changes where feasible. We have also improved our package and created an analysis guide (available on our Github - https://github.com/gloewing/fastFMM and https://github.com/gloewing/photometry_fGLMM), showing users how to apply our methods and interpret their results. Below, we provide a detailed point-by-point response to the reviewers.

Reviewer #1:

Summary:

Fiber photometry has become a very popular tool in recording neuronal activity in freely behaving animals. Despite the number of papers published with the method, as the authors rightly note, there are currently no standardized ways to analyze the data produced. Moreover, most of the data analyses confine to simple measurements of averaged activity and by doing so, erase valuable information encoded in the data. The authors offer an approach based on functional linear mixed modeling, where beyond changes in overall activity various functions of the data can also be analyzed. More in-depth analysis, more variables taken into account, and better statistical power all lead to higher quality science.

Strengths:

The framework the authors present is solid and well-explained. By reanalyzing formerly published data, the authors also further increase the significance of the proposed tool opening new avenues for reinterpreting already collected data.

Thank you for your favorable and detailed description of our work!

Weaknesses:

However, this also leads to several questions. The normalization method employed for raw fiber photometry data is different from lab to lab. This imposes a significant challenge to applying a single tool of analysis.

Thank you for these important suggestions. We agree that many data pre-processing steps will influence the statistical inference from our method. Note, though, that this would also be the case with standard analysis approaches (e.g., t-tests, correlations) applied to summary measures like AUCs. For that reason, we do not believe that variability in pre-processing is an impediment to widespread adoption of a standard analysis procedure. Rather, we would argue that the sensitivity of analysis results to pre-processing choices should motivate the development of statistical techniques that reduce the need for pre-processing, and properly account for structure in the data arising from experimental designs. For example, even without many standard pre-processing steps, FLMM provides smooth estimation results across trial timepoints (i.e., the “functional domain”), has the ability to adjust for betweentrial and -animal heterogeneity, and provides a valid statistical inference framework that quantifies the resulting uncertainty. We appreciate the reviewer’s suggestion to emphasize and further elaborate on our method from this perspective. We have now included the following in the Discussion section:

“FLMM can help model signal components unrelated to the scientific question of interest, and provides a systematic framework to quantify the additional uncertainty from those modeling choices. For example, analysts sometimes normalize data with trial-specific baselines because longitudinal experiments can induce correlation patterns across trials that standard techniques (e.g., repeated measures ANOVA) may not adequately account for. Even without many standard data pre-processing steps, FLMM provides smooth estimation results across trial time-points (the “functional domain”), has the ability to adjust for between-trial and -animal heterogeneity, and provides a valid statistical inference approach that quantifies the resulting uncertainty. For instance, session-to-session variability in signal magnitudes or dynamics (e.g., a decreasing baseline within-session from bleaching or satiation) could be accounted for, at least in part, through the inclusion of trial-level fixed or random effects. Similarly, signal heterogeneity due to subject characteristics (e.g., sex, CS+ cue identity) could be incorporated into a model through inclusion of animal-specific random effects. Inclusion of these effects would then influence the width of the confidence intervals. By expressing one’s “beliefs” in an FLMM model specification, one can compare models (e.g., with AIC). Even the level of smoothing in FLMM is largely selected as a function of the data, and is accounted for directly in the equations used to construct confidence intervals. This stands in contrast to “trying to clean up the data” with a pre-processing step that may have an unknown impact on the final statistical inferences.”

Does the method that the authors propose work similarly efficiently whether the data are normalized in a running average dF/F as it is described in the cited papers? For example, trace smoothing using running averages (Jeong et al. 2022) in itself may lead to pattern dilution.

By modeling trial signals as “functions”, the method accounts for and exploits correlation across trial timepoints and, as such, any pre-smoothing of the signals should not negatively affect the validity of the 95% CI coverage. It will, however, change inferential results and the interpretation of the data, but this is not unique to FLMM, or many other statistical procedures.

The same question applies if the z-score is calculated based on various responses or even baselines. How reliable the method is if the data are non-stationery and the baselines undergo major changes between separate trials?

Adjustment for trial-to-trial variability in signal magnitudes or dynamics could be accounted for, at least in part, through the inclusion of trial-level random effects. This heterogeneity would then influence the width of the confidence intervals, directly conveying the effect of the variability on the conclusions being drawn from the data. This stands in contrast to “trying to clean up the data” with a pre-processing step that may have an unknown impact on the final statistical inferences. Indeed, non-stationarity (e.g., a decreasing baseline within-session) due to, for example, measurement artifacts (e.g., bleaching) or behavioral causes (e.g., satiation, learning) should, if possible, be accounted for in the model. As mentioned above, one can often achieve the same goals that motivate pre-processing steps by instead applying specific FLMM models (e.g., that include trial-specific intercepts to reflect changes in baseline) to the unprocessed data. One can then compare model criteria in an objective fashion (e.g., with AIC) and quantify the uncertainty associated with those modeling choices. Even the level of smoothing in FLMM is largely selected as a function of the data, and is accounted for directly in the equations used to construct confidence intervals. In sum, our method provides both a tool to account for challenges in the data, and a systematic framework to quantify the additional uncertainty that accompanies accounting for those data characteristics.

Finally, what is the rationale for not using non-linear analysis methods? Following the paper’s logic, non-linear analysis can capture more information that is diluted by linear methods.

This is a good question that we imagine many readers will be curious about as well. We have added in notes to the Discussion and Methods Section 4.3 to address this (copied below). We thank the reviewer for raising this point, as your feedback also motivated us to discuss this point in Part 5 of our Analysis Guide.

Methods

“FLMM models each trial’s signal as a function that varies smoothly across trial time-points (i.e., along the “functional domain”). It is thus a type of non-linear modeling technique over the functional domain, since we do not assume a linear model (straight line). FLMM and other functional data analysis methods model data as functions, when there is a natural ordering (e.g., time-series data are ordered by time, imaging data are ordered by x-y coordinates), and are assumed to vary smoothly along the functional domain (e.g., one assumes values of a photometry signal at close time-points in a trial have similar values). Functional data analysis approaches exploit this smoothness and natural ordering to capture more information during estimation and inference.”

Discussion

“In this paper, we specified FLMM models with linear covariate–signal relationships at a fixed trial time-point across trials/sessions, to compare the FLMM analogue of the analyses conducted in (Jeong et al., 2022). However, our package allows modeling of covariate–signal relationships with non-linear functions of covariates, using splines or other basis functions. One must consider, however, the tradeoff between flexibility and interpretability when specifying potentially complex models, especially since FLMM is designed for statistical inference.”

Reviewer #2:

Summary:

This work describes a statistical framework that combines functional linear mixed modeling with joint 95% confidence intervals, which improves statistical power and provides less conservative statistical inferences than in previous studies. As recently reviewed by Simpson et al. (2023), linear regression analysis has been used extensively to analyze time series signals from a wide range of neuroscience recording techniques, with recent studies applying them to photometry data. The novelty of this study lies in 1) the introduction of joint 95% confidence intervals for statistical testing of functional mixed models with nested random-effects, and 2) providing an open-source R package implementing this framework. This study also highlights how summary statistics as opposed to trial-by-trial analysis can obscure or even change the direction of statistical results by reanalyzing two other studies.

Strengths:

The open-source package in R using a similar syntax as the lme4 package for the implementation of this framework on photometry data enhances the accessibility, and usage by other researchers. Moreover, the decreased fitting time of the model in comparison with a similar package on simulated data, has the potential to be more easily adopted.

The reanalysis of two studies using summary statistics on photometry data (Jeong et al., 2022; Coddington et al., 2023) highlights how trial-by-trial analysis at each time-point on the trial can reveal information obscured by averaging across trials. Furthermore, this work also exemplifies how session and subject variability can lead to opposite conclusions when not considered.

We appreciate the in-depth description of our work and, in particular, the R package. This is an area where we put a lot of effort, since our group is very concerned with the practical experience of users.

Weaknesses:

Although this work has reanalyzed previous work that used summary statistics, it does not compare with other studies that use trial-by-trial photometry data across time-points in a trial. As described by the authors, fitting pointwise linear mixed models and performing t-test and BenjaminiHochberg correction as performed in Lee et al. (2019) has some caveats. Using joint confidence intervals has the potential to improve statistical robustness, however, this is not directly shown with temporal data in this work. Furthermore, it is unclear how FLMM differs from the pointwise linear mixed modeling used in this work.

Thank you for making this important point. We agree that this offers an opportunity to showcase the advantages of FLMM over non-functional data analysis methods, such as the approach applied in Lee et al. (2019). As mentioned in the text, fitting entirely separate models at each trial timepoint (without smoothing regression coefficient point and variance estimates across timepoints), and applying multiple comparisons corrections as a function of the number of time points has substantial conceptual drawbacks. To see why, consider that applying this strategy with two different sub-sampling rates requires adjustment for different numbers of comparisons, and could thus lead to very different proportions of timepoints achieving statistical significance. In light of your comments, we decided that it would be useful to provide a demonstration of this. To that effect, we have added Appendix Section 2 comparing FLMM with the method in Lee et al. (2019) on a real dataset, and show that FLMM yields far less conservative and more stable inference across different sub-sampling rates. We conducted this comparison on the delay-length experiment (shown in Figure 6) data, sub-sampled at evenly spaced intervals at a range of sampling rates. We fit either a collection of separate linear mixed models (LMM) followed by a Benjamini–Hochberg (BH) correction, or FLMM with statistical significance determined with both Pointwise and Joint 95% CIs. As shown in Appendix Tables 1-2, the proportion of timepoints at which effects are statistically significant with FLMM Joint CIs is fairly stable across sampling rates. In contrast, the percentage is highly inconsistent with the BH approach and is often highly conservative. This illustrates a core advantage of functional data analysis methods: borrowing strength across trial timepoints (i.e., the functional domain), can improve estimation efficiency and lower sensitivity to how the data is sub-sampled. A multiple comparisons correction may, however, yield stable results if one first smooths both regression coefficient point and variance estimates. Because this includes smoothing the coefficient point and variance estimates, this approach would essentially constitute a functional mixed model estimation strategy that uses multiple comparisons correction instead of a joint CI. We have now added in a description of this experiment in Section 2.4 (copied below).

“We further analyze this dataset in Appendix Section 2, to compare FLMM with the approach applied in Lee et al. (2019) of fitting pointwise LMMs (without any smoothing) and applying a Benjamini–Hochberg (BH) correction. Our hypothesis was that the Lee et al. (2019) approach would yield substantially different analysis results, depending on the sampling rate of the signal data (since the number of tests being corrected for is determined by the sampling rate). The proportion of timepoints at which effects are deemed statistically significant by FLMM joint 95% CIs is fairly stable across sampling rates. In contrast, that proportion is both inconsistent and often low (i.e., highly conservative) across sampling rates with the Lee et al. (2019) approach. These results illustrate the advantages of modeling a trial signal as a function, and conducting estimation and inference in a manner that uses information across the entire trial.”

In this work, FLMM usages included only one or two covariates. However, in complex behavioral experiments, where variables are correlated, more than two may be needed (see Simpson et al. (2023), Engelhard et al. (2019); Blanco-Pozo et al. (2024)). It is not clear from this work, how feasible computationally would be to fit such complex models, which would also include more complex random effects.

Thank you for bringing this up, as we endeavored to create code that is able to scale to complex models and large datasets. We agree that highlighting this capability in the paper will strengthen the work. We now state in the Discussion section that “[T]he package is fast and maintains a low memory footprint even for complex models (see Section 4.6 for an example) and relatively large datasets.” Methods Section 4.6 now includes the following:

Our fastFMM package scales to the dataset sizes and model specifications common in photometry. The majority of the analyses presented in the Results Section (Section 2) included fairly simple functional fixed and random effect model specifications because we were implementing the FLMM versions of the summary measure analyses presented in Jeong et al. (2022). However, we fit the following FLMM to demonstrate the scalability of our method with more complex model specifications:

We use the same notation as the Reward Number model in Section 4.5.2, with the additional variable TL_i,j,l_ denoting the Total Licks on trial j of session l for animal i. In a dataset with over 3,200 total trials (pooled across animals), this model took ∼1.2 min to fit on a MacBook Pro with an Apple M1 Max chip with 64GB of RAM. Model fitting had a low memory footprint. This can be fit with the code:

model_fit = fui(photometry ~ session + trial + iri + lick_time + licks + (session + trial + iri + lick_time + licks | id), parallel = TRUE, data = photometry_data)

This provides a simple illustration of the scalability of our method. The code (including timing) for this demonstration is now included on our Github repository.

Reviewer #3:

Summary:

Loewinger et al., extend a previously described framework (Cui et al., 2021) to provide new methods for statistical analysis of fiber photometry data. The methodology combines functional regression with linear mixed models, allowing inference on complex study designs that are common in photometry studies. To demonstrate its utility, they reanalyze datasets from two recent fiber photometry studies into mesolimbic dopamine. Then, through simulation, they demonstrate the superiority of their approach compared to other common methods.

Strengths:

The statistical framework described provides a powerful way to analyze photometry data and potentially other similar signals. The provided package makes this methodology easy to implement and the extensively worked examples of reanalysis provide a useful guide to others on how to correctly specify models.

Modeling the entire trial (function regression) removes the need to choose appropriate summary statistics, removing the opportunity to introduce bias, for example in searching for optimal windows in which to calculate the AUC. This is demonstrated in the re-analysis of Jeong et al., 2022, in which the AUC measures presented masked important details about how the photometry signal was changing.

Meanwhile, using linear mixed methods allows for the estimation of random effects, which are an important consideration given the repeated-measures design of most photometry studies.

We would like to thank the reviewer for the deep reading and understanding of our paper and method, and the thoughtful feedback provided. We agree with this summary, and will respond in detail to all the concerns raised.

Weaknesses:

While the availability of the software package (fastFMM), the provided code, and worked examples used in the paper are undoubtedly helpful to those wanting to use these methods, some concepts could be explained more thoroughly for a general neuroscience audience.

Thank you for this point. While we went to great effort to explain things clearly, our efforts to be concise likely resulted in some lack of clarity. To address this, we have created a series of analysis guides for a more general neuroscience audience, reflecting our experience working with researchers at the NIH and the broader community. These guides walk users through the code, its deployment in typical scenarios, and the interpretation of results.

While the methodology is sound and the discussion of its benefits is good, the interpretation and discussion of the re-analyzed results are poor:

In section 2.3, the authors use FLMM to identify an instance of Simpson’s Paradox in the analysis of Jeong et al. (2022). While this phenomenon is evident in the original authors’ metrics (replotted in Figure 5A), FLMM provides a convenient method to identify these effects while illustrating the deficiencies of the original authors’ approach of concatenating a different number of sessions for each animal and ignoring potential within-session effects.

Our goal was to demonstrate that FLMM provides insight into why the opposing within- and between-session effects occur: the between-session and within-session changes appear to occur at different trial timepoints. Thus, while the AUC metrics applied in Jeong et al. (2022) are enough to show the presence of Simpson’s paradox, it is difficult to hypothesize why the opposing within-/between-session effects occur. An AUC analysis cannot determine at what trial timepoints (relative to licking) those opposing trends occur.

The discussion of this result is muddled. Having identified the paradox, there is some appropriate speculation as to what is causing these opposing effects, particularly the decrease in sessions. In the discussion and appendices, the authors identify (1) changes in satiation/habitation/motivation, (2) the predictability of the rewards (presumably by the click of a solenoid valve) and (3) photobleaching as potential explanations of the decrease within days. Having identified these effects, but without strong evidence to rule all three out, the discussion of whether RPE or ANCCR matches these results is probably moot. In particular, the hypotheses developed by Jeong et al., were for a random (unpredictable) rewards experiment, whereas the evidence points to the rewards being sometimes predictable. The learning of that predictability (e.g. over sessions) and variation in predictability (e.g. by attention level to sounds of each mouse) significantly complicate the analysis. The FLMM analysis reveals the complexity of analyzing what is apparently a straightforward task design.

While we are disappointed to hear the reviewer felt our initial interpretations and discussion were poor, the reviewer brings up an excellent point re: potential reward predictability that we had not considered. They have convinced us that acknowledging this alternative perspective will strengthen the paper, and we have added it into the Discussion. We agree that the ANCCR/RPE model predictions were made for unpredictable rewards and, as the reviewer rightly points out, there is evidence that the animals may sense the reward delivery. After discussing extensively with the authors of Jeong et al. (2022), it is clear that they went to enormous trouble to prevent the inadvertent generation of a CS+, and it is likely changes in pressure from the solenoid (rather than a sound) that may have served as a cue. Regardless of the learning theory one adopts (RPE, ANCCR or others), we agree that this potential learned predictability could, at least partially, account for the increase in signal magnitude across sessions. As this paper is focused on analysis methods, we feel that we can contribute most thoughtfully to the dopamine–learning theory conversation by presenting this explanation in detail, for consideration in future experiments. We have substantially edited this discussion and, as per the reviewer’s suggestion, have qualified our interpretations to reflect the uncertainty in explaining the observed trends.

If this paper is not trying to arbitrate between RPE and ANCCR, as stated in the text, the post hoc reasoning of the authors of Jeong et al 2022 provided in the discussion is not germane. Arbitrating between the models likely requires new experimental designs (removing the sound of the solenoid, satiety controls) or more complex models (e.g. with session effects, measures of predictability) that address the identified issues.

Thank you for this point. We agree with you that, given the scope of the paper, we should avoid any extensive comparison between the models. To address your comment, we have now removed portions of the Discussion that compared RPE and ANCCR. Overall, we agree with the reviewer, and think that future experiments will be needed for conclusively testing the accuracy of the models’ predictions for random (unpredicted) rewards. While we understand that our description of several conversations with the Jeong et al., 2022 authors could have gone deeper, we hope the reviewer can appreciate that inclusion of these conversations was done with the best of intentions. We wish to emphasize that we also consulted with several other researchers in the field when crafting our discussion. We do commend the authors of Jeong et al., 2022 for their willingness to discuss all these details. They could easily have avoided acknowledging any potential incompleteness of their theory by claiming that our results do not invalidate their predictions for a random reward, because the reward could potentially have been predicted (due to an inadvertent CS+ generated from the solenoid pressure). Instead, they emphasized that they thought their experiment did test a random reward, to the extent they could determine, and that our results suggest components of their theory that should be updated. We think that engagement with re-analyses of one’s data, even when findings are at odds with an initial theoretical framing, is a good demonstration of open science practice. For that reason as well, we feel providing readers with a perspective on the entire discussion will contribute to the scientific discourse in this area.

Finally, we would like to reiterate that this conversation is happening at least in part because of our method: by analyzing the signal at every trial timepoint, it provides a formal way to test for the presence of a neural signal indicative of reward delivery perception. Ultimately, this was what we set out to do: help researchers ask questions of their data that may have been harder to ask before. We believe that having a demonstration that we can indeed do this for a “live” scientific issue is the most appropriate way of demonstrating the usefulness of the method.

Of the three potential causes of within-session decreases, the photobleaching arguments advanced in the discussion and expanded greatly in the appendices are not convincing. The data being modeled is a processed signal (∆F/F) with smoothing and baseline correction and this does not seem to have been considered in the argument. Furthermore, the photometry readout is also a convolution of the actual concentration changes over time, influenced by the on-off kinetics of the sensor, which makes the interpretation of timing effects of photobleaching less obvious than presented here and more complex than the dyes considered in the cited reference used as a foundation for this line of reasoning.

We appreciate the nuance of this point, and we have made considerable efforts in the Results and Discussion sections to caution that alternative hypotheses (e.g., photobleaching) cannot be definitively ruled out. In response to your criticism, we have consulted with more experts in the field regarding the potential for bleaching in this data, and it is not clear to us why photobleaching would be visible in one time-window of a trial, but not at another (less than a second away), despite high ∆F/F magnitudes in both time-windows. We do wish to point out that the Jeong et al. (2022) authors were also concerned about photobleaching as a possible explanation. At their request, we analyzed data from additional experiments, collected from the same animals. In most cases, we did not observe signal patterns that seemed to indicate photobleaching. Given the additional scrutiny, we do not think that photobleaching is more likely to invalidate results in this particular set of experiments than it would be in any other photometry experiment. While the role of photobleaching may be more complicated with this sensor than others in the references, that citation was included primarily as a way of acknowledging that it is possible that non-linearities in photobleaching could occur. Regardless, your point is well taken and we have qualified our description of these analyses to express that photobleaching cannot be ruled out.

Within this discussion of photobleaching, the characterization of the background reward experiments used in part to consider photobleaching (appendix 7.3.2) is incorrect. In this experiment (Jeong et al., 2022), background rewards were only delivered in the inter-trial-interval (i.e. not between the CS+ and predicted reward as stated in the text). Both in the authors’ description and in the data, there is a 6s before cue onset where rewards are not delivered and while not described in the text, the data suggests there is a period after a predicted reward when background rewards are not delivered. This complicates the comparison of this data to the random reward experiment.

Thank you for pointing this out! We removed the parenthetical on page 18 of the appendix that incorrectly stated that rewards can occur between the CS+ and the predicted reward.

The discussion of the lack of evidence for backpropagation, taken as evidence for ANCCR over RPE, is also weak.

Our point was initially included to acknowledge that, although our method yields results that conflict with the conclusions described by Jeong et al., 2022 on data from some experiments, on other experiments our method supports their results. Again, we believe that a critical part of re-analyzing shared datasets is acknowledging both areas where new analyses support the original results, as well as those where they conflict with them. We agree with the reviewer that qualifying our results so as not to emphasize support for/against RPE/ANCCR will strengthen our paper, and we have made those changes. We have qualified the conclusions of our analysis to emphasize they are a demonstration of how FLMM can be used to answer a certain style of question with hypothesis testing (how signal dynamics change across sessions), as opposed to providing evidence for/against the backpropagation hypothesis.

A more useful exercise than comparing FLMM to the methods and data of Jeong et al., 2022, would be to compare against the approach of Amo et al., 2022, which identifies backpropagation (data publicly available: DOI: 10.5061/dryad.hhmgqnkjw). The replication of a positive result would be more convincing of the sensitivity of the methodology than the replication of a negative result, which could be a result of many factors in the experimental design. Given that the Amo et al. analysis relies on identifying systematic changes in the timing of a signal over time, this would be particularly useful in understanding if the smoothing steps in FLMM obscure such changes.

Thank you for this suggestion. Your thoughtful review has convinced us that focusing on our statistical contribution will strengthen the paper, and we made changes to further emphasize that we are not seeking to adjudicate between RPE/ANCCR. Given the length of the manuscript as it stands, we could only include a subset of the analyses conducted on Jeong et al., 2022, and had to relegate the results from the Coddington et al., data to an appendix. Realistically, it would be hard for us to justify including analyses from a third dataset, only to have to relegate them to an appendix. We did include numerous examples in our manuscript where we already replicated positive results, in a way that we believe demonstrates the sensitivity of the methodology. We have also been working with many groups at NIH and elsewhere using our approach, in experiments targeting different scientific questions. In fact, one paper that extensively applies our method, and compares the results with those yielded by standard analysis of AUCs, is already published (Beas et al., 2024). Finally, in our analysis guide we describe additional analyses, not included in the manuscript, that replicate positive results. Hence there are numerous demonstrations of FLMM’s performance in less controversial settings. We take your point that our description of the data supporting one theory or the other should be qualified, and we have corrected that. Specifically for your suggestion of Amo et al. 2022, we have not had the opportunity to personally reanalyze their data, but we are already in contact with other groups who have conducted preliminary analyses of their data with FLMM. We are delighted to see this, in light of your comments and our decision to restrict the scope of our paper. We will help them and other groups working on this question to the extent we can.

Recommendations for the Authors:

Reviewer #2:

First, I would like to commend the authors for the clarity of the paper, and for creating an open-source package that will help researchers more easily adopt this type of analysis.

Thank you for the positive feedback!

I would suggest the authors consider adding to the manuscript, either some evidence or some intuition on how feasible would be to use FLMM for very complex model specifications, in terms of computational cost and model convergence.

Thank you for this suggestion. As we described above in response to Reviewer #2’s Public Reviews, we have added in a demonstration of the scalability of the method. Since our initial manuscript submission, we have further increased the package’s speed (e.g., through further parallelization). We are releasing the updated version of our package on CRAN.

From my understanding, this package might potentially be useful not just for photometry data but also for two-photon recordings for example. If so, I would also suggest the authors add to the discussion this potential use.

This is a great point. Our updated manuscript Discussion includes the following:

“The FLMM framework may also be applicable to techniques like electrophysiology and calcium imaging. For example, our package can fit functional generalized LMMs with a count distribution (e.g., Poisson). Additionally, our method can be extended to model time-varying covariates. This would enable one to estimate how the level of association between signals, simultaneously recorded from different brain regions, fluctuates across trial time-points. This would also enable modeling of trials that differ in length due to, for example, variable behavioral response times (e.g., latency-topress).”

Reviewer #3:

The authors should define ’function’ in context, as well as provide greater detail of the alternate tests that FLMM is compared to in Figure 7.

We include a description of the alternate tests in Appendix Section 5.2. We have updated the Methods Section (Section 4) to introduce the reader to how ‘functions’ are conceptualized and modeled in the functional data analysis literature. Specifically, we added the following text:

“FLMM models each trial’s signal as a function that varies smoothly across trial time-points (i.e., along the “functional domain”). It is thus a type of non-linear modeling technique over the functional domain, since we do not assume a linear model (straight line). FLMM and other functional data analysis methods model data as functions, when there is a natural ordering (e.g., time-series data are ordered by time, imaging data are ordered by x-y coordinates), and are assumed to vary smoothly along the functional domain (e.g., one assumes values of a photometry signal at close time-points in a trial have similar values). Functional data analysis approaches exploit this smoothness and natural ordering to capture more information during estimation and inference.”

Given the novelty of estimating joint CIs, the authors should be clearer about how this should be reported and how this differs from pointwise CIs (and how this has been done in the past).

We appreciate your pointing this out, as the distinction is nuanced. Our manuscript includes a description of how joint CIs enable one to interpret effects as statistically significant for time-intervals as opposed to individual timepoints. Unlike joint CIs, assessing significance with pointwise CIs suffers from multiple-comparisons problems. As a result of your suggestion, we have included a short discussion of this to our analysis guide (Part 1), entitled “Pointwise or Joint 95% Confidence Intervals.” The Methods section of our manuscript also includes the following:

“The construction of joint CIs in the context of functional data analysis is an important research question; see Cui et al. (2021) and references therein. Each point at which the pointwise 95% CI does not contain 0 indicates that the coefficient is statistically significantly different from 0 at that point. Compared with pointwise CIs, joint CIs takes into account the autocorrelation of signal values across trial time-points (the functional domain). Therefore, instead of interpreting results at a specific timepoint, joint CIs enable joint interpretations at multiple locations along the functional domain. This aligns with interpreting covariate effects on the photometry signals across time-intervals (e.g., a cue period) as opposed to at a single trial time-point. Previous methodological work has provided functional mixed model implementations for either joint 95% CIs for simple random-effects models (Cui et al., 2021), or pointwise 95% CIs for nested models (Scheipl et al., 2016), but to our knowledge, do not provide explicit formulas or software for computing joint 95% CIs in the presence of general random-effects specifications.”

The authors identify that many photometry studies are complex nested longitudinal designs, using the cohort of 8 animals used in five task designs of Jeong et al. 2022 as an example. The authors miss the opportunity to illustrate how FLMM might be useful in identifying the effects of subject characteristics (e.g. sex, CS+ cue identity).

This is a fantastic point and we have added the following into the Discussion:

“...[S]ignal heterogeneity due to subject characteristics (e.g., sex, CS+ cue identity) could be incorporated into a model through inclusion of animal-specific random effects.”

In discussing the delay-length change experiment, it would be more accurate to say that proposed versions of RPE and ANCCR do not predict the specific change.

Good point. We have made this change.

Minor corrections:

Panels are mislabeled in Figure 5.

Thank you. We have corrected this.

The Crowder (2009) reference is incorrect, being a review of the book with the book presumably being the correct citation.

Good catch, thank you! Corrected.

In Section 5 (first appendix), the authors could include the alternate spelling ’fibre photometry’ to capture any citations that use British English spelling.

This is a great suggestion, but we did not have time to recreate these figures before re-submission.

Section 7.4 is almost all quotation, though unevenly using the block quotation formatting. It is unclear why such a large quotation is included.

Thank you for pointing this out. We have removed this Appendix section (formerly Section 7.4) as the relevant text was already included in the Methods section.

References

Sofia Beas, Isbah Khan, Claire Gao, Gabriel Loewinger, Emma Macdonald, Alison Bashford, Shakira Rodriguez-Gonzalez, Francisco Pereira, and Mario A Penzo. Dissociable encoding of motivated behavior by parallel thalamo-striatal projections. Current Biology, 34(7):1549–1560, 2024.

Erjia Cui, Andrew Leroux, Ekaterina Smirnova, and Ciprian Crainiceanu. Fast univariate inference for longitudinal functional models. Journal of Computational and Graphical Statistics, 31:1–27, 07 2021. doi: 10.1080/10618600.2021.1950006.

Huijeong Jeong, Annie Taylor, Joseph R Floeder, Martin Lohmann, Stefan Mihalas, Brenda Wu, Mingkang Zhou, Dennis A Burke, and Vijay Mohan K Namboodiri. Mesolimbic dopamine release conveys causal associations. Science, 378(6626):eabq6740, 2022. doi: 10.1126/science.abq6740. URL https://www. science.org/doi/abs/10.1126/science.abq6740.

Rachel S Lee, Marcelo G Mattar, Nathan F Parker, Ilana B Witten, and Nathaniel D Daw. Reward prediction error does not explain movement selectivity in dms-projecting dopamine neurons. eLife, 8:e42992, apr 2019. ISSN 2050-084X. doi: 10.7554/eLife.42992. URL https://doi.org/10.7554/eLife.42992.

Fabian Scheipl, Jan Gertheiss, and Sonja Greven. Generalized functional additive mixed models. Electronic Journal of Statistics, 10(1):1455 – 1492, 2016. doi: 10.1214/16-EJS1145. URL https://doi.org/10.1214/16-EJS1145.

Author Response

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

eLife assessment

This important study combines psychophysics, fMRI, and TMS to reveal a causal role of FEF in generating an attention-induced ocular dominance shift, with potential relevance for clinical applications. The evidence supporting the claims of the authors is solid, but the theoretical and mechanistic interpretation of results and experimental approaches need to be strengthened. The work will be of broad interest to perceptual and cognitive neuroscience.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Based on a "dichoptic-background-movie" paradigm that modulates ocular dominance, the present study combines fMRI and TMS to examine the role of the frontoparietal attentional network in ocular dominance shifts. The authors claimed a causal role of FEF in generating the attention-induced ocular dominance shift.

Strengths:

A combination of fMRI, TMS, and "dichoptic-background-movie" paradigm techniques is used to reveal the causal role of the frontoparietal attentional network in ocular dominance shifts. The conclusions of this paper are mostly well supported by data.

Weaknesses:

(1) The relationship between eye dominance, eye-based attention shift, and cortical functions remains unclear and merits further delineation. The rationale of the experimental design related to the hemispheric asymmetry in the FEF and other regions should be clarified.

Thanks for the reviewer’s comments! We have further clarified the relationship between eye dominance shift, eye-based attention, and cortical functions in the Introduction and Discussion. In the Introduction, we introduce the modulating effects of eye-based attention on eye dominance. On one hand, eye-based attention can enhance eye dominance of the attended eye in real time (see page 3 first paragraph or below):

”For instance, presenting top-down attentional cues to one eye can intensify the competition strength of input signals in the attended eye during binocular rivalry (Choe & Kim, 2022; Zhang et al., 2012) and shift the eye balance towards the attended eye (Wong et al., 2021).”

On the other hand, prolonged eye-based attention can induce a shift of eye dominance to the unattended eye (see page 3 second paragraph or below):

“In Song et al. (2023)’s “dichoptic-backward-movie” adaptation paradigm (see Figure 1B), participants are presented with regular movie images in one eye (i.e., attended eye) while the other eye (i.e., unattended eye) received the backward movie images of the same episode. They were also instructed to try their best to follow the logic of the regular movie and ignore the superimposed backward movie. Therefore, the goal-directed eye-based attention was predominantly focused on the attended eye. Song et al. (2023) found that the predominance of the unattended eye in binocular rivalry increased after one hour of adaptation to the “dichoptic-backward-movie”, indicating a shift of perceptual ocular dominance towards the unattended eye. Since the overall energy of visual input from the two eyes was balanced throughout the adaptation period, the change of ocular dominance after adaptation is thought to result from unbalanced eye-based attention rather than unbalanced input energy as in typical short-term monocular deprivation (Bai et al., 2017; Lunghi et al., 2011; Zhou et al., 2014).”

Moreover, we discussed how FEF regulates attention-induced ocular dominance shift (see page 21 second paragraph to page 23 first paragraph or below, which also respond to this reviewer’s comment of Weakness #2):

“Then how does FEF regulate the attention-induced ocular dominance shift? Our previous work has found that the aftereffect (for simplicity, hereafter we use aftereffect to denote the attention-induced ocular dominance shift) can be produced only when the adapting stimuli involve adequate interocular competition, and is measurable only when the testing stimuli are not binocularly fused (Song et al., 2023). Given the indispensability of interocular competition, we explained those findings in the framework of the ocular-opponency-neuron model of binocular rivalry (Said & Heeger, 2013). The model suggests that there are some opponency neurons which receive excitatory inputs from monocular neurons for one eye and inhibitory inputs from monocular neurons for the other eye (e.g. AE-UAE opponency neurons receive excitatory inputs from the attended eye (AE) and inhibitory inputs from the unattended eye (UAE)). Then a difference signal is computed so that the opponency neurons fire if the excitatory inputs surpass the inhibitory inputs. Upon activation, the opponency neurons will in turn suppress the monocular neurons which send inhibitory signals to them.

Based on this model, we proposed an ocular-opponency-neuron adaptation account to explain the aftereffect, and pointed out that the attentional system likely modulated the AE-UAE ocular opponency neurons (Song et al., 2023). So why would FEF modulate the AE-UAE opponency neurons? The reason may be two fold. Firstly, understanding the logic during the dichoptic-backward-movie viewing may require filtering out the distracting information (from the unattended eye) and sustaining attention (to the attended eye), which is exactly the role of FEF (Esterman et al., 2015; Lega et al., 2019).

Secondly, due to the special characteristics of binocular vision system, filtering the distracting input from the unattended eye may have to rely on the interocular suppression mechanism. According to the ocular-opponency-neuron model, this is achieved by the firing of the AE-UAE opponency neurons that send inhibitory signals to the UAE monocular neurons.

As mentioned previously, the firing of the AE-UAE opponency neurons requires stronger activity for the AE monocular neurons than for the UAE monocular neurons. This is confirmed by the results shown in Figure 8 of Song et al. (2023) that monocular response for the attended eye during the entire adaptation phase was slightly stronger than that for the unattended eye. Accordingly, during adaptation the AE-UAE opponency neurons were able to activate for a longer period thus adapted to a larger extent than the UAE-AE opponency neurons. This would cause the monocular neurons for the unattended eye to receive less inhibition from the AE-UAE opponency neurons in the post-test as compared with the pre-test, leading to a shift of ocular dominance towards the unattended eye. In this vein, the magnitude of this aftereffect should be proportional to the extent of adaptation of the AE-UAE relative to UAE-AE opponency neurons. Attentional enhancement on the AE-UAE opponency neurons is believed to strengthen this aftereffect, as it has been found that attention can enhance adaptation (Dong et al., 2016; Rezec et al., 2004). Inhibition of FEF likely led such attentional modulation to be much less effective. Consequently, the AE-UAE opponency neurons might not have the chance to adapt to a sufficiently larger extent than the UAE-AE opponency neurons, leading to a statistically non-detectable aftereffect in Experiment 2. Therefore, the results of Experiments 2-4 in the present study suggest that within the context of the ocular-opponency-neuron adaptation account, FEF might be the core area to fulfill the attentional modulations on the AE-UAE opponency neurons.”

We used the experimental design with hemispheric asymmetry in the FEF and other regions for two reasons. First, many studies have shown that the dorsal attentional network has a functional right-hemisphere dominance (Duecker et al., 2013; Mayrhofer et al., 2019; Sack, 2010). This was also indicated by the results of Experiment 1 (Figure 3). Second, we found that a recent research applying TMS to FEF and IPS stimulated only the right hemisphere (Gallotto et al., 2022). Therefore, we selected the right FEF and right IPS as the target regions for cTBS. In the Methods section of Experiment 2, we have elucidated the reasons for the selection of cTBS target regions (see page 35, first paragraph or below):

“Given that the dorsal attentional network primarily consists of the FEF and the IPS (Corbetta & Shulman, 2002; Mayrhofer et al., 2019), with a functional right-hemisphere dominance (Duecker et al., 2013; Mayrhofer et al., 2019; Sack, 2010), we selected the right FEF and right IPS from the four clusters identified in Experiment 1 as the target regions for cTBS (Gallotto et al., 2022).”

(2) Theoretically, how the eye-related functions in this area could be achieved, and how it interacts with the ocular representation in V1 warrant further clarification.

Thanks for the reviewer’s comment! In the revised manuscript, we have discussed how FEF regulates attention-induced ocular dominance shift (see page 21 second paragraph to page 23 first paragraph or the quoted paragraphs under this reviewer’s first Public comment).

Reviewer #2 (Public Review):

Summary

Song et al investigate the role of the frontal eye field (FEF) and the intraparietal sulcus (IPS) in mediating the shift in ocular dominance (OD) observed after a period of dichoptic stimulation during which attention is selectively directed to one eye. This manipulation has been previously found to transiently shift OD in favor of the unattended eye, similar to the effect of short-term monocular deprivation. To this aim, the authors combine psychophysics, fMRI, and transcranial magnetic stimulation (TMS). In the first experiment, the authors determine the regions of interest (ROIs) based on the responses recorded by fMRI during either dichoptic or binocular stimulation, showing selective recruitment of the right FEF and IPS during the dichoptic condition, in line with the involvement of eye-based attention. In a second experiment, the authors investigate the causal role of these two ROIs in mediating the OD shift observed after a period of dichoptic stimulation by selectively inhibiting with TMS (using continuous theta burst stimulation, cTBS), before the adaptation period (50 min exposure to dichoptic stimulation). They show that, when cTBS is delivered on the FEF, but not the IPS or the vertex, the shift in OD induced by dichoptic stimulation is reduced, indicating a causal involvement of the FEF in mediating this form of short-term plasticity. A third control experiment rules out the possibility that TMS interferes with the OD task (binocular rivalry), rather than with the plasticity mechanisms. From this evidence, the authors conclude that the FEF is one of the areas mediating the OD shift induced by eye-selective attention.

Strengths

(1) The experimental paradigm is sound and the authors have thoroughly investigated the neural correlates of an interesting form of short-term visual plasticity combining different techniques in an intelligent way.

(2) The results are solid and the appropriate controls have been performed to exclude potential confounds.

(3) The results are very interesting, providing new evidence both about the neural correlates of eye-based attention and the involvement of extra-striate areas in mediating short-term OD plasticity in humans, with potential relevance for clinical applications (especially in the field of amblyopia).

Weaknesses

(1) Ethics: more details about the ethics need to be included in the manuscript. It is only mentioned for experiment 1 that participants "provided informed consent in accordance with the Declaration of Helsinki. This study was approved by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences". (Which version of the Declaration of Helsinki? The latest version requires the pre-registration of the study. The code of the approved protocol together with the code and date of the approval should be provided.) There is no mention of informed consent procedures or ethics approval for the TMS experiments. This is a huge concern, especially for brain stimulation experiments!

Response: Thanks for the reviewer’s comment! In the revised manuscript, we have provided the code of the approved protocol and date of the approval (see page 25 second paragraph or below):

“This study was approved (H21058, 11/01/2021) by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences.”

Indeed, ethics approval and informed consent were obtained for each experiment. To avoid duplication in the text, we only presented the ethics instructions in the Methods section of Experiment 1. We have now clarified in that section that all the experiments in this study were approved by the IRB in our Institute.

(2) Statistics: the methods section should include a sub-section describing in detail all the statistical analyses performed for the study. Moreover, in the results section, statistical details should be added to support the fMRI results. In the current version of the manuscript, the claims are not supported by statistical evidence.

Response: Thanks for the reviewer’s suggestion! In the Methods section of revised manuscript, we have added a section to describe the detailed statistical analyses for each experiment (see page 37 last paragraph for Experiment 2 and page 38 last paragraph for Experiment 3 or below):

“Statistical analyses were performed using MATLAB. A 3 (stimulation site: Vertex, FEF, IPS) × 2 (test phase: pre-test and post-test) repeated measures ANOVA was used to investigate the effect of cTBS delivery on ocular dominance shift. Moreover, for the blob detection test, the target detection rate of each experimental condition was calculated by dividing the summed number of detected blob targets by the total number of blob targets. Then, a 2 (eye: attended eye, unattended eye) × 3 (stimulation site: Vertex, FEF, IPS) repeated measures ANOVA on the detection performance was performed. Post-hoc tests were conducted using paired t-tests (2-tailed significance level at α = 0.05), and the resulting p-values were corrected for multiple comparisons using the false discovery rate (FDR) method (Benjamini & Hochberg, 1995).”

“In addition to the data analysis in Experiment 2, we complemented the standard inferential approach with the Bayes factor (van den Bergh et al., 2023; van Doorn et al., 2021; Wagenmakers et al., 2018), which allows quantifying the relative evidence that the data provide for the alternative (H1) or null hypothesis (H0). We conducted the Bayesian repeated measures ANOVA using JASP with default priors and computed inclusion Bayes factors (BFincl) which suggest the evidence for the inclusion of a particular effect calculated across matched models. A BF greater than 1 provides support for the alternative hypothesis. Specifically, a BF between 1 and 3 indicates weak evidence, a BF between 3 and 10 indicates moderate evidence, and a BF greater than 10 indicates strong evidence (van Doorn et al., 2021). In contrast, a BF below 1 provides evidence in favor of the null hypothesis.”

Furthermore, in the Results section of revised manuscript, we have added the statistical details to support the fMRI results (see page 9 last paragraph or below):

“To seek these brain regions, we used the AFNI program “3dttest++” to access the difference of ‘dichoptic-binocular’ contrast between the experimental and control runs. The AFNI program “ClustSim” was then applied for multiple comparison correction, yielding a minimum significant cluster size of 21 voxels (voxel wise p = .001; cluster threshold α = 0.05). We found 4 clusters showing stronger responses to the dichoptic movies than to the binocular movies especially in the experimental runs.”

(3) Interpretation of the results: the TMS results are very interesting and convincing regarding the involvement of the FEF in the build-up of the OD shift induced by dichoptic stimulation, however, I am not sure that the authors can claim that this effect is related to eye-based attention, as cTBS has no effect on the blob detection task during dichoptic stimulation. If the FEF were causally involved in eye-based attention, one would expect a change in performance in this task during dichoptic stimulation, perhaps a similar performance for the unattended and attended eye. The authors speculate that the sound could have an additional role in driving eye-based attention, which might explain the lack of effect for the blob discrimination task, however, this hypothesis has not been tested.

Response: Thanks for the reviewer’s comment! Following this reviewer’s insightful suggestion, we have conducted a new experiment to examine the effect of sound on blob detection task (see Experiment 4 in the revised manuscript). The procedure was similar to that of Experiment 2 except that the sound was no longer presented during the dichoptic-backward-movie adaptation. The results showed that the interocular difference of blob detection rate after sound elimination remained unaffected by the cTBS, which disagreed with our explanation in the previous version of manuscript. Based on the new data, we now question the validity to use the blob detection rate to precisely quantify eye-based attention, and have tried to explain why the blob detection results do not contradict with our account for the function role of FEF in modulating the aftereffect in the Discussion of the revised manuscript (see page 23 second paragraph to page 24 first paragraph or below):

“An unresolved issue is why inhibiting the cortical function of FEF did not impair the performance of blob detection task. One potential explanation is that the synchronized audio in Experiment 2 might help increase the length of time that the regular movie dominated awareness. However, the results of Experiment 4 did not support this explanation, in which the performance of blob detection survived from the inhibition of FEF even when silent movies were presented. Although this issue remains to be explored in future work, it does not contradict with our notion of FEF modulating AE-UAE opponency neurons. It should be noted that our notion merely states that FEF is the core area for attentional modulations on activities of AE-UAE opponency neurons. No other role of FEF during the adaptation is assumed here (e.g. boosting monocular responses or increasing conscious level of stimuli in the attended eye). In contrast, according to the most original definition, the blob detection performance serves as an estimation of visibility (or consciousness level) of the stimuli input from each eye, despite the initial goal of adopting this task is to precisely quantify eye-based attention (which might be impractical). Thus, according to our notion, inhibition of FEF does not necessarily lead to deteriorate performance of blob detection. Furthermore, our findings consistently indicated that the visibility of stimuli in the attended eye was markedly superior to that of stimuli in the unattended eye, yet the discrepancy in the SSVEP monocular responses between the two eyes was minimal though it had reached statistical significance (Song et al., 2023). Therefore, blob detection performance in our work may only faithfully reflect the conscious level in each monocular pathway, but it is probably not an appropriate index tightly associated with the attentional modulations on monocular responses in early visual areas. Indeed, previous work has argued that attention but not awareness modulates neural activities in V1 during interocular competition (Watanabe et al., 2011), but see (Yuval-Greenberg & Heeger, 2013). We have noticed and discussed the counterintuitive results of blob detection performance in our previous work (Song et al., 2023). Here, with the new counterintuitive finding that inhibition of FEF did not impair the performance of blob detection, we suspect that blob detection performance in the “dichoptic-backward-movie” adaptation paradigm may not be an ideal index that can be used to accurately quantify eye-based attention.

(4) Writing: in general, the manuscript is well written, but clarity should be improved in certain sections.

(a) fMRI results: the first sentence is difficult to understand at first read, but it is crucial to understand the results, please reformulate and clarify.

Response: Thanks for the reviewer’s suggestion! In the revised manuscript, we have reformulated this sentence (see page 9 last paragraph or below):

“It was only in the dichoptic condition of experimental runs that participants had to selectively pay more attention to one eye (i.e., eye-based attention). Therefore, we speculate that if certain brain regions exhibit greater activities in the dichoptic condition as compared to the binocular condition in the experimental runs but not in the control runs, the activation of these brain regions could be attributable to eye-based attention.”

(b) Experiment 3: the rationale for experiment one should be straightforward, without a long premise explaining why it would not be necessary.

Response: Thanks for the reviewer’s suggestion! In the revised manuscript, we have streamlined the lengthy premise explaining to make the rationale of Experiment 3 more straightforward (see page 15 last two paragraphs or below):

“The results of Experiment 2 support the notion that eye-based attention was the cause for attention-induced ocular dominance plasticity. However, an alternative account is that the significant two-way interaction between test phase and stimulation site did not stem from any persistent malfunction of FEF in modulating ocular dominance, but rather it was due to some abnormality of binocular rivalry measures in the post-test that occurred after stimulation at the FEF only (and not at the other two brain sites). For instance, stimulation at the FEF might simply reduce the ODI measured in the binocular rivalry post-test.

Therefore, we conducted Experiment 3 to examine how suppression of the three target sites would impact binocular rivalry performance, in case that any unknown confounding factors, which were unrelated to adaptation but related to binocular rivalry measures, contributed to the results.”

(c) Discussion: the language is a bit familiar here and there, a more straightforward style should be preferred (one example: p.19 second paragraph).

Response: Thanks for the reviewer’s suggestion! We have carefully revised the language in the discussion. The discussion following the example paragraph has been largely rewritten.

(5) Minor: the authors might consider using the term "participant" or "observer" instead of "subject" when referring to the volunteers who participated in the study.

Response: Thanks for the reviewer’s suggestion! In the revised manuscript, we have replaced the term “subject” with “participant”.

Reviewer #3 (Public Review):

Summary:

This study studied the neural mechanisms underlying the shift of ocular dominance induced by "dichoptic-backward-movie" adaptation. The study is self-consistent.

Strengths:

The experimental design is solid and progressive (relationship among three studies), and all of the raised research questions were well answered.

The logic behind the neural mechanisms is solid.

The findings regarding the cTMS (especially the position/site can be useful for future medical implications).

Weaknesses:

Why does the "dichoptic-backward-movie" adaptation matter? This part is severely missing. This kind of adaptation is neither intuitive like the classical (Gbison) visual adaptation, nor practical as adaptation as a research paradigm as well as the fundamental neural mechanism. If this part is not clearly stated and discussed, this study is just self-consistent in terms of its own research question. There are tons of "cool" phenomena in which the neural mechanisms are apparent as "FEF controls vision-attention" but never tested using TMS & fMRI, but we all know that this kind of research is just of incremental implications.

Response: Thanks for the reviewer’s comment! We designed the "dichoptic-backward-movie" adaptation to study the perceptual consequence and mechanisms of sustained attention to a monocular pathway. Since the overall visual input to both eyes during adaptation were identical, any effect (i.e. the change of ocular dominance in our study) after adaptation can be easily ascribed to unbalanced eye-based attention between the two eyes rather than unbalanced input energy across the eyes. In typical short-term monocular deprivation, input signal from one eye is blocked. Accordingly, attention is undoubtedly distributed to the non-deprived eye. The fact that in a short-term monocular deprivation paradigm the deprived eye is also the unattended eye prevents researchers from ascertaining whether unbalanced eye-based attentional allocation contributes to the shift of ocular dominance just like unbalanced visual input across the two eyes. That is why the “dichoptic-backward-movie” adaptation was adopted in the present study. This new paradigm balances the input energy across the eyes but leaves attention unbalanced across the eyes. In the revised manuscript, we have added the description of the “dichoptic-backward-movie” adaptation (see page 3 last paragraph and page 4 first paragraph or below). Hope this complementary information improves the clarity.

“In Song et al. (2023)’s “dichoptic-backward-movie” adaptation paradigm (see Figure 1B), participants are presented with regular movie images in one eye (i.e., attended eye) while the other eye (i.e., unattended eye) received the backward movie images of the same episode. They were also instructed to try their best to follow the logic of the regular movie and ignore the superimposed backward movie. Therefore, the goal-directed eye-based attention was predominantly focused on the attended eye. Song et al. (2023) found that the predominance of the unattended eye in binocular rivalry increased after one hour of adaptation to the “dichoptic-backward-movie”, indicating a shift of perceptual ocular dominance towards the unattended eye. Since the overall energy of visual input from the two eyes was balanced throughout the adaptation period, the change of ocular dominance after adaptation is thought to result from unbalanced eye-based attention rather than unbalanced input energy as in typical short-term monocular deprivation (Bai et al., 2017; Lunghi et al., 2011; Zhou et al., 2014).” In short-term monocular deprivation, input signal from one eye is blocked. Accordingly, attention is biased towards the non-deprived eye. However, it is difficult to tease apart the potential contribution of unbalanced eye-based attention from the consequence of the unbalanced input energy, as the deprived eye is also the unattended eye. Therefore, the advantage of the “dichoptic-backward-movie” adaptation paradigm is to balance the input energy across the eyes but leave attention unbalanced across the eyes.

Our previous work (Song et al., 2023) has shown that eye-based attention plays a role in the formation of ocular dominance shift following adaptation to dichoptic backward movie. However, because the “dichoptic-backward-movie” adaptation paradigm is new, to our knowledge, no literature has ever discovered the brain areas that are responsible for eye-based attention. Our fMRI experiment for the first time resolves this issue, which, we believe, is one of the novelties of the present study. Attention is a pretty general definition of our ability to select limited information for preferential or privileged processing, yet it includes numerous aspects (e.g. spatial attention for spatial locations, feature-based attention for visual features, object-based attention for objects, social attention for social cues, and eye-based attention for monocular pathways etc). Are we 100% sure that the same brain network always underlies every aspect of attention including eye-based attention? No test, no answer. Maybe the answer is Yes, but we are not aware of any evidence for that from literature. It is not unlikely that attention is like an elephant while researchers are like blind people touching the elephant from different angles. Even if all previous researchers have touched the side of the elephant and state that an elephant is no different from a wall, as long as one researcher grabs the elephant’s tail, the “wall” knowledge will be falsified. From this perspective of the essence of science (falsifiable), we have the confidence to say that our fMRI experiment on eye-based attention is novel, because to our knowledge our experiment is the first one to explore the issue. On the basis of the fMRI experiment (otherwise we would have no idea on which precise brain site to apply the cTBS), we could successfully complete the subsequent TMS experiments.

Of course, if the reviewer can kindly point out any previous neuroimaging work we missed that has already disclosed the neural mechanisms underlying human’s eye-based attention, we would truly appreciate the reviewer very much. But even so, we would like to emphasize that the purpose of the current study was actually not to use TMS & fMRI to confirm that “FEF controls visual attention”. As we mentioned in the Abstract and expanded the introduction in the last two paragraphs of Introduction, the goal of the TMS experiments is to examine the causal role of eye-based attention in producing the aftereffect of “dichoptic-backward-movie” adaptation. This research question is also new, thus we do not think the TMS experiments are incremental, either. Our findings provided direct causal evidence for the effect of FEF on modulating ocular dominance through eye-based attention. Please see the last two sentences in the first paragraph on page 20 in the revised manuscript or below,

“Interestingly, in our Experiment 2 this aftereffect was significantly attenuated after we temporarily inhibited the cortical function of FEF via cTBS. This finding indicates the crucial role of FEF in the formation of attention-induced ocular dominance shift.”

as well as the last sentence of the Abstract,

“…and in this network, FEF plays a crucial causal role in generating the attention-induced ocular dominance shift.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The hemispheric asymmetry in the eye-based attention-related cortex should be further examined and discussed. For example, IPS in both hemispheres was identified in the fMRI experiment. It is not clear why only the right IPS was stimulated in the TMS experiment.

Response: Thanks for the comment. We have elucidated the reasons for the experimental design with hemispheric asymmetry in FEF and IPS. Please see our response to the Weakness #1 raised by Reviewer #1 in the Public Review section.

(2) It is known that the frontoparietal cortex plays a role in the contralateral shift of attentional allocation. Meanwhile, the latest stage of ocular-specific representation is V1. The authors should discuss how the eye-related function can be achieved in FEF.

Response: Thanks for the comment. we have discussed how FEF regulates attention-induced ocular dominance shift (see page 21 second paragraph to page 23 first paragraph in the revised manuscript, and our response to the Weakness #2 raised by Reviewer #1 in the Public Review section).

(3) To further validate the role of FEF in eye-related attention shifts, the authors may consider using the traditional monocular deprivation paradigm with fMRI and TMS. It would be valuable to compare the neural mechanisms related to the classical monocular deprivation paradigm with the current findings.

Response: Thanks for the reviewer’s suggestion! That is indeed an interesting research topic that we are currently exploring. The current study investigated the attention-induced ocular dominance shift with the “dichoptic-backward-movie-adaptation” paradigm. This paradigm is substantially different from traditional short-term monocular deprivation. In our Neuroscience Bulletin paper (Song et al. 2023), we discuss the reason as follows.

“An alternative account of our results is the homeostatic plasticity mechanism. The function of this mechanism is to stabilize neuronal activity and prevent the neuronal system from becoming hyperactive or hypoactive. For this goal, the mechanism moves the neuronal system back toward its baseline after a perturbation [51, 52]. In our case, the aftereffect can be explained such that the visual system boosts the signals from the unattended eye to maintain the balance of the network’s excitability. However, this account cannot easily explain why the change of neural ocular dominance led by prolonged eye-based attention was observed here using the binocular rivalry testing stimuli, but absent in the previous research using the binocularly fused stimuli [11]. In contrast, a recent SSVEP study also using the binocularly fused stimuli has successfully revealed a shift of neural ocular dominance after two hours of monocular deprivation [31], which is in line with the homeostatic plasticity account. Therefore, the mechanisms underlying the “dichoptic-backward-movie” adaptation and monocular deprivation are probably not fully overlapped with each other; and the binocular rivalry mechanism described in the ocular-opponency-neuron model seems to be more preferable than the homeostatic plasticity mechanism in accounting for the present findings.”

Therefore, before asking whether FEF plays a role in the attention-induced ocular dominance shift in a traditional monocular deprivation paradigm, one should probably first examine whether attention also plays a role in traditional monocular deprivation, and whether the ocular-opponency-neuron adaptation account can also be used to explain the traditional monocular deprivation effect. Our newly accepted paper “Negligible contribution of adaptation of ocular opponency neurons to the effect of short-term monocular deprivation” (https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1282113/full) gives a generally negative answer to the second question. And as to the first question, we have one manuscript under review and another ongoing study. In other words, to get a satisfactory answer to this particular comment of this reviewer, we need to first obtain clear answers to the two above questions. We think this is far beyond the scope of one single manuscript.

(4) The authors only presented regular movies to the dominant eye to maximize the ocular dominance shift. This critical information of design should be clarified, not only in the method section.

Response: Thanks for the reviewer’s suggestion! In the Results section of Experiment 2, we have added a description of this critical information of design (see page 11 last paragraph to page 12 first paragraph or below):

“Then, participants adapted to the “dichoptic-backward-movie” in which regular movie images were presented to the dominant eye to maximize the effect of eye dominance shift (Song et al., 2023). Meanwhile they were asked to detect some infrequent blob targets presented on the movie images in one eye at the same time.”

(5) The frame rate of the movie is 30 fps, which is much lower than a typical 60 fps visual presentation, does this have an effect on the adaptation outcome?

Response: To our best of knowledge, there is no evidence that the frame rate of the movie influences the aftereffect of attention-induced ocular dominance shift. In our previous research, the frame rate of the movie during adaptation was 25 fps, which still produced a stable adaptation aftereffect (Song et al., 2023). And the frame rate of the movie was 30 fps in our monocular deprivation work (Lyu et al., 2020), which showed a similar monocular deprivation effect we previously observed in an altered reality study (Bai et al., 2017). The frame rate of the altered-reality video in Bai et al.’s (2017) work was 60 fps. All these clues suggest that the frame rate does not have an effect on the adaptation outcome.

(6) Figure 5: The ODSE derived from ODI in Experiment 3 should also be illustrated, for a better comparison with results from Experiment 2.

Response: Thanks for the reviewer’s suggestion! In the revised manuscript, we have added the results of ODSE in Experiment 3 to Figure 5 (see page 15 or below):

Author response image 1.

Figure 5. The results of (A) the ocular dominance index (ODI), (B) the ocular dominance shift effects (ODSE) in Experiment 2, (C) the ODI and (D) the ODSE in Experiment 3. The bars show the grand average data for each condition. The individual data are plotted with gray lines or dots. The dashed gray line represents the absolute balance point for the two eyes (ODI = 0.5). Error bars indicate standard errors of means. * p < .05; ** p < .01; n.s. p > .05.

(7) Spelling issues: "i.e." → "i.e.,"

Response: Thanks for the reviewer’s suggestion! In the revised manuscript, we have changed “i.e.” to “i.e.,”.

Reviewer #2 (Recommendations For The Authors):

Linked to weakness 3: Ideally, a control experiment with cTBS and dichoptic stimulation without sound but with the blob discrimination task should be performed to be able to make important claims about the neural mechanisms involved in eye-based attention.

Response: Thanks for the comment. We have performed a new experiment as the reviewer suggested. Please see our response to the Weakness #3 raised by Reviewer #2 in the Public Review section.

Reviewer #3 (Recommendations For The Authors):

(1) The neural mechanisms are so apparent. We all know the FEF\IPS\SC matter in vision and attention and gaze. This is not groundbreaking.

Response: As we addressed in our response to Reviewer #3’s public comment, the current study aimed at investigating the causal mechanism for eye-based attentional modulation of ocular dominance plasticity rather than simply the role of FEF\IPS\SC in visual attention. Moreover, eye-based attention is a less investigated aspect of visual attention. The neural mechanism underlying eye-based attention is still largely unknown, and seeking the brain areas for controlling eye-based attention is the necessary preparation work for applying the cTBS. We have responded in detail to Reviewer #3’s public comment why we think both the fMRI and TMS experiments are novel to the field, which we will not reiterate it here to avoid redundancy.

(2) Why does the "dichoptic-backward-movie" adaptation matter? Is playing a backward movie to one eye realistic? Does that follow the efficient coding? Is that a mere consequence of information theory?

Response: Thanks for the comments. We have added the description of the “dichoptic-backward-movie” adaptation paradigm in the revised manuscript (see page 3 last paragraph and page 4 first paragraph or our response to this reviewer’s Public comment).

Is it realistic to play backward movie to one eye? We feel this question is somehow ambiguous to us. If the reviewer means the technical operability for such stimulus presentation, we can assure it since we have used this paradigm in both the current and previously published studies. To be more specific, we made the video stimuli in advance. The left half of the video was the regular movie and the right half was the backward version of the same movie (or vice versa). When viewing such video stimuli through stereoscopes, participants could only see the left half of the video with the left eye and the right half of the video with the right eye. In other words, the regular movie and backward movie were viewed dichoptically. Alternatively, if the reviewer means that such dichoptic presentation rarely happens in real world thus not realistic, we agree with the reviewer on one hand. On the other hand, we have explained on page 3 last paragraph and page 4 first paragraph why it is a particular useful paradigm for the main purpose of the present study. Let us make a similar example. The phenomenon of binocular rivalry rarely happens in everyday life. So people may say binocular rivalry is not realistic. However, our visual system does have the ability to deal with such conflicting visual inputs across the eyes, even binocular rivalry is unrealistic! Sometimes it is fun to investigate those seemingly unrealistic functions of our brains since those may also reveal the mystery of our neural system. As we know, despite binocular rivalry is uncommon in daily life, it is frequently used to investigate awareness. And in our work, we use binocular rivalry to measure perceptual ocular dominance.

Finally, the reviewer queried about if the "dichoptic-backward-movie" adaptation paradigm follow efficient coding and information theory. The information theory and efficient coding assume that messages with low expectedness or of rare occurrence would attract more attention and induce larger neural responses than those with high expectedness. In the "dichoptic-backward-movie" adaptation paradigm, the backward movie should be less expected since the actions of the characters in the backward movie appeared illogical. Thus, according to the information theory and efficient coding, it would be expected that more attention was paid to the backward movie and thus the backward movie might dominate the awareness for a longer period during adaptation (Zhang et al., 2012). However, we instructed participants to follow the regular movie during adaptation. The results of blob detection task also showed a better task performance when the targets appeared in the eye presented with the regular movie, which contradicted with the prediction of the information theory and efficient coding. Thus, it seems not very likely that the "dichoptic-backward-movie" adaptation followed efficient coding and information theory.

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Author Response

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

eLife assessment

This study has uncovered some important initial findings about how certain extracellular vehicles (EVs) from the mother might impact the energy usage of an embryo. While the study's findings are in general solid, some experiments lack statistical power due to small sample sizes. The study's title might be a bit too assertive as the evidence linking maternal mtDNA transmission to changes in embryo energy use is still correlative.

We would like to express our sincere gratitude to the editors and reviewers for their invaluable comments on this work. Their feedback has been instrumental in enhancing the quality of our manuscript; we have incorporated their suggestions to the best of our abilities.

Reviewer #1 (Public Review):

Q1. Bolumar et al. isolated and characterized EV subpopulations, apoptotic bodies (AB), Microvesicles (MV), and Exosomes (EXO), from endometrial fluid through the female menstrual cycle. By performing DNA sequencing, they found the MVs contain more specific DNA sequences than other EVs, and specifically, more mtDNA were encapsulated in MVs. They also found a reduction of mtDNA content in the human endometrium at the receptive and post-receptive period that is associated with an increase in mitophagy activity in the cells, and a higher mtDNA content in the secreted MVs was found at the same time. Last, they demonstrated that the endometrial Ishikawa cell-derived EVs could be taken by the mouse embryos and resulted in altered embryo metabolism.

This is a very interesting study and is the first one demonstrating the direct transmission of maternal mtDNA to embryos through EVs.

A1. Thank you for your kind comments.

Reviewer #2 (Public Review):

Q2. In Bolumar, Moncayo-Arlandi et al. the authors explore whether endometrium-derived extracellular vesicles contribute mtDNA to embryos and therefore influence embryo metabolism and respiration. The manuscript combines techniques for isolating different populations of extracellular vesicles, DNA sequencing, embryo culture, and respiration assays performed on human endometrial samples and mouse embryos.

Vesicle isolation is technically difficult and therefore collection from human samples is commendable. Also, the influence of maternally derived mtDNA on the bioenergetics of embryos is unknown and therefore novel. However, several experiments presented in the manuscript fail to reach statistical significance, likely due to the small sample sizes. Additionally, the experiments do not demonstrate a direct effect of mtDNA transfer on embryo bioenergetics. This has the unfortunate consequence of making several of the authors' conclusions speculative.

In my opinion the manuscript supports the following of the authors' claims:

1) Different amounts of mtDNA are shed in human endometrial extracellular vesicles during different phases of the menstrual cycle

2) Endometrial microvesicles are more enriched for mitochondrial DNA sequences compared to other types of microvesicles present in the human samples

3) Fluorescently labelled DNA from extracellular vesicles derived from an endometrial adenocarcinoma cell line can be incorporated into hatched mouse embryos.

4) Culture of mouse embryos with endometrial extracellular vesicles can influence embryo respiration and the effect is greater when cultured with isolated exosomes compared to other isolated microvesicles

A2. Thank you for your detailed feedback. We have made every effort to enhance the manuscript in this revised version, ensuring that our conclusions are grounded in solid evidence and that they avoid any speculation.

My main concerns with the manuscript:

Q3. The authors demonstrate that microvesicles contain the most mtDNA, however, they also demonstrate that only isolated exosomes influence embryo respiration. These are two separate populations of extracellular vesicles.

A3. This manuscript focuses on the DNA content secreted by the endometrium and captured by the embryo. We identified both mitochondrial DNA and genomic DNA. We have found that mitochondrial DNA is predominantly secreted and encapsulated within microvesicles, while all three types of vesicles encapsulate genomic DNA. Specifically, based on the results we presented in Response A8 to the reviewers and included in the latest version of the manuscript, we observed that exosomes contain the highest amount of genomic DNA. Furthermore, exosomes have the greatest impact on embryo bioenergetics, suggesting that this DNA content may primarily exert this effect. We have thoroughly revised the manuscript, focusing our message on DNA content.

Q4. mtDNA is not specifically identified as being taken up by embryos only DNA.

A4. We agree with the reviewer; as we mention in answer A9, EdU does not specifically label mitochondrial DNA. To solve this issue, we incubated a synthetic molecule of labeled mtDNA with embryos and analyzed mtDNA incorporation using confocal microscopy. We co-cultured hatched mouse embryos (3.5 days) with an ATP8 sequence conjugated with Biotin overnight at 37ºC and 5% CO2. We then permeabilized embryos, incubated them with Streptavidine-Cy3 for 45 min, and visualized the results using an SP8 confocal microscope (Leica). We observed mtDNA internalization by cells of the hatched embryos; please see new supplementary Figure 7 and lines 234-237 on page 9 and lines 583-592 M&M on page 21.

Q5. The authors do not rule out that other components packaged in extracellular vesicles could be the factors influencing embryo metabolism.

A5. The vesicular subtypes contain molecules beyond DNA, such as microRNAs, proteins, or lipids. Our laboratory has studied the transmission of vesicles and their relationship with their contents (particularly microRNAs) and their connection to maternal-fetal communication. In this study, we focused on genomic/mitochondrial DNA. We cannot exclude the possibility that other molecules may influence metabolism; this statement is already noted in the discussion section on lines 328-331 on page 12.

Q6. Taken together, these concerns seem to contradict the implication of the title of the manuscript – the authors do not demonstrate that inheritance of maternal mtDNA has a direct causative effect on embryo metabolism.

A6. We have modified the title to better align with the manuscript’s results. The proposed new title for the manuscript is “Vertical transmission of maternal DNA through extracellular vesicles modulates embryo bioenergetics during the periconceptional period.”

Reviewer #1 (Recommendations for The Authors):

Q7. Would it be possible to validate the mtDNA content and mitophagy activity in different periods using the Ishikawa cells?

A7. Unfortunately, this validation cannot be achieved with in vitro cultures of cell lines, especially with a cell line such as the endometrial adenocarcinoma-derived Ishikawa cell line. While mimicking the menstrual cycle (as observed in Figure 3 of the manuscript) is entirely artificial, we believe that the statistically significant results obtained in human samples faithfully represent the biological processes involved. Using a cell line, in our opinion, would not provide us with novel information.

Q8. Characterization of the EVs subpopulations from Ishikawa cells and direct evidence to show the EdU labeled DNA is contained in the EVs are necessary.

A8. To address this concern, we designed a novel experiment. We cultured Ishikawa cells in the presence of Edu, isolated the three types of vesicles, and evaluated labeled DNA content by flow cytometry (as illustrated in Supplementary Figure 5). All three types of vesicles exhibited positive EdU-DNA labeling; notably, the exosomal fraction demonstrated substantially higher DNA content than the other vesicle populations. Please see new supplementary Figure 5 and lines 217-218 on page 9, and lines 576-582 of the M&M on pages 20-21.

Q9. Would EdU incorporate into the genomic DNA or mitochondrial DNA?

A9. EdU (5-ethynyl-2′-deoxyuridine) is a nucleoside analog of thymidine and becomes incorporated into DNA during active DNA synthesis. EdU labels all newly synthesized DNA, both genomic and mitochondrial; however, we cannot differentiate between them with this technique.

Q10. It is difficult to assess whether the EV-derived DNA was taken by the TE or ICM without immunostaining of cell lineage markers in mouse embryos.

A10. We did not aim to label the inner cell mass, as the vesicles primarily enter through trophectodermal cells. The images presented in Figure 4 and Supplementary Figure 5 depict trophectoderm cells.

Q11. It is also valuable to perform co-staining of Mitotracker to show the co-localization of EdU labelled DNA and the mitochondrial.

A11. Per the reviewer's suggestion, we conducted an experiment as described in the following text. We isolated MVs from the culture media of EdU-treated Ishikawa cells and co-incubated them with embryos overnight. The resulting images (See Author response image 1) show an embryo subjected to staining with EdU-tagged DNA labeled with Alexa Fluor 488 (green), Mitotracker Deep Red (red), and nuclei (blue). Detailed views of the embryo are presented in panels A and B. Notably, we observed co-localization of mitochondria and EdU-tagged DNA, as indicated by the white arrows. Despite this intriguing finding, we chose not to include these results in the initial version of the manuscript; however, if the editor deems it appropriate, we would be delighted to incorporate them into the final version. The experimental procedure for co-localization of EdU DNA-tagged with mitochondria involved the following steps: Mitotracker Deep Red FM (Thermo Fisher Scientific, M22426) was added to the embryo media at a final concentration of 200 nM, and the embryos were subsequently incubated for 45-60 minutes prior to fixation.

Author response image 1.

Co-localization of mitochondria and EdU-tagged DNA in mouse embryos. Representative micrograph of an embryo co-incubated with MVs isolated from the culture media of Ishikawa cells treated with EdU. EdU-tagged DNA was labeled with Alexa Fluro 488 (green). Mitotracker Deep Red (mitochondria; red) and nuclei (blue). A and B) magnified images of the embryo show detailed co-localization of mitochondria and EdU-tagged DNA (white arrows). Negative control) Embryos incubated with MVs isolated from control Ishikawa cells (without EdU incubation) and stained with the click-it reaction cocktail. A and B showed magnified images of the embryo. Notice the absence of EdU-Alexa Fluro 488 signals (green).

Reviewer #2 (Recommendations for The Authors):

Q12. It would be helpful if the authors could provide citations and rationale for why they chose specific molecular markers to validate the different population of extracellular vesicles.

A12. Different extracellular populations are defined by molecular marker signatures that reflect their origin. VDAC1 forms ionic channels in the mitochondrial membrane, has a role in triggering apoptosis, and has been described as characteristic of ABs.[1]

The ER protein Calreticulin has also been used as an AB marker [2]; however, other studies have noted the presence of Calreticulin in MVs. [1] This apparent non-specificity may derive from apoptotic processes, during which the ER membrane fragments and forms vesicles smaller than ABs, which would contain Calreticulin and sediment at higher centrifugal forces.[3,4] In fact, proteomic studies have linked the presence of Calreticulin with vesicular fractions of a size range relevant for MVs [5] and ABs [6].

ARF6, a GTP-binding protein implicated in cargo sorting and promoting MV formation, has been proposed as an MV marker. [7,8]

Classic markers of EXOs include molecules involved in biogenesis, such as tetraspanins (CD63, CD9, CD81), Alix, TSG101, and flotillin-1.[9,10] Nonetheless, studies have recently reported the widespread nature of such markers among various EV populations, although with different relative abundances (such as is the case for CD9, CD63, HSC70, and flotillin-1[11]). Notably, certain molecular markers (such as TSG101[1,11]) have been ratified as specific to EXOs.

References

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2. J. van Deun, P. Mestdagh, R. Sormunen, V. Cocquyt, K. Vermaelen, J. Vandesompele, M. Bracke, O. De Wever, A. Hendrix, J. Extracell. Vesicles. 2014, 3:24858, doi: 10.3402/jev.v3.24858.

3. L. Abas, C. Luschnig, Anal. Biochem. 2010, 401, 217-227, doi: 10.1016/j.ab.2010.02.030.

4. C. Lavoie, J. Lanoix, F. W. Kan, J. Paiement, J. Cell Sci. 1996, 109(6), 1415-1425.

5. M. Tong, T. Kleffmann, S. Pradhan, C. L. Johansson, J. DeSousa, P. R. Stone, J. L. James, Q. Chen, L. W. Chamley, Hum. Reprod. 2016, 31(4), 687-699, doi: 10.1093/humrep/dew004.

6. P. Pantham, C. A. Viall, Q. Chen, T. Kleffmann, C. G. Print, L. W. Chamley, Placenta. 2015, 36, 1463e1473, doi: 10.1016/j.placenta.2015.10.006.

7. V. Muralidharan-Chari, J. Clancy, C. Plou, M. Romao, P. Chavrier, G. Raposo, C. D'Souza-Schorey, Curr. Biol. 2009, 19, 1875-1885.

8. C. Tricarico, J. Clancy, C. D'Souza-Schorey, Small GTPases. 2016, 0(0), 1-13.

9. M. Colombo, G. Raposo, C. Théry, Annu. Rev. Cell. Dev. Biol. 2014, 30, 255-289, doi: 10.1146/annurev-cellbio-101512-122326.

10. S. Mathivanan, H. Ji, R. J. Simpson, J. Proteomics. 2010, 73(10), 1907-1920.

11. J. Kowal, G. Arras, M. Colombo, M. Jouve, J. P. Morath, B. Primdal-Bengtson, F. Dingli, D. Loew, M. Tkach, C. Théry, Proc. Natl. Acad. Sci. U. S. A. 2016, 113(8), E968-77.

Q13. The PCA analysis in supplementary figure 4 A&B needs more explanation for why they think separation of the two conditions based on principal component 1 is sufficient. The small number of replicates makes me concerned because principal component 2 does not show similarity of replicates for the DNase treated samples. Also, 4C has no description in the figure legend.

A13. The PCA results show a clear separation between the two conditions; we believe this separation is primarily driven by the differences observed in principal component 1 (PC1). We would like to address the concerns raised by the reviewer with the following points:

1. Interpretation of PCs: In PCA, the principal components represent orthogonal axes capturing the highest variance in the data. PC1 accounts for 56% and 57% of the variance in the two conditions, respectively. The significant variance explained by PC1 suggests that it effectively captures the major sources of variation between the samples.

2. Sample Replicates and Variability: The concern regarding the small number of replicates is acknowledged, and we understand its impact on the analysis. Despite the limited number of replicates, the consistent pattern of separation in PC1 between the two conditions provides confidence in the observed separation. We also agree that PC2 does not show an apparent similarity among the DNase-treated samples; however, this does not diminish the significance of PC1, which robustly separates the two conditions.

We include the Figure legend for 4C: “C) Principal component analysis shows EV sample grouping due to specificity in coding-gene sequences.

Q14. I am confused by the phrasing in the last two sentences of the top paragraph on page 7. Why would apoptotic bodies all have similar content if they encapsulate a greater amount of material making their contents less specific? Please clarify.

A14. This sentence intended to convey the fact that apoptotic bodies (ABs) are formed from apoptotic cells, they are larger in size, and their content is more non-specific - this non-specific nature arises as they do not encapsulate molecules specifically, unlike the other two types of vesicles. For more detailed information on ABs in human reproduction, we published an extensive review in 2018 (see below).

Simon C, Greening DW, Bolumar D, Balaguer N, Salamonsen LA, Vilella F. Extracellular Vesicles in Human Reproduction in Health and Disease. Endocr. Rev. 2018 Jun 1;39(3):292-332. doi: 10.1210/er.2017-00229. PMID: 29390102.

Q15. The first and last sentences of the last paragraph of page 8 seem to contradict each other. Please clarify.

A15. We observe an enrichment in the amount of mitochondrial DNA in samples during the receptive and post-receptive phases. While the data may not show statistical significance, we observed a trend towards greater enrichment in receptivity compared to pre-receptivity. The lack of significant differences could be attributed to inherent variability among patients. We have also altered the text on page 8 to avoid confusion.

Q16. Quantification of the rates of DNA incorporation into embryos would strengthen Figure 4 and Supplementary Figure 5.

A16. We acknowledge the reviewer's feedback, and in response, we conducted an assay to quantify the total DNA incorporated into the embryos. We isolated EVs from the control Ishikawa cell culture media and EdU-treated Ishikawa cell culture media to achieve this. Subsequently, we co-incubated both types of EVs with ten embryos overnight in G2 plus media at 37ºC and 5% CO2.

After co-incubation, we collected embryos and the culture media containing co-incubated EVs. We then isolated total DNA using the QIAamp® DNA Mini kit (Qiagen; 51304). To label the EdU-DNA particles, we performed a click-it reaction using the Click-iT™ EdU Alexa Fluor™ 488 flow cytometry assay Kit (Thermo Fisher Scientific, ref: C10420) per the manufacturer's instructions. Subsequently, we cleaned and purified DNA using AMPure beads XP (Beckman Coulter, A63882) and eluted DNA in 150 L of 0.1 M Tris-EDTA. Finally, we measured the fluorescence of each sample using a Victor3 plate reader (PerkinElmer). To ensure accuracy, we subtracted the background signal from non-labeled DNA-derived EVs and embryos incubated without EVs for each sample. Despite conducting the experiment twice, we encountered challenges in obtaining clear results, possibly due to the limitation of the technique's resolution.

Q17. If mtDNA is most enriched in MVs but only embryos cultured with Exos demonstrated differences in respiration the authors need to comment on this discrepancy.

A17. We ask the reviewer to refer to Answer A3; we have thoroughly revised the manuscript, focusing our message on DNA content.

Q18. The authors should change the definitive language in the title of the manuscript because all evidence presented is correlative.

A18.We have modified the title to better align with the manuscript's results. The proposed new title for the manuscript is “Vertical transmission of maternal DNA through extracellular vesicles modulates embryo bioenergetics during the periconceptional period.”

Q19. I realize this is beyond what the authors intend for the scope of this paper, however, on page 6 the authors describe membranous structures within the ABs but say they couldn't study their presence with organelle-specific markers. Why? Presence of organelles in these vesicles is very interesting!

A19. As the reviewer rightly points out, we did not study ABs in this manuscript. Analysis of the electron microscopy images suggests the presence of fragments of organelles, most likely originating from apoptotic processes; however, we did not use any specific markers to confirm our assertion. We have modified the text to avoid any confusion. Please see Page 6, Lines 120-121, for further details.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The authors have examined gene expression between life cycle stages in a range of brown macroalgae to examine whether there are conserved aspects of biological features. 

Strengths: 

The manuscript incorporates large gene expression datasets from 10 different species and therefore enables a comprehensive assessment of the degree of conservation of different aspects of gene expression and underlying biology. 

The findings represent an important step forward in our understanding of the core aspects of cell biology that differ between life cycle phases and provide a substantial resource for further detailed studies in this area. Convincing evidence is provided for the conservation of lifecycle-specific gene expression between species, particularly in core housekeeping gene modules. 

Weaknesses: 

I found a few weaknesses in the methodology and experimental design. I think the manuscript could have been clearer when linking the findings to the biology of the brown algae. 

Reviewer #2 (Public review): 

Summary: 

The manuscript by Ratchinski et al presents a comprehensive analysis of developmental and life history gene expression patterns in brown algal species. The manuscript shows that the degree of generation bias or generation-specific gene expression correlates with the degree of dimorphism. It also reports conservation of life cycle features within generations and marked changes in gene expression patterns in Ectocarpus in the transition between gamete and early sporophyte. The manuscript also reports considerable conservation of gene expression modules between two representative species, particularly in genes associated with conserved functional characteristics. 

Strengths: 

The manuscript represents a considerable "tour de force" dataset and analytical effort. While the data presented is largely descriptive, it is likely to provide a very useful resource for studies of brown algal development and for comparative studies with other developmental and life cycle systems. 

Weaknesses: 

Notwithstanding the well-known issues associated with inferring function from transcriptomics-only studies, no major weaknesses were identified by this reviewer. 

Reviewing Editor Comments:

The overall assessment of the reviewers does not contain major aspects of concern. We nevertheless recommend that the authors carefully consider the constructive comments, as this will further improve their manuscript. 

Reviewer #1 (Recommendations for the authors): 

(1) Line 32: The abstract states 'considerable conservation of co-expressed gene modules', but the degree of conservation between Ectocarpus and D. dichotoma appeared limited to specific subsets of genes with highly conserved housekeeping functions, e.g., translation. I think the wording of the abstract should be rephrased to better reflect this. 

We agree that genes with housekeeping functions figure strongly in the gene modules that showed strong conservation between Ectocarpus species 7 and D. dichotoma (and we actually highlight this point in the manuscript) but we do not believe that this invalidates the conservation. In the analysis shown in Figure 6A, for example, high scores were obtained for both connectivity and density for about a third of the gene modules and these modules cover broad range of cellular functions. This is a significant result given the large phylogenetic distance and we feel that "considerable conservation" is appropriate as a description of the level of correlation. 

(2) Introduction - The Introduction needs a better explanation of the biology of the life cycle phases. Some of this information is present in the 1st paragraph of Materials and Methods, although it would be preferable to include this information within the main text, ideally within the Introduction before the Results are described. For example, when are flagella present? The presence of flagella could be indicated in Figure 3. The ecology of the life cycle is also not described. Are life cycles present in the same ecological niche? Do they co-exist or occupy distinct environments? It would be useful to understand how the observed genotypes could relate to this wider aspect of the brown algal biology. 

We have added a sentence to explain that zoids (gametes and spores) are the only flagellated stages of the life cycle (line 678). In addition, in the legend for Figure 3, we have indicated which of the life cycle stages analysed in panel 3A consisted entirely or partially of flagellated cells. We have also added information about phenology to the Introduction. 

(3) Line 127. 'The proportion of generation specific genes was positively correlated with the level of dimorphism'. The level of dimorphism between species was not clear to me. This needs to be clearly displayed in Figure 1B. 

We had attempted to illustrate the level of dimorphism, using the size of each generation as a measurable proxy, in Figure S1 but we agree that the information was not very clearly presented. To improve clarity, we now provide independent size scales for each generation of the life cycle in this figure and state in the legend that "Size bars indicate the approximate sizes of each generation of each life cycle, providing an indication of the degree of dimorphism between the two generations.". In the text, Figure S1 is cited earlier in the paragraph but we now repeat the citation of the figure at the end of the sentence "The proportion of generation-specific genes (...) was positively correlated with the level of dimorphism" so that the reader can specifically consult the supplementary figure for this phenotypic parameter. 

(4) Line 267. Are there known differences in cell wall composition between life cycle phases or within each generation as individual life cycle phases mature (e.g., differences between unicellular and multicellular stages)? 

Detailed comparative analyses of cell wall composition at different stages of the life cycle have not been carried out for brown algae. However, Congo red stains Ectocarpus gametophytes but not sporophytes (Coelho et al., 2011), indicating a difference in cell wall composition between the two generations. Zoids (spores and gametes) do not have a cell wall and calcofluor white staining of meio-spores has indicated that a cell wall only starts to be deposited 24-48 hours post-release (Arun et al., 2013).

(5) Line 388. The authors should comment on the accuracy of OrthoFinder for different gene types across this degree of divergence (250 MYA). The best conservation was found in genes with housekeeping characteristics (line 401). It may be that these gene modules show the highest degree of conservation in expression patterns, but I also wonder whether they pattern may also emerge because finding true orthologues is easier for highly conserved gene families. 

We do not believe that this is the case because, as mentioned above, the "housekeeping" modules cover quite a broad range of cellular functions. Note also that the modules were given functional labels based on their being clearly enriched in genes corresponding to a particular class of function but not all the genes in a module have a predicted function that corresponds to the functional classification. 

However, we have carried out an analysis to look for evidence of the bias proposed by the reviewer. For this, we used BLASTp identity scores as an approximate proxy for pairwise identity between Ectocarpus species 7 and D. dichotoma one-to-one orthologues in each module and plotted the mean identity score for each module against the Fischer test p-value of the contingency table in Figure 6C (Author response image 1).

Author response image 1.

Plot of estimations of the mean percent shared identity between the orthologues within each module (based on mean BLASTp identity scores) against log10(pvalue) values obtained with the Fisher's exact test applied in Figure 6C to determine whether pairs of modules shared a greater number of one-to-one orthologues than expected from a random distribution. Error bars indicate the standard deviation. 

This analysis did not detect any correlation between the degree of sequence conservation of orthologues in a module and the degree of conservation of the module between Ectocarpus species 7 and D. dichotoma.

Minor comments 

(1) Line 650 loose should be lose.

The error has been corrected.

(2) Line 695 filtered through a 1 μm filter to remove multicellular gametophyte fractions. Is this correct? It seems too small to allow gametes to pass through. 

Yes, the text is correct, a 1 μm filter was used. The gametes do pass through this filter, presumably because they do not have a rigid cell wall, allowing them to squeeze through the filter when a light pressure is applied. 

(3) Line 709 - DDT should be DTT 

The error has been corrected.

Reviewer #2 (Recommendations for the authors): 

(1) It is not clear why the chosen species for analysis do not include fucoid algae, which display a high degree of dimorphism between generations and which are relatively well studied with respect to gene expression patterns during early development. Indeed, it was recently shown that gene expression patterns in developing embryos of Fucus spp. obey the "hourglass" pattern whereby gene expression shows a minima of transcription age index (i.e., higher expression of evolutionarily older genes) associated with differentiation at the phylotypic stage. I am somewhat surprised that the manuscript does not consider this feature in the analysis or discussion. 

Brown algae of the order Fucales have diploid life cycles and therefore do not alternate between a sporophyte and gametophyte generation. It is for this reason that we thought that it was more interesting to compare Ectocarpus species 7 with D. dichotoma, which has a haploid-diploid life cycle.

(2) In Discussion, the comparison of maternal to zygote transition in animals and land plants, which show a high degree of dimorphism, with Ectocarpus would be strengthened by data/discussion from other brown algae that show a high degree of dimorphism. 

Animals have diploid life cycles and dimorphism in that lineage generally refers to sexual rather than generational dimorphism. Land plants do have highly dimorphic haploiddiploid life cycles but it is unclear how this characteristic relates to events that occur during the maternal to zygote transition. In Ectocarpus, the transition from gamete to the first stages of sporophyte development involved more marked changes in gene expression than we observed when comparing the mature sporophyte and gametophyte generations (Figure 3C). At present, there is no evidence that events during these two transitions are correlated. The relationship between changes in gene expression during very early sporophyte development and during alternation of life cycle generations could be investigated further using a highly dimorphic kelp model system such as Saccharina latissima but we are not aware of any studies that have specifically addressed this point. 

(3) Since marked changes were observed during the transition from gamete to early sporophyte in Ectocarpus, it would be interesting to know how gene expression patterns change during the transition from gamete to partheno-sporophyte. Would the same patterns of downregulation and upregulation be expected? 

The sporophyte individuals derived from gamete parthenogenesis (parthenosporophytes) are indistinguishable morphologically and functionally from diploid sporophytes derived from gamete fusions (see line 76). They also express generation marker genes in a comparable manner (Peters et al., 2008). Based on these observations, we have treated partheno-sporophytes and diploid sporophytes as equivalent in our experiments. For clarity, we have now distinguished partheno-sporophyte from diploid sporophyte samples in Table S1. 

(4) The authors show a correlation between the degree of dimorphism and generation-biased or generation-specific expression. How was the degree of dimorphism quantified? 

The degree of dimorphism is illustrated in Figure S1 using the relative size of the two generations as a proxy. Size estimations are approximate because the size of an individual of a particular species is quite variable but the ten species nonetheless represent a very clear gradient of dimorphism due to the extreme differences in size between generations of species at each end of the scale, with the sporophyte generation being several orders of magnitude larger than the gametophyte generation or visa versa. 

References

Arun A, Peters NT, Scornet D, Peters AF, Cock JM, Coelho SM. 2013. Non-cell autonomous regulation of life cycle transitions in the model brown alga Ectocarpus. New Phytol 197:503– 510. doi:10.1111/nph.12007

Coelho SM, Godfroy O, Arun A, Le Corguillé G, Peters AF, Cock JM. 2011. OUROBOROS is a master regulator of the gametophyte to sporophyte life cycle transition in the brown alga Ectocarpus. Proc Natl Acad Sci USA 108:11518–11523. doi:10.1073/pnas.1102274108

Peters AF, Scornet D, Ratin M, Charrier B, Monnier A, Merrien Y, Corre E, Coelho SM, Cock JM. 2008. Life-cycle-generation-specific developmental processes are modified in the immediate upright mutant of the brown alga Ectocarpus siliculosus. Development 135:1503–1512.doi:10.1242/dev.016303

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Structural colors (SC) are based on nanostructures reflecting and scattering light and producing optical wave interference. All kinds of living organisms exhibit SC. However, understanding the molecular mechanisms and genes involved may be complicated due to the complexity of these organisms. Hence, bacteria that exhibit SC in colonies, such as Flavobacterium IR1, can be good models.

Based on previous genomic mining and co-occurrence with SC in flavobacterial strains, this article focuses on the role of a specific gene, moeA, in SC of Flavobacterium IR1 strain colonies on an agar plate. moeA is involved in the synthesis of the molybdenum cofactor, which is necessary for the activity of key metabolic enzymes in diverse pathways.

The authors clearly showed that the absence of moeA shifts SC properties in a way that depends on the nutritional conditions. They further bring evidence that this effect was related to several properties of the colony, all impacted by the moeA mutant: cell-cell organization, cell motility and colony spreading, and metabolism of complex carbohydrates. Hence, by linking SC to a single gene in appearance, this work points to cellular organization (as a result of cell-cell arrangement and motility) and metabolism of polysaccharides as key factors for SC in a gliding bacterium. This may prove useful for designing molecular strategies to control SC in bacterial-based biomaterials.

Strengths:

The topic is very interesting from a fundamental viewpoint and has great potential in the field of biomaterials.

Thank you for this.

The article is easy to read. It builds on previous studies with already established tools to characterize SC at the level of the flavobacterial colony. Experiments are well described and well executed. In addition, the SIBR-Cas method for chromosome engineering in Flavobacteria is the most recent and is a leap forward for future studies in this model, even beyond SC.

We appreciate these comments.

Weaknesses:

The paper appears a bit too descriptive and could be better organized. Some of the results, in particular the proteomic comparison, are not well exploited (not explored experimentally). In my opinion, the problem originates from the difficulty in explaining the link between the absence of moeA and the alterations observed at the level of colony spreading and polysaccharide utilization, and the variation in proteomic content.

We have looked at the organisation of the manuscript carefully in this revision, as suggested. In terms of the proteomics, there are a large number of proteins affected by the moeA deletion and not all could be followed up. We chose spreading, structural colour formation and starch degradation to follow up phenotypically, as the most likely to be relevant. For example, (L615-617) we discuss the downregulation of GldL (which is known to be involved Flavobacterial gliding motility [Shrivastava et al., 2013]) in the moeA KO as a possible explanation for the reduced colony spreading of this mutant. Changes in polysaccharide (starch) utilization were seen on solid medium, as well as in the proteomic profile where we observed the upregulation of carbohydrate metabolism proteins linked to PUL (polysaccharide utilisation locus) operons (Terrapon et al., 2015), such as PAM95095-90 (Figure 8), and other carbohydrate metabolism-related proteins, including a pectate lyase (Table S7) which is involved in starch degradation (Aspeborg et al., 2012). And as noted in L555-566 and Figure 9, alterations in starch metabolism were investigated experimentally.

First, the effect of moeA deletion on molybdenum cofactor synthesis should be addressed.

MoeA is the last enzyme in the MoCo synthesis pathway, thus if only MoeA is absent the cell would accumulate MPT-AMP (molybdopterin-adenosine monophosphatase) (Iobbi-Nivol & Leimkühler, 2013), and the expressed molybdoenzymes would not be functional. In L582-585, we commented how the lack of molybdenum cofactor may affect the synthesis of molybdoenzymes. However, if you meant to analyse the presence of the small molecules, i.e. the cofactors involved in these pathways, that was an assay we were not able to perform. However, in L585-587, we addressed how the deletion of moeA affected the proteins encoded by the rest of genes in the operon which is relevant to the question.

Second, as I was reading the entire manuscript, I kept asking myself if moeA (and by extension molybdenum cofactor) was really involved in SC or it was an indirect effect. For example, what if the absence of moeA alters the cell envelope because the synthesis of its building blocks is perturbed, then subsequently perturbates all related processes, including gliding motility and protein secretion? It would help to know if the effects on colony spreading and polysaccharide metabolism can be uncoupled. I don't think the authors discussed that clearly.

The message of the paper is that the moeA gene, as predicted from a previous genomics analysis, is important in SC. This is based on the representation of the moeA gene in genomes of bacteria that display SC. This analysis does not predict the mechanism. When knocked out, a significant change in structural colour occurred, supporting this hypothesis. Whether this effect is direct or indirect is difficult to assess, as this referee rightly suggests. In order to follow up this central result, we performed proteomics (both intra- and extracellular). As we observed, the deletion of a single gene generated many changes in the proteomic profile, thus in the biological processes. Based on the known functions of molybdenum cofactor, we could only hypothesize that pterin metabolism is important for SC, not exactly how.

We have discussed the links between gliding/spreading and polysaccharide metabolism more clearly, with reference to the literature, as quite a bit is known here including possible links to SC.

“Polysaccharide metabolism in IR1 has been linked to changes in colony color and motility through the study of fucoidan metabolism (van de Kerkhof et al., 2022). Polysaccharide degradation and gliding motility are coupled to the same mechanism: the phylum-specific type IX secretion system, used for the secretion of enzymes and proteins involved in both functions (McKee et al., 2021).” [L622-626]

Reviewer #2 (Public review):

Summary:

The authors constructed an in-frame deletion of moeA gene, which is involved in molybdopterin cofactor (MoCo) biosynthesis, and investigated its role in structural colors in Flavobacterium IR1. The deletion of moeA shifted colony color from green to blue, reduced colony spreading, and increased starch degradation, which was attributed to the upregulation of various proteins in polysaccharide utilization loci. This study lays the ground for developing new colorants by modifying genes involved in structural colors.

Major strengths and weaknesses:

The authors conducted well-designed experiments with appropriate controls and the results in the paper are presented in a logical manner, which supports their conclusions.

We appreciate these comments.

Using statistical tests to compare the differences between the wild type and moeA mutant, and adding a significance bar in Figure 4B, would strengthen their claims on differences in cell motility regarding differences in cell motility.

Thank you. Figure 4B contains the significance bars that represent the standard deviation of the mean value of the three replicates, but we have modified it to make them more clear.

Additionally, in the result section (Figure 6), the authors suggest that the shift in blue color is "caused by cells which are still highly ordered but narrower", which to my knowledge is not backed up by any experimental evidence.

Thanks. We mentioned that the mutant cells are narrower than the wild type based on the estimated periodicity resulting from the goniometry analysis (L427-430). We will now say “likely to be narrower based on the estimated periodicity from the optical analysis” rather than just “narrower”.

“This optical analysis aligns with visual observations, confirming the blue shift in ΔmoeA, and suggests that this change in SC is caused by cells which are likely to be narrower based on the estimated periodicity from the optical analysis.” [L409-411]

Overall, this is a well-written paper in which the authors effectively address their research questions through proper experimentation. This work will help us understand the genetic basis of structural colors in Flavobacterium and open new avenues to study the roles of additional genes and proteins in structural colors.

Much appreciated.

Recommendations for the authors:

Reviewing Editor Comments:

As you will see, the reviewers were rather positive about the paper but suggested a number of points to improve it, including a discussion of the direct role of moeA as well as specific editorial comments.

Reviewer #1 (Recommendations for the authors):

More specific comments to the authors:

(1( Line 300, Paragraph on bioinformatic analysis of molybdopterin operon : As written, it is not clear whether this operon is crucial for pterin cofactor synthesis or only some genes are involved. And what is the contribution of moeA?

Based on the bioinformatic analysis done in Zomer et al., 2024, we know the score of which genes of the molybdopterin cofactor synthesis operon may be more relevant to the display of SC, in addition to moeA. We chose moeA to KO as it had the highest score, being careful to delete the coding sequence and not any upstream promoter. The other genes in the predicted operon are moaE, moaC2, and moaA. Then in the proteomic analysis (L435-442), we analysed how the encoded proteins from this operon were upregulated (MoaA, MoaC2, and MobA), indicating also the unaltered proteins (MoeZ and MoaE) and the undetected proteins (MoaD and SumT). Nevertheless, the operon is crucial for pterin cofactor synthesis because it contains all the genes involved in the pathway, and moeA encoded the enzyme for the last reaction of the pathway, being the the molecule produced in the mutated pathway the adenylated molybdopterin (MPT-AMP) instead of molybdenum cofactor (MoCo).

(2) Paragraph line 342 on moeA mutant phenotyping :

Is the reduction in colony spreading caused by a defect in single-cell gliding motility or is the cause more complex? This can be quantified.

We believe the cause is more complex. As mentioned above, for example, in (L615-617) we discuss the downregulation of GldL (which is known to be involved Flavobacterial gliding motility [Shrivastava et al., 2013]) in the moeA KO as a possible explanation for the reduced colony spreading of this mutant. This cannot be explained simply by spreading, but must (from the optical analysis) indicate changes in cell organisation/dimensions.

(3) During the description of the moeA mutant phenotype (associated with Figures 2 and 4) and throughout the article, the optical properties are « functions » of colony spreading and moeA-dependent metabolism. However it is not quite clear if these two effects are independent or if one may be a consequence of the other.

As noted above, colony spreading alone does not explain the blue-shift in SC observed. Given the function of MoeA (molybdate insertion into MPT-AMP [adenylated molybdopterin], MoMPT [molybdenum-molybdopterin] formation) for the synthesis of MoCo (molybdenum cofactor), the primary effect seems to be on metabolism but as we are dealing with an influential enzymatic cofactor a number of secondary effects are likely, and indeed the proteomics supports this. It is likely that the effect on spreading is secondary as seen with the downregulation of GldL (see above), but we cannot be sure.

(4) Paragraph starting line 381 and Figure 5 on gliding motility:

Gliding motility has to be tested at the level of single cells, allowing a more thorough characterization of the spreading defects. In addition, since gliding is entangled with Type IX-dependent secretion in Flavobacteria, the authors should test if Type IXdependent was perturbed in the absence of moeA.

Based on the intracellular and extracellular proteomic analyses, the regulated T9SS proteins in the absence of moeA are the downregulation of GldL and SprT, and the upregulation of PorU. It shows the log2 FC (moeA/WT) of each these extracellular proteins:

Author response table 1.

<-1: downregulated in moeA KO, -1<X<1: no significant regulation, >1: upregulated in moeA KO, -: not detected

(5) L401: In my opinion, the section "Quantification of the optical responses of IR1 WT and ΔmoeA colonies" should be moved up, before the characterization of motility.

We have done this, as suggested. The section was moved from L401-423 to L388-411.

(6) L475: Proteome comparison: « Of the total known proteins in IR1, 27.5% (1,504 proteins) extracellular proteins were identified » Are some of these proteins also found in the cell fraction? Wouldn't it be more accurate to write that « 1504 proteins were found in the extracellular fraction"?

We have done this, as suggested.

“Of the total known proteins in IR1, 27.5% (1,504 proteins) proteins were detected in the extracellular fraction, 60.4% (909) were statistically significant (p<0.01), with 20.5% (186) considered downregulated, and 20% (182) upregulated in ΔmoeA (Figure 7B).” [L484-486]

How can the authors exclude contamination of the extracellular fraction? This could easily explain the number of proteins lacking secretion signals: "29.6% (55) were likely secreted through a non-classical way, lacking typical secretion sequence motifs in their N-terminus."

Based on the results from SecretomeP and SignalP, we excluded contamination, reducing the significant downregulated proteins from 186 (L476) to 69 (L486), and the upregulated ones from 182 (L477) to 111 (L500).

(7) L490: if the protein misannotated flagellin is highly downregulated, why not push the analysis a bit further and ask what true function may be perturbed? In addition, it should not be classified as a motility protein in Table S6 and considered as a motility protein in the article.

We reconsidered the information given by this and decided to remove it because after checking the homology of the polypeptide by Blast searching, we feel it is probably due to a missannotation.

As is, the whole proteomic section is not that useful. Too many functions are evoked and the reader is not directed toward any particular conclusion. The most convincing hits from the proteomic analysis should be confirmed using another method. Transcriptional regulation could be easily probed by RT-qPCR. Or, since genetics is possible, proteins could be tagged and levels compared by western blot maybe? Do knock-out of the encoding genes generate any phenotype on SC? This would bring weight to the proteomic analysis.

We have revised the proteomics section and removed functions that are not directly relevant to our conclusion.

We feel the most important observation suggested by proteomics was the possible link between moeA and starch metabolism, because the metabolism of complex polysaccharides is important in the Flavobacteriia and known to be linked to SC (van de Kerkhof et al., 2022). It was not possible to follow up every pathway suggested by the proteomics, but the study is appropriately performed with the correct statistics.

(8) Figure 9 : Does the absence of moeA affect the spreading of ASWS? Were colony sizes similar during the starch degradation assay? How can the authors rule out the idea that starch degradation is impacted by the difference in spreading rather than an independent function of moeA in starch metabolism? Slower spreading could lead to the accumulation of amylases, hence stronger activity. Why does starch degradation only accumulate at the center of the colony in the WT case?

The colonies of the WT and moeA had similar size during the starch degradation assay (2 days). However, after day 3, only WT colonies kept expanding on diameter.

Starch degradation is logically in the centre of the colony as it is where the greatest concentration of cells exists, secreting degradative enzymes, for the longest time. Presumably starch degradation at the colony edge is not yet seen as the action of extracellular enzymes is low and has not had time to degrade the starch to the point that there is no iodine staining.

“In contrast to other media where ΔmoeA colony expansion was less than WT, the ΔmoeA showed similar colony spreading and stronger starch degradation, supporting a role of moeA in complex polysaccharides metabolism.” [L562-565]

(9) Finally, I am not quite sure what the authors mean by « a role of moeA in complex polysaccharides metabolism ». Are they referring to enzymes secreted in the medium to degrade starch? or to the incorporation and use of starch degradation products?

We meant that the deletion of moeA showed an increase of extracellular starch degradation as seen in the iodine assay (Figure 9), as well as the upregulation of three different PUL operons (Figure 8).

Reviewer #2 (Recommendations for the authors):

The paper in general is well written with proper experimentation. However, here are a few recommendations for improving the writing and presentation, including minor corrections to the text and figures.

Thank you.

(1) It would be helpful for the readers if you could expand on "some metabolic pathways" in line 71. Please provide examples of metabolic pathways that are linked to SC.

We have done this.

“A recent bioinformatic study has shown the possible link of some metabolic pathways, such as carbohydrate, pterin, and acetolactate metabolism, to bacterial SC (Zomer et al., 2024).”[L70-72]

(2) "Line 79 : a bioinformatics analysis", please mention what kind of bioinformatics analysis was done and by whom to provide clarity for the readers: Either mention bio info analysis or give more details on what kind of bio info analysis and study done by whom"

We have clarified this, as suggested.

“A large-scale, genomic-based analysis of 117 bacteria strains (87 with SC and 30 without) identified genes potentially involved in SC by comparing gene presence/absence, providing a SC-score (Zomer et al., 2024). By this method, pterin pathway genes were strongly predicted to be involved in SC.” [L80-83]

(3) Please correct "Bacteria strains used in this study" to "bacterial" strains in Line 122.

We have done so.

(4) Please indicate in "Lines 394-396" that there were no vortex patterns observed in the moeA mutant.

We have done so.

“In contrast, ΔmoeA exhibited limited motility, with a more tightly packed cell organization and a fine, slow-moving layer at the edge (Figure 6, blue arrows), and did not show a ‘vortex’ pattern. This suggests that moeA deletion significantly impairs cell motility and colony expansion.” [428-L431]

(5) In Figure 4 it looks like with a different carbon source (ASWB with agar and Fucoidan (ASWBF)) the moeA mutant and wild type exchanges its phenotype compared to ASWBKC. Could you explain why this happens in the discussion by highlighting the differences between fucose and Kappa-Carrageenan or confirm if there are any differences in the carbohydrate utilization between the wild type and moeA mutant using biolog assays?

We have explained the differences. Biolog would not be appropriate as we are looking for metabolic processes of bacteria on surfaces (agar) and this is not necessarily appropriate to biolog, which we understand uses liquid cultivation in microplates.

“On different polysaccharide media, the ΔmoeA strain showed varied SC and colony expansion patterns: green/blue SC and low colony expansion on agar, intense blue SC and low colony expansion on kappa-carrageenan, dull green SC and low colony expansion on fucoidan, and blue/green SC with higher colony expansion on starch. Interestingly, the color phenotype of the WT and ΔmoeA exchanged their phenotype on kappa-carrageenan (a simple linear sulfated polysaccharide of D-galactopyranose) and fucoidan (a complex sulfated polysaccharide of fucose and other sugars as galactose, xylose, arabinose and rhamnose), showing the importance of the polysaccharide metabolism in SC. While reduced motility has been associated with dull or absent SC, and reduced polysaccharide metabolism (Kientz et al., 2012a; Johansen et al., 2018), ΔmoeA showed reduced motility, but an intense blue SC, and high polysaccharide metabolism. Based on these results, we established a link among polysaccharide metabolism, MoCo biosynthesis, and SC, showing that intense SC is not strictly dependent on motility.” [L636-648]

(6) In the discussion "Line 632" it is unclear what loss is being limited, and it would help strengthen your discussion if you could add references for lines: 633-636. There are a lot of hypotheses in lines 637-642, it would help the readers if you could clearly mention that these are hypotheses and will need experimental evidence or provide appropriate evidence to support these claims.

We have done this.

“Ecologically, we hypothesize that dense, highly structured bacterial colonies, such as necessary for the SC phenotype, can enhance the uptake of metabolic degradation products from complex polysaccharides. These large macromolecules are often partially hydrolyzed extracellularly because they are too large to pass through bacterial cell membranes. For example, marine Vibrionaceae strains that produce lower levels of extracellular alginate lyases tend to aggregate more strongly, potentially facilitating localized degradation and uptake of polysaccharides (D’Souza et al., 2023). Additionally, certain marine bacteria employ a "selfish" mechanism to internalize large polysaccharide fragments into their periplasmic space, minimizing loss to the environment and enhancing substrate utilization (Reintjes et al., 2017). Bacteria secrete enzymes into the surrounding environment to break these polysaccharides down into more easily absorbable monosaccharides or oligosaccharides. This mechanism suggests that the colony structure could create a physical barrier that keeps these products concentrated and near the cells, allowing the colony to efficiently access and utilize these products, preventing the leakage into the surrounding environment. While SC may also yield other ecological benefits associated with growth in biofilms, the highly structured colonies that characterize SC may be more resistant against invasion by competitor species scavenging for degradation products, than an unstructured biofilm. This model is consistent with the observation that SC is associated with polysaccharide metabolism genes, and with the recent observation that SC is mainly localized on surface and interface environments such as airwater interfaces, tidal flats, and marine particles (Zomer et al., 2024).” [L650-670]

(7) It would help the readers if you could expand on how polysaccharide metabolism is linked to motility in Line 610.

As indicated previously, this is known and we will clarify.

“Polysaccharide metabolism in IR1 has been linked to changes in colony color and motility through the study of fucoidan metabolism (van de Kerkhof et al., 2022).” [L622-623]

Author response:

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

eLife Assessment:

“…However, the findings are reliant on high concentrations of inhibitor drugs, and mechanistic details about the molecular interaction and respective functions of ABHD2 and mPRb are incomplete.”

As discussed below in the response to Reviewers the drug concentrations used span the full dose response of the active range of each drug. In cases where the drug concentrations required to block oocyte maturation where significantly higher than those reported in the literature, we considered those drugs ineffective. In terms of the molecular details of the mechanistic interaction between mPRb and ABHD2, we now provide additional data confirming their molecular interaction to produce PLA2 activity where each protein alone is insufficient. Although these new studies provide more mechanistic insights, there remains details of the ABHD2-mPR interactions that would need to be addressed in future studies which are beyond the scope of the current already extensive study.   

Public Reviews:

Reviewer 1

(1) The mechanism governing the molecular assembly of mPRbeta and ABHD2 remains unclear. Are they constitutively associated or is their association ligand-dependent? Does P4 bind not only to mPRbeta but also to ABHD2, as indicated in Figure 6J? In the latter case, the reviewer suggests that the authors conduct a binding experiment using labeled P4 with ABHD2 to confirm this interaction and assess any potential positive or negative cooperativity with a partner receptor.

The co-IP experiments presented in Figure 5E argue that the two receptors are constitutively associated at rest before exposure to P4; but at low levels since addition of P4 increases the association between mPRβ and ABHD2 by ~2 folds. Importantly, we know from previous work (Nader et al., 2020) and from imaging experiments in this study that mPR recycles in immature oocytes between the PM and the endosomal compartment. It is not clear at this point within which subcellular compartment the basal association of mPR and ABHD2 occurs. We have tried to elucidate this point but have not been able to generate a functional tagged ABHD2. We generated GFP-tagged ABHD2 at both the N- and C-terminus but these constructs where not functional in terms of their ability to rescue ABHD2 knockdown. This prevented us from testing the association dynamics between ABHD2 and mPR.   

Regarding whether ABHD2 in the oocyte directly binds P4 or not, we had in the initial submission no data directly supporting this rather we based the cartoon in Fig. 6J on the findings from Miller et al. (Science 2016) who showed that ABHD2 in sperm binds biotinylated P4. With the use of a new expression system to produce ABHD2 in vitro (please see below) we were able to try the experiment suggested by the Reviewer. In vitro expressed ABHD2 was incubated with biotinylated P4, and binding tested on a streptavidin column. Under these conditions we could not detect any specific binding of P4 to ABHD2, however, these experiments remain somewhat preliminary and would require validation using additional approaches to conclusively test whether Xenopus ABHD2 binds P4 or not. The discrepancy with the Miller et al. findings could be species specific as they tested mammalian ABHD2.  

(2) The authors have diligently determined the metabolite profile using numerous egg cells. However, the interpretation of the results appears incomplete, and inconsistencies were noted between Figure 2B and Supplementary Figure 2C. Furthermore, PGE2 and D2 serve distinct roles and have different elution patterns by LC-MS/MS, thus requiring separate measurements. In addition, the extremely short half-life of PGI2 necessitates the measurement of its stable metabolite, 6-keto-PGF1a, instead. The authors also need to clarify why they measured PGF1a but not PGF2a.

We believe the Reviewer meant to indicate discrepancies between Fig. 2E (not 2B) and Supp. Fig. 2C. Indeed, the Reviewer is correct, and this is because Fig. 2E shows pooled normalized data on a per PG species and frog, whereas Supp. Fig. 2E shows and example of absolute raw levels from a single frog to illustrate the relative basal abundance of the different PG species. We had failed to clarify this in the Supp. Fig. 2E figure legend, which we have now added in the revised manuscript. So, the discrepancies are due to variation between different donor animals which is highlighted in Supp. Fig. 2A. Furthermore, to minimize confusion, in the revised manuscript we revised Supp. Fig. 2C to show only PG levels at rest, to illustrate basal levels of the different PG species relative to each other, which is the goal of this supplemental figure. 

(3) Although they propose PGs, LPA, and S1P are important downstream mediators, the exact roles of the identified lipid mediators have not been clearly demonstrated, as receptor expression and activation were not demonstrated. While the authors showed S1PR3 expression and its importance by genetic manipulation, there was no observed change in S1P levels following P4 treatment (Supplementary Figure 2D). It is essential to identify which receptors (subtypes) are expressed and how downstream signaling pathways (PKA, Ca, MAPK, etc.) relate to oocyte phenotypes.

We agree conceptually with the Reviewer that identifying the details of the signaling of the different GPCRs involved in oocyte maturation would be interesting. However, our lipidomic data argue that the activation of a PLA2 early in the maturation process in response to P4 leads to the production of multiple lipid messengers that would activate GPCRs and branch out the signaling pathway to activate various pathways required for the proper and timely progression of oocyte maturation. Preparing the egg for fertilization is complex; so, it is not surprising that a variety of pathways are activated simultaneously to properly initiate both cytoplasmic and nuclear maturation to transition the egg from its meiotic arrest state to be ready to support the rapid growth during early embryogenesis. We focus on the S1P signaling pathway specifically because, as pointed out by the Reviewer, we could not detect an increase in S1P even though our metabolomic data collectively argued for an increase. Our results on the S1P pathway -as well as a plethora of other studies historically in the literature that we allude to in the manuscript- argue that these different GPCRs support and regulate oocyte maturation, but they are not essential for the early maturation signaling pathway. For example, for S1P, as shown in Figure 4, the delay/inhibition of oocyte maturation due to S1PR3 knockdown can be reversed at high levels of P4, which presumably leads to higher levels of other lipid mediators that would bypass the need for signaling through S1PR3. This is reminiscent of the kinase cascade driving oocyte maturation where there is significant redundancy and feedback regulation. Therefore, analyzing each receptor subtype that may regulate the different PG species, LPA, and S1P would be a tedious and time-consuming undertaking that goes beyond the scope of the current manuscript. More importantly based on the above arguments, we suggest that findings from such an analysis, similar to the conclusions from the S1PR3 studies (Fig. 4), would show a modulatory role on oocyte maturation rather than a core requirement for the maturation process as observed with mPR and ABHD2. Thus they would provide relatively little insights into the core signaling pathway driving P4-mediated oocyte maturation.

Reviewer 2:

(1) The ABHD2 knockdown and rescue, presented in Fig 1, is one of the most important findings. It can and should be presented in more detail to allow the reader to understand the experiments better. E.g.: the antisense oligos hybridize to both ABHD2.S and ABHD2.L, and they knock down both (ectopically expressed) proteins. Do they hybridize to either or both of the rescue constructs? If so, wouldn't you expect that both rescue constructs would rescue the phenotype since they both should sequester the AS oligo? Maybe I'm missing something here.

For the ABHD2 rescue experiment, the ABHD2 constructs (S or L) were expressed 48 hrs before the antisense was injected. The experiment was conducted in this way to avoid the potential confounding issue of both constructs sequestering the antisense. The assumption is that the injected RNA after protein expression would be degraded thus allowing the injected antisense to target endogenous ABHD2. The idea is to confirm that ABHD2.S expression alone is sufficient to rescue the antisense knockdown as confirmed experimentally.

However, to further confirm the rescue, we performed the experiment in a different chronological order, where we started with injecting the antisense to knock down endogenous ABHD2 and this was followed 24 hrs later by expressing wild type ABHD2.S. As shown in Author response image 1 this also rescues the knockdown.

Author response image 1.

ABHD2 knockdown and rescue. Oocytes were injected with control antisense (Ctrl AS) or specific ABHD2 antisense (AS) oligonucleotides and incubated at 18 oC for 24 hours. Oocytes were then injected with mRNA to overexpress ABHD.S for 48 hours and then treated with P4 overnight. The histogram shows % GVBD in naïve, oocytes injected with control or ABHD2 antisense with or without mRNA to overexpress ABHD2.S.

In addition, it is critical to know whether the partial rescue (Fig 1E, I, and K) is accomplished by expressing reasonable levels of the ABHD2 protein, or only by greatly overexpressing the protein. The author's antibodies do not appear to be sensitive enough to detect the endogenous levels of ABHD2.S or .L, but they do detect the overexpressed proteins (Fig 1D). The authors could thus start by microinjecting enough of the rescue mRNAs to get detectable protein levels, and then titer down, assessing how low one can go and still get rescue. And/or compare the mRNA levels achieved with the rescue construct to the endogenous mRNAs.

The dose response of ABHD2 protein expression in correlation with rescue of the ABHD2 knockdown is shown indirectly in Figure 1I and 1J. In experiments ABHD2 knockdown was rescued using either the WT protein or two mutants (H120A and N125A). All three constructs rescued ABHD2 KD with equal efficiency (Fig. 1I), eventhough their expression levels varied (Fig. 1J). The WT protein was expressed at significantly higher levels than both mutants, and N125A was expressed at higher levels than H120A (Fig. 1J), note the similar tubulin loading control. Crude estimation of the WBs argues for the WT protein expression being ~3x that of H120A and ~2x that of N125A, yet all three have similar rescue of the ABHD2 knockdown (Fig. 1I). This argues that low levels of ABHD2 expression is sufficient to rescue the knockdown, consistent with the catalytic enzymatic nature of the ABHD2 PLA2 activity.

Finally, please make it clear what is meant by n = 7 or n = 3 for these experiments. Does n = 7 mean 7 independently lysed oocytes from the same frog? Or 7 groups of, say, 10 oocytes from the same frog? Or different frogs on different days? I could not tell from the figure legends, the methods, or the supplementary methods. Ideally one wants to be sure that the knockdown and rescue can be demonstrated in different batches of oocytes, and that the experimental variability is substantially smaller than the effect size.

The n reflects the number of independent female frogs. We have added this information to the figure legends. For each donor frog at each time point 10-30 oocytes were used.

(2) The lipidomics results should be presented more clearly. First, please drop the heat map presentations (Fig 2A-C) and instead show individual time course results, like those shown in Fig 2E, which make it easy to see the magnitude of the change and the experiment-to-experiment variability. As it stands, the lipidomics data really cannot be critically assessed.

[Even as heat map data go, panels A-C are hard to understand. The labels are too small, especially on the heat map on the right side of panel B. The 25 rows in panel C are not defined (the legend makes me think the panel is data from 10 individual oocytes, so are the 25 rows 25 metabolites? If so, are the individual oocyte data being collapsed into an average? Doesn't that defeat the purpose of assessing individual oocytes?) And those readers with red-green colorblindness (8% of men) will not be able to tell an increase from a decrease. But please don't bother improving the heat maps; they should just be replaced with more informative bar graphs or scatter plots.]

We have revised the lipidomics data as requested by the Reviewer. The Reviewer asked that we show the data as a time course with each individual frog as in Fig. 2E. This turns out to be confusing and not a good way to present the data (please see Author response image 2).

Author response image 2.

Metabolite levels from 5 replicates of 10 oocytes each at each time point were measured and averaged per frog and per time point. Fold change was measured as the ratio at the 5- and 30-min time points relative to untreated oocytes (T0). FCs that are not statistically significant are shown as faded. Oocytes with mPR knockdown (KD) are boxed in green and ABHD2-KD in purple.

We therefore revised the metabolomics data as follow to improve clarity. The changes in the glycerophospholipids and sphingolipids determined on the Metabolon CLP platform (specific for lipids) are now shown as single metabolites clustered at the levels of species and pathways and arranged for the 5- and 30-min time points sequentially on the same heatmap as requested (Fig. 2B). This allows for a quick visual overview of the data that clearly shows the decrease in the lipid species following P4 treatment in the control oocytes and not in the mPR-KD or ABHD2-KD cells (Fig. 2B). The individual species are listed in Supplemental Tables 1 and 2. We also revised the Supplemental Tables to include the values for the non-significant changes, which were omitted from the previous submission.

We revised the metabolomics data from the HD4 platform in a similar fashion but because the lipid data were complimentary and less extensive than those from the CLP platform, we moved that heatmap to Supplemental Fig. 2B.

For the single oocyte metabolomics, we now show the data as the correlation between FC and p value, which clearly shows the upregulated (including LPA) and downregulated metabolites at T30 relative to T0 (Fig. 2C). The raw data is now shown in a new Supplemental Table 7.  

(3) The reticulocyte lysate co-expression data are quite important and are both intriguing and puzzling. My impression had been that to express functional membrane proteins, one needed to add some membrane source, like microsomes, to the standard kits. Yet it seems like co-expression of mPR and ABHD2 proteins in a standard kit is sufficient to yield progesterone-regulated PLA2 activity. I could be wrong here - I'm not a protein expression expert - but I was surprised by this result, and I think it is critical that the authors make absolutely certain that it is correct. Do you get much greater activities if microsomes are added? Are the specific activities of the putative mPR-ABHD2 complexes reasonable?

We thank the Reviewer for this insightful comment. We agree that this is a critical result that would benefit from cross validation, especially given the low level of PLA2 activity detected in the reticulocyte lysate expression system. We have therefore expanded these studies using another in vitro expression system with microsomal membranes based on tobacco extracts (ALiCE®Cell-Free Protein Synthesis System, Sigma Aldrich) to enhance production and stability of the expressed receptors as suggested by the Reviewer. We further prepared virus-like particles (VLPs) from cells expressing each receptor individually or both receptors together. We however could not detect any PLA2 activity from the VLPs. We thus focused on the coupled in vitro transcription/translation tobacco extracts that allow the expression of difficult-to-produce membrane proteins in microsomes. This kit targets membrane protein directly to microsomes using a microsome targeting melittin signal peptide. This system took significant time and effort to troubleshoot and adapt to mPR and ABHD2 expression. We were however ultimately able to produce significantly higher amounts of both ABHD2 and mPRb, which were readily detected by WBs (Supplemental Fig. 4I). In contrast, we could not reliably detect mPR or ABHD2 using WBs from reticulocyte lysates given the limited amounts produced.

Similarly to our previous findings with proteins produced in reticulocytes, expression of ABHD2 or mPRβ alone was not associated with an increase in PLA2 activity over a two-hour incubation period (Fig. 5C). It is worth noting here that the tobacco lysates had high endogenous PLA2 activity. However, co-expression of both mPRb and ABHD2 produced robust PLA2 activity that was significantly higher than that detected in reticulocyte lysate system (Fig. 5C). Surprisingly, however this PLA2 activity was P4 independent as it was observed when both receptors are co-expressed in the absence of P4.

These results validate our earlier conclusion that PLA2 activity requires both mPR and ABHD2, so their interaction in needed for enzymatic activity. It is interesting however that in the tobacco expression system this mPR-ABHD2 PLA2 activity becomes for the most part P4 independent. As the tobacco expression system forces both ABHD2 and mPR into microsomes using a signal sequence, the two receptors are enriched in the same vesicular compartment. As they can interact independently of P4 as shown in the co-IP experiments in immature oocytes (Fig. 5D), their forced co-expression in the same microsomal compartment could lead to their association and thus PLA2 activity. This is an attractive possibility that fits the current data, but would need independent validation.

Reviewer 3:

There were concerns with the pharmacological studies presented. Many of these inhibitors are used at high (double-digit micromolar) concentrations that could result in non-specific pharmacological effects and the authors have provided very little data in support of target engagement and selectivity under the multiple experimental paradigms. In addition, the use of an available ABHD2 small molecule inhibitor was lacking in these studies.

For the inhibitors used we performed a full dose response to define the active concentrations. So, inhibitors were not used at one high dose. We then compared the EC50 for each active inhibitor to the reported EC50 in the literature (Table 1). The inhibitors were deemed effective only if they inhibited oocyte maturation within the range reported in the literature. This despite the fact that frog oocytes are notorious in requiring higher concentrations of drug given their high lipophilic yolk content, which acts as a sponge for drugs. So our criteria for an effective inhibitor are rather stringent.  

Based on these criteria, only 3 inhibitors were ‘effective’ in inhibiting oocyte maturation: Ibuprofen, ACA and MP-A08 with relative IC50s to those reported in the literature of 0.7, 1.1, and 1.6 respectively. Ibuprofen targets Cox enzymes, which produce prostaglandins. We independently confirmed an increase in PGs in response to P4 in oocytes thus validating the drug inhibitory effect. ACA blocks PLA2 and inhibits maturation, a role supported by the metabolomics analyses that shows decrease in the PE/PE/LPE/LPC species; and by the ABHD2-mPR PLA2 activity following in vitro expression. Finally, MP-A08 blocks sphingosine kinase activity, which role is supported by the metabolomics showing a decrease in sphingosine levels in response to P4; and our functional studies validating a role for the S1P receptor 3 in oocyte maturation.     

As pointed out by the Reviewer, other inhibitors did block maturation at very high concentration, but we do not consider these as effective and have not implicated the blocked enzymes in the early steps of oocyte maturation. To clarify this point, we edited the summary panel (now Fig. 2D) to simplify it and highlight the inhibitors with an effect in the reported range in red and those that don’t inhibit based on the above criteria in grey. Those with intermediate effects are shown in pink. We hope these edits clarify the inhibitors studies.

Recommendations For the Authors

Reviewer 2:

(1) Introduction, para 1. Please change "mPRs mediated" to "mPR-mediated".

Done

(2) Introduction, para 2. Please change "cyclin b" to "cyclin B".

Done

(3) Introduction, para 2. Please change "that serves" to "which serves".

Done

(4) Introduction, para 4. I know that the authors have published evidence that "a global decrease in cAMP levels is not detectable" (2016), but old work from Maller and Krebs (JBC 1979) did see an early, transient decrease after P4 treatment, and subsequent work from Maller said that there was both a decrease in adenylyl cyclase activity and an increase in cAMP activity. Perhaps it would be better to say something like "early work showed a transitory drop in cAMP activity within 1 min of P4 treatment (Maller), although later studies failed to detect this drop and showed that P4-dependent maturation proceeds even when cAMP is high (25)".

We agree and thank the Reviewer for this recommendation. The text was revised accordingly.

(5) Results, para 1. Based on the results in Fig 1B, one should probably not assert that ABHD2 is expressed "at levels similar to those of mPRβ in the oocyte"-with different mRNAs and different PCR primers, it's hard to say whether they are similar or not. The RNAseq data from Xenbase in Supp Fig 1 supports the idea that the ABHD2 and mPRβ mRNAs are expressed at similar levels at the message level, although of course mRNA levels and protein levels do not correlate well when different gene products are compared (Wuhr's 2014 Curr Biol paper reported correlation coefficients of about 0.3).

We agree and have changed the text as follow to specifically point out to RNA: “we confirmed that ABHD2 RNA is expressed in the oocyte at levels similar to those of mPRβ RNA (Fig. 1B).”

(6) Results, para 2. It would be worth pointing out that since an 18 h incubation with microinjected antisense oligos was sufficient to substantially knock down both the ABHD2 mRNAs (Fig 1C) and the ectopically-expressed proteins (Fig 1D), the mRNA and protein half-lives must be fairly short, on the order of a few hours or less.

Done

(7) Figure 1. Please make the western blots (especially Fig 1D) and their labeling larger. These are key results and as it stands the labeling is virtually unreadable on printed copies of the figures. I'm not sure about eLife's policy, but many journals want the text in figures to be no smaller than 5-7 points at 100% size.

Likewise for many of the western blots in subsequent figures.

As requested by the Reviewer we have increased the font and size of all Western blots in the Figures.

(8) Figure 1E, G. I am not sure one should compare the effectiveness of the ABHD2 rescue (Fig 1E) and the mPRβ rescue (Fig 1G). Even if these were oocytes from the same frog, we do not know how the levels of the overexpressed ABHD2 and mPRβ proteins compare. E.g. maybe ABHD2 was highly overexpressed and mPRβ was overexpressed by a tiny amount.

Although this is a possibility, the expression levels of the proteins here is not of much concern because we previously showed that mPRβ expression effectively rescues mPRβ antisense knockdown which inhibits maturation (please see (Nader et al., 2020)). This argues that at the levels of mRNA injected mPR is functional to support maturation, yet it does not rescue ABHD2 knockdown to the same levels (Fig. 1G). With that it is fair to argue that mPRβ is not as effective at rescuing ABHD2 KD maturation.

(9) Inhibitor studies: There are two likely problems in comparing the observed potencies with legacy data - in vitro vs in vivo data and frog vs. mammalian data. Please make it clear what is being compared to what when you are comparing legacy data.

The legacy data are from the literature based on the early studies that defined the IC50 for inhibition primarily using in vivo models (cell line mostly) but not oocytes. Typically, frog oocytes require significantly higher concentrations of inhibitors to mediate their effect because of the high lipophilic yolk content which acts as a sponge for some drugs. So, the fact that the drugs that are effective in inhibiting oocyte maturation (ACA, MP-A08, and Ibuprofen) work in a similar or lower concentration range to the published IC<sub50</sub> gives us confidence as to the specificity of their effect. We have revised Table 1 to include the reference for each IC<sub50</sub> value from the literature to allow the reader to judge the exact model and context used.

(10) Isn't it surprising that Gas seems to promote maturation, given the Maller data (and data from others) that cAMP and PKA oppose maturation (see also the authors' own Fig 1A) and the authors' previous data sees no positive effect (minor point 7 above)?

We show that a specific Gas inhibitor NF-449 inhibits maturation (although at relatively high concentrations), which is consistent with a positive role for Gas in oocyte maturation. We argue based on the lipidomics data and the inhibitors data that GPCRs play a modulatory role and not a central early signaling role in terms of releasing oocyte meiotic arrest. They are likely to have effects on the full maturation of the egg in preparation for embryonic development. The actions of the multiple lipid messengers generated downstream of mPRβ activation are likely to act through GPCRs and could signal through Gas or other Ga or even through Gβγ. Minor point 7 refers to the size of Western blots.

(11) Page 9, bottom: "...one would predict activation of sphingosine kinases...." Couldn't it just be the activity of some constitutively active sphingosine kinase? Maybe replace "activation" with "activity".

A constitutively sphingosine kinase activity would not make sense as it needs to be activated by P4.

(12) Sometimes the authors refer to concentrations in molar units plus a power of 10 (e.g. 10-5 M) and sometime in µM or nM, sometimes even within the same paragraph. This makes it unnecessarily difficult to compare. Please keep consistent.

We replaced all the concentrations through the text to M with scientific notation for consistency as requested by the Reviewer.

(13) Fig 3I: "Sphingosine kinase" is misspelled.

This has been corrected. We thank the Reviewer for catching it.

(14) Legend to Fig. 5: Please change "after P4 treatment in reticulocytes" to "after P4 treatment in reticulocyte lysates".

Done

(15) Fig 6J. Doesn't the MAPK cascade inhibit MYT1? I.e. shouldn't the arrow be -| rather than ->?

Yes the Reviewer is correct. This has been changed. We thank the Reviewer for noticing this error.

(16) Materials and Methods, second paragraph. Please change "inhibitor's studies" to "inhibitor studies".

Corrected thanks.

(17) Table 1: Please be consistent in how you write Cox-2.

Done.

Reviewer #3:

The findings are of potential broad interest, but I have some concerns with the pharmacological studies presented. Many of these inhibitors are used at high (double-digit micromolar) concentrations that could result in non-specific pharmacological effects and the authors have provided very little data in support of target engagement and selectivity under the multiple experimental paradigms. Importantly, several claims regarding lipid metabolism signaling in the context of oocyte maturation are made without critical validation that the intended target is inactivated with reasonable selectivity across the proteome. Several of the inhibitors used for pharmacology and metabolomics are known covalent inhibitors (JZL184 and MJN110) that can readily bind additional lipases depending on the treatment time and concentration.

I did not find any data using the reported ABHD2 inhibitor (compound 183; PMID: 31525885). Is there a reason not to include this compound to complement the knockdown studies? I believe this is an important control given that not all lipid effects were reversed with ABHD2 knockdown. The proper target engagement and selectivity studies should be performed with this ABHD2 inhibitor.

We obtained aliquots the reported ABHD2 inhibitor compound 183 from Dr. Van Der Stelt and tested its effect on oocyte maturation at 10^-4M using both low (10^-7M) or high (10^-5M) P4 concentration. Compound 183 partially inhibited P4-mediated oocyte maturation. The new data was added to the manuscript as Supplemental Figure 3D.

Additional comments:

(1) Pristimerin was tested at low P4 concentration for effects on oocyte maturation. Authors should also test JZL184 and MJN110 under this experimental paradigm.

We have tested the effect of high concentration (2.10-^-5M) of JZL184 or MJN110 on oocyte maturation at low P4 concentration (Author response image 3).  MJN 110 did not have a prominent effect on oocyte maturation at low P4, whereas JZL184 inhibited maturation by 50%. However, this inhibition of maturation required concentrations of JZL 184 that are 10 times higher than those reported in rat and human cells (Cui et al., 2016; Smith et al., 2015), arguing against an important role for a monoacylglycerol enzymatic activity in inducing oocyte maturation.

Author response image 3.

The effect of MJN110 and JZL184 compounds on oocyte maturation at low P4 concentration. Oocytes were pre-treated for 2 hours with the vehicle or with the highest concentration of 2.10-^-5 M for both JZL184 or MJN110, followed by overnight treatment with P4 at 10-⁷M. Oocyte maturation was measured as % GVBD normalized to control oocytes (treated with vehicle) (mean + SEM; n = 2 independent female frogs for each compound).

2) Figure 4A showed different ct values of ODC between Oocytes and spleen, please explain them in the text. There is not any description regarding spleen information in Figure 4A, please make it clear in the text.

We thank the Reviewer for this recommendation. The text was revised accordingly.

(3) For Figures 3A, E, and I, there are different concentration settings for comparing the activity, is it possible to get the curves based on the same set of concentrations? The concentration gradient didn't include higher concentration points in these figures, thus the related values are incorrect. Please set more concentration points to improve the figures. And for the error bar, there are different display formats like Figure 4c and 4d, etc. Please uniform the format for all the figures. Additionally, for the ctrl. or veh., please add an error bar for all figures.

Some of the drugs tested were toxic to oocytes at high concentrations so the dose response was adjusted accordingly. The graphs were plotted to encompass the entire tested dose response. We could have plotted the data on the same x-axis range but that would make the figures uneven and awkward.

We are not clear what the Reviewer means by “The concentration gradient didn't include higher concentration points in these figures, thus the related values are incorrect.”

The error bars for all dose responses are consistent throughout all the Figures. They are different from those on bar graphs to improve clarity. If the Reviewer wishes to have the error bars on the bar graphs and dose response the same, we are happy to do so. 

For the inhibitor studies the data were normalized on a per frog basis to control for variability in the maturation rate in response to P4, which varies from frog to frog. It is thus not possible to add error bars for the controls.

(4) Please check the sentence "However, the concentration of HA130...... higher that......'; Change "IC50" to "IC50" in the text and tables. Table 1 lists IC50 values in the literature, but the references are not cited. Please include the references properly. For the IC50 value obtained in the research, please include the standard deviation in the table. For reference parts, Ref 1, 27, 32, 46, doublecheck the title format.

We edited the sentence as follows to be more clear: “However, this inhibition of maturation required high concentrations of HA130  -at least 3 orders of magnitude higher that the reported HA130 IC₅₀-…”

We changed IC50 to subscript in Table 1.

We added the relevant references in Table 1 to provide context for the cited IC50 values for the different inhibitors used.

We added SEM to the IC₅₀ for inhibition of oocyte maturation values in Table 1.

We checked the titles on the mentioned references and cannot identify any problems.

References

Cui, Y., Prokin, I., Xu, H., Delord, B., Genet, S., Venance, L., and Berry, H. (2016). Endocannabinoid dynamics gate spike-timing dependent depression and potentiation. eLife 5, e13185.

Nader, N., Dib, M., Hodeify, R., Courjaret, R., Elmi, A., Hammad, A.S., Dey, R., Huang, X.Y., and Machaca, K. (2020). Membrane progesterone receptor induces meiosis in Xenopus oocytes through endocytosis into signaling endosomes and interaction with APPL1 and Akt2. PLoS Biol 18, e3000901.

Smith, M., Wilson, R., O'Brien, S., Tufarelli, C., Anderson, S.I., and O'Sullivan, S.E. (2015). The Effects of the Endocannabinoids Anandamide and 2-Arachidonoylglycerol on Human Osteoblast Proliferation and Differentiation. PloS one 10, e0136546.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

The authors assess the effectiveness of electroporating mRNA into male germ cells to rescue the expression of proteins required for spermatogenesis progression in individuals where these proteins are mutated or depleted. To set up the methodology, they first evaluated the expression of reporter proteins in wild-type mice, which showed expression in germ cells for over two weeks. Then, they attempted to recover fertility in a model of late spermatogenesis arrest that produces immotile sperm. By electroporating the mutated protein, the authors recovered the motility of ~5% of the sperm, although the sperm regenerated was not able to produce offspring using IVF.

We actually did not write that “sperm regenerated was not able to produce offspring using IVF” but rather that IVF was not attempted because the number of rescued sperm was too low. To address this important point, the ability of sperm to produce embryos was therefore challenged by two different assisted reproduction technologies, that are IVF and ICSI. To increase the number of motile sperm for IVF experiments, we have injected both testes from one male. We also conducted intracytoplasmic sperm injection (ICSI) experiments, using only rescued sperm, identified as motile sperm with a normal flagellum. The results of these new experiments have demonstrated that the rescued ARMC2 sperm successfully fertilized eggs and produced embryos at the two-cell stage by IVF and blastocysts by ICSI. These outcomes are presented in Figure 12.

This is a comprehensive evaluation of the mRNA methodology with multiple strengths. First, the authors show that naked synthetic RNA, purchased from a commercial source or generated in the laboratory with simple methods, is enough to express exogenous proteins in testicular germ cells. The authors compared RNA to DNA electroporation and found that germ cells are efficiently electroporated with RNA, but not DNA. The differences between these constructs were evaluated using in vivo imaging to track the reporter signal in individual animals through time. To understand how the reporter proteins affect the results of the experiments, the authors used different reporters: two fluorescent (eGFP and mCherry) and one bioluminescent (Luciferase). Although they observed differences among reporters, in every case expression lasted for at least two weeks. 

The authors used a relevant system to study the therapeutic potential of RNA electroporation. The ARMC2-deficient animals have impaired sperm motility phenotype that affects only the later stages of spermatogenesis. The authors showed that sperm motility was recovered to ~5%, which is remarkable due to the small fraction of germ cells electroporated with RNA with the current protocol. The 3D reconstruction of an electroporated testis using state-of-the-art methods to show the electroporated regions is compelling. 

The main weakness of the manuscript is that although the authors manage to recover motility in a small fraction of the sperm population, it is unclear whether the increased sperm quality is substantial to improve assisted reproduction outcomes. The quality of the sperm was not systematically evaluated in the manuscript, with the endpoints being sperm morphology and sperm mobility. 

We would like to thank the reviewers for their comments. As previously stated above, we produced additional rescue experiments and performed CASA, morphology observation, IVF and ICSI with the rescued sperm. The rescued ARMC2 sperm exhibited normal morphology (new figure 11 and Supp Fig 8), motility (figure 11), and fecundity (figure 12).  Whereas sperm from untreated KO males were unable to fertilize egg by IVF, the rescued sperm fertilized eggs in vitro at a significant level (mean 62%, n=5), demonstrating that our strategy improves the sperm quality and assisted reproduction outcome (from 0 to 62%). 

Some key results, such as the 3D reconstruction of the testis and the recovery of sperm motility, are qualitative given the low replicate numbers or the small magnitude of the effects. The presentation of the sperm motility data could have been clearer as well. For example, on day 21 after Armc2-mRNA electroporation, only one animal out of the three tested showed increased sperm motility. However, it is unclear from Figure 11A what the percentage of sperm motility for this animal is since the graph shows a value of >5% and the reported aggregate motility is 4.5%. It would have been helpful to show all individual data points in Figure 11A. 

We provide now in figure 11A, a graph showing the percentage of rescued sperm for all animals. (scatter dot plot). Moreover, we performed additional CASA experiments to analyze in detail sperm motility (Figure 11A2-A3). Individual CASA parameters for motile sperm cells were extracted as requested by reviewer 3 and represented in a new graph (Fig 11 A2). 

The expression of the reporter genes is unambiguous; however, better figures could have been presented to show cell type specificity. The DAPI staining is diffused, and it is challenging to understand where the basement membranes of the tubules are. For example, in Figures 7B3 and 7E3, the spermatogonia seems to be in the middle of the seminiferous tubule. The imaging was better for Figure 8. Suboptimal staining appears to lead to mislabeling of some germ cell populations. For example, in Supplementary Figure 4A3, the round spermatid label appears to be labeling spermatocytes. Also, in some instances, the authors seem to be confusing, elongating spermatids with spermatozoa, such as in the case of Supplementary Figures 4D3 and D4.

Thanks for the comments, some spermatogenic cells were indeed mislabeled as you mentioned. We have therefore readjusted the labeling accordingly. We also changed spermatozoa to mature spermatids. The new sentence is now: “At the cellular level, fluorescence was detectable in germ cells (B1-B3) including Spermatogonia (Sg), Spermatocytes (Scytes),round Spermatids (RStids), mature spermatids (m-Sptids) and Sertoli cells (SC)”. Moreover, to indicate the localization of the basal membrane, we have also labelled myoid cells.

The characterization of Armc2 expression could have been improved as well. The authors show a convincing expression of ARMC2 in a few spermatids/sperm using a combination of an anti-ARMC2 antibody and tubules derived from ARMC2 KO animals. At the minimum, one would have liked to see at least one whole tubule of a relevant stage.  

Thanks for the remark. 

We present now new images showing transversal section of seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text.

Overall, the authors show that electroporating mRNA can improve spermatogenesis as demonstrated by the generation of motile sperm in the ARMC2 KO mouse model. 

Thank you

Reviewer #2 (Public Review): 

Summary: 

Here, the authors inject naked mRNAs and plasmids into the rete testes of mice to express exogenous proteins - GFP and later ARMC2. This approach has been taken before, as noted in the Discussion to rescue Dmc1 KO infertility. While the concept is exciting, multiple concerns reduce reviewer enthusiasm. 

Strengths: 

The approach, while not necessarily novel, is timely and interesting.  Weaknesses: 

Overall, the writing and text can be improved and standardized - as an example, in some places in vivo is italicized, in others it's not; gene names are italicized in some places, others not; some places have spaces between a number and the units, others not. This lack of attention to detail in the preparation of the manuscript is a significant concern to this reviewer - the presentation of the experimental details does cast some reasonable concern with how the experiments might have been done. While this may be unfair, it is all the reviewers have to judge. Multiple typographical and grammatical errors are present, and vague or misleading statements. 

Thanks for the comment, we have revised the whole manuscript to remove all the mistakes. We have also added new experiments/figures to strengthen the message. Finally, we have substantially modified the discussion.

Reviewer #3 (Public Review):

Summary: 

The authors used a novel technique to treat male infertility. In a proof-of-concept study, the authors were able to rescue the phenotype of a knockout mouse model with immotile sperm using this technique. This could also be a promising treatment option for infertile men. 

Strengths: 

In their proof-of-concept study, the authors were able to show that the novel technique rescues the infertility phenotype in vivo. 

Weaknesses: 

Some minor weaknesses, especially in the discussion section, could be addressed to further improve the quality of the manuscript. 

We have substantially modified the discussion, following the remarks of the reviewers.

It is very convincing that the phenotype of Armc2 KO mice could (at least in part) be rescued by injection of Armc2 RNA. However, a central question remains about which testicular cell types have been targeted by the constructs. From the pictures presented in Figures 7 and 8, this issue is hard to assess. Given the more punctate staining of the DNA construct a targeting of Sertoli cells is more likely, whereas the more broader staining of seminiferous tubules using RNA constructs is talking toward germ cells. Further, the staining for up to 119 days (Figure 5) would point toward an integration of the DNA construct into the genome of early germ cells such as spermatogonia and/or possibly to Sertoli cells. 

Thanks for the comment. We would like to recall the peculiar properties of the non-insertional Enhanced Episomes Vector (EEV) plasmid, which is a non-viral episome based on the Epstein-Barr virus (EBV: Epstein-Barr Virus). It allows the persistence of the plasmid for long period of time without integration. Its maintenance within the cell is made possible by its ability to replicate in a synchronous manner with the host genome and to segregate into daughter cells. This is due to the fact that EEV is composed of two distinct elements derived from EBV: an origin of replication (oriP) and an EpsteinBarr Nuclear Antigen 1 (EBNA1) expression cassette (Gil, Gallaher, and Berk, 2010).   The oriP is a locus comprising two EBNA1-binding domains, designated as the Family of Repeats (FR) and Dyad Symmetry (DS). The FR is an array of approximately 20 EBNA1-binding sites (20 repeats of 30 bp) with high affinity, while the DS comprises four lower-affinity sites operating in tandem (Ehrhardt et al., 2008). 

The 641-amino-acid EBNA1 protein contains numerous domains. The N-terminal domains are rich in glycines and alanines, which enable interaction with host chromosomes. The C-terminal region is responsible for binding to oriP (Hodin, Najrana, and Yates, 2013). The binding of EBNA1 to the DS element results in the recruitment of the origin of replication. This results in the synchronous initiation of extra-chromosomal EEV replication with host DNA at each S phase of the cell cycle (Düzgüneş, Cheung, and Konopka 2018). Furthermore, EBNA1 binding to the FR domain induces the formation of a bridge between metaphase chromosomes and the vector during mitosis. This binding is responsible for the segregation of the EEV episome in daughter cells (Düzgüneş, Cheung, and Konopka 2018). It is notable that EEV is maintained at a rate of 90-95% per cell division.

Because of the intrinsic properties of EEV described above, the presence of the reporter protein at 119 day after injection was likely due to the maintenance of the plasmid, mostly in Sertoli cells, and not to the DNA integration of the plasmid.

Of note, the specificity of EEV was already indicated in the introduction (lines 124-128 clean copy). Nevertheless, we have added more information about EEV to help the readers.  

Given the expression after RNA transfection for up to 21 days (Figure 4) and the detection of motile sperm after 21 days (Figure 11), this would point to either round spermatids or spermatocytes.  These aspects need to be discussed more carefully (discussion section: lines 549-574).

We added a sentence to highlight that spermatids are transfected and protein synthetized at this stage and this question is discussed in details (see lines 677-684 clean copy).

It would also be very interesting to know in which testicular cell type Armc2 is endogenously expressed (lines 575-591)

Thanks for the remarks. We present now new images showing the full seminiferous tubules as requested by reviewer 1 (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that Armc2 is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text. (lines 570-579 clean copy).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

The article is well-structured and easy to read. Nonetheless, there are typos and mistakes in some places that are distracting to the reader, such as the capitalization of the word "Oligo-" in the title of the manuscript, the use of the word "Materiel" in the title of the Materials and methods and the presence of space holders "Schorr staining was obtained from Merck (XXX)".  Thank you, we corrected the misspelling of "Materials and Methods" and corrected our error: "obtained from Merck (Darmstadt, Germany)". We also carefully corrected the manuscript to remove typos and mistakes.

The discussion is too lengthy, with much repetition regarding the methods used and the results obtained. For example, these are two sentences from the discussion. "The vector was injected via the rete testis into the adult Armc2 KO mice. The testes were then electroporated." I would recommend shortening these passages.

Thanks for your comments, we removed the sentences and we have substantially modified the discussion, following the remarks of the reviewers.

The work is extensive, and many experiments have been done to prove the points made. However, a more in-depth analysis of critical experiments would have benefited the manuscript significantly. A more thorough analysis of sperm mobility and morphology using the CASA system would have been an initial step.

In response to the observations made, additional CASA experiments and sperm motility analysis were conducted, as illustrated in Figure 11 (A2-A3). Individual CASA parameters for motile sperm cells were extracted as suggested and represented in a new graph (Fig 11 A2). We have observed significant differences between WT and rescued sperm. In particular, the VSL and LIN parameters were lower for rescued sperm. Nevertheless, these differences were not sufficient to prevent IVF, maybe because the curvilinear velocity (VCL) was not modified.

In the case of ARMC2 localization, an analysis of the different stages of spermatogenesis to show when ARMC2 starts to be expressed. 

Thanks for the remarks. This is an important remark pointed out by all reviewers. As explained above, we have performed more experiments. We present now new images showing transversal section of seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatid layers. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text. (lines 575579 clean copy).

Finally, exploring additional endpoints to understand the quality of the sperm generated, such as the efficiency of ICSI or sperm damage, could have helped understand the degree of the recovery.

This point was underlined in public review. We paste here our answer: “To address this important point, the ability of sperm to produce embryos was therefore challenged by two different assisted reproduction technologies, that are IVF and ICSI. To increase the number of motile sperm for IVF experiments, we have injected both testes from one male. We also conducted intracytoplasmic sperm injection (ICSI) experiments, using only rescued sperm, identified as motile sperm with a normal flagellum. The results of these new experiments have demonstrated that the rescued ARMC2 sperm successfully fertilized eggs and produced embryos at the two-cell stage by IVF and blastocysts by ICSI. These outcomes are presented in Figure 12.”

Reviewer #2 (Recommendations For The Authors):

38,74 intracellular

Thanks, we changed it accordingly: "Intracytoplasmic sperm injection (ICSI) is required to treat such a condition, but it has limited efficacy and has been associated with a small increase in birth defects" and "such as intracytoplasmic sperm injection (ICSI)".

39 "limited efficacy" Versus what? And for what reason? "small increase in birth defects" - compared to what? 

We changed to “… but it is associated with a small increase in birth defect with comparison to pregnancies not involving assisted conception.”

40 Just thinking through the logic of the argument thus far - the authors lay out that there are people with OAT (true), ICSI must be used (true), ICSI is bad (not convincing), and therefore a new strategy is needed... so is this an alternative to ICSI? And this is to restore fertility, not "restore spermatogenesis"

- because ICSI doesn't restore spermatogenesis. This logic flow needs to be cleaned up some

Thanks we changed it accordingly: “restore fertility.”

45 "mostly"?

Thank you, we removed the word: “We show that mRNA-coded reporter proteins are detected for up to 3 weeks in germ cells, making the use of mRNA possible to treat infertility.”

65 Reference missing. 

We added the following reference Kumar, N. and A. K. Singh (2015). "Trends of male factor infertility, an important cause of infertility: A review of literature." J Hum Reprod Sci 8(4): 191-196.

68 Would argue meiosis is not a reduction of the number of chromosomes - that happens at the ends of meiosis I and II - but the bulk of meiosis is doubling DNA and recombination; would re-word; replace "differentiation" with morphogenesis, which is much more commonly used:

Thank you, we have changed the sentence accordingly: "proliferation (mitosis of spermatogonia), reduction of the number of chromosomes (meiosis of spermatocytes), and morphogenesis of sperm (spermiogenesis)".

70 "almost exclusively" is an odd term, and a bit of an oxymoron - if not exclusively, then where else are they expressed? Can you provide some sense of scale rather than using vague words like "large", "almost", "several", "strongly" and "most...likely" - need some support for these claims by being more specific: 

Thanks for the comment, we changed the sentence: "The whole process involves around two thousand genes, 60% of which are expressed exclusively in the testes."

73 "severe infertility" is redundant - if they are infertile, is there really any more or less about it? I think what is meant is patients with immotile sperm can be helped by ICSI - so just be more specific... 

We changed the transition : “Among infertility disorders, oligo-astheno-teratozoospermia  (OAT) is the most frequent (50 % (Thonneau, Marchand et al. 1991); it is likely to be of genetic origin. Spermatocytograms of OAT patients show a decrease in sperm concentration, multiple morphological defects and defective motility. Because of these combined defects, patients are infertile and can only conceive by IntraCytoplasmic Sperm Injection (ICSI). IntraCytoplasmic Sperm Injection (ICSI) can efficiently overcome the problems faced. However, there are …”

75 "some" is vague - how many concerns, and who has them? Be specific!

Thanks for the comment, we removed the word.

76-7 Again, be specific - "real" has little meaning - what is the increased risk, in % or fold? This is likely a controversial point, so make sure you absolutely support your contention with data .

77 "these"? There was only one concern listed - increased birth defects; and "a number" is vague - what number, 1 or 1,000,000? A few (2-3), dozens, hundreds? 

Thanks for the comment, we have reworded the sentence: “Nevertheless, concerns persist regarding the potential risks associated with this technique, including blastogenesis defect, cardiovascular defect, gastrointestinal defect, musculoskeletal defect, orofacial defect, leukemia, central nervous system tumors, and solid tumors. Statistical analyses of birth records have demonstrated an elevated risk of birth defects, with a 30–40% increased likelihood in cases involving ICSI, and a prevalence of birth defects between 1% and 4%.” We have added a list of references to support these claims.

79-81 So, basically transgenesis? Again, vague terms "widely" - I don't think it's all that widely used yet... and references are missing to support the statement that integration of DNA into patient genomes is widely used. Give specific numbers, and provide a reference to support the contention. 

Thanks for the comment, we removed the word widely and add references.

81-5 Just finished talking about humans, but now it appears the authors have switched to talking about mice - got to let the readers know that! Unless you're talking about the Chinese group that deleted CCR5 in making transgenic humans? 

Your feedback is greatly appreciated. In response to your comments, the sentence in question has been amended to provide a more comprehensive understanding. Indeed, the text refers to experiences carried in mice. The revised wording is as follows: “Given the genetic basis of male infertility, the first strategy, tested in mice, was to overcome spermatogenic failure associated with monogenic diseases by delivery of an intact gene to deficient germ cells (Usmani, Ganguli et al. 2013). 

84-5 "efficiently" and "high" - provide context so the reader can understand what is meant - do the authors mean the experiments work efficiently, or that a high percentage of cells are transfected? And give some numbers or range of numbers - you're asking the readers to take your word for things when you choose adjectives - instead, provide values and let the readers decide for themselves.

Thanks for the comment, we have reworded the sentence: Gene therapy is effective in germ cells, as numerous publications have shown that conventional plasmids can be transferred into spermatogonia in several species with success, allowing their transcription in all cells of the germinal lineage (Usmani, Ganguli et al. 2013, Michaelis, Sobczak et al. 2014, Raina, Kumar et al. 2015, Wang, Liu et al. 2022).

93 Reference at the end of the sentence "most countries"

Thanks, we changed the sentence and added the reference: the new sentence is "… to avoid any eugenic deviations, transmissible changes in humans are illegal in 39 countries (Liu 2020)” (Liu, S. (2020). "Legal reflections on the case of genomeedited babies." Glob Health Res Policy 5: 24

93-4 Odd to say "multiple" and then list only one. 

Thanks for the comment, we have reworded the sentence: “Furthermore, the genetic modification of germ cell lines poses biological risks, including the induction of cancer, off-target effects, and cell mosaicism. Errors in editing may have adverse effects on future generations. It is exceedingly challenging to anticipate the consequences of genetic mosaicism, for instance, in a single individual. (Sadelain, Papapetrou et al. 2011, Ishii 2017).”

97 Is this really a "small" change? Again, would use adjectives carefully - to this reviewer, this is not a small change, but a significant one! And "should be" is not altogether convincing

Thanks for the comment, we have reworded the sentence: “Thanks to this change, the risk of genomic insertion is avoided, and thus there is no question of heritable alterations.”

What chance is there of retrotransposition? Is there any data in the literature for that, after injecting millions of copies of RNA one or more might be reverse transcribed and inserted into the genome?

This is certainly possible and is the putative origin for multiple intronless spermatid-expressed genes: 

The expert poses an interesting question, but one that unfortunately remains unanswered at present. Most papers on mRNA therapy state that there is no risk concerning genomic integration, but no reference is given (for instance see mRNA-based therapeutics: looking beyond COVID-19 vaccines. Lancet. 2024 doi: 10.1016/S0140-6736(23)02444-3). This is an important question, which deserves to be evaluated, but is beyond the scope of this manuscript. Nevertheless is remaining very debating (Igyarto and Qin 2024).

98 Odd to say "should be no risk" and then conclude with "there is no question" - so start the sentence with 'hedging', and then end with certainty - got to pick one or the other.

Thanks for the comment, we have reworded the sentence

99 "Complete" - probably not, would delete:

We removed the word: “The first part of this study presents a characterization of the protein expression patterns obtained following transfection of naked mRNA coding for reporter genes into the testes of mice”

101-2 Reference missing, as are numbers - what % of cases? 

Thank you, we changed the sentence and added the reference: “Among infertility disorders, oligoastheno-teratozoospermia  (OAT) is the most frequent (50 % (Thonneau, Marchand et al. 1991)” Thonneau, P., S. Marchand, A. Tallec, M. L. Ferial, B. Ducot, J. Lansac, P. Lopes, J. M. Tabaste and A. Spira (1991). "Incidence and main causes of infertility in a resident population (1,850,000) of three French regions (1988-1989)." Hum Reprod 6(6): 811-816.

103 Once again, the reference is missing:

We have added these references: (Colpi, Francavilla et al. 2018) (Cavallini 2006)

104-5 Awkward transition.

Thanks, we changed the transition: “The first part of this study presents a characterization of the protein expression patterns obtained following transfection of naked mRNA coding for reporter genes into the testes of mice. The second part is to apply the protocol to a preclinical mouse model of OAT.”

105 Backslash is odd - never seen it used in that way before

Removed

108 "completely infertile" is redundant;

Thank you, we changed it accordingly: “Patients and mice carrying mutations in the ARMC2 gene present a canonical OAT phenotype and are infertile”.

and is a KO mouse really "preclinical"? 

The definition of preclinical research, is research involving the use of animals to ascertain the potential efficacy of a drug, procedure, or treatment. Preclinical studies are conducted prior to any testing in humans. Our KO mouse model has been shown to mimic human infertility. Indeed Armc2-/-mice exhibit a phenotype that is identical to that observed in humans. Our study is in line with this definition. For this reason, we have decided to maintain our current position and to use the term "preclinical" in the article. 

110  Delete "sperm".

Thank you, we changed it accordingly: “The preclinical Armc2 deficient (Armc2 KO) mouse model is therefore a valuable model to assess whether in vivo injection of naked mRNA combined with electroporation can restore spermatogenesis”

111  "Easy"? Really? 

We changed it accordingly: “We chose this model for several reasons: first, Armc2 KO mice are sterile and all sperm exhibit short, thick or coiled flagella [13].”

112-3 "completely immobile" is redundant - either they are immobile or not.

Thank you, we changed it accordingly: “As a result, 100 % of sperm are immobile, thus it should be easy to determine the efficacy of the technique by measuring sperm motility with a CASA system.”

108-33 Condense this lengthy text into a coherent few sentences to give readers a sense of what you sought to accomplish, broadly how it was done, and what you found. This reads more like a Results section

Thanks for the comment, we shortened the text.

Materials and Methods 

The sections appear to have been written by different scientists - the authors should standardize so that similar detail and formatting are used - e.g., in some parts the source is in parentheses with catalog number, in others not, some have city, state, country, others do not... the authors should check eLife mandates for this type of information and provide. 

We are grateful for your feedback. We standardized the text, and if we had missed some, as outlined on the E-Life website, we can finish to format the article once it has been accepted for publication in the journal before sending the VOR.

134 Misspelling

We corrected the misspelling  

142 Just reference, don't need to spell it out.

Thanks, we changed it accordingly: “and the Armc2 KO mouse strain obtained by CRISPR-Cas9 (Coutton, Martinez et al. 2019). Experiments”

150 What is XXX?

We would like to express our gratitude for bringing this error to our attention. We have duly rectified the issue: “obtained from Merck (Darmstadt, Germany).”

157-60 Are enough details provided for readers to repeat this if necessary? Doesn't seem so to this reviewer; if kits were followed, then can say "using manufacturer's protocol", or refer to another manuscript - but this is too vague. 

Thanks, we change it accordingly: After expansion, plasmids were purified with a NucleoBond Xtra Midi kit (740410-50; Macherey-Nagel, Düren, Germany) using manufacturer's protocol.”

165 Again, too few details - how was it purified? What liquid was it in?

Thanks for the comment, the EEV plasmids were purified like all other plasmids. We change the text: “All plasmids,EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid ( given by Dr. Conti MD at UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOM-S017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation” 

170 Seems some words are missing - and will everyone know Dr. Conti by last name alone? Would spell out, and the details of the plasmid must either be provided or a reference given; how was amplification done? Purification? What was it resuspended in? 

Thank for the remark, the mcherry plasmids were purified like all other plasmids. We change the text: “All plasmids,EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid ( given by Dr. Conti MD, UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOM-S017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation”

175 Again, for this plasmid provide more information - catalog number, reference, etc; how amplified and purified, what resuspension buffer?

Thank you for the remark, as We mentioned, we add this sentence for the preparation: “All plasmids, EEV CAGs-GFP-T2A-Luciferase,((EEV604A-2), System Bioscience, Palo Alto, CA, USA), mCherry plasmid (given by Dr. Conti MD at UCSF, San Francisco, CA, USA) and EEV-Armc2-GFP plasmid (CUSTOMS017188-R2-3,Trilink,San Diego, USA) were amplified by bacterial transformation” and we add these sentence “The EEV-Armc2-GFP plasmid used for in vivo testes microinjection and electroporation was synthesized and customized by Trilink (CUSTOM-S017188-R2-3,San Diego, USA).”

183 What sequence, or isoform was used? Mouse or human? 

Thanks, we changed accordingly: “This non-integrative episome contains the mice cDNA sequences of Armc2 (ENSMUST00000095729.11)”

186-7 Provide sequence or catalog number; what was it resolubilized in?

Thanks we changed accordingly “the final plasmid concentration was adjusted to 9 μg μL-1 in water.” We provided the sequence of EEV-Armc2-GFP in supp data 6.

207-219 Much better, this is how the entire section needs to be written! 

237-240 Font

Thanks for the comment, we changed it accordingly

246 Cauda, and sperm, not sperm cells

Thanks for the comment, we changed it accordingly

255-6 Which was done first? Would indicate clearly.

Thanks for the comment, we changed the sentence: “Adult mice were euthanized by cervical dislocation and then transcardiac perfused  with 1X PBS”

281-2 Provide source for software - company, location, etc: 

We changed it accordingly: FIJI software (Opened source software) was used to process and analyze images and Imaris software (Oxford Instruments Tubney Woods, Abingdon, Oxon OX13 5QX, UK) for the 3D reconstructions.  

323 um, not uM. 

Thanks for the comment, we changed our mistake: “After filtration (100 µm filter)”

Results 

369 Weighed.  

Thanks for the comment, we changed our mistake: “the testes were measured and weighed”

371 No difference in what, specifically?

Thanks for the comment, we changed the sentence to: “No statistical differences in length and weight were observed between control and treated testes”

375 "was respected"? What does this mean?

Thanks for the comment, we changed the sentence to “The layered structure of germ cells were identical in all conditions”

378  This is highly unlikely to be true, as even epididymal sperm from WT animals are often defective - the authors are saying there were ZERO morphological defects? Or that there was no difference between control and treated? Only showing 2-3 sperm for control vs treatment is not sufficient.

Your observation that the epididymal spermatozoa from wild-type animals exhibited defective morphology is indeed true. The prevalence of these defects varies by strain, with an average incidence of 20% to 40% (Kawai, Hata et al., 2006; Fan, Liu et al., 2015). To provide a more comprehensive representation, we conducted a Harris-Shorr staining procedure and included a histogram of the percentage of normal sperm in each condition (new figure 2F4). Furthermore, Harris-Shorr staining of the epididymal sperm cells revealed that there were no discernible increases in morphological defects when mRNA and EEV were utilized, in comparison with the control. We add the sentence “At last, Harris-Shorr staining of the epididymal sperm cells demonstrated that there were no increases in morphological defects when mRNA and EEV were used in comparison with the control”.

379  "safe" is not the right word - better to say "did not perturb spermatogenesis". 

Thanks, we changed it accordingly: “these results suggest that in vivo microinjection and electroporation of EEV or mRNA did not perturb spermatogenesis”

382-3 This sentence needs attention, doesn't make sense as written: 

Thanks for the remark, we changed the sentence to: “No testicular lesions were observed on the testes at any post injection time”

389  How long after injection? 

Thanks for the comment, we changed the sentence to: “It is worth noting that both vectors induced GFP expression at one day post-injection”

390  Given the duration of mouse spermatogenesis (~35 days), for GFP to persist past that time suggests that it was maintained in SSCs? How can the authors explain how such a strong signal was maintained after such a long period of time? How stable are the episomally-maintained plasmids, are they maintained 100% for months? And if they are inherited by progeny of SSCs, shouldn't they be successively diluted over time? And if they are inherited by daughter cells such that they would still be expressed 49 days after injection, shouldn't all the cells originating from that SSC also be positive, instead of what appear to be small subsets as shown in Fig. 3H2? Overall, this reviewer is struggling to understand how a plasmid would be inherited and passed through spermatogenesis in the manner seen in these results. 

Thanks for the comment. 

This point was already underlined in public review. We paste here our answer: “The non-insertional Enhanced Episomes Vector (EEV) plasmid is a non-viral episome based on the Epstein-Barr virus (EBV: Epstein-Barr Virus). Its maintenance within the cell is made possible by its ability to replicate in a synchronous manner with the host genome and to segregate into daughter cells. This is due to the fact that EEV is composed of two distinct elements derived from EBV: an origin of replication (oriP) and an Epstein-Barr Nuclear Antigen 1 (EBNA1) expression cassette (Gil, Gallaher, and Berk, 2010).   The oriP is a locus comprising two EBNA1-binding domains, designated as the Family of Repeats (FR) and Dyad Symmetry (DS). The FR is an array of approximately 20 EBNA1-binding sites (20 repeats of 30 bp) with high affinity, while the DS comprises four lower-affinity sites operating in tandem (Ehrhardt et al., 2008). 

The 641-amino-acid EBNA1 protein contains numerous domains.The N-terminal domains are rich in glycines and alanines, which enable interaction with host chromosomes. The C-terminal region is responsible for binding to oriP (Hodin, Najrana, and Yates, 2013a). The binding of EBNA1 to the DS element results in the recruitment of the origin of replication. This results in the synchronous initiation of extra-chromosomal EEV replication with host DNA at each S phase of the cell cycle (Düzgüneş, Cheung, and Konopka 2018a). Furthermore, EBNA1 binding to the FR domain induces the formation of a bridge between metaphase chromosomes and the vector during mitosis. This binding is responsible for the segregation of the EEV episome in daughter cells (Düzgüneş, Cheung, and Konopka 2018b). It is notable that EEV is maintained at a rate of 90-95% per cell division.”

Because of the intrinsic properties of EEV described above, the presence of the reporter protein at 119 day after injection was likely due to the maintenance of the plasmid, mostly in Sertoli cells, and not to the DNA integration of the plasmid.

Of note, the specificity of EEV was already indicated in the introduction. Nevertheless, we have added more information about it to help the readers (lines 124-128 clean copy)  

398 Which "cell types"? 

Your feedback is greatly appreciated, and the sentence in question has been amended to provide a more comprehensive understanding. The revised wording is as follows: These results suggest that GFPmRNA and EEV-GFP targeted different seminiferous cell types, such as Sertoli cells and all germline cells, or that there were differences in terms of transfection efficiency.

409 Why is it important to inject similar copies of EEV and mRNA? Wouldn't the EEV be expected to generate many, many more copies of RNA per molecule than the mRNAs when injected directly?? 

We removed the word importantly. 

415 How is an injected naked mRNA stably maintained for 3 weeks? What is the stability of this mRNA?? Wouldn't its residence in germ cells for 21 days make it more stable than even the most stable endogenous mRNAs? Even mRNAs for housekeeping genes such as actin, which are incredibly stable, have half-lives of 9-10 hours.

We appreciate your inquiry and concur with your assessment that mRNA stability is limited.  It is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the expression of the GFP protein induced by the mRNA. To draw the reader's attention to this point, we have added the following sentence to the text “It is important to underline that the signal measured is the fluorescence emitted by the GFP. This signal is dependent of both the half-lives of the plasmid/mRNA and the GFP. Therefore, the kinetic of the signal persistence (which is called here expression) is a combination of the persistence of the vector and the synthetized protein. See lines 469-472 clean copy. 

This being said, it is difficult to compare the lifespan of a cellular mRNA with that of a mRNA that has been modified at different levels, including 5’Cap, mRNA body, poly(A)tail modifications, which both increase mRNA stability and translation (see The Pivotal Role of Chemical Modifications in mRNA Therapeutics  (2022) https://doi.org/10.3389/fcell.2022.901510). This question is discussed lines 687698 clean copy

467 "safely" should be deleted

Thanks, we removed the word: “To validate and confirm the capacity of naked mRNA to express proteins in the testes after injection and electroporation”

470  Except that apoptotic cells were clearly seen in Figure 2:

We would like to thank the reviewer for their comment. We agree that the staining of the provided sections were of heterogenous quality. To address the remark, we carried out additional HE staining for all conditions, and we now present testis sections correctly stained obtained in the different condition in Fig. 2 and Supp. 7. Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

471  "remanence"?

We appreciate your feedback and have amended the sentence to provide clear meaning. The revised wording is as follows: “The assessment of the temporal persistence of testicular mCherry fluorescent protein expression revealed a robust red fluorescence from day 1 post-injection, which remained detectable for at least 15 days (Fig. Supp. 3 B2, C2, and D2).”

489 IF measures steady-state protein levels, not translation; should say you determined when ARMC2 was detectable. 

Thanks for the remark, we changed the sentence to: “ By IF, we determined when ARMC2 protein was detectable during spermatogenesis.”

491 Flagella

Thanks for the comment, we changed our mistake: “in the flagella of the elongated spermatids (Fig 9A)”

Discussion 

The Discussion is largely a re-hashing of the Methods and Results, with additional background.

Message stability must be addressed - how is a naked mRNA maintained for 21 days?

As previously stated, it is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the synthetized GFP protein. This point and the stability of protein in the testis is now discussed lines 677-684 (clean copy).

556 How do the authors define "safe"?

Thanks for the comment, we changed the sentence to be clearer: “Our results also showed that the combination of injection and electroporation did not perturb spermatogenesis when electric pulses are carefully controlled”

563 Synthesized

Thanks, we changed it accordingly

602 Again, this was not apparent, as there were more apoptotic cells in Fig. 2 - data must be provided to show "no effect".

As previously stated, we carried out additional HE staining for all conditions, as can be observed in Fig. 2 . Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

629-30 This directly contradicts the authors' contention in the Introduction that ICSI was unsafe - how is this procedure going to be an advancement over ICSI as proposed, if ICSI needs to be used?? Why not just skip all this and do ICSI then?? Perhaps if this technique was used to 'repair' defects in spermatogonia or spermatocytes, then that makes more sense. But if ICSI is required, then this is not an advancement when trying to rescue a sperm morphology/motility defect.

In light of the latest findings (Fig 12), we have revised this part of the discussion and this paragraph no longer exist.

Nevertheless, to address specifically the reviewer’s remark, we would like to underline that ICSI with sperm from fertile donor is always more efficient than ICSI with sperm from patient suffering of OAT condition. Our strategy, by improving sperm quality, will improve the efficiency of ICSI and at the end will increase the live birth rate resulting from the first fresh IVF cycle.

640-2 What is meant by "sperm organelles" And what examples are provided for sperm proteins being required at or after fertilization? 

This paragraph was also strongly modified and the notion of protein persistence during spermatogenesis was discussed in the paragraph on fluorescent signal duration. See lines 698-705.

651 "Dong team"??

Thanks for the comment, we added the references. 

Figure 2D2 - tubule treated with EEV-GFP appears to have considerably more apoptotic cells - this reviewer counted ~10 vs 0 in control; also, many of the spermatocytes appear abnormal in terms of their chromatin morphology - the authors must address this by staining for markers of apoptosis - not fair to conclude there was no difference when there's a very obvious difference! 

We would like to thank the reviewer for their comment. This point was already addressed. As previously stated, we provide now new testis sections for all condition (see Fig. 2). Our observations revealed that the number of apoptotic cells remained consistent across all conditions.

Figure 2D3 staining is quite different than D1-2, likely a technical issue - looks like no hematoxylin was added? Need to re-stain so results can be compared to the other 2 figures 

As previously stated, we carried out additional HE staining for all conditions, and new images are provided, with similar staining. 

Figure 3 - the fluorescent images lack any context of tubule structure so it is nearly impossible to get a sense of what cells express GFP, or whether they're in the basal vs adluminal compartment - can the authors outline them? Indicate where the BM and lumen are. 

We would like to thank the reviewer for their comment. This figure provides actually a global view of the green fluorescent protein (GFP) expression at the surface of the testis. The entire testis was placed under an inverted epifluorescence microscope, and a picture of the GFP signal was recorded. For this reason, it is impossible to delineate the BM and the lumen. It should be noted that the fluorescence likely originates from different seminiferous tubules.

Author response image 1.

So, for Figure 3 if the plasmid is being uptaken by cells and maintained as an episome, is it able to replicate? Likely not. 

Yes! it is the intrinsic property of the episome, see the detailed explanation provided above about the EEV plasmid

So, initially, it could be in spermatogonia, spermatocytes, and spermatids. As time progressed those initially positive spermatids and then spermatocytes would be lost - and finally, the only cells that should be positive would be the progeny of spermatogonia that were positive - but, as they proliferate shouldn't the GFP signal decline? 

Because EEV is able  to replicate in a synchronous manner with the host genome and to segregate into daughter cells at a level of 90% of the mother cell, the expected decline is very slow.

And, since clones of germ cells are connected throughout their development, shouldn't the GFP diffuse through the intercellular bridges so entire clones are positive? Was this observed? 

We did not perform IF experiments further than 7 days after injection, a time too short to observe what the reviewer suggested. Moreover, if at 1 day after injection, GFP synthesized from injected EEV was found in both germ cells and Sertoli cells (Fig 7), after one week, the reporter proteins were only observable in Sertoli cells. This result suggests that EEV is maintained only in Sertoli cells, thus preventing the observation of stained clones.

Can these sections be stained for the ICB TEX14 so that clonality can be distinguished? Based on the apparent distance between cells, it appears some are clones, but many are not... 

We thank the reviewer for this suggestion but we are not able to perform testis sectioning and costaining experiments because the PFA treatment bleaches the GFP signal. We also tested several GFP antibodies, but all failed.  

Nevertheless, we were able to localize and identify transfected cells thank to the whole testis optical clearing, combined with a measure of GFP fluorescence and three-dimensional image reconstructions. 

For Figure 4, with the mRNA-GFP, why does the 1-day image (which looks similar to the plasmidtransfected) look so different from days 7-21? 

And why do days 7-21 look so different from those days in Fig 3? 

Thank you for your feedback. It is an excellent question. Because of the low resolution of the whole testis epifluorescences imaging and light penetration issue, we decided to carry-out whole testis optical clearing and three-dimensional image reconstructions experiments, in order to get insights on the transfection process. At day 1, GFP synthesized from EEV injection was found in spermatogonia, spermatocytes and Sertoli cells (Fig 7).  After one week, the reporter protein synthesized from injected EEV was only observable in Sertoli cells.

In contrast, for mRNA, on day 1 and day 7 post-injection, GFP fluorescent signal was associated with both Sertoli cells and germ cells. This explains why patterns between mRNA-GFP and EEV-GFP are similar at day 1 and different at day 7 between both conditions. 

Why do the authors think the signal went from so strong at 21 to undetectable at 28? What changed so drastically over those 7 days?

What is the half-life of this mRNA supposed to be? It seems that 21 days is an unreasonably long time, but then to go to zero at 28 seems also odd... Please provide some explanation, and context for whether the residence of an exogenous mRNA for 21 days is expected. 

As previously stated, it is our hypothesis that the source of the confusion lies in the fact that we injected mRNA coding for the GFP protein, rather than mRNA tagged with GFP. After a three-week observation period, we did not observe the mRNA, but we observed the GFP protein produced by the mRNA. The time of observation of the reporter proteins expressed by the respective mRNA molecules (mCherry, luciferase, or GFP) ranged from 15 to 21 days. Proteins have very different turnover rates, with half-lives ranging from minutes to days. Half-lives depend on proteins but also on tissues. As explained in the discussion, it has been demonstrated that proteins involved in spermatogenesis exhibit a markedly low turnover rate and this explains the duration of the fluorescent signal. 

The authors should immunostain testis sections from controls and those with mRNA and plasmid and immunostain with established germ cell protein fate markers to show what specific germ cell types are GFP+

Thank you for your feedback. As previously mentioned, we were unable to perform testis sectioning and co-staining because the PFA treatment bleaches the GFP signal and because we were unable to reveal GFP with an GFP antibody, for unknown reasons.

For the GFP signal to be maintained past 35 days, the plasmid must have integrated into SSCs - and for that to happen, the plasmid would have to cross the blood-testis-barrier... is this expected? 

We are grateful for your observation. 

First, as explained above, we do not think that the plasmid has been integrated. 

Concerning the blood-testing barrier.  It bears noting that electroporation is a technique that is widely utilized in biotechnology and medicine for the delivery of drugs and the transfer of genes into living cells (Boussetta, Lebovka et al. 2009). This process entails the application of an electric current, which induces the formation of hydrophilic pores in the lipid bilayer of the plasma membrane (Kanduser, Miklavcic et al. 2009). The pores remain stable throughout the electroporation process and then close again once it is complete. Consequently, as electroporation destabilizes the cell membrane, it can also destabilize the gap junctions responsible of the blood-testis barrier. This was actually confirmed by several studies, which have observed plasmid transfection beyond the blood-testis barrier with injection into rete testis following electroporation (Muramatsu, Shibata et al. 1997, Kubota, Hayashi et al. 2005, Danner, Kirchhoff et al. 2009, Kanduser, Miklavcic et al. 2009, Michaelis, Sobczak et al. 2014).

Figure 9 - authors should show >1 cell - this is insufficient; also, it's stated it's only in the flagella, but it also appears to be in the head as well. And is this just the principal piece?? And are the authors sure those are elongating vs condensing spermatids? Need to show multiple tubules, at different stages, to make these claims

We have partly answered to this question in the public review; We pastehere  our answer

“We present now new images showing the full seminiferous tubules as requested (see supp fig 6). In this new figure, it is clear that Armc2 is only expressed in spermatids. We have also added in this figure an analysis of the RNA-seq database produced by Gan's team (Gan, Wen et al. 2013), confirming that ArmC2 expression is predominantly expressed at the elongated spermatid stage. This point is now clearly indicated in the text.”

Concerning the localization of the protein in the head, we confirm that the base of the manchette is stained but we have no explanation so far. This point is now indicated in the manuscript.

Figure 10B2 image - a better resolution is necessary

We are grateful for your feedback. We concede that the quality of the image was not optimal. Consequently, We have replaced it with an alternative.

Figure 11 - in control, need to show >1 sperm; and lower-mag images should be provided for all samples to show population-wide effects; showing 1 "normal" sperm per group (white arrows) is insufficient: 

We are grateful for your feedback. We conducted further experiments and provide now additional images in Supp. figure 8.

Reviewer #3 (Recommendations For The Authors): 

In this study, Vilpreux et al. developed a microinjection/electroporation method in order to transfect RNA into testicular cells. The authors studied several parameters of treated testis and compared the injection of DNA versus RNA. Using the injection of Armc2 RNA into mice with an Armc2 knockout the authors were able to (partly) rescue the fertility phenotype. 

Minor points. 

Figure 6 + lines 553+554: might it be that the staining pattern primarily on one side of the testis is due to the orientation of the scissor electrode during the electroporation procedure and the migration direction of negatively charged RNA molecules (Figure 6)? 

Your input is greatly appreciated. We concur that the observed peripheral expression is due to both the electroporation and injection. Accordingly, we have amended the sentence as follows: "The peripheral expression observed was due to the close vicinity of cells to the electrodes, and to a peripheral dispersal of the injected solution, as shown by the distribution of the fluorescent i-particles NIRFiP-180."

Discussion of the safety aspect (lines 601-608): The authors state several times that there are no visible tissue changes after the electroporation procedure. However, in order to claim that this procedure is "safe", it is necessary to examine the offspring born after microinjection/electroporation. 

Your input is greatly appreciated. Consequently, the term "safe" has been replaced with "did not perturb spermatogenesis" in accordance with the provided feedback. Your assertion is correct; an examination of the offspring born would be necessary to ascertain the safety of the procedure. Due to the quantity of motile sperm obtained, it was not possible to produce offspring through natural mating. However, novel Armc2-/--rescued sperm samples have been produced and in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) experiments have been conducted. The results demonstrate that the Armc2-/--rescued sperm can successfully fertilize eggs and produce two-cell embryos by IVF and blastocysts by ICSI. These outcomes are visually represented in Figure 12. The development of embryos up to the blastocyst stage is a step in the right direction.

The discussion section could be shortened. Lines 632-646 are largely a repetition of the introductory section. In addition, the Dong paper (ref. 25) may be interesting; however, this part could also be shortened (lines 647-676). This reviewer would prefer the authors to focus on the technique (different application sites and applied nucleotides) and proof of concept for (partial) phenotype rescue in the knockout mice. 

Your contribution is highly valued. In light of your observations and the latest findings, we have substantially revised the discussion accordingly.

Line 63: oocytes rather than eggs.

We are grateful for your input, but we have decided to retain our current position and to use the term "eggs" rather than "oocytes" in our writing because the definition of an oocyte is a female gametocyte or germ cell involved in reproduction. In other words, oocyte corresponds to a germ cell inside the ovary and after ovulation become an egg.  

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This is an interesting and potentially important paper, which however has some deficiencies.

Strengths:

A significant amount of potentially useful data.

Weaknesses:

One issue is a confusion of thermal stability with solubility. While thermal stability of a protein is a thermodynamic parameter that can be described by the Gibbs-Helmholtz equation, which relates the free energy difference between the folded and unfolded states as a function of temperature, as well as the entropy of unfolding. What is actually measured in PISA is a change in protein solubility, which is an empirical parameter affected by a great many variables, including the presence and concentration of other ambient proteins and other molecules. One might possibly argue that in TPP, where one measures the melting temperature change ∆Tm, thermal stability plays a decisive or at least an important role, but no such assertion can be made in PISA analysis that measures the solubility shift.

We completely agree with the insightful comment from the reviewer and we are very grateful that the point was raised. Our goal was to make this manuscript easily accessible to the entire scientific community, not just experts in the field. In an attempt to simplify the language, we likely also simplified the underlying physical principles that these assays exploit. In defense of our initial manuscript, we did state that PISA measures “a fold change in the abundance of soluble protein in a compound-treated sample vs. a vehicle-treated control after thermal denaturation and high-speed centrifugation.” Despite this attempt to accurately communicate the reviewer’s point, we seem to have not been sufficiently clear. Therefore, we tried to further elaborate on this point and made it clear that we are measuring differences in solubility and interpreting these differences as changes in thermal stability. 

In the revised version of the manuscript, we elaborated significantly on our original explanation. The following excerpt appears in the introduction (p. 3):

“So, while CETSA and TPP measure a change in melting temperature (∆TM), PISA measures a change in solubility (∆SM).  Critically, there is a strong correlation between ∆TM and ∆SM, which makes PISA a reliable, if still imperfect, surrogate for measuring direct changes in protein thermal stability (Gaetani et al., 2019; Li et al., 2020). Thus, in the context of PISA, a change in protein thermal stability (or a thermal shift) can be defined as a fold change in the abundance of soluble protein in a compoundtreated sample vs. a vehicle-treated control after thermal denaturation and high-speed centrifugation. Therefore, an increase in melting temperature, which one could determine using CETSA or TPP, will lead to an increase in the area under the curve and an increase in the soluble protein abundance relative to controls (positive log2 fold change). Conversely, a decrease in melting temperature will result in a decrease in the area under the curve and a decrease in the soluble protein abundance relative to controls (negative log2 fold change).”

And the following excerpt appears in the results section (p. 4): 

“In a PISA experiment, a change in melting temperature or a thermal shift is approximated as a

significant deviation in soluble protein abundance following thermal melting and high-speed centrifugation. Throughout this manuscript, we will interpret these observed alterations in solubility as changes in protein thermal stability. Most commonly this is manifested as a log2 fold change comparing the soluble protein abundance of a compound treated sample to a vehicle-treated control (Figure 1 – figure supplement 1A).”

We have now drawn a clear distinction between what we were actually measuring (changes in solubility) and how we were interpreting these changes (as thermal shifts). We trust that the Reviewer will agree with this point, as they rightly claim that many of the observations presented in our work, which measures thermal stability, indirectly, are consistent with previous studies that measured thermal stability, directly. Again, we thank the reviewer for raising the point and feel that these changes have significantly improved the manuscript. 

Another important issue is that the authors claim to have discovered for the first time a number of effects well described in prior literature, sometimes a decade ago. For instance, they marvel at the differences between the solubility changes observed in lysate versus intact cells, while this difference has been investigated in a number of prior studies. No reference to these studies is given during the relevant discussion.

We thank the reviewer for raising this point. Our aim with this paper was to test the proficiency of this assay in high-throughput screening-type applications. We considered these observations as validation of our workflow, but admit that our choice of wording was not always appropriate and that we should have included more references to previous work. It was certainly never our intention to take credit for these discoveries. Therefore, we were more than happy to include more references in the revised version. We think that this makes the paper considerably better and will help readers better understand the context of our study.  

The validity of statistical analysis raises concern. In fact, no calculation of statistical power is provided.

As only two replicates were used in most cases, the statistical power must have been pretty limited. Also, there seems to be an absence of the multiple-hypothesis correction.

We agree with the reviewer that a classical comparison using a t-test would be underpowered comparing all log2 normalized fold changes. We know from the data and our validation experiments that stability changes that generate log2 fold changes of 0.2 are indicative of compound engagement. When we use 0.2 to calculate power for a standard two-sample t-test with duplicates, we estimated this to have a power of 19.1%. Importantly, increasing this to n=3 resulted in a power estimate of only 39.9%, which would canonically still be considered to be underpowered. Thus, it is important to note that we instead use the distribution of all measurements for a single protein across all compound treatments to calculate standard deviations (nSD) as presented in this work. Thus, rather than a 2-by-2 comparison, we are comparing two duplicate compound treatments to 94 other compound treatments and 18 DMSO vehicle controls. Moreover, we are using this larger sample set to estimate the sampling distribution. Estimating this with a standard z-test would result in a p-value estimate <<< 0.0001 using the population standard deviation. Additionally, rather than estimate an FDR using say a BenjaminiHochberg correction, we estimated an empirical FDR for target calls based on applying the same cutoffs to our DMSO controls and measuring the proportion of hits called in control samples at each set of thresholds. Finally, we note that several other PISA-based methods have used fold-change thresholds similar to, or less than, those employed in this work (PMID: 35506705, 36377428, 34878405, 38293219).  

Also, the authors forgot that whatever results PISA produces, even at high statistical significance, represent just a prediction that needs to be validated by orthogonal means. In the absolute majority of cases such validation is missing.

We appreciate this point and we can assure the reviewer that this point was not lost on us. To this point, we state throughout the paper that the primary purpose of this paper was to execute a chemical screen. Furthermore, we do not claim to present a definitive list of protein targets for each compound. Instead, our intention is to provide a framework for performing PISA studies at scale. In total, we quantified thousands of changes and feel that it would be unreasonable to validate the majority of these cases. Instead, as has been done for CETSA (PMID: 34265272), PISA (PMID: 31545609), and TPP (PMID: 25278616) experiments before, we chose to highlight a few examples and provide a reasonable amount of validation for these specific observations. In Figure 2, we show that two screening compounds—palbociclib and NVP-TAE-226—have a similar impact on PLK1 solubility as the two know PLK1 inhibitors. We then assay each of these compounds, alongside BI 2536, and show that the same compounds that impact the solubility of PLK1, also inhibit its activity in cell-based assays. Finally, we model the structure of palbociclib (which is highly similar to BI 2536) in the PLK1 active site. In Figure 4, we show that AZD-5438 causes a change in solubility of RIPK1 in cell- and lysate-based assays to a similar extent as other compounds known to engage RIPK1. We then test these compounds in cellbased assays and show that they are capable of inhibiting RIPK1 activity in vivo. Finally, in Figure 5, we show that treatment with tyrosine kinase inhibitors and AZD-7762 result in a decrease in the solubility of CRKL. We showed that these compounds, specifically, prevented the phosphorylation of CRKL at Y207. Next, we show that AZD-7762, impacts the thermal stability of tyrosine kinases in lysate-based PISA. Finally, we performed phosphoproteomic profiling of cells treated with bafetinib and AZD-7762 and find that the abundance of many pY sites is decreased after treatment with each compound. It is also worth stating that an important goal of this study was to determine the proficiency of these methods in identifying the targets of each compound. We do not feel that comprehensive validation of the “absolute majority of cases” would significantly improve this manuscript. 

Finally, to be a community-useful resource the paper needs to provide the dataset with a user interface so that the users can data-mine on their own.

We agree and are working to develop an extensible resource for this. Owing to the size and complexities there, that work will need to be included in a follow-up manuscript. For now, we feel that the supplemental table we provide can be easily navigated the full dataset. Indeed, this has been the main resource that we have been emailed about since the preprint was first made public. We are glad that the Reviewer considers this dataset to be a highly valuable resource for the scientific community.  

Reviewer #2 (Public Review):

Summary:

Using K562 (Leukemia) cells as an experimental model, Van Vracken et. al. use Thermal Proteome Profiling (TPP) to investigate changes in protein stability after exposing either live cells or crude cell lysates to a library of anti-cancer drugs. This was a large-scale and highly ambitious study, involving thousands of hours of mass spectrometry instrument time. The authors used an innovative combination of TPP together with Proteome Integral Solubility Alternation (PISA) assays to reduce the amount of instrument time needed, without compromising on the amount of data obtained.

The paper is very well written, the relevance of this work is immediately apparent, and the results are well-explained and easy to follow even for a non-expert. The figures are well-presented. The methods appear to be explained in sufficient detail to allow others to reproduce the work.

We thank the reviewer. One of our major goals was to make these assays and the resulting data approachable, especially for non-experts. We are glad that this turned out to be the case. 

Strengths:

Using CDK4/6 inhibitors, the authors observe strong changes in protein stability upon exposure to the drug. This is expected and shows their methodology is robust. Further, it adds confidence when the authors report changes in protein stability for drugs whose targets are not well-known. Many of the drugs used in this study - even those whose protein targets are already known - display numerous offtarget effects. Although many of these are not rigorously followed up in this current study, the authors rightly highlight this point as a focus for future work.

Weaknesses:

While the off-target effects of several drugs could've been more rigorously investigated, it is clear the authors have already put a tremendous amount of time and effort into this study. The authors have made their entire dataset available to the scientific community - this will be a valuable resource to others working in the fields of cancer biology/drug discovery.

We agree with the reviewer that there are more leads here that could be followed and we look forward to both exploring these in future work and seeing what the community does with these data.

Reviewer #3 (Public Review):

Summary:

This work aims to demonstrate how recent advances in thermal stability assays can be utilised to screen chemical libraries and determine the compound mechanism of action. Focusing on 96 compounds with known mechanisms of action, they use the PISA assay to measure changes in protein stability upon treatment with a high dose (10uM) in live K562 cells and whole cell lysates from K562 or HCT116. They intend this work to showcase a robust workflow that can serve as a roadmap for future studies.

Strengths:

The major strength of this study is the combination of live and whole cell lysates experiments. This allows the authors to compare the results from these two approaches to identify novel ligand-induced changes in thermal stability with greater confidence. More usefully, this also enables the authors to separate the primary and secondary effects of the compounds within the live cell assay.

The study also benefits from the number of compounds tested within the same framework, which allows the authors to make direct comparisons between compounds.

These two strengths are combined when they compare CHEK1 inhibitors and suggest that AZD-7762 likely induces secondary destabilisation of CRKL through off-target engagement with tyrosine kinases.

Weaknesses:

One of the stated benefits of PISA compared to the TPP in the original publication (Gaetani et al 2019) was that the reduced number of samples required allows more replicate experiments to be performed. Despite this, the authors of this study performed only duplicate experiments. They acknowledge this precludes the use of frequentist statistical tests to identify significant changes in protein stability. Instead, they apply an 'empirically derived framework' in which they apply two thresholds to the fold change vs DMSO: absolute z-score (calculated from all compounds for a protein) > 3.5 and absolute log2 fold-change > 0.2. They state that the fold-change threshold was necessary to exclude nonspecific interactors. While the thresholds appear relatively stringent, this approach will likely reduce the robustness of their findings in comparison to an experimental design incorporating more replicates. Firstly, the magnitude of the effect size should not be taken as a proxy for the importance of the effect.

They acknowledge this and demonstrate it using their data for PIK3CB and p38α inhibitors (Figures 2BC). They have thus likely missed many small, but biologically relevant changes in thermal stability due to the fold-change threshold. Secondly, this approach relies upon the fold-changes between DMSO and compound for each protein being comparable, despite them being drawn from samples spread across 16 TMT multiplexes. Each multiplex necessitates a separate MS run and the quantification of a distinct set of peptides, from which the protein-level abundances are estimated. Thus, it is unlikely the fold changes for unaffected proteins are drawn from the same distribution, which is an unstated assumption of their thresholding approach. The authors could alleviate the second concern by demonstrating that there is very little or no batch effect across the TMT multiplexes. However, the first concern would remain. The limitations of their approach could have been avoided with more replicates and the use of an appropriate statistical test. It would be helpful if the authors could clarify if any of the missed targets passed the z-score threshold but fell below the fold-change threshold.

The authors use a single, high, concentration of 10uM for all compounds. Given that many of the compounds likely have low nM IC50s, this concentration will often be multiple orders of magnitude above the one at which they inhibit their target. This makes it difficult to assess the relevance of the offtarget effects identified to clinical applications of the compounds or biological experiments. The authors acknowledge this and use ranges of concentrations for follow-up studies (e.g. Figure 2E-F). Nonetheless, this weakness is present for the vast bulk of the data presented.

We agree that there is potential to drive off-target effects at such high-concentrations. However, we note that the concentration we employ is in the same range as previous PISA/CETSA/TPP studies. For example, 10 µM treatments were used in the initial descriptions of TPP (Savitski et al., 2014) and PISA (Gaetani et al., 2019). We also note that temperature may affect off-rates and binding interactions (PMID: 32946682) potentiating the need to use compound concentrations to overcome these effects.

Additionally, these compounds likely accumulate in human plasma/tissues at concentrations that far exceed the compound IC50 values. For example, in patients treated with a standard clinical dose of ribocicilb, the concentration of the compound in the plasma fluctuates between 1 µM and 10 µM. (Bao, X., Wu, J., Sanai, N., & Li, J. (2019). Determination of total and unbound ribociclib in human plasma and brain tumor tissues using liquid chromatography coupled with tandem mass spectrometry. Journal of pharmaceutical and biomedical analysis, 166, 197–204. https://doi.org/10.1016/j.jpba.2019.01.017)

The authors claim that combining cell-based and lysate-based assays increases coverage (Figure 3F) is not supported by their data. The '% targets' presented in Figure 3F have a different denominator for each bar. As it stands, all 49 targets quantified in both assays which have a significant change in thermal stability may be significant in the cell-based assay. If so, the apparent increase in % targets when combining reflects only the subsetting of the data. To alleviate this lack of clarity, the authors could update Figure 3F so that all three bars present the % targets figure for just the 60 compounds present in both assays.

We spent much time debating the best way to present this data, so we are grateful for the feedback. Consistent with the Reviewer’s suggestion, we have included a figure that only considers the 60 compounds for which a target was quantified in both cell-based and lysate-based PISA (now Figure 3E). In addition, we included a pie chart that further illustrates our point (now Figure 3 – figure supplement 2A). Of the 60 compounds, there were 37 compounds that had a known target pass as a hit using both approaches, 6 compounds that had a known target pass as a hit in only cell-based experiments, and 6 compounds that had a known target pass as a hit in only lysate-based experiments.

Within the Venn diagram, we also included a few examples of compounds that fit into each category. Furthermore, we highlighted two examples of compound-target pairs that pass as a hit with one approach, but not the other (Figure 3 – figure supplement 2B,C). We would also like to refer the reviewer to Figure 4D, which indicates that BRAF inhibitors cause a significant change in BRAF thermal stability in lysates but not cells. 

Aims achieved, impact and utility:

The authors have achieved their main aim of presenting a workflow that serves to demonstrate the potential value of this approach. However, by using a single high dose of each compound and failing to adequately replicate their experiments and instead applying heuristic thresholds, they have limited the impact of their findings. Their results will be a useful resource for researchers wishing to explore potential off-target interactions and/or mechanisms of action for these 96 compounds, but are expected to be superseded by more robust datasets in the near future. The most valuable aspect of the study is the demonstration that combining live cell and whole cell lysate PISA assays across multiple related compounds can help to elucidate the mechanisms of action.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

More specifically:

P 1 l 20, we quantified 1.498 million thermal stability measurements.

It's a staggering assertion, and it takes some reading to realize that the authors mean the total number of proteins identified and quantified in all experiments. But far from all of these proteins were quantified with enough precision to provide meaningful solubility shifts.

We can assure the reviewer that we were not trying to deceive the readers. We stated ‘1.498 million thermal stability measurements.’ We did not say 1.498 million compound-specific thermal stability shifts.’ We assume that most readers will appreciate that the overall quality of the measurements will be variable across the dataset, e.g., in any work that describes quantitation of thousands of proteins in a proteomics dataset. In accordance with the Reviewer’s suggestion, we have weakened this statement. The revised version of the manuscript now reads as follows (p. 1): 

“Taking advantage of this advance, we quantified more than one million thermal stability measurements in response to multiple classes of therapeutic and tool compounds (96 compounds in living cells and 70 compounds in lysates).”

P 7 l 28. We observed a large range of thermal stability measurements for known compound-target pairs, from a four-fold reduction in protein stability to a four-fold increase in protein stability upon compound engagement (Figure 2A).

PISA-derived solubility shift cannot be interpreted simply as a "four-fold reduction/increase in protein stability".

We thank the Reviewer for highlighting this specific passage and agree that it was worded poorly. As such, we have modified the manuscript to the following (p. 8): 

“We observed a large range of thermal stability measurements for known compound-target pairs, from a four-fold reduction in protein solubility after thermal denaturation to a four-fold increase in protein solubility upon compound engagement (Figure 2A).”

P 8, l 6. Instead, we posit that maximum ligand-induced change in thermal stability is target-specific.

Yes, that's right, but this has been shown in a number of prior studies.

We agree with the reviewer and accept that we made a mistake in how we worded this sentence, which we regret upon reflection. As such, we have modified this sentence to the following:

“Instead, our data appears to be consistent with the previous observation that the maximum ligandinduced change in thermal stability is target-specific (Savitski et al., 2014; Becher et al., 2016).”

P 11 l 7. Combining the two approaches allows for greater coverage of the cellular proteome and provides a better chance of observing the protein target for a compound of interest. In fact, the main difference is that in-cell PISA provides targets in cases when the compound is a pro-drug that needs to be metabolically processed before engaging the intended target. This has been shown in a number of prior studies, but not mentioned in this manuscript.

While our study was not focused on the issue of pro-drugs, this is an important point and we would be happy to re-iterate it in our manuscript. We thank the Reviewer for the suggestion and have modified the manuscript to reflect this point (p. 19): 

“Cell-based studies, on the other hand, have the added potential to identify the targets of pro-drugs that must be metabolized in the cell to become active and secondary changes that occur independent of direct engagement (Savitski et al., 2014; Franken et al., 2015; Almqvist et al., 2016; Becher et al., 2016; Liang et al., 2022).”

While we are happy to make this change, we also would like to point out that the reviewer’s assertions that, “the main difference is that in-cell PISA provides targets in cases when the compound is a prodrug that needs to be metabolically processed before engaging the intended target” also may not fully capture the nuances of protein engagement effectors in the cellular context. Thus, we believe it is important to highlight the ability of cell-based assays to identify secondary changes in thermal stability.  

P 11 l 28. These data suggest that the thermal destabilization observed in cell-based experiments might stem from a complex biophysical rearrangement. That's right because it is not about thermal stability, but about protein solubility which is much affected by the environment.

We agree that the readout of solubility is an important caveat for nearly every experiment in the family of assays associated with ‘thermal proteome profiling’. Inherently complex biophysical arrangements could affect the inherent stability and solubility of a protein or complex. Thus, we would be happy to make the following change consistent with the reviewer’s suggestion (p. 12): 

“These data suggest that the decrease in solubility observed in cell-based experiments might stem from a complex biophysical rearrangement.”

P 12 l 7 A). Thus, certain protein targets are more prone to thermal stability changes in one experimental setting compared to the other. Same thing - it's about solubility, not stability.

We thank the Reviewer for the recommendation and have modified the revised manuscript as follows (p. 13):

“Thus, certain protein targets were more prone to solubility (thermal stability) changes in one experimental setting compared to the other (Huber et al., 2015).”

P13 l 15. While the data suggests that cell- and lysate-based PISA are equally valuable in screening the proteome for evidence of target engagement... No, they are not equally valuable - cell-based PISA can provide targets of prodrugs, which lysate PISA cannot.

We have removed this sentence to avoid any confusion. We will not place any value judgments on the two approaches. 

P 18 l 10. In general, a compound-dependent thermal shift that occurs in a lysate-based experiment is almost certain to stem from direct target engagement. That's true and has been known for a decade. Reference needed.

We recognize this oversight and would be happy to include references. The revised manuscript reads as follows: 

“In general, a compound-dependent thermal shift that occurs in a lysate-based experiment is almost certain to stem from direct target engagement (Savitski et al., 2014; Becher et al., 2016). This is because cell signaling pathways and cellular structures are disrupted and diluted. Cell-based studies, on the other hand, have the added potential to identify the targets of pro-drugs that must be metabolized in the cell to become active and secondary changes that occur independent of direct engagement (Savitski et al., 2014; Franken et al., 2015; Almqvist et al., 2016; Becher et al., 2016; Liang et al., 2022).”

P 18 l 29. the data seemed to indicate that the maximal PISA fold change is protein-specific. Therefore, a log2 fold change of 2 for one compound-protein pair could be just as meaningful as a log2 fold change of 0.2 for another. This is also not new information.

We again appreciate the Reviewer for highlighting this oversight. The revised manuscript reads as follows: 

“Ultimately, the data seemed to be consistent with previous studies that indicate the maximal change in thermal stability in protein specific (Savitski et al., 2014; Becher et al., 2016; Sabatier et al., 2022). Therefore, a log2 fold change of 2 for one compound-protein pair could be just as meaningful as a log2 fold change of 0.2 for another.”

P 19 l 5. Specifically, the compounds that most strongly impacted the thermal stability of targets, also acted as the most potent inhibitors. I wish this was true, but this is not always so. For instance, in Nat Meth 2019, 16, 894-901 it was postulated that large ∆Tm correspond to biologically most important sites ("hot spots") - the idea that was later challenged and largely discredited in subsequent studies.

Indeed, we agree with the Reviewer that there may be no essential connection between these. Rather, we are simply drawing conclusions from observations within the presented dataset. 

Saying nothing about the work presented in the paper that the reviewer notes above, the referenced definition is also more nuanced “…we hypothesized that ‘hotspot’ modification sites identified in this screen (namely, those significantly shifted relative to the unmodified, bulk and even other phosphomodiforms of the same protein) may represent sites with disproportionate effects on protein structure and function under specific cellular conditions.” Indeed, in the response to that work, Potel et al. (https://doi.org/10.1038/s41592-021-01177-5) “agree with the premise of the Huang et al. study that phosphorylation sites that have a significant effect on protein thermal stability are more likely to be functionally relevant, for example, by modulating protein conformation, localization and protein interactions.” 

Anecdotally, we also speculate that if we observe proteome engagement for two compounds (let’s say two ATP-competitive kinase inhibitors) that bind in the same pocket (let’s say the ATP binding site) and one causes a greater change in solubility, then it is reasonable to assume that it is a stronger evidence and we see evidence supporting this claim in Figure 2, Figure 3, Figure 4, and Figure 5.

It is also important to point out that previous work has also made similar points. This is highlighted in a review article by Mateus et al. (10.1186/s12953-017-0122-4). The authors state, “To obtain affinity estimates with TPP, a compound concentration range TPP (TPP-CCR) can be performed. In TPPCCR, cells are incubated with a range of concentrations of compound and heated to a single temperature.” In support of this claim, the authors reference two papers—Savitski et al., 2014 and Becher et al., 2016. We have updated this section in the revised manuscript (p. 20): 

“While the primary screen was carried out at fixed dose, the increased throughput of PISA allowed for certain compounds to be assayed at multiple doses in a single experiment. In these instances, there was a clear dose-dependent change in thermal stability of primary targets, off-targets, and secondary targets. This not only helped corroborate observations from the primary screen, but also seemed to provide a qualitative assessment of relative compound potency in agreement with previous studies (Savitski et al., 2014; Becher et al., 2016; Mateus et al., 2017). Specifically, the compounds that most strongly impacted the thermal stability of targets, also acted as the most potent inhibitors. In order to be a candidate for this type of study, a target must have a large maximal thermal shift (magnitude of log2 fold change) because there must be a large enough dynamic range to clearly resolve different doses.”

Also, the compound efficacy is strongly dependent upon the residence time of the drug, which may or may not correlate with the PISA shift. Also important is the concentration at which target engagement occurs (Anal Chem 2022, 94, 15772-15780).

In our study, the time and concentration of treatment and was fixed for all compounds at 30 minutes and 10 µM, respectively. Therefore, we do not believe these parameters will affect our conclusions.  

P 19 l 19. For example, we found that the clinically-deployed CDK4/6 inhibitor palbociclib is capable of directly engaging and inhibiting PLK1. This is a PISA-based prediction that needs to be validated by orthogonal means.

As we demonstrate in this work, the PISA assays serve as powerful screening methods, thus we agree that validation is important for these types of studies. To this end, we show the following:  

• Proteomics: Palbociclib causes a decrease in solubility following thermal melting in cells.

• Chemical Informatic: Palbociclib is structurally similar to BI 2536.

• Protein informatics: Modeling of palbociclib in empirical structures of the PLK1 active site generates negligible steric clashes. 

• Biochemical: Palbociclib inhibits PLK1 activity in cells.

We have changed this text to the following to clarify these points:

“For example, we found that the clinically-deployed CDK4/6 inhibitor palbociclib has a dramatic impact on PLK1 thermal stability in live cells, is capable of inhibiting PLK1 activity in cell-based assays, and can be modelled into the PLK1 active site.”

Reviewer #2 (Recommendations For The Authors):

I am wondering why the authors chose to use K562 (leukaemia) cells in this work as opposed to a different cancer cell line (HeLa? Panc1?). It would be helpful if the authors could present some rationale for this decision.

This is a great question. Two reasons really. First, they are commonly used in various fields of research, especially previous studies using proteome-wide thermal shift assays (PMID: 25278616, 32060372) and large scale chemical perturbations screens (PMID: 31806696). Second, they are a suspension line that makes executing the experiments easier because they do not need to be detached from a plate prior to thermal melting. We think this is a valuable point to make in the manuscript, such that non-experts understand this concept. We tried to communicate this succinctly in the revised manuscript, but would be happy to elaborate further if the Reviewer would like us to. 

“To enable large-scale chemical perturbation screening, we first sought to establish a robust workflow for assessing protein thermal stability changes in living cells. We chose K562 cells, which grow in suspension, because they have been frequently used in similar studies and can easily be transferred from a culture flask to PCR tubes for thermal melting (Savitski et al., 2014; Jarzab et al., 2020).”

I note that integral membrane proteins are over-represented among targets for anti-cancer therapeutics. To what extent is the membrane proteome (plasma membrane in particular) identified in this work? After examining the methods, I would expect at least some integral membrane proteins to be identified. Do the authors observe any differences in the behaviour of water-soluble proteins versus integral membrane proteins in their assays? It would be helpful if the authors could comment on this in a potential revision.

We agree this is an important point when considering the usage of PISA and thermal stability assays in general for specific classes of therapeutics. To address this, we explored what effect the analysis of thermal stability/solubility had on the proportion of membrane proteins in our data (Author response image 1). Annotations were extracted from Uniprot based on each protein being assigned to the “plasma membrane” (07/2024). We quantified 1,448 (16.5% of total proteins) and 1,558 (17.3% of total proteins) membrane proteins in our cell and lysate PISA datasets, respectively. We also compared the proportion of annotated proteins in these datasets to a recent TMTpro dataset (Lin et al.; PMID: 38853901) and found that the PISA datasets recovered a slightly lower proportion of membrane proteins (~17% in PISA versus 18.9% in total proteome analysis). Yet, we note that we expect more membrane proteins in urea/SDS based lysis methods compared to 0.5% NP-40 extractions.

Author response image 1.

We were not able to find an appropriate place to insert this data into the manuscript, so we have left is here in the response. If the Reviewer feels strongly that this data should be included in the manuscript, we would be happy to include these data.  

A final note: I commend the authors for making their full dataset publicly available upon submission to this journal. This data promises to be a very useful resource for those working in the field.

We thank the Reviewer for this and note that we are excited for this data to be of use to the community.

Reviewer #3 (Recommendations For The Authors):

There is no dataset PDX048009 in ProteomeXchange Consortium. I assume this is because it's under an embargo which needs to be released.

We can confirm that data was uploaded to ProteomeXchange.

MS data added to the manuscript during revisions was submitted to ProteomeXchange with the identifier – PDX053138.

Page 9 line 5 refers to 59 compounds quantified in both cell-based and lysate-based, but Figure 3E shows 60 compounds quantified in both. I believe these numbers should match.

We thank the Reviewer for catching this. In response to critiques from this Reviewer in the Public Review, we re-worked this section considerably. Please see the above critique/response for more details. 

Page 10, lines 26-28: It would help the reader if some of the potential 'artefactual effects of lysatebased analyses' were described briefly.

We thank the Reviewer for raising this point. The truth is, that we are not exactly sure what is happening here, but we know that, at least, for vorinostat, this excess of changes in lysate-based PISA is consistent across experiments. We also do not see pervasive issues within the plexes containing these compounds. Therefore, we do not think this is due to a mistake or other experimental error. We hypothesize that the effect might result from a change in pH or other similar property that occurs upon addition of the molecule, though we note that we have previously seen that vorinostat can induce large numbers of solubility changes in a related solvent shift assays (doi: 10.7554/eLife.70784). We have modified the text to indicate that we do not fully understand the reason for the observation (p. 11):

“It is highly unlikely that these three molecules actively engage so many proteins and, therefore, the 2,176 hits in the lysate-based screen were likely affected in part by consistent, but artefactual effects of lysate-based analyses that we do not fully understand (Van Vranken et al., 2021).”

Page 24, lines 29-30 appear to contain a typo. I believe the '>' should be '<' or the 'exclude' should be 'retain'.

The Reviewer is completely correct. We appreciate the attention to detail. This mistake has been corrected in the revised manuscript.  

Page 25, lines 5-7: The methods need to explain how the trimmed standard deviation is calculated.

We apologize for this oversight. To calculate the trimmed standard deviation, we used proteins that were measured in at least 30 conditions. For these, we then removed the top 5% of absolute log2 foldchanges (compared to DMSO controls) and calculated the standard deviation of the resulting set of log2 fold-changes. This is similar in concept to the utilization of “trimmed means” in proteomics data (https://doi.org/10.15252/msb.20145625), which helps to overcome issues due to extreme outliers in datasets. We have added the following statement to the methods to clarify this point (p. 27):

“Second, for each protein across all cells or lysate assays, the number of standard deviations away from the mean thermal stability measurement (z-score) for a given protein was quantified based on a trimmed standard deviation. Briefly, the trimmed standard deviation was calculated for proteins that were measured in at least 30 conditions. For these, we removed the top 5% of absolute log2 foldchanges (compared to DMSO controls) and calculated the standard deviation of the resulting set of log2 fold-changes.”

Page 25, lines 9-11 needs editing for clarity.

We tested empirical hit rates for estimation of mean and trimmed standard deviation (trimmedSD) thresholds to apply, to maximize sensitivity and minimizing the ‘False Hit Rate’, or the number of proteins in the DMSO control samples called as hits divided by the total number of proteins called as hits with a given threshold applied. 

Author response image 2.

Hit calling threshold setting based on maximizing the total hits called and minimizing the False Hit Rate in cells (number of DMSO hits divided by the total number of hits).

Author response image 3.

Hit calling threshold setting based on maximizing the total hits called and minimizing the False Hit Rate in lysates (number of DMSO hits divided by the total number of hits).

Figure 1 supplementary 2a legend states: '32 DMSO controls'. Should that be 64?

We thank the Reviewer for catching our mistake. This has been corrected in the revised manuscript. 

I suggest removing Figure 1 supplementary 3c which is superfluous as only the number it presents is already stated in the text (page 5, line 9).

We thank the Reviewer for the suggestion and agree that this panel is superfluous. It has been removed from the revised manuscript.

New data and tables added during revisions:  

(1) Table 3 – All log2 fold change values for the cell-based screen. Using this table, proteincentric solubility profiles can be plotted (as in Figures 2D and others). 

(2) Table 4 – All log2 fold change values for the lysate-based screen. Using this table, proteincentric solubility profiles can be plotted (as in Figures 2D and others). 

(3) Figure 1 – Figure supplement 3H – Table highlighting proteins that pass log2 fold change cutoffs, but not nSD cutoffs and vice versa. 

(4) Figure 2 – Panels H and I were updated with a new color scheme. 

(5) Figure 3 – Updated main figure and supplement at the request of Reviewer 3. 

• Figure 3E – Compares on-target hits for the cell- and lysate-based screens for all compounds for which a target was quantified in both screens. 

• Figure 3 – Figure supplement 2 – Highlights on-target hits in both screens, exclusively in cells, and exclusively in lysates. 

(6) Figure 5 – PISA data for K562 lysates treated with AZD-7762 at multiple concentrations.

• Figure 5F

• Figure 5 – Figure supplement 3A-C

• Figure 5 – Source data 2

(7) Figure 5 – Phosphoproteomic profiling of K562 cells treated with AZD7762 or Bafetinib. 

• Figure 5G

• Figure 5 – Figure supplement 4A-F

• Figure 5 – Source data 3 (phosphoproteome)

• Figure 5 – Source data 4 (associated proteome data)

Author Response

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

Reviewer #1 (Public Review):

Wang et al investigated the evolution, expression, and function of the X-linked miR-506 miRNA family. They showed that the miR-506 family underwent rapid evolution. They provided evidence that miR-506 appeared to have originated from the MER91C DNA transposons. Human MER91C transposon produced mature miRNAs when expressed in cultured cells. A series of mouse mutants lacking individual clusters, a combination of clusters, and the entire X-linked cluster (all 22 miRNAs) were generated and characterized. The mutant mice lacking four or more miRNA clusters showed reduced reproductive fitness (litter size reduction). They further showed that the sperm from these mutants were less competitive in polyandrous mating tests. RNA-seq revealed the impact of deletion of miR-506 on the testicular transcriptome. Bioinformatic analysis analyzed the relationship among miR-506 binding, transcriptomic changes, and target sequence conservation. The miR-506-deficient mice did not have apparent effect on sperm production, motility, and morphology. Lack of severe phenotypes is typical for miRNA mutants in other species as well. However, the miR-506-deficient males did exhibit reduced litter size, such an effect would have been quite significant in an evolutionary time scale. The number of mouse mutants and sequencing analysis represent a tour de force. This study is a comprehensive investigation of the X-linked miR-506 miRNA family. It provides important insights into the evolution and function of the miR-506 family.

The conclusions of this preprint are mostly supported by the data except being noted below. Some descriptions need to be revised for accuracy.

L219-L285: The conclusion that X-linked miR-506 family miRNAs are expanded via LINE1 retrotransposition is not supported by the data. LINE1s and SINEs are very abundant, accounting for nearly 30% of the genome. In addition, the LINE1 content of the mammalian X chromosome is twice that of the autosomes. One can easily find flanking LINE1/SINE repeat. Therefore, the analyses in Fig. 2G, Fig. 2H and Fig. S3 are not informative. In order to claim LINE1-mediated retrotransposition, it is necessary to show the hallmarks of LINE1 retrotransposition, which are only possible for new insertions. The X chromosome is known to be enriched for testis-specific multi-copy genes that are expressed in round spermatids (PMID: 18454149). The conclusion on the LINE1-mediated expansion of miR-506 family on the X chromosome is not supported by the data and does not add additional insights. I think that the LINE1 related figure panels and description (L219-L285) need to be deleted. In discussion (L557558), "...and subsequently underwent sequence divergence via LINE1-mediated retrotransposition during evolution" should also be deleted. This section (L219-L285) needs to deal only with the origin of miR506 from MER91C DNA transposons, which is both convincing and informative.

Reply: Agreed, the corresponding sentences were deleted.

Fig. 3A: can you speculate/discuss why the miR-506 expression in sperm is higher than in round spermatids?

Reply: RNAs are much less abundant in sperm than in somatic or spermatogenic cells (~1/100). Spermborne small RNAs represent a small fraction of total small RNAs expressed in their precursor spermatogenic cells, including spermatocytes and spermatids. Therefore, when the same amount of total/small RNAs are used for quantitative analyses, sperm-borne small RNAs (e.g., miR-506 family miRNAs) would be proportionally enriched in sperm compared to other spermatogenic cells. We discussed this point in the text (Lines 550-556).

**Reviewer #2 (Public Review):

In this paper, Wang and collaborators characterize the rapid evolution of the X-linked miR-506 cluster in mammals and characterize the functional reference of depleting a few or most of the miRNAs in the cluster. The authors show that the cluster originated from the MER91C DNA transposon and provide some evidence that it might have expanded through the retrotransposition of adjacent LINE1s. Although the animals depleted of most miRNAs in the cluster show normal sperm parameters, the authors observed a small but significant reduction in litter size. The authors then speculate that the depletion of most miRNAs in the cluster could impair sperm competitiveness in polyandrous mating. Using a successive mating protocol, they show that, indeed, sperm lacking most X-linked miR-506 family members is outcompeted by wild-type sperm. The authors then analyze the evolution of the miR-506 cluster and its predicted targets. They conclude that the main difference between mice and humans is the expansion of the number of target sites per transcript in humans.

The conclusions of the paper are, in most cases, supported by the data; however, a more precise and indepth analysis would have helped build a more convincing argument in most cases.

(1) In the abstracts and throughout the manuscript, the authors claim that "... these X-linked miRNA-506 family miRNA [...] have gained more targets [...] " while comparing the human miRNA-506 family to the mouse. An alternative possibility is that the mouse has lost some targets. A proper analysis would entail determining the number of targets in the mouse and human common ancestor.

Reply: This question alerted us that we did not describe our conclusion accurately, causing confusion for this reviewer. Our data suggest that although the sheer number of target genes remains the same between humans and mice, the human X-linked miR-506 family targets a greater number of genes than the murine counterpart on a per miRNA basis. In other words, mice never lost any targets compared to humans, but per the miR-506 family miRNA tends to target more genes in humans than in mice.

We revised the text to more accurately report our data. The pertaining text (lines 490-508) now reads: “Furthermore, we analyzed the number of all potential targets of the miR-506 family miRNAs predicted by the aforementioned four algorithms among humans, mice, and rats. The total number of targets for all the X-linked miR-506 family miRNAs among different species did not show significant enrichment in humans (Fig. S9C), suggesting the sheer number of target genes does not increase in humans. We then compared the number of target genes per miRNA. When comparing the number of target genes per miRNA for all the miRNAs (baseline) between humans and mice, we found that on a per miRNA basis, human miRNAs have more targets than murine miRNAs (p<0.05, t-test) (Fig. S9D), consistent with higher biological complexity in humans. This became even more obvious for the X-linked miR-506 family (p<0.05, t-test) (Fig. S9D). In humans, the X-linked miR-506 family, on a per miRNA basis, targets a significantly greater number of genes than the average of all miRNAs combined (p<0.05, t-test) (Fig. S9D). In contrast, in mice, we observed no significant difference in the number of targets per miRNA between X-linked miRNAs and all of the mouse miRNAs combined (mouse baseline) (Fig. S9D). These results suggest that although the sheer number of target genes remains the same between humans and mice, the human X-linked miR-506 family targets a greater number of genes than the murine counterpart on a per miRNA basis.”

We also changed “have gained” to “have” throughout the text to avoid confusion.

(2) The authors claim that the miRNA cluster expanded through L1 retrotransposition. However, the possibility of an early expansion of the cluster before the divergence of the species while the MER91C DNA transposon was active was not evaluated. Although L1 likely contributed to the diversity within mammals, the generalization may not apply to all species. For example, SINEs are closer on average than L1s to the miRNAs in the SmiR subcluster in humans and dogs, and the horse SmiR subcluster seems to have expanded by a TE-independent mechanism.

Reply: Agreed. We deleted the data mentioned by this reviewer.

(3) Some results are difficult to reconcile and would have benefited from further discussion. The miR-465 sKO has over two thousand differentially expressed transcripts and no apparent phenotype. Also, the authors show a sharp downregulation of CRISP1 at the RNA and protein level in the mouse. However, most miRNAs of the cluster increase the expression of Crisp1 on a reporter assay. The only one with a negative impact has a very mild effect. miRNAs are typically associated with target repression; however, most of the miRNAs analyzed in this study activate transcript expression.

Reply: Both mRNA and protein levels of Crisp1 were downregulated in KO mice, and these results are consistent with the luciferase data showing overexpression of these miRNAs upregulated the Crisp1 3’UTR luciferase activity. We agree that miRNAs usually repress target gene expression. However, numerous studies have also shown that some miRNAs, such as human miR-369-3, Let-7, and miR-373, mouse miR-34/449 and the miR-506 family, and the synthetic miRNA miRcxcr4, activate gene expression both in vitro (1, 2) and in vivo (3-6). Earlier reports have shown that these miRNAs can upregulate their target gene expression, either by recruiting FXR1, targeting promoters, or sequestering RNA subcellular locations (1, 2, 6). We briefly discussed this in the text (Lines 605-611).

(4) More information is required to interpret the results of the differential RNA targeting by the murine and human miRNA-506 family. The materials and methods section needs to explain how the authors select their putative targets. In the text, they mention the use of four different prediction programs. Are they considering all sites predicted by any method, all sites predicted simultaneously by all methods, or something in between? Also, what are they considering as a "shared target" between mice and humans? Is it a mRNA that any miR-506 family member is targeting? Is it a mRNA targeted by the same miRNA in both species? Does the targeting need to occur in the same position determined by aligning the different 3'UTRs?

Reply: Since each prediction method has its merit, we included all putative targets predicted by any of the four methods. The "shared target" refers to a mRNA that any miR-506 family member targets because the miR-506 family is highly divergent among different species. We have added the information to the “Large and small RNA-seq data analysis” section in Materials and Methods (Lines 871-882).

(5) The authors highlight the particular evolution of the cluster derived from a transposable element. Given the tendency of transposable elements to be expressed in germ cells, the family might have originated to repress the expression of the elements while still active but then remained to control the expression of the genes where the element had been inserted. The authors did not evaluate the expression of transcripts containing the transposable element or discuss this possibility. The authors proposed an expansion of the target sites in humans. However, whether this expansion was associated with the expansion of the TE in humans was not discussed either. Clarifying whether the transposable element was still active after the divergence of the mouse and human lineages would have been informative to address this outstanding issue.

Reply: Agreed. The MER91C DNA transposon is denoted as nonautonomous (7); however, whether it was active during the divergence of mouse and human lineages is unknown. To determine whether the expansion of the target sites in humans was due to the expansion of the MER91C DNA transposon, we analyzed the MER91C DNA transposon-containing transcripts and associated them with our DETs. Of interest, 28 human and 3 mouse mRNAs possess 3’UTRs containing MER91C DNA sequences, and only 3 and 0 out of those 28 and 3 genes belonged to DETs in humans and mice, respectively (Fig. S9E), suggesting a minimal effect of MER91C DNA transposon expansion on the number of target sites. We briefly discussed this in the text (Lines 511-518).

Post-transcriptional regulation is exceptionally complex in male haploid cells, and the functional relevance of many regulatory pathways remains unclear. This manuscript, together with recent findings on the role of piRNA clusters, starts to clarify the nature of the selective pressure that shapes the evolution of small RNA pathways in the male germ line.

Reply: Agreed. We appreciate your insightful comments.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors conducted a comprehensive study of the X-linked miR-506 family miRNAs in mice on its origin, evolution, expression, and function. They demonstrate that the X-linked miR-506 family, predominantly expressed in the testis, may be derived from MER91C DNA transposons and further expanded by retrotransposition. By genetic deletion of different combinations of 5 major clusters of this miRNA family in mice, they found these miRNAs are not required for spermatogenesis. However, by further examination, the mutant mice show mild fertility problem and inferior sperm competitiveness. The authors conclude that the X-linked miR-506 miRNAs finetune spermatogenesis to enhance sperm competition.

Strengths:

This is a comprehensive study with extensive computational and genetic dissection of the X-linked miR506 family providing a holistic view of its evolution and function in mice. The finding that this family miRNAs could enhance sperm competition is interesting and could explain their roles in finetuning germ cell gene expression to regulate reproductive fitness.

Weaknesses:

The authors specifically addressed the function of 5 clusters of X-link miR-506 family containing 19 miRNAs. There is another small cluster containing 3 miRNAs close to the Fmr1 locus. Would this small cluster act in concert with the 5 clusters to regulate spermatogenesis? In addition, any autosomal miR-506 like miRNAs may compensate for the loss of X-linked miR-506 family. These possibilities should be discussed.

Reply: The three FmiRs were not deleted in this study because the SmiRs are much more abundant than the FmiRs in WT mice (Author Response image 1, heatmap version of Fig. 5C). Based on small RNA-seq, some FmiRs, e.g., miR-201 and miR-547, were upregulated in the SmiRs KO mice, suggesting that this small cluster may act in concert with the other 5 clusters and thus, worth further investigation. To our best knowledge, all the miR-506 family miRNAs are located on the X chromosome, although some other miRNAs were upregulated in the KO mice, they don’t belong to the miR-506 family. We briefly discussed this point in the text (Lines 635-638).

Author response image 1.

sRNA-seq of WT and miR-506 family KO testis samples.

Direct molecular link to sperm competitiveness defect remains unclear but is difficult to address.

Reply: In this study, we identified a target of the miR-506 family, i.e. Crisp1. KO of Crisp1 in mice, or inhibition of CRISP1 in human sperm (7, 8), appears to phenocopy the quinKO mice, displaying largely normal sperm motility but compromised ability to penetrate eggs. The detailed mechanism warrants further investigation in the future.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Lines 84-85: "Several cellular events are unique to the male germ cells, e.g., meiosis, genetic recombination, and haploid male germ cell differentiation (also called spermiogenesis)". This statement is not accurate. Please revise. Meiosis and genetic recombination are common to both male and female germ cells. They are highly conserved in both sexes in many species including mouse.

Reply: Agreed. We have revised the sentence and it now reads: “Several cellular events are unique to the male germ cells, e.g., postnatal formation of the adult male germline stem cells (i.e., spermatogonia stem cells), pubertal onset of meiosis, and haploid male germ cell differentiation (also called spermiogenesis) (9)” (Lines 83-86).

Lines 163-164: "we found that Slitrk2 and Fmr1 were syntenically linked to autosomes in zebrafish and birds (Fig. 1A), but had migrated onto the X chromosome in most mammals". This description is not accurate. Chr 4 in zebrafish and birds is syntenic to the X chromosome in mammals. The term "migrated" is not appropriate. Suggestion: Slitrk2 and Fmr1 mapped to Chr 4 (syntenic with mammalian X chromosome) in zebrafish and birds but to the X chromosome in most mammals.

Reply: Agreed. Revised as suggested.

Reviewer #2 (Recommendations For The Authors):

(1) In the significance statement, the authors mention that the mutants are "functionally infertile," although the decrease in competitiveness is partial. I suggest referring to them as "functionally sub-fertile."

Reply: Agreed. Revised as suggested.

(2) I will urge the authors to explain in more detail how some figures are generated and what they mean. Some critical information needs to be included in various panels.

(2a) Figure S1. The phastCons track does not seem to align as expected with the rest of the figure. The highest conservation peak is only present in humans, and the sequence conserved in the sea turtle has the lowest phastCons score. I was expecting the opposite from the explanation.

Reply: The tracks for phyloP and phastCons are the scores for all 100 species, whereas the tracks with the species names on the left are the corresponding sequences aligned to the human genome. We have revised our figure to make it clearer.

(2b) Figure 2A and Figure S2C. Although all the functional analysis of the manuscript has been done in mice, the alignments showing sequence conservation do not include the murine miRNAs. Please include the mouse miRNAs in these panels.

Reply: The mouse has Mir-506-P7 with the conserved miRNA-3P seed region, which was included in the lower panel in Figure S2C. However, mice do not have Mir-506-P6, which may have been lost or too divergent to be recognized during the evolution and thus, were not included in Figure 2A and the upper panel in Figure S2C.

(2c) Figure S7H. The panel could be easier to read.

Reply: Agreed. We combined all the same groups and turned Figure S7H (now Figure S6H) into a heatmap.

(2d) The legend of Figure 6G reads, "The number of target sites within individual target mRNAs in both humans and mice ." Can the author explain why the value 1 of the human "Number of target sites" is connected to virtually all the "Number of target sites" values in mice?

Reply: Sorry for the confusion. For example, for gene 1, we have 1 target site in the human and 1 target site in the mouse; but for gene 2, we have 1 target site in the human and multiple sites in the mouse; therefore, the value 1 is connected to more than one value in the mouse.

Reviewer #3 (Recommendations For The Authors):

CRISP1 and EGR1 protein localization in WT and mutant sperm by immunostaining would be helpful.

Reply: Agreed. We performed immunostaining for CRISP1 on WT sperm, and the new results are presented in Figure S8D. CRISP1 seems mainly expressed in the principal piece and head of sperm.

The detailed description of the generation of various mutant lines should be included in the Methods.

Reply: We added more details on the generation of knockout lines in the Materials and Methods (686701).

References:

(1) S. Vasudevan, Y. Tong, J. A. Steitz, Switching from repression to activation: microRNAs can upregulate translation. Science 318, 1931-1934 (2007).

(2) R. F. Place, L. C. Li, D. Pookot, E. J. Noonan, R. Dahiya, MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105, 1608-1613 (2008).

(3) Z. Wang et al., X-linked miR-506 family miRNAs promote FMRP expression in mouse spermatogonia. EMBO Rep 21, e49024 (2020).

(4) S. Yuan et al., Motile cilia of the male reproductive system require miR-34/miR-449 for development and function to generate luminal turbulence. Proc Natl Acad Sci U S A 116, 35843593 (2019).

(5) S. Yuan et al., Oviductal motile cilia are essential for oocyte pickup but dispensable for sperm and embryo transport. Proc Natl Acad Sci U S A 118 (2021).

(6) M. Guo et al., Uncoupling transcription and translation through miRNA-dependent poly(A) length control in haploid male germ cells. Development 149 (2022).

(7) V. G. Da Ros et al., Impaired sperm fertilizing ability in mice lacking Cysteine-RIch Secretory Protein 1 (CRISP1). Dev Biol 320, 12-18 (2008).

(8) J. A. Maldera et al., Human fertilization: epididymal hCRISP1 mediates sperm-zona pellucida binding through its interaction with ZP3. Mol Hum Reprod 20, 341-349 (2014).

(9) L. Hermo, R. M. Pelletier, D. G. Cyr, C. E. Smith, Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microsc Res Tech 73, 241-278 (2010).

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study seeks to establish accurate computational models to explore the role of hydrodynamic interactions on energy savings and spatial patterns in fish schools. Specifically, the authors consider a system of (one degree-of-freedom) flapping airfoils that passively position themselves with respect to the streamwise direction, while oscillating at the same frequency and amplitude, with a given phase lag and at a constant cross-stream distance. By parametrically varying the phase lag and the cross-stream distance, they systematically explore the stability and energy costs of emergent configurations. Computational findings are leveraged to distill insights into universal relationships and clarify the role of the wake of the leading foil.

We would like to thank the referee for their careful read of the manuscript and for their constructive feedback. We appreciate it.

Strengths:

(1) The use of multiple computational models (computational fluid dynamics, CFD, for full Navier-Stokes equations and computationally efficient inviscid vortex sheet, VS, model) offers an extra degree of reliability of the observed findings and backing to the use of simplified models for future research in more complex settings.

(2) The systematic assessment of the stability and energy savings in multiple configurations of pairs and larger ensembles of flapping foils is an important addition to the literature.

(3) The discovery of a linear phase-distance relationship in the formation attained by pairs of flapping foils is a significant contribution, which helps compare different experimental observations in the literature.

(4) The observation of a critical size effect for in-line formations of larger, above which cohesion and energetic benefits are lost at once, is a new discovery in the field.

Thank you for this list of strength – we are delighted that these ideas were clearly communicated in our manuscript.

Note that Newbolt et al. PNAS, 2019 reported distance as a function of phase for pairs of flapping hydrofoils, and Li et al, Nat. Comm., 2020 also reported phase-distance relationship in robotic and biological fish (calling it Vortex Phase Matching). We compiled their results, together with our and other numerical and experimental results, showing that the linear distance-phase relationship is universal.

Weaknesses:

(1) The extent to which observations on one-degree-of-freedom flapping foils could translate to real fish schools is presently unclear so some of the conclusions on live fish schools are likely to be overstated and would benefit from some more biological framing.

Thank you for bringing up this point. Indeed, flapping foils that are free to translate in both the x- and y-directions and rotate in the x-y plane could drift apart in the y-direction. However, this drift occurs at a longer time scale than the forward swimming motion; it is much slower. For this reason, we feel justified to ignore it for the purpose of this study, especially that the pairwise equilibria in the swimming x-direction are reached at a faster time scale.

Below, we include two snapshots taken from published work from the group of Petros Koumoutsakos (Gazzola et al, SIAM 2014). The figures show, respectively, a pair and a group of five undulating swimmers, free to move and rotate in the x-y plane. The evolution of the two and five swimmers is computed in the absence of any control. The lateral drift is clearly sub-dominant to the forward motion. Similar results were reported in Verma et al, PNAS 2018.

These results are independent on the details of the flow interactions model. For example, similar lateral drift is observed using the dipole model dipole model (Kanso & Tsang, FDR 2014, Tsang & Kanso, JNLS 2023).

Another reason why we feel justified to ignore these additional degrees of freedom is the following: we assume a live fish or robotic vehicle would have feedback control mechanisms that correct for such drift. Given that it is a slowly-growing drift, we hypothesize that the organism or robot would have sufficient time to respond and correct its course.

Indeed, in Zhu et al. 2022, an RL controller, which drives an individual fish-like swimmer to swim at a given speed and direction, when applied to pairs of swimmers, resulted in the pair "passively" forming a stable school without any additional information about each other.

We edited the main manuscript in page 4 of the manuscript to include reference to the work cited here and to explain the reasons for ignoring the lateral drift.

Citations:  

Gazzola, M., Hejazialhosseini, B., & Koumoutsakos, P. (2014). Reinforcement learning and wavelet adapted vortex methods for simulations of self-propelled swimmers. SIAM Journal on Scientific Computing, 36(3), B622-B639. DOI: https://doi.org/10.1137/130943078

Verma, S., Novati, G., & Koumoutsakos, P. (2018). Efficient collective swimming by harnessing vortices through deep reinforcement learning. Proceedings of the National Academy of Sciences, 115(23), 5849-5854. DOI: https://doi.org/10.1073/pnas.1800923115

Tsang, A. C. H. & Kanso, E., (2013). Dipole Interactions in Doubly Periodic Domains. Journal of Nonlinear Science 23 (2013): 971-991. DOI: https://doi.org/10.1007/s00332-013-9174-5

Kanso, E., & Tsang, A. C. H. (2014). Dipole models of self-propelled bodies. Fluid Dynamics Research, 46(6), 061407. DOI: https://doi.org/10.1088/0169-5983/46/6/061407

Zhu, Y., Pang, J. H., & Tian, F. B. (2022). Stable schooling formations emerge from the combined effect of the active control and passive self-organization. Fluids, 7(1), 41. DOI: https://doi.org/10.3390/fluids7010041

Author response image 1.

Antiphase self-propelled anguilliform swimmers. (a) – (d) Wavelet adapted vorticity fields at, respectively, t = T, t = 4T, t = 10T. (e) Absolute normalized velocities |U|/L. (f) Swimmers’ centre of mass trajectories.

Author response image 2.

Parallel schooling formation. (a) – (d) wavelet adapted vorticity fields at, respectively, t = T, t = 4T, t = 7T, t = 10T. (e) Absolute normalized velocities |U|/L. (f) Swimmers’ center of mass trajectories.

(2) The analysis of non-reciprocal coupling is not as novel as the rest of the study and potentially not as convincing due to the chosen linear metric of interaction (that is, the flow agreement).

We thank the referee for this candid and constructive feedback. In fact, we view this aspect of the study as most “revolutionary” because it provides a novel approach to pre-computing the locations of stable equilibria even without doing expensive all-to-all coupled simulations or experiments.

Basically, the idea is the following: you give me a flow field, it doesn’t matter how you obtained it, whether from simulations or experimentally, and I can tell you at what locations in this flow field a virtual flapping swimmer would be stable and save hydrodynamic energy!

In the revised version, we changed page 3 and 7 in main text, and added a new section “Diagnostic tools” in SI to better illustrate this.

Overall, this is a rigorous effort on a critical topic: findings of the research can offer important insight into the hydrodynamics of fish schooling, stimulating interdisciplinary research at the interface of computational fluid mechanics and biology.

We thank the referee again for their careful read of the manuscript and their constructive feedback.

Reviewer #2 (Public Review):

The document "Mapping spatial patterns to energetic benefits in groups of flow-coupled swimmers" by Heydari et al. uses several types of simulations and models to address aspects of stability of position and power consumption in few-body groups of pitching foils. I think the work has the potential to be a valuable and timely contribution to an important subject area. The supporting evidence is largely quite convincing, though some details could raise questions, and there is room for improvement in the presentation. My recommendations are focused on clarifying the presentation and perhaps spurring the authors to assess additional aspects:

We would like to thank the referee for their careful read of the manuscript and for their constructive feedback. We appreciate it.

(1) Why do the authors choose to set the swimmers free only in the propulsion direction? I can understand constraining all the positions/orientations for investigating the resulting forces and power, and I can also understand the value of allowing the bodies to be fully free in x, y, and their orientation angle to see if possible configurations spontaneously emerge from the flow interactions. But why constrain some degrees of freedom and not others? What's the motivation, and what's the relevance to animals, which are fully free?

We would like to thank the referee for raising this point. It is similar to the point raised above by the first referee. As explained above the reason is the following: in freely-swimming, hydrodynamically-interacting “fish,” the lateral drift is sub-dominant to the forward swimming motion. Therefore, we ignore it in the model. Please see our detailed response above for further clarification, and see changes in page 4 in the main manuscript.

(2) The model description in Eq. (1) and the surrounding text is confusing. Aren't the authors computing forces via CFD or the VS method and then simply driving the propulsive dynamics according to the net horizontal force? It seems then irrelevant to decompose things into thrust and drag, and it seems irrelevant to claim that the thrust comes from pressure and the drag from viscous effects. The latter claim may in fact be incorrect since the body has a shape and the normal and tangential components of the surface stress along the body may be complex.

Thank you for pointing this out! It is indeed confusing.

In the CFD simulations, we are computing the net force in the swimming x-direction direction by integrating using the definition of force density in relation to the stress tensor. There is no ambiguity here.

In the VS simulations, however, we are computing the net force in the swimming x-direction by integrating the pressure jump across a plate of zero thickness. There is no viscous drag. Viscous drag is added by hand, so-to-speak. This method for adding viscous drag in the context of the VS model is not new, it has been used before in the literature as explained in the SI section “Vortex sheet (VS) model” (pages 30 and 31).

.

(3) The parameter taudiss in the VS simulations takes on unusual values such as 2.45T, making it seem like this value is somehow very special, and perhaps 2.44 or 2.46 would lead to significantly different results. If the value is special, the authors should discuss and assess it. Otherwise, I recommend picking a round value, like 2 or 3, which would avoid distraction.

Response: The choice of dissipation time is both to model viscous effect and reduce computational complexity. Introducing it is indeed introduces forcing to the simulation. Round value, like 2 or 3, is equal to an integer multiple of the flapping period, which is normalized to T=1, Therefore, an integer value of  would cause forcing at the resonant frequency and lead to computational blow up. To avoid this effect, a parameter choice of  = 2.45, 2.44 or 2.46 would be fine and would lead to small perturbation to the overall simulation, compared to no dissipation at all. This effect is studied in detail in the following published work from our group:

Huang, Y., Ristroph, L., Luhar, M., & Kanso, E. (2018). Bistability in the rotational motion of rigid and flexible flyers. Journal of Fluid Mechanics, 849, 1043-1067. DOI: https://doi.org/10.1017/jfm.2018.446

(4) Some of the COT plots/information were difficult to interpret because the correspondence of beneficial with the mathematical sign was changing. For example, DeltaCOT as introduced on p. 5 is such that negative indicates bad energetics as compared to a solo swimmer. But elsewhere, lower or more negative COT is good in terms of savings. Given the many plots, large amounts of data, and many quantities being assessed, the paper needs a highly uniform presentation to aid the reader.

Thank you for pointing this out! We updated Figures 3,6 as suggested.

(5) I didn't understand the value of the "flow agreement parameter," and I didn't understand the authors' interpretation of its significance. Firstly, it would help if this and all other quantities were given explicit definitions as complete equations (including normalization). As I understand it, the quantity indicates the match of the flow velocity at some location with the flapping velocity of a "ghost swimmer" at that location. This does not seem to be exactly relevant to the equilibrium locations. In particular, if the match were perfect, then the swimmer would generate no relative flow and thus no thrust, meaning such a location could not be an equilibrium. So, some degree of mismatch seems necessary. I believe such a mismatch is indeed present, but the plots such as those in Figure 4 may disguise the effect. The color bar is saturated to the point of essentially being three tones (blue, white, red), so we cannot see that the observed equilibria are likely between the max and min values of this parameter.

Thank you for pointing this out! You are correct in your understanding of the flow agreement parameter, but not in your interpretation.

Basically, “if the match were perfect, then the swimmer would generate no relative flow and thus no thrust,” means that “such a location could not be is an equilibrium.” Let me elaborate. An equilibrium is one at which the net thrust force is zero. The equilibrium is stable if the slope of the thrust force is negative. Ideally, this is what maximizing the flow agreement parameter would produce.

For example, consider an ideal fluid where the flow velocity is form  in vertical direction. Consider a “ghost swimmer” heaving at a velocity  . Under this scenario, flow agreement and thrust parameters are

Let’s now consider a balance of forces on the “ghost swimmer.” The ghost swimmer is in relative equilibrium if and only if:

It gives us

We then consider stability at this equilibrium by calculating the derivative of thrust parameter over phase

The corresponding values at equilibria are

Thus, when taking the positive which means the equilibria is a stable fixed point. We included this analysis in a new section in the SI page 32.

(6) More generally, and related to the above, I am favorable towards the authors' attempts to find approximate flow metrics that could be used to predict the equilibrium positions and their stability, but I think the reasoning needs to be more solid. It seems the authors are seeking a parameter that can indicate equilibrium and another that can indicate stability. Can they clearly lay out the motivation behind any proposed metrics, and clearly present complete equations for their definitions? Further, is there a related power metric that can be appropriately defined and which proves to be useful?

Thank you – these are excellent suggestions. Indeed, we needed to better explain the motivation and equations. Perhaps the main idea for these metrics can be best understood when explained in the context of the simpler particle model, which we now do in the SI and explain the main text.

(7) Why do the authors not carry out CFD simulations on the larger groups? Some explanations should be given, or some corresponding CFD simulations should be carried out. It would be interesting if CFD simulations were done and included, especially for the in-line case of many swimmers. This is because the results seem to be quite nuanced and dependent on many-body effects beyond nearest-neighbor interactions. It would certainly be comforting to see something similar happen in CFD.

We are using a open-source version of the Immersed Boundary Method that is not specifically optimized for many interacting swimmers. Therefore, the computational cost of performing CFD simulations for more swimmers is high. Therefore, we used the CFD simulations sporadically with fewer simmers (2 or 3) and we performed systematic simulations in the context of the VS model.

For the same Reynolds number in Figure 1, we simulated three and four swimmers in CFD: three swimmers forms a stable formation, four swimmers don’t, consistent with the VS model, with the forth swimmer colliding with the third one. Results are included in the SI figure 8 of the main text.

(8) Related to the above, the authors should discuss seemingly significant differences in their results for long in-line formations as compared to the CFD work of Peng et al. [48]. That work showed apparently stable groups for numbers of swimmers quite larger than that studied here. Why such a qualitatively different result, and how should we interpret these differences regarding the more general issue of the stability of tandem groups?

Thank you for bringing up this important comparison. Peng et al. [48] (Hydrodynamic schooling of multiple self-propelled flapping plates) studied inline configuration of flapping airfoils at Reynolds number =200. There are several differences between their work and ours. The most important one is that they used a flexible plate, which makes the swimmer more adaptive to changes in the flow field, e.g. changes in tailbeat amplitude and changes in phase along its body and diverts some of the hydrodynamic energy to elastic energy. We edited the main text page 10 at the end of section “Critical size of inline formations beyond which cohesion is lost” to explain this distinction.

(9) The authors seem to have all the tools needed to address the general question about how dynamically stable configurations relate to those that are energetically optimal. Are stable solutions optimal, or not? This would seem to have very important implications for animal groups, and the work addresses closely related topics but seems to miss the opportunity to give a definitive answer to this big question.

Indeed, that is exactly the point – in pairwise formations, stable configurations are also energetically optimal! In larger groups, there is no unique stable configuration – each stable configuration is associated with a different degree of energy savings. Interestingly, when exploring various equilibrium configurations in a school of four, we found the diamond formation of D. Weihs, Nature, 1972 to be both stable and most optimal among the configurations we tested. However, claiming this as a global optimum may be misleading – our standpoint is that fish schools are always dynamic and that there are opportunities for energy savings in more than one stable configuration.

We added a section in new text “Mapping emergent spatial patterns to energetic benefits”, and added a new figure in the maintext (Fig. 10) and a new figure in the SI (Fig. S. 8)

(10) Time-delay particle model: This model seems to construct a simplified wake flow. But does the constructed flow satisfy basic properties that we demand of any flow, such as being divergence-free? If not, then the formulation may be troublesome.

The simplified wake flow captures the hydrodynamic trail left by the swimmer in a very simplified manner. In the limit of small amplitude, it should be consistent with the inviscid vortex sheet shed of T. Wu’s waving swimmer model (Wu TY. 1961).

The model was compared to experiments and used in several recent publications from the Courant Institute (Newbolt et al. 2019, 2022, 2024).

Citations:  

Wu, T. Y. T. (1961). Swimming of a waving plate. Journal of Fluid Mechanics, 10(3), 321-344. DOI: https://doi.org/10.1017/S0022112061000949

Newbolt, J. W., Lewis, N., Bleu, M., Wu, J., Mavroyiakoumou, C., Ramananarivo, S., & Ristroph, L. (2024). Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves. Nature Communications, 15(1), 3462. DOI: https://doi.org/10.1038/s41467-024-47525-9

Newbolt, J. W., Zhang, J., & Ristroph, L. (2022). Lateral flow interactions enhance speed and stabilize formations of flapping swimmers. Physical Review Fluids, 7(6), L061101. DOI: https://doi.org/10.1103/PhysRevFluids.7.L061101

Newbolt, J. W., Zhang, J., & Ristroph, L. (2019). Flow interactions between uncoordinated flapping swimmers give rise to group cohesion. Proceedings of the National Academy of Sciences, 116(7), 2419-2424.  DOI: https://doi.org/10.1073/pnas.1816098116

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Congratulations on such a comprehensive and well-thought-out study; I truly enjoyed reading it and have only a couple of suggestions that I believe will help further strengthen the paper. I am including a bunch of references here that are very familiar to me without the expectation of you to include them all, just to point at areas that I feel you might consider useful.

We thank the referee again for their careful read of the manuscript and for their constructive feedback. We appreciate it.

First, I believe that some more rationale is needed to justify the chosen modeling framework. I am fully aware of how difficult is to run these simulations, but I see some critical assumptions that need to be at least spelled out for the reader to appreciate the limitations of the study: (1) Constraining the cross-stream coordinate (a stability analysis should include perturbations on the cross-stream coordinate as well, see, for example, https://doi.org/10.1017/flo.2023.25 -- I know this is much simpler as it discards any vortex shedding) and (2) Assuming equal frequency and amplitude (there are studies showing variation of tail beat frequency in animals depending on their position in the school, see, for example, https://doi.org/10.1007/s00265-014-1834-4).

Thank you for these suggestions. These are indeed important and interesting points to discuss in the manuscript. See response above regarding point 1. Regarding point 2, this is of course important and will be pursued in future extensions of this work. We edited the intro and discussion of the main text to explain this.

In the paper “Stability of schooling patterns of a fish pair swimming against a flow”, The authors considered a pair of swimmers swimming in a channel. They analyzed stability of the system and find multiple equilibria of the system, including inline and staggered formation, and a special formation of perpendicular to the wall. Studying fish school in confined domain and analyzing their stability is very interesting. We added citation to this paper in the discussion section at the end of page 10.

In the paper “Fish swimming in schools save energy regardless of their spatial position”, the authors measured the reduction in power of fish by measuring tail beat frequency and oxygen consumption and compared them to measurements in solitary fish. They found that in a school of fish, individuals always save power comparing to swimming alone.  However, there is one important caveat in this study: they considered a larger school of fish and expressed the results in terms of pairwise configurations (see schematics we draw below). This is misleading because it may suggest that formations with only two fish provide benefits each other, while in fact, the data is obtained from a larger school with many neighbors. They only consider a fish’s relationship to its nearest neighbor. But in a large school, other neighbors will also have influence on their energy consumption.  In the schematics below, we emphasized on several focal fishes, marking them as red, green, and blue. We also marked their nearest neighbors using the same color, but lighter. The nearest neighbors are what the authors are considering to show its neighbor relationship. For example, a problematic one is the red fish, for which its nearest neighbor is behind it, but indeed, its power saving may come from the other neighbors, which are around or ahead it.

Author response image 3.

Second, I would like to see more biology context with respect to limitations that are inherent to a purely mechanical model, including, neglecting vision that we know plays a synergistic role in determining schooling patterns. For example, a recent study https://doi.org/10.1016/j.beproc.2022.104767 has presented experiments on fish swimming in the dark and in bright conditions, showing that it is unlikely that hydrodynamics alone could explain typically observed swimming patterns in the literature.

Thank you for this suggestion and for sharing us with the paper “Collective response of fish to combined manipulations of illumination and flow”. This is a great study, and we are sorry to have missed it.

In this paper, the authors found that when having illumination, fish swim more cohesively, which is in consistent with another paper we already cited “The sensory basis of schooling by intermittent swimming in the rummy-nose tetra (Hemigrammus rhodostomus)”. Another important conclusion in this paper is that when having brighter illumination and with flow, fish school spend more time side by side. This connects well to the conclusion in another paper we cited “Simple phalanx pattern leads to energy saving in cohesive fish schooling,” where at lower flow speed in a water channel, fish tended to form a dynamic school while at higher flow speed, they organized in a side-by-side/ phalanx configuration. This conclusion is consistent with our study that in side-by-side formation, fish share power saving.

Importantly, it is well known that both vision and flow sensing play important roles in fish schooling. This study aimed to merely explore what is possible through passive hydrodynamic interactions, without visual and flow sensing and response. We clarify this in the revised version of the manuscript.

Third, I am not too convinced about the flow agreement metric, which only accounts for linear interactions between the foils. More sophisticated approaches could be utilized as the one proposed here https://doi.org/10.1017/jfm.2018.369, based on a truly model-agnostic view of the interaction - therein, the authors show non-reciprocal (in strength and time-scale) coupling between two in-line flapping foils using information theory. I also would like to mention this older paper https://doi.org/10.1098/rsif.2012.0084, where an equivalent argument about the positioning of a trailing fish with respect to a leading robotic fish is made from experimental observations.

Thank you for these remarks and for sharing these two interesting papers.

The flow agreement metric is not specific to two fish, as we show in Fig. 6 of the manuscript. We edited the manuscript and SI to better explain the motivation and implementation of the flow agreement parameter. We edited the main text, see revisions on page 7, and added a new section call “diagnostic tools.”.

In the paper “An information-theoretic approach to study fluid–structure interactions”, the authors calculate the transfer entropy between two oscillating airfoils when they are hydrodynamically coupled.  This is an interesting study! We will apply this approach to analyzing larger schools in the future. We cited this paper in the introduction.

In the paper “Fish and robots swimming together: attraction towards the robot demands biomimetic locomotion”, the authors found that fish will swim behind an artificial fish robot, especially when the fish robot is beating its tail instead of static. At specific conditions, the fish hold station behind the robot, which may be due to the hydrodynamic advantage obtained by swimming in the robot’s wake. DPIV resolved the wake behind a static/ beating fish robot, but did not visualize the flow field when the fish is there. This study is similar to a paper we already cited “In-line swimming dynamics revealed by fish interacting with a robotic mechanism”, in which, they considered fish-foil interaction. In the revised manuscript, we cite both papers.

For the reviewer’s comments about flow agreement only accounts for linear interactions between the foils, we want to explain more to clarify this. The flow agreement parameter is a nonlinear metric, which considered the interaction between a virtual swimmer and an arbitrary unsteady flow field. Although the metric is a linear function of swimmer’s speed, it is indeed a nonlinear function of spacing and phase, which are the quantities we care about. Moreover, the flow field can by generated by either experiment or CFD simulation, and behind one or more swimmers. It is true that it is a one way coupled system since the virtual swimmer does not perturb the flow field.

Again, this is great work and I hope these suggestions are of help.

Thank you again! We are delighted to receive such a positive and constructive feedback.

Reviewer #2 (Recommendations For The Authors):

(1) About Figure 1: Panel C should be made to match between CFD and VS with regard to the swimmer positions. Also, if the general goal of the figure is to compare CFD and VS, then how about showing a difference map of the velocity fields as a third column of panels across A-D?

Thank you for pointing this out. Figure 1 C is updated accordingly.

The general goal is to show the CFD and VS simulations produce qualitatively similar results. Some quantities are not the same across models, e.g. the swimming speed of swimmers are different, but the scaled distance is the same.

(2) Figure 3: In A, it would be nice to keep the y-axis the same across all plots, which would aid quick visual comparison. In B, the legend labels for CFD and VS should be filled in with color so that the reader can more easily connect to the markers in the plot.

Thank you for pointing this out, we’ve updated figure 3 and 6.

(3) Figures 4, 9, and Supplementary Figures too: As mentioned previously, the agreement parameter plots are saturated in the color map, possibly obscuring more detailed information.

Thank you for pointing this out. The goal is to show that there is a large region with positive flow agreement parameter.

We picked up the flow agreement behind a single swimmer in VS simulation (Fig.4B) and added the counter lines to it (represents 0.25 and 0.5).  Not many details are hidden by the saturated colormap.

Author response image 4.

We also updated Fig 4 and Fig 9 accordingly.

(4) Figure 6: Is this CFD or VS? Why show one or the other and not both? In B, it seems that there are only savings available and no energetically costly positions. This seems odd. In C, it seems the absolute value on dF/dd is suppressing some important information about stability - the sign of this seems important. In E, the color bar seems to be reflected from what is standard, i.e. 0 on the left and 100 on the right, as in F.

Thank you for asking. Fig. 6 is based only on VS simulations. There are hundreds of simulations in this figure, we are not running CFD simulations to save computational effort. Representative CFD simulations are shown in Figure 1,2,3, for comparison. We added a sentence in the figure caption for clarification.

In C, since  is always negative for emergent formations (only stable equilibria can appear during forward time simulation), we are showing its absolute value for comparison.

In E, we are flipping this because larger flow agreement parameter corresponds to more power saving, in the other word, negative changes in COT.

(5) Fig. 8: For cases such as in D that have >100% power savings, does this mean that the swimmer has work done by the flow? How to interpret this physically for a flapping foil and biologically for a fish?

Yes, it means the hydrofoil/fish gets a free ride, and even able to harvest energy from the incoming flow. Actually, similar phenomenon has been reported in the biology and engineering literature. For example, Liao et al. 2003, Beal et al. 2006 found that live or dead fish can harvest energy from incoming vortical flow by modulating their body curvature.

In engineering, Chen et al. 2018, Ribeiro et al. 2021 have found that the following airfoil in a tandem/ inline formation can harvest energy from the wake of leading swimmer in both simulation and experiemnts.

Citations:  

Liao, J. C., Beal, D. N., Lauder, G. V., & Triantafyllou, M. S. (2003). Fish exploiting vortices decrease muscle activity. Science, 302(5650), 1566-1569. DOI: https://doi.org/10.1126/science.1088295

Beal, D. N., Hover, F. S., Triantafyllou, M. S., Liao, J. C., & Lauder, G. V. (2006). Passive propulsion in vortex wakes. Journal of fluid mechanics, 549, 385-402. DOI: https://doi.org/10.1017/S0022112005007925

Chen, Y., Nan, J., & Wu, J. (2018). Wake effect on a semi-active flapping foil based energy harvester by a rotating foil. Computers & Fluids, 160, 51-63. DOI: https://doi.org/10.1016/j.compfluid.2017.10.024

Ribeiro, B. L. R., Su, Y., Guillaumin, Q., Breuer, K. S., & Franck, J. A. (2021). Wake-foil interactions and energy harvesting efficiency in tandem oscillating foils. Physical Review Fluids, 6(7), 074703. DOI: https://doi.org/10.1103/PhysRevFluids.6.074703

Author response:

The following is the authors’ response to the original reviews

Reviewer 1:

(1) Figure 2 is mentioned before Figure 1

We thank the reviewer for pointing this out, this was a mistake. What was meant by Figure 2 was actually Figure 1. This has been corrected in the manuscript.

(2) Figure 1c: red is used to indicate cell junctions on raw data, but also the error.

The color red is used to indicate cell junctions on raw data on figure 1c left, while it is used to indicate the error on figure 1c right.

The Lagrangian error can be negative right? This is not reflected by the error scale which goes from 0% to 100%

A negative Lagragian error would mean that the distance between real and simulated cellular junctions decreased over time. We effectively treat this case as if there was no displacement, and the error is hence 0%.

Why do you measure the error in percent?

The error is measured in percentages because it is relative to the apical length of a cell.

(3) Figure 2: The distinction between pink and red in e_2(t) is very difficult. What do the lines indicate?

The lines indicate directions of the eigen vectors of the strain rate tensor at every material particle of the embryo.

(4) L156 "per unit length": Rather per unit time?

We thank the reviewer for pointing this out. We apologize for this mistake. "per unit length" has been changed to "per unit time"

(5) L159 "Eigen vectors in this sense": is there another sense?

"In this sense" is referring to the geometric description of eigen vectors. The phrase has been removed

(6) L164 "magnitude of the rate of change underwent by a particle at the surface of the embryo in the three orthogonal spatial directions of most significant rate of change."

Would a decomposition in two directions within the surface's tangent plane and one perpendicular to it not be better?

We also performed the decomposition of the strain rate tensor as suggested within the surface's tangent plane and one perpendicular to it, but did not notice any tangible differences in the overall analysis, especially after derivation of the scalar field.

(7) L174 "morphological activity": I think this notion is never defined

By morphological activity we mean any noticeable shape changes

(8) L177: I did not quite understand this part

This part tries to convey that the scalar strain rate field evidences coordinated cell behaviors by highlighting wide regions of red that traverse cell boundaries (e.g. fig.2b, $t=5.48hpb$). At the same time, the strain rate field preserves cell boundaries, highlighted by bands of red at cellular intersections, when cell coordinated cell behaviors are not preponderant (e.g. fig.2b, $t=4hpb$).

(9) Ll 194 "Unsurprisingly, these functions play an important role in many branches of science including quantum mechanics and geophysics Knaack and Stenflo (2005); Dahlen and Tromp (2021)." Does this really help in understanding spherical harmonics?

This comment was made with the aim of showing to the reader that Spherical Harmonics have proved to be useful in other fields. Although it does not help in understanding spherical harmonics, it establishes that they can be effective.

(10) Figure 3a: I do not find this panel particularly helpful. What does the color indicate? What are the prefactors of the spherical harmonics?

This panel showcases the restriction of the strain rate scalar field to the spherical harmonics with the l and m specified. Each material particle of the embryo surface at the time  is colored with respect to the value of . The values are computed according to equation 2 and are showcased in figure 3c.

(11) L 265: Please define "scalogram" as opposed to a spectrogram.

Scalograms are the result of wavelet transforms applied to a signal. Although spectrogram can specifically refer to the spectrum of frequencies resulting for example from a Fourier transform, the term can also be used in a broader sense to designate any time-frequency representation. In the context of this paper, we used it interchangeably with scalogram. We have changed all occurrences of spectrogram to scalogram in the revised manuscript.

(12) L 299 "the analysis was carried out the 64-cell stage.": Probably 'the analysis was carried out at the 64-cell stage'

We thank the reviewer for pointing this out. The manuscript was revised to reflect the suggested change.

(13) L 340 "Another outstanding advantage over traditional is": Something seems to be missing in this sentence.

We thank the reviewer for pointing this out. We have modified the sentence in the revised manuscript. It now reads “Another outstanding advantage of our workflow over traditional methods is that our workflow is able to compress the story of the development ... ”.

(14) Ll 357 "on the one hand, the overall spatial resolution of the raw data, on the other hand, the induced computational complexity.": Is there something missing in this sentence

The sentence tries to convey the idea that in implementing our method, there is a comprise to be made between the choice of the number of particles on the constructed mesh and the computational complexity induced by this choice. There is also a comprise to be made between this choice of the number of particles and the spatial resolution of the original dataset.

Reviewer 2:

(1) The authors should clearly state to which data this method has been applied in this paper. Also, to what kind of data can this method be applied? For instance, should the embryo surface be segmented?

The method has been applied on 3D+time imaging data of ascidian embryonic development data hosted on the morphonet (morphonet.org) platform. The data on the morphonet platform comes in two formats: closed surface meshes of segmented cells spatially organized into the embryo, and 3D voxelated images of the embryo. The method was first designed for the former format and then extended to the later. There is no requirement for the embryo surface to be segmented.

(2) In this paper, it is essential to understand the way that the authors introduced the Lagrangian markers on the surface of the embryo. However, understanding the method solely based on the description in the main text was difficult. I recommend providing a detailed explanation of the methodology including equations in the main text for clarity.

We believe that adding mathematical details of the method into the text will cloud the text and make it more difficult to understand. Interested readers can refer to the supplementary material for detailed explanation of the method.

(3) In eq.(1) of the supplementary information, d(x,S_2(t)) could be a distance function between S_1 and S_2 although it was not stated. How was the distance function between the surfaces defined?

What was meant here was d(x,S_1(t)) where x is a point of S_2(t). d(x,S_1(t)) referring to the distance between point x and S_1(t). The definition of the distance function has been clarified in the supplementary information.

(4) In the section on the level set scheme of supplementary information, the derivation of eq.(4) from eq.(3) was not clear.

We added an intermediary equation for clarification.

(5) Why is a reference shape S_1(0) absent at t=0?

A reference shape S_1(0) is absent at t=0 precisely because that is what we are trying to achieve: construct an evolving Lagrangian surface S_2(t) matching S_1(t) at all times.

(6) In Figure 2(a), it is unclear what was plotted. What do the colors mean? A color bar should be provided.

The caption of the figure describes the colors: “a) Heatmap of the eigenvector fields of the strain rate tensor. Each row represents a vector field distinguished by a distinct root color (\textit{yellow, pink, white}). The gradient from the root color to red represents increasing magnitudes of the strain rate tensor.”

(7) With an appropriate transformation, it would be possible to create a 2D map from a 3D representation shown in for instance Figure 2. Such a 2D representation would be more tractable for looking at the overall activities.

We thank the reviewer for pointing this out. In Figure 4b of the supplementary information, we provide a 2D projection of the scalar strain rate field.

(8) The strain rate is a second-order tensor that contains rich information. In this paper, the information in the tensor has been compressed into a scalar field by taking the square root of the sum of the squares of the eigenvalues. However, such a representation may not distinguish important events such as stretching and compression of the tissue. The authors should provide appropriate arguments regarding the limitations of this analysis.

The tensor form of the strain rate field is indeed endowed with more information than the scalar eigen value field derived. However, our objective in this project was not to exhaust the richness of the strain rate tensor field but rather to serve as a proof of concept that our global approach to studying morphogenesis could in fact unveil sufficiently rich information on the dynamical processes at play. Although not in the scope of this project, a more thorough exploration of the strain rate tensor field could be the object of future investigations.

(9) The authors claimed that similarities emerge between the spatiotemporal distribution of morphogenesis processes in the previous works and the heatmaps in this work. Some concrete data should be provided to support this claim.

All claims have been backed with references to previous works. For instances, looking at figure 2b, the two middle panels on the lower row (5.48hpf, 6.97hpf), we explained that the concentration of red refers respectively to endoderm invagination during gastrulation, and zippering during neurulation [we cited Hashimoto et al. (2015)]. Here, we relied on eye observation to spot the similarities. The rest of the paper provides substantial and robust additional support for these claims using spectral decomposition in space and time.

(10) The authors also claimed that "A notable by-product of this scalar field is the evidencing of the duality of the embryo as both a sum of parts constituted of cells and an emerging entity in itself: the strain rate field clearly discriminates between spatiotemporal locations where isolated single cell behaviours are preponderant and those where coordinated cell behaviours dominate." The authors should provide specific examples and analysis to support this argument.

Here, we relied on eye observation to make this claim. This whole section of the paper “Strain rate field describes ascidian morphogenesis” was about computing, plot and observing the strain rate field.

However, specific examples were provided. This paragraph was building towards this statement, and the evidence was scattered through the paragraph. We have now revised the sentence to ensure that we highlight specific examples:

“A notable by-product of this scalar field is the evidencing of the duality of the embryo as both a sum of parts constituted of cells and an emerging entity in itself: the strain rate field clearly discriminates between spatiotemporal locations where isolated single cell behaviours are preponderant (e.g. fig.2b, $t=4hpb$) and those where coordinated cell behaviours dominate (e.g. fig.2b, $t=5.48hpb$).”

(11) The authors should provide the details of the analysis method used in Figure 3b, including relevant equations. In particular, it would be helpful to clarify the differences that cause the observed differences between Figure 3b and Figure 3c.

Figure 3b was introduced with the sentence: “In analogy to Principal Components Analysis, we measure the average variance ratio over time of each harmonic with respect to the original signal (Fig.3b).” explaining the origin of variance ratio values used in figure 3b. We have now added the mathematical expression to further clarify.

(12) The authors found that the variance ratio of Y_00 was 64.4%. Y_00 is a sphere, indicating that most of the activity can be explained by a uniform activity. Which actual biological process explains this symmetrical activity?

The reviewer makes a good point which also gave us a lot to think about during the analysis. Observing that the contribution of Y00 peaks during synchronous divisions, which are interestingly restricted only to the animal pole, we conjecture that localized morphological ripples and can be felt throughout the embryo. 

(13) The contribution of other spherical harmonics than Y_00 and Y_10 should be shown.

Other spherical harmonics contributed individual to less than 1% and we did not find it important to include them in the main figure. We will add supplementary material.

Author response:

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

Reviewer #1 (Public Review):

In their manuscript entitled: "Is tumor mutational burden predictive of response to immunotherapy?", Gurjao and colleagues discuss the use of tumor mutational burden (TMB) as a predictive biomarker for cancer patients to respond to immune checkpoint blockage (ICB). By analyzing a large cohort of 882 patient samples across different tumor types they find either little or no association of TMB to the response of ICB. In addition, they showed that finding the optimal cutoff for patient stratification lead to a severe multiple testing problem. By rigorously addressing this multiple testing problem only non-small cell lung cancer out of 10 cancer types showed a statistically significant association of TMB and response to ICB. Nevertheless, it is clearly shown that in any case the rate of misclassification is too high that TMB alone would qualify as a clinically suitable biomarker for ICB response. Finally, the authors demonstrate with a simple mathematical model that only a few strong immunogenic mutations would be sufficient for an ICB response, thereby showing that also patients with a low TMB score could benefit from immunotherapy. The manuscript is clearly written, the results are well presented and the applied methods are state-of-the-art.

We would like to thank the reviewer for their thoughtful suggestions and efforts towards improving our manuscript. We address below the reviewer’s recommendations.

Reviewer #1 (Recommendations For The Authors):

(1) The method used for mutation call can also influence the TMB score. Mutation data was downloaded from public databases and not re-called for this study, a potential caller bias could be present. What was the calling strategy of the used data sets? For the present study, I don't think that this is crucial because different callers or post-call processing would be used at different sites to determine TMB. I think it should the mutation calling bias should also be discussed in the manuscript as another shortcoming for TMB as a biomarker for ICB response.

We thank the reviewer for this comment. Mutational data was not aggregated across studies and caller bias would thus not have any impact on the results of this manuscript. In addition, we further clarified the role of mutation calling bias in the Discussions section.

“Although attractive and scalable, TMB does not consider the effect of specific mutations (missense, frameshift etc), their presentation and clonality (19), nor the state of the tumour, its microenvironment, and interactions with the immune system that can be integrated into potentially better predictors of response to ICB (43, 44). In addition, another major limitation of TMB is the lack of standardized measures. This includes the lack of standard sequencing methods to assess TMB: TMB can be measured from Whole-Exome sequencing, Whole-Genome sequencing, targeted panel and even RNA sequencing. This also includes biases introduced by using different mutation calling pipelines resulting in different TMB, sequencing depth and different characteristics of the samples (e.g. low purity samples typically yield lower TMB).”

(2) In their mathematical model of neoantigens and immunogenicity it is assumed that the probability of a mutation to be immunogenic is constant for all mutations. In reality this is certainly not satisfied. However, the central conclusion from the model still holds. I think that this is important to discuss in the manuscript.

We thank the reviewer for this suggestion and now consider the case where each mutation has its own probability p(i) of being immunogenic.

“Our model shows that achieving about constant 𝑃{𝑖𝑚𝑚𝑢𝑛𝑒 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒} for 𝑁 > 10 − 20 mutations, requires and . The same argument holds when each mutation has its  own probability to be immunogenic 𝑝(𝑖), then , where is the mean probability of a mutation to be immunogenic. Thus only the average probability of a mutation to be immunogenic matters. In summary, we find that the model agrees with clinical data if individual non-synonymous mutations have, on average, 𝑝~10 − 20% chance for triggering an immune response.”

(3) In the mathematical formula on page 8, C_N^k is the binomial coefficient. This should be stated or written out.

Thank you for pointing this out. Corrected.

“Due to immunodominance, only a few 𝑘crit immunogenic mutations are sufficient to elicit a full k𝑐𝑟𝑖𝑡 immune response. Hence, the probability for a cancer with 𝑁 (=TMB) mutations to elicit an immune response is then the probability of having 𝑘 or more immunogenic mutations among :

which is the CDF of a binomial distribution.”

(4) The mathematical model provides an explanation that tumors with a low TMB can also respond on ICB. It cannot explain tumors with high TMB lacking ICB response. An explanation of this phenomenon is discussed in the paper but I think also the impact of the tumor immune microenvironment should be mentioned here.

As we explained in the presentation of the model, even immunogenic tumors elicit response to ICB with some probability. In the revision we write:

“𝑃{𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒} = 𝑃{𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒|𝑖𝑚𝑚𝑢𝑛𝑒 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒} · 𝑃{𝑖𝑚𝑚𝑢𝑛𝑒 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒}, where 𝑃{𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒|𝑖𝑚𝑚𝑢𝑛𝑒 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒} is the probability of clinical response, given that cancer elicits an immune response which is complex and depends on many factors including tumor immune microenvironment. Yet the prerequisite for the clinical response is the immune response 𝑃{𝑖𝑚𝑚𝑢𝑛𝑒 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒} that we focus on.”

Reviewer #2 (Public Review):

The manuscript points out that TMB cut-offs are not strong predictors of response to immunotherapy or overall survival. By randomly shuffling TMB values within cohorts to simulate a null distribution of log-rank test p-values, they show that under correction, the statistical significance of previously reported TMB cut-offs for predicting outcomes is questionable.

We would like to thank the reviewer for their thoughtful suggestions and efforts towards improving our manuscript.

There is a clinical need for a better prediction of treatment response than TMB alone can provide. However, no part of the analysis challenges the validity of the well-known pan-cancer correlation between TMB and immunotherapy response.

We address the pan-cancer correlation in the supplemental text and Figure S3. We realized the supplemental text was missing in eLife submission and included in the bioRxiv only. We apologize for this oversight. In particular, we show that the “well-known pan-cancer correlation” is largely based on a few outlier cancer subtypes - MSI colorectal cancers and uveal/ ocular melanomas. We show that when we remove these cancer types from the pan-cancer dataset, the correlation becomes non-significant for the remaining 15 cancer types.

The failure to detect significant TMB cut-offs may be due to insufficient power, as the examined cohorts have relatively low sample sizes. A power analysis would be informative of what cohort sizes are needed to detect small to modest effects of TMB on immune response.

Since we see no effect, we cannot perform a power analysis. Moreover, increasing cohort sizes cannot increase the effect -- dramatic misclassification of responders (the fraction of responders below the treatment cutoff) would remain the same, making TMB unsuitable for clinical decision-making.

The manuscript provides a simple model of immunogenicity that is tailored to be consistent with a claimed lack of relationship between TMB and response to immunotherapy. Under the model, if each mutation that a tumor has acquired has a relatively high probability of being immunogenic (~10%, they suggest), and if 1-2 immunogenic mutations is enough to induce an immune response, then most tumors produce an immune response, and TMB and response should be uncorrelated except in very low-TMB tumors.

Contrary to reviewer’s suggestion, our modeling is not tailored to be consistent with the lack of association between TMB and response. On the contrary, we found the model has two regimes: the first regime (where p<<1) in which higher TMB leads to a higher probability of response, which doesn’t agree with the data , and the second regime (p~0.1) in which cancers with TMB>10-20 are immunogenic, consistent with the clinical data.

We further expanded on these key points in the Results:

“The model shows two different behaviors. If individual mutations are unlikely to be immunogenic (𝑝 ≪ 1) , e.g. due to a low probability of being presented, the probability of response increases gradually with TMB (Figure 5B). The neoantigen theory generally expects such gradual increase in immunogenicity of cancer with TMB. Yet, available data (Figure 2) don’t show such a trend.

On the contrary, if mutations are more likely to be immunogenic 𝑝~0. 1, the probability of response quickly saturates (Figure 5C), making such tumors respond to ICB irrespective of TMB, as we observed in clinical data.”

We also expanded on these key points in the Introduction:

“We develop a simple model that is based on the neoantigen theory and find that it has two regimes. In one regime, the probability of response increases gradually with TMB, as commonly believed. Yet in the other, the probability of response saturates after a few mutations, making a chance to respond independent of TMB. Our analysis of the clinical data is consistent with the latter regime. Thus our model shows that the neoantigen theory is fully consistent with the lack of association between TMB and response.”

The question then becomes whether the response is sufficient to wipe out tumor cells in conjunction with immunotherapy, which is essentially the same question of predicting response that motivated the original analysis. While TMB alone is not an excellent predictor of treatment response, the pan-cancer correlation between TMB and response/survival is highly significant, so the model's only independent prediction is wrong.

Our study indicates that TMB is a very poor predictor (writing that it’s “not an excellent predictor of treatment response” is understatement). Moreover we show that a widely believed “pan-cancer correlation” is shaky as well (Supplemental text and Figure S3). So we don’t see any contradictions between the model and the data.

Additionally, experiments to predict and validate neoepitopes suggest that a much smaller fraction of nonsynonymous mutations produce immune responses1,2.

We agree with the reviewer. That’s exactly what the model suggests.

A key idea that is overlooked in this manuscript is that of survivorship bias: self-evidently, none of the mutations found at the time of sequencing have been immunogenic enough to provoke a response capable of eliminating the tumor. While the authors suggest that immunoediting "is inefficient, allowing tumors to accumulate a high TMB," the alternative explanation fits the neoepitope literature better: most mutations that reach high allele frequency in tumor cells are not immunogenic in typical (or patient-specific) tumor environments. Of course, immunotherapies sometimes succeed in overcoming the evolved immune evasion of tumors. Higher-TMB tumors are likely to continue to have higher mutation rates after sequencing; increased generation of new immunogenic mutations may partially explain their modestly improved responses to therapy.

We disagree with reviewers' assertion that survivorship bias could explain observed phenomena. If immunogenic mutations that arise during cancer development were eliminated (by purifying selection, i.e. reduced fitness or cellular death) then observed mutations would carry noticeable signatures of purifying selection. On the contrary, cancer genomic data shows incredibly weak signals of purifying selection on non-synonymous mutations (Weghorn and Sunyaev, Nature Genetics 2017), and observed passenger mutations are practically indistinguishable from random in their effect on proteins (McFarland et al PNAS 2013).

We do agree with the statement that “most mutations … in tumor cells are not immunogenic”. In fact that’s exactly what our model predicts: (1-p)~90% of mutations in the model are non-immunogenic, while remaining p~10% being sufficient to trigger an immune response. We clarify this in the text of the paper: “On the contrary, if mutations are more likely to be immunogenic 𝑝~0. 1, the probability of response quickly saturates (Figure 5C), making such tumors respond to ICB irrespective of TMB, as we observed in clinical data. ”

Reviewer #2 (Recommendations For The Authors):

Abstract

Defining TMB as "number of non-synonymous mutations": while TMB is not consistently defined throughout the literature, it is usually given as a rate rather than a total count, and sometimes synonymous mutations are included. Consider adopting the definition used by the TMB Harmonization Project: "number of somatic mutations per megabase of interrogated genomic sequence.3"

We thank the reviewer for their comment,

Be more specific about your findings, so that abstract readers can get some understanding of your proposed explanation for the "immunogenicity of neoantigens and the lack of association between TMB and response."

We thank the reviewer for their comment. We modified the abstract to explain that the theory we developed expands the neoantigen theory yet can be consistent with the observed lack of association between TMB and response:

"Second, we develop a model that expands the neoantigen theory and can be consistent with both immunogenicity of neoantigens and the lack of association between TMB and response. Our analysis shows that the use of TMB in clinical practice is not supported by available data and can deprive patients of treatment to which they are likely to respond.”

Introduction

Again, consider using a more standard definition of TMB.

We thank the reviewer for their comment. Our study did not seek to harmonize TMB across the datasets and we thus used the total number of mutations rather than the mutational rate often used for comparison across different datasets.

Expand the introduction to provide a preview of the purpose and direction of your analysis. The current draft reveals only that the analysis will relate to TMB.

We expanded the introduction providing the motivation, the approach, and the summary of main findings.

“Using a biomarker to stratify and prioritize patients for treatment runs a risk of depriving patients who have a chance to respond to a life-saving treatment. High variability of response makes relying on a predictor particularly risky. Hence, we revisit original data that were used to establish correlation between TMB and response. We tested TMB as a predictor of both binary responder/non-responder labels from original clinical studies, as well as continuous survival data. We also investigated whether a TMB threshold could distinguish patients with high and low survival after multiple hypothesis testing. We find that no TMB threshold performs better on the clinical data than on randomized ones.

We further show that irrespective of the strategy to choose the threshold, even if we were to employ the optimal TMB cutoff, it would still lead to about 25% of responders falling below the treatment prioritization threshold. In addition, we re-examine the pan-cancer association of TMB with response rate to ICB.

“Finally we revisit the neoantigen theory that was the rationale for using TMB as a predictor of response to immunotherapy. The theory stipulates that non-synonymous mutations can lead to the production of unique antigens (_neo_antigens) that are recognized by the immune system as foreign, triggering the immune response to cancer. The theory further assumes that the more mutations a cancer has, the more likely it triggers the immune system, and the more likely it will benefit from immunotherapy. We develop a simple model that is based on the neoantigen theory and find that it has two regimes. In one regime, the probability of response increases gradually with TMB, as commonly believed. Yet in the other, the probability of response saturates after a few mutations, making a chance to respond independent of TMB. Our analysis of the clinical data is consistent with the latter regime. Thus our model shows that the neoantigen theory is fully consistent with the lack of association between TMB and response.”

Section: Is TMB associated with response after treatment?

The claim that after excluding melanoma and some colorectal cancers, there is no relationship between TMB and response rates in pan-cancer studies cites references 12 and 14. In reference 12 (Yarchoan et al.), it is clear from glancing at their Figure 1 that a pan-cancer correlation between TMB and response would remain with these cancer types excluded. This discrepancy requires explanation. "Supplementary text" is cited for this claim, but it was not included in the file that I received.

We address the pan-cancer correlation in the supplemental text and Figure S3. While the figure was available, we realized the supplemental text was missing in eLife submission. We apologize for this oversight.

Plots of survival and TMB do not show "visible correlation": Please strengthen this claim with an appropriate statistical test.

We expand the figure caption to explain the following:

“Plots of progression-free survival and TMB for melanoma and lung cancer ICB cohorts show the lack of correlation or of an obvious TMB cutoff. Computing a simple correlation for survival and censored data cannot correctly represent the dependence since patients who are alive live longer than the reported survival, and limiting correlation to patients who are dead would bias the analysis. Thus other survival statistics are used through the paper.”

Section: Model reconciles neoantigen theory and data

Page 8: In the probability formula, the C term is not defined. My guess is that it means choose(N, k).

Please clarify.

Thank you for pointing this out. Corrected using more conventional notation.

which is the CDF of a binomial distribution.

Page 8: Assuming the above, P(immune response) = P(X >= k_crit); where X~Bin(N, p). The formula should be explicitly introduced in terms of the CDF of the binomial distribution to prevent readers from thinking the wheel is being re-invented.

We thank the reviewer for pointing this out, we modified the equation in the text to make it easier to see this point (see above). We refrain from going further since the CDF of a binomial distribution doesn’t have a closed form and can only be written as the regularized incomplete beta function.

Page 9: Missing word in "allowing cancers with as little as mutations to be"

We thank the reviewer for pointing this out, we modified the text accordingly.

See comments in public review. In brief, I think a convincing case is made regarding the significance of TMB cut-offs as predictors of survival within cancer types, but frankly this elementary model is not compelling.

Section: Materials and Methods

In the manuscript, it is stated that TMB is accepted as reported by data sources. Since most of the comparisons in the manuscript are within-data-source, that is acceptable. However, it should be ensured that TMB measurements are comparable between samples within each source. For example, when TMB is reported as a total mutation count, it can be verified that all samples have the same coverage, or measurement can be converted to mutations per megabase of coverage. In the same vein, if this manuscript's definition of TMB only includes nonsynomous mutations, it should be confirmed that the TMB reported by data sources excludes synonymous mutations.

We thank the reviewer for their comment. We leverage total TMB as reported in the original studies claiming an association between TMB and response/ survival.

Figure S2: Instead of writing "the Youden index associated cutoffs is also plotted," it can be stated that the asterisk represents the Youden index cutoff, or a legend can be added that provides this information.

We thank the reviewer for pointing this out, we modified the text accordingly.

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Tiedje et al. investigated the transient impact of indoor residual spraying (IRS) followed by seasonal malaria chemoprevention (SMC) on the plasmodium falciparum parasite population in a high transmission setting. The parasite population was characterized by sequencing the highly variable DBL$\alpha$ tag as a proxy for var genes, a method known as varcoding. Varcoding presents a unique opportunity due to the extraordinary diversity observed as well as the extremely low overlap of repertoires between parasite strains. The authors also present a new Bayesian approach to estimating individual multiplicity of infection (MOI) from the measured DBL$\alpha$ repertoire, addressing some of the potential shortcomings of the approach that have been previously discussed. The authors also present a new epidemiological endpoint, the so-called "census population size", to evaluate the impact of interventions. This study provides a nice example of how varcoding technology can be leveraged, as well as the importance of using diverse genetic markers for characterizing populations, especially in the context of high transmission. The data are robust and clearly show the transient impact of IRS in a high transmission setting, however, some aspects of the analysis are confusing.

(1) Approaching MOI estimation with a Bayesian framework is a well-received addition to the varcoding methodology that helps to address the uncertainty associated with not knowing the true repertoire size. It's unfortunate that while the authors clearly explored the ability to estimate the population MOI distribution, they opted to use only MAP estimates. Embracing the Bayesian methodology fully would have been interesting, as the posterior distribution of population MOI could have been better explored. 

We thank the reviewer for appreciating the extension of var_coding we present here. We believe the comment on maximum _a posteriori (MAP) refers to the way we obtained population-level MOI from the individual MOI estimates. We would like to note that reliance on MAP was only one of two approaches we described, although we then presented only MAP.  Having calculated both, we did not observe major differences between the two, for this data set.  Nonetheless, we revised the manuscript to include the result based on the mixture distribution which considers all the individual MOI distributions in the Figure supplement 6.

(2) The "census population size" endpoint has unclear utility. It is defined as the sum of MOI across measured samples, making it sensitive to the total number of samples collected and genotyped. This means that the values are not comparable outside of this study, and are only roughly comparable between strata in the context of prevalence where we understand that approximately the same number of samples were collected. In contrast, mean MOI would be insensitive to differences in sample size, why was this not explored? It's also unclear in what way this is a "census". While the sample size is certainly large, it is nowhere near a complete enumeration of the parasite population in question, as evidenced by the extremely low level of pairwise type sharing in the observed data. 

We consider the quantity a census in that it is a total enumeration or count of infections in a given population sample and over a given time period. In this sense, it gives us a tangible notion of the size of the parasite population, in an ecological sense, distinct from the formal effective population size used in population genetics. Given the low overlap between var repertoires of parasites (as observed in monoclonal infections), the population size we have calculated translates to a diversity of strains or repertoires.  But our focus here is in a measure of population size itself.  The distinction between population size in terms of infection counts and effective population size from population genetics has been made before for pathogens (see for example Bedford et al. for the seasonal influenza virus and for the measles virus (Bedford et al., 2011)), and it is also clear in the ecological literature for non-pathogen populations (Palstra and Fraser, 2012). 

We completely agree with the dependence of our quantity on sample size. We used it for comparisons across time of samples of the same depth, to describe the large population size characteristic of high transmission which persists across the IRS intervention. Of course, one would like to be able to use this quantity across studies that differ in sampling depth and the reviewer makes an insightful and useful suggestion.  It is true that we can use mean MOI, and indeed there is a simple map between our population size and mean MOI (as we just need to divide or multiply by sample size, respectively) (Table supplement 7).  We can go further, as with mean MOI we can presumably extrapolate to the full sample size of the host population, or to the population size of another sample in another location. What is needed for this purpose is a stable mean MOI relative to sample size.  We can show that indeed in our study mean MOI is stable in that way, by subsampling to different depths our original sample (Figure supplement 8 in the revised manuscript). We now include in the revision discussion of this point, which allows an extrapolation of the census population size to the whole population of hosts in the local area.

We have also clarified the time denominator: Given the typical duration of infection, we expect our population size to be representative of a per-generation measure_._

(3) The extraordinary diversity of DBL$\alpha$ presents challenges to analyzing the data. The authors explore the variability in repertoire richness and frequency over the course of the study, noting that richness rapidly declined following IRS and later rebounded, while the frequency of rare types increased, and then later declined back to baseline levels. The authors attribute this to fundamental changes in population structure. While there may have been some changes to the population, the observed differences in richness as well as frequency before and after IRS may also be compatible with simply sampling fewer cases, and thus fewer DBL$\alpha$ sequences. The shift back to frequency and richness that is similar to pre-IRS also coincides with a similar total number of samples collected. The authors explore this to some degree with their survival analysis, demonstrating that a substantial number of rare sequences did not persist between timepoints and that rarer sequences had a higher probability of dropping out. This might also be explained by the extreme stochasticity of the highly diverse DBL$\alpha$, especially for rare sequences that are observed only once, rather than any fundamental shifts in the population structure.

We thank the reviewer raising this question which led us to consider whether the change in the number of DBLα types over the course of the study (and intervention) follows from simply sampling fewer P. falciparum cases. We interpreted this question as basically meaning that one can predict the former from the latter in a simple way, and that therefore, tracking the changes in DBLα type diversity would be unnecessary.  A simple map would be for example a linear relationship (a given proportion of DBLα types lost given genomes lost), and even more trivially, a linear loss with a slope of one (same proportion).  Note, however, that for such expectations, one needs to rely on some knowledge of strain structure and gene composition. In particular, we would need to assume a complete lack of overlap and no gene repeats in a given genome. We have previously shown that immune selection leads to selection for minimum overlap and distinct genes in repertoires at high transmission (see for example (He et al., 2018)) for theoretical and empirical evidence of both patterns). Also, since the size of the gene pool is very large, even random repertoires would lead to limited overlap (even though the empirical overlap is even smaller than that expected at random (Day et al., 2017)). Despite these conservators, we cannot a priori assume a pattern of complete non-overlap and distinct genes, and ignore plausible complexities introduced by the gene frequency distribution.  

To examine this insightful question, we simulated the loss of a given proportion of genomes from baseline in 2012 and examined the resulting loss of DBLα types. We specifically cumulated the loss of infections in individuals until it reached a given proportion (we can do this on the basis of the estimated individual MOI values). We repeated this procedure 500 times for each proportion, as the random selection of individual infection to be removed, introduces some variation. Figure 2 below shows that the relationship is nonlinear, and that one quantity is not a simple proportion of the other.  For example, the loss of half the genomes does not result in the loss of half the DBLα types. 

Author response image 1.

Non-linear relationship between the loss of DBLα types and the loss of a given proportion of genomes. The graph shows that the removal of parasite genomes from the population through intervention does not lead to the loss of the same proportion of DBLα types, as the initial removal of genomes involves the loss of rare DBLα types mostly whereas common DBLα types persist until a high proportion of genomes are lost. The survey data (pink dots) used for this subsampling analysis was sampled at the end of wet/high transmission season in Oct 2012 from Bongo District from northern Ghana. We used the Bayesian formulation of the _var_coding method proposed in this work to calculate the multiplicity of infection of each isolate to further obtain the total number of genomes. The randomized surveys (black dots) were obtained based on “curveball algorithm” (Strona et al., 2014) which keep isolate lengths and type frequency distribution.

We also investigated whether the resulting pattern changed significantly if we randomized the composition of the isolates.  We performed such randomization with the “curveball algorithm” (Strona et al., 2014). This algorithm randomizes the presence-absence matrix with rows corresponding to the isolates and columns, to the different DBLα types; importantly, it preserves the DBLα type frequency and the length of isolates. We generated 500 randomizations and repeated the simulated loss of genomes as above. The data presented in Figure 2 above show that the pattern is similar to that obtained for the empirical data presented in this study in Ghana. We interpret this to mean that the number of genes is so large, that the reduced overlap relative to random due to immune selection (see (Day et al., 2017)) does not play a key role in this specific pattern. 

Reviewer #2 (Public Review):  

In this manuscript, Tiedje and colleagues longitudinally track changes in parasite numbers across four time points as a way of assessing the effect of malaria control interventions in Ghana. Some of the study results have been reported previously, and in this publication, the authors focus on age-stratification of the results. Malaria prevalence was lower in all age groups after IRS. Follow-up with SMC, however, maintained lower parasite prevalence in the targeted age group but not the population as a whole. Additionally, they observe that diversity measures rebounds more slowly than prevalence measures. Overall, I found these results clear, convincing, and well-presented. They add to a growing literature that demonstrates the relevance of asymptomatic reservoirs.  There is growing interest in developing an expanded toolkit for genomic epidemiology in malaria, and detecting changes in transmission intensity is one major application. As the authors summarize, there is no one-size-fits-all approach, and the Bayesian MOIvar estimate developed here has the potential to complement currently used methods. I find its extension to a calculation of absolute parasite numbers appealing as this could serve as both a conceptually straightforward and biologically meaningful metric. However, I am not fully convinced the current implementation will be applied meaningfully across additional studies. 

(1) I find the term "census population size" problematic as the groups being analyzed (hosts grouped by age at a single time point) do not delineate distinct parasite populations. Separate parasite lineages are not moving through time within these host bins. Rather, there is a single parasite population that is stochastically divided across hosts at each time point. I find this distinction important for interpreting the results and remaining mindful that the 2,000 samples at each time point comprise a subsample of the true population. Instead of "census population size", I suggest simplifying it to "census count" or "parasite lineage count".  It would be fascinating to use the obtained results to model absolute parasite numbers at the whole population level (taking into account, for instance, the age structure of the population), and I do hope this group takes that on at some point even if it remains outside the scope of this paper. Such work could enable calculations of absolute---rather than relative---fitness and help us further understand parasite distributions across hosts.

Lineages moving exclusively through a given type of host or “patch”  are not a necessary requirement for enumerating the size of the total infections in such subset.  It is true that what we have is a single parasite population, but we are enumerating for the season the respective size in host classes (children and adults). This is akin to enumerating subsets of a population in ecological settings where one has multiple habitat patches, with individuals able to move across patches.

Remaining mindful that the count is relative to sample size is an important point. Please see our response to comment (2) of reviewer 1, also for the choice of terminology. We prefer not to adopt “census count” as a census in our mind is a count, and we are not clear on the concept of lineage for these highly recombinant parasites.  Also, census population size has been adopted already in the literature for both pathogens and non-pathogens, to make a distinction with the notion of effective population size in population genetics (see our response to reviewer 1) and is consistent with our usage as outlined in the introduction. 

Thank you for the comment on an absolute number which would extrapolate to the whole host population.  Please see again our response to comment (2) of reviewer 1, on how we can use mean MOI for this purpose once the sampling is sufficient for this quantity to become constant/stable with sampling effort.

(2) I'm uncertain how to contextualize the diversity results without taking into account the total number of samples analyzed in each group. Because of this, I would like a further explanation as to why the authors consider absolute parasite count more relevant than the combined MOI distribution itself (which would have sample count as a denominator). It seems to me that the "per host" component is needed to compare across age groups and time points---let alone different studies.

Again, thank you for the insightful comment. We provide this number as a separate quantity and not a distribution, although it is clearly related to the mean MOI of such distribution. It gives a tangible sense for the actual infection count (different from prevalence) from the perspective of the parasite population in the ecological sense. The “per host” notion which enables an extrapolation to any host population size for the purpose of a complete count, or for comparison with another study site, has been discussed in the above responses for reviewer 1 and now in the revision of the discussion.

(3) Thinking about the applicability of this approach to other studies, I would be interested in a larger treatment of how overlapping DBLα repertoires would impact MOIvar estimates. Is there a definable upper bound above which the method is unreliable? Alternatively, can repertoire overlap be incorporated into the MOI estimator? 

This is a very good point and one we now discuss further in our revision. There is no predefined upper bound one can present a priori. Intuitively, the approach to estimate MOI would appear to breakdown as overlap moves away from extremely low values, and therefore for locations with low transmission intensity.  Interestingly, we have observed that this is not the case in our paper by Labbe et al. (Labbé et al., 2023) where we used model simulations in a gradient of three transmission intensities, from high to low values. The original _var_coding method performed well across the gradient. This robustness may arise from a nonlinear and fast transition from low to high overlap that is accompanied by MOI changing rapidly from primarily multiclonal (MOI > 1) to monoclonal (MOI = 1). This matter clearly needs to be investigated further, including ways to extend the estimation to explicitly include the distribution of overlap.

Smaller comments:

- Figure 1 provides confidence intervals for the prevalence estimates, but these aren't carried through on the other plots (and Figure 5 has lost CIs for both metrics). The relationship between prevalence and diversity is one of the interesting points in this paper, and it would be helpful to have CIs for both metrics when they are directly compared. 

Based on the reviewer’s advice we have revised both Figure 4 and Figure 5, to include the missing uncertainty intervals. The specific approach for each quantity is described in the corresponding caption.

Reviewer #3 (Public Review): 

Summary: 

The manuscript coins a term "the census population size" which they define from the diversity of malaria parasites observed in the human community. They use it to explore changes in parasite diversity in more than 2000 people in Ghana following different control interventions. 

Strengths: 

This is a good demonstration of how genetic information can be used to augment routinely recorded epidemiological and entomological data to understand the dynamics of malaria and how it is controlled. The genetic information does add to our understanding, though by how much is currently unclear (in this setting it says the same thing as age-stratified parasite prevalence), and its relevance moving forward will depend on the practicalities and cost of the data collection and analysis. Nevertheless, this is a great dataset with good analysis and a good attempt to understand more about what is going on in the parasite population. 

Census population size is complementary to parasite prevalence where the former gives a measure of the “parasite population size”, and the latter describes the “proportion of infected hosts”.  The reason we see similar trends for the “genetic information” (i.e., census population size) and “age-specific parasite prevalence” is because we identify all samples for var_coding based on the microscopy (i.e., all microscopy positive _P. falciparum isolates). But what is more relevant here is the relative percentage change in parasite prevalence and census population size following the IRS intervention. To make this point clearer in the revised manuscript we have updated Figure 4 and included additional panels plotting this percentage change from the 2012 baseline, for both census population size and prevalence (Figure 4EF). Overall, we see a greater percentage change in 2014 (and 2015), relative to the 2012 baseline, for census parasite population size vs. parasite prevalence (Figure 4EF) as a consequence of the significant changes in distributions of MOI following the IRS intervention (Figure 3). As discussed in the Results following the deployment of IRS in 2014 census population size decreased by 72.5% relative to the 2012 baseline survey (pre-IRS) whereas parasite prevalence only decreased by 54.5%. 

With respect to the reviewer’s comment on “practicalities and cost”, var_coding has been used to successfully amplify _P. falciparum DNA collected as DBS that have been stored for more than 5-years from both clinical and lower density asymptomatic infection, without the additional step and added cost of sWGA ($8 to $32 USD per isolates, for costing estimates see (LaVerriere et al., 2022; Tessema et al., 2020)), which is currently required by other molecular surveillance methods (Jacob et al., 2021; LaVerriere et al., 2022; Oyola et al., 2016). _Var_coding involves a single PCR per isolate using degenerate primers, where a large number of isolates can be multiplexed into a single pool for amplicon sequencing.  Thus, the overall costs for incorporating molecular surveillance with _var_coding are mainly driven by the number of PCRs/clean-ups, the number samples indexed per sequencing run, and the NGS technology used (discussed in more detail in our publication Ghansah et al. (Ghansah et al., 2023)). Previous work has shown that _var_coding can be use both locally and globally for molecular surveillance, without the need to be customized or updated, thus it can be fairly easily deployed in malaria endemic regions (Chen et al., 2011; Day et al., 2017; Rougeron et al., 2017; Ruybal-Pesántez et al., 2022, 2021; Tonkin-Hill et al., 2021).

Weaknesses: 

Overall the manuscript is well-written and generally comprehensively explained. Some terms could be clarified to help the reader and I had some issues with a section of the methods and some of the more definitive statements given the evidence supporting them. 

Thank you for the overall positive assessment. On addressing the “issues with a section of the methods” and “some of the more definitive statements given the evidence supporting them”, it is impossible to do so however, without an explicit indication of which methods and statements the reviewer is referring to. Hopefully, the answers to the detailed comments and questions of reviewers 1 and 2 address any methodological concerns (i.e., in the Materials and Methods and Results). To the issue of “definitive statements”, etc. we are unable to respond without further information.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors):

Line 273: there is a reference to a figure which supports the empirical distribution of repertoire given MOI = 1, but the figure does not appear to exist.

We now included the correct figure for the repertoire size distribution as Figure supplement 3 (previously published in Labbé et al (Labbé et al., 2023)). This figure was accidently forgotten when the manuscript was submitted for review, we thank the reviewer for bringing this to our attention.

Line 299: while this likely makes little difference, an insignificant result from a Kolmogorov-Smirnov test doesn't tell you if the distributions are the same, it only means there is not enough evidence to determine they are different (i.e. fail to reject the null). Also, what does the "mean MOI difference" column in supplementary table 3 mean? 

The mean MOI difference is the difference in the mean value between the pairwise comparison of the true population-level MOI distribution, that of the population-level MOI estimates from either pooling the maximum a posteriori (MAP) estimates per individual host or the mixture distribution, or that of the population-level MOI estimates from different prior choices. This is now clarified as requested in the Table supplements 3 - 6. 

Figure 4: how are the confidence intervals for the estimated number of var repertoires calculated? Also should include horizontal error bars for prevalence measures.

The confidence intervals were calculated based on a bootstrap approach. We re-sampled 10,000 replicates from the original population-level MOI distribution with replacement. Each resampled replicate is the same size as the original sample. We then derive the 95% CI based on the distribution of the mean MOI of those resampled replicates. This is now clarified as requested in the Figure 4 caption (as well as Table supplement 7 footnotes). In addition, we have also updated Figure 4AB and have included the 95% CI for all measures for clarity. 

Reviewer #2 (Recommendations For The Authors): 

-  I would like to see a plot like Supplemental Figure 8 for the upsA DBLα repertoire size. 

The upsA repertoire size for each survey and by age group has now been provided as requested in Figure supplement 5AB. 

-  Supplemental Table 2 is cut off in the pdf. 

We have now resolved this issue so that the Table supplement 2 is no longer cut off.  

Reviewer #3 (Recommendations For The Authors): 

The manuscript terms the phrase "census population size". To me, the census is all about the number of individuals, not necessarily their diversity. I appreciate that there is no simple term for this, and I imagine the authors have considered many alternatives, but could it be clearer to say the "genetic census population size"? For example, I found the short title not particularly descriptive "Impact of IRS and SMC on census population size", which certainly didn't make me think of parasite diversity.

Please see our response to comment (2) of reviewer 1. We prefer not to add “genetic” to the phrase as the distinction from effective population size from population genetics is important, and the quantity we are after is an ecological one. 

The authors do not currently say much about the potential biases in the genetic data and how this might influence results. It seems likely that because (i) patients with sub-microscopic parasitaemia were not sampled and (ii) because a moderate number of (likely low density) samples failed to generate genetic data, that the observed MOI is an overestimate. I'd be interested to hear the authors' thoughts about how this could be overcome or taken into account in the future. 

We thank the reviewer for this this comment and agree that this is an interesting area for further consideration. However, based on research from the Day Lab that is currently under review (Tan et al. 2024, under review), the estimated MOI using the Bayesian approach is likely not an “overestimate” but rather an “underestimate”. In this research by Tan et al. (2024) isolate MOI was estimated and compared using different initial whole blood volumes (e.g., 1, 10, 50, 100 uL) for the gDNA extraction. Using _var_coding and comparing these different volumes it was found that MOI was significantly “underestimated” when small blood volumes were used for the gDNA extraction, i.e., there was a ~3-fold increase in median MOI between 1μL and 100μL blood. Ultimately these findings will allow us to make computational corrections so that more accurate estimates of MOI can be obtained from the DBS in the future.

The authors do not make much of LLIN use and for me, this can explain some of the trends. The first survey was conducted soon after a mass distribution whereas the last was done at least a year after (when fewer people would have been using the nets which are older and less effective). We have also seen a rise in pyrethroid resistance in the mosquito populations of the area which could further diminish the LLIN activity. This difference in LLIN efficacy between the first and last survey could explain similar prevalence, yet lower diversity (in Figures 4B/5). However, it also might mean that statements such as Line 478 "This is indicative of a loss of immunity during IRS which may relate to the observed loss of var richness, especially the many rare types" need to be tapered as the higher prevalence observed in this age group could be caused by lower LLIN efficacy at the time of the last survey, not loss of immunity (though both could be true).  

We thank the reviewer for this question and agree that (i) LLIN usage and (ii) pyrethroid resistance are important factors to consider. 

(i) Over the course of this study self-reported LLIN usage the previous night remained high across all age groups in each of the surveys (≥ 83.5%), in fact more participants reported sleeping under an LLIN in 2017 (96.8%) following the discontinuation of IRS compared to the 2012 baseline survey (89.1%). This increase in LLIN usage in 2017 is likely a result of several factors including a rebound in the local vector population making LLINs necessary again, increased community education and/or awareness on the importance of using LLINs, among others. Information on the LLINs (i.e., PermaNet 2.0, Olyset, or DawaPlus 2.0) distributed and participant reported usage the previous night has now been included in the Materials and Methods as requested by the reviewer.

(ii) As to the reviewer’s question on increased in pyrethroid resistance in Ghana over the study period, research undertaken by our entomology collaborators (Noguchi Memorial Insftute for Medical Research: Profs. S. Dadzie and M. Appawu; and Navrongo Health Research Centre:  Dr. V. Asoala) has shown that pyrethroid resistance is a major problem across the country, including the Upper East Region. Preliminary studies from Bongo District (2013 - 2015), were undertaken to monitor for mutations in the voltage gated sodium channel gene that have been associated with knockdown resistance to pyrethroids and DDT in West Africa (kdr-w). Through this analysis the homozygote resistance kdr-w allele (RR) was found in 90% of An. gambiae s.s. samples tested from Bongo, providing evidence of high pyrethroid resistance in Bongo District dating back to 2013, i.e., prior to the IRS intervention (S. Dadzie, M. Appawu, personal communication). Although we do not have data in Bongo District on kdr-w from 2017 (i.e., post-IRS), we can hypothesize that pyrethroid resistance likely did not decline in the area, given the widespread deployment and use of LLINs.

Thus, given this information that (i) self-reported LLIN usage remained high in all surveys (≥ 83.5%), and that (ii) there was evidence of high pyrethroid resistance in 2013 (i.e., kdr-w (RR) _~_90%), the rebound in prevalence observed for the older age groups (i.e., adolescents and adults) in 2017 is therefore best explained by a loss of immunity.

I must confess I got a little lost with some of the Bayesian model section methods and the figure supplements. Line 272 reads "The measurement error is simply the repertoire size distribution, that is, the distribution of the number of non-upsA DBLα types sequenced given MOI = 1, which is empirically available (Figure supplement 3)." This does not appear correct as this figure is measuring kl divergence. If this is not a mistake in graph ordering please consider explaining the rationale for why this graph is being used to justify your point. 

We now included the correct figure for the repertoire size distribution as Figure supplement 3 (previously published in Labbé et al (Labbé et al., 2023)). This figure was accidently forgotten when the manuscript was submitted for review, we thank the reviewer for bringing our attention to this matter. We hope that the inclusion of this Figure as well as a more detailed description of the Bayesian approach helps to makes this section in the Materials and Methods clearer for the reader. 

I was somewhat surprised that the choice of prior for estimating the MOI distribution at the population level did not make much difference. To me, the negative binomial distribution makes much more sense. I was left wondering, as you are only measuring MOI in positive individuals, whether you used zero truncated Poisson and zero truncated negative binomial distributions, and if not, whether this was a cause of a lack of difference between uniform and other priors. 

Thank you for the relevant question. We have indeed considered different priors and the robustness of our  estimates to this choice and have now better described this in the text. We focused on individuals who had a confirmed microscopic asymptomatic P. falciparum infection for our MOI estimation, as median P. falciparum densities were overall low in this population during each survey (i.e., median ≤ 520 parasites/µL, see Table supplement 1). Thus, we used either a uniform prior excluding zero or a zero truncated negative binomial distribution when exploring the impact of priors on the final population-level MOI distribution.  A uniform prior and a zero-truncated negative binomial distribution with parameters within the range typical of high-transmission endemic regions (higher mean MOI with tails around higher MOI values) produce similar MOI  estimates at both the individual and population level. However, when setting the parameter range of the zero-truncated negative binomial to be of those in low transmission endemic regions where the empirical MOI distribution centers around mono-clonal infections with the majority of MOI = 1 or 2 (mean MOI » 1.5, no tail around higher MOI values), the final population-level MOI distribution does deviate more from that assuming the aforementioned prior and parameter choices. The final individual- and population-level MOI estimates are not sensitive to the specifics of the prior MOI distribution as long as this distribution captures the tail around higher MOI values with above-zero probability.   

The high MOI in children <5yrs in 2017 (immediately after SMC) is very interesting. Any thoughts on how/why? 

This result indicates that although the prevalence of asymptomatic P. falciparum infections remained significantly lower for the younger children targeted by SMC in 2017 compared 2012, they still carried multiclonal infections, as the reviewer has pointed out (Figure 3B). Importantly this upward shift in the MOI distributions (and median MOI) was observed in all age groups in 2017, not just the younger children, and provides evidence that transmission intensity in Bongo has rebounded in 2017, 32-months a er the discontinuation of IRS.  This increase in MOI for younger children at first glance may seem to be surprising, but instead likely shows the limitations of SMC to clear and/or supress the establishment of newly acquired infections, particularly at the end of the transmission season following the final cycle of SMC (i.e., end of September 2017 in Bongo District; NMEP/GHS, personal communication) when the posttreatment prophylactic effects of SMC would have waned (Chotsiri et al., 2022).  

Line 521 in the penultimate paragraph says "we have analysed only low density...." should this not be "moderate" density, as low density infections might not be detected? The density range itself is not reported in the manuscript so could be added. 

In Table supplement 1 we have provided the median, including the inter-quartile range, across each survey by age group. For the revision we have now provided the density min-max range, as requested by the reviewer. Finally, we have revised the statement in the discussion so that it now reads “….we have analysed low- to moderate-density, chronic asymptomatic infections (see Table supplement 1)……”.   

Data availability - From the text the full breakdown of the epidemiological survey does not appear to be available, just a summary of defined age bounds in the SI. Provision of these data (with associated covariates such as parasite density and host characteristics linked to genetic samples) would facilitate more in-depth secondary analyses. 

To address this question, we have updated the “Data availability statement” section with the following statement: “All data associated with this study are available in the main text, the Supporting Information, or upon reasonable request for research purposes to the corresponding author, Prof. Karen Day (karen.day@unimelb.edu.au).”  

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Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This study makes an interesting finding: a polyunsaturated fatty acid, Lin-Glycine, increases the conductance of KCNQ1/KCNE1 channels by stabilizing a state of the selectivity filter that allows K+ conduction. The stabilization of a conducting state appears well supported by single-channel analysis, though some method details are missing. The linkage to PUFA action through the selectivity filter is supported by the disruption of PUFA effects by mutation of residues which change conformation in two KCNQ1 structures from the literature. Claims about differences in Lin-Glycine binding to these two structural conformations seem to lack clear support, thus the claim seems speculative that PUFAs increase Gmax by binding to a crevice in the pore domain. A potentially definitive functional experiment is conducted by single-channel recordings with selectivity filter domain mutation Y315F which ablates the Lin-Glycine effect on Gmax. However, this appears to be an n=1 experiment. Overall, the major claim of the abstract is supported: "... that the selectivity filter in KCNQ1 is normally unstable ... and that the PUFA-induced increase in Gmax is caused by a stabilization of the selectivity filter in an open-conductive state." However, the claim in the abstract that selectivity filter instability "explains the low open probability" seems too general.

We thank the reviewer for the comments, and we would like to address the main concern regarding the single channels. We now state the number of experiments used for the single channel analysis. We agree that the claim in the abstract seems too general and we now made it more specific to our findings.

Reviewer #2 (Public Review):

Golluscio et al. address one of the mechanisms of IKs (KCNQ1/KCNE1) channel upregulation by polyunsaturated fatty acids (PUFA). PUFA is known to upregulate KCNQ1 and KCNQ1/KCNE1 channels by two mechanisms: one shifts the voltage dependence to the negative direction, and the other increases the maximum conductance (Gmax). While the first mechanism is known to affect the voltage sensor equilibrium by charge effect, the second mechanism is less known. By applying the single-channel recordings and mutagenesis on the putative binding sites (most of them related to the selectivity filter), they concluded that the selectivity filter is stabilized to a conductive state by PUFA binding.

Strengths:

They mainly used single-channel recordings and directly assessed the behavior of the selectivity filter. The method is straightforward and convincing enough to support their claims.

Weaknesses:

The structural model they used is the KCNQ1 channel without KCNE1 because KCNQ1/KCNE1 channel complex is not available yet. As the binding site of PUFAs might overlap with KCNE1, it is not very clear how PUFA binds to the KCNQ1 channel in the presence of KCNE1.

Using other previous PUFA-related KCNQ1 mutants will strengthen their conclusions. For example, the Gmax of the K326E mutant is reduced by PUFA binding. Examining whether K326E shows reduced numbers of non-empty sweeps in the single-channel recordings will be a good addition.

We thank the reviewer for the public review. We would like to address the main weak points of the comments. As a structure of KCNQ1/KCNE1 in complex is not available yet, we used KCNQ1 alone. We believe that the PUFA and KCNE1 binding sites will not overlap as we previously presented data in agreement with the idea that KCNE1 rotates the VSD relative the PD (Wu et al., 2021). This would leave enough space for both PUFA and KCNE1, so that PUFA can bind to the crevice (K326 and D301) without competing with KCNE1.  We appreciate the suggestion of adding single-channel recordings of K326E mutant and we agree it would make a valuable addition to strengthen our conclusions. However, single channel recordings for KCNQ1 are very challenging and time consuming to obtain, so we would like to keep this in consideration for future studies.

Reviewer #3 (Public Review):

This manuscript reveals an important mechanism of KCNQ1/IKs channel gating such that the open state of the pore is unstable and undergoes intermittent closed and open conformations. PUFA enhances the maximum open probability of IKs by binding to a crevice adjacent to the pore and stabilizing the open conformation. This mechanism is supported by convincing single-channel recordings that show empty and open channel traces and the ratio of such traces is affected by PUFA. In addition, mutations of the pore residues alter PUFA effects, convincingly supporting that PUFA alters the interactions among these pore residues.

Strengths:

The data are of high quality and the description is clear.

Weaknesses:

Some comments about the presentation.

(1) The structural illustrations in this manuscript in general need to be more clarified.

(2) The manuscript heavily relies on the comparison between the S4-down and S4-up structures (Figures 3, 4, and 7) to illustrate the difference between the extracellular side of the pore and to lead to the hypothesis of open-state stability being affected by PUFA. This may mislead the readers to think that the closed conformation of the channel in the up-state is the same as that in the down-state.

We thank the reviewer for the public review, and we would like to address the comments about the presentation. We agree that the structural illustrations need to be more detailed, and we amended our previous illustrations. We have now included a new Figure 3 with a more detailed legend and a new Figure 4 that includes more information, such as the main chain of the whole selectivity filter and surrounding peptide.

We have now added some clarification regarding the structures of KCNQ1 with S4-down and S4-up to clarify that the closed conformation of the channel in the up-state is different from that in the down-state. We also emphasize this difference in the Discussion.

Recommendations for the authors:

Reviewer #1:

(1) Explain more thoroughly how the single-channel recordings were done:

- How was Lin-Glycine applied in these experiments? The patch configuration is unclear. Was Lin-Glycine added to the patch pipette? If not, why is Lin-Glycine expected to reach the proposed binding site in the outer leaflet? Were controls time-matched applications of vehicles with ethanol?

Data were collected using the cell attached patch configuration to minimize disruption to the patch and avoid rundown problems due to the loss of PIP2. Lin-Glycine was solubilized in DMSO and the desired concentration was added directly to the bath. We had no a priori reason to know if the PUFA would reach the proposed binding site but the consistency at which there was an increase in channel activity 5-10 minutes after addition to the bath convinced us that it was indeed reaching the binding site. This time frame fits with our prior experience with mefenamic acid effects on single channels (Wang et al 2020). The mefenamic acid binding site is external to the membrane so the drug must enter the cell and cross the patch membrane to affect channel activity. In addition, shown below is a previous recording from our lab, where nothing was added to the bath over a 55-minute time while recording consecutive files.  This shows the typical behavior of IKs, with activity tending to cluster with a few active sweeps in between many blank sweeps.  The behavior in this patch contrasts with that seen in the presence of Lin-glycine, where the clusters of activity spread over an increasing number of sweeps.

In addition, we have previously shown that 0.1% DMSO (concentration used in the present study) does not affect the GV of KCNQ1 but there is a non-significant decrease in tail current amplitudes of about 14% (Eldstrom et al., 2021). As such we do not think that the effects we see with Lin-Glycine, with an increase in activity can be explained by vehicle effects alone.

Author response image 1.

We added some more details in the section Material and Method.

- How well the replicates match the representative data in Figures 1, S1, and 6 is unclear (except for average current and Po in the last second of the traces from Figure 1). Are the results in Fig 6 n=1? 

We now show in a data supplement that 3 replicates were used to access the change in channel activity upon addition of Lin-glycine.

- Diary plots (as in Werry et al. 2013) and additional descriptions of the timeline of Lin-Glycine application and analyses could add credibility to interpretations. 

We added a Diary plot of for the First latency to open in Supplementary Figure S1.

- Amounts of plasmids and lipofectamine that were used in transfections are missing. 

We added the information in Material and Method section as follow:

“Single channel currents were recorded from transiently transfected mouse ltk- fibroblast cells (LM cells) using 1.5 mL Lipofectamine 2000 (Thermo Fisher Scientific). Cells were transfected with 1.5 mg of pcDNA3 containing a linked KCNE1-KCNQ1 construct 20, to ensure fully KCNE1-saturated complexes, in addition to a plasmid containing green fluorescent protein (GFP) to identify transfected cells”

- Inclusion/exclusion criteria for patches analyzed are missing. 

We added the information in Material and Method section as follow:

“Only patches that were largely free of endogenous currents and had few channels, such that there were several blank sweeps to average for use for leak subtraction, were analyzed.”

- Whether blinding, randomization, or pre-determined n values were employed is not mentioned. 

No blinding, randomization or pre-determined n values were employed.

- Analysis methods are sometimes unclear: How was Po calculated? Representative sweeps appear to have been leak and capacitance subtracted. How was that done? 

Po was estimated from all-point amplitude histogram as follow: Po = Sum (iN/(iestimateNtotal), where N is the number of points for a specific current i in the histogram, iestimate = 0.4 pA from the peak of the histogram, and Ntotal = 10,000 is the total number of points in the last second of the trace. p = 0.75 ± 0.12 (n = 8) and p = 0.87 ± 0.04 (n = 3) for Control and Lin-Glycine, respectively.

Leak and capacitance were subtracted with averaged empty sweeps.

(2) The change of cells used for whole cell vs single channel (oocytes vs mouse ltk- fibroblast cells) could be discussed. These cells likely have different lipids in their membranes. Is there any other evidence that PUFAs have the same effects on KCNE1-KCNQ1 in these cells? Does the V0.5 shift? 

A similar effect on Gmax, in both oocytes and mouse ltk-fibroblast cells, is shown in Figure 1 and 2. In Figure 2, the shift in latency suggests a shift in V0.5, suggesting the binding of PUFA to Site I.

(3) The manuscript associates selectivity filter changes with S4 being up or down. It would help to clarify whether there was a change in [K+] in the two KCNQ1 structures used for modeling, as Mandala and MacKinnon (2023) state: "We note that one interesting difference between the two up structures regards the occupancy of K+ ions in the selectivity filter (SI Appendix, Fig. S5 C and D). In the polarized sample, due to the low extravesicular concentration of K+, density is only visible at the first and third positions in the selectivity filter, while density is present at all four positions in the unpolarized sample. Similar differences were observed in our previous study on Eag (20) and are qualitatively consistent with crystal structures of KcsA solved under symmetrical high and low K+ concentrations (45)." 

Our studies states that there are some differences in the two structures with S4 in up-state and S4 in down-state and a reorganization of the pore. As for the change in [K+] occupancy in the two structures, we are not sure as our knowledge only come from what stated in Mandala and Mackinnon (2023). Mandala and MacKinnon did not discuss the selectivity filter in the down state structure in their paper and there are no K ions in any of their pdb files. So, we don’t know how many K+ ions there are in the down state.

(4) The manuscript states " PUFAs increase Gmax by binding to a crevice in the pore domain" and "we elucidated that Lin-Glycine binds to a crevice between K326 and D301", this seems speculative without any actual binding studies or concrete structural evidence. A quantitative structural modeling analysis of whether changes in the crevice change the theoretical binding of Lin-Glycine might provide a stronger basis for speculation. 

We toned down these statements in Results and Discussion to:

“Crevice residues affect PUFA ability to increase Gmax"

And

Discussion: “We tested the hypothesis that the effect of Lin-Glycine involved conformational changes in the selectivity filter following PUFA binding to two residues K326 and D301 at the pore domain. Those residues delimit a small crevice that seems to change in size in different structures with S4 up or S4 down (Figure 3, D-F).”

(5) The several figures detailing differences in selectivity filter conformation in the KCNQ1 structures are interesting and relevant in that they identify the movement of residues such as Y315 that, when mutated, ablate Lin-Glycine effect on Gmax. It would help to clarify whether T312 and I313 also move between the two selectivity filter conformations. 

From the morph of the selectivity filter in the two conformations, it is noticeable that the changes and residue movements involve only residues at the upper part of the selectivity filter (including Y315 and D317). T312 and I313, are in the lower part of the selectivity filter and do not seem to move or rotate from their position between the two conformations of the selectivity filter.

We now include a Supplementary Figures S3 and S4 that show the extent of movement of each residue in the pore region and a short description of this in the Results section.

(6) The claim in the abstract that selectivity filter instability "explains the low open probability" seems too general. Lin-Glycine seems to increase the likelihood of conduction by 2.5-fold, but it was not clear whether open probability ceases to be low or whether other mechanisms also keep Po low. 

We reword this sentence to “Our results suggest that the selectivity filter in KCNQ1 is normally unstable, contributing to the low open probability, and that the PUFA-induced increase in Gmax is caused by a stabilization of the selectivity filter in an open-conductive state..”

Reviewer #2:

(1) While all the electrophysiological recordings used KCNQ1/KCNE1 channels, all the structural models they used are KCNQ1 channels (without KCNE1). I know it is because the KCNQ1/KCNE1 complex structure is unavailable. However, according to their previous results, KCNQ1 alone is also upregulated by PUFAs. I am curious about what the single-channel recordings of KCNQ1 alone look like in the presence and absence of PUFAs. 

We would love to include single-channel recordings of KCNQ1, but they are extremely hard to measure due to the small size and flickering nature of the channel.

(2) As mentioned above, we do not have the KCNQ1/KCNE1 structure yet have the KCNQ1/KCNE3 structures (Sun and MacKinnon, Cell, 2020). According to the PDBs (6V00 or 6V01), the clevis (K326 and D301) looks covered by KCNE3. Is it true that PUFAs do not upregulate KCNQ1/KCNE3? If true, KCNE1 may not cover the clevis, so the binding mode should differ from the KCNQ1/KCNE3 structures. Please discuss the possible blocking of the clevis by KCNE proteins. 

We previously presented data that is consistent with that KCNE1 rotates the VSD towards the PD (Wu et al., 2021). This mechanism would leave room for PUFA and KCNE1, so that PUFA can bind to the crevice (K326 and D301). So we think that this rotation will prevent PUFA and KCNE1 from competing for the same space. As for KCNQ1/KCNE3 we currently do not have any evidence about a possible upregulation by PUFA.

(3) In the cryoEM structure with S4 resting (Figure 3F), the clevis looks too narrow for PUFA to bind. Is there any (either previous or current) evidence supporting that PUFA binding is state-dependent? 

Because PUFAs integrate first into the bilayer and then diffuse towards its binding site on the channel, it would be hard to test a state-dependence of the binding. In addition, once PUFAs are in the bilayer, the rate of binding/unbinding is quite fast (within the ns range according to our previous MD simulations), whereas opening/closing rate is very slow (100 ms-s). So, the combination of slow wash in/washout, fast binding/unbinding, and slow opening/closing would make it very difficult to test the state-dependence of the binding by using a fast perfusion or different voltage protocols.  

(4) In the previous report (Liin et al. Cell Reports, 2018), K326 is the most critical site for PUFA binding. Why the K326 mutants are not included in the current study? I also would like to see the single-channel recordings of the K326E mutant, which showed a smaller Gmax. Does the PUFA application reduce the probability of non-empty traces in this mutant? 

As Liin et al. reported, mutations of K326 reduce the ability of PUFA to increase the Gmax. In this work, we wanted to gain further biophysical information on the mechanism that leads to an increase in Gmax, considering the knowledge we had from work conducted in our lab previously. We therefore focused here on residues downstream of K326 that we think are important for inducing the conformational changes at the selectivity filter. We agree that single channel experiments on K326E would be very interesting but that has to be for a future study.

Minor points 

(1) Liin et al. used S209F (Po of 0.4) and I204F (Po of 0.04) mutants. Their single-channel recordings would be a good addition. 

We thank the reviewer for the suggestion. However, single channels analysis on S209F and I204F were previously shown (Eldstrom et al., 2010).

(2) I would like to see how the Site I mutations (R2Q/Q3R) affect (or do not affect) the single-channel recordings (open probability and latency). 

Thank you for the excellent suggestion. It would be interesting to assess the behavior of the channel when mutations occur at Site I. However, we think this information will not add any more detail to this study as we focus here our attention on the mechanism for Gmax increase. Single channels recordings are extremely hard to get, therefore we chose to include only mutations at Site II for this study.

(3) I would like the G-V curves for all the mutations at 0 and 20 uM of Lin-Glycine (Figure 3C and Figures 5A and B). 

We now added the G-V curves in Supplementary Figure S7.

(4) I assume all the PUFAs have a similar effect on the selectivity filter, but a few other examples of PUFAs would be nice to see. 

We anticipate that PUFAs and analogues with similar properties to Lin-Glycine would increasing the Gmax by a similar mechanism, because other PUFAs have been previously shown to increase the Gmax (Bohannon et al., 2020).

(5) Although the probabilities of non-empty sweeps are written in the manuscript, bar graph presentations would be a nice addition to Figures 2 and 6. 

We have added bar graphs of non-empty sweeps for Fig 2 and 6 in.

(6) Is there no statistical significance for D317E and T309S in Figure 5A? 

No statistical significance for D317E and T309S

(7) There is no reference to Figure 7 in the manuscript. 

A reference to Figure 7 has been added to the manuscript in the following paragraph.

“Taken together, our results suggest that the binding of PUFA to Site II increases Gmax by promoting a series of interactions that stabilize the channel pore in the conductive state. For instance, we speculate that in the conductive state, hydrogen bonds between W304-D317 and W305-Y315, which are likely absent in the non-conductive conformation of KCNQ1, are created and that PUFA binding to Site II favors the transition towards the conductive state of the channel (Figure 7)”

Reviewer #3:

(1) Clarify the structural figures. Figures 3 D, E, and F - explain what the colors indicate. 

A more detailed description of Figure 3 has been added to the legend.

“D, E and F) Structure of crevice between S5 and S6 in KCNQ1 with S4 up (D and E) and S4 down (F). Residues that surround the crevice from S6 shown in blue (K326, T327, S330, V334) and from S5 in red (D301, A300, L303, F270). Remaining KCNQ1 residues shown in purple…, linoleic acid (LIN: gold color)”

Fig 4. Only side chains of the residues are shown, making it hard to relate the figure to the familiar K channel selectivity filter. The main chain of the entire selectivity should be shown to orient readers to the familiar view of the K channel selectivity filter. In addition, the structures shown are only part of the selectivity filter, it should be specified which part of the selectivity filter is shown. These will also help the discussion at the bottom of page 10 and subsequent text. 

We now provide a new Figure 4 with more details such as the main chain of the whole selectivity filter and surrounding peptide.

(2) Cautions should be stated clearly when the structural comparison between the S4-up and S4-down is made that the structure of the pore when it is closed with S4-up may differ from the structure of the pore with S4-down. 

We now state in addition “Clearly, there will be other differences in the pore domain between structures with activated and resting VSDs, for example the state of the activation gate.”