eLife Assessment

This is a potentially important work on the organization of visual information in the rodent superior colliculus. It reports that the selectivity of neurons to line orientation and motion in the visual image is largely governed by the sensitivities of retinal neurons and their ordered projection to the superior colliculus. If confirmed, these conclusions could substantially revise prior thinking in this field. However, in the present state, the methods and analysis are incomplete and cannot justify all the claims.

Reviewer #1 (Public review):

Summary:

When contemplating the role of any sensory area in the brain, an essential question is: How much of the neural code is inherited from the inputs versus constructed de novo by the local circuitry? This study tackles that important question for the case of the mouse superior colliculus (SC), a visual brain area that receives direct input from the retina. The specific aspects of the neural code are the representation of line orientation and direction of motion in the visual image. Over the past 10 years or so, there have been reports that the preferred directions and orientations of neurons vary systematically across the SC in a global map that is not present in the retina, and therefore computed locally.

Here, the authors revisit this question by expanding the range of measurements: They record from the axonal boutons of retinal ganglion cells in the input layer of the SC, from the post-synaptic neurons there, and from neurons in deeper layers of the SC. They conclude that at any given location in the SC, the signals in retinal boutons recapitulate the tuning of retinal ganglion cells, and that SC neurons follow that organization, though it is lost in the deeper layers. Notably, they find no evidence for a global map of these response properties other than what is contributed by retinal input.

Strengths:

The study combines multiple recording methods - calcium imaging and electrical recording - to capture the activity of retinal inputs to the colliculus, the tuning of neurons in the superficial layers close to the input, as well as neurons in deeper layers. Furthermore, the work connects to the recent literature on gradients of tuning properties among retinal ganglion cells. All these set the stage for testing the correspondence between retinal inputs and collicular outputs.

Weaknesses:

The methods used to identify direction-selective and orientation-selective neurons based on visual responses are overly permissive and don't match common usage in this research area. Furthermore, the measurements covered only a small fraction of the visual field, which limits their ability to distinguish between different hypotheses for the global map of visual response properties. Relatedly, the claim that retinal input patterns explain much of the layout in the superior colliculus should be made more quantitative.

Reviewer #2 (Public review):

In this study, the authors investigate the spatial organization of direction and orientation selectivity in the mouse superior colliculus (SC) and its retinal inputs. By combining two-photon imaging of retinal boutons and SC neurons with Neuropixels recordings, they assess whether tuning preferences form structured maps or are arranged in a salt-and-pepper fashion. They further compare SC tuning organization to previously described retinal geometric models to determine the extent to which collicular responses inherit retinal topography. The authors conclude that SC inherits a cardinally biased topographic scaffold from the retina, which progressively weakens with depth, and that strong global maps are absent.

A major strength of the study is the impressive combination of methodologies, including imaging of retinal boutons, imaging of SC neurons, and large-scale electrophysiological recordings across SC depth. The direct comparison to retinal geometric models is particularly valuable, as it situates the SC within a broader framework of retinotopic information transfer and advances our understanding of how retinal computations are transformed in downstream targets.

A limitation of the study, however, is that the imaging experiments sample only a relatively small and spatially homogeneous region of the visual field, whereas the electrophysiological recordings cover a different portion of SC. This separation makes it difficult to form a fully integrated, global picture of the spatial organization of direction and orientation selectivity across the entire collicular map.

Reviewer #3 (Public review):

Summary:

The authors studied the organisation of orientation and direction-selective retinal ganglion cells' boutons in the mouse superior colliculus. They confirmed the results already published (Molotkov, 2023; de Malmazet, 2024; Vita, 2024; Laniado, 2025), that retinal ganglion cells' boutons in the superior colliculus conserve the retinal organisation. Thereby, orientation and direction preferences of retinal boutons at each collicular location reflect the tuning of retinal ganglion cells found at the corresponding retinal location, that is covering a matching receptive field location.

The authors also studied the organization of orientation and direction-selective neurons in the superior colliculus. They report a lack of functional organisation in the superior colliculus for neurons preferring the same stimulus orientation or direction of movement. This goes against several published reports (Ahmadlou and Heimel, 2015; Liang et al., 2023; De Malmazet et al., 2018; Feinberg and Meister, 2014; Kasai and Isa, 2021; Li et al., 2020) but echoes a study from Chen et al. (Chen, 2021). The latter authors contested the strength of the anatomical clustering of tuned alike direction-selective neurons. They found, however, that in about a quarter of their recordings, direction-selective cells with similar preferred directions did cluster anatomically in the superior colliculus.

Here, the authors of the current manuscript under review report that local clustering of tuning was weak in all neural populations and confined to very small spatial scales (10-20 μm). This is one order of magnitude smaller than previously reported clusters of around 100-300μm wide. Therefore, the authors conclude that orientation and direction tuning in the mouse superior colliculus follows a salt and pepper organisation.

Strengths & Weaknesses:

Although the authors performed a solid analysis contesting the functional clustering of direction and orientation selective neurons, there seemed to be some elements in their data in favour of a functional clustering of neurons.

As an illustration, the authors plotted in Figure 1Q the distribution of preferred orientations from all their recorded orientation-selective cells. The curve shows a clear bias, indicating that neurons preferring horizontal orientations were found two times more often than neurons encoding any other orientations. Moreover, the authors recorded all their neurons from a defined anatomical location of the colliculus, marked by the dotted rectangle in Figure 3A-C. Therefore, this suggests that orientation-selective cells in this particular collicular location are biased towards preferring horizontal orientations. This supports an anatomical clustering of tuned alike orientation-selective cells in the superior colliculus.

Similarly, Figure 1P shows a bias in the preferred directions of direction-selective neurons in the same recording area. Cells tended to respond more to upward and forward-moving stimuli. The bias is more modest than the one described above for preferred orientations. However, it still seems significant. For example, cells preferring upwards movements appeared to be four times more abundant than cells preferring downward movements. As a consequence, it indicates that preferred directions might not be uniformly distributed and equally represented across the superior colliculus.

These anatomical biases are also visible in the receptive field analysis of the paper. In Figure 3G, the authors plotted the distribution of preferred orientations for every 10-degree bins within the recorded field of view. Out of 26 bins containing more than one neuron, only 6 seemed to include cells not overwhelmingly preferring a single orientation. These were located towards the top right of the figure. Therefore, over almost 80% of the recorded superior colliculus, the data seem in agreement with the view that orientation-selective cells tend to prefer the same orientation at a given receptive location.

The patch analysis in Figures 4G and H also seems to show some degree of coherence in the preferred orientation and direction of neighbouring tuned collicular cells. In both Figures 4 G and H, clear patches of similar preferred orientation and direction appeared to emerge. For example, in Figure 4H, there is a predominance of horizontally tuned patches. This was expected given the recording bias consisting of a majority of horizontally tuned cells. In addition, vertical and 45-degree patches are also visible, in blue and red, respectively. These patches overlap with the corresponding retinotopic locations in Figure 3G, where the histograms show that cells tend to prefer the same orientations, horizontal, vertical or 45 degrees.

It is important to note that in the previous studies on functional clustering of orientation and direction, variability in the tuning of cells within clusters was always reported (Ahmadlou and Heimel, 2015; Chen et al., 2021; De Malmazet et al., 2018; Feinberg and Meister, 2014; Kasai and Isa, 2021; Li et al., 2020). This was more marked for direction-selective cells than for orientation-selective cells. In general, cells preferring all four cardinal directions were often recorded at any given collicular location. Similarly, orientation-selective cells could be found to prefer deviant orientations compared to adjacent cells. Therefore, it is not surprising to see locally mixed tuning in collicular neurons. However, what appeared significant in these studies was the overall proportion of cells with similar tuning in patches of the superior colliculus. As described above, this also seems to be the case in the data of this manuscript.

To conclude, it seems that authors tend to overlook the sources of agreement between their data and previous reports showing functional clustering of cells in the superior colliculus. Instead, the authors tend to emphasise the dissimilarities and variability to put forward a contentious view on the organisation of orientation and direction selectivity in neurons of the superior colliculus. This, I fear, is detrimental to the field because it creates a sort of manufactured chaos that produces unnecessary confusion for readers who do not attentively read the manuscript. It would be valuable for the authors to consider rewriting the manuscript, acknowledging where their data, in fact, support some level of functional clustering.

Author Response:

We thank the reviewers and editors for their thoughtful and constructive assessment. We are encouraged that the reviewers viewed the combination of retinal bouton imaging, collicular neuron imaging, and depth-resolved electrophysiology, together with the comparison to retinal geometric models, as a strength of the study. As the reviewers note, our findings are consistent with previous in vitro studies showing topographic organization of tuning in the retina and with recent work demonstrating the precision of retinotopic mapping from retina to superior colliculus (SC). In revision, we will refine our definition of tuning, sharpen our claims about the spatial organization across SC and its correspondence to retinal topography, and make clearer our aim of reconciling seemingly opposing findings in the literature. In addition, we will provide a detailed response to all other points raised by the reviewers.

A central point raised in the reviews concerns our definition of direction- and orientation-selective cells. We agree that relying only on statistical significance is not sufficient for our purposes, because the resulting classification can depend on recording duration and statistical power. In the revised manuscript, we will therefore introduce thresholding criteria for direction and orientation selectivity indices (DSI and OSI) in addition to significance-based testing. We will also make clearer that our primary question is which stimulus directions and orientations are represented at a given receptive field location, rather than the distribution of preferences among neurons classified as purely direction- or orientation-selective.

We will also revise the text to define more precisely what our data do and do not establish about the large-scale organization across SC. Our intended conclusion is not that we identify a novel global organization, which would require sampling a larger portion of visual space, but rather that the regions we sampled are not well explained by previously proposed global maps in which each visual field location is dominated by a single tuning preference and the same organization is conserved across individuals. Instead, our data are more consistent with a retinal organization of biases toward specific directions and orientations that vary systematically across visual space.

We will further clarify how we quantified the correspondence between our data and the previously established retinal model of direction and orientation tuning. In the current manuscript, we report the errors between model predictions and measured tuning preferences at the corresponding visual field locations. We then assess model performance by comparing the distribution of these errors with the errors obtained from two surrogate datasets: one in which the correspondence between visual field location and tuning preference is destroyed, and one in which the prior distribution of tuning preferences is assumed to be uniform. In the revised manuscript, we will make the interpretation of this comparison more explicit, so that the reported errors are clearly presented as the relevant effect-size measure alongside significance.

Finally, we appreciate the reviewers’ concern that the manuscript may currently emphasize disagreement with previous studies too strongly. We will revise the Discussion to better acknowledge where our data support some degree of local bias or weak clustering, while clarifying that we do not find evidence for a robust, stereotyped global map that is consistent across animals. Our goal is to sharpen the manuscript so that it better reconciles seemingly divergent findings in the literature rather than setting them in opposition.

eLife Assessment

This important study advances our understanding of the neural substrate of planning trajectories towards a goal by using recurrent neural networks. The manuscript provides solid evidence for most of the claims, but it remains unclear whether the dynamics do indeed bear the defining characteristics of attractors, and the interpretation and scope of some claims may need to be reassessed in light of prior work. The work will be of broad interest to theoretical and systems neuroscientists and to cognitive scientists.

Reviewer #1 (Public review):

Summary:

This work builds a theory to implement planning trajectories towards a goal in a known environment, inspired by analyses of prefrontal neural recordings. Unlike standard neural architectures for this task, such as value-based learning and successor representations, their proposed theory is able to adapt to novel goal locations within a trial. The key to the theory is that future times are represented by orthogonal groups of neurons. The recurrent connectivity between groups of neurons selective to specific future times and locations reflects the learned knowledge of the task. Finally, the authors show that standard networks trained on the task approximate their proposed theory.

Strengths:

The structure of the work is clear, and the presentation of the results is very well written, which is particularly noticeable given the consequential amount of results presented. The authors are able to link their theory with experimental findings in neural recordings. The reverse-engineering of trained recurrent neural networks is very thorough, by analyzing both dynamics and connectivity. The assumptions and predictions of their model are clearly stated.

Weaknesses:

It is unclear whether their proposed theory, "space-time attractors", actually is an attractor network. The authors used recurrent neural networks with very few timesteps, and long single neuron time constants with respect to the task time scales. Attractor networks, as the ones the authors cite, refer to networks that generate nontrivial patterns of activity through recurrent interactions, after long periods of time.

The authors gloss over how the reward inputs are calculated. Computing these reward inputs should be part of the planning process, and the authors are implicitly leaving this problem aside. How does the reward input, which includes future time and location, depend on the actions that have not yet been taken by the agent? It feels like most of the planning computation is already provided by these reward inputs at the beginning of the trial. It could be that the network is only learning to process the planned sequence of actions present in the inputs.

Reviewer #2 (Public review):

This well-written manuscript proposes to use attractors in space and time (STA) as a mechanistic explanation for planning in the prefrontal cortex. The main conceptual hypothesis is that planning is implemented as attractor dynamics in a representation that encodes states at each time step jointly. Depending on inputs, the network relaxes to a trajectory that already contains future states that will be visited at each time step, rather than computing a scalar value at each point in time and space like other classical approaches from RL. The authors compare this approach to implementations such as TD learning and successor representation, and further show that trained recurrent neural networks on specific tasks involving planning develop structured subspaces resembling the ones postulated in STA.

The idea of treating attracting trajectories unfolding in time as the computational substrate for planning is very interesting and potentially important. The explicit construction of a state x time representational space and its implementation via recurrent dynamics are appealing and convincing in the idealized tasks considered. I found the manuscript to be refreshingly explicit regarding several of the assumptions and limitations of the models, for example, the fact that certain advantages can be viewed as properties of the state space itself and not necessarily of a fundamentally new planning mechanism.

Overall, the manuscript presents a cool attractor model that extends in time and explores its performance in a subset of illustrative tasks involving planning. My doubts concern mostly the interpretation and scope of the claims made in the manuscript. Here are a few comments where I detail my questions/concerns:

(1) The authors nicely discuss that much of the difference between STA and classical TD or SR agents is "in some sense a property of the state space rather than the decision making algorithm," and that TD and SR could in principle be implemented in a comparable space x time representation. This is fair, but it also suggests that the central contribution of the manuscript lies primarily in the representational factorization (state x time tiling) and its dynamical implementation via attractors, rather than in a fundamentally new planning algorithm or theory, mechanistic or not. I think theory should be distinguished from mechanism, and it would therefore help the reader to describe the conceptual advancement more as a novel mechanism or implementation than a novel (mechanistic) theory for decision/planning.

(2) Related to my previous point, I think it would be helpful to position STA more explicitly relative to computational/theoretical literature in which attractor networks encode temporally ordered patterns (so effectively including future times). For example, classical extensions of Hopfield networks with asymmetric connectivity implement retrieval of sequences and ordered transitions between patterns (Sompolinsky & Kanter, 1986). More recently, sequential attractors and limit-cycle dynamics have been constructed in structured recurrent networks by the Morrison group (Parmelee et al., 2021). These works do not implement an explicit discretized state x future-time tiling as in STA and do not specifically discuss the usage for planning. However, they do provide concrete precedents for attractor dynamics over temporally structured trajectories in terms of mechanism. It would be useful to discuss this literature and clarify a little what's new mechanistically in the view of the authors.

(3) A central claim of the manuscript is that space-time trajectories are attractors of the STA dynamics. The manuscript does provide empirical evidence consistent with attractor-like behavior. However, it is not explicitly shown whether trajectory representations persist in the absence of sustained external inputs. So it's not clear to me whether the trajectories should be interpreted as intrinsic attractors of the recurrent system, which can be selected by delivering transient inputs, or whether they must be stabilized by a specific continuous external drive. It would be useful if the author could clarify/discuss this point.

As far as I understand it, reward information is provided as input to specific populations encoding future time steps, and that's essential for rapid adaptation without rewiring connectivity. How such future-time-specific reward inputs would be generated and routed to distinct neural populations isn't entirely clear to me. Since this seems to be an essential component of the model, I think it would be important to discuss more deeply the source and plausibility of these reward signals related to different timesteps.

(4) The authors note that vanilla STA scales linearly with planning horizon, and discuss potentially hierarchical extensions for longer horizons. They acknowledge that learning abstractions remains an open challenge, yet the examples of planning in the manuscript are restricted to very short temporal horizons and limited branching complexity. It is not obvious to me in what cases the current implementation and interpretation of STA remains viable (for example, in terms of relaxation iterations) as the horizon and branching factor increase. Relatively simple planning can be managed by simpler, less costly models/algorithms, whereas complex planning is a lot harder to deal with, and it's something that a mechanistic "theory" should address. In the context of the claims of the paper in its present form, I think this is possibly the most important conceptual and practical limitation in the manuscript.

(5) The RNN analyses show that trained networks develop structured subspaces aligned with future time indices and exhibit perturbation behavior consistent with attractor-like dynamics. The manuscript also explicitly notes differences between the trained RNN and the handcrafted STA (e.g., long-range couplings between subspaces and differences in behavior of lower-value trajectories under perturbation), which I much appreciated. My doubt is on the specificity of this result, as trained RNNs on fixed-horizon tasks can develop latent dimensions correlated with temporal progress within a trial or time-to-goal. I think it would help the reader to clarify whether the results demonstrate that STA-like computations emerge in RNNs trained on planning tasks, or that RNNs generally develop some kind of structured spacetime representations when tasks involve future timesteps and some degree of flexibility in the decisions.

A few more minor points, mainly concerning clarity:

(1) The main dynamical equation combines a log-domain recurrent term, a floor operation, and a log-sum-exp normalization step, followed by exponentiation. The intuition/logic behind this specific formulation could be clarified for the reader. For example tt would be helpful to explain why the recurrent input appears inside a log, and also whether/how these operations relate to any multiplicative constraint.

(2) While the computational cost of successor representation in an expanded NT x NT representation is discussed, the corresponding scaling of STA in terms of number of units and connections (as a function, for example, of the planning horizon) isn't clear to me. Perhaps the authors could compare costs more explicitly.

(3) In the RNN analyses, structured subspaces aligned with future time indices are shown. I couldn't find a quantification of how much variance is captured by the subspaces, relative to other latent dimensions. Adding it would help get a feeling for the strength of the alignment.

eLife Assessment

This important study presents evidence that the Chromatin-linked adaptor for MSL complex proteins (CLAMP) GA-binding transcription factor (TF) regulates ~75% of HS-induced repression in Drosophila and suggests that CLAMP is the first known transcription factor to induce heat-stress-mediated repression of gene expression. While mechanistic details remain to be sorted out, this manuscript provides convincing evidence that novel pathways involving the CLAMP transcription factor repress gene expression during heat shock stress.

Reviewer #1 (Public review):

Summary:

This work aims to identify the transcription factor responsible for targeting constitutively active genes for repression during heat stress. While the mechanisms underlying heat-stress-induced gene activation are well characterized - primarily involving Heat Shock Factor (HSF), the GA-binding factor GAF, and RNA Polymerase II pausing regulators - far less is known about how repression of constitutive genes is directed. Because known activation factors such as HSF and GAF do not account for repression, the authors sought a DNA-binding factor that could selectively target these genes. They focused on CLAMP (Chromatin-linked adaptor for MSL complex proteins) for several reasons. First, CLAMP recognizes GA-rich DNA motifs similar to those bound by GAF, suggesting it could compete with GAF at regulatory elements and shift transcriptional outcomes. Second, CLAMP has been shown to antagonize GAF binding in certain genomic contexts, suggesting it could counteract activation mechanisms. Third, CLAMP interacts with Negative Elongation Factor (NELF), a factor known to regulate transcriptional repression during heat stress. Finally, CLAMP promotes long-range chromatin interactions, indicating it may influence local chromatin architecture during the heat-stress response. Together, these properties led the authors to hypothesize that CLAMP helps mediate heat-stress-induced transcriptional repression of constitutively active genes.

To test this hypothesis, the authors use immunofluorescence along with three techniques: (1) nascent RNA-sequencing (SLAM-seq) to define the function of CLAMP in heat stress-induced transcriptional activation and repression; (2) Micro-C to identify CLAMP-dependent and independent genome-wide, high-resolution local changes in chromatin organization after heat stress, and (3) HiChIP to identify CLAMP-bound 3D chromatin loop anchors associated with heat-stress-dependent transcriptional regulation.

Analysis of heat-shocked salivary glands or KC cells showed results that aligned across all experiments, indicating that CLAMP is the primary repressor of gene activation upon heat shock. CLAMP also inhibits chromatin loop formation.

Strengths:

The techniques used here are comprehensive, and impressively, the data is unambiguous.

Weaknesses:

These techniques do not reveal the molecular mechanisms, but the authors provide a strong rationale and molecular hypotheses for future studies to dissect.

Reviewer #2 (Public review):

In this manuscript, Aguilera et al. investigate the mechanisms underlying transcriptional repression of constitutively expressed genes during heat stress. While the activation of heat-shock genes has been extensively studied, the mechanisms responsible for widespread transcriptional repression remain poorly understood. The authors propose that the GA-binding transcription factor CLAMP acts as a major regulator of heat-stress-induced transcriptional repression in Drosophila. Using nascent RNA-sequencing approaches, they report that CLAMP contributes to the repression of a large fraction of genes whose transcription decreases upon heat stress. In addition, the authors generate high-resolution Micro-C datasets to analyze changes in chromatin architecture during heat stress and report widespread alterations in chromatin looping associated with transcriptional changes. Based on these results, the study proposes that CLAMP regulates repression through both direct transcriptional mechanisms and modulation of local 3D genome architecture.

The study addresses an important question in gene regulation: how transcription is rapidly repressed during environmental stress. The work is timely because most previous studies have focused on transcriptional activation of heat-shock genes, whereas repression mechanisms remain comparatively less understood. The integration of transcriptional profiling with high-resolution chromatin conformation data is a major strength of the manuscript and provides a valuable resource for the community studying genome organization and stress responses.

The nascent RNA-sequencing experiments appear carefully designed and allow the authors to capture rapid transcriptional responses to heat stress. These data provide convincing evidence that heat stress leads to widespread transcriptional repression of constitutive genes and that CLAMP contributes substantially to this process. The genomic analyses linking CLAMP binding to repressed genes are also informative and support the idea that CLAMP plays a direct regulatory role at many loci.

Another strength of the study is the generation of Micro-C datasets under heat stress conditions. These datasets provide a high-resolution view of chromatin architecture and reveal dynamic changes in local chromatin looping associated with transcriptional responses. The authors' analysis suggests that heat stress induces widespread reorganization of chromatin contacts, and that CLAMP may contribute to these structural changes. This dataset is likely to be useful for future studies exploring how environmental cues influence genome organization.

Despite these strengths, several aspects of the study would benefit from further clarification. First, the mechanism by which CLAMP mediates transcriptional repression remains insufficiently defined. While the data support a role for CLAMP in the repression of a subset of genes during heat stress, the molecular basis of this effect is not fully explored. Second, although the Micro-C dataset represents a valuable resource for studying chromatin architecture during heat stress, the functional interpretation of the observed structural changes could be further developed. In particular, it would be helpful to better establish the relationship between the identified chromatin loops and gene regulation, and to clarify whether these structural changes play a causal role in transcriptional repression or instead reflect broader chromatin reorganization associated with the stress response.

Reviewer #3 (Public review):

Summary:

Exposure to heat shock results in major changes to gene expression programs within the cell, and over the past decades, there has been extensive characterization of the mechanisms through which heat shock activates transcription. However, heat shock also leads to widespread repression of many genes, and the transcriptional mechanisms that mediate this repression have not been well understood. Here, the authors show that the transcription factor CLAMP mediates this heat shock-dependent repression via changes in local 3D chromatin looping. Intriguingly, CLAMP is already bound to chromatin prior to heat shock, but is necessary for the loss of local chromatin loops at its bound sites and repression of genes located within the loops. This study is significant because it defines chromatin looping, depending on a key transcription factor CLAMP, as the major mechanism through which genome-wide changes in gene repression occur in response to an inducible stimulus, heat shock.

Strengths:

The use of the SLAM-seq and Micro-C techniques to measure the necessity of CLAMP for heat shock-dependent transcription repression and local chromatin looping is excellent, and these approaches provide valuable insight into the role of CLAMP in heat shock-dependent repression that was not apparent with older approaches. The HiChIP approach provides an excellent method to test whether CLAMP is bound at sites where there are changes in looping upon heat shock, providing good support for their conclusions that CLAMP induces heat shock repression by decreasing loops. Appropriate controls are present, and there is robust statistical analysis of the bioinformatics data.

Weaknesses:

The study does not provide insight into how CLAMP mechanistically affects loops upon heat shock, although the discussion raises the possibility that this could result from biophysical changes since CLAMP is an intrinsically disordered protein.

eLife Assessment

This study investigates how the HIV inhibitor lenacapavir influences capsid mechanics and interactions with the nuclear pore complex. It provides important insights into how drug-induced hyperstabilization of the viral shell can compromise its structural integrity during nuclear entry. The modeling is technically sophisticated, and the analyses provide solid support for the mechanistic conclusions.

Reviewer #1 (Public review):

The paper from Hudait and Voth details a number of coarse-grained simulations as well as some experiments focused on the stability of HIV capsids in the presence of the drug lenacapavir. The authors find that LEN hyperstabilizes the capsid, making it fragile and prone to breaking inside the nuclear pore complex.

I found the paper interesting. I have a few suggestions for clarification and/or improvement.

(1) How directly comparable are the NPC-capsid and capsid-only simulations? A major result rests on the conclusion that the kinetics of rupture are faster inside the NPC, but are the numbers of LENs bound identical? Is the time really comparable, given that the simulations have different starting points? I'm not really doubting the result, but I think it could be made more rigorous/quantitative.

(2) Related to the above, it is stated on page 12 that, based on the estimated free-energy barrier, pentamer dissociation should occur in ~10 us of CG time. But certainly, the simulations cover at least this length of time?

(3) At first, I was surprised that even in a CG simulation, LEN would spontaneously bind to the correct site. But if I read the SI correctly, LEN was parameterized specifically to bind to hexamers and not pentamers. This is fine, but I think it's worth describing in the main text.

Comments on revisions:

I found that the authors addressed my concerns satisfactorily. The other reviewer raised a number of important points regarding the nuances of the model and the interpretation of the simulations, which the authors rebutted. I think the paper in its current form now is a worthwhile addition to the literature.

Reviewer #3 (Public review):

I have carefully reviewed the manuscript, the two referee reports, and the authors' detailed responses. I appreciate the substantial effort the authors have invested in addressing the reviewers' comments, and I also recognize the strength and ambition of the work. This is a technically sophisticated study that integrates coarse-grained modeling with live-cell imaging to address an important and timely question regarding HIV-1 capsid inhibition by lenacapavir.

Embedded within Reviewer #2's report are several substantive points that warrant careful consideration, particularly with respect to framing, terminology, and engagement with the broader literature. I view my role here is to distinguish those issues from claims that I do not find to be supported.

First, I do not agree with Reviewer #2's central assertion that the manuscript lacks novelty or fails to present meaningful new findings. While individual elements of the system studied here-capsid docking at the NPC, lenacapavir-induced capsid hyperstabilization, capsid rupture, and competition with FG- nucleoporins-have been observed previously, this work provides a coherent, mechanistic account of how these elements are coupled. In particular, the proposed sequence linking LEN-induced lattice hyperstabilization, preferential pentamer loss at the narrow end, NPC-induced mechanical stress, and failure of nuclear import represents a nontrivial integration that goes beyond prior phenomenological observations. I therefore do not view this work as redundant with existing literature.

That said, Reviewer #2 is correct to note that the manuscript would benefit from broader and more explicit engagement with recent independent studies, including computational and hybrid modeling efforts that address capsid mechanics, nuclear entry, and LEN effects using different frameworks. While the authors' bottom-up coarse-grained approach is clearly distinct and, in many respects, more systematically derived, eLife readers would benefit from a clearer discussion of how the present results relate to, complement, or differ from these other approaches. I strongly encourage the authors to add a short discussion paragraph situating their work within this broader context, without disparaging alternative models.

Second, I find that some mechanistic claims in the manuscript would benefit from more careful language distinguishing model-conditioned interpretation from de novo prediction. This applies in particular to discussions of LEN binding heterogeneity and stoichiometry, as well as to conclusions drawn from biased enhanced-sampling simulations. While I agree with the authors that parameterization does not invalidate mechanistic insight, it is important to be precise about what aspects of the behavior emerge from the simulations versus what is constrained by prior experimental knowledge. Modest tightening/revising of language (e.g., "suggests," "is consistent with," "within the model") would address this concern without weakening the scientific conclusions.

Third, Reviewer #2 raises a legitimate semantic issue regarding the use of the term "elasticity." The manuscript infers changes in capsid mechanical response using heterogeneous elastic network models, which quantify effective stiffness and deformability rather than elasticity in the macroscopic materials sense. I recommend that the authors clarify this definition explicitly in the text to avoid confusion and unnecessary debate.

Finally, I note that several of Reviewer #2's objections-particularly those asserting circular reasoning, misuse of enhanced sampling methods, or invalidity of coarse-grained predictions-reflect a misunderstanding of contemporary bottom-up coarse-grained modeling rather than genuine methodological flaws. I do not believe these points require further rebuttal or revision beyond what the authors have already provided.

In summary, in my view, the manuscript represents a solid contribution to the field, provided that the authors undertake a limited set of targeted revisions aimed at improving framing, clarity, and engagement with the broader literature. Addressing these points will strengthen the manuscript and ensure that its contributions are clearly and fairly communicated to the community.

Author response:

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

It is important to make a few key points about our work. First, our paper is largely a computational biophysics paper, augmented by experimental results. Generally speaking, computational biophysics work intends to achieve one of two things (or both). One is to provide more molecular level insight into various behaviors of biomolecular systems that have not been (or cannot be) provided by qualitative experimental results alone. The second general goal of computational biophysics it to formulate new hypotheses to be tested subsequently by experiment. In our paper, we have achieved both of these goals and then confirmed the key computational results by experiment.

eLife Assessment

This study investigates how the HIV inhibitor lenacapavir influences capsid mechanics and interactions with the nuclear pore complex. It provides important insights into how drug-induced hyperstabilization of the viral shell can compromise its structural integrity during nuclear entry. While the modeling is technically sophisticated and the results are promising, some mechanistic interpretations rely on assumptions embedded in the simulations, leaving parts of the evidence incomplete.

Given our response below, regarding the rigor and “completeness” of our work, we do not feel that an editorial judgement of “leaving parts of the evidence incomplete” is justified.

We also note that another recent experimental paper has validated essentially every prediction made in our eLife paper: https://www.biorxiv.org/content/10.64898/2026.01.05.697065v1

We thus disagree that the evidence we have presented in our paper is incomplete.

Public Reviews:

Reviewer #1 (Public review):

The paper from Hudait and Voth details a number of coarse-grained simulations as well as some experiments focused on the stability of HIV capsids in the presence of the drug lenacapavir. The authors find that LEN hyperstabilizes the capsid, making it fragile and prone to breaking inside the nuclear pore complex.

I found the paper interesting. I have a few suggestions for clarification and/or improvement. 

(1) How directly comparable are the NPC-capsid and capsid-only simulations? A major result rests on the conclusion that the kinetics of rupture are faster inside the NPC, but are the numbers of LENs bound identical? Is the time really comparable, given that the simulations have different starting points? I'm not really doubting the result, but I think it could be made more rigorous/quantitative.

We note (also in the manuscript) that it is difficult to compare the timescales obtained from coarse-grained MD simulations and experiments (“real time”) given that, by design, the CG simulations are accelerated to greatly enhance sampling. However, we can qualitatively compare the timescales of different CG simulations (without directly comparing the corresponding experimental timescales).

We agree with the reviewer that the starting point of NPC-capsid and capsid-only simulations is different, as is the biological environment in which the rupture occurs. When analyzing the NPC-only and capsid-only simulations, what was striking to us was that at the NPC the capsid-LEN complex ruptures in a multicomponent environment, where several FG-NUPs compete to displace the LENs. It is well established in experiments that LEN has a detrimental effect on capsid integrity.

In Figure 2, we plot the number of LEN molecules as a function of CG simulation time. The initial capsid-LEN complex was equilibrated without NPC and then placed at the cytoplasmic end of the NPC for docking. The number of LEN molecules for the capsid-only simulations and the NPC-docked simulations is nearly identical, and an insignificant number of LEN molecules unbind at the NPC. Hence, we added the following clarification:

Page 10, paragraph 11

“Note that the number of LEN molecules bound to the capsid for the free capsid and NPCdocked capsids are nearly identical. Hence, the disparity in timescale of lattice rupture is not only because of the effect of LEN on capsid lattice properties.”

Is the time really comparable, given that the simulations have different starting points?

Yes, the CG timescales of both the NPC and freely diffusing capsid unbiased simulations are comparable, since they were done using identical simulation settings.

(2) Related to the above, it is stated on page 12 that, based on the estimated free-energy barrier, pentamer dissociation should occur in ~10 us of CG time. But certainly, the simulations cover at least this length of time?

Our implicit solvent CG MD simulations are designed to access timescales far beyond the capabilities of the fully atomistic simulations. We reiterate here that it is difficult to directly compare the timescales obtained from CG MD simulations and experiments.

As described in the text, there are 12 pentamers in the capsid (7 in the wide end and 5 in the narrow end). For the narrow end to rupture, all 5 pentamers should progressively dissociate. In our unbiased simulations (Fig. S5), in 25 us of CG time, we observe (partial) dissociation of one or two pentamers. Hence, our unbiased CG simulation timescales were not long enough to observe rupturing of the narrow end.

(3) At first, I was surprised that even in a CG simulation, LEN would spontaneously bind to the correct site. But if I read the SI correctly, LEN was parameterized specifically to bind to hexamers and not pentamers. This is fine, but I think it's worth describing in the main text.

We modified (see below) the main text to include the details.

Page 4, paragraph 1

“We model LEN and CA interactions such that LEN molecules can only bind to CA hexamers, and all interactions to CA pentamers are turned off, as in experiments, CA selectively associates with hexamers (25, 36).”

Reviewer #2 (Public review):

Here, Hudait et al. use CG modeling to investigate the mechanism by which Lenacapavir (LEN) treats HIV capsids that dock to the nuclear pore complex (NPC). However, the manuscript fails to present meaningful findings that were previously unreported in the literature and is thus of low impact. Many claims made in the manuscript are not substantiated by the presented data. Key mechanistic details that the work purports to reveal are artifacts of the parameterization choices or simulation/analysis design, with the simulations said to reveal details that they were specifically biased to reproduce. This makes the manuscript highly problematic, as its contributions to the literature would represent misconceptions based on oversights in modeling and thus mislead future readers. 

We strongly disagree with these statements, and they do not reflect the facts. We provide a rebuttal to these statements in the “Author Response” statements below.

(1) Considering the literature, it is unclear that the manuscript presents new scientific discoveries. The following are results from this paper that have been previously reported:

(a) LEN-bound capsid can dock to the nuclear pore (Figure 2; see e.g. 10.1016/j.cell.2024.12.008 or 10.1128/mbio.03613-24). 

(b) NUP98 interacts with the docked capsid (Figure 2; see e.g. 10.1016/j.virol.2013.02.008 or 10.1038/s41586-023-06969-7 or 10.1016/j.cell.2024.12.008). 

(c) LEN and NUP98 compete for a binding interface (Figure 2; see e.g. 10.1126/science.abb4808 or 10.1371/journal.ppat.1004459). 

(d) LEN creates capsid defects (Figure 3 and 5, see e.g. 10.1073/pnas.2420497122). 

(e) RNP can emerge from a damaged capsid (Figure 3 and 5; see e.g. 10.1073/pnas.2117781119 or 10.7554/eLife.64776). 

(f) LEN hyperstabilizes/reduces the elasticity of the capsid lattice (Figure 6; see e.g. 10.1371/journal.ppat.1012537). 

The goal of our simulations (in combination with experiments from the Pathak group) is to provide molecular-level insight into the sequence of events of NPC docking of capsid and the effect of LEN binding leading to sequential dissociation of pentamers and leading to rupturing of the narrow end of the cone-shaped capsid. We also compare the events leading to capsid rupture at the NPC with the same for a freely diffusing capsid, akin to that in cytoplasm. The reviewer should carefully read the abstract of our paper. In fact, the above are all papers that present qualitative experimental results that help validate our model, but they do not provide details on the molecule-scale events. For example, the paper (10.1073/pnas.2420497122 written by our coauthors in the Pathak group) is extensively used to compare the behavior of LEN-bound capsid in the cytoplasm.

(2) The mechanistic findings related to how these processes occur are problematic, either based on circular reasoning or unsubstantiated, based on the presented data. In some cases, features of parameterization and simulation/analysis design are erroneously interpreted as predictions by the CG models. 

We strongly disagree with this assessment. Our CG NPC model is largely a “bottomup” model derived from molecular scale interactions sampled in atomistic simulations (see our previous paper in PNAS https://doi.org/10.1073/pnas.2313737121). The reviewer appears to be ignorant of the “bottom-up” approach based on rigorous statistical mechanics to derive moleculescale model (please refer to a detailed review on bottom-up coarse-graining: J. Chem. Theory. Comput., 2022, 18. 5759-5791).

Using the “bottom-up” CG model of the NPC, we predicted several molecular-level details of capsid import and docking to the NPC. Our key predictions were that there is an intrinsic capsid lattice elasticity and also the pleomorphic nature of the NPC channel is key for successful capsid docking https://doi.org/10.1073/pnas.2313737121). Our computational predictions have benn, for example, validated in a recently published paper by an experimental group: Hou, Z., Shen, Y., Fronik, S. et al. HIV-1 nuclear import is selective and depends on both capsid elasticity and nuclear pore adaptability. Nat Microbiol 10, 1868–1885 (2025). https://doi.org/10.1038/s41564025-02054-z). Our work is an excellent example of how systematically derived “bottom-up” CG models can accurately predict molecular details of complex biological processes.

We have now added the following statement:

Page 3, Paragraph 1

“Importantly, the computational predictions of capsid docking to the NPC central channel have been recently validated in a HIV-1 core import at the NPC using cryo-ET (33), demonstrating how systematically derived “bottom-up” CG models can accurately predict molecular details of complex biomolecular processes.”

(a) Claim: LEN-bound capsids remain associated with the NPC after rupture. CG simulations did not reach the timescale needed to demonstrate continued association or failure to translocate, leaving the claim unsubstantiated.

The reviewer fails to recognize that the statement is based on the experimental results of LEN-bound capsid that remains bound to the NPC after rupture and fails to translocate to the nuclear side (from the Pathak group in the section “Ruptured LEN-viral complexes remain bound to the NPC”). The Reviewers’ comment is incorrect. 

(b) Claim: LEN contributes to loss of capsid elasticity. The authors do not measure elasticity here, only force constants of fluctuations between capsomers in freely diffusing capsids. Elasticity is defined as the ability of a material to undergo reversible deformation when subjected to stress. Other computational works that actually measure elasticity (e.g., 0.1371/journal.ppat.1012537) could represent a point of comparison but are not cited. The changes in force constants in the presence of LEN are shown in Figure 6C, but the text of the scale bar legend and units of k are not legible, so one cannot discern the magnitude or significance of the change.

The concept of elasticity can extend down to the mesoscopic scale. Many examples can be found in the large number of elastic network models (ENMs) of proteins published by many authors. The reviewer also fails to comprehend the meaning of the effective spring constants in the HeteroENM model and how they relate to the response of the capsid to stress (e.g., in the NPC). Note, in the NPC central channel, the capsid encounters several nucleoporins (including disordered FG Nucleoporins that not have specific interactions to rest of the proteins), and also a confined environment. This environment can exert inward stress to the capsid, which is also reflected in stress on the capsid lattice. Furthermore, the cited computational AFM studies are very far from a realistic in vivo or even in vitro set of conditions. In contrast, our study presents a realistic environment which the capsid will encounter in NPC, and then these predictions are validated by experimental results.

(c) Claim: Capsid defects are formed along striated patterns of capsid disorder. Data is not presented that correlates defects/cracks with striations. 

We presented the data of formation of striated patterns of lattice stress in the capsid that runs from capsid narrow end to the wide end in coarse-grained model (https://doi.org/10.1073/pnas.2313737121), and atomistic model (https://doi.org/10.1073/pnas.2117781119). Both of our papers are extensively cited in the current manuscript. Also, when the capsid is ruptured, one cannot visualize the striated patterns.

(d) Claim: Typically 1-2 LEN, but rarely 3 bind per capsid hexamer. The authors state: "The magnitude of the attractive interactions was adjusted to capture the substoichiometric binding of LEN to CA hexamers (Faysal et al., 2024). ... We simulated LEN binding to the capsid cone (in the absence of NPC), which resulted in a substoichiometric binding (~1.5 LEN per CA hexamer), consistent with experimental data (Singh et al., 2024)." This means LEN was specifically parameterized to reproduce the 1-2 binding ratio per hexamer apparent from experiments, so this was a parameterization choice, not a prediction by CG simulations as the authors erroneously claim: "This indicates that the probability of binding a third LEN molecule to a CA hexamer is impeded, likely due to steric effects that prevent the approach of an incoming molecule to a CA hexamer where 2 LEN molecules are already associated. ... Approximately 20% of CA hexamers remain unoccupied despite the availability of a large excess of unbound LEN molecules. This suggests a heterogeneity in the molecular environment of the capsid lattice for LEN binding." These statements represent gross over-interpretation of a bias deliberately introduced during parameterization, and the "finding" represents circular reasoning. Also, if "steric effects" play any role, the authors could analyze the model to characterize and report them rather than simply speculate.

Reviewer comment: “This means LEN was specifically parameterized to reproduce the 1-2 binding ratio per hexamer apparent from experiments, so this was a parameterization choice, not a prediction by CG simulations as the authors erroneously claim.” – This comment by reviewer is deeply flawed and we strongly disagree. In our CG model there is no restriction on the number of LEN molecules that can bind to a CA hexamer. We again restate that, the experimental results on LEN binding to CA hexamers and inability of LEN to bind to pentamers were used as no allatom (AA) forcefield yet exists.

The steric effect of the lack of third LEN binding to a hexamer is a likely hypothesis (which one is allowed to make). More importantly, an investigation of the steric effect of LEN binding to the CA hexamer is not the main goal of the manuscript.

(e) Claim: Competition between NUP98 and LEN regulates capsid docking. The authors state: "A fraction of LEN molecules bound at the narrow end dissociate to allow NUP98 binding to the capsid ... Therefore, LEN can inhibit the efficient binding of the viral cores to the NPC, resulting in an increased number of cores in the cytoplasm." Capsid docking occurs regardless of the presence of LEN, and appears to occur at the same rate as the LEN-free capsid presented in the authors' previous work (Hudait &Voth, 2024). The presented data simply show that there is a fluctuation of bound LEN, with about 10 fewer (<5%) bound at the end of the simulation than at the beginning, and the curve (Figure 2A) does not clearly correlate with increased NUP98 contact. In that case, no data is shown that connects LEN binding with the regulation of the docking process. Further, the two quoted statements contradict each other. The presented data appear to show that NUP outcompetes LEN binding, rather than LEN inhibiting NUP binding. The "Therefore" statement is an attempt to reconcile with experimental studies, but is not substantiated by the presented data.

We disagree with this spurious statement, and we see no real contradiction. We have now added a minor clarification that LEN can inhibit efficient capsid binding at significantly high concentration.

Page 6, Paragraph 1

“Therefore, at significantly high concentration LEN can inhibit the efficient binding of the viral cores to the NPC, resulting in an increased number of cores in the cytoplasm.”

(f) Claim: LEN binding leads to spontaneous dissociation of pentamers. The CG simulation trajectories show pentamer dissociation. However, it is quite difficult to believe that a pentamer in the wide end of the capsid would dissociate and diffuse 100 nm away before a hexamer in the narrow end (previously between two pentamers and now only partially coordinated, also in a highly curved environment, and further under the force of the extruding RNA) would dissociate, as in Figure 2B. A more plausible explanation could be force balance between pent-hex versus hex-hex contacts, an aspect of CG parameterization. No further modeling is presented to explain the release of pentamers, and changes in pent-hex stiffness are not apparent in the force constant fluctuation analysis in Figure 6C.

This is both a misrepresentation of the simulations and a failure to understand them (as well as the supporting experiments) on the part of the reviewer. In the presence of LEN, the hexameric lattice is hyperstabilized. In contrast, the pentamers are not. As a consequence, the pentamers are dissociated. The pentamers at the narrow end are dissociated first, due to high curvature. The reviewer, from a point of being uninformed, simply speculates on what they think should happen. Moreover, as emphasized earlier and which the reviewer fails to comprehend is that ours is a “bottom-up CG model” so it predicts, not builds in, these effects.

(g) Claim: WTMetaD simulations predict capsid rupture. The authors state: "In WTMetaD simulations, we used the mean coordination number (Figure S6) between CA proteins in pentamers and in hexamers as the reaction coordinate." This means that the coordination number, the number of pent-hex contacts, is the bias used to accelerate simulation sampling. Yet the authors then interpret a change in coordination number leading to capsid rupture as a discovery, representing a fundamental misuse of the WTMetaD method. Changes in coordination number cannot be claimed as an emergent property when they are in fact the applied bias, when the simulation forced them to sample such states. The bias must be orthogonal to the feature of interest for that feature to be discoverable. While the reported free energies are orthogonal to the reaction coordinate, the structural and stepwise-mechanism "findings" here represent circular reasoning.

Unfortunately, the reviewer appears to be quite uninformed on the WTMetaD method and what it does. The chosen collective variable (CV) in our case is the coordination variable and the MetaD samples along that variable (the conditional free energy) as it is designed to do. The reviewer may wish to educate themself by reading Dama et al (https://doi.org/10.1103/PhysRevLett.112.240602). We also note that “emergent properties” are not along some other, uncoupled coordinate.

(3) Another major concern with this work is the excessive self-citation, and the conspicuous lack of engagement with similar computational modeling studies that investigate the HIV capsid and its interactions with LEN, capsid mechanical properties relevant to nuclear entry, and other capsidNPC simulations (e.g., 10.1016/j.cell.2024.12.008 and 10.1371/journal.ppat.1012537). Other such studies available in the literature include examination of varying aspects of the system at both CG and all-atom levels of resolution, which could be highly complementary to the present work and, in many cases, lend support to the authors' claims rather than detract from them. The choice to omit relevant literature implies either a lack of perspective or a lack of collegiality, which the presentation of the work suffers from. Overall, it is essential to discuss findings in the context of competing studies to give readers an accurate view of the state of the field and how the present work fits into it. It is appropriate in a CG modeling study to discuss the potential weaknesses of the methodology, points of disagreement with alternative modeling studies, and any lack of correlation with a broader range of experimental work. Qualitative agreement with select experiments does not constitute model validation. 

We disagree with this statement and point out where we have cited other work, including the ones mentioned above. However, our CG model is a largely bottom-up CG model which differs from other more ad hoc CG approaches (and some well-known CG models). We do not wish to emphasize the obvious flaws in those other CG approaches and models, since that is not the focus of our manuscript.

(4) Other critiques, questions, concerns:

(a) The first Results sub-heading presents "results", complete with several supplementary figures and a movie that are from a previous publication about the development of the HIV capsid-NPC model in the absence of LEN (Hudait &Voth, 2024). This information should be included as part of the introduction or an abbreviated main-text methods section rather than being included within Results as if it represents a newly reported advancement, as this could be misleading. 

The movie in question (capsid docking to NPC without LEN) is essential for comparison of LEN-binding dynamics. Different from our previous paper, we simulated significantly longer timescales of capsid docking and performed several additional analyses that is relevant to this paper. Moreover, the first section of the result is titled “Coarse-grained modeling and simulation”, hence we only present a summary of the CG models and key validation steps in this section.

(b) The authors say the unbiased simulations of capsid-NPC docking were run as two independent replicates, but results from only one trajectory are ever shown plotted over time. It is not mentioned if the time series data are averaged or smoothed, so what is the shadow in these plots (e.g., Figures 1,2, and Supplementary Figure 5)?

These simulations are the average from two replicas. “For all the plots, the solid lines are the mean values calculated from the time series of two independent replicas, and the shaded region is the standard deviation at each timestep.” This was mentioned in the original figure caption.

(c) Why do the insets showing LEN binding in Figure 2A look so different from the models they are apparently zoomed in on? Both instances really look like they are taken from different simulation frames, rather than being a zoomed-in view.

It is difficult to discern a high curvature region of the capsid due to object overlap of different regions of the capsid. This is likely a case of “perspective distortion” in image processing.

(d) What are the sudden jerks apparent in the SI movies? Perhaps this is related to the rate at which trajectory frames are saved, but occasionally, during the relatively smooth motion of the capsidNPC complex, something dramatic happens all of a sudden in a frame. For example, significant and apparently instantaneous reorientation of the cone far beyond what preceding motions suggest is possible (SI movie 2, at timestamp 0.22), RNP extrusion suddenly in a single frame (SI movie 2, at timestamp 0.27), and simultaneous opening of all pentamers all at once starting in a single frame (SI movie 2, at timestamp 0.33). This almost makes the movie look generated from separate trajectories or discontinuous portions of the same trajectory. If movies have been edited for visual clarity (e.g., to skip over time when "nothing" is happening and focus on the exciting aspects), then the authors should state so in the captions. 

This is due to the rate at which trajectory frames are saved for movie generation for faster processing of the movies. We added the following in movie caption: 

“The movie frames correspond to snapshots every 250000 𝜏_CG.” 

(e) Figure 3c presents a time series of the degree of defects at pent-hex and hex-hex interfaces, but I do not understand the normalization. The authors state, "we represented the defects as the number of under-coordinated CA monomers of the hexamers at the pentamer-hexamer-pentamer and hexamer-hexamer interface as N_Pen-Hex and N_Hex-Hex ... Note that in N_Pen-Hex and N_Hex-Hex are calculated by normalizing by the total number of CA pentamer (12) and hexamer rings (209) respectively." Shouldn't the number of uncoordinated monomers be normalized by the number of that type of monomer, rather than the number of capsomers/rings? E.g., 12*5 and 209*6, rather than 12 and 209?

We prefer to continue with the current normalization, since typically in the HIV-1 literature capsids are represented as a collection of hexamers and pentamers (rather than total number of CA monomers).

(f) The authors state that "Although high computational cost precluded us from continuing these CG MD simulations, we expect these defects at the hexamer-hexamer interface to propagate the high curvature ends of the capsid." The defects being reported are apparently propagating from (not towards) the high curvature ends of the capsid. 

We corrected the statement as follows:

“Although high computational cost precluded us from continuing these CG MD simulations, we expect these defects at the hexamer-hexamer interface to propagate from the high curvature to low curvature end of the capsid.”

(g) The first half of the paper uses the color orange in figures to indicate LEN, but the second half uses orange to indicate defects, and this could be confusing for some readers. Both LEN and "defects" are simply a cluster of spheres, so highlighted defects appear to represent LEN without careful reading of captions.

We only show LEN in Figure 1, and in rest of the figures the bound LEN molecules are not shown for clarity. The defects are shown in a darker shade of orange (amber). 

(h) SI Figure S3 captions says "The CA monomers to which at least one LEN molecule is bound are shown in orange spheres. The CA monomers to which no LEN molecule is bound are shown in white spheres. " While in contradiction, the main-text Fig 2 says "The CA monomers to which at least one LEN molecule is bound are shown in white spheres. The CA monomers to which no LEN molecule is bound are shown in orange spheres. " One of these must be a typo.

We have corrected the erroneous caption in Fig. S3. The color scheme in Fig. 2 and Fig. S3 are now consistent.

(i) The authors state that: "CG MD simulations and live-cell imaging demonstrate that LEN-treated capsids dock at the NPC and rupture at the narrow end when bound to the central channel and then remain associated to the NPC after rupture." However, the live cell imaging data do not show where rupture occurs, such that this statement is at least partially false. It is also unclear that CG simulations show that cores remain bound following rupture, given that simulations were not extended to the timescale needed to observe this, again rendering the statement partially false.

We modified the statement as follows:

“CG MD simulations complemented by the outcome of live-cell imaging demonstrate that LENtreated capsids dock at the NPC and rupture at the narrow end when bound to the central channel and then remain associated with the NPC after rupture.”

(j) The authors state: "We previously demonstrated that the RNP complex inside the capsid contributes to internal mechanical strain on the lattice driven by CACTD-RNP interactions and condensation state of RNP complex (Hudait &Voth, 2024). " In that case, why do the present CG models detect no difference in results for condensed versus uncondensed RNP?

In our previous paper, the difference from condensation state of RNP complex appear only in the pill-shaped capsid, and not in the cone-shaped capsid. In this manuscript, we only investigated the cone-shaped capsid.

(k) The authors state: "The distribution demonstrates that the binding of LEN to the distorted lattice sites is energetically favorable. Since LEN localizes at the hydrophobic pocket between two adjoining CA monomers, it is sterically favorable to accommodate the incoming molecule at a distorted lattice site. This can be attributed to the higher available void volume at the distorted lattice relative to an ordered lattice, the latter being tightly packed. This also allows the drug molecule to avoid the multitude of unfavorable CA-LEN interactions and establish the energetically favorable interactions leading to a successful binding event. " What multitude of unfavorable interactions are the authors referring to? Data is not presented to substantiate the claim of increased void volume between hexamers in the distorted lattice. Capsomer distortion is shown as a schematic in Figure 6A rather than in the context of the actual model.

“What multitude of unfavorable interactions are the authors referring to?” We have now added the following sentence to clarify

“Here we denote unfavorable CA-LEN interactions as all interactions other than the electrostatic and van der Waal interactions that lead to CA-LEN binding (17).”

“In the distorted lattice, there is an increase of void volume is based on standard solid-state physics understanding. We added the word “likely” in the statement. “. This can likely be attributed to the higher available void volume at the distorted lattice relative to an ordered lattice, the latter being tightly packed (41).”

Moreover, in one of our previous manuscripts, we established that compressive or expansive strain induces more closely packed or expanded lattice (A. Yu et al., Strain and rupture of HIV-1 capsids during uncoating. Proceedings of the National Academy of Sciences 119, e2117781119 (2022)).

(l) The authors state that "These striated patterns also demonstrate deviations from ideal lattice packing. " What does ideal lattice packing mean in this context, where hexamers are in numerous unique environments in terms of curvature? What is the structural reference point?

The ideal lattice packing definition is provided in our previous manuscripts: 1. A. Yu et al., Strain and rupture of HIV-1 capsids during uncoating. Proceedings of the National Academy of Sciences 119, e2117781119 (2022), 2. A. Hudait, G. A. Voth, HIV-1 capsid shape, orientation, and entropic elasticity regulate translocation into the nuclear pore complex. Proceedings of the National Academy of Sciences 121, e2313737121 (2024).

These manuscripts are cited in the previous statement. The ideal lattice packing is defined based on lattice separations in each core (in cryo-ET and atomistic simulations) using a local order parameter, which measures the near-neighbor contacts of a particle. Moreover, the ideal packing reference is calculated from all available capsid shapes (cone, ellipsoid, and tubular), and takes into account different curvatures.

(m) If pentamer-hexamer interactions are weakened in the presence of LEN, why are differences at these interfaces not apparent in the Figure 6C data that shows stiffening of the interactions between capsomer subunits?

We have added a statement as follows:

“Based on our analysis, we hypothesize that LEN binding hyperstabilzes the CA hexamerhexamer interactions relative to CA hexamer-pentamer interaction.”

(n) The authors state: "Lattice defects arising from the loss of pentamers and cracks along the weak points of the hexameric lattice drive the uncoating of the capsid." The word rupture or failure should be used here rather than uncoating; it is unclear that the authors are studying the true process of uncoating and whether the defects induced by LEN binding relate in any way to uncoating. 

We have now changed “uncoating” to “rupture” throughout the manuscript.

(o) The authors state: " LEN-treated broken cores are stabilized by the interaction with the disordered FG-NUP98 mesh at the NPC." But no data is presented to demonstrate that capsid stability is increased by NUP98 interaction. In fact, the presented data could suggest the opposite since capsids in contact with NUP98 in the NPC appeared to rupture faster than freely diffusing capsids.

We have modified the statement as follows

“We hypothesize that LEN-treated broken cores are stabilized by the interaction with the disordered FG-NUP98 mesh at the NPC.”

(p) The authors state: "LEN binding stimulates similar changes in free capsids, but they occur with lower frequency on similar time scales, suggesting that the cores docked at the NPC are under increased stress, resulting in more frequent weakening of the hexamer-pentamer and hexamerhexamer interactions, as well as more nucleation of defects at the hexamer-hexamer Interface. ... Our results suggest that in the presence of the LEN, capsid docking into the NPC central channel will increase stress, resulting in more frequent breaks in the capsid lattice compared to free capsids." The first is a run-on sentence. The results shown support that LEN stimulates changes in free capsids to happen faster, but not more frequently. The frequency with which an event occurs is separate from the speed with which the event occurs.

We have fixed the run-on sentence.

The results shown support that LEN stimulates changes in free capsids to happen faster, but not more frequently. The frequency with which an event occurs is separate from the speed with which the event occurs.

We disagree with the reviewer. The statement was intended to provide a comparison between free capsid and NPC-bound capsid.

(q) The authors state: "A possible mechanistic pathway of capsid disassembly can be that multiple pentamers are dissociated from the capsid sequentially, and the remaining hexameric lattice remains stabilized by bound LEN molecules for a time, before the structural integrity of the remaining lattice is compromised." This statement is inconsistent with experimental studies that say LEN does not lead to capsid disassembly, and may even prevent disassembly as part of its disruption of proper uncoating (e.g., 10.1073/pnas.2420497122 previously published by the authors).

We disagree with the interpretation of the reviewer. Our interpretation based on our results is LEN binding accelerates capsid rupture (from pentamer-rich high curvature ends), and the rest of the broken hexameric lattice is hyperstabilized. Ultimately, lattice rupture will lead to release the RNP, and hence the intended goal of the drug is achieved.

(r) Finally, it remains a concern with the authors' work that the bottom-up solvent-free CG modeling software used in this and supporting works is not open source or even available to other researchers like other commonly used molecular dynamics software packages, raising significant questions about transparency and reproducibility.

The simulations were performed in LAMMPS, which is open source. This software is already stated in the Methods. Input data is provided upon request.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 1: In part B, it appears the middle panel was screenshotted from a ppt, given the red line underneath Lenacapavir. You can export it to an image instead.

The figure is fixed.

(2) Figure 6: In part A, the LEN_d in the graph is illegible. Also, in the panel next to it, it also appears to have been screenshotted from a ppt.

The figure is fixed.

(3) Page 6: There's an errant quotation mark at the end of a paragraph.

Removed the errant quotation

Reviewer #2 (Recommendations for the authors):

The code used to perform bottom-up solvent-free CG modeling simulations is not made available.

This is not true. LAMMPS was used as stated in Methods.

eLife Assessment

In this fundamental work Horne et al present compelling evidence that YbjP is a novel binding partner of the TolC channel protein. The YbjP is characterized using cryo-EM, and its role probed using pull-down experiments, in vivo crosslinking, functional assays along with phylogenetic analysis which are all properly performed and presented and support the main conclusions. While the study does not identify a clear role for this protein, the revised manuscript offers improved clarity and contributes invaluable insight into membrane transport and antimicrobial resistance.

Reviewer #2 (Public review):

This article focuses on the study of two E. coli tripartite efflux pumps, both using TolC as a partner in the outer membrane, namely MacAB-TolC and AcrABZ-TolC.

By preparing MacAB-TolC in Peptidiscs rather than in detergent for cryo-EM structure determination, they visualized an extra protein localized around TolC. The resolution was sufficient to build part of the structure, and using the AlphaFold2 database and DALI topology recognition program, they identified it as the lipoprotein YbjP. This protein has an anchorage in the outer membrane, and it was suggested that it could act as a support for TolC, which is the only OMF that does not have an N-terminal extension anchored in the outer membrane, which is very puzzling for the community working in this field of research.

Authors used a large number of different approaches to evaluate the importance of YbjP (structure, genomic evolution, microbiology, photocrosslink in vivo, proteomic profile), but did not succeed in finding it a clear role so far, even if it could be important depending on environmental stress. Nevertheless, their results, obtained with extreme rigour, are of main interest for the comprehension of the complexity of such systems and deserve publication.

Comments on revisions:

Thank you for clarifying the points that puzzled me concerning the crosslink experiments. This version does not need further modifications.

Author Response:

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

Reviewer #1 (Public review):

The presentation and especially main-text illustrative material seem to focus disproportionately on MacAB-TolC-YbjP complex, and the AcrABZ-TolC-YbjP is relegated to supplementary data which is somewhat confusing. There is no high-resolution side view of the AcrABZ-TolC-YbjP side-by-side to MacAB-TolC-YbjP which may be helpful to spot parallels and differences in the organisation of the two systems.

This was previously presented in Supplementary Figure S2. However, because the models were shown at a small scale, we have now included the comparison in a main manuscript (Figure 4). This figure presents AcrABZ-TolC-YbjP and MacAB-TolC-YbjP side-by-side, a structural alignment of TolC-YbjP in the two pumps, and close-up views of the interaction interface.

Supplementary Figure 2 may also be better presented in the main text, as it shows specific displacements of residues upon binding of the YbjP relative to the apo-complexes, although this can be left at the authors' discretion.

We added more text to describe the displacements of residues upon YbjP binding: ‘Nonetheless, the side chains of a few residues in TolC, which mainly correspond to positively charged amino acids (R18, R24, K214, R227, R234), reorient to interact with the YbjP lipoprotein partner (Figure 2B).’

Reviewer #1 (Recommendations for the authors):

The work is of high quality and requires minimal modifications, which are mentioned as suggestions above and are mostly connected to the illustrative material.

One additional suggestion, which is connected to the earlier BioRxiv preprint, the data seen in Fig 6 of the preprint seems to have been edited out from the current version, and perhaps can be included in a revised version, as it seems to support the "rapid adaptation under stress" role for YbjP, which currently is only speculatively mentioned in p.11, line 365 of the manuscript.

We acknowledge that the BioRxiv preprint Figure 6 can support the rapid adaptation under stress role for YbjP. However, upon sequencing the ΔybjP strain from the Keio collection used in the preprint, we identified a large deletion in the yecT-flhD region. We therefore generated a new ΔybjP strain without the yecT-flhD deletion and repeated the experiment. However, the results with the corrected strain did not support the previous conclusion, and these data were consequently removed in the current manuscript.

Reviewer #2 (Public review):

In Figure 3C, the experiment performed with AcrA is clear and the extra band appears at the proper size. On the right panel, it is clear that the crosslink doesn't work when pBPA is placed on residues too far from TolC. Only when introduced on N113 or T110 does a band appear.

This is in accordance with an interaction in vivo. Nevertheless, 17 + 54 = 71kDa, which is more than the two bands appearing on the gel. This difference in size migration can occur, but it is not clear when looking at Figure S3. In Figure S3a, the purified proteins are highlighted at approximately the expected size (≈20kDa instead of 17 for YbjP and between 56 and 60kDa in two bands for TolC instead of 54kDa). On the right panel, it seems that the bands are present exactly at the same position, instead of an upper band as expected for the crosslinked YbjP-TolC (at 71kDa). It would be clearer if having the control of the same sample without illumination, revealed by anti-TolC, to see the difference.

We thank the reviewer for pointing out this discrepancy. We identified an error in the molecular weight ladder, as one band was missing. This has now been corrected: YbjP migrates just below 17 kDa, consistent with Figure 3C. In addition, we previously reported a size of 54 kDa for TolC, whereas matured TolC, after signal peptide cleavage, is actually 52 kDa.

We believe that the differences in the apparent molecular weight observed in Figures 3A, 3C and S3 (now S2) mainly result from tagging and post-translation modifications.

In Figure 3A, we used the soluble construct His-YbjP_28-1711 (theoretical M_w ~18 kDa), as also done for the controls in Figures 3C and S3 (now S2). However, for the crosslinking samples, we used full-length His-tagged YbjP, which carries a post-translational lipid modification (theoretical M_w ~19 kDa, considering the protein lipidation). The presence of the lipid chains alters the migration as this species migrates at ~15 kDa (Fig 3A). Increased hydrophobicity, due here to YbjP lipidation, could accelerate the migration (Emmanuel et al. 2025 FEBS Open Bio).

In Figure 3A, we used the TolC-FLAG whose apparent M_w is ~52 kDa, as previously reported (Fig S3, Fitzpatrick et al. 2017). In Figure S3 (now S2), we used His-tagged TolC (theoretical M_w 55 kDa) for the control, which migrates above 56 kDa. In the crosslinking samples, however, we detect tag-free, endogenous TolC, with a theoretical M_w of ~51 kDa.

In conclusion, the crosslinked complex composed of lipidated FL YbjP (~15 kDa) and endogenous TolC (~51 kDa) would be expected to migrate at ~66 kDa, which is consistent with what is observed in Figures 3C and S3 (now S2).

A second point that could be discussed further is the comparison of the structure of the pump in the presence of the peptidoglycan with the images previously obtained by tomography. It is not totally clear to me if YbjP could have been positioned in these maps.

There is density corresponding to YbjP in the map obtained in the presence of peptidoglycan. To improve clarity, we have specified the location of the peptidoglycan relative to the pumps in the revised Figure 4, and Supplementary Figure S4, together with the position of YbjP. In both figures, the lipoprotein appears distant from the peptidoglycan density.

Reviewer #2 (Recommendations for the authors):

In addition, please add explanations in the legend of Figure 3C concerning the structures.

We added the following description of the structures: ‘As shown underneath, AcrA residues Q136 and Y137, proximal to TolC in the structure of the AcrABZ-TolC pump (PDB 5NG5), were replaced by pBPA. For YbjP, the two residues N113 and T110 proximal to TolC in the MacAB-TolC-YbjP complex (PDB 9QGY) and the three residues N43, N90 and H104 distal to TolC were mutated.’

It would be clearer if having the control of the same sample without illumination, revealed by anti-TolC, to see the difference.

As the amount of crosslinked material is low, samples were enriched via His-tag purification of YbjP prior to Western blotting. In the absence of illumination (see sample N113, UV-), no crosslink would be formed, and therefore TolC would not be co-purified.

In addition, some typo errors have been noted.

Table S1 minus is missing for the defocus range for AcrABZ-TolC-YbjP.

Thank you for noting the typo. We have added the minus sign.

Table S3, please specify what is N in the legend.

N is the stoichiometry parameter, which is now specified in the table legend.

Line 237, I suppose it has to refer to Figure S6, not S5.

Thank you for noting the error. We have verified the text matches the figures here and in the entire manuscript.

Several errors are present in the legend of Figure 6.

No letters are indicated for the different panels; line 841 must be C, F and I; the indicated colors for the differentially expressed proteins do not correspond to the volcano plots.

Thank you for suggesting the improvements for the labels. We have modified the plot accordingly.

Reference Glavier 2020 has been cited as Glacier on line 72.

We have modified the writing accordingly and checked the reference.

eLife Assessment

This is an important study that takes a key step towards understanding developmental disorders linked to mutations in the O-GlcNAc transferase enzyme by generating a mouse model harboring the C921Y mutation. While the mechanisms remain open, the study thoroughly examines behavioral and anatomical differences in these mice and provides convincing evidence for behavioral hyperactivity and learning/memory deficits, as well as phenotypic differences in skull and brain formation. This study will be of interest to those studying neurodevelopmental disorders and associated mechanisms.

Reviewer #1 (Public review):

This study established a C921Y OGT-ID mouse model, systematically demonstrating in mammals the pathological link between O-GlcNAc metabolic imbalance and neurodevelopmental disorders (cortical malformation, microcephaly) as well as behavioral abnormalities (hyperactivity, impulsivity, learning/memory deficits). Researchers comprehensively assessed the model phenotype through integrated multi-level analysis methods, including long-term behavioral monitoring, high-resolution brain structural imaging (micro-CT and MRI), histopathology, and quantitative proteomics.

The core strength of this study lies in its multimodal experimental design. The evidence chain spanning in vivo behavior, brain structure, and molecular characteristics demonstrates high consistency and correlation. Of particular note is the combination of non-invasive behavioral tracking with quantitative neuroimaging techniques, providing objective validation for the observed phenotypes. The findings support the authors' core conclusion: O-GlcNAc homeostasis imbalance correlates with neurodevelopmental deficits, including structural abnormalities in specific brain regions and altered cognitive behaviors. Furthermore, this model reproduces certain clinical features observed in human patients.

Nevertheless, several avenues remain open for further exploration. For instance, sample sizes in certain omics analyses remain relatively small, and investigations into downstream molecular mechanisms are still confined to the level of correlation-direct causal validation through genetic or pharmacological interventions is still required. Furthermore, as this model focuses on a single recurrent mutation, the generalizability of its findings to other OGT-ID variants remains to be verified.

It provides the first actionable vertebrate model for neurodevelopmental disorders with unclear mechanisms, filling a critical gap in this field. The multidimensional research methods established in the paper-such as the digital behavioral phenotyping workflow-also offer valuable references for related disease studies.

Reviewer #2 (Public review):

Summary:

The authors are trying to understand why certain mutants of O-GlcNAc transferase (OGT) appear to cause developmental disorders in humans. As an important step towards that goal, the authors generated a mouse model with one of these mutations that disrupts OGT activity. They then go on to test these mice for behavioral differences, finding that the mutant mice exhibit some signs of hyperactivity and differences in learning and memory. They then examine alterations to the structure of the brain and skull, and again find changes in the mutant mice that have been associated with developmental disorders. Finally, they identify proteins that are up or down regulated between the two mice as potential mechanisms to explain the observations.

Strengths:

The major strength of this manuscript is the creation of this mouse model, as a key step in beginning to understand how OGT mutants cause developmental disorders. This line will prove important for not only the authors but other investigators as well, enabling the testing of various hypotheses and potentially treatments. The experiments are also rigorously performed and the conclusions are well supported by the data.

Weaknesses:

The only weakness is a lack of mechanistic insight. However, this certainly may come in the future through more targeted experimentation using this mouse model. I do not recommend that these experiments need to be performed in this manuscript.

Comments on revisions:

The authors have addressed all of my suggestions proactively.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

This study established a C921Y OGT-ID mouse model, systematically demonstrating in mammals the pathological link between O-GlcNAc metabolic imbalance and neurodevelopmental disorders (cortical malformation, microcephaly) as well as behavioral abnormalities (hyperactivity, impulsivity, learning/memory deficits). However, critical flaws in the current findings require resolution to ensure scientific rigor.

The most concerning finding appears in Figure S12. While Supplementary Figure S12 demonstrates decreased OGA expression without significant OGT level changes in C921Y mutants via Western blot/qPCR, previous reports (Florence Authier, et al., Dis Model Mech. 2023) described OGT downregulation in Western blot and an increase in qPCR in the same models. The opposite OGT expression outcomes in supposedly identical mouse models directly challenge the model's reliability. This discrepancy raises serious concerns about either the experimental execution or the interpretation of results. The authors must revalidate the data with rigorous controls or provide a molecular biology-based explanation.

We thank the reviewer for their time and effort in improving the quality of our manuscript.

We would like to point out that the results presented in the previous Fig. S12 (now Fig. S13) are from different ages of the mice and restricted to the prefrontal cortex, compared to the previous report (Florence Authier, et al., Dis Model Mech. 2023) where we showed OGT and OGA mRNA/protein expression in total brain homogenates. In this previous study, we observed a significant reduction in OGT protein levels while OGT mRNA levels were significantly increased in the brains of 3 months old mutant C921Y compared to WT controls. However, in our current study (Figure S12, now S13), OGA and OGT mRNA/protein expression have been a) restricted to the pre-frontal cortex and b) are from 4 months old male mice. Therefore, a direct comparison of findings from total brain vs. prefrontal cortex would be speculative. In our present work, OGT protein levels are not changed in the pre-frontal cortex, while OGT mRNA levels are increased (similarly to the total brain data), albeit not significantly.

It is plausible that the different levels of OGT protein expression in total brain (previous study) and prefrontal cortex (current study) potentially reflect regional differences in the regulation of OGT protein levels/stability, since OGT mRNA levels are increased in both cases. This notion is also supported by additional analyses in three other brain regions (hippocampus, striatum and cerebellum) and these data are now included in Figures S13 and S14.

A few additional comments to the author may be helpful to improve the study.

Major

(1) While this study systematically validated multi-dimensional phenotypes (including neuroanatomical abnormalities and behavioral deficits) in OGT C921Y mutant mice, there is a lack of relevant mechanisms and intervention experiments. For example, the absence of targeted intervention studies on key signaling pathways prevents verification of whether proteomics-identified molecular changes directly drive phenotypic manifestations.

We agree with the reviewer that the suggested experiments would further strengthen our work. However, the extensive nature of the suggested studies would result in considerable delay in sharing this work with the scientific and patient communities. Nevertheless, we appreciate the reviewers’ comment and will continue to work along these lines, and report in a follow up manuscript in the future.

(2) Although MRI detected nodular dysplasia and heterotopia in the cingulate cortex, the cellular basis remains undefined. Spatiotemporal immunofluorescence analysis using neuronal (NeuN), astrocytic (GFAP), and synaptic (Synaptophysin) markers is recommended to identify affected cell populations (e.g., radial glial migration defects or intermediate progenitor differentiation abnormalities).

Following the reviewers’ suggestion, we have performed additional analyses to identify the cellular composition of the observed nodular dysplasia using neuronal and glial markers. These new analyses indicate that the nodular collections in the layers II/III were predominantly neurons, for example see cresyl violet (Fig. 6E). Moreover, we have also performed immunofluorescence imaging using NeuN and GFAP (Fig. 6G-H), which reflect that the dystrophic collections are predominantly neurons. To further corroborate these findings, we have also performed multiplex IHC analyses, presented in Fig. S12, which indicate that: i) the nodular cortical malformations were populated by neurons and oligodendrocytes and ii) predominantly affected layers II-V, as reflected by the distribution of neuronal markers Reelin and POU class 3 homeobox 2 (POU3F2), and collectively (Fig. 6 and Fig. S12) reflect neuronal disorganisation due to migration defects rather than differentiation defects. We appreciate the reviewers’ suggestion to perform spatiotemporal analyses of these cellular features; however, tissue from defined stages of development is not available. 

(3) While proteomics revealed dysregulation in pathways including Wnt/β-catenin and mTOR signaling, two critical issues remain unresolved: a) O-GlcNAc glycoproteomic alterations remain unexamined; b) The causal relationship between pathway changes and O-GlcNAc imbalance lacks validation. It is recommended to use co-immunoprecipitation or glycosylation sequencing to confirm whether the relevant proteins undergo O-GlcNAc modification changes, identify specific modification sites, and verify their interactions with OGT.

We agree with the referee that these experiments would further strenghten the work. However, we respectfully point out that the inference that altered proteins must themselves be O-GlcNAc modified is not necessarily correct. For instance, O-GlcNAcylation of unknown protein kinase X, E3 ligase/DUB, Y or transcription factor Z could indirectly affect these pathways/proteins. Nevertheless, we have performed further experiments to explore whether Wnt/β-catenin and mTOR signalling are functionally affected, as pointed out by the referee. In the qPCR analyses, we did not observe significant changes in expression of Wnt target genes (Cdkn1a, Ccnd1, Myc, Ramp3, Tfrc), neither in protein levels of key proteins involved in Wnt/β-catenin (non-phosphorylated β-catenin) and mTOR (phosphorylated rpS6) signalling by western blots (data not shown). These results suggest that both pathways are not functionally deregulated in prefrontal cortex of adult OGT^C921Y mice to a significant extent.

(4) Given that OGT-ID neuropathology likely originates embryonically, we recommend serial analyses from E14.5 to P7 to examine cellular dynamics during critical corticogenesis phases.

We appreciate the reviewers’ suggestion to perform spatiotemporal analyses of these cellular dynamics; however, tissue from defined stages of development is not available. As stated above, we want to share our current findings with the scientific and patient communities in a timely manner, and the suggested experiments could form the foundation of a follow up study in the future.

(5) The interpretation of Figure 8A constitutes overinterpretation. Current data fail to conclusively demonstrate impairment of OGT's protein interaction network and lack direct evidence supporting the proposed mechanisms of HCF1 misprocessing or OGA loss.

Thank you for the comment. To avoid misleading the readers, we have removed panel A from the previous version of Figure 8 and updated the version of record.

Reviewer #2 (Public review):

Summary:

The authors are trying to understand why certain mutants of O-GlcNAc transferase (OGT) appear to cause developmental disorders in humans. As an important step towards that goal, the authors generated a mouse model with one of these mutations that disrupts OGT activity. They then go on to test these mice for behavioral differences, finding that the mutant mice exhibit some signs of hyperactivity and differences in learning and memory. They then examine alterations to the structure of the brain and skull and again find changes in the mutant mice that have been associated with developmental disorders. Finally, they identify proteins that are up- or down-regulated between the two mice as potential mechanisms to explain the observations.

Strengths:

The major strength of this manuscript is the creation of this mouse model, as a key step in beginning to understand how OGT mutants cause developmental disorders. This line will prove important for not only the authors but other investigators as well, enabling the testing of various hypotheses and potentially treatments. The experiments are also rigorously performed, and the conclusions are well supported by the data.

Weaknesses:

The only weakness identified is a lack of mechanistic insight. However, this certainly may come in the future through more targeted experimentation using this mouse model.

We agree with the reviewer that the suggested experiments would further strengthen our work. However, the extensive nature of the suggested studies would result in considerable delay in sharing this work with the scientific and patient communities. Nevertheless, we appreciate the reviewers’ comment and will continue to work along these lines, and report in a follow up manuscript in the future.

Recommendations for the authors:

Editor's note:

Should you choose to revise your manuscript, if you have not already done so, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and, where appropriate, 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

Statistics including exact p-values have been included in the main text for all key questions where appropriate.

Reviewer #1 (Recommendations for the authors):

(1) In Figure 1F, the y-axis labels and scale values are partially obscured by graphical elements, compromising accurate interpretation of the data range.

Panel 1F has been adjusted to make the y-axis label visible.

(2) Regarding the histological analyses in Figure 6, the current H&E staining and Luxol Fast Blue myelin staining results lack age-matched wild-type control samples processed in parallel, which undermines experimental comparability. To enhance methodological rigor, control group staining results should be displayed adjacent to each experimental group image.

The original Figure 6 already contained comparison between WT and OGT^C921Y tissues. The Figure has been updated with additional data from the WT and C921Y mutant groups shown side by side.

Reviewer #2 (Recommendations for the authors):

(1) I believe that Figures S1 and S2 were switched during the submission. The legends are correct, so the authors should just be careful with the order when they upload the final versions.

Figures S1 and S2 have been re-ordered.

(2) On page 18, the authors state, "Although no significant changes in the expression of OGT were observed in OGTC921Y cortex (Figure S12A, C), there was a significant increase in OGT/OGA protein ratio in OGTC921Y mice (Fig. S12D). As a functional consequence, global O-GlcNAcylation of proteins in the brain was drastically impaired in the OGTC921Y brain compared to WT (Figure S12E, F).

To me, this statement suggests that the incorrect ratio of OGT to OGA is responsible for the altered O-GlcNAc levels. I think this is missing important information. The authors are, I'm sure, aware that OGT and OGA expression is linked to O-GlcNAc levels. I think it would be better to describe the situation here as the tissue attempting to respond to lower OGT activity by lowering OGA levels. However, the tissue is not fully successful, resulting in lower overall O-GlcNAc levels as seen by RL2. If the difference were only driven by the OGT/OGA ratio, one would expect increased O-GlcNAc levels due to decreased OGA. I think it is important to point out more details here for non-expert readers.

Thank you for the insightful comment, we have included these aspects in the revised text, please see page 20.

(3) I am a little surprised that the authors did not explore differences in O-GlcNAc-modified proteins through a more targeted enrichment of these proteins for analysis of potential modification differences, in addition to just changes in protein abundance.

We agree that these experiments would further strengthen the work. However, it is not known yet whether OGT-CDG is caused by loss of O-GlcNAc modification on specific proteins or due to as yet to decipher mechanisms (e.g. OGT interactome, HCF1 processing, feedback on OGA levels) which we are not able to confirm in the current manuscript. Therefore, as a starting point, we have performed whole proteome analysis to establish candidate hypothesis which could lead to discovering cellular and molecular mechanisms underlying OGT-CDG. Lastly, we appreciate the reviewers’ comment and will continue to work along these lines, and report in a follow up manuscript in the future.

eLife Assessment

This important study presents a compelling link between nutrient signaling and chromosome regulation, demonstrating that reduced activity in a central nutrient-sensing pathway improves chromosome stability and alters gene expression through effects on cohesin. The convincing evidence from a combination of genetic, biochemical and cell biological approaches supports a model in which TORC1-dependent phosphorylation of Mis4 and the cohesin subunit Psm1/Smc1 can modulate cohesin loading to enhance faithful chromosome transmission. While the underlying mechanisms and biological importance of this newly described circuit are not yet fully known, the overall body of evidence is strong and supports the main conclusions.

Reviewer #1 (Public review):

Summary:

In this study, Besson et al. investigate how environmental nutrient signals regulate chromosome biology through the TORC1 signaling pathway in Schizosaccharomyces pombe. Specifically, the authors explore the impact of TORC1 on cohesin function-a protein complex essential for chromosome segregation and transcriptional regulation. Through a combination of genetic screens, biochemical analysis, phospho-proteomics, and transcriptional profiling, they uncover a functional and physical interaction between TORC1 and cohesin. The data suggest that reduced TORC1 activity enhances cohesin binding to chromosomes and improves chromosome segregation, with implications for stress-responsive gene expression, especially in subtelomeric regions.

Strengths:

This work presents a compelling link between nutrient sensing and chromosome regulation. The major strength of the study lies in its comprehensive and multi-disciplinary approach. The authors integrate genetic suppression screens, live-cell imaging, chromatin immunoprecipitation, co-immunoprecipitation, and mass spectrometry to uncover the functional connection between TORC1 signaling and cohesin. The use of phospho-mutant alleles of cohesin subunits and their loader provides mechanistic insight into the regulatory role of phosphorylation. The addition of transcriptomic analysis further strengthens the biological relevance of the findings and places them in a broader physiological context. Altogether, the dataset convincingly supports the authors' main conclusions and opens up new avenues of investigation.

Points that remain open but are appropriately discussed by the authors:

(1) The authors propose that nutrient status influences cohesin regulation. While this is not directly tested under defined nutrient conditions (e.g., by systematically examining cohesin dynamics or phosphorylation across nutrient states), the rationale is well explained in the text, and the study provides a strong foundation for addressing this question in future work.

(2) The upstream signaling cascade downstream of TORC1 remains to be fully elucidated. In particular, the identity of the relevant kinases (e.g., whether Sck1/Sck2 or other effectors are involved) and whether TORC1 directly phosphorylates Mis4 or Psm1 are not resolved. The authors acknowledge these mechanistic gaps, which represent logical next steps rather than shortcomings of the current study.

Reviewer #2 (Public review):

Summary:

In this study the authors follow up on a previous suppressor screen of a temperature-sensitive allele of mis4 (mis4-G1487D), the cohesin loading factor in S. pombe, and identify additional suppressor alleles tied to the S. pombe TORC1 complex. Their analysis suggests that these suppressor mutations attenuate TORC1 activity while enhanced TORC1 activity is deleterious in this context. Suppression of TORC1 activity also ameliorates chromosome segregation and spindle defects observed in the mis4-G1487D strain, although some more subtle effects are not reconstituted. The authors provide evidence that this genetic suppression is also tied to the reconstitution of cohesin loading. Moreover, disrupting TORC1 also enhances Mis4/cohesin association with chromatin (likely reflecting enhanced loading) in WT cells while rapamycin treatment can enhance the robustness of chromosome transmission. These effects likely arise directly through TORC1 or its downstream effector kinases as TORC1 co-purifies with Mis4 and Rad21; these factors are also phosphorylated in a TORC1-dependent fashion. Disrupting Sck2, a kinase downstream of TORC1, also suppresses the mis4-G1487D allele while simultaneous disruption of Sck1 and Sck2 enhances cohesin association with chromatin, albeit with differing effects on phosphorylation of Mis4 and Psm1/Scm1. Phosphomutants of Mis4 and Psm1 that mimic observed phosphorylation states identified by mass spectrometry that are TORC1-dependent also suppressed phenotypes observed in the mis4-G1487D background. Lastly, the authors provide evidence that the mis4-G1487D background and TORC1 mutant backgrounds display an overlap in the dysregulation of genes that respond to environmental conditions.

Overall, the authors provide compelling evidence from genetics, biochemistry and cell biology to support a previously unknown mechanism by which nutrient sensing regulates cohesin loading with implications for the stress response. The technical approaches are generally sound, well-controlled, and comprehensive.

The specific points that I raised in the first review have been addressed by changes/additions to the manuscript or have been determined to be beyond the scope of the study by the authors.

One major question that remains open is the relationship between local changes in cohesin loading and gene expression through this TORC1 regulatory signaling pathway and the details of the underlying mechanisms.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study, Besson et al. investigate how environmental nutrient signals regulate chromosome biology through the TORC1 signaling pathway in Schizosaccharomyces pombe. Specifically, the authors explore the impact of TORC1 on cohesin function - a protein complex essential for chromosome segregation and transcriptional regulation. Through a combination of genetic screens, biochemical analysis, phospho-proteomics, and transcriptional profiling, they uncover a functional and physical interaction between TORC1 and cohesin. The data suggest that reduced TORC1 activity enhances cohesin binding to chromosomes and improves chromosome segregation, with implications for stress-responsive gene expression, especially in subtelomeric regions.

Strengths:

This work presents a compelling link between nutrient sensing and chromosome regulation. The major strength of the study lies in its comprehensive and multi-disciplinary approach. The authors integrate genetic suppression screens, live-cell imaging, chromatin immunoprecipitation, co-immunoprecipitation, and mass spectrometry to uncover the functional connection between TORC1 signaling and cohesin. The use of phospho-mutant alleles of cohesin subunits and their loader provides mechanistic insight into the regulatory role of phosphorylation. The addition of transcriptomic analysis further strengthens the biological relevance of the findings and places them in a broader physiological context. Altogether, the dataset convincingly supports the authors' main conclusions and opens up new avenues of investigation.

Weaknesses:

While the study is strong overall, a few limitations are worth noting. The consistency of cohesin phosphorylation changes under different TORC1-inhibiting conditions (e.g., genetic mutants vs. rapamycin treatment) is unclear and could benefit from further clarification. The phosphorylation sites identified on cohesin subunits do not match known AGC kinase consensus motifs, raising the possibility that the modifications are indirect. The study relies heavily on one TORC1 mutant allele (mip1-R401G), and additional alleles could strengthen the generality of the findings. Furthermore, while the results suggest that nutrient availability influences cohesin function, this is not directly tested by comparing growth or cohesin dynamics under defined nutrient conditions.

We thank the reviewer for his overall positive assessment and constructive criticism. We broadly agree with the few limitations he pointed out, which we will comment on below.

(1) The consistency of cohesin phosphorylation changes under different TORC1-inhibiting conditions (e.g., genetic mutants vs. rapamycin treatment) is unclear and could benefit from further clarification.

The basis of our study was to search for suppressor mutants, a situation in which an unviable strain becomes viable. It turns out that the suppressor mutants affect TORC1, necessarily in a partial manner given that TORC1 kinase activity is essential for proliferation. Likewise rapamycin partially inhibits TORC1 and does not prevent proliferation of wild-type S. pombe cells. TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. In addition, it is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al., 2013, PMID: 23888043). Therefore, both hypomorphic TORC1 genetic mutants and rapamycin treatment result in partial inhibition of TORC1 kinase activity. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. Nevertheless, both conditions suppress the heatsensitive phenotype of the mis4 mutant, although the suppressor effect of rapamycin is weaker. Consequently, some phosphorylation sites involved in mis4-ts suppression may behave similarly in rapamycin and TORC1 mutants (i.e. Psm1-S1022), while others (i.e. Mis4-183) may behave differently.

It is clear that there are phenotypic differences between the suppression of mis4-ts by rapamycin treatment or by genetic alteration of TORC1. This can be seen also in our ChIP analysis of Rad21 distribution at CARs. The trend is upward, but the pattern is not identical. We have added the following text to summarize the above considerations:

“It is important to note at this stage that, although rapamycin and TORC1 mutants both decrease TORC1 kinase activity, the two are not equivalent. The mechanisms by which TORC1 kinase activity is reduced are different, and TORC1 mutants suppress the mis4G1487D phenotype more effectively than rapamycin. It is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al, 2013). TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. It is therefore remarkable that negative regulation of TORC1 by rapamycin or a genetic mutation both alleviate mis4G14878D phenotypes and have a fairly similar effect on cohesin dynamics.”

(2) The phosphorylation sites identified on cohesin subunits do not match known AGC kinase consensus motifs, raising the possibility that the modifications are indirect.

The genetic and biochemical analyses provided in this study show that the AGC kinases Sck1 and Sck2 influence cohesin phosphorylation and function. Whether Sck1, Sck2 or TORC1 directly phosphorylates cohesin components are the next questions to address. The fact that the phosphorylation of Psm1-S1022 and Mis4-S183 were never abolished in the sck1-2 mutants may suggest they are indirectly involved. This should be taken with caution because we have been using deletion mutants. In this situation, cells adapt and other kinases may substitute, at least partially (Plank et al, 2020, PMID: 32102971). Asking whether cohesin components display consensus sites for AGC kinases is a complementary approach. The consensus site for Sck1 and Sck2 is unknown. If we assume some conservation with budding yeast SCH9, the consensus sequence would be RRxS/T. Psm1S1022 (DQMSP) and Mis4-S183 (QLCSP) do not fit the consensus. However, this kind of information should be taken with care as many SCH9-dependent phosphorylation sites did not fall within the consensus in a study using analogue-sensitive AGC kinases and phosphoproteomics (Plank et al, 2020, PMID: 32102971). Alternatively, Sck1-2 may regulate other kinases. Indeed Psm1-S1022 and Mis4-183 lie within CDK consensus sites and Psm1-S1022 phosphorylation is Pef1-dependent. In summary, yes, the changes may be indirect, that remains to be seen, but in any case they are influenced by TORC1 signalling. The following paragraph was added:

“The consensus site for Sck1 and Sck2 is unknown. If we assume some conservation with budding yeast SCH9, the consensus sequence would be RRxS/T. Psm1-S1022 (DQMSP) and Mis4-S183 (QLCSP) do not fit the consensus. However, this should be taken with care as many SCH9-dependent phosphorylation sites did not fall within the consensus in a study using analogue-sensitive AGC kinases and phosphoproteomics (Plank et al, 2020). Alternatively, Sck1-2 may regulate other kinases. Indeed Psm1-S1022 and Mis4-183 lie within CDK consensus sites and Psm1-S1022 phosphorylation is Pef1-dependent.”

(3) The study relies heavily on one TORC1 mutant allele (mip1-R401G), and additional alleles could strengthen the generality of the findings.

It is true that we focused our attention on mip1-R401G, which is present in all the experiments presented. That said, other alleles were used in one or more figures. Five mip1 alleles and one tor2 allele were identified as mis4-ts suppressors (Fig. 1). We have also shown that another mip1 allele, mip1-Y533A, created by another group (Morozumi et al, 2021), is also a suppressor of mis4-ts and affects the phosphorylation of Mis4-S183 and Psm1-S1022 (Fig. 1, Figure 5—figure supplement 1). To this we can add the effect of mutants that render TORC1 hyperactive (Fig. 1E, Fig. 2H) as well as AGC kinase mutants (Figure 5—figure supplement 3.). And finally, the effect of rapamycin. So yes, mip1-R401G has been used extensively, but we have still broadly covered the TORC1 signalling pathway.

(4) Furthermore, while the results suggest that nutrient availability influences cohesin function, this is not directly tested by comparing growth or cohesin dynamics under defined nutrient conditions

We agree that studying the dynamics of cohesin, genome folding and gene expression in relation to nutrient availability is a very exciting topic, and we hope to address these issues in detail in the future.

Reviewer #2 (Public review):

Summary:

In this study, the authors follow up on a previous suppressor screen of a temperaturesensitive allele of mis4 (mis4-G1487D), the cohesin loading factor in S. pombe, and identify additional suppressor alleles tied to the S. pombe TORC1 complex. Their analysis suggests that these suppressor mutations attenuate TORC1 activity, while enhanced TORC1 activity is deleterious in this context. Suppression of TORC1 activity also ameliorates chromosome segregation and spindle defects observed in the mis4-G1487D strain, although some more subtle effects are not reconstituted. The authors provide evidence that this genetic suppression is also tied to the reconstitution of cohesin loading. Moreover, disrupting TORC1 also enhances Mis4/cohesin association with chromatin (likely reflecting enhanced loading) in WT cells, while rapamycin treatment can enhance the robustness of chromosome transmission. These effects likely arise directly through TORC1 or its downstream effector kinases, as TORC1 co-purifies with Mis4 and Rad21; these factors are also phosphorylated in a TORC1-dependent fashion. Disrupting Sck2, a kinase downstream of TORC1, also suppresses the mis4-G1487D allele while simultaneous disruption of Sck1 and Sck2 enhances cohesin association with chromatin, albeit with differing effects on phosphorylation of Mis4 and Psm1/Scm1. Phosphomutants of Mis4 and Psm1 that mimic observed phosphorylation states identified by mass spectrometry that are TORC1-dependent also suppressed phenotypes observed in the mis4-G1487D background. Last, the authors provide evidence that the mis4-G1487D background and TORC1 mutant backgrounds display an overlap in the dysregulation of genes that respond to environmental conditions, particularly in genes tied to meiosis or other "stress".

Overall, the authors provide compelling evidence from genetics, biochemistry, and cell biology to support a previously unknown mechanism by which nutrient sensing regulates cohesin loading with implications for the stress response. The technical approaches are generally sound, well-controlled, and comprehensive.

Specific Points:

(1) While the authors favor the model that the enhanced cohesin loading upon diminished TORC1 activity helps cells to survive harsh environmental conditions, as starvation of S. pombe also drives commitment to meiosis, it seems as plausible that enhanced cohesin loading is related to preparing the chromosomes to mate.

(2) Related to Point 1, the lab of Sophie Martin previously published that phosphorylation of Mis4 characterizes a cluster of phosphotargets during starvation/meiotic induction (PMID: 39705284). This work should be cited, and the authors should interrogate how their observations do or do not relate to these prior observations (are these the same phosphosites?).

We agree this is a possibility and the following paragraph was added in the discussion section:

“TORC1-based regulation of cohesin may be relevant to preparing cells for meiosis. Since nitrogen deprivation stimulates meiosis initiation, subsequent TORC1 down-regulation may regulate the cohesin complex, preparing the chromosomes for fusion and meiosis. A recent phosphoproteomic study conducted by Sophie Martin's laboratory showed that Mis4-S107 phosphorylation increases during cellular fusion (Bérard et al, 2024). It is unknown whether the phosphorylation of S107 is controlled by TORC1 signalling. As the phosphorylation of Mis4-S183 and Psm1-S1022 was not detected in these experiments, the potential involvement of the TORC1-cohesin axis in the sexual programme remains to be investigated.”

(3) It would be useful for the authors to combine their experimental data sets to interrogate whether there is a relationship between the regions where gene expression is altered in the mis4-G1487D strain and changes in the loading of cohesin in their ChIP experiments.

(4) Given that the genes that are affected are predominantly sub-telomeric while most genes are not affected in the mis4-G1487D strain, one possibility that the authors may wish to consider is that the regions that become dysregulated are tied to heterochromatic regions where Swi6/HP1 has been implicated in cohesin loading

We agree that it would be interesting to see if there are correlations between cohesin positioning, heterochromatin and gene expression. That said, this would need to be done at the whole-genome level and include many other parameters (genome folding, histone modifications, Pol2 occupancy). These issues require substantial investment and may be addressed in a follow-up project.

(5) It would be helpful to show individual data points from replicates in the bar graphs - it is not always clear what comprises the data sets, and superplots would be of great help.

We verified that the figure captions clearly indicate the data sets considered, their mean, standard deviation, and statistical analysis method. As for the type of plot, we used the tools at our disposal.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Besson et al. investigate how the nutrient-responsive TORC1 signaling pathway modulates cohesin function in S. pombe. Using a genetic screen, the authors identify TORC1 mutants that suppress the thermosensitive growth defects of a cohesin loader mutant (mis4-G1487D). They show that reducing TORC1 activity-either genetically or pharmacologically-enhances cohesin binding to chromosomal sites (CARs), improves chromosome segregation, and alters the phosphorylation state of cohesin and its loader. They also show, through coimmunoprecipitation, that TORC1 and cohesin physically associate, and that this functional interaction extends to the transcriptional regulation of stress-responsive, subtelomeric genes. Together, the data suggest that environmental cues influence chromosome stability and gene expression via a TORC1-cohesin axis.

Overall, the study is well-supported by thoughtful genetic epistasis analyses and a combination of genetic, biochemical, cell biological, and transcriptomic approaches. While not all data are equally strong, the cumulative evidence convincingly supports the authors' conclusions.

Specific Concerns and Suggestions

(1) Figure 2A - Division rates of wild-type and mip1-R401G cells are missing and should be provided for proper comparison.

This is now done in revised Figure 2A. We also made a change in the manuscript, replacing “The mip1-R401G mutation efficiently suppressed the proliferation and viability defects (Figure 2A)” by “The mip1-R401G mutation efficiently attenuated the proliferation and viability defects (Figure 2A)”, to acknowledge the fact that the proliferation rate did not return to wild-type levels.

(2) Figure 3 - Figure Supplement 1 - The authors claim that "Rapamycin treatment during a single cell cycle provoked a similar effect although less pronounced." However, for most CARs, the effect appears insignificant. This should be acknowledged in the text.

The text has been changed accordingly:

“Rapamycin treatment during a single cell cycle provoked a similar stimulation of Rad21 binding at CARs (Figure 3—figure supplement 1), albeit with noticeable differences. In mis4+ cells, both mip1-R401G and rapamycin induced a significant increase in Rad21 binding at several CARs (tRNA-left, cc2, 3323, NTS, Tel1-R). However, some CARs that exhibited increased Rad21 binding in the mip1 mutant did not respond significantly to rapamycin (dg2-R, tRNA-R). Conversely, rapamycin (but not mip1-R401G) induced a significant increase in Rad21 binding at imr2-L and CAR1806 (Figure 3D and Figure 3— figure supplement 1). In the mis4-G1487D mutant background, mip1-R401G induced a significant increase in Rad21 binding at all examined sites (Figure 3B). Similarly, rapamycin did increase Rad21 binding at all sites but only at the Tel1-R site did this reach statistical significance (Figure 3—figure supplement 1).”

(3) Figure 4 - The analysis of interactions between TORC1 and the cohesin complex is somewhat limited. The authors may wish to test interactions between Mip1 and cohesin subunits (e.g., Rad21). More interestingly, it would be valuable to explore whether MIP1 mutations that suppress cohesin mutants affect the interaction between Tor2 and Rad21.

We have added some additional data that answer this question (Figure 4—figure supplement 1) and a paragraph in the manuscript:

“Tor2, the kinase subunit of TORC1, is particularly well detected in Rad21 and Mis4 coimmunoprecipitation experiments (Figure 4 and Figure 4—figure supplement 1). To determine whether the R401G mutation in Mip1 affects these interactions, coimmunoprecipitation experiments were repeated in both the mip1-R401G and mip1+ contexts. The data obtained indicate that Tor2 co-immunoprecipitation with Mis4 and Rad21 is largely unaffected by the mip1-R401G mutation (Figure 4—figure supplement 1). If mip1-R401G affects the regulation of cohesin by TORC1, this does not appear to stem from a gross defect in their interaction, at least at this level of resolution.”

(4) Figure 5 - There appears to be a lack of correlation between cohesin subunit phosphorylation in TORC1-reducing mutants and in response to rapamycin. The reason for this discrepancy is unclear.

This point was addressed in the previous section (Public review, reviewer 1, point 1). The response is pasted below:

The basis of our study was to search for suppressor mutants, a situation in which an unviable strain becomes viable. It turns out that the suppressor mutants affect TORC1, necessarily in a partial manner given that TORC1 kinase activity is essential for proliferation. Likewise rapamycin partially inhibits TORC1 and does not prevent proliferation of wild-type S. pombe cells. TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. In addition, it is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al., 2013, PMID: 23888043). Therefore, both hypomorphic TORC1 genetic mutants and rapamycin treatment result in partial inhibition of TORC1 kinase activity. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. Nevertheless, both conditions suppress the heatsensitive phenotype of the mis4 mutant, although the suppressor effect of rapamycin is weaker. Consequently, some phosphorylation sites involved in mis4-ts suppression may behave similarly in rapamycin and TORC1 mutants (i.e. Psm1-S1022), while others (i.e. Mis4-183) may behave differently.

It is clear that there are phenotypic differences between the suppression of mis4-ts by rapamycin treatment or by genetic alteration of TORC1. This can be seen also in our ChIP analysis of Rad21 distribution at CARs. The trend is upward, but the pattern is not identical. We have added the following text to summarize the above considerations:

“It is important to note at this stage that, although rapamycin and TORC1 mutants both decrease TORC1 kinase activity, the two are not equivalent. The mechanisms by which TORC1 kinase activity is reduced are different, and TORC1 mutants suppress the mis4G1487D phenotype more effectively than rapamycin. It is known that bona fide TORC1 substrates respond differently to rapamycin. Some phosphosites show acute sensitivity, while others are less sensitive or even insensitive (Kang et al, 2013). TORC1 mutants cause a constitutive decrease in activity with possible adaptive effects, whereas rapamycin is applied for a single cell cycle. While the lists of affected TORC1 substrates may overlap, they are unlikely to be identical. Furthermore, the phosphorylation level of the relevant substrates is not necessarily altered to the same extent. It is therefore remarkable that negative regulation of TORC1 by rapamycin or a genetic mutation both alleviate mis4G14878D phenotypes and have a fairly similar effect on cohesin dynamics.”

(5) The phosphorylation sites examined on cohesin subunits are not canonical AGC kinase consensus motifs, suggesting they are unlikely to be direct targets of Sck1 or Sck2. I suggest that this point should be mentioned in the manuscript.

This is now done:

“The consensus site for Sck1 and Sck2 is unknown. If we assume some conservation with budding yeast SCH9, the consensus sequence would be RRxS/T. Psm1-S1022 (DQMSP) and Mis4-S183 (QLCSP) do not fit the consensus. However, this should be taken with care as many SCH9-dependent phosphorylation sites did not fall within the consensus in a study using analogue-sensitive AGC kinases and phosphoproteomics (Plank et al, 2020). Alternatively, Sck1-2 may regulate other kinases. Indeed Psm1-S1022 and Mis4-183 lie within CDK consensus sites and Psm1-S1022 phosphorylation is Pef1-dependent.”

(6) Figure 5 - Figure Supplement 3 - The reduction in Psm1 phosphorylation in the sck1Δ sck2Δ double mutant is not convincing without replicates and statistical analysis.

This is now done and the data are presented in Figure 5—figure supplement 3. Panel D shows the data for Psm1-S1022p and Panel E for Mis4-S183p. Each graph shows the mean ratios +/- SD from 3 experiments.

(7) Figure 5C - It would be helpful if the authors validated the effect of pef1 deletion on Mis4 phosphorylation by Western blotting, rather than relying solely on mass spectrometry data.

This is now done. The data appears in Figure 5—figure supplement 2, panel B.

(8) The statement: "The frequency of chromosome segregation defects of mis4‐G1487D was markedly reduced in a sck2‐deleted background and further decreased by the additional deletion of sck1 (Figure 5-figure supplement 3)" is not supported by the data. According to the figure, the difference between sck2Δ and sck1Δ sck2Δ is not statistically significant.

The sentence was changed to:

“The frequency of chromosome segregation defects in the mis4-G1487D strain remained unchanged in a sck1-deleted background, but was significantly reduced when either the sck2 or both the sck1 and sck2 genes were deleted (Figure 5—figure supplement 3).”

(9) Figure 6A - The data shown are not convincing. The double mutants carrying the phosphomimetic and phospho-null psm1 alleles should be shown on the same plate for direct comparison.

This is now done. The new data are shown Figure 6A.

(10) Figure 6E - The wild-type control is missing. Including it would provide an essential reference point to assess whether the mutants rescue cohesin binding to wild-type levels.

This is true that the effects were small when compared to wild-type but still significant when compared to mis4-G1487D. The comparison with wild-type is now available in Figure 6—figure supplement 1 and the paragraph was modified accordingly:

“Cohesin binding to CARs as assayed by ChIP tend to increase for the mutants mimicking the non-phosphorylated state and to decrease with the phospho-mimicking forms (Figure 6E). The rescue of mis4-G1487D by the non-phosphorylatable form was modest but significant, notably within centromeric regions (imr2-L, dg2-R) and at the telomere (Tel1-R) site (Figure 6E and see Figure 6—figure supplement 1 for comparison with wild-type levels). Conversely, the mutant mimicking the phosphorylated state displayed a significant reduction of Rad21 binding at those sites as well as to several other sites at the centromere (cc2, tRNA-R), CAR2898, and at the ribosomal non-transcribed spacer site NTS).”

Limitations of the Study (not requiring additional experiments for publication, but worth noting).

(11) The authors suggest that nutrient status affects cohesin, but this is not directly demonstrated-e.g., by comparing growth or cohesin dynamics or phosphorylation under defined nutrient conditions. That said, the paper is sufficiently detailed to allow this question to be addressed in follow-up work.

We agree that studying the dynamics of cohesin, genome folding and gene expression in relation to nutrient availability is a very exciting topic, and we hope to address these issues in detail in the future.

(12) The upstream signaling cascade remains unresolved. The identity of kinases downstream of TORC1 (e.g., whether Sck1/Sck2 or other factors are responsible) and whether TORC1 directly phosphorylates Mis4 or Psm1 are not established.

This is something we can all agree on, and it might be something we look at in a future project.

(13) The conclusions rely heavily on one TORC1 mutant allele (mip1-R401G). While this allele is informative, additional alleles or orthogonal methods could further support the generality of the findings.

It is true that we focused our attention on mip1-R401G, which is present in all the experiments presented. That said, other alleles were used in one or more figures. Five mip1 alleles and one tor2 allele were identified as mis4-ts suppressors (Fig. 1). We have also shown that another mip1 allele, mip1-Y533A, created by another group (Morozumi et al, 2021), is also a suppressor of mis4-ts and affects the phosphorylation of Mis4-S183 and Psm1-S1022 (Fig. 1, Figure 5—figure supplement 1). To this we can add the effect of mutants that render TORC1 hyperactive (Fig. 1E, Fig. 2H) as well as AGC kinase mutants (Figure 5—figure supplement 3.) and finally, the effect of a transient treatment with rapamycin. So yes, mip1-R401G has been used extensively, but we have still broadly covered the TORC1 signalling pathway.

Reviewer #2 (Recommendations for the authors):

(1) Given the lack of CTCF in fission yeast, it is worth noting that cohesin ChIP data nonetheless can predict topological domains, which reinforces its important role in dictating chromatin folding (PMID: 39543681).

We thank the reviewer for this suggestion. We now refer to this study in the discussion section.

(2) Providing context for the S. pombe nomenclature for the conserved cohesin subunits would help the reader navigate the manuscript, possibly using a cartoon as for the TORC complexes. For example, Psm1 (aka Smc1) is not introduced and therefore its phosphorylation comes into the manuscript without explanation.

Cohesin subunits and their names are given in the introduction section.

eLife Assessment

This convincing study examines a novel interaction of RAB5 with VPS34 complex II. Structural data are combined with site-directed mutagenesis, sequence analysis, biochemistry, yeast mutant analysis, and prior data on RAB1-VPS34 and RAB5-VPS34 interactions to provide a new perspective on how RAB GTPases recruit related but distinct VPS34 complexes to different organelles. The judgment is that this work represents a fundamental advance in our understanding of VPS34 localization and regulation.

Reviewer #1 (Public review):

Summary:

This manuscript presents high resolution cryoEM structures of VPS34-complex II bound to Rab5A at 3.2A resolution. The Williams group previously reported the structure of VPS34 complex II bound to Rab5A on liposomes using tomography, and therefore the previous structure, although very informative, was at lower resolution.

The first new structure they present is of the 'REIE>AAAA' mutant complex bound to RAB5A. The structure resembles the previously determined one except an additional molecule of RAB5A was observed bound to the complex in a new position, interacting with the solenoid of VPS15.

Although this second binding site exhibited reduced occupancy of RAB5A in the structure, the authors determined an additional structure in which the primary binding site was mutated to prevent RAB5A binding ('REIE>ERIR'). In this structure, there is no RAB5A bound to the primary binding site on VPS34, but the RAB5A bound to VPS15 now has strong density. The authors note that the way in which RAB5A interacts with each site is distinct, though both interfaces involve the switch regions. The authors confirm the location of this additional binding site using HDX-MS.

The authors then determine multiple structures of the wild-type complex bound to RAB5A from a single sample, as they use 3D classifications to separate out versions of the complex bound to 0, 1, or 2 copies of RAB5A. Overall the structure of VPS34-Complex II does not change between the different states, and the data indicate that both RAB5A binding sites can be occupied at the same time.

The authors then design a new mutant form of the complex (SHMIT>DDMIE) that is expected to disrupt the interaction at the secondary site between VPS15 and RAB5A. This mutation had a minor impact on the Kd for RAB5A binding, but when combined with the REIE>ERIR mutation of the primary binding site, RAB5A binding to the complex was abolished.

Comparison of sequences across species indicated that the RAB5A binding site on VPS15 was conserved in yeast while the RAB5A binding site on VPS34 is not.

The authors tested the impact of a correspond yeast Vps15 mutation (SHLITY>DDLIEY) predicted to disrupt interaction with yeast Rab5/Vps21, and found this mutant Vps15 protein was mislocalized and caused defective CPY processing.

The authors then compare these structures of the RAB5A-class II complex to recently published structures from the Hurley group of the RAB1A-class I complex, and find that in both complexes the Rab protein is bound to the VPS34 binding site in a somewhat similar manner. However, a key difference is the position of VPS34 is slightly different in the two complexes because of the unique ATL14L and UVRAG subunits in the class I and class II complexes, respectively. This difference creates a different RAB binding pocket that explains the difference in RAB specificity between the two complexes.

Finally, the higher resolution structures enable the authors to now model portions of BECLIN1 and UVRAG that were not previously modeled in the cryoET structure.

Strengths:

Overall I found this to be an interesting and comprehensive study of the structural basis for interaction of RAB5A with VPS34-complex II. The authors have performed experiments to validate their structural interpretations, and they present a clear and thorough comparative analysis of the Rab binding sites in the two different VPS34 complexes. The result is a much better understanding of how two different Rab GTPases specifically recruit two different, but highly similar complexes to the membrane surface.

Weaknesses:

No significant weaknesses noted.

Reviewer #2 (Public review):

The work by Spokaite et al describes the discovery of a novel Rab5 binding site present in complex II of class III PI3K using a combination of HDX and Cryo EM. Extensive mutational and sequence analysis define this as the primordial Rab5 interface. The data presented are convincing that this is indeed a biologically relevant interface, and is important in defining mechanistically how vps34 complexes are regulated.

This paper is a very nice expansion of their previous cryo-ET work from 2021, and is an excellent companion piece on high resolution cryo-EM of the complex I class III complex bound to Rab1 from the Hurley lab in 2025. Overall, this work is of excellent technical quality, and answers important unexplained observations on some unexpected mutational analysis from the previous work.

They used their increased affinity vps34 mutant to determine the 3.2 ang structure of Rab5 bound to vps34-CII. Clear density was seen for the original Rab5 interface, but an additional site was observed. Based on this structure they mutated out the vps34 interface, allowing for a high resolution structure of the Rab5 bound at the Vps15 interface.

They extensively validated the vps15 interface in the yeast variant of vps34, showing that the Vp215-Rab5 (Vps21) interface identified is critical in controlling complex II vps34 recruitment.

The major strengths of this paper are that the experiments appear to be done carefully and rigorously and I have very few experimental suggestions.

Here is what I recommend based on some very minor weaknesses I observed

(1) My main concern has to do a little bit with presentation. My main issue is how the authors use mutant description. They clearly indicate the mutant sequence in the human isoform (for example see Fig 2A, Vps15 described as 579-SHMIT-583>DDMIE), however, when they shift to the yeast version they shift to saying vps15 mutant, but don't define the mutant, Fig 2G). I would recommend they just include the same sequence numbering and WT to mutant replacement every time a new mutant (or species) is described. It is always easier to interpret what is being shown when the authors are jumping between species when the exact mutant is included. This is particularly important in this paper, where we are jumping between both different subunits and different species, so clear description in figure/figure legends makes it much easier to read for non-specialists.

(2) The HDX data very clearly shows that Rab5 is likely able to bind at both sites, which back ups the cryo EM data nicely. I am slightly confused by some of the HDX statements described in the methods.

(3) The authors state "Only statistically significant peptides showing a difference greater than 0.25 Da and greater than 5% for at least two timepoints were kept." This seems to be confusing why they required multiple timepoints, and before they also describe that they required a p value of less than 0.05. It might be clearer to state that significant differences required a 0.25 Da, 5%, and p value of <0.05 (n=3). Also what do they mean by kept? Does this mean that they only fully processed the peptides with differences.

(4) They show peptide traces for a selection in the supplement, but it would be ideal to include the full set of HDX data as an excel file, including peptides with no differences as there is a lot of additional information (deuteration levels for everything) that would be useful to share, as recommended from the Masson et al 2019 recommendations paper. This may be attached but this reviewer could not see an example of it in the shared data dropbox folder.

Comments on revisions:

The authors have addressed all of my issues.

Reviewer #3 (Public review):

Summary:

The manuscript of Spokaite et al. focuses on the Vps34 complex involved in PI3P production. This complex exists in two variants, one (class I) specific for autophagy, and a second one (class II) specific for the endocytic system. Both differ only in one subunit. The authors previously showed that the Vps34 complexes interact with Rab GTPases, Rab1 or Rab5 (for class II), and the identified site was found at Vps34. Now, the authors identify a conserved and overlooked Rab5 binding site in Vps15, which is required for the function of the Class II complex. In support of this, they show cryo-EM data with a second Rab5 bound to Vps15, identify the corresponding residues, and show by mutant analysis that impaired Rab5 binding also results in defects using yeast as a model system.

Overall, this is a most complete study with little to criticize. The paper shows convincingly that the two Rab5 binding sites are required for Vps34 complex II function, with the Vps15 binding site being critical for endosomal localization. The structural data is very much complete. What I am missing are a few controls that show that the mutations in Vps15 do not affect autophagy. I also found the last paragraph of the results section a bit out of place, even though this is a nice observation that the N-terminal part of BECLIN has these domains. However, what does it add to the story?

Comments on revisions:

The authors answered all my questions. I have no further requests.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript presents high-resolution cryoEM structures of VPS34-complex II bound to Rab5A at 3.2A resolution. The Williams group previously reported the structure of VPS34 complex II bound to Rab5A on liposomes using tomography, and therefore, the previous structure, although very informative, was at lower resolution.

The first new structure they present is of the 'REIE>AAAA' mutant complex bound to RAB5A. The structure resembles the previously determined one, except that an additional molecule of RAB5A was observed bound to the complex in a new position, interacting with the solenoid of VPS15.

Although this second binding site exhibited reduced occupancy of RAB5A in the structure, the authors determined an additional structure in which the primary binding site was mutated to prevent RAB5A binding ('REIE>ERIR'). In this structure, there is no RAB5A bound to the primary binding site on VPS34, but the RAB5A bound to VPS15 now has strong density. The authors note that the way in which RAB5A interacts with each site is distinct, though both interfaces involve the switch regions. The authors confirm the location of this additional binding site using HDX-MS.

The authors then determine multiple structures of the wild-type complex bound to RAB5A from a single sample, as they use 3D classifications to separate out versions of the complex bound to 0, 1, or 2 copies of RAB5A. Overall, the structure of VPS34-Complex II does not change between the different states, and the data indicate that both RAB5A binding sites can be occupied at the same time.

The authors then design a new mutant form of the complex (SHMIT>DDMIE) that is expected to disrupt the interaction at the secondary site between VPS15 and RAB5A. This mutation had a minor impact on the Kd for RAB5A binding, but when combined with the REIE>ERIR mutation of the primary binding site, RAB5A binding to the complex was abolished.

Comparison of sequences across species indicated that the RAB5A binding site on VPS15 was conserved in yeast,while the RAB5A binding site on VPS34 is not.

The authors tested the impact of a corresponding yeast Vps15 mutation (SHLITY>DDLIEY) predicted to disrupt interaction with yeast Rab5/Vps21, and found that this mutant Vps15 protein was mislocalized and caused defective CPY processing.

The authors then compare these structures of the RAB5A-class II complex to recently published structures from the Hurley group of the RAB1A-class I complex, and find that in both complexes the Rab protein is bound to the VPS34 binding site in a somewhat similar manner. However, a key difference is that the position of VPS34 is slightly different in the two complexes because of the unique ATL14L and UVRAG subunits in the class I and class II complexes, respectively. This difference creates a different RAB binding pocket that explains the difference in RAB specificity between the two complexes.

Finally, the higher resolution structures enable the authors to now model portions of BECLIN1 and UVRAG that were not previously modeled in the cryoET structure.

Strengths:

Overall, I found this to be an interesting and comprehensive study of the structural basis for the interaction of RAB5A with VPS34-complex II. The authors have performed experiments to validate their structural interpretations, and they present a clear and thorough comparative analysis of the Rab binding sites in the two different VPS34 complexes. The result is a much better understanding of how two different Rab GTPases specifically recruit two different, but highly similar complexes to the membrane surface.

Weaknesses:

No significant weaknesses were noted.

Reviewer #2 (Public review):

Summary:

The work by Spokaite et al describes the discovery of a novel Rab5 binding site present in complex II of class III PI3K using a combination of HDX and Cryo EM. Extensive mutational and sequence analysis define this as the primordial Rab5 interface. The data presented are convincing that this is indeed a biologically relevant interface, and is important in defining mechanistically how VPS34 complexes are regulated.

This paper is a very nice expansion of their previous cryo-ET work from 2021, and is an excellent companion piece on high-resolution cryo-EM of the complex I class III complex bound to Rab1 from the Hurley lab in 2025. Overall, this work is of excellent technical quality and answers important unexplained observations on some unexpected mutational analysis from the previous work.

They used their increased affinity VPS34 mutant to determine the 3.2 ang structure of Rab5 bound to VPS34-CII. Clear density was seen for the original Rab5 interface, but an additional site was observed. Based on this structure, they mutated out the VPS34 interface, allowing for a high-resolution structure of the Rab5 bound at the VPS15 interface.

They extensively validated the VPS15 interface in the yeast variant of VPS34, showing that the Vp215-Rab5 (VPS21) interface identified is critical in controlling complex II VPS34 recruitment.

The major strengths of this paper are that the experiments appear to be done carefully and rigorously, and I have very few experimental suggestions.

Here is what I recommend based on some very minor weaknesses I observed

(1) My main concern has to do a little bit with presentation. My main issue is how the authors use mutant description. They clearly indicate the mutant sequence in the human isoform (for example, see Figure 2A, VPS15 described as 579-SHMIT-583>DDMIE); however, when they shift to the yeast version, they shift to saying VPS15 mutant, but don't define the mutant, Figure 2G). I would recommend they just include the same sequence numbering and WT to mutant replacement every time a new mutant (or species) is described. It is always easier to interpret what is being shown when the authors are jumping between species, when the exact mutant is included. This is particularly important in this paper, where we are jumping between different subunits and different species, so a clear description in the figure/figure legends makes it much easier to read for non-specialists.

The reviewer has made an excellent point here. To clarify the yeast mutation, we have revised the manuscript main text to refer to the yeast mutant as SHLITY>DDLIEY, and we have added this to the legend for Figs. 2F,G.

(2) The HDX data very clearly shows that Rab5 is likely able to bind at both sites, which back ups the cryo EM data nicely. I am slightly confused by some of the HDX statements described in the methods.

(3) The authors state, "Only statistically significant peptides showing a difference greater than 0.25 Da and greater than 5% for at least two timepoints were kept." This seems to be confusing as to why they required multiple timepoints, and before they also describe that they required a p-value of less than 0.05. It might be clearer to state that significant differences required a 0.25 Da, 5%, and p-value of <0.05 (n=3). Also, what do they mean by kept? Does this mean that they only fully processed the peptides with differences?

(4) They show peptide traces for a selection in the supplement, but it would be ideal to include the full set of HDX data as an Excel file, including peptides with no differences, as there is a lot of additional information (deuteration levels for everything) that would be useful to share, as recommended from the Masson et al 2019 recommendations paper. This may be attached, but this reviewer could not see an example of it in the shared data dropbox folder.

We have revised the HDX method description to clarify. All peptides were kept and fully processed. However, for the results displayed, we have illustrated only peptides meeting the criteria described.

The Excel file for all peptides (as recommended by Masson et al) was deposited with PRIDE, with the identifier with the dataset identifier PXD061277, in addition, we have included this excel file in our supplementary material.

Reviewer #3 (Public review):

Summary:

The manuscript of Spokaite et al. focuses on the Vps34 complex involved in PI3P production. This complex exists in two variants, one (class I) specific for autophagy, and a second one (class II) specific for the endocytic system. Both differ only in one subunit. The authors previously showed that the Vps34 complexes interact with Rab GTPases, Rab1 or Rab5 (for class II), and the identified site was found at Vps34. Now, the authors identify a conserved and overlooked Rab5 binding site in Vps15, which is required for the function of the Class II complex. In support of this, they show cryo-EM data with a second Rab5 bound to Vps15, identify the corresponding residues, and show by mutant analysis that impaired Rab5 binding also results in defects using yeast as a model system.

Overall, this is a most complete study with little to criticize. The paper shows convincingly that the two Rab5 binding sites are required for Vps34 complex II function, with the Vps15 binding site being critical for endosomal localization. The structural data is very much complete.

Weaknesses:

What I am missing are a few controls that show that the mutations in Vps15 do not affect autophagy. I am wondering if this mutant is still functional in autophagy. This can be simply tested by sorting of Atg8 to the vacuole lumen using established assays or by following PhoΔ60 sorting. This analysis would reveal that the corresponding mutant is specific for the Class II complex.

One of the first noted features of the VPS34 complexes was that the ATG14-containing complex (VPS34-CI) is important for autophagy, while the VPS38 (yeast orthologue of UVRAG) subunit characteristic of VPS34-CII is important for endocytic sorting (PMID 11157979). However, the VPS34, VPS15 and BECLIN1 subunits are required are present in both complexes, as such, mutations of them may affect both processes.

We agree with the reviewer that is an important undertaking to examine the effect of the SHLITY>DDLIEY mutation in yeast Vps15 on autophagy. However, the focus of the current manuscript is VPS34-complex II and RAB5 interaction/activation. An autophagy effect would be more relevant for VPS34 complex I and RAB1. We have not presented any results for human VPS34-complex I - RAB1 nor yeast Vps34-complex I – Ypt1 (yeast RAB1 orthologue). We are preparing another manuscript focusing entirely on this, and it is not a simple story. While we think this is an important question, we believe that this is beyond the scope of the current manuscript.

It would be helpful if the authors could clarify whether they believe that Vps34 kinase activity is stimulated by Rab binding or whether this stimulation is a consequence of better membrane localization of Vps34. In other words, is the complex active with soluble PI3P in solution, and does the activity change if Rab5 is added to the complex? This might have been addressed in the past, but I did not see evidence for this, as the authors only addressed the activity of the Vps34 complexes on membranes.

The reviewer has raised an excellent question, which was addressed briefly in the introduction to the manuscript. We have now somewhat expanded on these issues near the end of the discussion in the revised manuscript. In our previously published study, we found that soluble RAB5-GTP did not stimulate the complex II activity (supplementary figure 2b of PMID: 33692360). This is consistent with our finding in this manuscript showing that RAB5 did not cause large conformational changes in solution. However, our previous single-molecule study showed that once complex II is recruited to the membrane by RAB5, and RAB5 increases the turnover rate on membranes, indicating an additional allosteric activation (Figure 7 of PMID: 33137306). This study indicated that the primary the role of RAB5 is to anchor complex II on the membrane. Once the complex is anchored on the membrane by RAB5, the kinase domain is in the vicinity of its substrate, PI, leading to higher turnover.

The Echelon Class III PI3K ELISA Kit (Echelon, K-3000) comes with a soluble PI, diC8 to measure the VPS34 activity, and it is certainly active with this soluble substrate. However, if the substrate is in membranes, the VPS34 activity is greatly dependent on the character of the membrane.

I also found the last paragraph of the results section a bit out of place, even though this is a nice observation that the N-terminal part of BECLIN has these domains. However, what does it add to the story?

The reviewer is correct that the high-resolution features of BECLIN1 at the base of the V-shaped complex that we observed are not related to RAB5 binding, but they are characteristic of VPS34-CII and likely to be important for the specific role of VPS34-CII. This is the first high-resolution structure of the VPS34-CII that has been reported, and we believe it would be irresponsible not to briefly describe them, since they are unique to VPS34-CII. For this reason, we have placed this section at the end of the results, and we now clarify that we do not see a relevance to RAB5 function, but we describe the arrangement of a region (the BH3) that has been functionally noted in many previous studies, in the absence of a structure.

Reviewing Editor Comments:

Please address the following suggestions for minor changes to the manuscript. Use your best scientific judgment in addressing the comments and describe the modifications together with your reasoning in a cover letter. We look forward to seeing the revised version of this very nice study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I found a portion of the description of the cryoEM complexes on the top of page 9 to be redundant with similar descriptions near the top of page 7, and it was not clear to me at first that these were describing the same structures. Part of my confusion was due to the redundancy, including the statement near the bottom of page 7: 'Models were built and refined for all RAB5associated VPS34-CII assemblies', and then the similar statement on page 9: 'We fit and refined atomic models into both densities'. I believe these are describing the same models? To clarify for the reader, perhaps on page 9, the authors could begin this part with a statement such as "as described above", and eliminate the redundant descriptions.

The reviewer is correct. Both sections describe the same set of cryo-EM classes from the same sample. The only difference is what we analysed in the two sections: number of RAB5s bound in the first section and the effect of RAB5 binding in the second section. We have revised the text to make this clear, and to make the second section more succinct.

Reviewer #3 (Recommendations for the authors):

(1) The authors show nicely that a mutation in Vps15 disrupts binding to Vps21 in vivo, with defects in the endocytic pathway as analyzed by CPY sorting. I am wondering if this mutant is still functional in autophagy. This can be simply tested by sorting of Atg8 to the vacuole lumen using established assays or by following Pho∆60 sorting. This analysis would reveal that the corresponding mutant is specific for the Class II complex. If the authors were to find evidence that this Vps15 mutant also affects autophagy, it would indicate that there is possibly also another Rab1 binding site in Vps15.

As we stated above, an autophagy effect would be more relevant for VPS34 complex I and RAB1. We have not presented any results for human VPS34-complex I - RAB1 nor yeast Vps34-complex I – Ypt1 (yeast RAB1 orthologue). We are preparing another manuscript focusing entirely on this, and it is not a simple story. While we think this is an important question, we believe that this is beyond the scope of the current manuscript.

(2) It would be helpful if the authors could clarify whether they believe that Vps34 kinase activity is stimulated by Rab binding or whether this stimulation is a consequence of better membrane localization of Vps34. In other words, is the complex active with soluble PI3P in solution, and does the activity change if Rab5 is added to the complex? This might have been addressed in the past, but I did not see evidence for this, as the authors only addressed the activity of the Vps34 complexes on membranes.

As in our response to reviewer #3 above, this point was addressed in previous publications and was described in the introduction to our manuscript.

eLife Assessment

This important study provides compelling evidence that fever-like temperatures enhance the export of Plasmodium falciparum transmembrane proteins, including the cytoadherence protein PfEMP1 and the nutrient channel PSAC, to the red blood cell surface, thereby increasing cytoadhesion. Using rigorous and well-controlled experiments, the authors convincingly demonstrate that this effect results from accelerated protein trafficking rather than changes in protein production or parasite development. These findings significantly advance our understanding of parasite virulence mechanisms and offer insights into how febrile episodes may exacerbate malaria severity.

Reviewer #1 (Public review):

Summary:

The manuscript from Jones and colleagues investigates a previously described phenomenon in which P. falciparum malaria parasites display increased trafficking of proteins displayed on the surface of infected RBCs as well as increased cytoadherence in response to febrile temperatures. While this parasite response was previously described, it was not uniformly accepted, and conflicting reports can be found in the literature. This variability likely arises due to differences in the methods employed and the degree of temperature increase that the parasites were exposed to. Here the authors are very careful to employ a temperature shift that likely reflects what is happening in infected humans and that they demonstrate is not detrimental to parasite viability or replication. In addition, they go on to investigate what steps in protein trafficking are affected by exposure to increased temperature and show that the effect is not specific to PfEMP1 but rather likely affects all transmembrane domain containing proteins that are trafficked to the RBC. They also detect increased rates of phosphorylation of trafficked proteins, consistent with overall increased protein export.

Strengths:

The authors used a relatively mild increase in temperature (39 degrees) that they demonstrate is not detrimental to parasite viability or replication. This enabled them to avoid potential complications of more severe heat shock that might have affected previously published studies. They employed a clever method of fractionation of RBCs infected with a var2csa-nanoluc fusion protein expressing parasite line to determine which step in the export pathway was likely accelerating in response to increased temperature. This enabled them to determine that export across the PVM is being affected. They also explored changes in phosphorylation of exported proteins and demonstrated that the effect is not limited to PfEMP1 but appears to affect numerous (or potentially all) exported transmembrane domain containing proteins.

Impact and conclusions:

The study shows that protein export, including PfEMP1 and PSAC, are accelerated in response to mild heat shock. This has implications for disease severity as well as our understanding of protein trafficking in these unique organisms. There is increasing interest in asymptomatic infections, which have been proposed to be a major reservoir for transmission and generally are not associated with fever. It will be interesting to consider whether reduced (or slower) trafficking of these proteins has a selective advantage for parasites in asymptomatic infections.

Reviewer #2 (Public review):

This manuscript describes experiments characterising how malaria parasites respond to physiologically relevant heat-shock conditions. The authors show, quite convincingly, that moderate heat-shock appears to increase cytoadherance, likely by increasing trafficking of surface proteins involved in this process.

While generally of a high quality and including a lot of data, I have a few small questions and comments, mainly regarding data interpretation.

(1) The authors use sorbitol lysis as a proxy for trafficking of PSAC components. This is a very roundabout way of doing things and does not, I think, really show what they claim. There could be a myriad of other reasons for this increased activity (indeed, the authors note potential PSAC activation under these conditions). One further reason could be a difference in the membrane stability following heat shock, which may affect sorbitol uptake, or the fragility of the erythrocytes to hypotonic shock. I really suggest that the authors stick to what they show (increased PSAC) without trying to use this as evidence for increased trafficking of a number of non-specified proteins that they cannot follow directly.

(2) Supplementary Figure 6C/D: The KAHRP signal does not look like it should. In fact, it doesn't look like anything specific. The HSP70-X signal is also blurry and overexposed. These pictures cannot be used to justify the authors' statements about a lack of colocalisation in any way.

(3) Figure 6: This experiment confuses me. The authors purport to fractionate proteins using differential lysis, but the proteins they detect are supposed to be transmembrane proteins and thus should always be found associated with the pellet, whether lysis is done using equinatoxin or saponin. Have they discovered a currently unknown trafficking pathway to tell us about? Whilst there is a lot of discussion about the trafficking pathways for TM proteins through the host cell, a number of studies have shown that these proteins are generally found in a membrane-bound state. The authors should elaborate, or choose an experiment that is capable of showing compartment-specific localisation of membrane-bound proteins (protease protection, for example).

(4) The red blood cell contains, in addition to HSP70-X, a number of human HSPs (HSP70 and HSP90 are significant in this current case). As the name suggests, these proteins non-specifically shield exposed hydrophobic domains revealed upon partial protein unfolding following thermal insult. I would thus have expected to find significantly more enrichment following heat shock, but this is not the case. Is it possible that the physiological heat shock conditions used in this current study are not high enough to cause a real heat shock?

Comments on Revision:

Although in any study there are going to be residual weaknesses, this reviewer is happy to see that the authors have gone to lengths to address many of my main concerns, and also those of other reviewers.

Reviewer #3 (Public review):

Summary:

In this paper it is established that high fever-like 39oC temperatures cause parasite infected red blood cells become stickier. It is thought that high temperatures might help the spleen to destroy parasite infected cells, so they become stickier to remain trapping in blood vessels, so they stop passing through the spleen.

Strengths:

The strength of this research is that it shows that fever-like temperatures can cause parasite infected red blood cells to stick to surfaces designed to mimic the walls of small blood vessels. In a natural infection this would cause parasite infected red blood cells to stop circulating through the spleen where the parasites would be destroyed by the immune system. It is thought that fevers could lead to infected red blood cells becoming stiffer and therefore more easily destroyed in the spleen. Parasites respond to fevers by making their red blood cells stickier, so they stop flowing around the body and into the spleen. The experiments here prove fever temperatures increase the export of Velcro-like sticky proteins onto the surface of the infected red blood cells and are very thorough and convincing.

Weaknesses:

Minor weaknesses in the original version have now been satisfactorily addressed with additional work which is very convincing.

Author Response:

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

eLife Assessment

This important study provides compelling evidence that fever-like temperatures enhance the export of Plasmodium falciparum transmembrane proteins, including the cytoadherence protein PfEMP1 and the nutrient channel PSAC, to the red blood cell surface, thereby increasing cytoadhesion. Using rigorous and well-controlled experiments, the authors convincingly demonstrate that this effect results from accelerated protein trafficking rather than changes in protein production or parasite development. These findings significantly advance our understanding of parasite virulence mechanisms and offer insights into how febrile episodes may exacerbate malaria severity.

We thank all reviewers for their constructive feedback on our manuscript.

We believe we have addressed all the questions in the rebuttal below in writing, including planned experiments we will perform to strengthen the conclusions of the manuscript.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript from Jones and colleagues investigates a previously described phenomenon in which P. falciparum malaria parasites display increased trafficking of proteins displayed on the surface of infected RBCs, as well as increased cytoadherence in response to febrile temperatures. While this parasite response was previously described, it was not uniformly accepted, and conflicting reports can be found in the literature. This variability likely arises due to differences in the methods employed and the degree of temperature increase to which the parasites were exposed. Here, the authors are very careful to employ a temperature shift that likely reflects what is happening in infected humans and that they demonstrate is not detrimental to parasite viability or replication. In addition, they go on to investigate what steps in protein trafficking are affected by exposure to increased temperature and show that the effect is not specific to PfEMP1 but rather likely affects all transmembrane domain-containing proteins that are trafficked to the RBC. They also detect increased rates of phosphorylation of trafficked proteins, consistent with overall increased protein export.

Strengths:

The authors used a relatively mild increase in temperature (39 degrees), which they demonstrate is not detrimental to parasite viability or replication. This enabled them to avoid potential complications of a more severe heat shock that might have affected previously published studies. They employed a clever method of fractionation of RBCs infected with a var2csa-nanoluc fusion protein expressing parasite line to determine which step in the export pathway was likely accelerating in response to increased temperature. This enabled them to determine that export across the PVM is being affected. They also explored changes in phosphorylation of exported proteins and demonstrated that the effect is not limited to PfEMP1 but appears to affect numerous (or potentially all) exported transmembrane domain-containing proteins.

Weaknesses:

All the experiments investigating changes resulting from increased temperature were conducted after an increase in temperature from 16 to 24 hours, with sampling or assays conducted at the 24 hr mark. While this provided consistency throughout the study, this is a time point relatively early in the export of proteins to the RBC surface, as shown in Figure 1E. At 24 hrs, only approximately 50% of wildtype parasites are positive for PfEMP1, while at 32 hrs this approaches 80%. Since the authors only checked the effect of heat stress at 24 hrs, it is not possible to determine if the changes they observe reflect an overall increase in protein trafficking or instead a shift to earlier (or an accelerated) trafficking. In other words, if a second time point had been considered (for example, 32 hrs or later), would the parasites grown in the absence of heat stress catch up?

We did not assess cytoadhesion at later stages, but in the supplementary figures we show that at 40 hours post infection both heat stress and control conditions have comparable proportions of VAR2CSA-positive iRBCs, whilst they differ at 24h. This is true for the DMSO (control wildtype resembling) HA-tagged lines of HSP70x and PF3D7_072500 (Supplementary Figures 9 and 12 respectively). In the light that protein levels appear not changed, we conclude that trafficking is accelerated during these earlier timepoints, but remains comparable at later stages. This would still increase the overall bound parasite mass as parasites start to adhere earlier during or after a heat stress.

Reviewer #2 (Public review):

This manuscript describes experiments characterising how malaria parasites respond to physiologically relevant heat-shock conditions. The authors show, quite convincingly, that moderate heat-shock appears to increase cytoadherance, likely by increasing trafficking of surface proteins involved in this process.

While generally of a high quality and including a lot of data, I have a few small questions and comments, mainly regarding data interpretation.

(1) The authors use sorbitol lysis as a proxy for trafficking of PSAC components. This is a very roundabout way of doing things and does not, I think, really show what they claim. There could be a myriad of other reasons for this increased activity (indeed, the authors note potential PSAC activation under these conditions). One further reason could be a difference in the membrane stability following heat shock, which may affect sorbitol uptake, or the fragility of the erythrocytes to hypotonic shock. I really suggest that the authors stick to what they show (increased PSAC) without trying to use this as evidence for increased trafficking of a number of non-specified proteins that they cannot follow directly.

This is a valid point, however, uninfected RBCs do not lyse following heat stress, nor do much younger iRBCs, indicating that the observed effect is specific to infected RBCs at a defined stage. The sorbitol sensitivity assay is performed at 37°C under normal conditions after cells are returned to non–heat stress temperatures, so the effect is not due to transient changes in membrane permeability at elevated temperature.

Planned experiment: However, to increase the strength of our conclusions and further test our hypothesis, we will perform sorbitol sensitivity assays on >20 hours post infection iRBCs following heat stress in the presence and absence of furosemide, a PSAC inhibitor. If iRBC lysis is abolished with furosemide present, this would confirm that the effect is PSAC-dependent. However, the effect could also possibly be due to altered PSAC activity during heat stress which is maintained at lower temperatures, as outlined in the discussion.

New Results:

We performed sorbitol sensitivity assays on >20 hours post-infection iRBCs following heat stress in the presence and absence of the PSAC inhibitor furosemide. These additional experiments were added to the supplementary figures (Supplementary Figure 3). Importantly, sorbitol-mediated lysis of iRBCs, with or without prior heat stress, was reduced when furosemide was present, demonstrating that the observed effect is likely PSAC-dependent. We also observed that uninfected RBCs did not lyse with sorbitol, regardless of heat stress, confirming that the effect is specific to infected cells.

(2) Supplementary Figure 6C/D: The KAHRP signal does not look like it should. In fact, it doesn't look like anything specific. The HSP70-X signal is also blurry and overexposed. These pictures cannot be used to justify the authors' statements about a lack of colocalisation in any way.

Planned experiment: We agree that the IFAs are not the best as presented and will include better quality supplementary images in a revised version.

New Results:

Immunofluorescence microscopy, including the localisation of the two HA-tagged proteins (PF3D7_1039000 and PF3D7_0702500), has been repeated and higher-quality images are now included in the updated manuscript (Supplementary Figures 9 and 11). These images include co-staining with the P. falciparum proteins KAHRP and SPB1 to assess possible co-localisations. Furthermore, following the reviewer’s suggestion, we have softened the statement regarding PF3D7_1039000-HA to better reflect the data, changing “...does not colocalise” to “...does not strongly colocalise”.

(3) Figure 6: This experiment confuses me. The authors purport to fractionate proteins using differential lysis, but the proteins they detect are supposed to be transmembrane proteins and thus should always be found associated with the pellet, whether lysis is done using equinatoxin or saponin. Have they discovered a currently unknown trafficking pathway to tell us about? Whilst there is a lot of discussion about the trafficking pathways for TM proteins through the host cell, a number of studies have shown that these proteins are generally found in a membrane-bound state. The authors should elaborate, or choose an experiment that is capable of showing compartment-specific localisation of membrane-bound proteins (protease protection, for example).

We do not believe we identified a novel trafficking pathway, but that we capture trafficking intermediates of PfEMP1 between the PVM and the RBC periphery, in either small vesicles, and possibly including Maurer’s clefts. These would still be membrane embedded, but because of their small size, not be pelleted using the centrifugation speeds in our study (we did not use ultracentrifugation). This explanation, we believe, is in line with the current hypothesis of PfEMP1 and other exported TMD protein trafficking to the periphery or the Maurer’s clefts.

(4) The red blood cell contains, in addition to HSP70-X, a number of human HSPs (HSP70 and HSP90 are significant in this current case). As the name suggests, these proteins non-specifically shield exposed hydrophobic domains revealed upon partial protein unfolding following thermal insult. I would thus have expected to find significantly more enrichment following heat shock, but this is not the case. Is it possible that the physiological heat shock conditions used in this current study are not high enough to cause a real heat shock?

As noted by the reviewer, we do not see enrichment of red blood cell heat shock proteins following heat stress, either with FIKK10.2-TurboID or in the phosphoproteome. We used a physiologically relevant heat stress that significantly modifies the iRBC, as shown by our functional assays. While a higher temperature might induce an association of red blood cell heat shock proteins, such conditions may not accurately reflect the most commonly found in the context of malaria infection.

Reviewer #3 (Public review):

Summary:

In this paper, it is established that high fever-like 39 C temperatures cause parasite-infected red blood cells to become stickier. It is thought that high temperatures might help the spleen to destroy parasite-infected cells, and they become stickier in order to remain trapped in blood vessels, so they stop passing through the spleen.

Strengths:

The strength of this research is that it shows that fever-like temperatures can cause parasite-infected red blood cells to stick to surfaces designed to mimic the walls of small blood vessels. In a natural infection, this would cause parasite-infected red blood cells to stop circulating through the spleen, where the parasites would be destroyed by the immune system. It is thought that fevers could lead to infected red blood cells becoming stiffer and therefore more easily destroyed in the spleen. Parasites respond to fevers by making their red blood cells stickier, so they stop flowing around the body and into the spleen. The experiments here prove that fever temperatures increase the export of Velcro-like sticky proteins onto the surface of the infected red blood cells and are very thorough and convincing.

Weaknesses:

A minor weakness of the paper is that the effects of fever on the stiffness of infected red blood cells were not measured. This can be easily done in the laboratory by measuring how the passage of infected red blood cells through a bed of tiny metal balls is delayed under fever-like temperatures.

Previous work by Marinkovic et al. (cited in this manuscript) reported that all RBCs, both infected and uninfected, increase in stiffness at 41 °C compared with 37 °C, with trophozoites and schizonts exhibiting a particularly pronounced increase. We agree that it would be interesting to determine whether similar changes occur at physiological fever-like temperatures, and whether this increase in stiffness coincides with the period of elevated protein trafficking. However, here we focused on enhanced protein export using multiple complementary approaches, and have chosen to address rigidity questions in a different study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

As mentioned above, a second time point in many of the assays (for example, 36 hrs or later) would be useful to determine if heat stress simply accelerates trafficking of proteins to the RBC or if instead it results in an overall increase in trafficking.

As mentioned earlier: We did not assess cytoadhesion at later stages, but in the supplementary figures we show that at 40 hours post infection both heat stress and control conditions have comparable proportions of VAR2CSA-positive iRBCs. This is true for the DMSO (control wildtype resembling) HA-tagged lines of HSP70x and PF3D7_072500 (Supplementary Figures 9 and 12 respectively). The end level of VAR2CSA is the same in both conditions, but at 24 hours post infection it is higher following heat stress, indicating that trafficking is accelerated.

In the text, the authors frequently mention changes in the parasites' phenotype in response to heat stress; however, the way it is described is a bit ambiguous and can be confusing. For example, on page 3, they state that "Following heat stress, significantly more iRBCs (57.6% +/-19.4%) cytoadhered.....". From this sentence, it is not initially clear if the end result is cytoadherence of 57.6% of iRBCs or if this refers to an increase of 57.6%. This could be stated explicitly (e.g., "an increase of 57.6% +/- 19.4%") to avoid confusion. Similar descriptions of the results are found throughout the paper.

We agree this is confusing and altered the text accordingly.

The authors might consider citing and discussing the paper from Andrade et al (Nat Med, 2020, 26:1929-1940), which describes longer circulation times (less cytoadherence) by parasites in the dry season (asymptomatic patients) than in febrile patients in the wet season (stronger cytoadhesion of younger stages). This would seem to be consistent with the data presented here.

We are aware of the Andrade study, but chose not to cite it in this context since the reported differences in cytoadhesion appear more consistent with PfEMP1 expression levels, as hypothesized by the authors, than with altered trafficking.

Reviewer #2 (Recommendations for the authors):

General comments on the text:

(1) "Approximately 10% of the proteins encoded by P. falciparum are predicted to be exported beyond the parasite plasma membrane (PPM) into the parasitophorous vacuole lumen (PVL) and subsequently across the parasitophorous vacuole membrane (PVM) into the RBC cytosol."

To my knowledge, it has not been really demonstrated that all exported proteins take this route (transfer step in the PVL), and how transmembrane proteins transfer from the parasite to the erythrocyte is still poorly understood. I recommend that the authors rephrase this for precision.

We agree with this reviewer and will change the statement.

Changes:

We have clarified these statements to accurately reflect the current understanding of protein export. Approximately 10% of P. falciparum encoded proteins are predicted to be exported beyond the parasite plasma membrane, with many thought to pass through the parasitophorous vacuole lumen (PVL) and parasitophorous vacuole membrane (PVM) into the RBC cytosol, although the exact routes for transmembrane proteins are not fully understood.”

(2) "Charnaud et al. 25, but not Cobb et al. 26, found HSP70x to be essential for normal PfEMP1 trafficking, although both studies concluded that HSP70x is dispensable for intraerythrocytic parasite growth at 37 {degree sign}C."

The trafficking block in Charnaud is likely due to a delay in parasite development and cannot thus really be directly related to PfEMP1 trafficking.

Charnaud et al., report: “Microscopy of Giemsa stained IE indicated that ΔHsp70-x appeared similar to CS2 with no obvious abnormalities (Fig 2c). To more accurately quantify changes in maturation through the cell cycle, the DNA content of parasites stained with ethidium bromide was measured by flow cytometry (Fig 2d). This indicated that most parasites had the same DNA content at each timepoint and were maturing at the same rate.”

Thus, we cannot conclude that the trafficking phenotype reported in the Charnaud study can be attributed to a growth delay. This is also supported by only minor changes in the transcriptome, which would likely be more widely perturbed if there was a significant growth delay. However, we will change the statement “Charnaud et al., found HSP70x to be essential for normal PfEMP1 trafficking”, to ”…important for PfEMP1 trafficking” to more precisely reflect the data.

(3) "NanoLuciferase (NanoLuc) fusion proteins and compartment-specific isolation confirmed a greater abundance of PfEMP1 in the RBC cytosol following heat stress."

Please see my comments about the differentiation between soluble and TM-containing proteins. One would expect that PfEMP1 is membrane-integrated, and thus should not be found in the cytosol (implying a soluble form).

See our response above.

(4) "Importantly, heat stress did not accelerate parasite development through the asexual life cycle (Supplementary Figure 1)."

The authors should constrain this statement to the time frame in which the heat-shock was given. Previous publications have shown a speeded-up development only in younger-stage parasites, which the authors did not study.

We will re-phrase.

Changes:

We have rephrased the sentence to clarify the time window of heat stress: ”Importantly, heat stress between 16-24 hours post-invasion did not accelerate parasite development through the asexual life cycle (Supplementary Figure 1).” The supplementary figure title has also been updated to match.

(5) I recommend that the authors include line numbers. This makes the reviewers' lives much easier.

We agree and apologize for this oversight.

We now added line numbers.

Reviewer #3 (Recommendations for the authors):

(1) All the experiments have been performed to a very high standard, and I have no major questions about the results. However, the paper would go up to the next level if the effect of fever temperatures on the stiffness of the iRBCs had been investigated by measuring the passage of iRBCs through an artificial spleen where a bed of metal spheres mimics interendothelial splenic slits.

See our comment from above.

(2) With respect to Figures 5E, 6C, and 6E, why was there not a decrease in bioluminescence levels at 39 {degree sign}C for Sap and NP40 to match the increase in EqtII?

The assay is not performed as a sequence of permeabilisation steps. Instead, samples are split into three parallel treatments: one with EqtII, one with Saponin, and one with NP40. The protein measured in each case reflects the total released under that specific condition rather than being cumulative. Therefore, the NP40 fraction includes proteins from the Saponin-accessible compartment, the EqtII-accessible compartment, and the parasite cytosol.

(3) In the Supplementary gene maps, I could not read the white text on the black gene boxes.

We apologize: these have not converted well and will be altered with the revised version.

Changes

We have significantly increased the size of all fonts within the gene maps and improved the resolution of the figures to improve readability.

(4) In Figure S6, why does HSP70-x look different between parts C and D IFAs, with the latter showing much more export?

We agree these IFAs are not optimal and we will provide better images.

New Results:

Immunofluorescence microscopy, including the localisation of the two HA-tagged proteins (PF3D7_1039000 and PF3D7_0702500), has been repeated and higher-quality images are now included in the updated manuscript (Supplementary Figures 9 and 11). These figures now include multiple images of HA-tagged staining to more accurately represent the observed localisation and export patterns.

(5) Would the authors care to comment on what kinase might be additionally phosphorylating at 39 {degree sign}C?

We presume these are Maurer’s clefts FIKK kinases as most of the hyperphosphorylated proteins are MC residents. However, without directly testing for this using conditional KO parasite lines, we cannot exclude that host kinases are also playing a role.

(6) Could the additional assembly of PSAC at the iRBC membrane be important for survival at 39 {degree sign}C?

We have tested to see if nutrient uptake helps parasite survival during heat stress in the presence of furosemide and lower nutrient concentrations, but did not see a difference in growth following heat stress compared to control temperature conditions.

New Results:

We have added a new supplementary figure (Supplementary Figure 4) detailing experiments testing parasite growth under altered nutrient availability using two approaches (sub-lethal furosemide concentrations or reduced-nutrient RPMI) and with or without a 40°C heat stress applied between 16-24 hpi.

The main text now references this data: “Culturing parasites in sub-lethal furosemide concentrations or in reduced nutrient media lead to reduced parasitaemia (Supplementary Figure 4). However, the parasitaemia is not further reduced following heat stress. This shows that increased PSAC levels/activity do not enhance parasite survival under conditions of limited nutrient availability either from furosemide-induced nutrient deprivation or a reduced nutrient media composition.”

These experiments show that nutrient uptake does not improve parasite survival during heat stress compared to control temperature conditions.

(7) Would the authors like to speculate on how higher temperatures increase the transport of exported proteins with TMDs?

There are many possible explanations, one of which is that unfolding of the hydrophobic TMD domains is favoured at elevated temperatures. However, we have no data to support this hypothesis and therefore refrained from particularly stating this possibility.

eLife Assessment

Du et al. present a valuable study examining neural activation in medial prefrontal cortex (mPFC) subpopulations projecting to the basolateral amygdala (BLA) and nucleus accumbens (NAc) during behavioral tasks assessing anxiety, social preference, and social dominance. The strength of the evidence linking in vivo neural physiology to behavioral outcomes was considered solid; however, the electrophysiology data and their interpretation were less well received. Overall, the reviewers felt that the revised work provides insight into how distinct mPFC→BLA and mPFC→NAc pathways influence anxiety, exploration, and social behaviors.

Reviewer #1 (Public review):

Summary:

It is well known that neurons in the medial prefrontal cortex (mPFC) are involved in higher cognitive functions such as executive planning, motivational processing and internal state mediated decision-making. These internal states often correlate with the emotional states of the brain. While several studies point to the role of mPFC in regulating behavior based on such emotional states, the diversity of information processing in its sub-populations remains a less explored territory. In this study, the authors try to address this gap by identifying and characterizing some of these sub-populations in mice using a combination of projection-specific imaging, function-based tagging of neurons, multiple behavioral assays and ex-vivo patch clamp recordings.

Strengths:

The authors targeted mPFC projections to the nucleus accumbens (NAc) and basolateral amygdala (BLA). Using the open field task (OFT), the authors identified four relevant behavioral states as well as neurons active while the animal was in the center region ("center-ON neurons"). By characterizing single unit activity and using dimensionality reduction, the authors show differentiated coding of behavioral events at both the projection and functional levels. They further substantiate this effect by showing higher sensitivity of mPFC-BLA center-ON neurons during time spent in the open arms of the elevated plus maze (EPM). The authors then pivoted to the three-chamber social interaction (SI) assay to show the different subsets of neurons encode preference of social stimulus over non-social. This reveals an interesting diversity in the function of these sub-populations on multiple levels. Lastly, the authors used the tube test as a manipulation of the anxiety state of mice and compared behavioral differences before/after in the OFT and social interaction tasks. This experiment revealed that "losers" of the tube test spend less time in the center of the open field while "winners" show a stronger preference for the familiar mouse over the object. Using patch-clamp experiments, the authors also found that "winners" exhibit stronger synaptic transmission in the mPFC-NAc projection while "losers" exhibit stronger synaptic transmission in the mPFC-BLA projection. Given the popularity of the tube test assay in rank determination, this provides useful insights into possible effects on anxiety levels and synaptic plasticity. Overall, the many experiments performed by the authors reveal interesting differences in mPFC neurons relative to their involvement in high or low anxiety behaviors, social preference and social rank.

Weaknesses:

The authors focused primarily on female mice limiting generalizability and leaving the readers with questions about the impact of sex differences on their results. The tube test is used as a manipulation of the "emotional state" in several of the experiments. While the authors show the changes to corticosterone levels as a consequence of win/loss in the tube test, stronger claims might be made with comparisons to other gold standard stressors such as forced social defeat or social isolation.

Reviewer #2 (Public review):

Summary:

The goal of this proposal was to understand how two separate projection neurons from the medial prefrontal cortex, those innervating the basolateral amygdala (BLA) and nucleus accumbens (NAc), contribute to the encoding of emotional behaviors. The authors record the activity of these different neuron classes across three different behavioral environments. They propose that, although both populations are involved in emotional behavior, the two populations have diverging activity patterns in certain contexts. A subset of projections to the NAc appear particularly important for social behavior. They then attempt to link these changes to the emotional state of the animal and changes in synaptic connectivity.

Strengths:

The behavioral data builds on previous studies of these projection neurons supporting distinct roles in behavior and extend upon previous work by looking at the heterogeneity within different projection neurons across contexts, this is important to understand the "neural code" within the PFC that contributes to such behaviours and how it is relayed to other brain structures.

Weaknesses:

The diversity of neurons mediating these projections and their targeting within the BLA and NAc is not explored. These are not homogeneous structures and so one possibility is that some of the diversity within their findings may relate to targeting of different sub-structures within BLA or NAc or the diversity of projection neuron subtypes that mediate these pathways. This is an important future direction for this work but does not detract from the main finding as reported. The electrophysiological data in Figure 7 have significant experimental confounds that makes their interpretation challenging.

Author Response:

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

Public review:

Reviewer #1 (Public review):

Weaknesses:

The authors focused primarily on female mice without commenting on the effect that sex differences would have on their results.

We agree that sex is an important biological variable. Our experiments were performed primarily in female mice to align with the higher prevalence of affective disorders in females and to maintain consistency across experiments. We now explicitly acknowledge this as a limitation in the Discussion and note that future studies will be needed to determine whether the projection-specific coding principles identified here generalize to male animals. Relevant literature on sex-specific mPFC→BLA/NAc function has also been incorporated.

While the authors have identified relevant behavioral states across the various behavioral tasks, there is still a missing link between them and "emotional states" - the phrase used by them emphatically throughout the manuscript. The authors have neither provided adequate references to satisfy this gap nor shared any data pertaining to relevant readouts such as cortisol levels.

We appreciate the reviewer’s concern regarding the use of the term “emotional states.” In the revised manuscript, we have clarified our terminology and now use “behavioral states associated with affective valence” where appropriate. We have also added references supporting the use of open field center vs. corner occupancy, elevated plus maze performance, and social interaction assays as established proxies for anxiety-like and affect-related behaviors.

Importantly, to provide physiological support for these interpretations, we now include data showing that repeated win/loss outcomes in the tube test are associated with increased corticosterone levels in loser mice. These results indicate that the behavioral manipulations used in this study are accompanied by measurable physiological changes linked to stress-related processes.

Both the projection-specific recordings and patch-clamp experiments, including histology reports in the manuscript, would provide essential information for anyone trying to replicate the results, especially since it's known that sub-populations in the BLA and NAc can have vastly different functions.

We agree that detailed reporting of projection targeting is important for reproducibility. We have expanded the Methods and Results to more clearly describe viral targeting, recording locations, and histological verification of mPFC projections to the lateral BLA and NAc shell. We also now explicitly acknowledge the anatomical and cellular heterogeneity within these regions as a limitation and discuss this as an important direction for future work.

The population-level analysis in the manuscript requires more rigor to reduce bias and statistical controls for establishing the significance of their results.

We have strengthened the statistical analyses throughout the manuscript. Specifically, we have incorporated permutation-based controls for key analyses, clarified how behavioral and neural features were defined, and provided additional details on dimensionality reduction and clustering approaches. Exact p values, sample sizes, and statistical tests are now reported throughout the manuscript and figure legends.

Lastly, the tube test is used as a manipulation of the "emotional state" in several of the experiments. While the tube test can cause a temporary spike in anxiety of the participating mice, it is not known to produce a sustained effect - unless there are additional interventions such as forced social defeat. Thus, additional controls for these experiments are essential to support claims based on changes in the emotional state of mice.

We agree that the tube test is not a classical chronic stress paradigm such as social defeat. In our study, the tube test was used to establish social hierarchy rather than to model sustained stress. We have revised the manuscript to clarify this point and have tempered our language accordingly. At the same time, our corticosterone measurements indicate that repeated social competition induces measurable physiological changes, suggesting that the paradigm captures aspects of social hierarchy–related stress. We now frame these effects conservatively and acknowledge the need for future studies using additional stress paradigms.

Apart from the methodology, the manuscript could also be improved with the addition of clear scatter points in all the plots along with detailed measures of the statistical tests such as exact p values and size of groups being compared.

We have revised all figures to include individual data points (scatter overlays) wherever appropriate and have improved reporting of statistical details, including exact p values and group sizes, to enhance transparency and reproducibility.

Taken together, these revisions clarify our interpretations, improve methodological transparency, and strengthen the rigor of the analyses while preserving the main conclusions of the study.

Reviewer #2 (Public Review):

Weaknesses:

The diversity of neurons mediating these projections and their targeting within the BLA and NAc is not explored. These are not homogeneous structures and so one possibility is that some of the diversity within their findings may relate to targeting of different sub-structures within each region.

We agree that both the basolateral amygdala (BLA) and nucleus accumbens (NAc) are highly heterogeneous. Our study was designed to focus on projection-defined mPFC outputs (presynaptic activity) rather than resolving postsynaptic subregional or cell-type diversity. We have now:

- Clarified targeting strategies (PL→NAc shell and PL→BLA basal region)

- Added histological descriptions of injection and recording sites

- Expanded the Discussion to acknowledge how subregional and cellular heterogeneity may contribute to the observed variability

We also highlight this as an important direction for future work.

The electrophysiological data have significant experimental confounds and more methodological information is required to support other conclusions related to these data.

We have significantly strengthened the electrophysiological component by:

- Providing detailed recording conditions (access resistance, membrane properties, inclusion criteria)

- Clarifying stimulus protocols and normalization procedures

- Including representative traces and quantification of exclusion rates

- Addressing potential confounds such as viral expression variability and stimulation parameters

These revisions improve both interpretability and reproducibility of the electrophysiological findings.

Reviewer #3 (Public Review):

Major Weaknesses:

(1) The manuscript does not clearly and consistently specify the sex of the mice used for behavioral and imaging experiments. Given the known influence of sex on emotional behaviors and neural activity, this omission raises concerns about the generalizability of the findings. The authors should make clear throughout the manuscript whether male, female, or mixed-sex cohorts were used and provide a rationale for their choice. If only one sex was used, the potential limitations of this approach should be explicitly discussed.

We agree that sex is an important biological variable. We have now clearly specified throughout the manuscript that experiments were performed primarily in female mice and have added a rationale for this choice in the Methods. Briefly, we focused on females to align with the higher prevalence of affective disorders in females and to maintain consistency across experiments. We now explicitly acknowledge this as a limitation in the Discussion and note that future studies will be needed to determine whether these findings generalize to male animals.

(2) Mice lacking "center-ON" neurons were excluded from analysis, yet the manuscript draws broad conclusions about the encoding of emotional states by mPFC pathways. It is critical to justify this exclusion and discuss how it may limit the generalizability of the findings. The inclusion of data or contextualization for animals without center-ON neurons would strengthen the interpretation.

We thank the reviewer for raising this important point. Mice lacking identifiable center-ON neurons were excluded from analyses that specifically relied on this functional classification, as inclusion of such datasets would preclude meaningful comparison of this neuronal population. We have now clarified this criterion in the Methods and Results. Importantly, this exclusion does not affect analyses performed at the population level or those not dependent on center-ON classification. We now explicitly discuss this limitation and note that variability in the presence of center-ON neurons may reflect biological heterogeneity across animals.

(3) The manuscript lacks baseline activity comparisons for mPFC→BLA and mPFC→NAc pathways across subjects. Providing baseline data would contextualize the observed activity changes during behavior testing and help rule out inter-individual variability as a confounding factor.

We have added baseline comparisons of mPFC→BLA and mPFC→NAc activity across subjects to control for inter-individual variability and better contextualize behavior-related changes.

(4) Extensive behavioral testing across multiple paradigms may introduce stress and fatigue in the animals, which could confound the induction of emotional states. The authors should describe the measures taken to minimize these effects (e.g., recovery periods, randomized testing order) and discuss their potential impact on the results.

We now provide detailed descriptions of experimental design, including habituation, randomized testing order, and recovery periods between assays. We also discuss potential cumulative stress effects as a limitation.

(5) Grooming is described as a "non-anxiety" behavior, which conflicts with its established role as a stress-relieving behavior that may indicate anxiety. This discrepancy requires clarification, as the distinction is central to the conclusions about the mPFC→BLA pathway's role in differentiating anxiety-related and non-anxiety behaviors.

We thank the reviewer for this important clarification. We agree that grooming can be associated with both stress-related and self-soothing behaviors. In the revised manuscript, we have clarified that grooming is not strictly a “non-anxiety” behavior but instead represents a distinct behavioral state that may reflect stress regulation or internal state transitions. We have revised the text accordingly to avoid oversimplification and to better align with the literature.

(6) While the study highlights pathway-specific neural activity, it lacks a cohesive integration of these findings with the behavioral data. Quantifying the overlap or decorrelation of neuronal activity patterns across tasks would solidify claims about the specialization of mPFC→NAc and mPFC→BLA pathways. Likewise, the discussion should be expanded to place these findings in light of prior studies that have probed the roles of these pathways in social/emotion/valence-related behaviors.

We agree that stronger integration between neural and behavioral findings would strengthen the manuscript. In the revised version, we have added quantitative analyses examining the similarity and divergence of activity patterns across behavioral contexts (e.g., cross-context comparisons and correlation-based analyses). We have also expanded the Discussion to better integrate our findings with prior studies on mPFC→NAc and mPFC→BLA pathways in reward, aversion, and social behavior, thereby providing a more cohesive interpretation of pathway-specific functions.

Minor Weaknesses:

(1) The manuscript does not explicitly state whether the same mice were used across all behavioral assays. This information is critical for evaluating the validity of group comparisons. Additionally, more detail on sample sizes per assay would improve the manuscript's transparency.

(2) In Figure 2G, the difference between BLA and NAc activity during exploratory behaviors (sniffing) is difficult to discern. Adjusting the scale or reformatting the figure would better illustrate the findings.

(3) While the characteristics of the first social stimulus (M1) are specified, there is no information about the second social stimulus (M2). This omission makes it difficult to fully interpret the findings from the three-chamber test.

(4) The methods section lacks detailed information about statistical approaches and animal selection criteria. Explicitly outlining these procedures would improve reproducibility and clarity.

We have addressed all these minor concerns, including:

- Clarifying whether the same mice were used across assays

- Reporting sample sizes for each experiment

- Improving figure clarity (e.g., scaling, labeling, scatter points)

- Providing details for social stimuli (M1 vs. M2)

- Expanding statistical methods and animal selection criteria

Summary

In summary, we have made substantial revisions to:

- Improve conceptual precision (behavior vs. emotional state)

- Increase methodological transparency and statistical rigor

- Strengthen physiological validation

- Clarify experimental design and limitations

- Enhance integration with existing literature

We believe these revisions significantly improve the clarity, rigor, and interpretability of the manuscript, and we are grateful for the reviewers’ guidance in strengthening this work.

eLife Assessment

This is a detailed and well-designed simulation study of the utility of replication metrics in animal-to-human study translations in bridging the gap between laboratory discoveries and health practice, a critical consideration in turning laboratory scientific research findings into tangible, real-world applications, to directly help human health. The study approaches are solid, and the findings are important, as they offer insights into clinical research translations to advance health decision-making. There is some potential for the strength and applicability of the presented evidence to be improved upon revision.

Reviewer #1 (Public review):

A well-designed and preregistered simulation study investigating whether replication-success metrics can be applied to assess animal-to-human translation. The study is comprehensive, uses realistic parameter settings, and provides valuable insights into how different metrics behave under varied conditions.

Strengths:

(1) Methodologically rigorous and transparently preregistered.

(2) Comprehensive simulation design covering a wide range of plausible scenarios.

(3) Clear description of metrics and decision rules.

(4) Valuable contribution to understanding the limitations of applying replication metrics to translation questions.

Weaknesses:

(1) The conceptual distinction between replication and translation could be more clearly emphasized.

(2) Interpretation of results is dense and can be challenging to follow without a clear and summarized.

(3) Some simulation parameters (effect sizes, heterogeneity, and number of animal studies) require more substantial justification.

(4) Practical recommendations could be more explicit to guide applied researchers.

Reviewer #2 (Public review):

Summary:

The authors attempt to address the issue of high rates of translation failure from animal studies to humans in the literature, where promising results in animal studies fail when conducting human clinical trials. Using parameters from a previous meta-analysis on prenatal amino acid supplementation and the effects it has on maternal blood pressure, the authors assessed the performance of the metrics used and whether they can quantify translation success. Performing a simulation study, the authors compared nine translation success metrics and found that no one method was uniformly optimal. The authors list several limitations of the study, such as comparability of effect sizes between animal and human studies, different goals of animal studies versus human studies, and the focus of the study on one aspect (statistics of translation) is part of a broader, more complex decision-making process before proceeding to human trials. The authors recommend using multiple metrics in combination while taking into consideration their strengths and weaknesses to assess the translation of animal studies to human outcomes. The paper achieves the aim of providing a model with several metrics to evaluate translation success from animal studies to humans.

Strengths:

(1) Utilizing 9 different translation success metrics in combination provides strong flexibility in evaluating whether results in animal studies can translate to humans. This would allow researchers to evaluate translation success using multiple different metrics according to the context of the study.

(2) The authors accommodated for the limited sample size in animal studies, which are typically underpowered, and also caution that special attention should be given to heterogeneity when interpreting translation results.

(3) Overall, this approach has the potential to be applied to other biomedical studies, provided the limitations for each of the metrics are considered. It would provide a useful tool in assessing translation from animals to humans, in addition to other factors such as safety, pharmacokinetics, etc.

Weaknesses:

While the study has several strengths, there are some limitations.

(1) Preclinical animal study sizes tend to be much smaller than human studies, which results in underpowered results. The authors adjusted for this by pooling animal study data. However, high heterogeneity in the animal studies can affect translation results.

(2) The study focuses only on evaluating the statistical component of translation, which is only one aspect of the decision-making process to move on to human trials. The study does not take into account safety and toxicological profiles, pharmacokinetics, or genetics, which are important considerations that influence the overall effect in humans.

Reviewer #3 (Public review):

Summary:

This paper focused on how to navigate the complex decision-making process of whether to go into human trials. This is a critical topic considering the well-documented challenges in replicating and translating findings. While these are two distinct topics (i.e., replication and translation), they are related, and the authors simulated many conditions to assess the utility of replication assessment metrics.

Strengths:

A major strength of the study is the detailed approach to identifying relevant conditions and metrics, and to providing rich results that outline the strengths and weaknesses of each metric. Any simulation study is challenged by trying to identify the most relevant variables of interest, and this study provided sound justification for its chosen variables of interest. While this study does not make a strong recommendation (which I see as a strength), it does provide a comprehensive overview of the various metrics and conditions that were investigated.

Weaknesses:

The weaknesses of the study are the limited focus on specific metrics, the assumptions, particularly in the limited number of human study variables, and the less-than-ideal approachable summary of findings for a non-technical audience.

Conclusion:

This paper provides a much-needed investigation and discussion of how decisions are made when assessing whether to go into human trials. This is an important topic that productively challenges the status quo, considering documented challenges in replication and translation in biomedical research.

eLife Assessment

The study investigates how CD1c-restricted T cells respond to Mtb-infected APCs, leading to increased cytokine production and cytotoxic activity that may help control Mtb infection. While the work is important and will interest researchers in the field, the supporting evidence is incomplete and could be strengthened by additional experiments. Experiments would: (i) evaluate THP1-CD1c cells to determine whether MHC surface expression is reduced or entirely abolished, (ii) enhance confidence in the purity of the CD1c-specific T cell population isolated from blood, and (iii) suggest what additional signal THP1-CD1c cells treated with Mtb express that is absent from the untreated cells.

Reviewer #1 (Public review):

Summary:

T cells that recognize lipids - CD1c - are frequent in circulation; however, their role in infection is unclear. This study aims to understand how Mtb infection can shape the responses of CD1c-specific T cells. CD1c is expressed in MTB granuloma, but in lower amounts than in nearby inflamed tissue. Mtb infection downregulates the expression of CD1c on monocyte-derived DCs. Single-cell RNA sequencing revealed the cytotoxic program inherent to the lipid-CD1c-specific T cells. Using an in vitro APC system where CD1c expression remains intact upon Mtb infection, the authors suggest that these T cells react better to Mtb-infected than uninfected Cd1c-expressing APC and reduce Mtb burden in infected cells. Therefore, Cd1c downregulation could be an immune evasion strategy used by Mtb.

Strengths:

This study asks an important question. The single-cell transcription analysis suggests the inherent cytotoxic program of lipid-CD1c cells and provides insights into their phenotypic and potential functional profiles. Function experiments suggest that these autoreactive T cells can react to Mtb infection, adding to the paradigm of infection control by these non-conventional T cell populations.

Weaknesses:

The study lacks sufficient rigor; conclusions may be strengthened with the incorporation of more controls, and some deeper characterization of the THP1 system and the CD1c-specific T cells isolated from blood. Crucial conclusions are drawn from the cell mixing experiments involving the engineered THP-1 system and CD1c-lipid-specific T cells from blood. These cells need more in-depth characterization. The expression of MHC-I/II is clearly reduced in THP1-CD1c cells. However, it is important to ensure that it is completely abolished, since a residual expression can skew the result with activation of conventional T cells in the blood or low levels of conventional T cells that may be present in the CD1c-tetra/multimer sorted T cells. CD1c-tetra/multimer sorting should include more markers than used in this study.

Figure 2: The immunohistochemistry appears to be shown only for one biopsy; it may be worth quantifying the immunohistochemistry of all five. The expression of CD1 molecules goes up during the differentiation of MoDC. And Mtb infection prevents or dampens the upregulation. Does Mtb infection downregulate the CD1 expression of mature DCs? Can the effect of Mtb on the expression of CD1a,b,c molecules be investigated using CD1c-expressing DCs from blood? What could be the reason THP-1 cells do not downregulate CD1 molecules upon Mtb infection, and how about the expression of CD1a and b?

Figure 3: (F) What does the X-axis read for the no infection group? The value for MOI = 0 should be incorporated for the infected T cell group.

Figure 4: In the lysis assay, THP1-CD1c cells (uninfected and infected) incubated alone should be incorporated.

Figure 5: A quantitative brief on the single cell TCR sequencing - including how many T cells were sequenced and the frequency of different clone including EM1 and EM2 - should be shown.

Reviewer #2 (Public review):

Summary:

The study by Milton et al titled "Human CD1c-autoreactive T cells recognise Mycobacterium tuberculosis-infected antigen-presenting cells and display cytotoxic effector programmes" characterises CD1c-restricted autoreactive T cells and their potential role in controlling Mtb infection. The authors develop a well-controlled system to assay for the functioning/activation of autoreactive T cells. They report the presence of CD1c-restricted autoreactive T cells in the circulating blood of healthy donors. They show that these T cells respond to CD1c and get activated even in the absence of any exogenous antigen. They next show that CD1c, along with CD1a and b, are typically downregulated on APCs during Mtb infection. These autoreactive T cells are cytotoxic, indicating they respond to Mtb treatment and/or to changes in the T cell ratio. The autoreactive T cells could effectively lyse Mtb-infected or PAMP-stimulated CD1c+APCs. Next, using TCR sequencing, they show that T cell responses were mediated by specific TCR clones with common sequence features. They show that these autoreactive T cells could curtail Mtb growth as measured by luminescence. Finally, using scRNAseq, they selectively identify the CD1c-reactive T cell pool and detect enrichment of typical effector memory CD4 and CD8 cells expressing cytolytic markers such as Granzyme, granulolysin, etc. The lung biopsy staining, along with the other data presented here, suggests that while CD1c-restricted T cells could have potential anti-bacterial roles, Mtb downregulation effectively shuts down this mechanism for TB control.

Strengths:

The study is designed well and has developed many exciting tools to generate specific information.

Weaknesses:

The study has weaknesses in two important parameters - novelty and relevance in controlling TB. Further, the results could be better presented and discussed to allow easy understanding of the experimental design. For example, at several places, UV-killed or live Mtb were used. What is the rationale behind that? Why use irradiated THP1-CD1c cells for activating T cells?

While functional assays identified only CD4+ cells as CD1c-restricted, scRNAseq shows that both CD4+ and CD8+ cells exhibit this phenotype. Identifying the specific lipid antigen presented by CD1c could add greater value to the study.

Since autoreactivity was independent of exogenous antigen, the cytotoxic activity should also be independent of exogeneous antigens? What additional signal a THP1-CD1c cells treated with UV-killed Mtb express that is absent from the untreated cells?

The relative Mtb growth assay is confusing. CD1c cells with Mtb infection triggers massive lytic response, as shown in Figure 4. Under similar conditions, in Figure 6, the authors report a significant decline in Mtb growth in these cells. The problem is that with the kind of lytic response observed, a lot more Mtb could be present extracellularly and would evade killing. How do we reconcile the two observations?

Reviewer #3 (Public review):

Summary:

Despite the rising global prevalence of TB, the role of non-classical T-cell pathways in host immunity remains unclear. The present study by Milton et al. is a novel contribution to the field of unconventional T-cell immunity in Mtb infection. The study addresses the role of CD1c-autoreactive T-cells and demonstrates that upon Mtb infection, these cells are significantly activated, resulting in increased cytokine production and cytotoxicity, and a reduction in the bacterial burden, specifically against Mtb-infected CD1c+ APCs (antigen-presenting cells). This defines their role as a plausible candidate for lipid-directed immunity in TB, complementary to MHC-restricted responses.

Strengths:

The manuscript is well written, and the novelty, impact, and limitations of this study are precisely highlighted by the authors.

Weaknesses:

The authors mention that they did not identify any specific lipids presented by CD1c on Mtb-infected APCs, making it unclear whether they are of host or bacterial origin. This leaves a gap in understanding why the response is enhanced in Mtb-infected cells, whether it is through altered self-presentation of lipids arising from Mtb-induced changes, infection-induced stress signals, or Mtb lipids, or through CD1c-dependent co-stimulation/infection signals. Direct lipid identification via lipidomics/MS of CD1c-bound lipids from Mtb-infected APCs would clarify whether the enhancement arises from altered self-lipids or subtle Mtb lipids.

eLife Assessment

This study provides valuable findings regarding potential correlates of protection against the African swine fever virus. The evidence supporting the claims is solid, and the results are highly relevant to the field. Further analysis using larger number of animals and other virus strains will help validate the importance of these findings and assess the relevance of the immune parameters associated with protection. The work will be of broad interest to veterinary immunologists, and particularly those working on African swine fever.

Reviewer #1 (Public review):

The study by Lotonin et al. investigates correlates of protection against African swine fever virus (ASFV) infection. The study is based on a comprehensive work, including the measurement of immune parameters using complementary methodologies. An important aspect of the work is the temporal analysis of the immune events, allowing to capture the dynamics of the immune responses induced after infection. Also, the work compares responses induced in farm and SPF pigs, showing the later an enhanced capacity to induce a protective immunity. Overall, the results obtained are interesting and relevant for the field. The findings described in the study further validate work form previous studies (critical role of virus-specific T cell responses), and provide new evidence on the importance of a balanced innate immune response during the immunization process. This information increases our knowledge on basic ASF immunology, one of the important gaps in ASF research that needs to be addressed for a more rational design of effective vaccines. As discussed in the manuscript, the results provide targets which can be further validated in other models, such as immunization using live attenuated vaccines.

Overall the conclusions of the work are well supported by the results, and most of the issues mentioned during the review have been properly addressed during the revision, improving the quality of the final manuscript. While some limitations remain, I consider that they do not invalidate the results obtained and are well discussed by the authors.

The study is highly relevant for the field, representing a step forward in our understanding of ASF protective immunity, providing immune targets to be further explored in other models and during vaccine development.

Reviewer #2 (Public review):

Summary:

In the current study the authors attempt to identify correlates of protection for improved outcomes following re-challenge with ASFV. An advantage is the study design which compares the responses to a vaccine like mild challenge and during a virulent challenge months later. It is a fairly thorough description of the immune status of animals in terms of T cell responses, antibody responses, cytokines and transcriptional responses and the methods appear largely standard. The comparison between SPF and farm animals is interesting and probably useful for the field in that it suggests that SPF conditions might not fully recapitulate immune protection in the real world. I thought some of the conclusions were over-stated and there are several locations where the data could be presented more clearly.

Strengths:

The study is fairly comprehensive in the depth of immune read-outs interrogated. The potential pathways are systematically explored. Comparison of farm animals and SPF animals gives insights into how baseline immune function can differ based on hygiene, which would also likely inform interpretation of vaccination studies going forward.

Weaknesses:

There are limited numbers of animals assessed.

Comments on revisions:

The authors mostly addressed my comments to the previous version. However, in the discussion they added comments relating to and an interpretation based on their own unpublished data and I think that those statements should be removed because the data are not included in this publication and cannot be cited.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The study by Lotonin et al. investigates correlates of protection against African swine fever virus (ASFV) infection. The study is based on a comprehensive work, including the measurement of immune parameters using complementary methodologies. An important aspect of the work is the temporal analysis of the immune events, allowing for the capture of the dynamics of the immune responses induced after infection. Also, the work compares responses induced in farm and SPF pigs, showing the latter an enhanced capacity to induce a protective immunity. Overall, the results obtained are interesting and relevant for the field. The findings described in the study further validate work from previous studies (critical role of virus-specific T cell responses) and provide new evidence on the importance of a balanced innate immune response during the immunization process. This information increases our knowledge on basic ASF immunology, one of the important gaps in ASF research that needs to be addressed for a more rational design of effective vaccines. Further studies will be required to corroborate that the results obtained based on the immunization of pigs by a not completely attenuated virus strain are also valid in other models, such as immunization using live attenuated vaccines.

While overall the conclusions of the work are well supported by the results, I consider that the following issues should be addressed to improve the interpretation of the results:

We thank Reviewer #1 for their thoughtful and constructive feedback, which significantly contributed to improving the clarity and quality of our manuscript. Below, we respond to each of the reviewer’s comments and describe the revisions that were incorporated.

(1) An important issue in the study is the characterization of the infection outcome observed upon Estonia 2014 inoculation. Infected pigs show a long period of viremia, which is not linked to clinical signs. Indeed, animals are recovered by 20 days post-infection (dpi), but virus levels in blood remain high until 141 dpi. This is uncommon for ASF acute infections and rather indicates a potential induction of a chronic infection. Have the authors analysed this possibility deeply? Are there lesions indicative of chronic ASF in infected pigs at 17 dpi (when they have sacrificed some animals) or, more importantly, at later time points? Does the virus persist in some tissues at late time points, once clinical signs are not observed? Has all this been tested in previous studies?

Tissue samples were tested for viral loads only at 17 dpi during the immunization phase, and long-term persistence of the virus in tissues has not been assessed in our previous studies. At 17 dpi, lesions were most prominently observed in the lymph nodes of both farm and SPF pigs. In a previous study using the Estonia 2014 strain (doi: 10.1371/journal.ppat.1010522), organs were analyzed at 28 dpi, and no pathological signs were detected. This finding calls into question the likelihood of chronic infection being induced by this strain.

(2) Virus loads post-Estonia infection significantly differ from whole blood and serum (Figure 1C), while they are very similar in the same samples post-challenge. Have the authors validated these results using methods to quantify infectious particles, such as Hemadsorption or Immunoperoxidase assays? This is important, since it would determine the duration of virus replication post-Estonia inoculation, which is a very relevant parameter of the model.

We did not perform virus titration but instead used qPCR as a sensitive and standardized method to assess viral genome loads. Although qPCR does not distinguish between infectious and non-infectious virus, it provides a reliable proxy for relative viral replication and clearance dynamics in this model. Unfortunately, no sample material remains from this experiment, but we agree that subsequent studies employing infectious virus quantification would be valuable for further refining our understanding of viral persistence and replication following Estonia 2014 infection.

(3) Related to the previous points, do the authors consider it expected that the induction of immunosuppressive mechanisms during such a prolonged virus persistence, as described in humans and mouse models? Have the authors analysed the presence of immunosuppressive mechanisms during the virus persistence phase (IL10, myeloid-derived suppressor cells)? Have the authors used T cell exhausting markers to immunophenotype ASFV Estonia-induced T cells?

We agree with the reviewer that the lack of long-term protection can be linked to immunosuppressive mechanisms, as demonstrated for genotype I strains (doi: 10.1128/JVI.00350-20). The proposed markers were not analyzed in this study but represent important targets for future investigation. We addressed this point in the discussion.

(4) A broader analysis of inflammatory mediators during the persistence phase would also be very informative. Is the presence of high VLs at late time points linked to a systemic inflammatory response? For instance, levels of IFNa are still higher at 11 dpi than at baseline, but they are not analysed at later time points.

While IFN-α levels remain elevated at 11 dpi, this response is typically transient in ASFV infection and likely not linked to persistent viremia. We agree that analyzing additional inflammatory markers at later time points would be valuable, and future studies should be designed to further understand viral persistence.

(5) The authors observed a correlation between IL1b in serum before challenge and protection. The authors also nicely discuss the potential role of this cytokine in promoting memory CD4 T cell functionality, as demonstrated in mice previously. However, the cells producing IL1b before ASFV challenge are not identified. Might it be linked to virus persistence in some organs? This important issue should be discussed in the manuscript.

We agree that identifying the cellular source of IL-1β prior to challenge is important, and this should be addressed in subsequent studies. We included a discussion on the potential link between elevated IL-1β levels and virus persistence in certain organs.

(6) The lack of non-immunized controls during the challenge makes the interpretation of the results difficult. Has this challenge dose been previously tested in pigs of the age to demonstrate its 100% lethality? Can the low percentage of protected farm pigs be due to a modulation of memory T and B cell development by the persistence of the virus, or might it be related to the duration of the immunity, which in this model is tested at a very late time point? Related to this, how has the challenge day been selected? Have the authors analysed ASFV Estonia-induced immune responses over time to select it?

In our previous study, intramuscular infection with ~3–6 × 10² TCID₅₀/mL led to 100% lethality (doi: 10.1371/journal.ppat.1010522), which is notably lower than the dose used in the present study, although the route here was oronasal. The modulation of memory responses could be more thoroughly assessed in future studies using exhaustion markers. The challenge time point was selected based on the clearance of the virus from blood and serum. We agree that the lack of protection in some animals is puzzling and warrants further investigation, particularly to assess the role of immune duration, potential T cell exhaustion caused by viral persistence, or other immunological factors that may influence protection. Based on our experience, vaccine virus persistence alone does not sufficiently explain the lack-of-protection phenomenon. We incorporated these important aspects into the revised discussion.

(7) Also, non-immunized controls at 0 dpc would help in the interpretation of the results from Figure 2C. Do the authors consider that the pig's age might influence the immune status (cytokine levels) at the time of challenge and thus the infection outcome?

We support the view that including non-immunized controls at 0 dpc would strengthen the interpretation of cytokine dynamics and will consider this in future experimental designs. Regarding age, while all animals were within a similar age range at the time of challenge, we acknowledge that age-related differences in immune status could influence baseline cytokine levels and infection outcomes, and this is an important factor to consider.

(8) Besides anti-CD2v antibodies, anti-C-type lectin antibodies can also inhibit hemadsorption (DOI: 10.1099/jgv.0.000024). Please correct the corresponding text in the results and discussion sections related to humoral responses as correlates of protection. Also, a more extended discussion on the controversial role of neutralizing antibodies (which have not been analysed in this study), or other functional mechanisms such as ADCC against ASFV would improve the discussion.

The relevant text in the Results and Discussion sections was revised accordingly, and the discussion was extended to more thoroughly address the roles of antibodies.

Reviewer #2 (Public review):

Summary:

In the current study, the authors attempt to identify correlates of protection for improved outcomes following re-challenge with ASFV. An advantage is the study design, which compares the responses to a vaccine-like mild challenge and during a virulent challenge months later. It is a fairly thorough description of the immune status of animals in terms of T cell responses, antibody responses, cytokines, and transcriptional responses, and the methods appear largely standard. The comparison between SPF and farm animals is interesting and probably useful for the field in that it suggests that SPF conditions might not fully recapitulate immune protection in the real world. I thought some of the conclusions were over-stated, and there are several locations where the data could be presented more clearly.

Strengths:

The study is fairly comprehensive in the depth of immune read-outs interrogated. The potential pathways are systematically explored. Comparison of farm animals and SPF animals gives insights into how baseline immune function can differ based on hygiene, which would also likely inform interpretation of vaccination studies going forward.

Weaknesses:

Some of the conclusions are over-interpreted and should be more robustly shown or toned down. There are also some issues with data presentation that need to be resolved and data that aren't provided that should be, like flow cytometry plots.

We appreciate the feedback from the Reviewer #2 and acknowledge the concerns raised regarding data presentation. In the revised manuscript, we clarified our conclusions where needed and ensured that interpretations were better aligned with the data shown.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) In the Introduction, more details on the experimental model would be appreciated. A short summary of findings obtained with this model in previous works from the authors would help to better understand the context of the study.

Basic information on the model was added in the Introduction section of the revised manuscript.

(2) In Figure 1, the addition of more time points on the x-axes would help the interpretation of the figures.

We agree and have added extra time points to the x-axes.

(3) To better understand the results in Figure 2A, a figure showing cytokine levels post-Estonia infection of only challenged pigs would help, indicating protected and non-protected animals as in Figure 2C. This figure would be better linked to the corresponding dot plot (Figure 2B).

Our statistical analyses in Figure 2A are based on using both challenged and non-challenged pigs to assess differences between SPF and farm pigs. We prefer not to remove the non-challenged pigs in order to avoid losing statistical power. Moreover, even when non-challenged and challenged pigs are displayed in the plots, upregulation of IFN-α and IL-8 can be visualized and remains consistent with the positive and negative correlates of protection shown in Figure 2C.

(4) Dark red colour associated with SPF non-protected is difficult to differentiate from light red in some figures.

We thank the reviewer for this remark. To preserve the color scheme across the paper, we changed the circle data points to squares for the non-protected SPF pig in the most crowded figures: Figures 1–3 and Supplementary Figures 2 and 8.

(5) In Supplementary figures 12-16, grouping of the animal numbers (SPF vs farm) would facilitate the interpretation of the results.

Information on the animal numbers for each group (SPF vs. farm) has been added to the figure captions.

(6) Are the results shown in Figure 8 based on absolute scores as mentioned? Results from 0 dpc are not shown. Is that correct?

That is correct. BTM expression values are absolute and could not be normalized, as RNA was not isolated either immediately before the challenge or on day 0 post-challenge. This information is now clarified in the figure captions.

Reviewer #2 (Recommendations for the authors):

(1) The authors use the words "predicted" and "predicts" although they haven't used any methods to show that this is true, such as a multivariate analysis. I don't think correlation coefficients are sufficient to indicate prediction. This needs to be fixed.

We agree with this and have made changes in the text to avoid this impression.

(2) "Lower baseline immune activation was linked to increased protective immunity." Presumably, the authors mean prior to challenge, not prior to "vaccination"?

In this sentence written in the Abstract, we refer to baseline immune activation in the steady state, i.e., prior to any infection, as demonstrated in a previous study by Radulovic et al. (2022). The sentence was adapted accordingly. This concept is further explored in the Discussion section.

(3) The abstract mentioned the comparison between farm and SPF pigs, but didn't provide any context for those findings. It could be added here.

In the new version, we have added information on this model in the Introduction section.

(4) Figure legends need N to be indicated. For example, the viral load figures don't appear to be representative of all 9 or 5 animals. Is there a reason why not all were challenged, and how were those 5 challenged selected?

Numbers of animals in each group were added to the figure captions. We have also provided details regarding the animals sacrificed at different time points of the experiment in the ‘Animal experiment’ section of the Methods.

(5) 1A doesn't have a legend to indicate whether dark or light color indicates sampling.

Fair point. We have added the information to the figure.

(6) For Figure 3C, it's not clear how the correlation is presented. The legend indicates in writing that the color indicates the outcome it correlates with, but the legend suggests that it is r.

The method of presenting correlation data is consistent across all figures, including Figure 3C. The color reflects the direction and strength of the correlation, corresponding to the r coefficient obtained from correlating immunological parameters with clinical scores. We have clarified this description in the figure caption to improve readability.

(7) For some of the correlation data in 2D and 3C, it would be nice to provide the plots in the supplemental. Also, are there enough data points for a robust interpretation of correlation curves?

We agree that providing the plots will improve clarity and have included them in the supplementary material. While we acknowledge that the number of data points is modest, we believe it is sufficient to support a robust interpretation of the correlation curves. Corresponding p-value cutoffs are noted in the figure captions.

(8) The figure 2C method of indicating significance is confusing. There must be a clearer way to present this figure.

Analyzing statistical significance for the dataset shown in Figure 2C is challenging due to the small number of animals. We carefully considered alternative ways of presenting statistical significance, however, given the limited group sizes, we believe that the current approach provides the most transparent and informative representation of the data.

For clarity, we divided the animals into SPF and farm groups, as well as into protected (4 SPF, 2 farm pigs) and non-protected (1 SPF, 3 farm pigs) categories, and performed both group-based (unpaired t-test) and time-based (mixed-effects analysis) comparisons. All significant differences were added to the plots so that readers could directly visualize the observed trends and compare them with the correlation analysis presented in Figure 2D.

(9) Please note that "viremia" means the presence of a virus specifically in the blood. Other descriptions of viral load should be used if this was not measured.

We have clarified this in the text. When referring to organs, we use the term “viral loads.”

(10) The way of putting a square around boxes that are significant can be misleading when a box is surrounded by other significant comparisons. Like for Figure 6B - probably all of these are really significant, but I can't tell for sure.

Good point. We changed rectangles to circles for better readability of the figures.

(11) There is a potential argument that these correlates of protection might only be valid for this specific vaccine. It should be noted that comparisons of multiple vaccines would be needed before assuming the correlates are broadly relevant.

We agree with this statement and address it in the Discussion section.

(12) For the circled pathways in Figure 9, it is not clear from the diagram if there is a directionality to the involvement of those pathways. Modulated or induced?

When discussing pathways identified by transcriptome analysis, we are always referring to their induction, as this is based on the normalized enrichment score (NES). We have now specified this in the figure caption.

(13) The authors speculate about NK cells, but this is based on transcriptional pathways identified and the literature. Is there any indication from the flow cytometry data whether activated NK cells versus NKT cells are associated with protection? Also, the memory phenotype of those cells?

Regarding NK cells, the BTM analysis was corroborated by the flow cytometry data shown in Supplementary Figure 8. NK cells were defined as CD3^-CD8α⁺. Specific markers to distinguish NKT cells or to assess memory phenotypes were not included in our panel.

(14) In the discussion, "Our study demonstrates that T cell activation represents a robust correlate of protection against ASFV" doesn't indicate whether they mean after vaccination or after challenge. Re-using the same time points throughout the manuscript compounds this confusion.

In this case, we mean that T cell activation upon immunization/vaccination and challenge correlates with protection. This information has been added to the sentence. Although some time points overlap between the immunization and challenge phases, we consistently use “dpi” and “dpc” to clearly distinguish them.

(15) Flow cytometry gating strategies should be provided in the supplemental, particularly since this species is less frequently studied using flow cytometry; it would be helpful to understand gating and expression levels of key markers.

We have provided the gating strategy in Supplementary Figure 7, which is also referenced in the “Flow cytometry and hematology analysis” section of the Methods.

(16) Some of the discussion is a bit long and repetitive - e.g. the parts on antibodies and the last paragraph with multiple other parts of the discussion and manuscript.

While we agree that some sections are extensive, we think that this level of detail is necessary to integrate the different datasets and to place our findings in the context of previous literature.

eLife Assessment

This study provides an important contribution by showing that whiteflies and planthoppers use salivary effectors to suppress plant immunity through the receptor-like protein RLP4, suggesting convergent evolution in these insect lineages. The topic is of clear interest for understanding plant-insect interactions and offers ideas that could stimulate further research in the field. The authors provide convincing evidence for the functional roles of the salivary effectors.

Reviewer #1 (Public review):

Summary:

This manuscript investigates how herbivorous insects, specifically whiteflies and planthoppers, utilize salivary effectors to overcome plant immunity by targeting the RLP4 receptor.

Strengths:

The authors present a strong case for the independent evolution of these effectors and provide compelling evidence for their functional roles.

Comments on revisions:

The authors have addressed all my concerns.

Reviewer #2 (Public review):

Summary:

The authors tested an interesting hypothesis that white flies and planthoppers independently evolved salivary proteins to dampen plant immunity by targeting a receptor like protein. Unlike previously reported receptor like proteins with large ligand-binding domains, the NtRLP4 here has a malectin LRR domain. Interestingly, it also associates with the adaptor SOBIR1. While the function of this protein remains to be further explored, the authors provide strong evidence showing it's the target of salivary proteins as the insects' survival strategy.

The authors have nicely addressed the questions I raised.

I noticed two small points the authors may modify:<br /> - Line 16: delete "on"<br /> - Line 185: Replace "is resistant to B. tabaci infestation" with "confers resistance against B. tabaci".

Reviewer #3 (Public review):

Summary:

In this study, Wang et al., investigates how herbivorous insects overcome plant receptor-mediated immunity by targeting plant receptor-like proteins. The authors identify two independently evolved salivary effectors, BtRDP in whiteflies and NlSP694 in brown planthoppers, that promote the degradation of plant RLP4 through the ubiquitin-dependent proteasome pathway.

Strengths:

This work highlights a convergent evolutionary strategy in distinct insect lineages and advances our understanding of insect-plant coevolution at the molecular level.

Comments on revisions:

The authors have satisfactorily addressed all the issues I raised.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript investigates how herbivorous insects, specifically whiteflies and planthoppers, utilize salivary effectors to overcome plant immunity by targeting the RLP4 receptor.

Thank you for your comments.

Strengths:

The authors present a strong case for the independent evolution of these effectors and provide compelling evidence for their functional roles.

Thank you for your help in improving our manuscript

Reviewer #2 (Public review):

Summary:

The authors tested an interesting hypothesis that white flies and planthoppers independently evolved salivary proteins to dampen plant immunity by targeting a receptor-like protein. Unlike previously reported receptor-like proteins with large ligand-binding domains, the NtRLP4 here has a malectin LRR domain. Interestingly, it also associates with the adaptor SOBIR1. While the function of this protein remains to be further explored, the authors provide strong evidence showing it's the target of salivary proteins as the insects' survival strategy.

Thank you for your comments.

Major points:

The authors mixed the concepts of LRR-RLPs with malectin LRR-RLPs. These are two different type of receptors. While LRR-RLPs are well studied, little is known about malectin LRR-RLPs. The authors should not simply apply the mode of function of LRR-RLPs to RLP4 which is a malectin LRR-RLP. In addition, LRR-RLPs that function as ligand-binding receptors typically possess >20 LRRs, whereas RLP4 in this work has a rather small ectodomain. It remains unclear whether it will function as a PRR. I can't agree with the author's logic of testing uninfested plants for proving a PRR's function. The function of a pattern recognition receptor depends on perceiving the corresponding ligand. As shown by the data provided, RLP4-OE plants have altered transcriptional profile indicating activated defense, suggesting it's unlikely a PRR. An alternative explanation is needed. More work on BAK1 will also help to clarify the ideas proposed by the authors.

We sincerely thank the reviewer for the insightful and constructive comments, which have helped us critically re-evaluate our interpretation of RLP4 function. In the revised manuscript, we have addressed this important point by adding a detailed discussion of an alternative explanation for RLP4’s role in plant defense. Specifically, we now explicitly distinguish between classical LRR-RLPs and malectin-domain-containing RLPs, and we acknowledge that RLP4 may not function as a canonical PRR. We also discuss the structural features of RLP4, including its malectin-like domain and relatively small LRR region, and the observation that NtRLP4 overexpression lines exhibit altered transcriptional profiles even in the absence of insect infestation. Based on these lines of evidence, we propose that RLP4 may instead act as a regulatory component within plant immune signaling networks, modulating defense outputs rather than functioning as a direct ligand receptor. The revised discussion now reads as follows: “Together, this study reveals that suppressing PRR-mediated plant immunity may be a conserved strategy employed by herbivorous insects for successful feeding. We demonstrate that whiteflies and planthoppers have independently evolved salivary effectors that facilitate the ubiquitin-dependent degradation of defensive RLP4 in host plants, thereby dampen RLP4-mediated plant immunity (Fig. 6). Nevertheless, the precise mechanism by which RLP4 contributes to plant defense warrants further consideration. While it may function as a canonical PRR that perceives insect-derived molecular patterns, several lines of evidence point to an alternative interpretation. Structurally, RLP4 differs from classical LRR-RLP: it contains a malectin-like domain and a relatively small LRR domain, contrasting with typical LRR-RLPs that often possess large LRRs dedicated to ligand binding. Functionally, NtRLP4 overexpression lines exhibit significantly altered transcriptional profiles and dysregulated SA/JA pathways even in the absence of insect infestation, a phenotype inconsistent with canonical PRRs, which typically remain quiescent until ligand perception. These findings point to an alternative explanation: rather than functioning as a classical PRR that recognizes insect-derived molecules, RLP4 may act as a regulatory component within plant immune signaling networks. Elucidating the precise mechanism of RLP4 in conferring plant defense against herbivorous insects will therefore be an important focus of future research” in Line 392-407.

Reviewer #3 (Public review):

Summary:

In this study, Wang et al., investigate how herbivorous insects overcome plant receptor-mediated immunity by targeting plant receptor-like proteins. The authors identify two independently evolved salivary effectors, BtRDP in whiteflies and NlSP694 in brown planthoppers, that promote the degradation of plant RLP4 through the ubiquitin-dependent proteasome pathway. NtRLP4 from tobacco and OsRLP4 from rice are shown to confer resistance against herbivores by activating defense signaling, while BtRDP and NlSP694 suppress these defenses by destabilizing RLP4 proteins.

Thank you for your comments.

Strengths:

This work highlights a convergent evolutionary strategy in distinct insect lineages and advances our understanding of insect-plant coevolution at the molecular level.

Two minor comments:

In line 140, yeast two-hybrid (Y2H) was used to screen for interacting proteins in plants. However, it is generally difficult to identify membrane receptors using Y2H. Please provide more methodological details to justify this approach, or alternatively, include a discussion explaining this.

Thank you for pointing this out. It is true that Y2H is generally difficult to identify membrane receptors. To address this limitation, we used truncated versions of RLP4s lacking the signal peptide and transmembrane domains in point-to-point Y2H assays. In addition, the interactions between BtRDP and RLP4s were further validated by Co-IP and BiFC experiments. In the revised manuscript, we have clarified this methodological detail as follows: “Given that Y2H is generally difficult to identify membrane receptors, the truncated versions of NtRLP4/SlRLP4/OsRLP4 lacking the signal peptide and transmembrane domains were used” in Linr 636-638.

In Figure S12C, the interaction between the two proteins appears to be present in the nucleus as well. Please provide a possible explanation for this observation.

Thank you for pointing this out. During revision, we further examined the subcellular localization of NtRLP4 and found that NtRLP4-GFP could also be detected in the nucleus when expressed alone (Fig. S18), suggesting that NtRLP4 may have additional functions beyond serving as a cell surface pattern recognition receptor. In the revised manuscript, we discussed that NtRLP4 might play other roles in addition to PRRs in the discussion section as follow: “Together, this study reveals that suppressing PRR-mediated plant immunity may be a conserved strategy employed by herbivorous insects for successful feeding. We demonstrate that whiteflies and planthoppers have independently evolved salivary effectors that facilitate the ubiquitin-dependent degradation of defensive RLP4 in host plants, thereby dampen RLP4-mediated plant immunity (Fig. 6). Nevertheless, the precise mechanism by which RLP4 contributes to plant defense warrants further consideration. While it may function as a canonical PRR that perceives insect-derived molecular patterns, several lines of evidence point to an alternative interpretation. Structurally, RLP4 differs from classical LRR-RLP: it contains a malectin-like domain and a relatively small LRR domain, contrasting with typical LRR-RLPs that often possess large LRRs dedicated to ligand binding. Functionally, NtRLP4 overexpression lines exhibit significantly altered transcriptional profiles and dysregulated SA/JA pathways even in the absence of insect infestation, a phenotype inconsistent with canonical PRRs, which typically remain quiescent until ligand perception. These findings point to an alternative explanation: rather than functioning as a classical PRR that recognizes insect-derived molecules, RLP4 may act as a regulatory component within plant immune signaling networks. Elucidating the precise mechanism of RLP4 in conferring plant defense against herbivorous insects will therefore be an important focus of future research” in Line 392-407.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The authors have addressed all my concerns.

Thank you for your help in improving our manuscript

Reviewer #2 (Recommendations for the authors):

This work is quite interesting. It's not necessary to prove RLP4 as a PRR to show the merit of this discovery. The current logic is forced and thus the conclusion not convincing. Finding an alternative explanation will be more helpful.

Thank you for your valuable suggestions. In the revised version, we discussed the alternative explanation as follow: “Together, this study reveals that suppressing PRR-mediated plant immunity may be a conserved strategy employed by herbivorous insects for successful feeding. We demonstrate that whiteflies and planthoppers have independently evolved salivary effectors that facilitate the ubiquitin-dependent degradation of defensive RLP4 in host plants, thereby dampen RLP4-mediated plant immunity (Fig. 6). Nevertheless, the precise mechanism by which RLP4 contributes to plant defense warrants further consideration. While it may function as a canonical PRR that perceives insect-derived molecular patterns, several lines of evidence point to an alternative interpretation. Structurally, RLP4 differs from classical LRR-RLP: it contains a malectin-like domain and a relatively small LRR domain, contrasting with typical LRR-RLPs that often possess large LRRs dedicated to ligand binding. Functionally, NtRLP4 overexpression lines exhibit significantly altered transcriptional profiles and dysregulated SA/JA pathways even in the absence of insect infestation, a phenotype inconsistent with canonical PRRs, which typically remain quiescent until ligand perception. These findings point to an alternative explanation: rather than functioning as a classical PRR that recognizes insect-derived molecules, RLP4 may act as a regulatory component within plant immune signaling networks. Elucidating the precise mechanism of RLP4 in conferring plant defense against herbivorous insects will therefore be an important focus of future research” in Line 392-407.

Inappropriate descriptions still exist at multiple places across the manuscript and damages the merit of this work. I highly recommend the authors to consult an expert in plant PRR research for proof reading. The language editing service the authors used only provided limited help in this case. Here are a few examples:

We sincerely thank the reviewer for the critical and constructive comments. We agree that precise language is essential for conveying scientific findings. In the revised version, we have refined the text with the help of colleagues who have expertise in plant immunity, aiming to ensure the descriptions are as precise and professional as possible.

Line 16: Using "depend" ignores the fact that many biotic invaders are recognized by NLRs. The authors can simply use the word "use" or "utilize".

Thank you for your suggestion. We corrected it in the revised version.

Line 20:"target defensive RLP4, therefor minimizing the plant immunity" is a strange saying. "dampen RLP4-mediated plant immunity"will be better.

Thank you for your suggestion. We corrected it in the revised version.

Line 49: as far as I know, only LRR-RLPs use SOBIR1 as adaptor. The authors should introduce this specific point. The mode of action of other type of LRR-RLPs are less clear.

Thank you for your suggestion. In the revised version, we re-introduce this as follow: “As RLPs lack the intracellular signaling domains, they are anticipated to associate with adaptor kinases to form the bimolecular receptor kinases. For example, suppressor of BAK1-interacting receptor-like kinase 1 (SOBIR1) is reported to act as a common adaptor for most, if not all, of the leucine-rich repeat RLP (LRR-RLP)” in Line 48-52, “The receptor-like kinase SOBIR1, which contained a kinase domain, has been widely reported to be required for the function of LRR-RLPs in the innate immunity. However, whether SOBIR1 interacted with malectin-LRR RLP remains largely unknown” in Line 170-173.

Line 67: There are quite a few publications showing that insect salivary proteins dampen plant immunity.

Sorry for the inaccurate description. We agree that an accumulated literature describes the suppression of plant immunity by insect salivary proteins. However, the specific molecular mechanism by which these proteins target plant PRRs is still poorly understood. In the revised version, we specified that “it remains largely unknown how insects cope with plant PRRs” in Line 68-69.

Line 149: I don't understand what "point-to-point Y2H" is.

Thank you for your comment. We agree that the term "pairwise Y2H" is more commonly used in the literature than "point-to-point Y2H." To avoid any confusion and to align with standard terminology, we have replaced "point-to-point Y2H" with "pairwise Y2H" throughout the revised manuscript.

Line 179: Replace with "NtRLP4 and NtSOBIR1 confers resistance to B. tabaci". You don't say a protein is resistant to a insect infestation. The same applies for Line 209-210.

Thank you for your suggestion. We corrected it in the revised version.

Minor points:

Line 91-92: Lengthy text for simple results.

Line 98: "which was significantly different from the actin or ribosomal 18S rRNA" can be deleted. It's self-evident that actin and 18S rRNA are controls. The same applies to Line 101.

Line 130: unnecessary sentence, delete.

The use of verb forms needs further correction.

Thank you for your valuable suggestion. In the revised manuscript, we have revised the text accordingly. We truly appreciate your help in improving our manuscript.

eLife Assessment

This study uses a Bayesian framework to characterize latent brain state dynamics associated with memory encoding and performance in children, as measured with functional magnetic resonance imaging. The novelty of the approach offers valuable insights into memory-related brain activity, but the consideration of developmental changes in memory and brain dynamics, and the evidence to support the proposed mapping between specific states and distinct aspects of memory, are incomplete. This work will be of interest to researchers interested in cognitive neuroscience and the development of memory.

Reviewer #1 (Public review):

Summary:

Zeng et al. characterized the dynamic brain states that emerged during episodic encoding and the reactivation of these states during the offline rest period in children aged 8-13. In the study, participants encoded scene images during fMRI and later performed a memory recognition test. The authors adopted the BSDS approach and identified four states during encoding, including an "active-encoding" state. The occupancy rate of, and the state transition rates towards, this active-encoding state positively predicted memory accuracy across participants. The authors then decoded the brain states during pre- and post-encoding rests with the model trained on the encoding data to examine state reactivation. They found that the state temporal profile and transition structure shifted from encoding to post-encoding rest. They also showed that the mean lifetime and stability (measured with self-transition probability) of the "default-mode" state during post-encoding rest predict memory performance.

Strengths:

How brain dynamics during encoding and offline rest support long-term memory remains understudied, particularly in children. Thus, this study addresses an important question in the field. The authors implemented an advanced computational framework to identify latent brain states during encoding and carefully characterized their spatiotemporal features. The study also showed evidence for the behavioral relevance of these states, providing valuable insights into the link between state dynamics and successful encoding and consolidation.

Weaknesses:

(1) If applicable, please provide information on the decoding performance of states during pre- and post-encoding rests. The Methods noted that the authors applied a threshold of 0.1 z-scored likelihood, and based on Figure S2, it seems like most TRs were assigned a reinstated state during post-encoding rest. It would be useful to know, for the decodable TRs, how strong the evidence was in favor of one state over others. Further, was decoding performance better during post- vs. pre- encoding rest? This is critical for establishing that these states were indeed "reinstated" during rest. The authors showed individual-specific correlations between encoding and post-encoding state distribution, which is an important validation of the method, but this result alone is not sufficient to suggest that the states during encoding were the ones that occurred during rest. The authors found that the state dynamics vary substantially between encoding and rest, and it would be helpful to clarify whether these differences might be related to decoding performance. I am also curious whether, if the authors apply the BSDS approach to independently identify brain states during rest periods (instead of using the trained model from encoding), they find similar states during rest as those that emerged during encoding?

(2) During post-encoding rest, the intermediate activation state (S1) became the dominant state. Overall, the paper did not focus too much on this state. For example, when examining the relationship between state transitions and memory performance, the authors also did not include this state as a part of the analyses presented in the paper (lines 203-211). Could the author report more information about this state and/or discuss how this state might be relevant to memory formation and consolidation?

(3) Two outcome measures from the BSDS model were the occupancy rate and the mean lifetime. The authors found a significant association with behavior and occupancy rate in some analyses, and mean lifetime in others. The paper would benefit from a stronger theoretical framing explaining how and why these two different measures provide distinct information about the brain dynamics, which will help clarify the interpretation of results when association with behavior was specific to one measure.

(4) For performance on a memory recognition test, d' is a more common metric in the literature as it isolates the memory signal for the old items from response bias. According to Methods (line 451), the authors have computed a different metric as their primary behavioral measure (hits + correction rejections - misses - false alarms). Please provide a rationale for choosing this measure instead. Have the authors considered computing d' as well and examining brain-behavior relationships using d'?

(5) While this study examined brain state dynamics in children, there was no adult sample to compare with. Therefore, it is hard to conclude whether the findings are specific to children (or developing brains). It would be helpful to discuss this point in the paper.

Reviewer #2 (Public review):

This paper investigates the latent dynamic brain states that emerge during memory encoding and predict later memory performance in children (N = 24, ages: 8 -13 years). A novel computational approach (Bayesian Switching Dynamic Systems, BSDS) discovers latent brain states from fMRI data in an unsupervised and parameter-free manner that is agnostic to external stimuli, resulting in 4 states: an active-encoding state, a default-mode state, an inactive state, and an intermediate state. The key finding is that the percentage of time occupied in the active-encoding state (characterized by greater activity in hippocampal, visual, and frontoparietal regions), as well as greater transitions to this state, predicts memory accuracy. Memory accuracy was also predicted by the mean lifetime and transitions to the default-mode state (characterized by greater activity in medial prefrontal cortex and posterior cingulate cortex) during post-encoding rest. Together, the results provide insights into dynamic interactions between brain regions that may be optimal for encoding novel information and consolidating memories for long-term retention.

The approach is interesting and important for our understanding of neural mechanisms of memory during development, as we know less about dynamic interactions between memory systems in development.

Moreover, the novel methodology may be broadly useful beyond the questions addressed in this study. The manuscript is well-written and concise. Nonetheless, there are several areas for improvement:

(1) The study focuses on middle childhood, but there is a lack of engagement in the Introduction or Discussion about what is known about memory development and the brain during this period. Many of the brain regions examined in this study, particularly frontoparietal regions, undergo developmental changes that could influence their involvement in memory encoding and consolidation. The paper would be strengthened by more directly linking the findings to what is already known about episodic memory development and the brain.

(2) A more thorough overview of the BSDS algorithm is needed, since this is likely a novel method for most readers. Although many of the nitty-gritty details can be referenced in prior work, it was unclear from the main text if the BSDS algorithm discovered latent states based on activation patterns, functional connectivity, or both. Figure 1F is not very informative (and is missing labels).

(3) A further confusion about the BSDS algorithm was whether it necessarily had to work on the rest data. Figure 4A suggests that each TR was assigned one of the four states based on the maximum win from the log-likelihood estimation. Without more details about how this algorithm was applied to the rest data, it is difficult to evaluate the claim on page 14 about the spontaneous emergence of the states at rest.

(4) Although the BSDS algorithm was validated in prior simulations and task-based fMRI using sustained block designs in adults, it is unclear whether it is appropriate for the kind of event-related design used in the current study. Figure 1G shows very rapid state changes, which is quantified in the low mean lifetime of the states (between 1-3 TRs on average) in Figure 4C. On the one hand, it is a strength of the algorithm that it is not necessarily tied to external stimuli. On the other hand, it would be helpful to see simulations validating that rapid transitions between states in fMRI data are meaningful and not due to noise.

(5) The Methods section mentions that participants actively imagined themselves within the encoded scenes and were instructed to memorize the images for a later test during the post-encoding rest scan. This detail needs to be included in the main text and incorporated into the interpretation of the findings, as there are likely mechanistic differences between spontaneous memory replay/reinstatement vs. active rehearsal.

(6) Information about the general linear model used to discover the 16 ROIs that showed a subsequent memory effect are missing, such as: covariates in the model (motion, etc.), group analysis approach (parametric or nonparametric), whether and how multiple-comparisons correction was performed, if clusters were overlapping at all or distinct, if the total number of clusters was 16 or if this was only a subset of regions that showed the effect.

Reviewer #3 (Public review):

Summary:

This paper uses a novel method to look at how stable brain states and the transitions between them promote memory formation during encoding and post-encoding rest in children. I think the paper has some weaknesses (detailed below) that mean that the authors fall short of achieving their aims. Although the paper has an interesting methodological approach, the authors need better logic, and are potentially "double dipping" in their results - meaning their logic is circular. I think the method that they are using could be useful to the broader neuroimaging community, although they need to make this argument clearer in the paper.

Strengths:

The paper is interesting in that they use a novel method to look at brain state dynamics and how they might support memory.

Weaknesses:

The paper has several weaknesses:

(1) The authors use children as their study subjects but fail to reconcile why children are used, if the same phenomena are expected to be seen in adults (or only children), and if and how their findings change with age across an age range that ranges from middle childhood into early adolescence. They need to include more consideration for the development of their subject population. The authors should make it clear why and how memory was tested in children and not adults. Are adults and children expected to encode and consolidate in a similar manner to children? Do the findings here also apply to adults? Do the findings here also apply to adults? How was the age range of 8-13-year-old children selected? Why didn't the authors look at change with age? Does memory performance change with age? Do the BSDS dynamics change with age in the authors' sample?

(2) The authors look for brain state dynamics within a preselected set of ROIs that are selected because they display a subsequent memory effect. This is problematic because the state that is most associated with subsequent memory (S3, or State 3) is also the one that shows most activity in these regions (that have already been a priori selected due to displaying a subsequent memory effect). This logic is circular. It would be helpful if they could look at brain state dynamics in a more ROI agnostic whole brain approach so that we can learn something beyond what a subsequent memory analysis tells us. I think the authors are "double dipping" in that they selected regions for further analysis based on a subsequent memory association (remembered > forgotten contrast) and then found states within those regions showing a subsequent memory effect to further analyze for being associated with subsequent memory. Would it be possible instead to do a whole-brain analysis (something a bit more agnostic to findings) using the BSDS framework, and then, from a whole-brain perspective, look for particular brain states associated with subsequent memory? As it stands, it looks like S3 (state 3) has greater overall activation in all brain regions associated with subsequent memory, so it makes sense that this brain state is also most associated with subsequent memory. The BSDS analysis is therefore not adding anything new beyond what the authors find with the simple subsequent memory contrast that they show in Figure 1C. This particularly effects the following findings: (a) active-encoding state occupancy rate correlated positively with memory accuracy, (b) transitions to the active-encoding state were beneficial / Conversely, transitions toward the inactive state (S4) were detrimental, with incoming transitions showing negative correlations with memory accuracy / The active-encoding state serves as a "hub" configuration that facilitates memory formation, while pathways leading to this state enhance performance and transitions away from it impair encoding.

(3) The task used to test memory in children seems strange. Why should children remember arbitrary scenes? How this was chosen for encoding needs to be made clear. There needs to be more description of the memory task and why it was chosen. Why was scene encoding chosen? What does scene encoding have to do with the stated goal of (a) "Understanding how children's brains form lasting memories", (b) "optimizing education" and (c) "identifying learning disabilities"? What was the design of the recognition memory test? How many novel scenes were included in the test, and how were they chosen? How close were the "new" images to previously seen "old" images? Was this varied parametrically (i.e., was the similarity between new and old images assessed and quantified?)

(4) They ultimately found four brain states during encoding. It would be helpful if they could make the logic and foundation for arriving at this number clear.

(5) There is already extant work on whether brain states during post-encoding rest predict memory outcomes. This work needs to be cited and referred to. The present manuscript needs to be better situated within prior work. The authors should look at the work by Alexa Tompary and Lila Davachi. They have already addressed many of the questions that the authors seek to answer. The authors should read their papers (and the papers they cite and that cite them) and then situate their work within the prior literature.

More minor weaknesses:

(1) The authors should back up the claim that "successful episodic memory formation critically depends on the temporal coordination between these systems. Brain regions must coordinate their activity through dynamic functional interactions, rapidly reconfiguring their activity and connectivity patterns in response to changing cognitive demands and stimulus characteristics." Do they have any specific evidence supporting this claim?

(2) These claims seem overstated: "this work has broad implications for understanding memory function in children, for developing educational interventions that enhance memory formation, and enabling early identification of children at risk for learning disabilities." Can the authors add citations that would support these claims, or if not, remove them?

Author response:

eLife Assessment

This study uses a Bayesian framework to characterize latent brain state dynamics associated with memory encoding and performance in children, as measured with functional magnetic resonance imaging. The novelty of the approach offers valuable insights into memory-related brain activity, but the consideration of developmental changes in memory and brain dynamics, and the evidence to support the proposed mapping between specific states and distinct aspects of memory, are incomplete. This work will be of interest to researchers interested in cognitive neuroscience and the development of memory.

We are grateful to the editor and reviewers for their positive feedback and constructive evaluation. Their comments have identified important areas where the manuscript can be strengthened. Below, we outline our planned revisions.

Reviewer #1 (Public review):

Zeng et al. characterized the dynamic brain states that emerged during episodic encoding and the reactivation of these states during the offline rest period in children aged 8-13. In the study, participants encoded scene images during fMRI and later performed a memory recognition test. The authors adopted the BSDS approach and identified four states during encoding, including an "active-encoding" state. The occupancy rate of, and the state transition rates towards, this active-encoding state positively predicted memory accuracy across participants. The authors then decoded the brain states during pre- and post-encoding rests with the model trained on the encoding data to examine state reactivation. They found that the state temporal profile and transition structure shifted from encoding to post-encoding rest. They also showed that the mean lifetime and stability (measured with self-transition probability) of the "default-mode" state during post-encoding rest predict memory performance. How brain dynamics during encoding and offline rest support long-term memory remains understudied, particularly in children. Thus, this study addresses an important question in the field. The authors implemented an advanced computational framework to identify latent brain states during encoding and carefully characterized their spatiotemporal features. The study also showed evidence for the behavioral relevance of these states, providing valuable insights into the link between state dynamics and successful encoding and consolidation.

We thank Reviewer #1 for the positive feedback on our study. And we would like to thank you for the reviewer's constructive feedback. We plan to incorporate detailed methodological justifications and a thorough limitation analysis. We also plan to enhance the overall logical coherence of the manuscript, ensuring a more robust and scientifically sound presentation.

Weaknesses:

(1) If applicable, please provide information on the decoding performance of states during pre- and post-encoding rests. The Methods noted that the authors applied a threshold of 0.1 z-scored likelihood, and based on Figure S2, it seems like most TRs were assigned a reinstated state during post-encoding rest. It would be useful to know, for the decodable TRs, how strong the evidence was in favor of one state over others. Further, was decoding performance better during post- vs. pre- encoding rest? This is critical for establishing that these states were indeed "reinstated" during rest. The authors showed individual-specific correlations between encoding and post-encoding state distribution, which is an important validation of the method, but this result alone is not sufficient to suggest that the states during encoding were the ones that occurred during rest. The authors found that the state dynamics vary substantially between encoding and rest, and it would be helpful to clarify whether these differences might be related to decoding performance. I am also curious whether, if the authors apply the BSDS approach to independently identify brain states during rest periods (instead of using the trained model from encoding), they find similar states during rest as those that emerged during encoding?

We plan three additional analyses to strengthen the evidence for state reinstatement during rest: First, we will report quantitative decoding confidence metrics for each decoded time point, including the log-likelihood between the winning state and the next-best state. We will compare these distributions between pre- and post-encoding rest to test whether decoding quality differs between conditions, as the reviewer suggests. Second, we will provide a more detailed characterization of the decoding process, including the proportion of TRs that survive the log-likelihood threshold of 0.1 during pre- vs. post-encoding rest and whether this proportion relates to memory performance. Third, we will train an independent BSDS model directly on the rest data (rather than using the encoding-trained model) and assess the degree of correspondence between the independently discovered rest states and the encoding states in terms of amplitude profiles and covariance structures. Convergence between the two approaches would provide strong validation that the encoding-defined states genuinely re-emerge at rest. Together with our evidence from our previous analyses, these additional analyses will strengthen our claims.

(2) During post-encoding rest, the intermediate activation state (S1) became the dominant state. Overall, the paper did not focus too much on this state. For example, when examining the relationship between state transitions and memory performance, the authors also did not include this state as a part of the analyses presented in the paper (lines 203-211). Could the author report more information about this state and/or discuss how this state might be relevant to memory formation and consolidation?

We thank the reviewer for this suggestion. During encoding, S1 had the lowest occupancy (~10%) and showed no significant relationship with memory performance, which led us to interpret it as a non-essential transient configuration. In the revision, we will provide a more thorough characterization of S1, and conduct correlation analyses to probe whether its dynamic properties during post-encoding rest correlate with individual memory performance.

(3) Two outcome measures from the BSDS model were the occupancy rate and the mean lifetime. The authors found a significant association with behavior and occupancy rate in some analyses, and mean lifetime in others. The paper would benefit from a stronger theoretical framing explaining how and why these two different measures provide distinct information about the brain dynamics, which will help clarify the interpretation of results when association with behavior was specific to one measure.

We thank the reviewer for this suggestion. Occupancy rate and mean lifetime, while related, capture fundamentally different aspects of brain state dynamics. Occupancy rate reflects the total proportion of time the brain spends in a given state, capturing the overall prevalence of that configuration across the scanning session. Mean lifetime, by contrast, measures the average uninterrupted duration of each state visit, indexing the temporal stability or persistence of a given network configuration once it is entered. Critically, two states could have identical occupancy rates but very different mean lifetimes, a state visited frequently but briefly versus one visited rarely but sustained, implying distinct underlying neural dynamics. In the context of memory, high occupancy of the active-encoding state may reflect repeated engagement of encoding-optimal circuits, while long mean lifetime of the default-mode state during rest may reflect sustained consolidation-related processing. We will expand the theoretical framework in the revised manuscript to articulate these distinctions and connect them to extant findings suggesting that temporal stability versus frequency of state visits may have dissociable behavioral correlates in working memory and episodic memory (He et al., 2023; Stevner et al., 2019).

(4) For performance on a memory recognition test, d' is a more common metric in the literature as it isolates the memory signal for the old items from response bias. According to Methods (line 451), the authors have computed a different metric as their primary behavioral measure (hits + correction rejections - misses - false alarms). Please provide a rationale for choosing this measure instead. Have the authors considered computing d' as well and examining brain-behavior relationships using d'?

Our primary memory recognition metric computed as (hits + correct rejections − misses − false alarms) / total trials, provides an unbiased linear estimate of discrimination ability that is mathematically consistent with d' in directional effects. We selected this measure because it is particularly robust with limited trial counts per condition (Verde et al., 2006; Wickens, 2001). Nonetheless, we agree that reporting d' is important for comparability with the broader literature. In the revision, we will compute d' for each participant and conduct parallel brain–behavior correlation analyses to demonstrate that our findings are robust across both metrics.

(5) While this study examined brain state dynamics in children, there was no adult sample to compare with. Therefore, it is hard to conclude whether the findings are specific to children (or developing brains). It would be helpful to discuss this point in the paper.

We thank the reviewer for raising this point. While several studies have documented memory-related replay and reinstatement in adults at both the regional and systems levels(Tambini et al., 2017; Wimmer et al., 2020), few have examined whether analogous state-level reinstatement occurs in children. Our study was motivated by this gap: we sought to test whether children show dynamic brain state reinstatement mechanisms similar to those described in adults. However, we acknowledge that without a direct adult comparison, we cannot determine whether the observed patterns are unique to children or reflect general principles of episodic memory organization. In the revised manuscript, we will: (a) frame the study more carefully as examining whether established state-level consolidation mechanisms also operate during childhood, (b) discuss findings in relation to adult studies, and (c) include exploratory analyses of age-related variability in both memory performance and BSDS dynamics within our sample, while acknowledging that the narrow age range (8–13) and small sample size limit the power of such developmental analyses. We will clearly identify the absence of an adult comparison as a limitation.

Reviewer #2 (Public review):

This paper investigates the latent dynamic brain states that emerge during memory encoding and predict later memory performance in children (N = 24, ages: 8 -13 years). A novel computational approach (Bayesian Switching Dynamic Systems, BSDS) discovers latent brain states from fMRI data in an unsupervised and parameter-free manner that is agnostic to external stimuli, resulting in 4 states: an active-encoding state, a default-mode state, an inactive state, and an intermediate state. The key finding is that the percentage of time occupied in the active-encoding state (characterized by greater activity in hippocampal, visual, and frontoparietal regions), as well as greater transitions to this state, predicts memory accuracy. Memory accuracy was also predicted by the mean lifetime and transitions to the default-mode state (characterized by greater activity in medial prefrontal cortex and posterior cingulate cortex) during post-encoding rest. Together, the results provide insights into dynamic interactions between brain regions that may be optimal for encoding novel information and consolidating memories for long-term retention.

We thank Reviewer #2 for recognizing the novelty and broader utility of our methodology and for noting that the manuscript is well-written and concise.

Weaknesses:

(1) The study focuses on middle childhood, but there is a lack of engagement in the Introduction or Discussion about what is known about memory development and the brain during this period. Many of the brain regions examined in this study, particularly frontoparietal regions, undergo developmental changes that could influence their involvement in memory encoding and consolidation. The paper would be strengthened by more directly linking the findings to what is already known about episodic memory development and the brain.

We thank the reviewer for this suggestion. In response, we will substantially expand the Introduction and Discussion to situate our findings within the developmental cognitive neuroscience literature on episodic memory. In particular, we will address the protracted developmental trajectory of frontoparietal regions, the well-documented maturation of hippocampal–cortical connectivity during middle childhood, and how these developmental changes may influence the brain state configurations we observed (He et al., 2023; Ryali et al., 2016). This will provide the necessary developmental context for interpreting our state dynamics results.

(2) A more thorough overview of the BSDS algorithm is needed, since this is likely a novel method for most readers. Although many of the nitty-gritty details can be referenced in prior work, it was unclear from the main text if the BSDS algorithm discovered latent states based on activation patterns, functional connectivity, or both. Figure 1F is not very informative (and is missing labels).

We thank the reviewer for this suggestion. We agree that a more accessible overview of the BSDS algorithm (Lee et al., 2025; Taghia et al., 2018) is needed. In the revision, we will expand the Methods and provide a concise algorithmic overview in the main text that clarifies the following key points: (a) BSDS operates on multivariate time series from the ROIs and infers latent brain states defined jointly by their mean activation patterns (amplitude vectors) and inter-regional covariance matrices (functional connectivity); (b) it employs a hidden Markov model framework with Bayesian inference and automatic relevance determination to identify the number of states without manual specification; and (c) state assignments are made at each TR, yielding a temporal sequence that enables computation of occupancy rates, mean lifetimes, and transition probabilities. We will also revise Figure 1F to include appropriate labels and a clearer schematic of the model's inputs, latent structure, and outputs.

(3) A further confusion about the BSDS algorithm was whether it necessarily had to work on the rest data. Figure 4A suggests that each TR was assigned one of the four states based on the maximum win from the log-likelihood estimation. Without more details about how this algorithm was applied to the rest data, it is difficult to evaluate the claim on page 14 about the spontaneous emergence of the states at rest.

The key methodological point is that the BSDS model, once trained on encoding data, can be applied to new (rest) time series via log-likelihood estimation: for each TR during rest, the model computes the log-likelihood of each state given the observed multivariate signal, and the state with the maximum log-likelihood is assigned to that TR. This "decoding" approach tests whether the spatial configurations learned during encoding are present during rest, rather than fitting new states de novo. We applied a threshold to the log-likelihood values to exclude TRs where the evidence for any single state was weak, thus controlling for potential misassignment. We will substantially clarify this process in the revised Methods and main text, and as described in our response to Reviewer #1 point 1, we will also conduct additional analyses to address the concerns raised.

(4) Although the BSDS algorithm was validated in prior simulations and task-based fMRI using sustained block designs in adults, it is unclear whether it is appropriate for the kind of event-related design used in the current study. Figure 1G shows very rapid state changes, which is quantified in the low mean lifetime of the states (between 1-3 TRs on average) in Figure 4C. On the one hand, it is a strength of the algorithm that it is not necessarily tied to external stimuli. On the other hand, it would be helpful to see simulations validating that rapid transitions between states in fMRI data are meaningful and not due to noise.

This is an important methodological question. The rapid state changes observed in our event-related design (mean lifetimes of 1–3 TRs) differ from the longer state durations typically observed with block designs(He et al., 2023; Zeng et al., 2024), where sustained cognitive demands stabilize brain configurations. We believe these rapid transitions are consistent with the inherent dynamics of event-related encoding, where each trial involves rapid shifts between sensory processing, memory binding, and attentional engagement. Several considerations support the meaningfulness of these transitions: (a) the identified states have interpretable amplitude profiles consistent with well-established memory-related brain systems; (b) state dynamics show statistically significant, directionally consistent correlations with subsequent memory performance; and (c) the transition structure during encoding is distinct from that observed during rest, indicating sensitivity to task demands. Nonetheless, we acknowledge the concern about noise and will conduct additional analyses in the revision to address the concerns raised.

(5) The Methods section mentions that participants actively imagined themselves within the encoded scenes and were instructed to memorize the images for a later test during the post-encoding rest scan. This detail needs to be included in the main text and incorporated into the interpretation of the findings, as there are likely mechanistic differences between spontaneous memory replay/reinstatement vs. active rehearsal.

We thank the reviewer for this suggestion. We will include these experimental details in the main text and incorporate it into the interpretation of our findings in the context of spontaneous memory replay/reinstatement vs. active rehearsal (Liu et al., 2019; Wimmer et al., 2020).

(6) Information about the general linear model used to discover the 16 ROIs that showed a subsequent memory effect are missing, such as: covariates in the model (motion, etc.), group analysis approach (parametric or nonparametric), whether and how multiple-comparisons correction was performed, if clusters were overlapping at all or distinct, if the total number of clusters was 16 or if this was only a subset of regions that showed the effect.

We apologize for the missing methodological details. In the revised manuscript, we will provide complete information on the general linear model used to identify the 16 ROIs, including: the event regressors and parametric modulators included in the model, nuisance covariates (motion parameters, white matter and CSF regressors), the group-level analysis approach and statistical thresholding, the method for multiple-comparisons correction, whether the 16 ROIs represent all significant clusters or a subset, and whether any clusters were spatially overlapping. We will also clarify how peak voxels were selected for ROI definition.

Reviewer #3 (Public review):

This paper uses a novel method to look at how stable brain states and the transitions between them promote memory formation during encoding and post-encoding rest in children. I think the paper has some weaknesses (detailed below) that mean that the authors fall short of achieving their aims. Although the paper has an interesting methodological approach, the authors need better logic, and are potentially "double dipping" in their results - meaning their logic is circular. I think the method that they are using could be useful to the broader neuroimaging community, although they need to make this argument clearer in the paper.

We thank Reviewer #3 for recognizing the novelty of our approach and its potential utility for the broader neuroimaging community.

(1) The authors use children as their study subjects but fail to reconcile why children are used, if the same phenomena are expected to be seen in adults (or only children), and if and how their findings change with age across an age range that ranges from middle childhood into early adolescence. They need to include more consideration for the development of their subject population. The authors should make it clear why and how memory was tested in children and not adults. Are adults and children expected to encode and consolidate in a similar manner to children? Do the findings here also apply to adults? How was the age range of 8-13-year-old children selected? Why didn't the authors look at change with age? Does memory performance change with age? Do the BSDS dynamics change with age in the authors' sample?

Our study was motivated by the observation that while adult studies have documented memory replay and reinstatement, very little is known about whether these dynamic state-level mechanisms operate during middle childhood, a period characterized by substantial improvements in episodic memory ability and ongoing maturation of frontoparietal and hippocampal–cortical circuits. The age range of 8–13 was defined a priori based on typical developmental classifications of middle childhood through early adolescence, representing a period when episodic memory abilities are developing rapidly.

In response to the reviewer's specific questions: (a) we will conduct exploratory analyses testing whether memory accuracy, BSDS state dynamics (occupancy, mean lifetime, transitions), and brain–behavior correlations vary as a function of age within our sample; (b) we will clearly discuss whether adults are expected to show similar patterns, drawing on the extant adult literature; and (c) we will acknowledge as a limitation that our sample size (N = 24) and narrow age range provide limited statistical power for detecting continuous age-related changes, and that a dedicated cross-sectional or longitudinal developmental design would be needed to draw firm conclusions about developmental trajectories. Please also see responses to Reviewer #1 point 5 and Reviewer #2 point 1.

(2) The authors look for brain state dynamics within a preselected set of ROIs that are selected because they display a subsequent memory effect. This is problematic because the state that is most associated with subsequent memory (S3, or State 3) is also the one that shows most activity in these regions (that have already been a priori selected due to displaying a subsequent memory effect). This logic is circular. It would be helpful if they could look at brain state dynamics in a more ROI agnostic whole brain approach so that we can learn something beyond what a subsequent memory analysis tells us. I think the authors are "double dipping" in that they selected regions for further analysis based on a subsequent memory association (remembered > forgotten contrast) and then found states within those regions showing a subsequent memory effect to further analyze for being associated with subsequent memory. Would it be possible instead to do a whole-brain analysis (something a bit more agnostic to findings) using the BSDS framework, and then, from a whole-brain perspective, look for particular brain states associated with subsequent memory? As it stands, it looks like S3 (state 3) has greater overall activation in all brain regions associated with subsequent memory, so it makes sense that this brain state is also most associated with subsequent memory. The BSDS analysis is therefore not adding anything new beyond what the authors find with the simple subsequent memory contrast that they show in Figure 1C. This particularly effects the following findings: (a) active-encoding state occupancy rate correlated positively with memory accuracy, (b) transitions to the active-encoding state were beneficial / Conversely, transitions toward the inactive state (S4) were detrimental, with incoming transitions showing negative correlations with memory accuracy / The active-encoding state serves as a "hub" configuration that facilitates memory formation, while pathways leading to this state enhance performance and transitions away from it impair encoding.

We appreciate this critique, which raises an important concern about analytical circularity.

a) Why BSDS adds information beyond the static subsequent memory contrast. The reviewer notes that S3 (the active-encoding state) shows high activation in the same regions selected by the subsequent memory contrast, and therefore questions whether BSDS provides new information. We respectfully argue that BSDS captures dimensions of neural organization that a static contrast cannot. Specifically: (a) the subsequent memory contrast identifies which regions are differentially active for remembered vs. forgotten items, averaged across the entire encoding session, it provides no temporal information about when or for how long these regions are co-active; (b) BSDS reveals the moment-to-moment temporal evolution of brain states, including the duration and stability of each configuration (mean lifetime), which independently predicts behavior; (c) BSDS uniquely captures transition dynamics, the rates and patterns of switching between states, which we show are predictive of memory in ways not derivable from the contrast map (e.g., transitions from S2→S3 positively predict memory, transitions toward S4 negatively predict memory); and (d) BSDS characterizes the full covariance structure among regions within each state, revealing distinct connectivity patterns (e.g., the high clustering coefficient and global efficiency of S3), which are not captured by univariate activation contrasts. Thus, while the ROI selection is informed by the subsequent memory effect, the information BSDS extracts from those regions, temporal dynamics, transition patterns, and multivariate covariance, is orthogonal to the information used for selection.

b) Additional validation. To directly address the circularity concern empirically, we will conduct additional analysis using ROIs from previous studies (e.g. network templates) / meta-analyses/Neurosynth ROIs (He et al., 2023; Meer et al., 2020; Taghia et al., 2018), without resorting to selection based on the subsequent memory contrast.

(3) The task used to test memory in children seems strange. Why should children remember arbitrary scenes? How this was chosen for encoding needs to be made clear. There needs to be more description of the memory task and why it was chosen. Why was scene encoding chosen? What does scene encoding have to do with the stated goal of (a) "Understanding how children's brains form lasting memories", (b) "optimizing education" and (c) "identifying learning disabilities"? What was the design of the recognition memory test? How many novel scenes were included in the test, and how were they chosen? How close were the "new" images to previously seen "old" images? Was this varied parametrically (i.e., was the similarity between new and old images assessed and quantified?)

Scene encoding was chosen for several reasons: (a) scenes are rich, complex stimuli that engage the hippocampal–parahippocampal memory system, eliciting robust subsequent memory effects suitable for BSDS modeling; (b) scene encoding recruits distributed networks spanning visual cortex, MTL, and frontoparietal regions, enabling detection of multi-region brain states; and (c) scene encoding paradigms have been widely used in both adult and developmental studies of episodic memory and replay(Tambini et al., 2017; Tompary et al., 2017), facilitating comparison with prior work.

Regarding the recognition test: participants viewed 200 images (100 old, 100 new), with novel scenes drawn from the same categories (buildings and natural scenes) but chosen to be perceptually distinct from studied images. Similarity between old and new images was not parametrically manipulated or quantified: we will note this limitation. We will also expand the main text to include full task details and have deleted claims about implications for educational optimization and learning disability identification (see also Reviewer #3 point 7).

(4) They ultimately found four brain states during encoding. It would be helpful if they could make the logic and foundation for arriving at this number clear.

The number of brain states is not predetermined by the user but is automatically determined by the BSDS algorithm through Bayesian automatic relevance determination (ARD). The model is initialized with a maximum number of possible states, and during inference, states that contribute minimally to explaining the data are effectively pruned, their associated parameters are driven to near-zero by the ARD prior. In our data, the model converged on four states. This is a key advantage of BSDS over conventional HMM approaches, which require the user to specify the state number a priori. We will clarify this process in the revised Methods and Results, referencing the original BSDS methodology paper (Taghia et al., 2018) for full mathematical details.

(5) There is already extant work on whether brain states during post-encoding rest predict memory outcomes. This work needs to be cited and referred to. The present manuscript needs to be better situated within prior work. The authors should look at the work by Alexa Tompary and Lila Davachi. They have already addressed many of the questions that the authors seek to answer. The authors should read their papers (and the papers they cite and that cite them) and then situate their work within the prior literature.

We agree that the manuscript must be better situated within the existing literature on post-encoding rest and memory consolidation. We will revise the Introduction and Discussion to further discuss with the foundational work in adults by Tompary & Davachi (2017, Neuron; 2024, eLife) on consolidation-related hippocampal–mPFC representational overlap, as well as Tambini & Davachi (2013, PNAS; 2019, Trends in Cognitive Sciences) on hippocampal persistence during post-encoding rest and awake reactivation(Tambini et al., 2019; Tambini et al., 2017; Tompary et al., 2017). We will explicitly discuss how our BSDS-based approach to state-level reinstatement complements and extends these earlier findings, which largely focused on region-specific pattern similarity or hippocampal–cortical connectivity, by characterizing reinstatement at the level of dynamic, whole-network configurations.

(6) The authors should back up the claim that "successful episodic memory formation critically depends on the temporal coordination between these systems. Brain regions must coordinate their activity through dynamic functional interactions, rapidly reconfiguring their activity and connectivity patterns in response to changing cognitive demands and stimulus characteristics." Do they have any specific evidence supporting this claim?

The claim that episodic memory depends on temporal coordination and dynamic functional interactions is supported by several lines of evidence: (a) within our study, the significant correlations between state transition rates and memory performance directly demonstrate that dynamic inter-state communication predicts memory outcomes; (b) studies showing that hippocampal–prefrontal theta coherence during encoding predicts subsequent memory (e.g., Zielinski et al., 2020)(Zielinski et al., 2020); and (c) recent work demonstrating that rapid reconfiguration of large-scale brain networks supports cognitive functions including working memory (Shine et al., 2018; Braun et al., 2015)(Braun et al., 2015; Shine et al., 2018) and episodic encoding (Phan et al., 2024)(Phan et al., 2024) We will revise this passage to include specific citations and to make clear that our own transition–behavior correlations constitute direct evidence for this claim.

(7) These claims seem overstated: "this work has broad implications for understanding memory function in children, for developing educational interventions that enhance memory formation, and enabling early identification of children at risk for learning disabilities." Can the authors add citations that would support these claims, or if not, remove them?

We thank the reviewer for raising this point. We agree that the current framing overstates the practical implications. We have now removed these claims and remark on future studies that are needed here.

References

(1) Braun, U., Schafer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., . . . Bassett, D. S. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A, 112(37), 11678-11683.

(2) He, Y., Liang, X., Chen, M., Tian, T., Zeng, Y., Liu, J., . . . Qin, S. (2023). Development of brain-state dynamics involved in working memory. Cerebral Cortex.

(3) Lee, B., Young, C. B., Cai, W., Yuan, R., Ryman, S., Kim, J., . . . Menon, V. (2025). Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease. Nature Communications, 16(1), 2433.

(4) Liu, Y., Dolan, R. J., Kurth-Nelson, Z., & Behrens, T. E. J. (2019). Human Replay Spontaneously Reorganizes Experience. Cell, 178(3), 640-652.e614.

(5) Meer, J. N. v. d., Breakspear, M., Chang, L. J., Sonkusare, S., & Cocchi, L. (2020). Movie viewing elicits rich and reliable brain state dynamics. Nature Communications, 11(1), 5004.

(6) Phan, A. T., Xie, W., Chapeton, J. I., Inati, S. K., & Zaghloul, K. A. (2024). Dynamic patterns of functional connectivity in the human brain underlie individual memory formation. Nature Communications, 15(1), 8969.

(7) Ryali, S., Supekar, K., Chen, T., Kochalka, J., Cai, W., Nicholas, J., . . . Menon, V. (2016). Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling. PLoS Comput Biol, 12(12), e1005138.

(8) Shine, J. M., & Poldrack, R. A. (2018). Principles of dynamic network reconfiguration across diverse brain states. Neuroimage, 180, 396-405.

(9) Stevner, A. B. A., Vidaurre, D., Cabral, J., Rapuano, K., Nielsen, S. F. V., Tagliazucchi, E., . . . Kringelbach, M. L. (2019). Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature Communications, 10(1), 1035.

(10) Taghia, J., Cai, W., Ryali, S., Kochalka, J., Nicholas, J., Chen, T., & Menon, V. (2018). Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition. Nature Communications, 9(1), 2505.

(11) Tambini, A., & Davachi, L. (2019). Awake Reactivation of Prior Experiences Consolidates Memories and Biases Cognition. Trends in Cognitive Sciences, 23(10), 876-890.

(12) Tambini, A., Rimmele, U., Phelps, E. A., & Davachi, L. (2017). Emotional brain states carry over and enhance future memory formation. Nature Neuroscience, 20(2), 271-278.

(13) Tompary, A., & Davachi, L. (2017). Consolidation Promotes the Emergence of Representational Overlap in the Hippocampus and Medial Prefrontal Cortex. Neuron, 96(1), 228-241.e225.

(14) Verde, M. F., Macmillan, N. A., & Rotello, C. M. (2006). Measures of sensitivity based on a single hit rate and false alarm rate: The accuracy, precision, and robustness of′, A z, and A’. Perception & psychophysics, 68(4), 643-654.

(15) Wickens, T. D. (2001). Elementary signal detection theory: Oxford university press.

(16) Wimmer, G. E., Liu, Y., Vehar, N., Behrens, T. E. J., & Dolan, R. J. (2020). Episodic memory retrieval success is associated with rapid replay of episode content. Nature Neuroscience, 23(8), 1025-1033.

(17) Zeng, Y., Xiong, B., Gao, H., Liu, C., Chen, C., Wu, J., & Qin, S. (2024). Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning. Proceedings of the National Academy of Sciences, 121(52), e2405850121.

(18) Zielinski, M. C., Tang, W., & Jadhav, S. P. (2020). The role of replay and theta sequences in mediating hippocampal-prefrontal interactions for memory and cognition. Hippocampus, 30(1), 60-72.

eLife Assessment

This important study elucidates the role of the exocyst component EXOC6A at distinct stages of ciliogenesis, which advances our understanding of ciliary membrane remodeling and cilium formation. The authors provide compelling evidence through high quality light and electron microscopic imaging, and careful analysis of knockout cell lines, that EXOC6A interacts with myosin-Va and is dynamically recruited via dynein-, microtubule-, and actin-dependent mechanisms, to support proper formation of the ciliary membrane. The study will be of interest to cell biologists and other researchers interested in vesicular trafficking, organellar membrane dynamics, and ciliogenesis.

Reviewer #2 (Public review):

Summary:

The molecular mechanisms underlying ciliogenesis are not well understood. Previously, work from the same group (Wu et al., 2018) identified myosin-Va as an important protein in transporting preciliary vesicles to the mother vesicles, allowing for initiation of ciliogenesis. The exocyst complex has previously been implicated in ciliogenesis and protein trafficking to cilia. Here, Lin et al. investigate the role of exocyst complex protein EXOC6A in cilia formation. The authors find that EXOC6A localizes to preciliary vesicles, ciliary vesicles, and the ciliary sheath. EXOC6A colocalizes with Myo-Va in the ciliary vesicle and the ciliary sheath, and both proteins are removed from fully assembled cilia. EXOC6A is not required for Myo-Va localization, but Myo-VA and EHD1 are required for EXOC6A to localize in ciliary vesicles. The authors propose that EXOC6A vesicles continually remodel the cilium: FRAP analysis demonstrates that EXOC6A is a dynamic protein, and live imaging shows that EXOC6A fuses with and buds off from the ciliary membrane. Loss of EXOC6A reduces, but does not eliminate, the number of cilia formed in cells. Any cilia that are still present are structurally abnormal, with either bent morphologies or transition zone defects. Overall, the analyses and imaging are well done, and the conclusions are well supported by the data. The work will be of interest to cell biologists, especially those interested in centrosomes and cilia.

Strengths:

The TEM micrographs are of excellent quality. The quality of the imaging overall is very good, especially considering that these are dynamic processes occurring in a small region of the cell. The data analysis is well done and the quantifications are very helpful. The manuscript is well-written and the final figure is especially helpful in understanding the model.

The manuscript has greatly improved after revision. In particular, testing GPR161 and BBS9 localization is helpful evidence to demonstrate that transition zone function is disrupted when EXOC6A is lost. The generation of a second knockout clone and tests of antibody specificity are also great additions.

Weaknesses:

None

Reviewer #3 (Public review):

Summary:

Lin et al report on the dynamic localization of EXOC6A and Myo-Va at pre-ciliary vesicles, ciliary vesicles, and ciliary sheath membrane during ciliogenesis using three-dimensional structured illumination microscopy and ultrastructure expansion microscopy. The authors further confirm the interaction of EXOC6A and Myo-Va by co-immunoprecipitation experiments and demonstrated the requirement of EHD1 for the EXOC6A-labeled ciliary vesicles formation. Additional experiments using gene-silencing by siRNA and pharmacological tools identified the involvement of dynein-, microtubule-, and actin in the transport mechanism of EXOC6A-labeled vesicles to the centriole, as they have previously reported for Myo-Va. Notably, loss of EXOC6A severely disrupts ciliogenesis, with the majority of cells becoming arrested at the ciliary vesicle (CV) stage, highlighting the involvement of EXOC6A at later stages of ciliogenesis. As the authors observe dynamic EXOC6A-positive vesicle release and fusion with the ciliary sheath, this suggests a role in membrane and potentially membrane protein delivery to the growing cilium past the ciliary vesicle stage. While CEP290 localization at the forming cilium appears normal the recruitment of other transition zone components, exemplified by several MKS and NPHP module components, was also impaired in EXOC6A-deficient cells.

Strengths:

- By applying different microscopy approaches, the study provides deeper insight into the spatial and temporal localization of EXOC6A and Myo-Va during ciliogenesis.

- The combination of complementary siRNA and pharmacological tools targeting different components strengthens the conclusions.

- This study reveals a new function of EXOC6A in delivering membrane and membrane proteins during ciliogenesis, both to the ciliary vesicle as well as to the ciliary sheath.

- The overall data quality is high. The investigation of EXOC6A at different time points during ciliogenesis is well schematized and explained.

- The authors confirmed central antibody reagents used in this study and validated key experiments by using two independent knockout clones (for which sequencing information was provided).

Weaknesses:

- The precise molecular function of EXOC6A remains open, as the presented data suggests no involvement of other exocyst components.

Taken together, the authors achieved their goal to elucidate the role of EXOC6A in ciliogenesis, demonstrating its involvement in vesicle trafficking and membrane remodeling in both early and late stages of ciliogenesis. Their findings are supported by experimental evidence. This work is likely to have an impact on the field by expanding our understanding of the molecular machinery underlying cilia biogenesis, particularly the coordination between exocyst components and cytoskeletal transport systems. The methods and data presented offer valuable tools for dissecting vesicle dynamics and cilium formation, providing a foundation for future research into ciliary dysfunction and related diseases. By connecting vesicle trafficking to structural maturation of an organelle, the study adds important context to the broader description of cellular architecture and organelle biogenesis.

Comments on revisions:

We very much appreciate the extra work you put into improving your manuscript and want to congratulate you on your important discovery. We encourage you to keep up the good work!

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The study by Lin et al. studies the role of EXOC6A in ciliogenesis and its relationship with the interactor myosin-Va using a range of approaches based on the RPE1 cell line model. They establish its spatio-temporal organization at centrioles, the forming ciliary vesicle and ciliary sheath using ExM, various super-resolution techniques, and EM, including correlative light and electron microscopy. They also perform live imaging analyses and functional studies using RNAi and knockout. They establish a role of EXOC6A together with myosin-Va in Golgi-derived, microtubule- and actin-based vesicle trafficking to and from the ciliary vesicle and sheath membranes. Defects in these functions impair robust ciliary shaft and axoneme formation due to defective transition zone assembly.

Strengths:

The study provides very high-quality data that support the conclusions. In particular, the imaging data is compelling. It also integrates all findings in a model that shows how EXOC6A participates in multiple stages of ciliogenesis and how it cooperates with other factors.

Weaknesses:

The precise role of EXOC6A remains somewhat unclear. While it is described as a component of the exocyst, the authors do not address its molecular functions and whether it indeed works as part of the exocyst complex during ciliogenesis.

We sincerely thank Reviewer 1 for the thoughtful evaluation of our manuscript and the constructive comments provided. We are especially grateful for the recognition of the quality and significance of our imaging data and the comprehensive model we propose regarding EXOC6A’s role in ciliogenesis. We did not address the function of other components of the exocyst complex during ciliogenesis. However, in our biochemical analyses, Myosin‑Va specifically co‑immunoprecipitated with EXOC6A but not with other exocyst subunits tested (EXOC5 and EXOC7) (Fig. 4E) indicating a selective interaction between EXOC6A and the Myo‑Va transport machinery.

Reviewer #2 (Public review):

Summary:

The molecular mechanisms underlying ciliogenesis are not well understood. Previously, work from the same group (Wu et al., 2018) identified myosin-Va as an important protein in transporting preciliary vesicles to the mother vesicles, allowing for initiation of ciliogenesis. The exocyst complex has previously been implicated in ciliogenesis and protein trafficking to cilia. Here, Lin et al. investigate the role of exocyst complex protein EXOC6A in cilia formation. The authors find that EXOC6A localizes to preciliary vesicles, ciliary vesicles, and the ciliary sheath. EXOC6A colocalizes with Myo-Va in the ciliary vesicle and the ciliary sheath, and both proteins are removed from fully assembled cilia. EXOC6A is not required for Myo-Va localization, but Myo-VA and EHD1 are required for EXOC6A to localize in ciliary vesicles. The authors propose that EXOC6A vesicles continually remodel the cilium: FRAP analysis demonstrates that EXOC6A is a dynamic protein, and live imaging shows that EXOC6A fuses with and buds off from the ciliary membrane. Loss of EXOC6A reduces, but does not eliminate, the number of cilia formed in cells. Any cilia that are still present are structurally abnormal, with either bent morphologies or the absence of some transition zone proteins. Overall, the analyses and imaging are well done, and the conclusions are well supported by the data. The work will be of interest to cell biologists, especially those interested in centrosomes and cilia.

Strengths:

The TEM micrographs are of excellent quality. The quality of the imaging overall is very good, especially considering that these are dynamic processes occurring in a small region of the cell. The data analysis is well done and the quantifications are very helpful. The manuscript is well-written and the final figure is especially helpful in understanding the model.

Weaknesses:

Additional information about the functional and mechanistic roles of EXOC6A would improve the manuscript greatly.

We sincerely thank Reviewer 2 for the thoughtful and encouraging evaluation of our work. We are grateful for the recognition of the strengths of our study, including the quality of the TEM micrographs, the rigor of our imaging and data analysis, and the clarity of our manuscript and proposed model.

We have expanded our analyses in the revised manuscript to better define EXOC6A’s contribution to ciliary function. Specifically, we examined the trafficking of two critical ciliary membrane-associated proteins: GPR161, a G-protein-coupled receptor involved in Sonic hedgehog (Shh) signaling, and BBS9, a core component of the BBSome complex essential for ciliary membrane protein transport. Our new data (Fig. 7C) show that both GPR161 and BBS9 fail to localize to the cilium in EXOC6A knockout cells, in contrast to wild-type controls where their ciliary localization is robust. This new evidence significantly strengthens the understanding of EXOC6A’s role.

Reviewer #3 (Public review):

Summary:

Lin et al report on the dynamic localization of EXOC6A and Myo-Va at pre-ciliary vesicles, ciliary vesicles, and ciliary sheath membrane during ciliogenesis using three-dimensional structured illumination microscopy and ultrastructure expansion microscopy. The authors further confirm the interaction of EXOC6A and Myo-Va by co-immunoprecipitation experiments and demonstrated the requirement of EHD1 for the EXOC6A-labeled ciliary vesicles formation. Additional experiments using gene-silencing by siRNA and pharmacological tools identified the involvement of dynein-, microtubule-, and actin in the transport mechanism of EXOC6A-labeled vesicles to the centriole, as they have previously reported for Myo-Va. Notably, loss of EXOC6A severely disrupts ciliogenesis, with the majority of cells becoming arrested at the ciliary vesicle (CV) stage, highlighting the involvement of EXOC6A at later stages of ciliogenesis. As the authors observe dynamic EXOC6A-positive vesicle release and fusion with the ciliary sheath, this suggests a role in membrane and potentially membrane protein delivery to the growing cilium past the ciliary vesicle stage. While CEP290 localization at the forming cilium appears normal, the recruitment of other transition zone components, exemplified by several MKS and NPHP module components, was also impaired in EXOC6A-deficient cells.

Strengths:

(1) By applying different microscopy approaches, the study provides deeper insight into the spatial and temporal localization of EXOC6A and Myo-Va during ciliogenesis.

(2) The combination of complementary siRNA and pharmacological tools targeting different components strengthens the conclusions.

(3) This study reveals a new function of EXOC6A in delivering membrane and membrane proteins during ciliogenesis, both to the ciliary vesicle as well as to the ciliary sheath.

(4) The overall data quality is high. The investigation of EXOC6A at different time points during ciliogenesis is well schematized and explained.

Weaknesses:

(1) Since many conclusions are based on EXOC6A immunostaining, it would strengthen the study to validate antibody specificity by demonstrating the absence of staining in EXOC6A-deficient cells.

(2) While the authors generated an EXOC6A-deficient cell line, off-target effects can be clone-specific. Validating key experiments in a second independent knockout clone or rescuing the phenotype of the existing clone by re-expressing EXOC6A would ensure that the observed phenotypes are due to EXOC6A loss rather than unintended off-target effects.

(3) Some experimental details are lacking from the materials and methods section. No information on how the co-immunoprecipitation experiments have been performed can be found. The concentrations of pharmacological agents should be provided to allow proper interpretation of the results, as higher or lower doses can produce nonspecific effects. For example, the concentrations of ciliobrevin and nocodazole used to treat RPE1 cells are not specified and should be included. More precise settings for the FRAP experiments would help others reproduce the presented data. Some details for the siRNA-based knockdowns, such as incubation times, can only be found in the figure legends.

Taken together, the authors achieved their goal of elucidating the role of EXOC6A in ciliogenesis, demonstrating its involvement in vesicle trafficking and membrane remodeling in both early and late stages of ciliogenesis. Their findings are supported by experimental evidence. This work is likely to have an impact on the field by expanding our understanding of the molecular machinery underlying cilia biogenesis, particularly the coordination between the exocyst complex and cytoskeletal transport systems. The methods and data presented offer valuable tools for dissecting vesicle dynamics and cilium formation, providing a foundation for future research into ciliary dysfunction and related diseases. By connecting vesicle trafficking to structural maturation of an organelle, the study adds important context to the broader description of cellular architecture and organelle biogenesis.

We sincerely thank Reviewer 3 for the thorough and thoughtful assessment of our manuscript. We greatly appreciate the recognition of the strengths of our study, including the use of advanced microscopy techniques, complementary functional tools, and the conceptual contributions regarding EXOC6A's role in vesicle trafficking and membrane remodeling during ciliogenesis.

Below, we detail how we have addressed the specific suggestions for improvement:

(1) Validation of EXOC6A Immunostaining Specificity

To directly address the reviewer’s concern regarding antibody specificity, we have included new control immunofluorescence panels in Figure S3E-F, which show a complete loss of EXOC6A signal in two independent knockout (KO) clones. These data confirm the specificity of the EXOC6A antibody used throughout the study and reinforce the accuracy of our localization analyses at different stages of ciliogenesis.

(2) Addressing Potential Clone-Specific or Off-Target Effects

To ensure that the observed phenotypes are attributable to EXOC6A loss and not due to off-target effects, we performed parallel analyses using two independent KO clones, all of which exhibited identical defects in ciliogenesis, including arrest at the ciliary vesicle stage and impaired cilia assembly (Fig. S3C-D).

In addition, we conducted rescue experiments by re-expressing EXOC6A in the KO background, which effectively restored ciliogenesis. Quantitative analysis of the rescue data has been added to the revised manuscript (Figure S6B), providing further support that the observed phenotype is specifically due to EXOC6A deficiency.

(3) Expanded Methodological Details

- A detailed protocol for co-immunoprecipitation experiments, including lysis conditions, antibody concentrations, and washing steps.

- The precise concentrations and treatment durations for all pharmacological agents used, including ciliobrevin and nocodazole.

- Comprehensive details on the siRNA-mediated knockdowns, including oligonucleotide sequences, transfection reagents, and incubation durations.

Recommendations for the authors:

Reviewing Editor Comments:

After further consultation, all 3 reviewers agreed that this is an important study with highquality data, in particular the imaging data. They also considered most of the evidence convincing, but overall they termed it "solid" for two main reasons: first, they would have liked to see a validation of the EXOC6A antibody specificity, and second, they suggest that you demonstrate for at least key experiments the phenotypes with a second KO clone, to exclude clonal effects. In principle, rescue would be suited to address this, but the issue here is that the presented rescue is not very robust.

We sincerely thank the Editor and all reviewers for their constructive and thoughtful evaluation of our manuscript. We are especially grateful for the recognition of the highquality imaging data, the experimental rigor, and the significance of our findings to the field of ciliogenesis.

We fully acknowledge the two principal concerns raised during further consultation: (1) the need for validation of EXOC6A antibody specificity, and (2) the importance of confirming the phenotypes in an independent knockout clone to exclude clonal artifacts. We have taken both of these points seriously and have now addressed them through additional experiments and analyses, as detailed below:

(1) Validation Using Independent Knockout Clones

To rigorously validate antibody specificity and eliminate the possibility of clonal variation, we have characterized a second independent EXOC6A knockout (KO) clone. We confirmed complete loss of EXOC6A expression in both clones using three orthogonal approaches: genotyping, immunoblotting, and immunofluorescence (Fig. S3). Both KO clones exhibit indistinguishable phenotypes, including arrest at the ciliary vesicle stage and impaired cilia formation (Fig. S3D). 

(2) Rescue Phenotype Validation with Statistical Significance

In response to concerns about the robustness of the rescue, we have now included statistical analysis of the rescue experiments. A two-tailed Student’s t-test comparing ciliogenesis between the EXOC6A KO and rescue (GFP-EXOC6A re-expression) conditions shows a statistically significant improvement (p = 0.0041) (Fig. S6B). While we acknowledge that the rescue is partial—likely due to limitations of overexpression systems—the statistically significant recovery provides strong genetic evidence that the phenotypes are specific and reversible. These data are now included in the revised Figure S6.

(3) Functional Consequences of EXOC6A Loss on Ciliary Membrane Protein Trafficking

To further strengthen the mechanistic conclusions, we expanded our study to include the trafficking of two functional ciliary membrane proteins. We show that in EXOC6A KO cells, both BBS9 (a component of the BBSome complex) and GPR161 (a GPCR involved in Shh signaling) fail to enter the cilium. These results suggest that EXOC6A is required not only for early structural events in ciliogenesis, but also for establishing a competent transition zone, critical for ciliary membrane protein recruitment. These findings are detailed in the revised Figure 7C and corresponding Results.

We believe that these additional experiments and clarifications directly address the concerns and significantly strengthen the robustness and impact of our study.

The reviewers also made additional suggestions regarding functional and mechanistic insights that would strengthen the manuscript even further.

Reviewer #1 (Recommendations for the authors):

(1) The authors should include control IF panels for the specificity of the EXOC6A stainings at the various ciliogenesis stages using the KO cell line.

We thank the reviewer for this important suggestion. We have now included the requested immunofluorescence (IF) control panels to validate the specificity of the EXOC6A antibody. As shown in the newly added Figure S3, EXOC6A immunofluorescence signal is completely absent in EXOC6A knockout (KO) cells at CV (Fig. S3E) and cilia membrane (Fig. S3F) stages, whereas robust and stage-specific signals are observed in wild-type cells. These results confirm the specificity of the endogenous EXOC6A staining used throughout the study and validate the spatiotemporal localization patterns reported in the main figures.

(2) It would be informative to compare EXOC6A KO and RNAi to determine whether the only partially impaired ciliogenesis phenotype may be a consequence of cellular adaptation.

We appreciate the reviewer’s concern regarding potential cellular adaptation or clonespecific effects. To address this, we examined the ciliogenesis phenotype in two independent EXOC6A KO clones generated using distinct sgRNA targeting strategies. As shown in Figure S3, two independent KO clones displayed a highly consistent phenotype characterized by a pronounced arrest at the ciliary vesicle (CV) stage and a significant reduction in mature cilium formation.

The reproducibility of this phenotype across multiple independently derived clones strongly argues against clonal variability or long-term adaptive compensation as the underlying cause. Instead, these results support the conclusion that the observed ciliogenesis defects are a direct and specific consequence of EXOC6A loss.

(3) It remains unclear whether EXOC6A's function in ciliogenesis is part of the exocyst complex. This is currently implied by the context in which it is introduced and discussed, although the authors avoid any direct statement about this. Do the authors observe similar phenotypes by knocking down any other exocyst subunit? In any case, this issue should be discussed.

We thank the reviewer for raising this conceptual point. This study did not explore the functions of other components of the exocytosis complex during ciliogenesis, which warrants further investigation in the future. However, in our biochemical analyses, Myosin ‑Va specifically co‑immunoprecipitated with EXOC6A but not with other exocyst subunits tested (EXOC5 and EXOC7) (Fig. 4E) indicating a selective interaction between EXOC6A and the Myo‑Va transport machinery.

Reviewer #2 (Recommendations for the authors):

To clarify the roles of EXOC6A in ciliogenesis, I suggest the following:

(1) Myo-Va is involved in both the intracellular and extracellular ciliogenesis pathways. The authors show that EXOC6A has a role in the intracellular ciliogenesis pathway. Does it also participate in the extracellular pathway?

We thank the reviewer for this insightful question. Given that Myo-Va functions in both intracellular and extracellular ciliogenesis pathways, it is indeed plausible that EXOC6A may also participate in the extracellular pathway. However, the current study was specifically focused on elucidating the molecular mechanisms of intracellular ciliogenesis using RPE1 cells, which exclusively undergo this pathway. Assessing EXOC6A’s role in the extracellular pathway would require the use of specialized models (e.g., polarized epithelial cells such as MDCK or IMCD3), which fall beyond the scope of this manuscript.

(2) In the live imaging movies (Fig 3C, 3D, supp movie 4 and 5), the authors observe tubular structures and puncta with EXOC6A and conclude that these are dynamic vesicles/membranes. While the movies are suggestive of membrane-like behavior, it would be helpful to show that these puncta and tubules have membrane, perhaps by astaining with a membrane dye.

We appreciate the reviewer’s suggestion to validate the membrane identity of EXOC6Apositive structures. While we did not perform membrane dye staining in the current study, we agree this approach would provide additional confirmation. Nevertheless, the dynamic behaviors observed in our live-cell imaging—including membrane-like tubulation, fusion, and fission—strongly support the interpretation that EXOC6A puncta and tubules

(3) It is unclear how the EXOC6A tubules and vesicles are delivered, and the extent to which MyoVa plays a role. The authors co-label EXOC6A and MyoVa in Supp Fig 2, but EXOC6A dynamics seem very different here, as compared to Fig 3D - there are fewer tubules and puncta and less movement of either tubules or puncta between time points. Does expression of MyoVa decrease EXOC6A membrane dynamics? Or is it required for EXOC6A membrane dynamics?

We thank the reviewer for this observation. The apparent differences in EXOC6A dynamics between Supplementary Figure 2 and Figure 3D most likely reflect cell-to-cell variability in dynamic behavior, which is common in live-cell imaging. Both figures were derived from the same stable cell line co-expressing EXOC6A and Myo-Va-GTD. Moreover, our analysis shows that Myo-Va-GTD overexpression does not suppress EXOC6A dynamics, nor is it required for membrane remodeling per se. However, Myo-Va is essential for EXOC6A recruitment to the ciliary vesicle, as shown by the loss of EXOC6A localization in Myo-Va KO cells (Fig. 4A).

(4) The authors show that loss of EXOC6A affects the localization of some transition zone proteins. Does this subsequently lead to defects in transition zone function?

We agree with the reviewer that structural defects in the transition zone (TZ) should be linked to its function. To address this, we examined the localization of two wellcharacterized ciliary membrane-associated proteins: BBS9 and GPR161. Both proteins failed to localize to the cilia in EXOC6A knockout cells, despite proper recruitment in wildtype controls (Fig. 7C). Although we did not examine the exact functions of GPR161 and BBS9, our results suggest that the loss of EXOC6A may impair TZ function, particularly its gating capacity for membrane protein trafficking.

(5) Additional information about how the MKS proteins are regulated by EXOC6A would be helpful to understand the mechanisms by which EXOC6A builds the transition zone. Does EXOC6A directly bind to MKS proteins, or are the MKS proteins delivered by EXOC6A-containing vesicles during ciliogenesis?

We appreciate the reviewers' questions regarding the mechanistic relationship between EXOC6A and MKS module proteins. In this study, we did not explore the mechanism by which EXOC6A constructs the transition zone. This is an interesting topic worthy of further investigation in the future.

Reviewer #3 (Recommendations for the authors):

Recommended modifications:

(1) The co-immunoprecipitation experiments suggest an interaction between EXOC6A and Myo-Va; however, the presence of a faint band in the IgG control raises some uncertainty. To reinforce this conclusion, the authors could demonstrate that the interaction is absent in the EXOC6A knockout cell line.

We thank the reviewer for this careful observation. We acknowledge the presence of a faint Myo‑Va signal in the IgG control lane. Myosin‑Va is a highly abundant cytoskeletal motor protein and can occasionally exhibit low‑level nonspecific binding to agarose beads during immunoprecipitation assays. Importantly, the Myo‑Va signal co‑immunoprecipitated with endogenous EXOC6A is substantially stronger and specifically enriched compared with the IgG control, supporting a specific interaction.

(2) Figure S5: The partial rescue of the EXOC6A phenotype is not entirely convincing. A statistical test to assess the significance of the observed differences may help to strengthen the authors' conclusion.

We appreciate the reviewer’s suggestion to validate the rescue experiment. We have now performed a pairwise two‑tailed Student’s t‑test comparing ciliogenesis efficiency between EXOC6A knockout cells and rescue cells expressing GFP‑EXOC6A. As shown in the revised Figure S6 (original Figure S5), re‑expression of EXOC6A resulted in a statistically significant recovery of ciliogenesis (p = 0.0041). While the rescue is partial—likely due to inherent limitations of plasmid‑based expression systems, including variable transfection efficiency and imperfect restoration of endogenous protein levels—the statistically significant improvement confirms that the ciliogenesis defect is specifically caused by EXOC6A loss. Figure S6 and its legend have been updated accordingly.

(3) A detailed description of the EXOC6A knockout strategy should be included.

The Method section has been expanded to include a comprehensive description of the CRISPR/Cas9 ‑ mediated EXOC6A knockout strategy, including sgRNA sequences, genomic target sites, and validation approaches. Additionally, we now include Figure S3, demonstrating complete loss of EXOC6A protein expression in two independent knockout clones, confirming the efficiency and specificity of the gene‑editing strategy.

(4) The labeling in Figure 6 is confusing; assigning a separate letter to each panel would improve clarity.

Figure 6 has been reorganized for clarity: the original panels have been subdivided and relabeled as 6A/6A’ and 6B/6B’, respectively. The figure legend and all corresponding references in the main text have been updated accordingly.

(5) Lines 109-112: The cell line used is not well described. While experts might understand that Dox is used to induce expression of the transgenes, this should be better explained for non-expert readers.

We have revised the text to clearly explain that doxycycline (Dox) is used to induce transgene expression via a Tet‑On inducible system. This clarification has been added to the main text.

(6) Line 180: replace "labels" with "structures".

We have revised the text as suggested.

(7) Line 189: the EXOC6A recruitment to the membrane structures seems to be occurring on a short timescale that should be specified. In this context, "immediately" appears unscientific.

We have revised the sentence to specify that EXOC6A recruitment occurs within seconds, based on our live‑cell imaging data, providing a more accurate temporal description.

(8) Lines 280-282: We recommend rewording to soften this statement. Actin and microtubule inhibitors affect the entire cytoskeletal network; more specific experiments would be required to assess whether the transport of vesicles is defective.

We have reworded the statement to indicate that the accumulation of these vesicles at the mother centrioles is highly sensitive to disruption of dynein or microtubules, suggesting that efficient transport of these vesicles may depend on the integrity of the microtubule network. However, more experiments are required to confirm this conclusion. 

(9) Lines: 428-433: Similarly, we recommend rewording this statement as it presents the authors' current model, which is in line with the presented data but would require more rigorous investigation.

We have revised this section to describe the mechanism as a working model supported by our data, while acknowledging that further investigation will be required to fully establish the proposed hierarchy and molecular details.

Questions and comments to consider:

(1) 15-30% of cells can form cilia-like structures in the EXOC6A KO cells, although membrane transport should be reduced. It would be interesting to investigate whether these cilia are only formed intracellularly and fail to reach the cell surface.

We thank the reviewer for this insightful question. Using both immunofluorescence and electron microscopy, we observed that a subset of ciliary membranes in EXOC6A KO cells do appear to fuse with the plasma membrane. However, due to the low frequency and heterogeneous morphology of these structures, we were unable to reliably quantify this population. 

(2) In the Western blot shown in Figure 4, EXOC6A appears at multiple molecular weights when detected with the anti-EXOC6A antibody. Providing a possible explanation for this shift would be helpful.

We clarify that the apparent molecular weight shift likely results from gel distortion during electrophoretic separation. Importantly, the specificity of the major EXOC6A band was rigorously validated by its complete absence in EXOC6A knockout lysates, confirming that the detected signal corresponds to EXOC6A.

(3) The Western blot in Figure 5B is not fully convincing; including additional independent blots would be nice.

We thank the reviewer for this suggestion. Figure 5B has been replaced with a blot from an independent experiment, improving clarity and reproducibility.

(4) According to the materials and methods section, siRNA-mediated knockdown of targets was performed using a single siRNA per gene, which could result in off-target effects. It would be advised to use several different siRNAs for a single target to exclude off-target effects, cite references or, in case this has been done.

We appreciate this concern. The siRNAs used in this study were previously validated in our earlier work (Wu et al., Nat Cell Biol 2018), where both specificity and efficiency were rigorously tested. We have now explicitly cited this reference in the Materials and Methods section to justify the selection of these reagents.

(5) The abbreviation CFLEM is uncommon for correlative (fluorescence) light and electron microscopy; the authors should consider using the standard abbreviation CLEM.

We have replaced “CFLEM” with the standard term CLEM (Correlative Light and Electron Microscopy) throughout the manuscript and figure legends.

(6) The term "M-centriole" is uncommon and should at least be introduced. The use of the term "mother centriole" is recommended.

We have replaced “M‑centriole” with the standard term “mother centriole” throughout the manuscript and figures.

eLife Assessment

This fundamental study combines in vitro reconstitution experiments and molecular dynamics simulations to elucidate how membrane lipids are transported from the outer to the inner membrane of mitochondria. The authors provide convincing evidence that a positive membrane curvature is critical for membrane lipid extraction. The work will be of broad interest to cell biologists and biochemists.

Reviewer #1 (Public review):

Lipid transfer proteins (LTPs) play a crucial role in the intramembrane lipid exchange within cells. However, the molecular mechanisms that govern this activity remain largely unclear. Specifically, the way in which LTPs surmount the energy barrier to extract a single lipid molecule from a lipid bilayer is not yet fully understood. This manuscript investigates the influence of membrane properties on the binding of Ups1 to the membrane and the transfer of phosphatidic acid (PA) by the LTP. The findings reveal that Ups1 shows a preference for binding to membranes with positive curvature. Moreover, coarse-grained molecular dynamics simulations indicate that positive curvature decreases the energy barrier associated with PA extraction from the membrane. Additionally, lipid transfer assays conducted with purified proteins and liposomes in vitro demonstrate that the size of the donor membrane significantly impacts lipid transfer efficiency by Ups1-Mdm35 complexes, with smaller liposomes (characterized by high positive curvature) promoting rapid lipid transfer.

This study offers significant new insights into the reaction cycle of phosphatidic acid (PA) transfer by Ups1 in mitochondria. The experiments are technically robust and carefully interpreted by the authors. They provide compelling evidence that a positive membrane curvature and the presence of negatively charged phospholipids govern the transfer of PA by the mitochondrial lipid transfer protein Ups1-Mdm35.

Reviewer #2 (Public review):

Summary:

Lipid transfer between membranes is essential for lipid biosynthesis across different organelle membranes. Ups1-Mdm35 is one of the best-characterized lipid transfer proteins, responsible for transferring phosphatidic acid (PA) between the mitochondrial outer membrane (OM) and inner membrane (IM), a process critical for cardiolipin (CL) synthesis in the IM. Upon dissociation from Mdm35, Ups1 binds to the intermembrane space (IMS) surface of the OM, extracts a PA molecule, re-associates with Mdm35, and moves through the aqueous IMS to deliver PA to the IM. Here, the authors analyzed the early steps of this PA transfer - membrane binding and PA extraction - using a combination of in vitro biochemical assays with lipid liposomes and purified Ups1-Mdm35 to measure liposome binding, lipid transfer between liposomes, and lipid extraction from liposomes. The authors found that membrane curvature, a previously overlooked property of the membrane, significantly affects PA extraction but not PA insertion into liposomes. These findings were further supported by MD simulations.

Strengths:

The experiments are well-designed, and the data are logically interpreted. The present study provides an important basis for understanding the mechanism of lipid transfer between membranes.　

Weaknesses:

The physiological relevance of membrane curvature in lipid extraction and transfer still remains open.

Comments on revisions:

The authors have addressed most of my previous concerns, and the manuscript now looks much stronger.

Reviewer #3 (Public review):

The manuscript by Sadeqi et al. studies the interactions between the mitochondrial protein Ups1 and reconstituted membranes. The authors apply synthetic liposomal vesicles to investigate the role of pH, curvature, and charge on the binding of Ups1 to membranes and its ability to extract PA from them. The manuscript is well written and structured. The authors provide all relevant information and reference the appropriate literature in their introduction. The underlying question of how the energy barrier for lipid extraction from membranes is overcome by Ups1 is interesting, and the data presented by the authors offer a valuable new perspective on this process. It is also certainly a challenging in vitro reconstitution experiment, as the authors aim to disentangle individual membrane properties (e.g., curvature, charge, and packing density) to study protein adsorption and lipid transfer.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Lipid transfer proteins (LTPs) play a crucial role in the intramembrane lipid exchange within cells. However, the molecular mechanisms that govern this activity remain largely unclear. Specifically, the way in which LTPs surmount the energy barrier to extract a single lipid molecule from a lipid bilayer is not yet fully understood. This manuscript investigates the influence of membrane properties on the binding of Ups1 to the membrane and the transfer of phosphatidic acid (PA) by the LTP. The findings reveal that Ups1 shows a preference for binding to membranes with positive curvature. Moreover, coarse-grained molecular dynamics simulations indicate that positive curvature decreases the energy barrier associated with PA extraction from the membrane. Additionally, lipid transfer assays conducted with purified proteins and liposomes in vitro demonstrate that the size of the donor membrane significantly impacts lipid transfer efficiency by Ups1-Mdm35 complexes, with smaller liposomes (characterized by high positive curvature) promoting rapid lipid transfer.

This study offers significant new insights into the reaction cycle of phosphatidic acid (PA) transfer by Ups1 in mitochondria. Notably, the authors present compelling evidence that, alongside negatively charged phospholipids, positive membrane curvature enhances lipid transfer - an effect that is particularly relevant at the mitochondrial outer membrane. The experiments are technically robust, and my primary feedback pertains to the interpretation of specific results.

(1) The authors conclude from the lipid transfer assays (Figure 5) that lipid extraction is the rate-limiting step in the transfer cycle. While this conclusion seems plausible, it should be noted that the authors employed high concentrations of Ups1-Mdm35 along with less negatively charged phospholipids in these reactions. This combination may lead to binding becoming the rate-limiting factor. The authors should take this point into consideration. In this type of assay, it is challenging to clearly distinguish between binding, lipid extraction, and membrane dissociation as separate processes.

We have included a detailed consideration of this issue on page 11 of the revised manuscript.

(2) The authors should discuss that variations in the size of liposomes will also affect the distance between them at a constant concentration, which may affect the rate of lipid transfer. Therefore, the authors should determine the average size and size distribution of liposomes after sonication (by DLS or nanoparticle analyzer, etc.)

We have included DLS measurements for all lipid sizes (page 6) (SupFig. 2A). Due to the sensitivity of the intensity distribution in DLS measurements by larger particles, we also conducted cryo-EM analysis of vesicles with different sizes (page 6) (SupFig. 2B).

We also now discuss the challenges posed by a fixed membrane-binding surface, which can lead to variations in vesicle spacing when using liposomes of different sizes and its possible influence on the interpretation of results (page 10-11).

(3) The authors use NBD-PA in the lipid transfer assays. Does the size of the donor liposomes affect the transfer of NBD-PA and DOPA similarly? Since NBD-labeled lipids are somewhat unstable within lipid bilayers (as shown by spontaneous desorption in Figure 5B), monitoring the transfer of unlabeled PA in at least one setting would strengthen the conclusion of the swap experiments.

To experimentally address this comment, we explored several different approaches. We first performed transfer experiments using unlabelled lipids, following the general procedures described in the manuscript. After the transfer reaction, we attempted to separate donor and acceptor vesicles by centrifugation and subsequently analyzed the samples by high-resolution mass spectrometry and thin-layer chromatography. Despite considerable effort, we were not able to reliably separate the differently sized liposomes. In particular, small liposomes proved difficult to handle during centrifugation, which is a well-known challenge (Kučerka et al. 1994, BBA; Boucrot et al. 2012, Cell). In addition, liposomes exhibited a tendency to cross-link in the presence of protein, further complicating the separation. Even if this separation step were straightforward, an important limitation of such an approach is that it is very difficult to monitor lipid transfer with sufficient time resolution. Much of the relevant activity occurs within the first 20–30 seconds, and precise interruption at defined time points would be essential.

We therefore set out to establish a fluorescence-based assay that would allow us to follow lipid transfer in real time. For this, we adapted a dequenching-type assay based on a PE coupled fluorescein dye, whose fluorescence is quenched in the proximity of negative charges (e.g., negatively charged lipid headgroups). In principle, this assay should allow us to monitor the movement of negatively charged PA lipids away from donor membranes. Although a fluorescein-based passive lipid-transfer assay has been described previously (Richens et al., 2017), it is used only rarely in the lipid-transfer field. While establishing this assay, we encountered several technical challenges. For example, immediately after protein addition, fluorescence intensity changed in unexpected ways that could not be attributed to lipid transfer. Such effects have been reported in the literature (Wall et al., 1995) and are most likely caused by changes in membrane charge density upon protein binding. After extensive fine -tuning of the experimental conditions and careful evaluation of the data, we were ultimately able to demonstrate that lipid-transfer rates are significantly higher with smaller than with larger liposomes. These results confirm our initial observations, and importantly, they were obtained using unlabelled PA.

The revised manuscript now includes this independent lipid-transfer assay demonstrating the transfer of non-labelled PA (page 11) (SupFig. 4).

(4) The present study suggests that membrane domains with positive curvature at the outer membrane may serve as starting points for lipid transport by Ups1-Mdm35. Is anything known about the mechanisms that form such structures? This should be discussed in the text.

We included a detailed consideration of this interesting point in the discussion section on page 13-14.

Reviewer #2 (Public review):

Summary:

Lipid transfer between membranes is essential for lipid biosynthesis across different organelle membranes. Ups1-Mdm35 is one of the best-characterized lipid transfer proteins, responsible for transferring phosphatidic acid (PA) between the mitochondrial outer membrane (OM) and inner membrane (IM), a process critical for cardiolipin (CL) synthesis in the IM. Upon dissociation from Mdm35, Ups1 binds to the intermembrane space (IMS) surface of the OM, extracts a PA molecule, re-associates with Mdm35, and moves through the aqueous IMS to deliver PA to the IM. Here, the authors analyzed the early steps of this PA transfer - membrane binding and PA extraction - using a combination of in vitro biochemical assays with lipid liposomes and purified Ups1-Mdm35 to measure liposome binding, lipid transfer between liposomes, and lipid extraction from liposomes. The authors found that membrane curvature, a previously overlooked property of the membrane, significantly affects PA extraction but not PA insertion into liposomes. These findings were further supported by MD simulations.

Strengths:

The experiments are well-designed, and the data are logically interpreted. The present study provides an important basis for understanding the mechanism of lipid transfer between membranes.

Weaknesses:

The physiological relevance of membrane curvature in lipid extraction and transfer still remains open.

We thank the reviewer for the constructive feedback on our work. We agree that the physiological relevance of membrane curvature in lipid extraction and transfer remains an open question. Our data show that Ups1 binding to native-like OM membranes under physiological pH conditions is curvature-dependent, supporting the idea that this mechanism may optimize lipid transfer in vivo. While the intricate biophysical basis of this behaviour can only be dissected in vitro, these findings offer valuable insight into how curvature may functionally regulate Ups1 activity in the cellular context. To directly test this, it will be important in future studies to identify Ups1 mutants that lack curvature sensitivity and assess their performance in vivo, which will help clarify the physiological importance of this mechanism.

Reviewer #3 (Public review):

The manuscript by Sadeqi et al. studies the interactions between the mitochondrial protein Ups1 and reconstituted membranes. The authors apply synthetic liposomal vesicles to investigate the role of pH, curvature, and charge on the binding of Ups1 to membranes and its ability to extract PA from them. The manuscript is well written and structured. With minor exceptions, the authors provide all relevant information (see minor points below) and reference the appropriate literature in their introduction. The underlying question of how the energy barrier for lipid extraction from membranes is overcome by Ups1 is interesting, and the data presented by the authors could offer a valuable new perspective on this process. It is also certainly a challenging in vitro reconstitution experiment, as the authors aim to disentangle individual membrane properties (e.g., curvature, charge, and packing density) to study protein adsorption and lipid transfer. I have one major suggestion and a few minor ones that the authors might want to consider to improve their manuscript and data interpretation:

Major Comments:

The experiments are performed with reconstituted vesicles, which are incubated with recombinant protein variants and quantitatively assessed in flotation and pelleting assays. According to the Materials and Methods section, the lipid concentration in these assays is kept constant at 5 µM. However, the authors change the size of the vesicles to tune their curvature. Using the same lipid concentration but varying vesicle sizes results in different total vesicle concentrations. Moreover, larger vesicles (produced by freeze-thawing and extrusion) tend to form a higher proportion of multilamellar vesicles, thus also altering the total membrane area available for binding. Could these differences in the experimental system account for the variation in binding? To address this, the authors would need to perform the experiments either under saturated (excess protein) conditions or find an experimental approach to normalize for these differences.

To experimentally address this comment, we have conducted a detailed structural analysis of liposomes of different sizes using cryo-EM to determine the degrees of multi-lamellarity and to estimate how much membrane surface is available for protein binding. We found that while indeed as expected liposomes extruded through a 400 nm sized filter showed about 75 % of the initially calculated membrane surface is still available (SupFig. 3A). For 50 nm extruded liposomes, this number went up to about 93 % and for sonicated liposomes the number was about 94 %. Given the fact that we found about 70 % binding of Ups1 to sonicated liposomes, while this number went down to about 40 % with 50 nm liposomes and to about 30 % for 400 nm extruded liposomes, we can rule out that the effects we observe are due to an increased or decreased available membrane binding area.

Additionally, we performed experiments with increasing amounts of lipids to analyse the impact of lipid concentration on Ups1 membrane binding, when comparing 400 nm extruded liposomes with sonicated liposomes. Interestingly, while we do observe an increased binding of Ups1 to sonicated liposomes with concentrations varying between 2.5 mM to 10 mM no major increase in binding was observed with 400 nm extruded liposomes. Ups1 membrane binding to sonicated liposomes highly exceeded binding to 400 nm extruded liposomes under all tested conditions (page 7) (SupFig. 3B).

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors:):

(1) Figures 1, 2, and 3 - In the flotation assays, the Ups1-containing fractions differ between experiments. The presence of liposomes in these fractions should be confirmed, for example, by fluorescence measurements. In relation to this, the broad low MW bands in Supplementary Figure 3 may reflect liposomes (mixed micelles of lipids and SDS?), as their fractionation patterns coincide with those of Ups1 at pH 5.5 -6.7 but deviate at pH 7.0 and 7.5. Could the authors clarify this discrepancy?

Flotation profiles vary with changing conditions of the experiment. We have included a picture of a gel showing the Coomassie staining and the fluorescence of the used lipids side by side to show that the protein bands co-migrate together with liposomes (SupFig. 5). 

(2) Figures 2, 3, and 5 - The sizes of the liposomes (400 nm and 50 nm) should be experimentally confirmed, e.g., by dynamic light scattering (DLS).

We have included DLS measurements confirming the differences of liposome sizes. Please see answer to point 2 of Reviewer 1.

(3) Figure 4C - The free energy landscape for different phospholipids is interesting. What about other acidic phospholipids, such as PS?

This is indeed an interesting point. Our molecular dynamics simulations show that PE has a similar free energy landscape to PA while PC is significantly different. This might point into the direction that the headgroup size plays a major role. For intra-mitochondrial PS transport a specific protein complex consisting of Ups2/Mdm35 has been identified, and it will be an interesting question for future studies if PS transfer is regulated by similar factors.

(4) Supplementary Figure 2 - The deformation of liposomes by Ups1 is interesting. Does this depend on the presence of PA or other acidic phospholipids?

We asked ourself the same question throughout the project. As pointed out in the manuscript, the membrane-deforming activity of Ups1 is relatively mild when compared to proteins found for example in endocytosis. This made a proper static analysis challenging. We weren’t able to unambiguously show whether other acidic phospholipids showed comparable effects to PA.

(5) It may not be easy to assess experimentally, but the OM in mitochondria should have scramblase activity. Then, such scramblase activity could influence the observed effects of membrane curvature on Ups1-mediated PA transfer.

(6) It would be helpful to discuss this possibility in the manuscript.

In the revised version of the manuscript, we now discuss the existence of scramblases, such as Sam50 and VDAC, in the outer mitochondrial membrane with regard to their likely effect on membrane packing (page 13 - 14). As for a co-reconstitution experiment we considered the in vitro analysis of the impact that a scramblase in liposomes might have on lipid transfer outside the scope of this study. 

(7) Figure 6 is not referenced in the main text.

Thank you, this oversight was corrected.

(8) The non-abbreviated forms of LUV and SUV should be defined in the text upon first use.

We now include a definition in the manuscript.

(9) The term "transfer velocity" would be better expressed as "transfer rate".

We agree, and we changed the wording accordingly.

Reviewer #3 (Recommendations for the authors):

(1) As flotation assays are a central technique of the study, readers who are not familiar with this method could benefit from a few explanatory sentences and appropriate references in the introduction section.

Figure 1B now contains an updated version of a cartoon outlining the flotation assay and a description in the manuscript (page 4) that should make it easier to understand the assay. We have also included a direct reference within the methods section to a paper describing this assay in more detail.

(2) Related to the major point, but also to improve the manuscript overall, the authors could add DLS (for size distribution and zeta potential) and cryo-EM (for multilamellarity analysis) data. This would aid future efforts to reproduce their observations.

In the revised version of the manuscript we include DLS and zeta potential measurements as well as a detailed analysis of liposome multilamellarity by cryo-EM (also see answer to point 2 by Reviewer 1) (SupFig. 2A & B; SupFig. 3E).

(3) Could the authors state the specific zeta potentials of the negatively charged (under varying pH) and neutral liposomes and relate these to natural membranes?

We have included zeta potential measurements of differently charged liposomes in and changed the text accordingly (page 8) (SupFig. 3E).

(4) Changes in pH affect several characteristics of membranes (including lipid dipoles, charge, packing density, fluidity, and phase separation), particularly charge density. This experimental system does not allow all of these factors to be disentangled and studied separately. Some of the observations presented in Figures 2 and 5 could also be explained by these effects.

The effects of pH on various membrane properties, such as lipid headgroup dipoles, lipid packing, interfacial tension, and others, are well described in the literature. For example, it was implied that increasing pH leads to phosphatidic acid (PA) becoming more negatively charged when in proximity to phosphatidylethanolamine (PE). We already discuss this effect in the manuscript, as our observation that Ups1 binding to membranes depends on negatively charged lipids but nevertheless increases with decreasing pH is unexpected.

As pointed out, many of the parameters mentioned above are beyond control in our assays, and a systematic analysis of each of these factors with respect to Ups1 membrane binding and lipid transfer would be well beyond the scope of this manuscript. We have therefore included a passage discussing this issue in more detail (page 4-5).

(5) Is the curvature simulated in the theoretical models comparable to the curvature of the liposome systems (e.g., a sphere of 100 nm diameter)?

The simulated curvature spans a defined range, with the highest curvature corresponding to vesicles with diameters of approximately 15 nm. This corresponds reasonably well to the vesicle size distribution as analyzed by cryo-EM.

Reference

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Lu, J., Chan, C., Yu, L., Fan, J., Sun, F., & Zhai, Y. (2020). Molecular mechanism of mitochondrial phosphatidate transfer by Ups1. Commun Biol, 3(1), 468. https://doi.org/10.1038/s42003-020-01121-x

Miliara, X., Garnett, J. A., Tatsuta, T., Abid Ali, F., Baldie, H., Perez-Dorado, I., Simpson, P., Yague, E., Langer, T., & Matthews, S. (2015). Structural insight into the TRIAP1/PRELI-like domain family of mitochondrial phospholipid transfer complexes. EMBO Rep, 16(7), 824-835. https://doi.org/10.15252/embr.201540229

Miliara, X., Tatsuta, T., Berry, J. L., Rouse, S. L., Solak, K., Chorev, D. S., Wu, D., Robinson, C. V., Matthews, S., & Langer, T. (2019). Structural determinants of lipid specificity within Ups/PRELI lipid transfer proteins. Nat Commun, 10(1), 1130. https://doi.org/10.1038/s41467-019-09089-x

Miliara, X., Tatsuta, T., Eiyama, A., Langer, T., Rouse, S. L., & Matthews, S. (2023). An intermolecular hydrogen-bonded network in the PRELID-TRIAP protein family plays a role in lipid sensing. Biochim Biophys Acta Proteins Proteom, 1871(1), 140867. https://doi.org/10.1016/j.bbapap.2022.140867

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Richens, J. L., Tyler, A. I. I., Barriga, H. M. G., Bramble, J. P., Law, R. V., Brooks, N. J., Seddon, J. M., Ces, O., & O'Shea, P. (2017). Spontaneous charged lipid transfer between lipid vesicles. Sci Rep, 7(1), 12606. https://doi.org/10.1038/s41598-017-12611-0

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Watanabe, Y., Tamura, Y., Kawano, S., & Endo, T. (2015). Structural and mechanistic insights into phospholipid transfer by Ups1-Mdm35 in mitochondria. Nat Commun, 6, 7922. https://doi.org/10.1038/ncomms8922

Reviewer #3 (Public review):

Summary:

This manuscript by Kim and Parsons presents an overview of the nitroreductase/metronidazole (NTR/MTZ) cell ablation system.

Strengths:

This manuscript nicely places the NTR/MTZ system in the context of other cell ablation methods, with a discussion of their respective advantages and disadvantages. This review is particularly useful for highlighting the many ways the NTR/MTZ system has been applied to study the regeneration of multiple cell types and to model different degenerative human diseases. The review concludes with a discussion on recent improvements made to the system and practical considerations and "best practices" for NTR-based experiments. This review could be a helpful resource, especially for researchers new to regeneration or cell ablation studies.

Weaknesses:

Although the NTR/MTZ system has been used in other model organisms, this review is primarily focused on its uses in zebrafish. While this is understandable given the wide adoption of NTR/MTZ in the zebrafish field, discussion of the unique considerations and/or challenges for non-zebrafish systems would be an interesting addition and could broaden the potential audience for this review. Additional minor revisions, as suggested below, could also improve readability.

Reviewer #2 (Public review):

Summary:

Kim and Parsons reviewed the nitroreductase (NTR)/prodrug system: when engineered cells expressing the enzyme NTR are treated with prodrug (e.g. metronidazole), NTR converts the prodrug into a cytotoxic compound that kills these cells. The review covers how the system has been developed, spatiotemporal control of targeted cell ablation, and its broad utility to study regenerative mechanisms, model human diseases, and screen chemicals to discover pro-regenerative and protective compounds. They further discussed the newer version of NTR, a more potent prodrug, and experimental design, which not only expands the possible utility of the NTR/prodrug system, but also allows the research community to develop a precise, reproducible and versatile platform.

Strengths:

The review summarized landmark work application of the NTR/prodrug system, and recent studies, with focus on the model organism zebrafish. The review provides a good gateway to understanding the system and considering regenerative studies.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #1 (Public review):

Summary:

Kim and Parsons present a timely overview of the NTR/prodrug system and its applications in regenerative biology research, with particular emphasis on tissue-specific cell ablation. The system has substantially advanced the field by enabling non-invasive, conditional cell elimination, and has proven especially powerful in zebrafish, though applications in other classical model organisms are also noted. The review covers the historical origins of the NTR system, its use in regeneration studies, small-molecule screening, and genetic and CRISPR-based screening, as well as future directions, including the development of the highly efficient NTR2 enzyme variant.

Strengths:

This is a useful and well-structured contribution. The manuscript is a valuable resource for the regeneration biology community.

Weaknesses:

The impact and scientific value of this paper could be meaningfully enhanced by addressing several points outlined below. The concerns centre on completeness, conceptual precision, and the depth of mechanistic discussion.

(1) Title: Species specificity.

Given that the review's primary focus is the zebrafish model, it would be appropriate to include the species name in the title. This would improve discoverability and accurately set the scope of the article for prospective readers.

(2) Subchapter: Physical injury.

The subchapter enumerates different types of physical injury models but would benefit from a more substantive comparative discussion. In particular, the authors are encouraged to address the following:

(2.1) Outcome comparison: Surgical and other invasive approaches cause damage to entire tissue structures comprising multiple cell types, whereas tissue-specific genetic ablation eliminates a defined cell population while leaving the surrounding architecture largely intact. This fundamental distinction has direct implications for the interpretation of regenerative outcomes and should be clearly articulated.

(2.2) Inflammatory response: Invasive injuries typically trigger a robust inflammatory response, which itself can be a potent driver of regeneration. By contrast, genetic cell ablation may elicit a qualitatively different inflammatory reaction. A comparative discussion of this distinction would help readers appreciate a critical limitation of genetic ablation systems relative to models of natural, accidental tissue damage.

(3) Subchapter: Cell-specific toxins.

This subchapter would benefit from several targeted expansions:

(3.1) Off-target effects: The authors should include evidence that the exemplified drugs have known off-target activities, with a discussion of how these confounded the interpretation of experimental data. At least a few concrete published examples should be cited.

(3.2) Completeness of the toxin list: The current list appears illustrative rather than comprehensive. A more complete enumeration would be valuable, particularly for neurotoxins and drugs targeting sensory cells, as these are highly relevant to the zebrafish regeneration field.

(3.3) Interspecies differences: It would be informative to specify whether drug specificity differs across species, as this is a practical consideration for researchers working in organisms other than zebrafish.

(4) Subchapter: Optogenetic cell ablation.

The authors note that optogenetic cell ablation has not yet been applied in conventional regeneration studies. It would strengthen this section to include a discussion of the underlying reasons for this gap, whether technical or biological, so that readers can appreciate the barriers and potential for future adoption.

(5) Terminology: "Suicide gene".

The use of the term "suicide gene" to nitroreductase is conceptually imprecise and merits reconsideration. Strictly speaking, a suicide gene is one whose expression alone is sufficient to kill the cell, as in the case of genes encoding direct triggers of apoptosis or the catalytic A subunit of diphtheria toxin (DTA). NTR does not meet this criterion: it requires the exogenous administration of a prodrug (e.g., metronidazole) to produce a cytotoxic metabolite, and is therefore only conditionally lethal.

It is worth noting that nitroreductases evolved in bacteria and fungi as enzymes involved in chemoprotection and detoxification, converting potentially toxic and mutagenic nitroaromatic compounds into less harmful metabolites (PMID: 18355273). This biological context further underscores that NTR is not inherently a lethal protein. The authors are encouraged to replace or qualify the term "suicide gene" and instead adopt terminology that more accurately reflects the conditional, prodrug-dependent nature of the system.

(6) NTR/MTZ in regenerative studies: Mechanistic depth.

While the review catalogues several studies employing the NTR/MTZ system, it lacks mechanistic depth regarding the cellular basis of ablation. The following questions should be addressed, where evidence exists in the literature:

(6.1) Temporal dynamics of cell death: What is known about the kinetics of NTR/MTZ-induced lethality across different tissue types in larval and adult zebrafish, as well as other organisms? Are there age- and tissue-specific differences in the speed or completeness of ablation?

(6.2) Mechanism of cell death: What is the cellular basis of NTR/MTZ-induced cytotoxicity in zebrafish? In particular, do the toxic metabolites preferentially cause mitochondrial damage or nuclear DNA damage, and what downstream death pathways are engaged?

(6.3) Proliferative versus post-mitotic cells: Are proliferating and non-proliferating cells equally sensitive to the NTR/MTZ system, or does the proliferative status of a cell influence susceptibility? This is a practically important question for researchers designing ablation experiments in tissues with mixed cell populations.

(6.4) Ablation of progenitor cells: Are there published examples demonstrating that co-ablation of differentiated functional cells and organ-specific progenitor cells abolishes regenerative capacity? Such examples would be highly informative in illustrating the system's power to dissect the cellular requirements for regeneration.

Addressing the points above, particularly the comparative discussion of injury models and inflammatory responses, the clarification of terminology, and the mechanistic discussion of NTR/MTZ-induced cell death would substantially strengthen the review's scientific contribution and utility.

eLife Assessment

This Review Article synthesizes the development, applications, and recent technical advances of the nitroreductase/prodrug system, highlighting how it enables precise spatiotemporal cell ablation and experimental platforms for studying regenerative mechanisms and screening for pro-regenerative or protective compounds. Together, the article provides a conceptual and practical overview that will help researchers adopt and further develop this versatile approach in regenerative biology. It will be of interest to researchers studying regeneration, disease modelling, and targeted cell ablation, particularly those working with zebrafish and other genetic model systems.

eLife Assessment

This article describes the comprehensive metabolic phenotype of a mouse model of Down Syndrome, together with supporting transcriptomic, metabolomic, and biochemical data. While the work is largely descriptive, the evidence presented is convincing and highlights similarities and differences in male and female mice. This is a valuable study that provides essential groundwork for the further genetic dissection of dosage-sensitive genes causing metabolic dysregulation in Down Syndrome.

Reviewer #1 (Public review):

Summary:

Chen et al. describe metabolic phenotypes in Dp16 Down Syndrome mice, specifically the Dp(16)1Yey/+ mice - segmental duplication model carrying a majority of the triplicated Hsa21 gene orthologs. The group has performed metabolic phenotyping data in chow and high-fat diets, as well as undertaking a transcriptomic and metabolomic approach in tissues such as white and brown adipose tissues, liver, skeletal muscle, and hypothalamus to reveal both shared and sex-specific differences. The group describes sexual dimorphism in body weight, body temperature, food intake, and physical activity. Core shared features are insulin resistance, glucose intolerance, impaired lipid clearance, and dyslipidaemia in the Dp16 mice. They report tissue signatures of immune activation and a pro-inflammatory state, ER and oxidative stress, fibrosis, impaired glucose and fatty acid catabolism, altered lipid and bile acid profiles, and reduced mitochondrial respiration in Dp16 mice.

Strengths:

Overall, this is a good study with detailed, comprehensive data from an excellent group who have previously published on metabolic phenotyping of 2 other Down Syndrome mouse models. Although somewhat descriptive, it does certainly add to the current field and understanding of strengths and weaknesses of Down Syndrome mouse models, as well as identifying new features whilst strengthening previously suggested mechanisms.

Weaknesses:

Many aspects of this study have been described in other Down syndrome mouse models, though there are certainly aspects that are new. It would be useful if the authors could do a direct critique and comparison with previous publications in the area, utilising the same Down Syndrome mouse model. There are also a few limitations in the number of animals used and the interpretation of the data that should be acknowledged.

Reviewer #2 (Public review):

Summary:

Human DS is associated with metabolic dysfunction in humans, but the precise details of this have not been studied in detail. Here, the authors use a mouse model of DS to study systemic metabolic and transcriptional responses in key metabolic tissues to provide a deep understanding of the metabolic changes associated with DS. As part of his work, the authors also aimed to help inform the selection of a mouse model that best reflects the metabolic profile of DS, through comparison with other DS model metabolic data.

The data presented in this model will be of interest to those in the field of metabolism. The immediate impact is unclear, but the breadth of data presented makes this a very useful resource.

Strengths:

(1) This work builds on other comprehensive analyses that the authors have performed in other DS mouse models.

(2) The authors note common metabolic disturbances between male and female mice (e.g., insulin resistance) alongside clearly sexually dimorphic phenotypes (e.g., body weight). Studying both sexes in this context is important.

(3) The authors have written the paper in a way that integrates a large number of observations well. There is complex data, and a high degree of sexual dimorphism. The study has generated a valuable and wide-ranging dataset comprising molecular, biochemical, and physiological data that will be useful for further, more mechanistic studies of metabolism in DS.

(4) For specific observations, like the findings of altered body temperature in male and female mice, the authors undertake follow-up hypothesis-driven analyses of BAT mitochondria and specific hormones. Although these analyses do not explain the change in temperature, they ensure the study is not purely descriptive in nature.

Weaknesses:

(1) Assessing metabolism using dynamic testing is a strength. ITT, GTT and LTTs are included.

(2) The dosing for GTTs, ITTs and LTTs was performed per body weight. But the mice under chow and HFD had different body weights. This may compromise the interpretation of the data. Further, ITTs are presented as percentage change, and this can be heavily influenced by baseline glucose measures. The changes appear quite dramatic, so can the authors plot the raw data instead?

(3) In addition, throughout the manuscript, it is not clear which tissues are the most dominant in disrupting metabolism. The ITT and GTT are composite measures across tissues. Tissue-specific analyses using a clamp technique or isolated tissues may provide more clarity here.

(4) One of the aims of the study was "to help inform the selection of mouse model that best reflects the metabolic profile of DS". The discussion does not contain a comparison between the previous work on different strains and relative to known human data.

(5) Data availability. Raw metabolomic data should be made available.

Reviewer #3 (Public review):

Summary:

The article by Chen et al. describes the comprehensive metabolic profiling of DP16 mice, a Down syndrome model that carries a duplicated segment of the mouse chromosome syntenic to human chromosome 21. The authors note that this model is superior to previously used models, based on genetics, as ~65% of the chromosome 21 orthologues. The metabolic phenotypes also appear to be more consistent with those observed in humans with Down Syndrome. The study lays the groundwork for a more detailed genetic dissection of dosage-sensitive genes that contribute to the metabolic deficits observed in Down Syndrome.

Strengths:

There is an enormous amount of data in this manuscript, and the methods are described with adequate attention to detail. A strength of the manuscript is that both male and female mice were analyzed, so that concordant and discordant phenotypes were identified. Both males and females had evidence of insulin resistance. Transcriptomic and metabolomic data revealed impaired pathways for lipid metabolism, a pro-inflammatory state, reduced mitochondrial health and oxidative stress. Although the effects of a high-fat diet on weight gain were divergent, this diet caused worsened insulin resistance in both males and females.

The discussion is excellent. Limitations of the study are well described. This reviewer does not identify any critical missing data.

Weaknesses:

It might have been helpful to have included blood pressure measurements, given the differences in 19-Nor-deoxycorticosterone. The discussion references several articles that describe sex-dependent differences in metabolic phenotypes in humans with Down syndrome, and it might have been helpful to state more explicitly whether these differences correlate with those observed here in mice.

eLife Assessment

The use of DNA tethers is a useful advance for studying how motor proteins respond to load. The authors use a convincing methodology to investigate the detachment and reattachment kinetics of kinesin-1, 2, and 3 motors against loads oriented parallel to the microtubule. As the manuscript stands, the conclusions drawn from the experiments, as well as the overall interpretation of the results, are incompletely supported by the presented data, and the novelty over previous reports appears less clear.

Reviewer #1 (Public review):

Summary:

Noell et al have presented a careful study of the dissociation kinetics of Kinesin (1,2,3) classes of motors moving in vitro on a microtubule. These motors move against the opposing force from a ~1 micron DNA strand (DNA tensiometer) that is tethered to the microtubule and also bound to the motor via specific linkages (Figure 1A). The authors compare the time for which motors remain attached to the microtubule when they are tethered to the DNA, versus when they are not. If the former is longer, the interpretation is that the force on the motor from the stretched DNA (presumed to be working solely along the length of the microtubule) causes the motor's detachment rate from the microtubule to be reduced. Thus, the specific motor exhibits "catch-bond" like behaviour.

Strengths:

The motivation is good - to understand how kinesin competes against dynein through the possible activation of a catch bond. Experiments are well done, and there is an effort to model the results theoretically.

Weaknesses:

The motivation of these studies is to understand how kinesin (1/2/3) motors would behave when they are pitted in a tug of war against dynein motors as they transport cargo in a bidirectional manner on microtubules. Earlier work on dynein and kinesin motors using optical tweezers has suggested that dynein shows a catch bond phenomenon, whereas such signatures were not seen for kinesin. Based on their data with the DNA tensiometer, the authors would like to claim that (i) Kinesin1 and Kinesin2 also show catch-bonding and (ii) the earlier results using optical traps suffer from vertical forces, which complicates the catch-bond interpretation.

While the motivation of this work is reasonable, and the experiments are careful, I find significant issues that the authors have not addressed:

(1) Figure 1B shows the PREDICTED force-extension curve for DNA based on a worm-like chain model. Where is the experimental evidence for this curve? This issue is crucial because the F-E curve will decide how and when a catch-bond is induced (if at all it is) as the motor moves against the tensiometer. Unless this is actually measured by some other means, I find it hard to accept all the results based on Figure 1B.

(2) The authors can correct me on this, but I believe that all the catch-bond studies using optical traps have exerted a load force that exceeds the actual force generated by the motor. For example, see Figure 2 in reference 42 (Kunwar et al). It is in this regime (load force > force from motor) that the dissociation rate is reduced (catch-bond is activated). Such a regime is never reached in the DNA tensiometer study because of the very construction of the experiment. I am very surprised that this point is overlooked in this manuscript. I am therefore not even sure that the present experiments even induce a catch-bond (in the sense reported for earlier papers).

(3) I appreciate the concerns about the Vertical force from the optical trap. But that leads to the following questions that have not at all been addressed in this paper:

(i) Why is the Vertical force only a problem for Kinesins, and not a problem for the dynein studies?

(ii) The authors state that "With this geometry, a kinesin motor pulls against the elastic force of a stretched DNA solely in a direction parallel to the microtubule". Is this really true? What matters is not just how the kinesin pulls the DNA, but also how the DNA pulls on the kinesin. In Figure 1A, what is the guarantee that the DNA is oriented only in the plane of the paper? In fact, the DNA could even be bending transiently in a manner that it pulls the kinesin motor UPWARDS (Vertical force). How are the authors sure that the reaction force between DNA and kinesin is oriented SOLELY along the microtubule?

(4) For this study to be really impactful and for some of the above concerns to be addressed, the data should also have included DNA tensiometer experiments with Dynein. I wonder why this was not done?

While I do like several aspects of the paper, I do not believe that the conclusions are supported by the data presented in this paper for the reasons stated above.

Reviewer #2 (Public review):

Summary:

To investigate the detachment and reattachment kinetics of kinesin-1, 2, and 3 motors against loads oriented parallel to the microtubule, the authors used a DNA tensiometer approach comprising a DNA entropic spring attached to the microtubule on one end and a motor on the other. They found that for kinesin-1 and kinesin-2, the dissociation rates at stall were smaller than the detachment rates during unloaded runs. With regard to the complex reattachment kinetics found in the experiments, the authors argue that these findings were consistent with a weakly-bound 'slip' state preceding motor dissociation from the microtubule. The behavior of kinesin-3 was different and (by the definition of the authors) only showed prolonged "detachment" rates when disregarding some of the slip events. The authors performed stochastic simulations that recapitulate the load-dependent detachment and reattachment kinetics for all three motors. They argue that the presented results provide insight into how kinesin-1, -2, and -3 families transport cargo in complex cellular geometries and compete against dynein during bidirectional transport.

Strengths:

The present study is timely, as significant concerns have been raised previously about studying motor kinetics in optical (single-bead) traps where significant vertical forces are present. Moreover, the obtained data are of high quality, and the experimental procedures are clearly described.

Weaknesses:

However, in the present version of the manuscript, the conclusions drawn from the experiments, the overall interpretation of the results, and the novelty over previous reports appear less clear.

Major comments:

(1) The use of the term "catch bond" is misleading, as the authors do not really mean consistently a catch bond in the classical sense (i.e., a protein-protein interaction having a dissociation rate that decreases with load). Instead, what they mean is that after motor detachment (i.e., after a motor protein dissociating from a tubulin protein), there is a slip state during which the reattachment rate is higher as compared to a motor diffusing in solution. While this may indeed influence the dynamics of bidirectional cargo transport (e.g., during tug-of-war events), the used terms (detachment (with or without slip?), dissociation, rescue, ...) need to be better defined and the results discussed in the context of these definitions. It is very unsatisfactory at the moment, for example, that kinesin-3 is at first not classified as a catch bond, but later on (after tweaking the definitions) it is. In essence, the typical slip/catch bond nomenclature used for protein-protein interaction is not readily applicable for motors with slippage.

(2) The authors define the stall duration as the time at full load, terminated by >60 nm slips/detachments. Isn't that a problem? Smaller slips are not detected/considered... but are also indicative of a motor dissociation event, i.e., the end of a stall. What is the distribution of the slip distances? If the slip distances follow an exponential decay, a large number of short slips are expected, and the presented data (neglecting those short slips) would be highly distorted.

(3) Along the same line: Why do the authors compare the stall duration (without including the time it took the motor to reach stall) to the unloaded single motor run durations? Shouldn't the times of the runs be included?

(4) At many places, it appears too simple that for the biologically relevant processes, mainly/only the load-dependent off-rates of the motors matter. The stall forces and the kind of motor-cargo linkage (e.g., rigid vs. diffusive) do likely also matter. For example: "In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to maintain force generation and, hence, are distinct from true detachment events." I disagree. The kinesin force at reattachment (after slippage) is much smaller than at stall. What helps, however, is that due to the geometry of being held close to the microtubule (either by the DNA in the present case or by the cargo in vivo) the attachment rate is much higher. Note also that upon DNA relaxation ,the motor is likely kept close to the microtubule surface, while, for example, when bound to a vesicle, the motor may diffuse away from the microtubule quickly (e.g., reference 20).

(5) Why were all motors linked to the neck-coil domain of kinesin-1? Couldn't it be that for normal function, the different coils matter? Autoinhibition can also be circumvented by consistently shortening the constructs.

(6) I am worried about the neutravidin on the microtubules, which may act as roadblocks (e.g. DOI: 10.1039/b803585g), slip termination sites (maybe without the neutravidin, the rescue rate would be much lower?), and potentially also DNA-interaction sites? At 8 nM neutravidin and the given level of biotinylation, what density of neutravidin do the authors expect on their microtubules? Can the authors rule out that the observed stall events are predominantly the result of a kinesin motor being stopped after a short slippage event at a neutravidin molecule?

(7) Also, the unloaded runs should be performed on the same microtubules as in the DNA experiments, i.e., with neutravidin. Otherwise, I do not see how the values can be compared.

(8) If, as stated, "a portion of kinesin-3 unloaded run durations were limited by the length of the microtubules, meaning the unloaded duration is a lower limit." corrections (such as Kaplan-Meier) should be applied, DOI: 10.1016/j.bpj.2017.09.024.

(9) Shouldn't Kaplan-Meier also be applied to the ramp durations ... as a ramp may also artificially end upon stall? Also, doesn't the comparison between ramp and stall duration have a problem, as each stall is preceded by a ramp ...and the (maximum) ramp times will depend on the speed of the motor? Kinesin-3 is the fastest motor and will reach stall much faster than kinesin-1. Isn't it obvious that the stall durations are longer than the ramp duration (as seen for all three motors in Figure 3)?

(10) It is not clear what is seen in Figure S6A: It looks like only single motors (green, w/o a DNA molecule) are walking ... Note: the influence of the attached DNA onto the stepping duration of a motor may depend on the DNA conformation (stretched and near to the microtubule (with neutravidin!) in the tethered case and spherically coiled in the untethered case).

(11) Along this line: While the run time of kinesin-1 with DNA (1.4 s) is significantly shorter than the stall time (3.0 s), it is still larger than the unloaded run time (1.0 s). What do the authors think is the origin of this increase?

(12) "The simplest prediction is that against the low loads experienced during ramps, the detachment rate should match the unloaded detachment rate." I disagree. I would already expect a slight increase.

(13) Isn't the model over-defined by fitting the values for the load-dependence of the strong-to-weak transition and fitting the load dependence into the transition to the slip state?

(14) "When kinesin-1 was tethered to a glass coverslip via a DNA linker and hydrodynamic forces were imposed on an associated microtubule, kinesin-1 dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (37)." This statement appears not to be true. In reference 37, very similar to the geometry reported here, the microtubules were fixed on the surface, and the stepping of single kinesin motors attached to large beads (to which defined forces were applied by hydrodynamics) via long DNA linkers was studied. In fact, quite a number of statements made in the present manuscript have been made already in ref. 37 (see in particular sections 2.6 and 2.7), and the authors may consider putting their results better into this context in the Introduction and Discussion. It is also noteworthy to discuss that the (admittedly limited) data in ref. 37 does not indicate a "catch-bond" behavior but rather an insensitivity to force over a defined range of forces.

Reviewer #3 (Public review):

Summary:

Several recent findings indicate that forces perpendicular to the microtubule accelerate kinesin unbinding, where perpendicular and axial forces were analyzed using the geometry in a single-bead optical trapping assay (Khataee and Howard, 2019), comparison between single-bead and dumbbell assay measurements (Pyrpassopoulos et al., 2020), and comparison of single-bead optical trap measurements with and without a DNA tether (Hensley and Yildiz, 2025).

Here, the authors devise an assay to exert forces along the microtubule axis by tethering kinesin to the microtubule via a dsDNA tether. They compared the behavior of kinesin-1, -2, and -3 when pulling against the DNA tether. In line with previous optical trapping measurements, kinesin unbinding is less sensitive to forces when the forces are aligned with the microtubule axis. Surprisingly, the authors find that both kinesin-1 and -2 detach from the microtubule more slowly when stalled against the DNA tether than in unloaded conditions, indicating that these motors act as catch bonds in response to axial loads. Axial loads accelerate kinesin-3 detachment. However, kinesin-3 reattaches quickly to maintain forces. For all three kinesins, the authors observe weakly attached states where the motor briefly slips along the microtubule before continuing a processive run.

Strengths:

These observations suggest that the conventional view that kinesins act as slip bonds under load, as concluded from single-bead optical trapping measurements where perpendicular loads are present due to the force being exerted on the centroid of a large (relative to the kinesin) bead, needs to be reconsidered. Understanding the effect of force on the association kinetics of kinesin has important implications for intracellular transport, where the force-dependent detachment governs how kinesins interact with other kinesins and opposing dynein motors (Muller et al., 2008; Kunwar et al., 2011; Ohashi et al., 2018; Gicking et al., 2022) on vesicular cargoes.

Weaknesses:

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results should be addressed in the manuscript.

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

In the single-molecule extension traces (Figure 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g., the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3?

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis?

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state?

Author response:

Reviewer 1 (Public review):

(1) Figure 1B shows the PREDICTED force-extension curve for DNA based on a worm-like chain model. Where is the experimental evidence for this curve? This issue is crucial because the F-E curve will decide how and when a catch-bond is induced (if at all it is) as the motor moves against the tensiometer. Unless this is actually measured by some other means, I find it hard to accept all the results based on Figure 1B.

The Worm-Like-Chain model for the elasticity of DNA was established by early work from the Bustamante lab (Smith et al., 1992)  and Marko and Siggia (Marko and Siggia, 1995), and was further validated and refined by the Block lab (Bouchiat et al., 1999; Wang et al., 1997). The 50 nm persistence length is the consensus value, and was shown to be independent of force and extension in Figure 3 of Bouchiat et al (Bouchiat et al., 1999). However, we would like to stress that for our conclusions, the precise details of the Force-Extension relationship of our dsDNA are immaterial. The key point is that the motor stretches the DNA and stalls when it reaches its stall force. Our claim of the catch-bond character of kinesin is based on the longer duration at stall compared to the run duration in the absence of load. Provided that the motor is indeed stalling because it has stretched out the DNA (which is strongly supported by the repeated stalling around the predicted extension corresponding to ~6 pN of force), then the stall duration depends on neither the precise value for the extension nor the precise value of the force at stall.

(2) The authors can correct me on this, but I believe that all the catch-bond studies using optical traps have exerted a load force that exceeds the actual force generated by the motor. For example, see Figure 2 in reference 42 (Kunwar et al). It is in this regime (load force > force from motor) that the dissociation rate is reduced (catch-bond is activated). Such a regime is never reached in the DNA tensiometer study because of the very construction of the experiment. I am very surprised that this point is overlooked in this manuscript. I am therefore not even sure that the present experiments even induce a catch-bond (in the sense reported for earlier papers).

It is true that Kunwar et al measured binding durations at super-stall loads and used that to conclude that dynein does act as a catch-bond (but kinesin does not) (Kunwar et al., 2011). However, we would like to correct the reviewer on this one. This approach of exerting super-stall forces and measuring binding durations is in fact less common than the approach of allowing the motor to walk up to stall and measuring the binding duration. This ‘fixed trap’ approach has been used to show catch-bond behavior of dynein (Leidel et al., 2012; Rai et al., 2013) and kinesin (Kuo et al., 2022; Pyrpassopoulos et al., 2020). For the non-processive motor Myosin I, a dynamic force clamp was used to keep the actin filament in place while the myosin generated a single step (Laakso et al., 2008). Because the motor generates the force, these are not superstall forces either.

(3) I appreciate the concerns about the Vertical force from the optical trap. But that leads to the following questions that have not at all been addressed in this paper:

(i) Why is the Vertical force only a problem for Kinesins, and not a problem for the dynein studies?

Actually, we do not claim that vertical force is not a problem for dynein; our data do not speak to this question. There is debate in the literature as to whether dynein has catch bond behavior in the traditional single-bead optical trap geometry - while some studies have measured dynein catch bond behavior (Kunwar et al., 2011; Leidel et al., 2012; Rai et al., 2013), others have found that dynein has slip-bond or ideal-bond behavior (Ezber et al., 2020; Nicholas et al., 2015; Rao et al., 2019). This discrepancy may relate to vertical forces, but not in an obvious way.

(ii) The authors state that "With this geometry, a kinesin motor pulls against the elastic force of a stretched DNA solely in a direction parallel to the microtubule". Is this really true? What matters is not just how the kinesin pulls the DNA, but also how the DNA pulls on the kinesin. In Figure 1A, what is the guarantee that the DNA is oriented only in the plane of the paper? In fact, the DNA could even be bending transiently in a manner that it pulls the kinesin motor UPWARDS (Vertical force). How are the authors sure that the reaction force between DNA and kinesin is oriented SOLELY along the microtubule?

We acknowledge that “solely” is an absolute term that is too strong to describe our geometry. We will soften this term in our revision to “nearly parallel to the microtubule”. In the Geometry Calculations section of Supplementary Methods, we calculate that if the motor and streptavidin are on the same protofilament, the vertical force will be <1% of the horizontal force. We also note that if the motor is on a different protofilament, there will be lateral forces and forces perpendicular to the microtubule surface, except they are oriented toward rather than away from the microtubule. The DNA can surely bend due to thermal forces, but because inertia plays a negligible role at the nanoscale (Howard, 2001; Purcell, 1977), any resulting upward forces will only be thermal forces, which the motor is already subjected to at all times.

(4) For this study to be really impactful and for some of the above concerns to be addressed, the data should also have included DNA tensiometer experiments with Dynein. I wonder why this was not done?

As much as we would love to fully characterize dynein here, this paper is about kinesin and it took a substantial effort. The dynein work merits a stand-alone paper.

While I do like several aspects of the paper, I do not believe that the conclusions are supported by the data presented in this paper for the reasons stated above.

The three key points the reviewer makes are the validity of the worm-like-chain model, the question of superstall loads, and the role of DNA bending in generating vertical forces. We hope that we have fully addressed these concerns in our responses above.

Reviewer #2 (Public review):

Major comments:

(1) The use of the term "catch bond" is misleading, as the authors do not really mean consistently a catch bond in the classical sense (i.e., a protein-protein interaction having a dissociation rate that decreases with load). Instead, what they mean is that after motor detachment (i.e., after a motor protein dissociating from a tubulin protein), there is a slip state during which the reattachment rate is higher as compared to a motor diffusing in solution. While this may indeed influence the dynamics of bidirectional cargo transport (e.g., during tug-of-war events), the used terms (detachment (with or without slip?), dissociation, rescue, ...) need to be better defined and the results discussed in the context of these definitions. It is very unsatisfactory at the moment, for example, that kinesin-3 is at first not classified as a catch bond, but later on (after tweaking the definitions) it is. In essence, the typical slip/catch bond nomenclature used for protein-protein interaction is not readily applicable for motors with slippage.

We appreciate the reviewer’s point and we will work to streamline and define terms in our revision.

(2) The authors define the stall duration as the time at full load, terminated by >60 nm slips/detachments. Isn't that a problem? Smaller slips are not detected/considered... but are also indicative of a motor dissociation event, i.e., the end of a stall. What is the distribution of the slip distances? If the slip distances follow an exponential decay, a large number of short slips are expected, and the presented data (neglecting those short slips) would be highly distorted.

The reviewer brings up a good point that there may be undetected slips. To address this question, we plotted the distribution of slip distances for kinesin-3, which by far had the most slip events. As the reviewer suggested, it is indeed an exponential distribution. Our preliminary analysis suggests that roughly 20% of events are missed due to this 60 nm cutoff. This will change our unloaded duration numbers slightly, but this will not alter our conclusions.\

(3) Along the same line: Why do the authors compare the stall duration (without including the time it took the motor to reach stall) to the unloaded single motor run durations? Shouldn't the times of the runs be included?

The elastic force of the DNA spring is variable as the motor steps up to stall, and so if we included the entire run duration then it would be difficult to specify what force we were comparing to unloaded. More importantly, if we assume that any stepping and detachment behavior is history independent, then it is mathematically proper to take any arbitrary starting point (such as when the motor reaches stall), start the clock there, and measure the distribution of detachments durations relative to that starting point.

More importantly, what we do in Fig. 3 is to separate out the ramps from the stalls and, using a statistical model, we compute a separate duration parameter (which is the inverse of the off-rate) for the ramp and the stall. What we find is that the relationship between ramp, stall, and unloaded durations is different for the three motors, which is interesting in itself.

(4) At many places, it appears too simple that for the biologically relevant processes, mainly/only the load-dependent off-rates of the motors matter. The stall forces and the kind of motor-cargo linkage (e.g., rigid vs. diffusive) do likely also matter. For example: "In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to maintain force generation and, hence, are distinct from true detachment events." I disagree. The kinesin force at reattachment (after slippage) is much smaller than at stall. What helps, however, is that due to the geometry of being held close to the microtubule (either by the DNA in the present case or by the cargo in vivo) the attachment rate is much higher. Note also that upon DNA relaxation, the motor is likely kept close to the microtubule surface, while, for example, when bound to a vesicle, the motor may diffuse away from the microtubule quickly (e.g., reference 20).

We appreciate the reviewer’s detailed thinking here, and we offer our perspective. As to the first point, we agree that the stall force is relevant and that the rigidity of the motor-cargo linkage will play a role. The goal of the sentence on pulling cargo that the reviewer highlights is to set up our analysis of slips, which we define as rearward displacements that don’t return to the baseline before force generation resumes. We agree that force after slippage is much smaller than at stall, and we plan to clarify that section of text. However, as shown in the model diagram in Fig. 5, we differentiate between the slip state (and recovery from this slip state) and the detached state (and reattachment from this detached state). This delineation is important because, as the reviewer points out, if we are measuring detachment and reattachment with our DNA tensiometer, then the geometry of a vesicle in a cell will be different and diffusion away from the microtubule or elastic recoil perpendicular to the microtubule will suppress this reattachment.

Our evidence for a slip state in which the motor maintains association with the microtubule comes from optical trapping work by Tokelis et al (Toleikis et al., 2020) and Sudhakar et al (Sudhakar et al., 2021). In particular, Sudhakar used small, high index Germanium microspheres that had a low drag coefficient. They showed that during ‘slip’ events, the relaxation time constant of the bead back to the center of the trap was nearly 10-fold slower than the trap response time, consistent with the motor exerting drag on the microtubule. (With larger beads, the drag of the bead swamps the motor-microtubule friction.) Another piece of support for the motor maintaining association during a slip is work by Ramaiya et al. who used birefringent microspheres to exert and measure rotational torque during kinesin stepping (Ramaiya et al., 2017). In most traces, when the motor returned to baseline following a stall, the torque was dissipated as well, consistent with a ‘detached’ state. However, a slip event is shown in S18a where the motor slips backward while maintaining torque. This is best explained by the motor slipping backward in a state where the heads are associated with the microtubule (at least sufficiently to resist rotational forces). Thus, we term the resumption after slip to be a rescue from the slip state rather than a reattachment from the detached state.

To finish the point, with the complex geometry of a vesicle, during slip events the motor remains associated with the microtubule and hence primed for recovery. This recovery rate is expected to be the same as for the DNA tensiometer. Following a detachment, however, we agree that there will likely be a higher probability of reattachment in the DNA tensiometer due to proximity effects, whereas with a vesicle any elastic recoil or ‘rolling’ will pull the detached motor away from the microtubule, suppressing reattachment. We plan to clarify these points in the text of the revision.

(5) Why were all motors linked to the neck-coil domain of kinesin-1? Couldn't it be that for normal function, the different coils matter? Autoinhibition can also be circumvented by consistently shortening the constructs.

We chose this dimerization approach to focus on how the mechoanochemical properties of kinesins vary between the three dominant transport families. We agree that in cells, autoinhibition of both kinesins and dynein likely play roles in regulating bidirectional transport, as will the activity of other regulatory proteins. The native coiled-coils may act as as ‘shock absorbers’ due to their compliance, or they might slow the motor reattachment rate due to the relatively large search volumes created by their long lengths (10s of nm). These are topics for future work. By using the neck-coil domain of kinesin-1 for all three motors, we eliminate any differences in autoinhibition or other regulation between the three kinesin families and focus solely on differences in the mechanochemistry of their motor domains.

(6) I am worried about the neutravidin on the microtubules, which may act as roadblocks (e.g. DOI: 10.1039/b803585g), slip termination sites (maybe without the neutravidin, the rescue rate would be much lower?), and potentially also DNA-interaction sites? At 8 nM neutravidin and the given level of biotinylation, what density of neutravidin do the authors expect on their microtubules? Can the authors rule out that the observed stall events are predominantly the result of a kinesin motor being stopped after a short slippage event at a neutravidin molecule?

We will address these points in our revision.

(7) Also, the unloaded runs should be performed on the same microtubules as in the DNA experiments, i.e., with neutravidin. Otherwise, I do not see how the values can be compared.

We will address this point in our revision.

(8) If, as stated, "a portion of kinesin-3 unloaded run durations were limited by the length of the microtubules, meaning the unloaded duration is a lower limit." corrections (such as Kaplan-Meier) should be applied, DOI: 10.1016/j.bpj.2017.09.024.

(9) Shouldn't Kaplan-Meier also be applied to the ramp durations ... as a ramp may also artificially end upon stall? Also, doesn't the comparison between ramp and stall duration have a problem, as each stall is preceded by a ramp ...and the (maximum) ramp times will depend on the speed of the motor? Kinesin-3 is the fastest motor and will reach stall much faster than kinesin-1. Isn't it obvious that the stall durations are longer than the ramp duration (as seen for all three motors in Figure 3)?

The reviewer rightly notes the many challenges in estimating the motor off-rates during ramps. To estimate ramp off-rates and as an independent approach to calculating the unloaded and stall durations, we developed a Markov model coupled with Bayesian inference methods to estimate a duration parameter (equivalent to the inverse of the off-rate) for the unloaded, ramp, and stall duration distributions. With the ramps, we have left censoring due to the difficulty in detecting the start of the ramps in the fluctuating baseline, and we have right censoring due to reaching stall (with different censoring of the ramp duration for the three motors due to their different speeds). The Markov model assumes a constant detachment probability and history independence, and thus is robust even in the face of left and right censoring (details in the Supplementary section). This approach is preferred over Kaplan-Meier because, although these non-parametric methods make no assumptions for the distribution, they require the user to know exactly where the start time is.

Regarding the potential underestimate of the kinesin-3 unloaded run duration due to finite microtubule lengths. The first point is that the unloaded duration data in Fig. 2C are quite linear up to 6 s and are well fit by the single-exponential fit (the points above 6s don’t affect the fit very much). The second point is that when we used our Markov model (which is robust against right censoring) to estimate the unloaded and stall durations, the results agreed with the single-exponential fits very well (Table S2). For instance, the single-exponential fit for the kinesin-3 unloaded duration was 2.74 s (2.33 – 3.17 s 95% CI) and the estimate from the Markov model was 2.76 (2.28 – 3.34 s 95% CI). Thus, we chose not to make any corrections due to finite microtubule lengths.

(10) It is not clear what is seen in Figure S6A: It looks like only single motors (green, w/o a DNA molecule) are walking ... Note: the influence of the attached DNA onto the stepping duration of a motor may depend on the DNA conformation (stretched and near to the microtubule (with neutravidin!) in the tethered case and spherically coiled in the untethered case).

In Figure S6A kymograph, the green traces are GFP-labeled kinesin-1 without DNA attached (which are in excess) and the red diagonal trace is a motor with DNA attached. There are also two faint horizontal red traces, which are labeled DNA diffusing by (smearing over a large area during a single frame). Panel S6B shows run durations of motors with DNA attached. We agree that the DNA conformation will differ if it is attached and stretched (more linear) versus simply being transported (random coil), but by its nature this control experiment is only addressing random coil DNA.

(11) Along this line: While the run time of kinesin-1 with DNA (1.4 s) is significantly shorter than the stall time (3.0 s), it is still larger than the unloaded run time (1.0 s). What do the authors think is the origin of this increase?

Our interpretation of the unloaded kinesin-DNA result is that the much slower diffusion constant of the DNA relative to the motor alone enables motors to transiently detach and rebind before the DNA cargo has diffused away, thus extending the run duration. In contrast, such detachment events for motors alone normally result in the motor diffusing away from the microtubule, terminating the run. This argument has been used to reconcile the longer single-motor run lengths in the gliding assay versus the bead assay (Block et al., 1990). Notably, this slower diffusion constant should not play a role in the DNA tensiometer geometry because if the motor transiently detaches, then it will be pulled backward by the elastic forces of the DNA and detected as a slip or detachment event. We will address this point in the revision.

(12) "The simplest prediction is that against the low loads experienced during ramps, the detachment rate should match the unloaded detachment rate." I disagree. I would already expect a slight increase.

Agreed. We will change this text to: “The prediction for a slip bond is that against the low loads experienced during ramps, the detachment rate should be equal to or faster than the unloaded detachment rate.”

(13) Isn't the model over-defined by fitting the values for the load-dependence of the strong-to-weak transition and fitting the load dependence into the transition to the slip state?

Essentially, yes, it is overdefined, but that is essentially by design and it is still very useful. Our goal here was to make as simple a model as possible that could account for the data and use it to compare model parameters for the different motor families. Ignoring the complexity of the slip and detached states, a model with a strong and weak state in the stepping cycle and a single transition out of the stepping cycle is the simplest formulation possible. And having rate constants (k_S-W and k_slip in our case) that vary exponentially with load makes thermodynamic sense for modeling mechanochemistry (Howard, 2001). Thus, we were pleasantly surprised that this bare-bones model could recapitulate the unloaded and stall durations for all three motors (Fig. 5C-E).

(14) "When kinesin-1 was tethered to a glass coverslip via a DNA linker and hydrodynamic forces were imposed on an associated microtubule, kinesin-1 dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (37)." This statement appears not to be true. In reference 37, very similar to the geometry reported here, the microtubules were fixed on the surface, and the stepping of single kinesin motors attached to large beads (to which defined forces were applied by hydrodynamics) via long DNA linkers was studied. In fact, quite a number of statements made in the present manuscript have been made already in ref. 37 (see in particular sections 2.6 and 2.7), and the authors may consider putting their results better into this context in the Introduction and Discussion. It is also noteworthy to discuss that the (admittedly limited) data in ref. 37 does not indicate a "catch-bond" behavior but rather an insensitivity to force over a defined range of forces.

The reviewer misquoted our sentence. The actual wording of the sentence was: “When kinesin-1 was connected to micron-scale beads through a DNA linker and hydrodynamic forces parallel to the microtubule imposed, dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (Urbanska et al., 2021).” The sentence the reviewer quoted was in a previous version that is available on BioRxiv and perhaps they were reading that version. Nonetheless, in the revision we will note in the Discussion that this behavior was indicative of an ideal bond (not a catch-bond), and we will also add a sentence in the Introduction highlighting this work.

Reviewer #3 (Public review):

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results should be addressed in the manuscript.

This is an interesting point. To address it, we calculated the predicted stiffness of the dsDNA by taking the slope of theoretical force-extension curve in Fig. 1B. Below 650 nm extension, the stiffness is <0.001 pN/nM; it reaches 0.01 pN/nM at 855 nm, and at 960 nm where the force is 6 pN the stiffness is roughly 0.2 pN/nm. That value is higher than the quoted 0.05 pN/nm trap stiffness, but for reference, at this stiffness, an 8 nm step leads to a 1.6 pN jump in force, which is reasonable. Importantly, the stiffness of kinesin motors has been estimated to be in the range of 0.3 pN (Coppin et al., 1996; Coppin et al., 1997). Granted, this stiffness is also nonlinear, but what this means is that even at stall, our dsDNA tether has a similar predicted compliance to the motor that is pulling on it. We will address this point in our revision.  

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

Please see our response to Reviewer #2 Major Comment #4 above, which asks this same question in the context of intracellular cargo. We plan to address this in our revision. Regarding a dynamic tether, we agree that’s interesting – there are kinesins that have a second, non-canonical binding site that achieves this tethering (ncd and Cin8); p150glued likely does this naturally for dynein-dynactin-activator complexes; and we speculated in a review some years ago (Hancock, 2014) that during bidirectional transport kinesin and dynein may act as dynamic tethers for one another when not engaged, enhancing the activity of the opposing motor.

In the single-molecule extension traces (Figure 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g., the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3?

Due to the compliance of the dsDNA, the 95% limits for the initial attachment position are +/- 290 nm (Fig. S2). Thus, some apparent ‘jumps’ from the detached state are expected. We will take a closer look at why there are jumps for kinesin-2 that aren’t apparent for kinesin-1 or -3.

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis?

Yes. Please see response to Reviewer #2 points (8) and (9) above.

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state?

In the model, the slip state and the detached states are conceptually similar; they only differ in the sequence (slip to detached) and the transition rates into and out of them. The simple answer is: yes, the slips could be explained by fast reattachments from the detached state. In that case, the slip state and recovery could be called a “detached state with fast reattachment kinetics”. However, the key data for defining the kinetics of the slip and detached states is the distribution of Recovery times shown in Fig. 4D-F, which required a triple exponential to account for all of the data. If we simplified the model by eliminating the slip state and incorporating fast reattachment from a single detached state, then the distribution of Recovery times would be a single-exponential with a time constant equivalent to t₁, which would be a poor fit to the experimental distributions in Fig. 4D-F.

We appreciate the efforts and helpful suggestions of all three reviewers and the Editor.

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Coppin, C.M., J.T. Finer, J.A. Spudich, and R.D. Vale. 1996. Detection of sub-8-nm movements of kinesin by high-resolution optical-trap microscopy. Proc Natl Acad Sci U S A. 93:1913-1917.

Coppin, C.M., D.W. Pierce, L. Hsu, and R.D. Vale. 1997. The load dependence of kinesin's mechanical cycle. Proc Natl Acad Sci U S A. 94:8539-8544.

Ezber, Y., V. Belyy, S. Can, and A. Yildiz. 2020. Dynein Harnesses Active Fluctuations of Microtubules for Faster Movement. Nat Phys. 16:312-316.

Hancock, W.O. 2014. Bidirectional cargo transport: moving beyond tug of war. Nat Rev Mol Cell Biol. 15:615-628.

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eLife Assessment

The nematode C. elegans is an ideal model in which to achieve the ambitious goal of a genome-wide atlas of protein expression and localization. In this paper, the authors explore the utility of a new and efficient method for labeling proteins with fluorescent tags, evaluating its potential to be the basis for a larger, genome-wide effort that is likely to be very useful for the community. While the evidence for the method itself is solid, carrying out this project at a large scale will require significant additional feasibility studies.

Reviewer #1 (Public review):

Summary:

Eroglu and Hobert demonstrate that injecting CRISPR guides and repair constructs to target three genes at a time, tagging each with a different fluorescent protein, and selecting which gene to tag with which fluorophore based on genes' expression levels, can improve the efficiency of gene tagging.

Strengths:

This manuscript demonstrates that three genes can be targeted efficiently with three different fluorophores. It also presents some practical considerations, like using the fluorophore least complicated by agar/worm autofluorescence for genes with low expression levels, and cost calculations if the same methods were used on all genes.

Weaknesses:

Eroglu has demonstrated in a previous publication that single-stranded DNA injection can increase the efficiency of CRISPR in C. elegans while inserting two fluorescent proteins and a co-CRISPR marker into three loci. The current work is, therefore, an incremental advance. In general, I applaud the authors' willingness to think ahead to how whole proteome tagging might be accomplished, but I predict that the advance here will be one of many small advances that will get the field to that goal. The title vastly oversells the advance in my view, and the first sentence of the Discussion seems a more apt summary of the key advance here.

Some injections target genes on the same chromosome together, which will create unnecessary issues when doing necessary backcrossing, especially if the mutation rate is increased by CRISPR. Also, the need for backcrossing and perhaps sequencing made me wonder if injecting 3 together really is helpful vs targeting each gene separately, since only 5 worms need to be injected.

The limited utility of current blue fluorescent proteins makes me wonder if it's worth using at all at this stage, before there are better blue (or far red) fluorescent proteins.

Some literature reviews, particularly in the Introduction and Abstract, rely too much on recent examples from the authors' laboratory instead of presenting the state of the field. I'd like to have known what exactly has been done with simultaneous injection targeting multiple loci more thoroughly, comparing what has been accomplished to date by various laboratories' advances to date.

Reviewer #2 (Public review):

The manuscript by Eroglu and Hobert presents a set of strains each harboring up to three fluorescently tagged endogenous proteins. While there is technically nothing wrong with the method and the images are beautiful, we struggled to appreciate the advance of this work - who is this paper for?

As a technical method, the advance is minimal since the first author had already demonstrated that three mutations (fluorophore insertion and co-CRISPR marker) could be introduced simultaneously.

As a pilot for creating genome-scale resources, it is not clear whether three different fluorophores in one animal, while elegantly designed and implemented, will be desired by the broader community.

Finally, the interpretation of the patterns observed in the created lines is somewhat lacking. A Table with all the observations must be included. This can replace the descriptions of the observations with the different lines, which could be somewhat laborious for the reader, and are often wrong. There are numerous mistaken expectations of protein expression here, but two examples include:

(1) The expectation that ACDH-10 is enriched in the intestine and epidermal tissues (hypodermis)<br /> There are multiple paralogs of this protein (see WormPaths or WormFlux) that may share functions in different tissues. There is also no reason to assume that fatty acid metabolism does not occur in other tissues (including the germline). Finally, there are no published studies about this enzyme, so we really don't know for sure what it's doing.

(2) The expectation that HXK-1 is ubiquitously expressed<br /> Three paralogous enzymes are all associated with the same reaction, and we have shown that these three function redundantly in vivo, perhaps in different tissues (PMID: 40011787). Moreover, single-cell RNA-seq data (PMID: 38816550) also show enrichment of hxk-1 in gonadal sheath cells.

The table should have at least the following information: gene/protein name - Wormbase ID - TPM levels of single cell data assigned to tissues for L2, L4, and adult (all published) - tissues in which expression is observed in the lines presented by the authors.

Reviewer #3 (Public review):

Summary:

The authors argue that establishing the expression pattern and subcellular localisation of an animal's proteome will highlight many hypotheses for further study. To make this point and show feasibility, they developed a pipeline to knock in DNA encoding fluorescent tags into C. elegans genes.

Strengths:

The authors effectively make the points above. For example, they provide evidence of two populations of mitochondria in the C. elegans germline that differ qualitatively in the proteins they express. They also provide convincing evidence that labelling the whole proteome is an achievable goal with relatively limited resources and time.

Weaknesses:

Cell biology in C. elegans is challenging because of the small size of many of its cells, notably neurons. This can make establishing the sub-cellular localisation of a fluorescently tagged protein, or co-localizing it with another protein, tricky. The authors point out in their introduction that advances in light microscopy, such as diSPIM, STED, and ISM (a close relative of SIM), have increased the resolution of light microscopy. They also point out that recent advances in expansion microscopy can similarly help overcome the resolution limit.

(1) Have the authors investigated if the three fluorescent tags they use are appropriate for super-resolution microscopy of C. elegans, e.g., STED or SIM? Would Elektra be better than mTAGBFP2? How does mScarlet3-S2 compare to mScarlet 3?

(2) Have the authors investigated what tags could be used in expansion microscopy - that is, which retain antigenicity or even fluorescence after the protocol is applied? It may be useful to add different epitope tags to the knock-in cassettes for this purpose.

The paper is fine as it stands. The experiments above could add value to it and future-proof it, but are not essential. If the experiments are not attempted, the authors could refer to the points above in the discussion.

Author Response:

eLife Assessment

The nematode C. elegans is an ideal model in which to achieve the ambitious goal of a genome-wide atlas of protein expression and localization. In this paper, the authors explore the utility of a new and efficient method for labeling proteins with fluorescent tags, evaluating its potential to be the basis for a larger, genome-wide effort that is likely to be very useful for the community. While the evidence for the method itself is solid, carrying out this project at a large scale will require significant additional feasibility studies.

We appreciate the editor’s recognition that the evidence for our method is solid and that a genome-wide protein atlas in C. elegans would be highly valuable to the community. However, we respectfully disagree that significant additional feasibility studies are required. As comparison, the yeast proteome-wide GFP tagging project (Huh et al., Nature 2003) achieved ~75% coverage of ~6,000 proteins directly from an established protocol without any prior significant feasibility studies, at least to our knowledge. While the C. elegans genome is 3 times in size, we would argue that our tagging protocol may even be less labor intensive as it does not involve any cloning and the screening is visual, requiring no molecular biology skills. Reviewer 3 notes: “They also provide convincing evidence that labelling the whole proteome is an achievable goal with relatively limited resources and time.”

Our pilot study validates all key parameters for genome-wide scaling: editing efficiency at novel loci with untested reagents, viability of tagged worms, and detectability of multiple spectrally separated fluorophores across expression ranges. These address the core technical, biological, and practical challenges of large-scale endogenous tagging in a multicellular organism, leaving no fundamental barriers in our view.

The proposed cost and timeline align quite favorably with established large-scale consortium projects: e.g., ENCODE pilot analyzed 1% of the human genome at ~$55 million over 4 years; Mouse Knockout Consortium scaled to ~20,000 genes over 20 years (ongoing) with ~$100 million; Human Protein Atlas mapped ~87% of proteins with antibodies in fixed cells (through much more labor intensive methods) over 20+ years at >$100 million. With ~8% of C. elegans genes already tagged (WormTagDB), scaling our protocol to the proteome is feasible, potentially covering the genome in 5-6 years by a single lab or faster with distributed effort at a reagent cost of merely $2.2 million. The main barriers now are funding commitment and assembling collaborators, not further feasibility testing.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Eroglu and Hobert demonstrate that injecting CRISPR guides and repair constructs to target three genes at a time, tagging each with a different fluorescent protein, and selecting which gene to tag with which fluorophore based on genes' expression levels, can improve the efficiency of gene tagging.

Strengths:

This manuscript demonstrates that three genes can be targeted efficiently with three different fluorophores. It also presents some practical considerations, like using the fluorophore least complicated by agar/worm autofluorescence for genes with low expression levels, and cost calculations if the same methods were used on all genes.

Weaknesses:

Eroglu has demonstrated in a previous publication that single-stranded DNA injection can increase the efficiency of CRISPR in C. elegans while inserting two fluorescent proteins and a co-CRISPR marker into three loci. The current work is, therefore, an incremental advance. In general, I applaud the authors' willingness to think ahead to how whole proteome tagging might be accomplished, but I predict that the advance here will be one of many small advances that will get the field to that goal.

Our manuscript indeed builds on prior multiplex editing (including our own co-CRISPR work), but the manuscript's primary contribution is not a novel technical breakthrough per se. Instead, our main goal was to pilot and strategize a feasible path to whole-proteome tagging in C. elegans and importantly test the following key parameters: (1) success rate of triple pools with prior untested reagents at novel targets; (2) utility of fluorophores across expression levels; (3) major effects on tagged protein function. In prior multiplexing, we used two targets which we already knew could be edited quite efficiently, with the 3rd target a point mutation with nearly 100% efficiency. Thus, it was not at all clear that picking 3 random genes and replacing the 3rd highly efficient locus with another less efficient large insertion would work or be sufficiently scalable for thousands of novel genes with unvalidated reagents at first pass.

The title vastly oversells the advance in my view, and the first sentence of the Discussion seems a more apt summary of the key advance here.

Some injections target genes on the same chromosome together, which will create unnecessary issues when doing necessary backcrossing, especially if the mutation rate is increased by CRISPR.

We disagree with the reviewer’s assessment of the need for backcrossing, for two reasons: (1) Prior studies have shown that off-target mutations are not a serious concern in C. elegans (reviewed in PMID: 26336798 and PMID: 24685391). For instance, WGS of strains after CRISPR/Cas9 found negligible off-target effects (PMID: 25249454, PMID: 30420468 – using similar RNP/ssDNA method and multiple guides; PMID: 23979577, PMID: 27650892 using other methods). Targeted sequencing studies have reported similar findings, using various CRISPR/Cas9 methods, with essentially no mutations at sites other than the intended target (PMID: 23995389; PMID: 23817069). (2) If the goal is to tag the entire genome, the introduction of backcrossing should not reasonably be a routine part of the initial tagging.

Lastly, if one wants to backcross at a later stage, the existence of tags on the same chromosome is actually an advantage because it permits selection for recombinants with wild-type chromosomes.

Also, the need for backcrossing and perhaps sequencing made me wonder if injecting 3 together really is helpful vs targeting each gene separately, since only 5 worms need to be injected.

Apart from our disagreement regarding backcrossing, we are puzzled by the reviewer’s comment that tagging each gene separately may not be considered helpful. Why would one do single tagging at a time, rather than triple tagging if the whole point of the paper is to demonstrate the scalability of tagging? Meaning, that one can shortcut tagging all genes by a factor of 3 through joint tagging? It is important to keep in mind that the rate limiting step for tagging the whole genome is the number of injections that can be done per day. Since there is no cloning to generate the repair templates/guides and all other reagents are commercially available and not sample specific, these can be prepared quite rapidly. Being able to isolate multiple lines (together or independently) from the same injection increases throughput 3-fold and in our view does not provide any disadvantages as individual tags can be isolated independently if desired.

Beyond the numerous technical advantages pooling provides (also lower cost and throughput for making injection mixes as well as imaging), our results show that it yields epistemic benefits as well: we would never have noted the subcellular pattern in Fig. 6B, C with different sets of mitochondria being marked by different mitochondrial proteins had we imaged them separately or even aligned to a pan-mitochondrial landmark. As we mentioned in the discussion, grouping proteins predicted to localize to the same compartment together can simultaneously test how uniform or differentiated such compartments are during the screen.

The limited utility of current blue fluorescent proteins makes me wonder if it's worth using at all at this stage, before there are better blue (or far red) fluorescent proteins.

We do not think that the utility of current BFPs is very limiting. The theoretical brightness of mTagBFP2 is comparable to that of EGFP (PMID: 30886412), which was useful for the bulk of currently tagged proteins. Due to modestly higher autofluorescence in the blue spectrum, the practical brightness is somewhat less ideal, but we have shown that many proteins are expressed high enough to be detected quite well with mTagBFP2 by eye at low magnification. We also note that many tags that are not visible by eye under a dissection scope become visible with long exposure cameras of widefield microscopes or modern confocal (GaAsP) detectors, so the list of genes detectable with mTagBFP2 is likely to be much higher. We routinely use mTagBFP2 to super-resolve subnuclear structures with endogenous tags (e.g., in the nucleolus), with some tags having lower annotated FPKMs than the genes tested here.

Some literature reviews, particularly in the Introduction and Abstract, rely too much on recent examples from the authors' laboratory instead of presenting the state of the field. I'd like to have known what exactly has been done with simultaneous injection targeting multiple loci more thoroughly, comparing what has been accomplished to date by various laboratories' advances to date.

We are not sure what the reviewer is referring to when bemoaning that the Abstract and Introduction are too focused on our paper and not presenting the state of the field. In the Abstract, we do not refer to any literature. In the Introduction, we cite 28 papers, 6 of those from our lab (4 of which providing examples of protein tags). We do not believe that this can be fairly called an unbalanced presentation of the state of the field.

This being said, we will gladly expand our Introduction to provide more background on co-CRISPRing. Labs have routinely used co-conversion (“coCRISPR”) markers for picking out their intended edits (e.g., point mutations or insertions), as it has been shown by multiple groups that a CRISPR/Cas9 edit at one locus correlates with efficiency at other simultaneous targets (PMID: 25161212). Generally, making point mutations with the Cas9/RNP protocol is highly efficient, especially at specific loci such as dpy-10. However, multiple FP-sized insertions have not been routinely attempted. We and only one other group have successfully attempted it using previously working targets and reagents (e.g., 28% in PMID: 26187122). Importantly, the efficiency of such multiple insertions has never been assessed at scale and using entirely untested reagents at novel sites – critical parameters to determine for a whole genome approach. So, we test here (1) the efficiency of triple insertions and (2) the chance of getting them with new and untested guides and reagents.

In our view, since we have to use some injection/coCRISPR marker anyway for those genes which are not expressed at dissecting-scope visible levels (likely most genes), using highly expressed intended targets as improvised markers in a pooled approach makes our approach much more efficient. It allows us to find the worms with the highest chance of yielding CRISPR insertions, which we can screen with higher power methods for the dimmer targets, while enabling us to co-isolate other intended targets. Insertions, being often heterozygous in F1, can be segregated independently if desired, or homozygosed together to facilitate maintenance then outcrossed individually by those interested in studying specific genes in more detail.

In the revised version of this manuscript, we will discuss some of these points in the first paragraph of the results section:

“In C. elegans, screening for novel CRISPR/Cas9-induced genomic edits is facilitated either by use of co-injection markers (i.e., plasmids that form extrachromosomal arrays) that yield phenotypes or fluorescence in progeny of successfully injected worms, or co-editing well characterized loci using established and highly efficient reagents which likewise yield visible phenotypes. In the latter approach, termed “co-CRISPR”, worms edited at the marker locus are most likely to also carry the intended edit (Arribere et al., 2014).”

“These attempts pooled reagents previously established to work efficiently and targeted genes that were known to yield functional fusion proteins when tagged. Thus, while in principle current methods could allow tagging of at least 3 independent loci in one injection if a co-CRISPR marker is omitted, it is not known to what extent such an approach could be generalized across the genome with previously unvalidated reagents (i.e., guides and repair template homology arms) at novel loci.”

Reviewer #2 (Public review):

The manuscript by Eroglu and Hobert presents a set of strains each harboring up to three fluorescently tagged endogenous proteins. While there is technically nothing wrong with the method and the images are beautiful, we struggled to appreciate the advance of this work - who is this paper for?

We consider this paper to have two purposes: (1) motivate the community to come together to consider such genome-wide tagging approach; (2) provide a reference point for funding agencies that such an aim is not unreasonable and will provide novel interesting insights.

As a technical method, the advance is minimal since the first author had already demonstrated that three mutations (fluorophore insertion and co-CRISPR marker) could be introduced simultaneously.

We agree that the basic principle is similar. However, it was not clear that triple pooling three novel large edits would work, given the numbers in our original paper or that it would be scalable.

The dpy-10 coCRISPR marker previously used is a highly efficient single site, with close to 100% hit rate. We also knew in the earlier study that the two pooled insertions already worked quite efficiently and did not disrupt the function of targeted proteins. Exchanging these plus dpy-10 for three novel tags was not guaranteed to succeed for many potential reasons, including both biological and technical. For instance, such a “marker free” approach necessitates that a significant number of targets in the genome should be expressed highly enough to be visible by fluorescence stereomicroscopy when tagged with current best fluorophores. The chance of disrupting gene function by tagging was also not explored in detail in C. elegans, nor whether one untested guide is generally sufficient. We think that establishing these parameters was meaningful and necessary for the goal of whole genome tagging. We have clarified some of these points in the text.

As a pilot for creating genome-scale resources, it is not clear whether three different fluorophores in one animal, while elegantly designed and implemented, will be desired by the broader community.

The usage of three different fluorophores is largely driven by the ability to co-inject and therefore cut injection effort by a factor of three. Moreover, having all three fluorophores together facilitates imaging and maintenance. Lastly, co-labeling has the potential to reveal unexpected patterns of co-localization or lack thereof (example: two mitochondrial proteins that we found to not have overlapping distribution). We clarified this point in the revised text in both the results and discussion.

Finally, the interpretation of the patterns observed in the created lines is somewhat lacking. A Table with all the observations must be included. This can replace the descriptions of the observations with the different lines, which could be somewhat laborious for the reader, and are often wrong. There are numerous mistaken expectations of protein expression here, but two examples include:

We are not convinced that expectations are mistaken. Below we respond to the reviewer’s specific examples and we are open to hear from the reviewer about additional cases.

(1) The expectation that ACDH-10 is enriched in the intestine and epidermal tissues (hypodermis).

There are multiple paralogs of this protein (see WormPaths or WormFlux) that may share functions in different tissues. There is also no reason to assume that fatty acid metabolism does not occur in other tissues (including the germline). Finally, there are no published studies about this enzyme, so we really don't know for sure what it's doing.

The expression of acdh-10 is annotated in multiple scRNA datasets as intestine and epidermal enriched (Packer et al 2019, highest intestine and hyp; Ghaddar et al 2023 intestine, sheath and BWM, and even oocyte). We did not mean to imply that fatty acid metabolism does not occur in the gonad, nor that a paralog of acdh-10 could not be performing the same function in tissues where acdh-10 is not expressed.

However, this raises an important question: why have different paralogs doing the same thing? Duplicate genes with the same function are generally not evolutionarily stable (PMID: 11073452, PMID: 24659815). That there are such striking tissue specific expression patterns of an essential or widely expressed protein class suggests that paralogs of the gene likely differ in some meaningful parameter that might align with tissue-specific functional needs or regulation. The reviewer’s statement that “there are no published studies about this enzyme, so we really don't know for sure what it's doing” is in fact an excellent demonstration of our point; finding out where the duplicates are expressed can provide a starting point to uncover potential differences between the paralogs. At the very least it can delineate to what degree paralogs diverge in their expression across the proteome and identify which such cases merit further study. In a more ideal scenario, prior information of protein function could indicate that the involved pathway requires tissue specific regulation.

(2) The expectation that HXK-1 is ubiquitously expressed.

Three paralogous enzymes are all associated with the same reaction, and we have shown that these three function redundantly in vivo, perhaps in different tissues (PMID: 40011787).

The cited paper (PMID: 40011787) does not show where they are expressed. We discussed redundancy/paralogs above in point 1, and in our view the same applies here. They may perform the same reaction but are likely to differ in some meaningful way, be it regulation or rate of activity, for them to be stably maintained as functional genes over evolution.

Moreover, single-cell RNA-seq data (PMID: 38816550) also show enrichment of hxk-1 in gonadal sheath cells.

We note that the Ghaddar et al. and CeNGEN/Taylor et al. datasets do not. The scRNA paper cited by the referee (PMID: 38816550) also shows enrichment in neurons and pharynx, which we did not note. In our view, these in fact further support our goals: often, transcript datasets alone (frequently used to infer tissue function) do not sufficiently predict protein expression. One can post hoc find an scRNA-seq dataset that aligns somewhat with our protein observations, but how does one know which to trust a priori? Disagreements between transcript datasets will ultimately require resolution at the protein level, in our view.

To clarify these points, we will add the following to the discussion section:

“We also noted unexpected cell type dependent distributions of proteins involved in broadly important metabolic processes such as ACDH-10, which was depleted from the germline compared to other tissues, and HXK-1, which was highly enriched in the gonadal sheath. Notably, for these as well as other cases, scRNA-seq datasets were not sufficient to deduce a priori the observed cell type specific differences at the protein level. Importantly, many genes encoding metabolic enzymes including acdh-10 and hxk-1 have paralogs that likely perform similar catalytic functions. Yet, duplicate genes with identical functions are generally not evolutionarily stable (Adler et al., 2014; Lynch and Conery, 2000); thus such genes are likely to differ in some meaningful parameter (e.g., regulation or activity) that might align with tissue-specific functional needs. Fully annotating the expression patterns of paralogs at the protein level could indicate which tissues require unique metabolic needs and indicate which paralogous genes have undergone sub- versus neo-functionalization. For those proteins that are less functionally understood, unexpected distributions might indicate which merit further study.”

The table should have at least the following information: gene/protein name - Wormbase ID - TPM levels of single cell data assigned to tissues for L2, L4, and adult (all published) - tissues in which expression is observed in the lines presented by the authors.

We will add this information to the table including annotated expression levels in young adults from various datasets (but not larval datasets as we did not image these). We note that each of these studies use different pipelines and report different metrics (scaled TPM/Z-score versus Seurat average expression versus TPM), so comparisons between them are not informative unless they are integrated and analyzed together.

Reviewer #3 (Public review):

Summary:

The authors argue that establishing the expression pattern and subcellular localisation of an animal's proteome will highlight many hypotheses for further study. To make this point and show feasibility, they developed a pipeline to knock in DNA encoding fluorescent tags into C. elegans genes.

Strengths:

The authors effectively make the points above. For example, they provide evidence of two populations of mitochondria in the C. elegans germline that differ qualitatively in the proteins they express. They also provide convincing evidence that labelling the whole proteome is an achievable goal with relatively limited resources and time.

We are grateful for the referee’s appreciation that whole proteome tagging is feasible.

Weaknesses:

Cell biology in C. elegans is challenging because of the small size of many of its cells, notably neurons. This can make establishing the sub-cellular localisation of a fluorescently tagged protein, or co-localizing it with another protein, tricky. The authors point out in their introduction that advances in light microscopy, such as diSPIM, STED, and ISM (a close relative of SIM), have increased the resolution of light microscopy. They also point out that recent advances in expansion microscopy can similarly help overcome the resolution limit.

(1) Have the authors investigated if the three fluorescent tags they use are appropriate for super-resolution microscopy of C. elegans, e.g., STED or SIM? Would Elektra be better than mTAGBFP2? How does mScarlet3-S2 compare to mScarlet 3?

All three tags work for ISM (i.e., Airyscan). We previously tried Electra (not for the genes tested here) but could not isolate positive tags. Given Electra is not that much brighter on paper than mTagBFP2 we did not pursue it further, though we recognize that these may simply have been unlucky injections. mScarlet3-S2 is quite a bit dimmer than mScarlet3 on paper – the advantage is that it has higher photostability. In our view, the limiting factor will be having FPs that are bright enough to screen, image and scale to the whole genome, so brightness will likely provide an advantage over photostability at this stage.

(2) Have the authors investigated what tags could be used in expansion microscopy - that is, which retain antigenicity or even fluorescence after the protocol is applied? It may be useful to add different epitope tags to the knock-in cassettes for this purpose.

mSG and mSc3 retain fluorescence after fixing with formaldehyde. We have not tested mTagBFP2 fluorescence in fixed worms. We agree that adding different epitope tags would be useful.

The paper is fine as it stands. The experiments above could add value to it and future-proof it, but are not essential. If the experiments are not attempted, the authors could refer to the points above in the discussion.

eLife Assessment

This important study reports that genome-wide signatures of relaxed purifying selection in genes associated with male fertility may reflect an evolutionary response to reduced sperm competition in the gorilla mating system. The authors present compelling data that robustly support their central conclusion. This work will be of broad interest to investigators in evolutionary biology and reproductive biology.

Reviewer #1 (Public review):

This manuscript describes the pattern of relaxed selection observed at spermatogenesis genes in gorillas, presumably due to the low sperm competition associated with single-male polygyny. The analyses to detect patterns of selection are very thorough, as are the follow-up analyses to characterize the function of these genes. Furthermore, the authors take the extra steps of in vivo determination of function with a Drosophila model.

This is an excellent paper. It addresses the interesting phenomenon of relaxation of selection as a genomic signal of reproductive strategies using multiple computational approaches and follow-up analyses by pulling in data from GO, mouse knockouts, human infertility database, and even Drosophila RNAi experiments. I really appreciate the comprehensive and creative approach to analyze and explore the data. As far as I can tell, the analyses were performed soundly and statistics are appropriate. The Introduction and Discussion sections are thoughtful and well-written. I have no major criticisms of the manuscript, just a few minor thoughts.

In the "Caveats and Limitations" section of the Discussion, the first paragraph of this section states the obvious that genetic manipulation of gorillas is not feasible. Beyond a reminder to the reader that this was a rationale for the Drosophila work, it isn't really adding much insight.

I do agree with one of the initial reviewers that a comparative approach would add powerful perspective on the evolution of these genes. At the same time, I agree with the authors that the present work is comprehensive and can stand in its own in providing convincing evidence that many male reproductive genes in gorillas have experienced relaxed selection, without reference to other species with similar mating systems. I do not think that the elephant seal data adds a useful perspective.

Reviewer #3 (Public review):

In this study the authors tested for alterations in selection intensity across ~13,000 protein coding genes along the gorilla lineage in order to test the hypothesis that the evolution of a polygynous social system resulted in relaxed selective constraint through a reduction in sperm competition. Of these genes, 578 exhibited signatures of relaxed purifying selection that were enriched for functions in male germ cells including meiosis and sperm biology. These genes were also more likely to be expressed in male germ cells and to contain deleterious mutations. Functional analysis of genes not previously implicated in male reproduction identified 41 new genes essential to male fertility in a Drosophila model. Moreover, genes under relaxed selective constraint in the gorilla lineage were more likely to contain loss of function variants in a cohort of infertile men. The authors conclude that their results support the hypothesis that the emergence of a polygynous social system may have reduced the degree of selective pressures exerted through sperm competition.

(1) The identification of novel genes involved in spermatogenesis using signatures of relaxed selective constraint coupled to in vivo RNAi in Drosophila offers a proof of principal as to the power of evolutionarily-informed functional genomics that has been largely underutilized.

(2) The analysis is restricted to protein-coding regions of genes that have single, orthologous sequences spanning 261 mammalian species, and as such is a non-random set of 13,310 genes that have higher evolutionary conservation. While this approach is necessary for the analyses being performed, it excludes non-coding regions, recently duplicated genes/gene families, and rapidly evolving genes, which are all likely subject to stronger selection as compared to evolutionarily conserved genes (and gene regions). Thus, the conclusions of relaxed selective constraint as being pervasive could be missing a large number of the most strongly selected genes, many of which may include sex and reproduction related genes.

(3) The identification of genes showing relaxed selection along the gorilla lineage, which are overrepresented in male reproduction, supports the hypothesis that the emergency of polygyny resulted in relaxed sperm competition and is the driving force behind their observations. To more fully test this hypothesis the authors contrast their findings to observations in elephant seals, however of the 573 genes under relaxed selection in gorillas only 14 show a similar pattern. These genes are not enriched for male reproductive function, and may be under-powered or result from variation in reproductive strategies in gorillas as compared to elephant seals that mate seasonally.

(4) The comparisons of human males with infertility to a large number of healthy males from a separate cohort can lead to genetic differences related to population structure or differences in study recruitment independent of infertility, and care must be taken to avoid confounding. Population structure is more likely to affect patterns of rare variation (including loss of function mutations), even when controls are ascertained using similar enrollment criteria, geographic regions, racial/ethnic and national identities. In this study, the MERGE cohort is largely recruited from Germany, vs. a geographically more broadly recruited control cohort gnomeAD. The authors performed a sub-cohort analysis among individuals identified as having predominantly European genetic ancestry within MERGE, to that of non-Finnish European individuals from genomeAD, and find similar results, thus strengthening their findings.

Author Response:

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

Reviewer #1 (Public Review):

This manuscript describes the pattern of relaxed selection observed at spermatogenesis genes in gorillas, presumably due to the low sperm competition associated with single-male polygyny. The analyses to detect patterns of selection are very thorough, as are the follow-up analyses to characterize the function of these genes. Furthermore, the authors take the extra steps of in vivo determination of function with a Drosophila model.

This is an excellent paper. It addresses the interesting phenomenon of relaxation of selection as a genomic signal of reproductive strategies using multiple computational approaches and follow-up analyses by pulling in data from GO, mouse knockouts, human infertility database, and even Drosophila RNAi experiments. I really appreciate the comprehensive and creative approach to analyze and explore the data. As far as I can tell, the analyses were performed soundly and statistics are appropriate. The Introduction and Discussion sections are thoughtful and well-written. I have no major criticisms of the manuscript.

We thank you for your kind words!

The main area that I would suggest for improvement is in the "Caveats and Limitations" section of the Discussion. Currently, the first paragraph of this section states the obvious that genetic manipulation of gorillas is not feasible. Beyond a reminder to the reader that this was a rationale for the Drosophila work, it isn't really adding much insight. The second paragraph is a brief discussion of the directionality of change. I think it comes across as overly simplistic, with a sort of "well, we can never know" feel. Obviously, there are plenty of researchers who do model change to infer direction and causation, and there are plenty of published papers attempting to do so with respect to mating systems in primates.

We understand these statements might seem trivial, but they are meant to fully acknowledge, particularly to non-evolutionary biologists, the fact that we can’t do the genetics to “prove” these putatively deleterious mutations really are so (hence the statement about forward/reverse genetic experiments), nor causation (since this mating system evolved once in the history of gorillas we cannot know directionality in this lineage, although we could infer it if we had species in which different stages were extant, for example).”

I do not think the authors need to remove these paragraphs, but I do encourage them to turn the "Caveats and Limitations" section into something more meaningful by addressing limitations of the work that was actually done rather than limitations of hypothetical things that were not done. A few areas come to mind. First, the authors should discuss the effect of gene-tree vs species-tree inconsistencies in the analyses, which could affect the identification of gorilla-specific amino acid changes and/or the dN/dS estimates. Incomplete lineage sorting is very common in primates including the gorilla-chimp-human splits (Rivas-González et al. 2023). It would be nice to hear the authors' thoughts on how that might affect their analyses. Second, the dN/dS-based analyses assume the neutrality of synonymous substitutions. Of course, that assumption is not completely true; it might be true enough, and the authors should at least note it as a caveat. Third, and potentially related, is the consideration that these protein-coding genes may be functioning in other ways such as via antisense transcription. The genes under relaxed selection may be on their way to becoming pseudogenes and evolving as such at the sequence level, but many pseudogenes continue to be transcribed sense or anti-sense in a regulatory purpose. I don't think there is a way to incorporate this into the authors' analyses but it would be nice to see it acknowledged as a caveat or limitation.

We thank you for the helpful suggestion and have added a discussion of these issues in the reworked Caveats and limitations section (lines 639 - 710).

Reviewer #1 (Recommendations for The Authors):

This is an excellent paper with thorough and creative approaches to address an interesting connection between genotype and phenotype. Stylistically the paper is very well written.

We thank you for your kind words.

Page 3: I suggest deleting the word "vaginal" so the sentence reads "... the evolution of female traits such as anatomical features that allow female control...". Most of the well-documented examples of cryptic female choice are in animals that do not have vaginas like insects, fish, and birds, including the reference given at the end of the sentence (Brennan et al. 2007 on waterfowl).

We agree and have made this edit.

Page 3: I would delete the words "multimale-multifemale" when discussing gorillas, to make the sentence read "Most gorillas, for example, live in groups with age-graded...". The use of "multimale-multifemale" here is not exactly wrong, but can be confusing to the reader since the authors essentially use "multimale-multifemale" as a synonym for "polygamous" in the previous paragraph.

We agree and have made this edit.

The writing in the Materials and Methods fluctuates between present and past tense. The authors should pick a consistent style, probably past tense by convention.

We have edited the Materials and Methods only to use past tense.

"Drosophila" is italicized sometimes, but not sometimes not. Make consistent.

To ensure consistency, italics were used only when genus and species were shown together (i.e., Drosophila melanogaster).

In the main text, a few reference typos/confusions:

Box 1, Figure 1B caption: I believe this "Dixson, n.d." reference should be Dixson (2009), if it refers to the book (Oxford Press).

Yes, that is the case. Thank you for having spotted this. The reference has been corrected.

Page 21: The authors use the term "false exons" and "fake exons" in the same paragraph. Are these the same thing? If so, just use "false exons" both times.

These are the same, we have changed fake to false.

Page 22-23, maybe elsewhere: The Smith et al. reference includes Martin's first name.

Thank you for bringing this issue to our attention. The reference has been corrected.

Page 25: in the parenthetical listing of scientific species names, the word "and" should not be italicized. In this same section, there's really no reason to include "gorilla" as the subspecies. It isn't given for the other species.

Corrected.

Page 27: Missing period in the second paragraph after "(Guyonnet et al. 2012)".

Corrected.

Page 29: Should read "... available in gnomAD that would allow us to exclude..." (or possibly "... available in gnomAD that would allow the exclusion of ...").

Corrected.

Page 33, figure legend off Appendix Figure 1A: "gray line" not "gray liner".

Corrected.

Box 1, Figure 1A: This is confusing in a few ways. First, the gorilla red dot is labeled "Gorilla", but the chimpanzee and bonobo dots are not labeled. Perhaps in the legend the colors could be indicated, such as "... percentage of body mass for gorilla (red), common chimpanzee (dark blue), and bonobo (light blue)"? Secondly, the bar chart shows the testes/body mass ratio but it is not clear what they are scaled to. Should there be a second y-axis on the right side of the plot?

The bar chart showed the testis weight/body weight ratio (log), but it is not really necessary. We have removed the bar chart and labeled chimpanzees and gorillas.

Figure 1D: I found myself confused by the vertical label of "Percent of genes with w>1 in Gorilla". Because all genes are in the stacked histogram, my first thought was that ~99% of the genes have w>1 (gray). Would be more clear if the label was the same as 1G ("Percent of genes").

We agree and have made this change.

The text in the figures is extremely small. I don't know what it will look like once it is fully formatted for publication, so I'll leave those concerns to the editor/publisher.

We will wait until the proofs to determine if this figure needs to be split into multiple figures with larger text.

References in the reference section need a LOT of cleaning up. It does not appear that any manual editing was done. Please check for consistency in capitalization, italicization, abbreviations, missing information, etc. The level of neglect to this section is frankly unprofessional.

I (VJL) apologize for this; it is entirely my fault. To explain but not justify, I have dyslexia, and the shifting combination of text, numbers, punctuation, fonts, and font styles makes it difficult to see the inconsistencies. To mitigate this, I use a reference manager to format references (like everyone else) and almost always have someone proofread the reference section, but I didn’t do that with this manuscript. I apologize for the oversight. My dedicated co-authors have cleaned the reference section.

Reviewer #2 (Public Review):

As outlined in the public review, this is a nicely executed molecular evolutionary study. The analyses and overall patterns described in gorillas appear rigorous and convincing. The fundamental limitation here is a lack of comparative context to specifically establish the connection to mating system or the uniqueness of these overall patterns to gorillas.

We thank the reviewer for the compliments. However, there is some confusion about the hypothesis we tested. We hypothesized that genes involved in male reproductive biology would have relaxed selective constraints in gorillas because of their mating system, not that polygynous mating systems would lead to relaxed selection. While that may be true, it is not the hypothesis we tested, nor do we state that the overall pattern we observe is unique to gorillas. Our data, however, support our claims: 1) We performed an unbiased selection scan in gorillas and identified genes with K<1, an evolutionary signature of reduced selection intensity; 2) We found that those genes were enriched for male reproductive functions; and 3) Some of those genes had effects on male reproduction in both Drosophila screens and in infertile men. These are the results one would expect if our hypothesis were true.

To partly address the concern that our results do not have a connection to mating systems or may be an overall pattern rather than a gorilla-specific one, we ran RELAX using the same dataset but in the elephant seal, another species with a highly polygynous mating system. Although elephant seals are a polygynous species, they differ from gorillas in that their spermatogenesis does not undergo persistent deterioration, but instead follows a seasonal pattern. According to the comprehensive study by Laws (The Elephant Seal (Mirounga Leonina Linn.): III. The physiology of reproduction; Scientific Reports, 15, Falkland Islands Dependencies Survey, 1956], male gamete production is upregulated during the mating season and is mostly inactive throughout the rest of the year. Of the 573 genes with K<1 in gorillas only 14 also have K<1 in elephant seals, which had 350 genes with K<1. A GO analysis of the 350 elephant seal K<1 genes does not identify enrichment in spermatogenesis-related terms. In fact, the list of GO terms is quite broad. A potential, if admittedly speculative, interpretation of these findings is that although polygynous, the selective pressure on elephant seal spermatogenesis is not relaxed (unlike in gorillas) because of the seasonal nature of their mating period. In other words, by having a temporally narrower window for reproductive success than gorillas, the selective constraint on male gametogenesis in seals is not weakened. Regardless, the low overlap in relaxed genes between the two tested polygynous species support the view that this reproductive strategy is probably associated with different evolutionary signatures in the genome (depending on the species), a likely reflection of the complex, nuanced and multi-factorial aspects of such strategies. We include this analysis in the Appendix (lines 1112 - 1132).

While there is much that I like about the study and approach, this is a substantial shortcoming that really limits the significance of the, especially given that lineage specific patterns were also analyzed by Scally et al. (2012) over a decade ago.

While Scally et al. (2012) reported the initial sequencing, assembly, and analyses of the gorilla genome, the method they used to characterize selective pressure on coding genes - the branch and branch-site model implemented in PAML - is misspecified to detect relaxed selection (PMID: 25540451). Under relaxed selection, the d_N/d_S of sites under purifying selection will move towards 1, the d_N/d_S of sites under positive selection will also move towards 1, and some sites will not experience a change in d_N/d_S. The PAML test used Scally et al. (2012) averages d_N/d_S across all sites, rather than having distinct rate categories for each of the three selection classes. A change in d_N/d_S toward 1 under the PAML model can arise because the strength of positive selection is weaker in the foreground lineage than the background lineage, even if there is still positive selection acting on some sites. Averaging across all sites also means there is little power to detect relaxed selection, even if it is relaxed selection. Furthermore, the PAML test used by Scally et al. (2012) is underpowered to detect relaxed selection because it depends on selective regimes in background species. Scally et al. (2012) also used six species, which underpowers their test of relaxation, because if one or more of those species experience an increase in their d_N/d_S rate, the background rate will increase giving the appearance of a decrease in the gorilla lineage even if its d_N/d_S rate has not changed. We elaborate on this in the Appendix section (lines 1036 - 1073). Finally the method implemented in PAML does not allow for synonymous rate variation across sites or multi-nucleotide mutations per codon, ignoring synonymous rate variation dramatically inflates the false positive rates in selection tests (PMID: 32068869) as does ignoring multi-nucleotide mutations (PMID: 29967485 and PMID: 37395787); we have added a discussion of these issues in our Caveats and limitations section (lines 683 - 710).

Reviewer #2 (Recommendations for The Authors):

Specific comments

Framing: Overall, the connection between mating system is referred in variable levels of certainty, some appropriate, others overstated. The paper title uses 'coincident' which is appropriate, but also at odds with the stronger conclusions that are emphasized throughout. Elsewhere the phrasing is much stronger (abstract, discussion) implying a direct statistical association with mating system variation that has not been established. Elsewhere the term 'association' is used in the same manner, but in instances where a statistical association is tested and demonstrated (tests of enrichment, etc).

We are unsure why the Reviewer considers our claims overstatements. The patterns of molecular evolution we found are ‘associated,’ and 'coincident with,' and we believe our results are ‘compelling’. Our tests for relaxed and positive selection are statistically associated with a polygynous social system which we a priori hypothesized. We have taken care to ensure a more consistent framing of this connection throughout the manuscript to avoid potential misinterpretations of causality.

Page 7, elsewhere- It is essential to compare the reported patterns (percentage of relaxed genes in gorilla, patterns of enrichment, etc) to other primate lineages to identify if this number is enriched due to mating system or if these patterns are unusually for sperm genes across mammals. The implication here and throughout is that the specific pattern reflects specific aspects of gorilla mating biology, but this is never established. Additionally, it would be interesting to know the relative number of genes under positive selection across species (or across great apes).

We agree that if we were using a PAML-like approach that these controls would be informative. But with the RELAX method the foreground K is compared to the background K, K only becomes significantly less than one if there is relaxing in the intensity of selection in the foreground. If these patterns were common to sperm genes across mammals the background and foreground K would not be significantly different. Our a priori hypothesis was that genes related to male reproductive biology would show evidence of a decrease in the intensity of selection (both positive and purifying), which we tested and found to be true. In this regard, we can conclude that the gorilla mating system is associated with patterns of molecular evolution in the species’ genome.

While we too would find it interesting to know the relative number of genes under positive selection across species (or across great apes), that is not the study we performed and is beyond the scope of this one (and we only identified 96 genes that were positively selected in gorilla suggesting that few genes are positively selected across species).

Page 8, bottom, elsewhere- "13,491 background set" elsewhere this is 13,310 (abstract). The number of genes here is different, and the set seems to change across multiple parts of the paper without explanation. This could be a simple typo, however, it may affect statistical analysis if the problem is widespread, especially when assessing enrichment of (presumably) small sets of genes.

This is partly true and partly a typo. We generated 13,491 alignments, 13,310 of which had HUGO gene symbols. These 13,310 genes were used in all subsequent studies. We have re-written the text to clarify this point, and have added a statement: “We thus generated a dataset of 13,491 orthologous coding gene alignments from the genomes of 261 Eutherian mammals, corresponding to 62.7% of all protein-coding genes in the gorilla genome. Of the 13,491 alignments, 13,310 had an identifiable HUGO gene symbol and were used in all subsequent analyses (lines 158 - 162).”

Related to this, it is difficult to determine how many genes these GO associations are based on. Even small numbers of genes can result in very significant results with these tests. How many genes are these associations based on? This connection is a key component of the overall narrative that changes in sperm competition have a large effect on genome-wide shifts.

All analyses are based on the 13,310 genes with identifiable HUGO gene symbols, including over-representation analyses (ORA). Our dataset submitted with this manuscript includes these 13,310 genes (as well as the genes with K<1 and K>1). The number of genes used as the foreground is the 578 with K<1, these genes are given in Figure 1 – source data 3. The minimum number of genes annotated in a GO or pathway term was 3. While it is unlikely that statistically significant GO term enrichments result from a few genes annotating to each term, that scenario would produce small P-values, the false discovery rate would be high and readers can decide what false discovery they are willing to accept.

How many of these 578 genes are plausibly related to reproduction? Apologies if I missed this detail, but Figure 3 does not convey this. Could you speak to this directly in the text and include a table or supplemental table of the GO terms to show the differences in enrichment between classes of genes, and counts per term?

These data are included in Figure – 3 source data 1.

One of the key results is the relative frequency of relaxed constraint versus positive selection. This is expected on some level as the form of recurrent positive directional selection detected with these models is usually relatively rare. However, it is not at all clear that it is rarer in gorillas versus other mammals, as implied.

Our comparison of relaxed constraint to positive selection was to explore if more genes experienced one pattern of molecular evolution or the other within gorillas, we do not imply that it is rarer in gorillas than in other mammals.

Likewise, I was wondering how the dataset itself may be biased toward this result. If I understand correctly, you are requiring very high levels of conservation (251/261 genes) for inclusion in the dataset, resulting in ~60% of all gorilla genes being included. Rapidly evolving genes that are targets of recurrent positive selection often also tend not be highly conserved across such a deep phylogenetic sample. It would be good to acknowledge this potential bias when implying meaning to the differences in relative rates of the two forms of selection.

Our results are unlikely to be subject to this bias. The RELAX test relies on accurately estimating K in background lineages, which requires that we include as many species as possible. The tradeoff is a reduction in the number of genes included in the dataset due to evolutionary dynamics across a wide range of species. However, it's not that 40% of the genes are excluded because they are evolving so rapidly we cannot identify or align them, it mainly reflects the fact that we cannot identify the gene in 251 of the 261 species included in the dataset (due to gene loss, etc).

Page 9 - The results here (and in Figure 3D) shows that relaxed genes are enriched broadly across spermatogenesis cell types except for Sertoli cells. But the Sertoli cells and a few non-significant cell types are the only thing to compare to. Instead, it would be interesting to identify single cell expression patterns from other tissues- or even bulk RNA as sc-RNA may be limited in the species. This would show that these genes are enriched in testis compared to other tissues, as opposed to just being broadly expressed. Additionally, the authors could compare to the other primate testis sc-RNA available in Murat et al. Without such comparisons the interpretations here seem limited.

We did not test whether K<1 were enriched in other cell types because: 1) we had an a priori hypothesis that genes with K<1 would be enriched in cells involved in male reproduction, rather than enriched in cell types in the testis compared to any other cell type; and 2) The number of genes with K<1 is relatively small and the number of known cell-types in very large, at least one estimate points to ~400 major cell types in a higher primate (PMID: 37722043). Using a P-value of 0.05 from a hypergeometric or Fisher's exact test and a Bonferroni correction to control for multiple hypothesis testing, we would need the P-value for enrichment in any cell type to be 0.000125, which we are unlikely to achieve.

More comprehensive functional comparisons could provide evidence that even though relaxed constraint is present in all lineages, perhaps relaxed constraints in the gorilla lineages are more related to sperm formation and function.

The RELAX test is a relative one; while relaxed constraint may be present in other lineages, to observe a statistically significant K<1 in gorillas the degree of relaxation would have to have a greater effect size in gorilla than in other lineages.

I was also a little unclear what to make of the interpretation of K<1 versus K >1 enrichment by cell type. The enrichment of K<1 is called out as noteworthy because this is when the spermatogenesis specific genes begin to be expressed, but then the K > 1 result is dismissed as occurring during pachytene which is a transcriptional permissive state of testis. To be clear, pachytene is also a critical checkpoint for fertility and enhanced purifying selection at this step could be reasonably interpreted as being at odds with the entire erosion of reproduction argument. This seems to be a selective interpretation for the overall narrative. Also, permissive transcription is not only limited to the pachytene stage and the relaxation of constraint concomitant with increased specificity and permissive expression during the later stages of spermatogenesis is a well-known result in mammals, and not anything that can be ascribed gorillas and their change in mating system.

We agree with the Reviewer’s comment and have removed the K<1 versus K>1 interpretation from the manuscript.

Page 13 - The LOF enrichment identified from this random sampling is borderline significant. An improved approach would be to perform permutations of random samplings and identify the range of significance based on 1000+ permutations.

We have redone the burden test with population-matched groups to confirm the reliability of this association (lines 435 - 446). In addition, we now acknowledge in the Caveats and limitation section that our observations could benefit from a permutation analysis (lines 695 - 697).

Page 17, bottom- Statements like these are overstating the correlation as the comparative analyses were not shown.

We agree and have edited the text to avoid potential overstatements.

This is good to include the role of female reproductive tract. Shouldn't the unbiased screen pull these out anyway? The authors did find some female GO terms enriched. What additional information or experiments would be needed to test the hypothesis of female compensation? The expectations for this should be made clearer.

Given the nature of these putative female compensatory mechanisms (primarily acting on the oviduct and lower uterus, as speculated in lines 586 – 601), it is currently impossible to functionally test them in gorillas. The continued development of in vitro systems mimicking the female reproductive tract may allow such studies in the future.

Page 18, middle- Pleiotropy is an important consideration and this paragraph discusses some valuable points. However, this is another section that could be improved by discussing the relaxed constraints in later spermatogenesis, which likely suggests that genes expressed in later stages are less pleiotropic and more testis- specific.

We agree and have added a brief discussion of this in lines 619 - 622: “It is also possible that the negative consequences of deleterious pleiotropy become less pronounced at later stages of spermatogenesis as meiotic and post-meiotically expressed genes are enriched for testis-specific functions (PMID: 36544022).”

Page 27, Bottom- The criteria for selection of genes to target here is interesting and disconnected from the claimed interpretation of the results. If you're targeting genes with reliable expression in Drosophila, it is not surprising that a percentage of them will lead to fertility loss. Shouldn't the background be a random set of testis-expressed genes? This test would show that relaxed constraint is a strong way to screen for fertility genes. Additionally, the authors previously showed that these genes were enriched in SC-rna in gorilla,- and likely other species. Suggesting that you identified genes 'lacking evidence' of a role in spermatogenesis in previous studies is misleading, when many of these genes are present in testis RNA datasets and enriched for sperm go terms. I would argue that genes found to be expressed in testis and spermatogenesis specific cell types, certainly have evidence of being involved in spermatogenesis.

We thank you for the helpful suggestion. We have generated a new background group composed of a random set of testis-expressed genes. More specifically, by looking at previously published Drosophila testis expression data (PMID: 30249207), we randomly selected 156 genes with TPM>1 (transcript per million) and determined the percentage of them with reported spermatogenic / male fertility defects in Drosophila. We observed that 18 (11.5%) had been previously demonstrated to be functionally required for male reproductive fitness. This percentage is slightly higher than what we had previously observed for a random selection of Drosophila genes (9.6% - an update, using the latest available data, to the 7.7% reported in the original version). Nevertheless, both figures are still well below the 27.6% hit rate we found for the Drosophila orthologs of the gorilla K<1 genes. We have added this new information to the manuscript (lines 380 - 386).

Regarding the potential correlation between expression and function in spermatogenesis, we and others have shown that the majority of the protein-coding genome is expressed during spermatogenesis in both vertebrate and invertebrate species (PMID: 39388236). Although the reasons for such widespread transcription in the male germ line are not entirely clear, it advises a cautious approach in terms of correlating expression with function. Indeed, our recent analysis of 920 genes reliably expressed in insect and mammalian spermatogenesis revealed that only 27.2% of them caused male reproductive impairment when individually silenced in the Drosophila testis (PMID: 39388236). Since genetic redundancy is a factor that needs to be taken into consideration when dealing with such a central biological process for the survival of a species, we take the more stringent approach of only considering a gene to be functionally involved in spermatogenesis if there is phenotypical evidence (from our RNAi assay or from previous publications) that its disruption is associated with spermatogenic impairment and/or abnormal fertility. We have added this clarification to the manuscript (lines 349 - 363).

Page 17 "Our data ... suggests that gorillas may be at the lowest limit of male reproductive function that can be maintained by natural selection (at least in mammals or vertebrates)." I realize this is the speculation section, but this is a massive overstatement. There is absolutely nothing in your data or results that support this statement, nor is this supported by the extensive comparative reproductive data in mammals. For example, there are many mammalian systems that show lower metrics of reproductive function than gorillas. For example, the sperm abnormality indices in Box 1F are nowhere near as severe as found in many species that still somehow manage to reproduce.

We agree and have edited the text to avoid potential overstatements (see above).

Reviewer #3 (Recommendations for The Authors):

(1) More discussion is needed as to whether their results could be explained by a reduction in effective population size in gorillas.

Thank you for raising this important point. As you know, reduced effective population size can lead to an increased load of deleterious mutations/relaxed selection intensity. However, we do not believe that it substantially affects our observations. Indeed, relatively few genes have K<1 and those are enriched in sperm biology. Given that a reduced effective population size will plausibly increase the load of deleterious mutations and relaxed selection across many genes, it is unlikely that such a broad phenomenon would result in a specific enrichment in genes related to male reproductive biology. We have added this reasoning to the Caveats and limitations section (lines 675 - 682).

(2) Properly controlled genetic association testing when performing a burden test is essential, and methods that allow for some variants to be associated with increased fertility should be considered. Rare variants are much more likely to show population-specific differences, and selecting humans from two potentially very different cohorts and sample sizes can easily lead to confounding. I suggest performing a principal component analysis to ascertain the degree of genetic differentiation between these cohorts, and use this to guide the selection of a subset of the control cohort as well.

We agree and have replicated this analysis using only individuals of European descent; our conclusions have not changed but the P-values have become lower (lines 435 - 446).

(3) Citations should also be included in Table 1, for each relevant phenotype. You may also want to consider a more general comparison of p-values and effect sizes of genome-wide association studies for human male infertility to test for an enrichment in/nearby genes showing relaxed selection along the gorilla lineage. In other words, do the relaxed genes in the gorilla lineage have an enrichment of small p-values for being associated with male infertility.

Citations have been included in Table 1, as suggested, and the table has been updated to include the latest reported phenotypes.

eLife Assessment

This study presents compelling evidence supporting the therapeutic potential of trained immunity in Colitis. The study is important for the field of trained immunity and is a welcome addition to the focus issue on trained immunity.

Reviewer #1 (Public review):

Summary:

This study presents an interesting investigation into the role of trained immunity in inflammatory bowel disease, demonstrating that β-glucan-induced reprogramming of innate immune cells can ameliorate experimental colitis. The findings are novel and clinically relevant, with potential implications for therapeutic strategies in IBD. The combination of functional assays, adoptive transfer experiments, and single-cell RNA sequencing provides comprehensive mechanistic insights. However, some aspects of the study could benefit from further clarification to strengthen the conclusions.

Strengths:

(1) This study elegantly connects trained immunity with IBD, demonstrating how β-glucan-induced innate immune reprogramming can mitigate chronic inflammation.

(2) Adoptive transfer experiments robustly confirm the protective role of monocytes/macrophages in colitis resolution.

(3) Single-cell RNA sequencing provides mechanistic depth, revealing the expansion of reparative Cx3cr1⁺ macrophages and their contribution to epithelial repair.

(4) The work highlights the therapeutic potential of trained immunity in restoring gut homeostasis, offering new directions for IBD treatment.

Weaknesses:

While β-glucan may exert its training effect on hematopoietic stem cells, performing ATAC-seq on HSCs or monocytes to profile chromatin accessibility at antibacterial defense and mucosal repair-related genes would further validate the trained immunity mechanism. Alternatively, the authors could acknowledge this as a study limitation and future research direction.

Comments on revisions:

My concerns have been fully addressed. I have no additional comments.

Reviewer #2 (Public review):

This study investigates how BG-induced myeloid reprogramming influences inflammatory bowel disease in a mouse model of DSS-induced colitis. The authors use in vivo functional experiments, adoptive transfer, and scRNA-seq to assess whether innate immune reprogramming can confer protection in colitis.

In the revised versions of the manuscript, the authors clarified the mechanistic scope of the study, softened the conclusions, and acknowledged the lack of direct epigenetic validation of trained immunity in this model. The manuscript now also better emphasizes the context-dependent nature of BG-induced reprogramming.

While some aspects remain correlative and will require further investigation, the central findings are well supported.

Overall, this work provides a meaningful contribution to the field, and I support its publication.

Comments on revisions:

No further comments.

Reviewer #3 (Public review):

Summary:

In the present work, the authors offer evidence for the therapeutic potential of trained immunity in the context of inflammatory bowel disease (IBD). Prior research has demonstrated that innate cells pre-treated (trained) with β-glucan show an enhanced pro-inflammatory response upon a second challenge with the same or different stimulus. While an increased immune response can be beneficial and protect against bacterial infections, there is also the risk that it will worsen symptoms in various inflammatory disorders.

Remarkably, the authors show that β-glucan training of bone marrow hematopoietic progenitors and peripheral monocytes mitigates the pro-inflammatory effects of colitis, with protection extending to naïve recipients of the trained cells. Additionally, the authors demonstrate that mice preconditioned with β-glucan have enhanced resistance to Staphylococcus aureus and Salmonella typhimurium infections, indicating heightened immune responses.<br /> Using a dextran sulfate sodium (DSS)-induced model of colitis, β-glucan pre-treatment significantly dampens disease severity. Importantly, the use of Rag1^-/- mice, which lack adaptive immune cells, confirms that the protective effects of β-glucan are mediated by innate immune mechanisms. Further, experiments using Ccr2^-/- mice underline the necessity of monocyte recruitment in mediating this protection, highlighting CCR2 as a key factor in the mobilization of β-glucan trained monocytes to inflamed tissues. In addition, β-glucan training highlights a distinct monocyte subpopulation with enhanced activation and phagocytic capacity. These monocytes, marked by increased expression of Cx3cr1, are suggestive of an increased ability to infiltrate inflamed colonic tissue and differentiate into macrophages.

Transcriptomic profiling reveals that β-glucan training upregulates genes associated with pattern recognition, antimicrobial defense, immunomodulation, and interferon signaling pathways, suggesting broad functional reprogramming of the innate immune compartment. Moreover, among the trained monocyte and macrophage subsets, gene expression signatures are associated with tissue and mucosal repair, suggesting a role in promoting resolution and regeneration following inflammatory insult. Furthermore, this was coupled with analysis of chromatin accessibility in publicly available data.

Strengths:

By employing a range of well-characterized murine models, the authors investigate specific mechanisms involved in the effects of β-glucan training. Furthermore, the study provides functional evidence that the protection conferred by the trained cells persists within the hematopoietic progenitors and can be transferred to naïve recipients. The integration of transcriptomic profiling allows the identification of changes in key genes and molecular pathways underlying the trained immune phenotype.

Weaknesses:

Further studies would benefit from investigating the cytokine responses of intestinal macrophages, particularly CX3CR1⁺ macrophages, following ex vivo stimulation of previously BCG-trained cells. Moreover, assessing the metabolic state of these macrophages would provide valuable insight into the mechanisms underlying trained immunity in this context.

Impact:

Overall, the authors present a mechanistically insightful investigation that advances our understanding of trained immunity in IBD. This is an important study that demonstrates that β-glucan-trained innate cells can confer protection against colitis and promote mucosal repair through trained-immunity related mechanisms. These findings underscore the potential of harnessing innate immune memory as a therapeutic approach for chronic inflammatory diseases.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review):

Summary:

This study presents an interesting investigation into the role of trained immunity in inflammatory bowel disease, demonstrating that β-glucan-induced reprogramming of innate immune cells can ameliorate experimental colitis. The findings are novel and clinically relevant, with potential implications for therapeutic strategies in IBD. The combination of functional assays, adoptive transfer experiments, and single-cell RNA sequencing provides comprehensive mechanistic insights. However, some aspects of the study could benefit from further clarification to strengthen the conclusions.

We are grateful for the reviewer’s positive assessment of our study and constructive suggestions to improve the manuscript.

Strengths:

(1) This study elegantly connects trained immunity with IBD, demonstrating how βglucan-induced innate immune reprogramming can mitigate chronic inflammation.

(2) Adoptive transfer experiments robustly confirm the protective role of monocytes/macrophages in colitis resolution.

(3) Single-cell RNA sequencing provides mechanistic depth, revealing the expansion of reparative Cx3cr1⁺ macrophages and their contribution to epithelial repair.

(4) The work highlights the therapeutic potential of trained immunity in restoring gut homeostasis, offering new directions for IBD treatment.

Weaknesses:

While β-glucan may exert its training effect on hematopoietic stem cells, performing ATAC-seq on HSCs or monocytes to profile chromatin accessibility at antibacterial defense and mucosal repair-related genes would further validate the trained immunity mechanism. Alternatively, the authors could acknowledge this as a study limitation and future research direction.

We appreciate your comments on assessing the chormoatain accessibility of HSCs induced by b-glucan training, as epigenetic reprogramming is known to be one of the underlying mechanisms for trained immunity suggest by many groups including our group. To delineate the genome-wide epigenetic reprogramming induced by β-glucan (BG), we reanalyzed publicly available chromatin profiling datasets where ATACseq of HSC from control and β-glucan trained mice was performed (accession number: CRA014389). Comparative analysis revealed HSC from BG-trained mice demonstrated pronounced enrichment at promoters and distal intergenic regions—key regulatory loci governing transcriptional activity (Fig. S7A). This divergent genomic targeting was further corroborated by distinct signal distribution profiles (Fig. S7B), supporting pronounced upregulation-driven remodeling of the epigenomic landscape induced by BG treatment. Functional annotation of these epigenetically primed promoters via GO term analysis revealed significant enrichment of immune-relevant processes, including leukocyte migration, cell-cell adhesion, and chemotaxis (Fig. S7C). Consistently, KEGG pathway analysis highlighted the enrichment of signaling cascades such as chemokine signaling and cell adhesion molecules (Fig. S7D), reinforcing the involvement of BG-induced trained immunity in inflammatory and mucosal homing pathways.

Furthermore, promoter-centric enrichment of terms related to “defense response to bacterium” (Fig. S7E) underscored the role of BG in priming antibacterial transcriptional programs, which is a crucial axis for maintaining intestinal homeostasis. Locus-specific examination of chromatin states further validated BG-induced epigenetic modifications in the upstream regions of selected target genes, including Gbp5, Gbp2 and S100a8 and Nos2 (Fig. S7F). Collectively, our integrative reanalysis demonstrates that BG reshapes the epigenomic architecture at regulatory elements, thereby orchestrating immune gene expression programs directly relevant to IBD pathophysiology and mucosal immunity. (Line 201-211)

Reviewer 1 (Recommendations for the authors):

(1) It’s better to include a schematic summarizing the proposed mechanism for reader clarity.

We appreciate your comments and proposed a graphical abstract as in Author response image 1.

Author response image 1.

(2) Discuss potential off-target effects of β-glucan-induced trained immunity (e.g., risk of exacerbated inflammation in other contexts).

We appreciate this important comment regarding the potential off-target or side-effects of β-glucan induced trained immunity. As trained immunity is known to augment inflammatory responses upon heterologous stimulation and has been implicated in chronic inflammation–prone conditions such as atherosclerosis, this is an important consideration. Previous in vivo studies have shown that β-glucan pretreatment can enhance antibacterial or antitumor responses without inducing basal inflammation after one week of administration (PMID: 22901542, PMID: 30380404, PMID: 36604547, PMID: 33125892). Nevertheless, it remains possible that β-glucan–induced trained immunity could have unintended effects in certain contexts, which warrants further investigation and caution. We have discussed this potential caveat in the discussion (Lines 299-302)

Reviewer #2 (Public review):

Summary:

The study investigates whether β-glucan (BG) can reprogram the innate immune system to protect against intestinal inflammation. The authors show that mice pretreated with BG prior to DSS-induced colitis experience reduced colitis severity, including less weight loss, colon damage, improved gut repair, and lowered inflammation. These effects were independent of adaptive immunity and were linked to changes in monocyte function.

The authors show that the BG-trained monocytes not only help control inflammation but confer non-specific protection against experimental infections (Salmonella), suggesting the involvement of trained immunity (TI) mechanisms. Using single-cell RNA sequencing, they map the transcriptional changes in these cells and show enhanced differentiation of monocytes into reparative CX3CR1⁺ macrophages. Importantly, these protective effects were transferable to other mice via adoptive cell transfer and bone marrow transplantation, suggesting that the innate immune system had been reprogrammed at the level of stem/progenitor cells.

Overall, this study provides evidence that TI, often associated with heightened inflammatory programs, can also promote tissue repair and resolution of inflammation. Moreover, this BG-induced functional reprogramming can be further harnessed to treat chronic inflammatory disorders like IBD.

Strengths:

(1) The authors use advanced experimental approaches to explore the potential therapeutic use of myeloid reprogramming by β-glucan in IBD.

(2) The authors follow a data-to-function approach, integrating bulk and single-cell RNA sequencing with in vivo functional validation to support their conclusions.

(3) The study adds to the growing evidence that TI is not a singular pro-inflammatory program, but can adopt distinct functional states, including anti-inflammatory and reparative phenotypes, depending on the context.

We are grateful for your positive assessment of our study and recognition of its translational implications. We particularly appreciate the acknowledgment that our work expands the therapeutic potential of β-glucan–mediated trained immunity in ameliorating colitis.

Weaknesses:

(1) The epigenetic and metabolic basis of TI is not explored, which weakens the mechanistic claim of TI. This is especially relevant given that a novel reparative, antiinflammatory TI program is proposed.

We appreciate your valuable comment highlighting the importance of the epigenetic and metabolic basis of TI in providing mechanistic insight. While previous studies, including work from our group (S.-C. Cheng), have extensively characterized the epigenetic and metabolic signatures of monocytes from BG-trained mice—primarily in the context of inflammatory genes—we acknowledge that these aspects are not directly addressed in our current manuscript as the current manuscript was aimed to build on the foundation of β-glucan-induced trained immunity established by many other groups including us and address its potential as a therapeutic approaches in the colitis setup.

That being said, we fully agree with your comments to analyze the epigenetic profile on key pathways similar to the question raised by reviewer 1, we reanalyze the relevant public datasets and presenting summarize the finding in Supplementary Figure S7. ATAC-seq analysis further validated and provide the epigenetic basis of the enhanced inflammatory and antibacterial capacity of monocytes which are seeded back in the HSC compartment.

(2) The absence of a BG-only group limits interpretation of the results. Since the authors report tissue-level effects such as enhanced mucosal repair and transcriptional shifts in intestinal macrophages (colonic RNA-Seq), it is important to rule out whether BG alone could influence the gut independently of DSS-induced inflammation. Without a BG-only control, it is hard to distinguish a true trained response from a potential modulation caused directly by BG.

We thank the reviewer for this important suggestion. Although we did not perform qPCR for mucosal repair genes in Figure S1C and Figure S1D, our colon RNA-seq analysis in Figure 5G included a BG-only control group (Colitis_d0). These results indicate that BG preconditioning alone does not alter baseline expression of colon mucosal repair genes, supporting the conclusion that the observed effects occur in the context of DSS-induced inflammation.

(3) Although monocyte transfer experiments show protection in colitis, the fate of the transferred cells is not described (e.g., homing or differentiation into Cx3cr1⁺ macrophage subsets). This weakens the link between specific monocyte subsets and the observed phenotype.

We thank the reviewer for this important point. We acknowledge that direct in vivo tracking of the adoptively transferred monocytes to confirm their homing to the colon and differentiation into specific macrophage subsets would strengthen the mechanistic link. However, due to technical limitations in reliably tracing the fate of transferred cells in our experimental setting, we were unable to provide this direct evidence. Instead, we present a strong correlative and functional evidence chain that supports the proposed model:

(a) Following BG pretreatment, we observed a significant decrease in circulating Ly6Chi monocytes specifically at the peak of colitis (day 7, Fig. 5D), concurrent with a marked increase in monocytes/macrophages within the colonic lamina propria (Fig. 2D). This inverse relationship strongly suggests enhanced recruitment of monocytes from the blood into the inflamed colon upon BG training.

(b) Using CX3CR1-GFP reporter mice, we found that BG pretreatment led to an increased proportion of colonic myeloid cells in an intermediate state (P5: Ly6C⁺MHCII⁺CX3CR1⁺, Fig. 5F). This population represents monocytes actively undergoing differentiation into intestinal macrophages, supporting the idea that BG accelerates the monocyte-to-macrophage transition in situ.

(c) Our scRNA-seq analysis independently revealed an expansion of monocyte-derived macrophage clusters (e.g., Macro1, Macro2) in BG-treated mice, which express canonical tissue macrophage markers (including Cx3cr1) and genes associated with tissue repair (e.g., Vegfa, Fig. 4A, 5H, 5I).

These data collectively indicate that BG-trained monocytes exhibit enhanced capacity for colonic recruitment and preferential differentiation toward reparative macrophage subsets, which aligns with the protective phenotype observed after adoptive transfer. We have explicitly noted the absence of direct fate-mapping data as a limitation in the revised Discussion and agree that future studies employing advanced tracing techniques would be valuable to definitively establish this cellular trajectory. (Line 378-380)

(4) While scRNA-seq reveals distinct monocyte/macrophage subclusters (Mono1-3.), their specific functional roles remain speculative. The authors assign reparative or antimicrobial functions based on transcriptional signatures, but do not perform causal experiments (depletion or in vitro assays). The biological roles of these cells remain correlative.

We agree that the functional role of CX3CR1⁺ macrophages is not comprehensively validated and is currently inferred from scRNA-seq clustering. While our flow cytometry data show increased CX3CR1⁺ macrophages in the BG-TI group, and our CCR2 KO and monocyte adoptive transfer experiments indicate these macrophages are monocyte-derived, suggesting at least that β-glucan pretreatment alters the monocyte capacity which directly contribute to the enhanced colitis alleviation phenotype as observed. However, due to the fact that we fail to find a cluster dependent marker, which is also the current biggest caveats of the scRNAseq defined cell subclusters, we were not able to show direct casual evidence via specifically depleting subcluster cells. However, the result from the monocyte adoptive transfer experiment with Ccr2 KO mice experimental strongly suggest the presence of monocytes is crucial for this protective effect. We fully acknowledge this as a limitation of current study and clarify in the discussion that our conclusions regarding CX3CR1⁺ macrophage function are mainly based on transcriptional profiling and association with protective phenotypes, rather than direct causal evidence (Lines 400-404).

(5) While Rag1^-/- mice were used to rule out adaptive immunity, the potential role of innate lymphoid cells (ILCs), particularly ILC2s and ILC3s, which are known to promote mucosal repair (PMID: 27484190 IF: 7.6 Q1 IF: 7.6 Q1 IF: 7.6 Q1), was not explored. Given the reparative phenotype observed, the contribution of ILCs remains a confounding factor.

We appreciate your valuable comment regarding the potential role of ILCs in the observed mucosal repair. Indeed, in our current manuscript examining the BG-trained immunity effect, the contribution of ILCs was not evaluated. Due to the fact that adoptive transfer of trained monocytes into CCR2 KO mice could recapitulate the colitis alleviation phenotype, we think at least the β-glucan enhanced protection are dependent on trained monocytes. While acknowledge that the limitation and we could not rule out the possible role of ILCs in this process and discuss this limitation in the discussion in the revised manuscript.

The literature (PMID: 21502992; PMID: 32187516) supports a role for ILC3-mediated IL-22 production in tissue repair, which could overlap with our observed effects. However, our monocyte adoptive transfer experiments show that monocytes alone can alleviate DSS-induced colitis, suggesting a dominant role for monocytes in this context. Nonetheless, we will make it clear that ILC contributions cannot be excluded. (Line 322-326).

Reviewer 2 (Recommendations for the authors):

(1) The authors do not provide direct mechanistic evidence of TI (e.g., epigenetic and metabolic reprogramming). The absence of such data weakens the mechanistic strength of the TI claim. The authors should soften the terminology to BGinduced myeloid reprogramming suggestive of trained immunity, acknowledge, and discuss this limitation.

We appreciate your comment highlighting the lack of direct epigenetic and metabolic assessment in our current study. Previous work from our group (S.-C. Cheng) and others has extensively documented the epigenetic and metabolic profiles of monocytes from β-glucan–trained mice, focusing primarily on inflammatory-related genes. Based on this established foundation, our current manuscript focuses on exploring the translational potential of BG-induced trained immunity.

That said, as mentioned in our response to the identified weakness, we performed reanalysis from the public epigenetic datasets with a focus on pathways related to reparative and antibacterial functions and integrated this part in the revised manuscript (Fig S7, Lines 201-211).

(2) CX3CR1⁺ macrophages' role is not functionally validated. The data relies solely on scRNA-seq and cluster annotations, which are insufficient to confirm functional roles in vivo. Depletion or in vitro studies would provide stronger causal evidence. The authors should acknowledge this limitation in the Discussion.

We agree that the functional role of CX3CR1⁺ macrophages is not comprehensively validated and is currently inferred from scRNA-seq clustering. While our flow cytometry data show increased CX3CR1⁺ macrophages in the BG-TI group, and our CCR2 KO and monocyte adoptive transfer experiments indicate these macrophages are monocyte-derived, suggesting at least that β-glucan pretreatment alters the monocyte capacity which directly contribute to the enhanced colitis alleviation phenotype as observed. However, due to the fact that we fail to find a cluster dependent marker, which is also the current biggest caveats of the scRNAseq defined cell subclusters, we were not able to show a direct casual evidence. We fully acknowledge this as a limitation of current study and clarify in the discussion that our conclusions regarding CX3CR1⁺ macrophage function are mainly based on transcriptional profiling and association with protective phenotypes, rather than direct causal evidence (Lines 395-404).

(3) Rag1^-/- mice retain innate lymphoid cells (ILCs), particularly ILC3, which are mucosal and produce IL-22, contributing to tissue repair (PMID: 21502992; PMID: 32187516). The potential for BG to activate ILCs remains unexplored in this study. This limits the interpretation of whether the observed protection arises from monocyte/macrophage reprogramming or is partially mediated by residual ILC activity. The authors should explicitly acknowledge this limitation and discuss the possible contribution of ILCs to the observed phenotype.

We appreciate your valuable comment regarding the potential role of ILCs in the observed mucosal repair. Indeed, in our current manuscript examining the BG-trained immunity effect, the contribution of ILCs was not evaluated. Due to the fact that adoptive transfer of trained monocytes into CCR2 KO mice could recapitulate the colitis alleviation phenotype, we think at least the β-glucan enhanced protection are dependent on trained monocytes. While acknowledge that the limitation and we could not rule out the possible role of ILCs in this process and discuss this limitation in the discussion in the revised manuscript

The literature (PMID: 21502992; PMID: 32187516) supports a role for ILC3-mediated IL-22 production in tissue repair, which could overlap with our observed effects. However, our monocyte adoptive transfer experiments show that monocytes alone can alleviate DSS-induced colitis, suggesting a dominant role for monocytes in this context. Nonetheless, we will make it clear that ILC contributions cannot be excluded. (Line 322-327).

(4) Figure 1-It would help to clarify whether a BG-only control group (without DSS) was included in the design. This would be critical to determine if BG alone alters the colon. If omitted, the authors should clearly state this and consider adding such a group in future experiments. This would help define the baseline effects of BG and support the claim that its benefits are dependent on TI (upon second challenge - DSS).

We appreciate this valuable suggestion. While we did not perform qPCR to assess mucosal repair genes in Figure S1C and Figure S1D, our colon RNA-seq analysis in Figure 5G included a dedicated BG-only control group at based line before DSStreatment (Colitis_d0). These data indicate that BG preconditioning alone does not alter the baseline expression of colon mucosal repair genes.

(5) Figure 3 - It would strengthen the conclusions to include a vehicle-treated PBS BMT donor control group, or to state its absence. It is unclear whether the protective effect observed in recipients of BG-treated BM is due to trained immunity or to non-specific effects of transplantation, irradiation, or batch variation.

We fully agree with your comments that it is critical to including the vehicle-treated PBS BMT control to rule out any non-specific effects induced by transplantation, irradiation or batch variation. We actually did the blank PBS transfer control everytime after mice received irradiation treatment as a control to assess the successful induction of irradiation to get rid of bone marrow from irradiated mice. Mice that receive PBS only will die after 8 days while only mice receiving either bone marrow from PBScontrol or BG-treatment group will survive. We also perform flowcytometry to examine the successful BMT transplantation (Fig S5C). We have added part regarding the vehicle-treated control for BMT in the material method section for clarification (Lines 456-466).

(6) No gene expression or phenotypic data is provided for monocytes/macrophages in BMT recipients; therefore, it cannot be confidently stated that these cells were reprogrammed. Expression/phenotypic data should be added or discussed.

We thank the reviewer for raising this important point. We acknowledge that a detailed transcriptomic or phenotypic analysis of donor-derived tissue-resident myeloid cells in the BMT recipients would provide the most direct evidence for their reprogrammed state.

While our BMT study focused primarily on assessing the transferability of the protective phenotype via endpoint disease parameters and circulating immune cell composition, we present a coherent and compelling line of evidence supporting the conclusion that BG's training effect is maintained within the hematopoietic system of recipients and mediated by reprogrammed myeloid cells:

(a) A key finding is the significant increase in the proportion of donor-derived Ly6Chi monocytes in the peripheral blood of recipients receiving BG-trained bone marrow (Fig. 3J). This is not a bystander effect but direct evidence that the BG-induced on donor hematopoietic stem/progenitor cells instructs a biased differentiation program towards a specific effector precursor population within the new host, demonstrating the functional persistence of the trained state post-transplantation.

(b) The core of reprogramming in trained immunity lies in persistent epigenetic and functional changes. Our new analysis of public datasets (Fig. S7) confirms that BG directly reshapes the chromatin accessibility landscape in hematopoietic stem cells (HSCs), particularly at loci regulating immune and antibacterial responses. This provides the fundamental mechanism explaining how the trained phenotype is both long-lasting and transplantable: the reprogramming occurs at the progenitor level.

(c) The most causally compelling data in our study comes from the independent adoptive transfer experiment, where transfer of purified BG-trained monocytes alone was sufficient to ameliorate colitis in recipient mice (Fig. 3K, L). This definitively proves that the trained monocytes themselves carry the protective functional program. It strongly suggests that these reprogrammed monocytes/macrophages are the likely effectors mediating protection in the BMT model.

(d) Our interpretation aligns with well-established paradigms in the field. Precedent studies confirm that the BG-trained phenotype (e.g., enhanced cytokine potential) can be transferred via BMT or monocyte adoption. For instance, Haacke et al. (PMID: 40020679) demonstrated that splenic monocytes from BG-trained donors, when transferred into arthritic recipient mice, led to elevated inflammatory cytokine (e.g., Tnf, Il6) expression in recipient joints, directly proving the maintained functional reprogramming of trained cells in a heterologous host environment. This provides a strong precedent supporting the functional activity of transferred trained cells in our model.

(7) The study is consistent with emerging evidence that distinct TI programs may exist depending on the stimulus and context, including immunoregulatory and tissue-reparative responses (PMID: 35133977; PMID: 31732931; PMID: 32716363; PMID: 30555483). The authors should integrate this perspective into the Discussion to acknowledge that their findings may represent one example of such context-dependent, potentially reparative TI programs. This would place the study within the growing literature describing functional heterogeneity in innate immune training.

We appreciate this suggestion and have incorporated it into the discussion. In the revised manuscript, we discussed how our findings of BG-induced protective myeloid reprogramming align with the concept of tissue-reparative or immunoregulatory TI, which is distinct from the pro-inflammatory TI phenotypes described in other contexts. By highlighting the functional heterogeneity of innate immune training, we position our work as an example of a stimulus-specific, reparative TI program. (Lines 356-379)

Reviewer #3 (Public review):

Summary:

In the present work, Yinyin Lv et al offer evidence for the therapeutic potential of trained immunity in the context of inflammatory bowel disease (IBD). Prior research has demonstrated that innate cells pre-treated (trained) with β-glucan show an enhanced pro-inflammatory response upon a second challenge.

While an increased immune response can be beneficial and protect against bacterial infections, there is also the risk that it will worsen symptoms in various inflammatory disorders. In the present study, the authors show that mice preconditioned with β-glucan have enhanced resistance to Staphylococcus aureus infection, indicating heightened immune responses.

The authors demonstrate that β-glucan training of bone marrow hematopoietic progenitors and peripheral monocytes mitigates the pro-inflammatory effects of colitis, with protection extending to naïve recipients of the trained cells.

Using a dextran sulfate sodium (DSS)-induced model of colitis, β-glucan pre-treatment significantly dampens disease severity. Importantly, the use of Rag1^-/- mice, which lack adaptive immune cells, confirms that the protective effects of β-glucan are mediated by innate immune mechanisms. Further, experiments using Ccr2^-/- mice underline the necessity of monocyte recruitment in mediating this protection, highlighting CCR2 as a key factor in the mobilization of β-glucan-trained monocytes to inflamed tissues. Transcriptomic profiling reveals that β-glucan training upregulates genes associated with pattern recognition, antimicrobial defense, immunomodulation, and interferon signaling pathways, suggesting broad functional reprogramming of the innate immune compartment. In addition, β-glucan training induces a distinct monocyte subpopulation with enhanced activation and phagocytic capacity. These monocytes exhibit an increased ability to infiltrate inflamed colonic tissue and differentiate into macrophages, marked by increased expression of Cx3cr1. Moreover, among these trained monocyte and macrophage subsets, other gene expression signatures are associated with tissue and mucosal repair, suggesting a role in promoting resolution and regeneration following inflammatory insult.

Strengths:

(1) Overall, the authors present a mechanistically insightful investigation that advances our understanding of trained immunity in IBD.

(2) By employing a range of well-characterized murine models, the authors investigate specific mechanisms involved in the effects of β-glucan training.

(3) Furthermore, the study provides functional evidence that the protection conferred by the trained cells persists within the hematopoietic progenitors and can be transferred to naïve recipients. The integration of transcriptomic profiling allows the identification of changes in key genes and molecular pathways underlying the trained immune phenotype.

(4) This is an important study that demonstrates that β-glucan-trained innate cells confer protection against colitis and promote mucosal repair, and these findings underscore the potential of harnessing innate immune memory as a therapeutic approach for chronic inflammatory diseases.

Thank you for the positive evaluation and constructive feedback on our manuscript.

Weaknesses:

However, FPKM is not ideal for between-sample comparisons due to its within-sample normalization approach. Best practices recommend using raw counts (with DESeq2) for more robust statistical inference.

We appreciate the reminder about best practices for RNA-seq analysis. We apologize for the inaccurate description in the Materials and Methods section. For all differential expression analyses, we have in fact used raw count data as input for DESeq2. FPKM values were only used for visualization purposes, such as in heatmaps and clustering analyses. We correct this description in the revised manuscript to accurately reflect our analysis workflow. (Lines 488-499)

Reviewer 3 (Recommendations for the authors):

(1) Current best practices recommend working with raw count data when using DESeq2 to ensure statistically robust differential expression analysis between samples. However, for visualization and clustering, like heatmaps, FPKMs can be used. Could the authors explain why they have used FPKM for differential gene expression analysis?

We appreciate the reminder about best practices for RNA-seq analysis. We apologize for the inaccurate description in the Materials and Methods section. For all differential expression analyses, we have in fact used raw count data as input for DESeq2. FPKM values were only used for visualization purposes, such as in heatmaps and clustering analyses. We correct this description in the revised manuscript to accurately reflect our analysis workflow. (Lines 488-499)

Minor Comment

(1) Line 92: remove extra word "that".

We remove the extra word “that” from Line 92 in the revised manuscript.

(2) Line 201: please state here what "GBP" stands for, as it appears first.

We define “GBP” as “Guanylate-Binding Protein” at its first appearance in Line 201. (Lines 213)

(3) Line 235: consider rewriting "we analyzed the day 7 RNA-seq data, which revealed significant enrichment of the myeloid"; added spacing for "day 7", "which", and "the".

We revise the sentence in Line 235 to read: “We analyzed the day 7 RNA-seq data, which revealed significant enrichment of the myeloid…” to improve readability. (Lines

246-247)

(4) Line 290: consider rewriting " as seen in conditions such as rheumatoid arthritis and ...".

We revise Line 290 to: “as observed in conditions such as rheumatoid arthritis and…” for clarity. (Lines 301-302)

(5) Line 375-376: please check sentence starting lower case "with minor modifications, by assessing ".

We correct the sentence to start with a capital letter: “With minor modifications, by assessing…” (Lines 422-423)

(6) Line 399: kindly consider adding "was" after "cDNA".

We revise Line 399 to include “was” as suggested: “cDNA was synthesized…” (Lines 446)

(7) Line 346-347: consider adding "which" after "monocytes": "We transferred BGpreconditioned monocytes which significantly alleviated clinical symptoms".

We revise Line 346-347 to include “which” as suggested for grammatical clarity. (Lines 385-386)

eLife Assessment

The authors use a convincing methodology to investigate the detachment and reattachment kinetics of kinesin-1, 2, and 3 motors against loads oriented parallel to the microtubule. The findings are useful to the field in providing a clearer view of kinesin function under load. However, as the manuscript stands, the conclusions drawn from the experiments, as well as the overall interpretation of the results, are only partially supported by the presented data, and the novelty relative to previous reports appears less clear.

Reviewer #1 (Public review):

Summary:

Noell et al have presented a careful study of the dissociation kinetics of Kinesin (1,2,3) classes of motors moving in-vitro on a microtubule. These motors move against the opposing force from a ~1 micron DNA strand (DNA tensiometer) that is tethered to the microtubule and also bound to the motor via specific linkages (Fig 1A). Authors compare the time for which motors remain attached to the microtubule when they are tethered to the DNA, versus when they are not. If the former is longer, the intepretation is that the force on the motor from the stretched DNA (presumed to be working solely along the length of the microtubule) causes the motor's detachment rate from the microtubule to be reduced. Thus, the specific motor exhibits "catch-bond" like behaviour.

Strengths:

The motivation is good - to understand how kinesin competes against dynein through the possible activation of a catch bond. Experiments are well done and there is an effort to model the results theoretically.

Weaknesses:

The motivation of these studies is to understand how kinesin (1/2/3) motors would behave when they are pitted in a tug of war against dynein motors as they transport cargo in bidirectional manner on microtubules. Earlier work on dynein and kinesin motors using optical tweezers has suggested that dynein shows catch bond phenomenon, whereas such signatures were not seen for kinesin. Based on their data with DNA tensiometer, the authors would like to claim that (i) Kinesin1 and kinesin2 also show catch-bonding and (ii) The earlier results using optical traps suffer from vertical forces, which complicates the catch-bond interpretation.

Comments on revised version:

I am not fully convinced about the responses from authors, so I would like to retain my original assessment of the paper. The same may be made available for public viewing, along with the responses of the authors. Readers can go through both and form their opinion.

Reviewer #2 (Public review):

Summary:

To investigate the detachment and reattachment kinetics of kinesin-1, 2 and 3 motors against loads oriented parallel to the microtubule, the authors used a DNA tensiometer approach comprising a DNA entropic spring attached to the microtubule on one end and a motor on the other. They found that for kinesin-1 and kinesin-2 the dissociation rates at stall were smaller than the detachment rates during unloaded runs. With regard to the complex reattachment kinetics found in the experiments, the authors argue that these findings were consistent with a weakly-bound 'slip' state preceding motor dissociation from the microtubule. The behavior of kinesin-3 was different and (by the definition of the authors) only showed prolonged "detachment" rates when disregarding some of the slip events. The authors performed stochastic simulations which recapitulate the load-dependent detachment and reattachment kinetics for all three motors. They argue that the presented results provide insight into how kinesin-1, -2 and -3 families transport cargo in complex cellular geometries and compete against dynein during bidirectional transport.

Strengths:

The present study is timely, as significant concerns have been raised previously about studying motor kinetics in optical (single-bead) traps where significant vertical forces are present. Moreover, the obtained data are of high quality and the experimental procedures are clearly described.

Comments on revision:

The authors extensively entered into a scientific debate with the reviewers in their Response Letter. This led to a few changes and some (limited) new data in the manuscript. This is great and did improve the manuscript.

However, in the view of this reviewer, (i) a significant number of responses fall short of actually addressing the concerns of the three reviewers (e.g. wrt using the same kinesin-1 neck-coil domains for all motors) and or (ii) a significant number of arguments now only occur in the response letter but not in the manuscript. The authors may check themselves critically for both. In principle, each longer discussion in the response letter warrants mentioning the appropriate facts and arguments in the main text of the manuscript.

Reviewer #3 (Public review):

Summary:

Several recent findings indicate that forces perpendicular to the microtubule accelerate kinesin unbinding, where perpendicular and axial forces were analyzed using the geometry in a single-bead optical trapping assay (Khataee and Howard, 2019), comparison between single-bead and dumbbell assay measurements (Pyrpassopoulos et al., 2020), and comparison of single-bead optical trap measurements with and without a DNA tether (Hensley and Yildiz, 2025).

Here, the authors devise an assay to exert forces along the microtubule axis by tethering kinesin to the microtubule via a dsDNA tether. They compared the behavior of kinesin-1, -2, and -3 when pulling against the DNA tether. In line with previous optical trapping measurements, kinesin unbinding is less sensitive forces when the forces are aligned with the microtubule axis. Surprisingly, the authors find that both kinesin-1 and -2 detach from the microtubule more slowly when stalled against the DNA tether than in unloaded conditions, indicating that these motors act as catch bonds in response to axial loads. Axial loads accelerate kinesin-3 detachment. However, kinesin-3 reattaches quickly to maintain forces. For all three kinesins, the authors observe weakly-attached states where the motor briefly slips along the microtubule before continuing a processive run.

Strengths:

These observations suggest that the conventional view that kinesins act as slip bonds under load, as concluded from single-bead optical trapping measurements where perpendicular loads are present due to the force being exerted on the centroid of a large (relative to the kinesin) bead, need to be reconsidered. Understanding the effect of force on the association kinetics of kinesin has important implications for intracellular transport, where the force-dependent detachment governs how kinesins interact with other kinesins and opposing dynein motors (Muller et al., 2008; Kunwar et al., 2011; Ohashi et al., 2018; Gicking et al., 2022) on vesicular cargoes.

Weaknesses:

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results is not fully considered in the manuscript.

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

In the single-molecule extension traces (Fig. 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g. the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3? In their response, the authors provide an explanation of the appearance of jumps due to limited imaging speeds. The authors state that the qualitative difference in the kinesin-2 traces compared to the kinesin-1 an -3 traces may be due to the specific rebinding kinetics of kinesin-2.

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis? The authors addressed this concern by applying a Markov model to estimate the duration parameter.

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state? In their response, the authors addressed this question by explaining that a three-state model is required to model the recovery time distributions.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

(1) Figure 1B shows the PREDICTED force-extension curve for DNA based on a worm-like chain model. Where is the experimental evidence for this curve? This issue is crucial because the F-E curve will decide how and when a catch-bond is induced (if at all it is) as the motor moves against the tensiometer. Unless this is actually measured by some other means, I find it hard to accept all the results based on Figure 1B.

The Worm-Like-Chain model for the elasticity of DNA was established by early work from the Bustamante lab (Smith et al., 1992) and Marko and Siggia (Marko and Siggia, 1995), and was further validated and refined by the Block lab (Bouchiat et al., 1999; Wang et al., 1997). The 50 nm persistence length is the consensus value, and was shown to be independent of force and extension in Figure 3 of Bouchiat et al (Bouchiat et al., 1999). However, we would like to stress that for our conclusions, the precise details of the Force-Extension relationship of our dsDNA are immaterial. The key point is that the motor stretches the DNA and stalls when it reaches its stall force. Our claim of the catch-bond character of kinesin is based on the longer duration at stall compared to the run duration in the absence of load. Provided that the motor is indeed stalling because it has stretched out the DNA (which is strongly supported by the repeated stalling around the predicted extension corresponding to ~6 pN of force), then the stall duration depends on neither the precise value for the extension nor the precise value of the force at stall.

(2) The authors can correct me on this, but I believe that all the catch-bond studies using optical traps have exerted a load force that exceeds the actual force generated by the motor. For example, see Figure 2 in reference 42 (Kunwar et al). It is in this regime (load force > force from motor) that the dissociation rate is reduced (catch-bond is activated). Such a regime is never reached in the DNA tensiometer study because of the very construction of the experiment. I am very surprised that this point is overlooked in this manuscript. I am therefore not even sure that the present experiments even induce a catch-bond (in the sense reported for earlier papers).

It is true that Kunwar et al measured binding durations at super-stall loads and used that to conclude that dynein does act as a catch-bond (but kinesin does not) (Kunwar et al., 2011). However, we would like to correct the reviewer on this one. This approach of exerting super-stall forces and measuring binding durations is in fact less common than the approach of allowing the motor to walk up to stall and measuring the binding duration. This ‘fixed trap’ approach has been used to show catch-bond behavior of dynein (Leidel et al., 2012; Rai et al., 2013) and kinesin (Kuo et al., 2022; Pyrpassopoulos et al., 2020). For the non-processive motor Myosin I, a dynamic force clamp was used to keep the actin filament in place while the myosin generated a single step (Laakso et al., 2008). Because the motor generates the force, these are not superstall forces either.

(3) I appreciate the concerns about the Vertical force from the optical trap. But that leads to the following questions that have not at all been addressed in this paper:

(i) Why is the Vertical force only a problem for Kinesins, and not a problem for the dynein studies?

Actually, we do not claim that vertical force is not a problem for dynein; our data do not speak to this question. There is debate in the literature as to whether dynein has catch bond behavior in the traditional single-bead optical trap geometry - while some studies have measured dynein catch bond behavior (Kunwar et al., 2011; Leidel et al., 2012; Rai et al., 2013), others have found that dynein has slip-bond or ideal-bond behavior (Ezber et al., 2020; Nicholas et al., 2015; Rao et al., 2019). This discrepancy may relate to vertical forces, but not in an obvious way.

(ii) The authors state that "With this geometry, a kinesin motor pulls against the elastic force of a stretched DNA solely in a direction parallel to the microtubule". Is this really true? What matters is not just how the kinesin pulls the DNA, but also how the DNA pulls on the kinesin. In Figure 1A, what is the guarantee that the DNA is oriented only in the plane of the paper? In fact, the DNA could even be bending transiently in a manner that it pulls the kinesin motor UPWARDS (Vertical force). How are the authors sure that the reaction force between DNA and kinesin is oriented SOLELY along the microtubule?

We acknowledge that “solely” is an absolute term that is too strong to describe our geometry. We softened this term in our revision to “nearly parallel to the microtubule” (Line 464). In the Geometry Calculations section of Supplementary Methods, we calculate that if the motor and streptavidin are on the same protofilament, the vertical force will be <1% of the horizontal force. We also note that if the motor is on a different protofilament, there will be lateral forces and forces perpendicular to the microtubule surface, except they are oriented toward rather than away from the microtubule. The DNA can surely bend due to thermal forces, but because inertia plays a negligible role at the nanoscale (Howard, 2001; Purcell, 1977), any resulting upward forces will only be thermal forces, which the motor is already subjected to at all times.

(4) For this study to be really impactful and for some of the above concerns to be addressed, the data should also have included DNA tensiometer experiments with Dynein. I wonder why this was not done?

As much as we would love to fully characterize dynein here, this paper is about kinesin and it took a substantial effort. The dynein work merits a stand-alone paper.

While I do like several aspects of the paper, I do not believe that the conclusions are supported by the data presented in this paper for the reasons stated above.

The three key points the reviewer makes are the validity of the worm-like-chain model, the question of superstall loads, and the role of DNA bending in generating vertical forces. We hope that we have fully addressed these concerns in our responses above.

Reviewer #2 (Public review):

Major comments:

(1) The use of the term "catch bond" is misleading, as the authors do not really mean consistently a catch bond in the classical sense (i.e., a protein-protein interaction having a dissociation rate that decreases with load). Instead, what they mean is that after motor detachment (i.e., after a motor protein dissociating from a tubulin protein), there is a slip state during which the reattachment rate is higher as compared to a motor diffusing in solution. While this may indeed influence the dynamics of bidirectional cargo transport (e.g., during tug-of-war events), the used terms (detachment (with or without slip?), dissociation, rescue, ...) need to be better defined and the results discussed in the context of these definitions. It is very unsatisfactory at the moment, for example, that kinesin-3 is at first not classified as a catch bond, but later on (after tweaking the definitions) it is. In essence, the typical slip/catch bond nomenclature used for protein-protein interaction is not readily applicable for motors with slippage.

We acknowledge that our treatment of kinesin-3 was confusing. In response, we deleted any reference to kinesin-3 catch-bond in the Results section, and restricted it to the Discussion where it is interpretation. In Line 635 in the Discussion, we softened the statement of catch-bond activity to “…all three dominant kinesin transport families display catch-bond like behavior at stall…”. We acknowledge that, classically, the catch/slip bond nomenclature refers to simple protein-protein interactions and is easier to interpret there. However, the term ‘catch-bond’ has been used in the literature for myosin, dynein and kinesin, and thus we feel that it is sufficiently established to use it here.

(2) The authors define the stall duration as the time at full load, terminated by >60 nm slips/detachments. Isn't that a problem? Smaller slips are not detected/considered... but are also indicative of a motor dissociation event, i.e., the end of a stall. What is the distribution of the slip distances? If the slip distances follow an exponential decay, a large number of short slips are expected, and the presented data (neglecting those short slips) would be highly distorted.

The reviewer brings up a good point that there may be undetected slips. To address this question, we plotted the distribution of slip distances for kinesin-3, which by far had the most slip events. As the reviewer suggested, it is indeed an exponential distribution, and we calculated a corrected kinesin-3 stall duration due to these undetected slips. This data and analysis are included as a new Supplementary Figure S8. In the main text on Lines 283-293 we included the following text:

“It was notable that the kinesin-3 stall durations at high load are longer than the ramp durations at low load, because this indicates that the kinesin-3 off-rate slows with increasing load. However, because kinesin-3 had the most slip events at stall, we were concerned that there may be undetected slip events below the 60 nm threshold of detection that led to an overestimation of the kinesin-3 stall duration. To test this hypothesis, we plotted the distribution of kinesin-3 slip distances at stall, fit an exponential, and calculated the fraction of missed slip events (Fig. S8). From this analysis, we calculated a correction factor of 1.42 that brought the kinesin-3 stall duration down 1.33 s. Notably, this stall duration value is still well above the kinesin-3 ramp duration value of 0.75 s in Fig. 3C and thus does not qualitatively change our conclusions.”

We thank the reviewer for this suggestion.

(3) Along the same line: Why do the authors compare the stall duration (without including the time it took the motor to reach stall) to the unloaded single motor run durations? Shouldn't the times of the runs be included?

The elastic force of the DNA spring is variable as the motor steps up to stall, and so if we included the entire run duration then it would be difficult to specify what force we were comparing to unloaded. More importantly, if we assume that any stepping and detachment behavior is history independent, then it is mathematically proper to take any arbitrary starting point (such as when the motor reaches stall), start the clock there, and measure the distribution of detachments durations relative to that starting point. More importantly, what we do in Fig. 3 is to separate out the ramps from the stalls and, using a statistical model, we compute a separate duration parameter (which is the inverse of the off-rate) for the ramp and the stall. What we find is that the relationship between ramp, stall, and unloaded durations is different for the three motors, which is interesting in itself.

(4) At many places, it appears too simple that for the biologically relevant processes, mainly/only the load-dependent off-rates of the motors matter. The stall forces and the kind of motor-cargo linkage (e.g., rigid vs. diffusive) do likely also matter. For example: "In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to maintain force generation and, hence, are distinct from true detachment events." I disagree. The kinesin force at reattachment (after slippage) is much smaller than at stall. What helps, however, is that due to the geometry of being held close to the microtubule (either by the DNA in the present case or by the cargo in vivo) the attachment rate is much higher. Note also that upon DNA relaxation, the motor is likely kept close to the microtubule surface, while, for example, when bound to a vesicle, the motor may diffuse away from the microtubule quickly (e.g., reference 20).

We appreciate the reviewer’s detailed thinking here, and we offer our perspective. As to the first point, we agree that the stall force is relevant and that the rigidity of the motor-cargo linkage will play a role. The goal of the sentence on pulling cargo that the reviewer highlights is to set up our analysis of slips, which we define as rearward displacements that don’t return to the baseline before force generation resumes. We revised this sentence to the following: “In the context of pulling a large cargo through the viscous cytoplasm or competing against dynein in a tug-of-war, these slip events enable the motor to continue generating force after a small rearward displacement, rather than fully detaching and ‘resetting’ to zero load.” (Line 339-342)

It should be noted that, as shown in the model diagram in Fig. 5, we differentiate between the slip state (and recovery from this slip state) and the detached state (and reattachment from this detached state). This delineation is important because, as the reviewer points out, if we are measuring detachment and reattachment with our DNA tensiometer, then the geometry of a vesicle in a cell will be different and diffusion away from the microtubule or elastic recoil perpendicular to the microtubule will suppress this reattachment.

Our evidence for a slip state in which the motor maintains association with the microtubule comes from optical trapping work by Tokelis et al (Toleikis et al., 2020) and Sudhakar et al (Sudhakar et al., 2021). In particular, Sudhakar used small, high index Germanium microspheres that had a low drag coefficient. They showed that during ‘slip’ events, the relaxation time constant of the bead back to the center of the trap was nearly 10-fold slower than the trap response time, consistent with the motor exerting drag on the microtubule. (With larger beads, the drag of the bead swamps the motor-microtubule friction.) Another piece of support for the motor maintaining association during a slip is work by Ramaiya et al. who used birefringent microspheres to exert and measure rotational torque during kinesin stepping (Ramaiya et al., 2017). In most traces, when the motor returned to baseline following a stall, the torque was dissipated as well, consistent with a ‘detached’ state. However, a slip event is shown in S18a where the motor slips backward while maintaining torque. This is best explained by the motor slipping backward in a state where the heads are associated with the microtubule (at least sufficiently to resist rotational forces). Thus, we term the resumption after slip to be a rescue from the slip state rather than a reattachment from the detached state.

To finish the point, with the complex geometry of a vesicle, during slip events the motor remains associated with the microtubule and hence primed for recovery. This recovery rate is expected to be the same as for the DNA tensiometer. Following a detachment, however, we agree that there will likely be a higher probability of reattachment in the DNA tensiometer due to proximity effects, whereas with a vesicle any elastic recoil or ‘rolling’ will pull the detached motor away from the microtubule, suppressing reattachment. To address this point, we added in the Discussion on lines 654-656:

“Additionally, any ‘rolling’ of a spherical cargo following motor detachment will tend to suppress the motor reattachment rate.”

(5) Why were all motors linked to the neck-coil domain of kinesin-1? Couldn't it be that for normal function, the different coils matter? Autoinhibition can also be circumvented by consistently shortening the constructs.

We chose this dimerization approach to focus on how the mechoanochemical properties of kinesins vary between the three dominant transport families. We agree that in cells, autoinhibition of both kinesins and dynein likely play roles in regulating bidirectional transport, as will the activity of other regulatory proteins. The native coiled-coils may act as ‘shock absorbers’ due to their compliance, or they might slow the motor reattachment rate due to the relatively large search volumes created by their long lengths (10s of nm). These are topics for future work. By using the neck-coil domain of kinesin-1 for all three motors, we eliminate any differences in autoinhibition or other regulation between the three kinesin families and focus solely on differences in the mechanochemistry of their motor domains.

(6) I am worried about the neutravidin on the microtubules, which may act as roadblocks (e.g. DOI: 10.1039/b803585g), slip termination sites (maybe without the neutravidin, the rescue rate would be much lower?), and potentially also DNA-interaction sites? At 8 nM neutravidin and the given level of biotinylation, what density of neutravidin do the authors expect on their microtubules? Can the authors rule out that the observed stall events are predominantly the result of a kinesin motor being stopped after a short slippage event at a neutravidin molecule?

(7) Also, the unloaded runs should be performed on the same microtubules as in the DNA experiments, i.e., with neutravidin. Otherwise, I do not see how the values can be compared.

To address the question of neutravidin acting as a roadblock, we did the following. Because of the sequence of injections used to assemble the tensiometer in the flow cell, there are often some residual GFP-kinesin motors that aren’t attached to DNA and thus serve as internal controls for unloaded motility on the neutravidin-functionalized Mt. We quantified the run durations of these free kinesin-GFP and found that their run duration was 0.92 s (95% CI: 0.79 to 1.04 by MEMLET). This is slightly lower but not statistically different from the 1.04 s [0.78, 1.31] on control microtubules in Fig 2A. This result is included in Figure S6 in the revised manuscript.

We don’t have a precise estimate for the amount of neutravidin on the microtubules. Based on Fig. 3C of Korten and Diez (Korten and Diez, 2008), the reduction in the unloaded run duration that we see corresponds to a ~2% biotinylation ratio. We polymerize Mt with 10% biotinylated tubulin and add 8 nM neutravidin to the flow cell, so in principle the microtubules could be 10% biotin-streptavidin coated. However, there are a number of uncertainties that push this estimate lower – a) the precise degree of biotinylation, b) whether the %biotinylated tubulin in polymerized microtubules is lower than the mixing ratio due to unequal incorporation, and 3) what fraction of the biotinylated tubulin are occupied by the neutravidin when using this neutravidin flow-in method. Thus, our best estimate is ~2% biotin-streptavidin functionalization.

The ramp durations in Fig. 3 provide another argument that biotinylated microtubules are not affecting the motors. Compared to unloaded durations for each motor, the kinesin-1 ramps were longer, the kinesin-2 ramps were the same, and the kinesin-3 ramps were shorter duration. That argues against any systematic effect of biotinylation on motor run durations, with the caveat that family-dependent differences could in principle be masking an effect. The fact that ramp durations aren’t systematically longer or shorter than the unloaded run durations also argues that the stalls we see, which are at the expected extension length of the dsDNA, are not caused by neutravidin roadblocks.

The final point the reviewer brings up is whether neutravidin may be contributing to the rescues from slips events that we observe. This is difficult to fully rule out. However, because the unloaded run durations aren’t significantly altered by the biotin-streptavidin on the microtubules, we don’t expect the rescue events following a slip to be significantly affected. In principle, we could systematically increase and decrease the biotinylation and see whether the slip rescues change, but we haven’t done this.

(8) If, as stated, "a portion of kinesin-3 unloaded run durations were limited by the length of the microtubules, meaning the unloaded duration is a lower limit." corrections (such as Kaplan-Meier) should be applied, DOI: 10.1016/j.bpj.2017.09.024.

(9) Shouldn't Kaplan-Meier also be applied to the ramp durations ... as a ramp may also artificially end upon stall? Also, doesn't the comparison between ramp and stall duration have a problem, as each stall is preceded by a ramp ...and the (maximum) ramp times will depend on the speed of the motor? Kinesin-3 is the fastest motor and will reach stall much faster than kinesin-1. Isn't it obvious that the stall durations are longer than the ramp duration (as seen for all three motors in Figure 3)?

The reviewer rightly notes the many challenges in estimating the motor off-rates during ramps. To estimate ramp off-rates and as an independent approach to calculating the unloaded and stall durations, we developed a Markov model coupled with Bayesian inference methods to estimate a duration parameter (equivalent to the inverse of the off-rate) for the unloaded, ramp, and stall duration distributions. With the ramps, we have left censoring due to the difficulty in detecting the start of the ramps in the fluctuating baseline, and we have right censoring due to reaching stall (with different censoring of the ramp duration for the three motors due to their different speeds). The Markov model assumes a constant detachment probability and history-independence, and thus is robust even in the face of left and right censoring (details in the Supplementary section). This approach is preferred over Kaplan-Meier because, although non-parametric methods such as K-M make no assumptions for the distribution, they require the user to know exactly where the start time is.

Regarding the potential underestimate of the kinesin-3 unloaded run duration due to finite microtubule lengths. The first point is that the unloaded duration data in Fig. 2C are quite linear up to 6 s and are well fit by the single-exponential fit (the points above 6 s don’t affect the fit very much). The second point is that when we used our Markov model (which is robust against right censoring) to estimate the unloaded and stall durations, the results agreed with the single-exponential fits very well (Table S2). Specifically, the single-exponential fit for the kinesin-3 unloaded duration was 2.74 s (2.33 – 3.17 s 95% CI) and the estimate from the Markov model was 2.76 (2.28 – 3.34 s 95% CI). Thus, we chose not to make any corrections to the kinesin-3 unloaded run durations due to finite microtubule lengths. To address this point in the revision, we added the following note in Table S2: “* Because the Markov-Bayesian model, which is unaffected by left and right censoring of data gave same unloaded run durations for kinesin-3 as the MEMLET fit, we did not the kinesin-3 unloaded run durations for any right censoring due to finite microtubule lengths.” We also added the following point in the legend of Fig. S1: “A fraction of kinesin-3 unloaded run durations were limited by the length of the microtubules, but fitting to a model that took into account missed events gave a similar mean duration as an exponential fit, and so no correction was made (Table S2).”

(10) It is not clear what is seen in Figure S6A: It looks like only single motors (green, w/o a DNA molecule) are walking ... Note: the influence of the attached DNA onto the stepping duration of a motor may depend on the DNA conformation (stretched and near to the microtubule (with neutravidin!) in the tethered case and spherically coiled in the untethered case).

In Figure S6 kymograph, the green traces are GFP-labeled kinesin-1 without DNA attached (which are in excess) and the red diagonal trace is a motor with DNA attached. We clarified this in the revised Figure S6 legend. We agree that the DNA conformation will differ if it is attached and stretched (more linear) versus simply being transported (random coil), but by its nature this control experiment is only addressing random coil DNA.

(11) Along this line: While the run time of kinesin-1 with DNA (1.4 s) is significantly shorter than the stall time (3.0 s), it is still larger than the unloaded run time (1.0 s). What do the authors think is the origin of this increase?

We addressed this point in lines 200-212 of the revised manuscript:

“We carried out two additional control experiments. First, to confirm that the neutravidin used to link the DNA to the microtubule wasn’t affecting kinesin motility, we analyzed the run durations of kinesin-1 motors on neutravidin-coated microtubules and found no change compared to unlabeled microtubules (Fig. S6). Second, we measured the run duration of kinesin-1 linked to a DNA tether that was not bound to the microtubule and thus was being transported (Fig. S6). The kinesin-DNA run duration was 1.40 s, longer than the 1.04 s of motors alone (Fig. 2A). We interpret this longer duration to reflect the slower diffusion constant of the dsDNA relative to the motor alone, which enables motors to transiently detach and rebind before the DNA cargo has diffused away, thus extending the run duration (Block et al., 1990). Notably, this slower diffusion constant should not play a role in the DNA tensiometer geometry because if the motor transiently detaches, it will be pulled backward by the elastic forces of the DNA and detected as a slip or detachment event.“

(12) "The simplest prediction is that against the low loads experienced during ramps, the detachment rate should match the unloaded detachment rate." I disagree. I would already expect a slight increase.

Agreed. We changed this text (Lines 265-267) to: “The prediction for a slip bond is that against the low loads experienced during ramps, the detachment rate should be equal to or faster than the unloaded detachment rate.”

(13) Isn't the model over-defined by fitting the values for the load-dependence of the strong-to-weak transition and fitting the load dependence into the transition to the slip state?

Essentially, yes, it is overdefined, but that is essentially by design and the model is still very useful. Our goal here was to make as simple a model as possible that could account for the data and use it to compare model parameters for the different motor families. Ignoring the complexity of the slip and detached states, a model with a strong and weak state in the stepping cycle and a single transition out of the stepping cycle is the simplest formulation possible. And having rate constants (k_S-W and k_slip in our case) that vary exponentially with load makes thermodynamic sense for modeling mechanochemistry (Howard, 2001). Thus, we were pleasantly surprised that this bare-bones model could recapitulate the unloaded and stall durations for all three motors (Fig. 5C-E).

(14) "When kinesin-1 was tethered to a glass coverslip via a DNA linker and hydrodynamic forces were imposed on an associated microtubule, kinesin-1 dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (37)." This statement appears not to be true. In reference 37, very similar to the geometry reported here, the microtubules were fixed on the surface, and the stepping of single kinesin motors attached to large beads (to which defined forces were applied by hydrodynamics) via long DNA linkers was studied. In fact, quite a number of statements made in the present manuscript have been made already in ref. 37 (see in particular sections 2.6 and 2.7), and the authors may consider putting their results better into this context in the Introduction and Discussion. It is also noteworthy to discuss that the (admittedly limited) data in ref. 37 does not indicate a "catch-bond" behavior but rather an insensitivity to force over a defined range of forces.

The reviewer misquoted our sentence. The actual wording of the sentence was: “When kinesin-1 was connected to micron-scale beads through a DNA linker and hydrodynamic forces parallel to the microtubule imposed, dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics (Urbanska et al., 2021).” The sentence the reviewer quoted was in a previous version that is available on BioRxiv and perhaps they were reading that version. Nonetheless, in the Discussion of the revision, we added text to note that this behavior is indicative of an ideal bond (not a catch-bond) on Lines 480-483: “When kinesin-1 was connected to micron-scale beads through a DNA linker and hydrodynamic forces parallel to the microtubule imposed, dissociation rates were relatively insensitive to loads up to ~3 pN, inconsistent with slip-bond characteristics and instead characteristic of an ideal-bond.” We also added a sentence in the Introduction highlighting this work, Lines 84-87: “Fourth, when kinesin-1 was connected to a bead through a micron-long segment of DNA and hydrodynamic forces were imposed on the bead, motor interaction times were insensitive to hindering loads up to 3 pN, indicative of an ideal-bond.”

Reviewer #3 (Public review):

The authors attribute the differences in the behaviour of kinesins when pulling against a DNA tether compared to an optical trap to the differences in the perpendicular forces. However, the compliance is also much different in these two experiments. The optical trap acts like a ~ linear spring with stiffness ~ 0.05 pN/nm. The dsDNA tether is an entropic spring, with negligible stiffness at low extensions and very high compliance once the tether is extended to its contour length (Fig. 1B). The effect of the compliance on the results should be addressed in the manuscript.

This is an interesting point. We added the following paragraph in Lines 101-111 in the Geometry Consideration section of the Supplementary Methods.

“Another consideration when comparing the DNA tensiometer to optical trap measurements is the relative stiffness of the trap and dsDNA. Optical trap stiffnesses are generally in the range of 0.05 pN/nm [12,13]. To calculate the predicted stiffness of the dsDNA spring, we computed the slope of theoretical force-extension curve in Fig. 1B. The stiffness is highly nonlinear and is <0.001 pN/nM below 650 nm extension. At the predicted stall force of 6 pN (960 nm extension), the dsDNA stiffness ~0.2 pN/nm, which is stiffer than most optical traps, but it is similar to the estimated 0.3 pN/nm stiffness of kinesin motors themselves[12,13]. An 8 nm step at this stiffness leads to a 1.6 pN jump in force, so it is reasonable to expect that motors are dynamically stepping at stall. Therefore, there is no reason to expect that stiffness differences between optical traps and the dsDNA spring are affecting the motor detachment kinetics.”

Compared to an optical trapping assay, the motors are also tethered closer to the microtubule in this geometry. In an optical trap assay, the bead could rotate when the kinesin is not bound. The authors should discuss how this tethering is expected to affect the kinesin reattachment and slipping. While likely outside the scope of this study, it would be interesting to compare the static tether used here with a dynamic tether like MAP7 or the CAP-GLY domain of p150glued.

Please see our response to Reviewer #2 Major Comment #4 above, which asks this same question in the context of intracellular cargo. In response to the point from Reviewer #3, we added the following sentence on Lines 654-656: “Additionally, any ‘rolling’ of a spherical cargo following motor detachment will tend to suppress the motor reattachment rate.”

Regarding a dynamic tether, we agree that’s interesting – there are kinesins that have a second, non-canonical binding site that achieves this tethering (e.g. ncd and Cin8); p150glued likely does this naturally for dynein-dynactin-activator complexes; and we speculated in a review some years ago (Hancock, 2014) that during bidirectional transport kinesin and dynein may act as dynamic tethers for one another when not engaged, enhancing the activity of the opposing motor.

In the single-molecule extension traces (Figure 1F-H; S3), the kinesin-2 traces often show jumps in position at the beginning of runs (e.g., the four runs from ~4-13 s in Fig. 1G). These jumps are not apparent in the kinesin-1 and -3 traces. What is the explanation? Is kinesin-2 binding accelerated by resisting loads more strongly than kinesin-1 and -3?

We agree that at first glance those jumps are puzzling. To investigate this question the first thing we did was to go back to our tensiometer dataset and look systematically at jumps for all three motors. We found roughly 4-6 large jumps like these for all three motors (kinesin-1: 250 +/- 99 nm (mean +/- SD; N=5); kinesin-2: 249 +/- 165 nm (N=6); kinesin-3: 490 +/- 231 nm (N=4)). Thus, although the apparent jumps may be more pronounced due to the specific rebinding kinetics of kinesin-2, this behavior is not unique to this motor. (Note that the motor binding position distribution in Fig. S2 is taken from initial binding positions that follow a clear period of detachment; thus, not all jumps are captured there.)

Our interpretation is that these apparent jumps are simply a reflection of the long length and high compliance of the dsDNA tether. For instance, below 650 nm extension the stiffness, k <0.001 pN/nM (see Reviewer #3, point #1 above). Thus, we expect large fluctuations of the tethered motor when not bound to the microtubule. One reason that these events look like ‘jumps’ is that the sub-ms fluctuations during detached periods are not captured by the ~25 fps movies (40 ms frame acquisition time). Instead, the fitted Qdot position represents the average position during the acquisition window. Actually, due to these rapid fluctuations (and the limited depth of the TIRF illumination field) the position often can’t be determined during these periods of fluctuation (e.g. see gaps at ~2.5 s, 11 s and 24 s in Fig. 1F).

When comparing the durations of unloaded and stall events (Fig. 2), there is a potential for bias in the measurement, where very long unloaded runs cannot be observed due to the limited length of the microtubule (Thompson, Hoeprich, and Berger, 2013), while the duration of tethered runs is only limited by photobleaching. Was the possible censoring of the results addressed in the analysis?

Yes. Please see response to Reviewer #2 points (8) and (9) above.

The mathematical model is helpful in interpreting the data. To assess how the "slip" state contributes to the association kinetics, it would be helpful to compare the proposed model with a similar model with no slip state. Could the slips be explained by fast reattachments from the detached state?

In the model, the slip state and the detached states are conceptually similar; they only differ in the sequence (slip to detached) and the transition rates into and out of them. The simple answer is: yes, the slips could be explained by fast reattachments from the detached state. In that case, the slip state and recovery could be called a “detached state with fast reattachment kinetics”. However, the key data for defining the kinetics of the slip and detached states is the distribution of Recovery times shown in Fig. 4D-F, which required a triple exponential to account for all of the data. If we simplified the model by eliminating the slip state and incorporating fast reattachment from a single detached state, then the distribution of Recovery times would be a single-exponential with a time constant equivalent to t₁, which would be a poor fit to the experimental distributions in Fig. 4D-F.

Recommendations for the authors: 

Reviewing Editor Comments:

The reviewers are in agreement with the motivation and approach of this study. The use of DNA tethers is an important advance in tethering motor proteins to gain insight into how motors respond to load. However, all 3 reviewers express reservations on how well the results support the claims. In particular, the use of the term catch bond was problematic, with Reviewer #2 suggesting some alternative nomenclature. Reviewer #1 expressed concern with experimental evidence for the predicted force-extension curve shown in Figure 1. I agree with the reviewers that additional experimental evidence would be required to conclude the catch-bond detachment kinetics of kinesin.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) By eye, the run lengths, e.g., of kin-1 look very long in Figure S1 ... certainly above the expected 1 µm. Please check and comment.

We agree that the long runs do stick out by eye in this figure. To address this point, we analyzed the run lengths and run times from the kymograph shown in Fig. S1. Fitting the run duration distribution gave t = 1.31 s with a 95% CI of 0.96 to 1.67. This is slightly longer than the 1.04 s duration in Fig. 2A, but the 95% CI include this population mean, and so the S1 data are not statistically significantly different. The run time distribution from the S1 kymograph is given in Author response image 1.

Author response image 1.

(2) The upper right kymograph in Figure 4A does not show a motor return to the baseline. Also, the scale bars, etc., are unreadable. Please modify.

Our purpose for showing the kymographs in Fig. 4A was to show the specific features of slips and fast and slow reattachment. Because we blew up the kymographs to show those specific features, it precluded us from showing the entire return to baseline. As suggested, we magnified the scale bars and the labels on the kymograph labels to make them readable.

Reviewer #3 (Recommendations for the authors):

(1) The frequent references to 95% confidence intervals disrupt the flow of the text. Perhaps the confidence intervals could be listed in a table rather than in the body of the text.

We deleted those from the text; they are shown in Fig. 2D and listed in Table S2.

We appreciate the efforts and helpful suggestions of all three reviewers and the Editor.

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eLife Assessment

This important study demonstrates the power of the UniDesign computational framework in prospectively engineering a PAM-relaxed Staphylococcus aureus Cas9 variant with editing performance comparable to evolution-derived counterparts. The authors provide convincing evidence through rigorous biochemical validation across multiple human cell types, comprehensive deep-sequencing analyses, and direct comparisons with established variants, providing mechanistic insights into PAM specificity remodeling and Cas9 optimization. By establishing computational design as a viable alternative to directed evolution for CRISPR systems, this work will be of broad interest to the protein engineering, genome engineering, synthetic biology, and computational protein design communities.

Reviewer #1 (Public review):

Summary:

This manuscript by Xiong and colleagues presents a compelling validation of UniDesign, a fully computational protein design framework, by using it to engineer a novel, PAM-relaxed variant of Staphylococcus aureus Cas9 (SaCas9) named KRH. The core achievement is the successful de novo generation of a high-performance nuclease (E782K/N968R/R1015H) solely through in silico modeling, without any subsequent experimental optimization or directed evolution. The authors demonstrate that KRH expands the SaCas9 PAM specificity from NNGRRT to NNNRRT, achieving genome editing and base editing efficiencies across multiple human cell types that are comparable to, and sometimes exceed, the well-known evolution-derived KKH variant. The work positions UniDesign not merely as an analytical tool, but as a powerful engine for the generative design of complex molecular functions, offering a scalable and mechanistically insightful alternative to traditional experimental screening.

Strengths:

This is an outstanding manuscript that serves as a powerful proof-of-concept for the next generation of computational protein design. The primary selling point-the raw predictive and generative power of UniDesign-is convincingly demonstrated throughout.

The manuscript shows that the tool can:<br /> (1) successfully navigate a complex sequence landscape to identify a minimal set of three mutations (KRH) that remodel a critical protein-DNA interface;<br /> (2) accurately model and balance the delicate interplay between specific base contacts and non-specific backbone interactions to achieve relaxed PAM specificity;<br /> (3) deliver a final product whose performance is indistinguishable from, and in some cases superior to, a variant that required extensive wet-lab evolution.

The experimental validation is rigorous, thorough, and directly supports the computational predictions. This work will stand as a landmark study for the field, illustrating that computational design has matured to the point where it can reliably generate sophisticated tools for genome engineering.

(1) Demonstration of Generative Power:

The most significant finding is that UniDesign, without any experimental feedback, generated a variant (KRH) that matches the performance of the evolution-derived KKH. This is a remarkable achievement. The iterative design strategy-first reducing PAM bias (R1015H), then restoring binding through non-specific interactions (e.g., N968R, E782K)-is a textbook example of rational design, but it is executed entirely by the algorithm. This validates UniDesign's energy function and search algorithm as capable of capturing the subtle biophysical principles governing PAM recognition.

(2) Mechanistic Insight as a Built-in Feature:

A key advantage of UniDesign highlighted by this work is its inherent ability to provide mechanistic explanations. The computational models not only predicted which mutations would work (e.g., N968R over N968K in the KRH variant) but also why they work. The structural and energetic analyses showing the bidentate salt bridge formed by Arg968 versus the single bond formed by Lys968 (Figure 4A) is a perfect example of how the tool's output can rationalize functional differences, a level of insight that is rarely attainable from directed evolution campaigns alone.

(3) Scalability and Accessibility for Engineering:

The authors explicitly contrast UniDesign's efficiency (minutes to hours per design run) with the computational expense of methods like COMET and the experimental overhead of directed evolution. The improvements to UniDesign v1.2, specifically the mutation-count and sequence-uniqueness penalties, directly address a key challenge in computational design (generating diverse, low-energy point-mutant libraries). This positions the tool as a highly accessible and scalable platform for engineering other CRISPR systems, a point that will be of immense interest to the community.

Weaknesses:

(1) Title and Abstract Emphasis:

The title and abstract are effective but could be slightly sharpened to emphasize the primary message. Consider a title like "Fully computational design of a PAM-relaxed SaCas9 variant with UniDesign demonstrates power to match directed evolution." The abstract could more explicitly state upfront that the design was achieved without any experimental iteration.

(2) Figure 1, Panel M:

The data points in panel M are currently presented at a font size that makes them difficult to read, particularly the labels for the many triple-mutant variants. This density obscures the clear identification of the top-performing designs, such as the KRH variant selected for experimental validation. I recommend that the authors increase the font size of all text elements within this panel, including axis labels, tick marks, and data point labels, to improve legibility. If necessary, the panel dimensions can be adjusted or the layout reorganized to accommodate the larger text without compromising clarity. Ensuring this figure is readable is important, as it visually communicates the energetic convergence that led to the selection of KRH.

(3) Generality of the Design Strategy for Other PAM Positions:

The design strategy focused on relaxing specificity at the highly constrained third position of the PAM (the guanine in NNGRRT). How transferable is this specific strategy (i.e., disrupting a key specific contact and compensating with non-specific backbone binders) to relaxing other positions in the PAM or to other Cas enzymes with different PAM-interaction architectures? A short discussion on this point would help readers understand the broader applicability of the "fine-tuning the balance" principle.

Reviewer #2 (Public review):

Summary:

This manuscript describes the fully in silico design of a new variant of Staphylococcus aureus Cas9 (SaCas9) using an improved UniDesign workflow.

The design strategy consists of three sequential steps:<br /> (1) reducing positional bias at PAM position 3;<br /> (2) restoring DNA binding through nonspecific interactions;<br /> (3) combining individually favorable substitutions.

The overall pipeline is conceptually elegant and logically structured, and the genome-editing activity of the designed variants is comprehensively characterized. The resulting KRH variant exhibits relaxed PAM specificity, expanding the targeting range of SaCas9 across diverse cell types. Notably, the KRH variant demonstrates performance comparable to that of the evolution-derived KKH variant, underscoring the effectiveness of the proposed computational design framework.

Strengths:

The design pipeline is entirely computational and does not rely on experimental data for pretraining or iterative optimization.

Weaknesses:

The computationally generated KRH mutant differs from the experimentally evolved KKH variant by only a single residue, which may reflect insufficient exploration of the available sequence space.

Reviewer #3 (Public review):

Summary:

This study reports KRH, a SaCas9 variant computationally engineered via UniDesign to recognize an expanded NNNRRT PAM with substantially enhanced editing efficiency at non-canonical sites. KRH achieves genome- and base-editing efficiencies comparable to or exceeding the evolution-derived KKH variant across multiple human cell types, demonstrating that computational design can effectively remodel PAM specificity while preserving nuclease activity.

Strengths:

The research follows a clear line of reasoning, and the results appear sound. The computational design strategy presented offers a valuable alternative to directed evolution, with potential applicability beyond Cas9 engineering.

Weaknesses:

The benchmarking of the UniDesign method is insufficient. How its performance compares to other protein design algorithms, whether the energy function parameters were systematically optimized, and if the design strategy can be generalized to other Cas9 orthologs or genome engineering tasks.

Author Response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript by Xiong and colleagues presents a compelling validation of UniDesign, a fully computational protein design framework, by using it to engineer a novel, PAM-relaxed variant of Staphylococcus aureus Cas9 (SaCas9) named KRH. The core achievement is the successful de novo generation of a high-performance nuclease (E782K/N968R/R1015H) solely through in silico modeling, without any subsequent experimental optimization or directed evolution. The authors demonstrate that KRH expands the SaCas9 PAM specificity from NNGRRT to NNNRRT, achieving genome editing and base editing efficiencies across multiple human cell types that are comparable to, and sometimes exceed, the well-known evolution-derived KKH variant. The work positions UniDesign not merely as an analytical tool, but as a powerful engine for the generative design of complex molecular functions, offering a scalable and mechanistically insightful alternative to traditional experimental screening.

Strengths:

This is an outstanding manuscript that serves as a powerful proof-of-concept for the next generation of computational protein design. The primary selling point-the raw predictive and generative power of UniDesign-is convincingly demonstrated throughout.

The manuscript shows that the tool can:

(1) successfully navigate a complex sequence landscape to identify a minimal set of three mutations (KRH) that remodel a critical protein-DNA interface;

(2) accurately model and balance the delicate interplay between specific base contacts and non-specific backbone interactions to achieve relaxed PAM specificity;

(3) deliver a final product whose performance is indistinguishable from, and in some cases superior to, a variant that required extensive wet-lab evolution.

The experimental validation is rigorous, thorough, and directly supports the computational predictions. This work will stand as a landmark study for the field, illustrating that computational design has matured to the point where it can reliably generate sophisticated tools for genome engineering.

(1) Demonstration of Generative Power:

The most significant finding is that UniDesign, without any experimental feedback, generated a variant (KRH) that matches the performance of the evolution-derived KKH. This is a remarkable achievement. The iterative design strategy-first reducing PAM bias (R1015H), then restoring binding through non-specific interactions (e.g., N968R, E782K)-is a textbook example of rational design, but it is executed entirely by the algorithm. This validates UniDesign's energy function and search algorithm as capable of capturing the subtle biophysical principles governing PAM recognition.

(2) Mechanistic Insight as a Built-in Feature:

A key advantage of UniDesign highlighted by this work is its inherent ability to provide mechanistic explanations. The computational models not only predicted which mutations would work (e.g., N968R over N968K in the KRH variant) but also why they work. The structural and energetic analyses showing the bidentate salt bridge formed by Arg968 versus the single bond formed by Lys968 (Figure 4A) is a perfect example of how the tool's output can rationalize functional differences, a level of insight that is rarely attainable from directed evolution campaigns alone.

(3) Scalability and Accessibility for Engineering:

The authors explicitly contrast UniDesign's efficiency (minutes to hours per design run) with the computational expense of methods like COMET and the experimental overhead of directed evolution. The improvements to UniDesign v1.2, specifically the mutation-count and sequence-uniqueness penalties, directly address a key challenge in computational design (generating diverse, low-energy point-mutant libraries). This positions the tool as a highly accessible and scalable platform for engineering other CRISPR systems, a point that will be of immense interest to the community.

We sincerely thank the reviewer for the comprehensive summary and the highly positive and encouraging comments on our manuscript.

Weaknesses:

(1) Title and Abstract Emphasis: The title and abstract are effective but could be slightly sharpened to emphasize the primary message. Consider a title like "Fully computational design of a PAM-relaxed SaCas9 variant with UniDesign demonstrates power to match directed evolution." The abstract could more explicitly state upfront that the design was achieved without any experimental iteration.

We thank the reviewer for these valuable suggestions. We agree that our current title and abstract may be overly objective and neutral, and we will consider refining them during the formal revision.

(2) Figure 1, Panel M: The data points in panel M are currently presented at a font size that makes them difficult to read, particularly the labels for the many triple-mutant variants. This density obscures the clear identification of the top-performing designs, such as the KRH variant selected for experimental validation. I recommend that the authors increase the font size of all text elements within this panel, including axis labels, tick marks, and data point labels, to improve legibility. If necessary, the panel dimensions can be adjusted or the layout reorganized to accommodate the larger text without compromising clarity. Ensuring this figure is readable is important, as it visually communicates the energetic convergence that led to the selection of KRH.

We thank the reviewer for these valuable suggestions. We will refine the Fig. 1M during the formal revision.

(3) Generality of the Design Strategy for Other PAM Positions:

The design strategy focused on relaxing specificity at the highly constrained third position of the PAM (the guanine in NNGRRT). How transferable is this specific strategy (i.e., disrupting a key specific contact and compensating with non-specific backbone binders) to relaxing other positions in the PAM or to other Cas enzymes with different PAM-interaction architectures? A short discussion on this point would help readers understand the broader applicability of the "fine-tuning the balance" principle.

We thank the reviewer for this insightful question and suggestion. The current study builds upon our previous work on CRISPR–Cas PAM recognition modeling using UniDesign (PMID: 37078688), in which eight Cas9 proteins and two Cas12 proteins (each has a different PAM) were investigated. Our computational results demonstrated that UniDesign effectively captures the mutual preferences between natural PAMs and native PAM-interacting amino acids (PIAAs). For example, UniDesign accurately predicted the canonical PAMs of SpCas9 and SaCas9 as NGG and NNGRRT, respectively; conversely, given their canonical PAMs, UniDesign successfully recapitulated the corresponding PIAAs in both systems.

These findings provide the foundation for the present study and motivate our selection of SaCas9 as a representative system to explore PAM relaxation, thereby further demonstrating UniDesign’s predictive power through experimental validation. Although we did not perform similar PAM relaxation designs for other Cas9 or Cas12 proteins, we believe that the UniDesign framework is broadly generalizable and can be readily extended to these systems. We will include additional discussion to clarify this point and highlight the broader applicability of our design strategy.

Reviewer #2 (Public review):

Summary:

This manuscript describes the fully in silico design of a new variant of Staphylococcus aureus Cas9 (SaCas9) using an improved UniDesign workflow.

The design strategy consists of three sequential steps:

(1) reducing positional bias at PAM position 3;

(2) restoring DNA binding through nonspecific interactions;

(3) combining individually favorable substitutions.

The overall pipeline is conceptually elegant and logically structured, and the genome-editing activity of the designed variants is comprehensively characterized. The resulting KRH variant exhibits relaxed PAM specificity, expanding the targeting range of SaCas9 across diverse cell types. Notably, the KRH variant demonstrates performance comparable to that of the evolution-derived KKH variant, underscoring the effectiveness of the proposed computational design framework.

Strengths:

The design pipeline is entirely computational and does not rely on experimental data for pretraining or iterative optimization.

We thank the reviewer for the concise and accurate summary of our manuscript.

Weaknesses:

The computationally generated KRH mutant differs from the experimentally evolved KKH variant by only a single residue, which may reflect insufficient exploration of the available sequence space.

We thank the reviewer for this insightful critique. In the present study, our strategy was not to allow UniDesign to freely explore all 27 mutable positions simultaneously, but rather to constrain the search to point mutations (e.g., double or triple mutants) within the full sequence space (approximately 20^27). Even with this constraint, UniDesign effectively samples a substantially large design space compared to traditional protein engineering approaches.

Through iterative design, we observed that only certain residue types became enriched at a subset of positions when identifying effective double mutants. These enriched residues were then systematically combined to generate performance-enhancing triple mutants in an automated manner. Although we ultimately selected the KRH mutant for experimental validation due to its high similarity to the known KKH variant, UniDesign also proposed additional multi-mutants that are distinct from KKH.

Reviewer #3 (Public review):

Summary:

This study reports KRH, a SaCas9 variant computationally engineered via UniDesign to recognize an expanded NNNRRT PAM with substantially enhanced editing efficiency at non-canonical sites. KRH achieves genome- and base-editing efficiencies comparable to or exceeding the evolution-derived KKH variant across multiple human cell types, demonstrating that computational design can effectively remodel PAM specificity while preserving nuclease activity.

Strengths:

The research follows a clear line of reasoning, and the results appear sound. The computational design strategy presented offers a valuable alternative to directed evolution, with potential applicability beyond Cas9 engineering.

We thank the reviewer for the concise and accurate summary of our manuscript.

Weaknesses:

The benchmarking of the UniDesign method is insufficient. How its performance compares to other protein design algorithms, whether the energy function parameters were systematically optimized, and if the design strategy can be generalized to other Cas9 orthologs or genome engineering tasks.

We thank the reviewer for this valuable critique. The present study builds upon our previous work on CRISPR–Cas PAM recognition modeling using UniDesign (PMID: 37078688), in which many of these concerns were systematically addressed. In that study, UniDesign was benchmarked against Rosetta, a well-established protein design platform, across eight Cas9 proteins and two Cas12 proteins, each recognizing distinct PAM sequences.

Our results demonstrated that UniDesign effectively captures the mutual preferences between natural PAMs and native PAM-interacting amino acids (PIAAs) across these CRISPR–Cas systems. For example, UniDesign accurately predicted the canonical PAMs of SpCas9 and SaCas9 as NGG and NNGRRT, respectively; conversely, given their canonical PAMs, UniDesign successfully recapitulated the corresponding PIAAs in both systems.

These findings provide the foundation for the present study and motivate our selection of SaCas9 as a representative system to explore PAM relaxation, thereby further demonstrating UniDesign’s predictive power through experimental validation. Although we did not perform analogous PAM relaxation designs for other Cas9 or Cas12 proteins in this work, we believe that the UniDesign framework is broadly generalizable and can be readily extended to these systems. We will incorporate additional discussion in the revised manuscript to address these points and clarify the broader applicability of our approach.

eLife Assessment

The authors provide valuable data linking NAD+ dependent HSD3b6 gene expression in the eyelid to a vicious cycle involving decreased steroidogenesis and AR signaling, pro-inflammatory cytokine release, inflammation, CD38 activation, and further NAD+ decline, which induces meibomian gland atrophy leading to dry eye disease. Overall, the presented work provides evidence for the pathologic relationship between a pro-inflammatory environment, intracrine activity, and the NAD+ cofactor. However, the current study does not clearly establish the proposed intracrine mechanism and may largely reflect systemic hormonal effects resulting from the global Had3b6 knockout, leading to an incomplete narrative.

Reviewer #1 (Public review):

Summary:

While the results show some loss in the eyelid meibomian glands, there is significant gland retention in HSD3b6 KO mice, as shown in Figure 2. This is supported by the lack of DEG patterns showing downregulation of Meibum lipid genes (AWAT2, Far2, Soat1, Plin2, SCD, etc.), and no decrease in Pparg expression, known to be critical for meibomian gland lipid gene expression.

Weaknesses:

It should be noted that while the authors indicate that CD38 is significantly up-regulated in the HSD3b6 KO mouse, the increase was not sufficient to show a significant adjusted P-value. Bulk RNA sequencing also shows no significant change in meibum lipid gene expression for aged mice that are treated with 78c, an inhibitor of CD38, which the authors indicate increases NAD levels, leading to increased meibomian gland size compared to vehicle-treated mice. Unfortunately, there was no increase in meibum lipid gene expression with 78c, as identified by adjusted P-value. However, it should be noted that the supplemental file covering DEG expression was labeled as a Microarray analysis. This did not include the 78c+NMN treated mice, which the authors contend show a more impactful effect on the meibomian gland.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors demonstrate strong correlations between a pro-inflammatory state, the activity of an intracrine hormone (3 beta-hydroxysteroid dehydrogenase, 3B-HSD), and the NAD co-factor. Specifically, in a 3B-HSD knockout mouse, there was an upregulation in pro-inflammatory cytokines and increased CD38+ cells (CD38 is an enzyme that depletes NAD, a necessary cofactor for 3B-HSD activity). Conversely, induction of inflammation in the eyelids resulted in reductions in 3B-HSD activity. Supplementation with 5 alpha-dihydrotestosterone (DHT) or the NAD precursor NMN, and inhibition of CD38 activity (78c), corrected the pathologies observed in both the 3B-HSD knockout mouse and the pro-inflammatory model (LPS injection into eyelids).

Strengths:

The experiments were performed with good rigor, assessing the impact of inflammation and 3B-HSD activity using multiple model systems. The endpoints represented a combination of transcriptional changes, protein quantification, enzymatic activity, and immunofluorescent microscopy. The authors use human tissue from both younger and older individuals to justify their hypotheses that increased CD38 + cells and reduced 3B-HSD quantity exist in older individuals. The data provide the foundation for assessing more global changes to the tear film and ocular surface.

Weaknesses:

The main weaknesses of the study include the following:

(1) An absence of information on meibomian gland health, tear film, and ocular surface.

(2) Too few human subjects to validate the hypotheses.

Conclusion:

Overall, this study demonstrates an important relationship that exists between intracrine signaling, inflammation, and cofactor signaling. It represents a novel approach in therapeutic design for patients with meibomian gland dysfunction.

Reviewer #3 (Public review):

Summary:

The authors aimed to investigate whether disruption of intracrine steroid hormone metabolism contributes to meibomian gland dysfunction and proposed a "vicious cycle" of gland dysfunction and inflammation, using a global Had3b6 knockout mouse model. The work addresses an important aspect of MGD, but its impact may be limited unless the intracrine mechanism can be more clearly distinguished from systemic hormonal effects.

Strengths:

This study addressed an important question. The hormonal regulation of the meibomian gland has long been recognized. If clarified, the concept of local steroid metabolism influencing gland homeostasis could have implications for understanding disease mechanisms and identifying therapeutic targets.

Weaknesses:

The use of a global knockout makes it difficult to separate local intracrine effects from systemic hormonal changes, and key controls and hormone measurements are lacking.<br /> LPS-induced inflammation may not reflect the chronic nature of MGD.

Author Response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

While the results show some loss in the eyelid meibomian glands, there is significant gland retention in HSD3b6 KO mice, as shown in Figure 2. This is supported by the lack of DEG patterns showing downregulation of Meibum lipid genes (AWAT2, Far2, Soat1, Plin2, SCD, etc.), and no decrease in Pparg expression, known to be critical for meibomian gland lipid gene expression.

Weaknesses:

It should be noted that while the authors indicate that CD38 is significantly up-regulated in the HSD3b6 KO mouse, the increase was not sufficient to show a significant adjusted P-value. Bulk RNA sequencing also shows no significant change in meibum lipid gene expression for aged mice that are treated with 78c, an inhibitor of CD38, which the authors indicate increases NAD levels, leading to increased meibomian gland size compared to vehicle-treated mice. Unfortunately, there was no increase in meibum lipid gene expression with 78c, as identified by adjusted P-value. However, it should be noted that the supplemental file covering DEG expression was labeled as a Microarray analysis. This did not include the 78c+NMN treated mice, which the authors contend show a more impactful effect on the meibomian gland.

We thank the reviewer for the careful evaluation and insightful comments regarding the interpretation of meibomian gland phenotypes and gene expression profiles.

Regarding the point on the apparent retention of meibomian gland structure and the lack of downregulation of key lipid-related genes (e.g., Awat2, Far2, Soat1, Plin2, Scd, and Pparg), we agree that these observations are important for interpreting the extent of gland dysfunction. In the revised manuscript, we will more clearly present and discuss the RNA-seq data, including the expression profiles of representative meibomian gland lipid genes (and other DEGs), to better contextualize these findings.

With respect to Cd38 expression, we acknowledge that the statistical significance based on adjusted P-values was limited in the current microarray dataset. To address this point, we will perform additional validation using targeted quantitative PCR with specific primers to more accurately assess Cd38 expression changes.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors demonstrate strong correlations between a pro-inflammatory state, the activity of an intracrine hormone (3 beta-hydroxysteroid dehydrogenase, 3B-HSD), and the NAD co-factor. Specifically, in a 3B-HSD knockout mouse, there was an upregulation in pro-inflammatory cytokines and increased CD38+ cells (CD38 is an enzyme that depletes NAD, a necessary cofactor for 3B-HSD activity). Conversely, induction of inflammation in the eyelids resulted in reductions in 3B-HSD activity. Supplementation with 5 alpha-dihydrotestosterone (DHT) or the NAD precursor NMN, and inhibition of CD38 activity (78c), corrected the pathologies observed in both the 3B-HSD knockout mouse and the pro-inflammatory model (LPS injection into eyelids).

Strengths:

The experiments were performed with good rigor, assessing the impact of inflammation and 3B-HSD activity using multiple model systems. The endpoints represented a combination of transcriptional changes, protein quantification, enzymatic activity, and immunofluorescent microscopy. The authors use human tissue from both younger and older individuals to justify their hypotheses that increased CD38 + cells and reduced 3B-HSD quantity exist in older individuals. The data provide the foundation for assessing more global changes to the tear film and ocular surface.

Weaknesses:

The main weaknesses of the study include the following:

(1) An absence of information on meibomian gland health, tear film, and ocular surface.

(2) Too few human subjects to validate the hypotheses.

Conclusion:

Overall, this study demonstrates an important relationship that exists between intracrine signaling, inflammation, and cofactor signaling. It represents a novel approach in therapeutic design for patients with meibomian gland dysfunction.

We thank the reviewer for the positive evaluation of our study and for recognizing the rigor of the experiments, the use of multiple model systems, and the potential of the data to provide a foundation for further investigation.

Regarding the points raised under weaknesses, we agree that evaluation of meibomian gland function, tear film, and ocular surface phenotypes would provide important additional insight. In the present study, we focused primarily on the structural phenotype of the meibomian gland, particularly gland size, as a primary feature of MGD. We acknowledge that pathological assessments of gland function and ocular surface conditions have not been fully addressed. We will clearly state this limitation and expand the Discussion to position these aspects as important directions for future investigation.

With respect to the limited number of human samples, we acknowledge that this is an important consideration for validating the translational relevance of our findings. We will revise the manuscript to more explicitly address this limitation and interpret the human data with appropriate caution.

Reviewer #3 (Public review):

Summary:

The authors aimed to investigate whether disruption of intracrine steroid hormone metabolism contributes to meibomian gland dysfunction and proposed a "vicious cycle" of gland dysfunction and inflammation, using a global Had3b6 knockout mouse model. The work addresses an important aspect of MGD, but its impact may be limited unless the intracrine mechanism can be more clearly distinguished from systemic hormonal effects.

Strengths:

This study addressed an important question. The hormonal regulation of the meibomian gland has long been recognized. If clarified, the concept of local steroid metabolism influencing gland homeostasis could have implications for understanding disease mechanisms and identifying therapeutic targets.

Weaknesses:

The use of a global knockout makes it difficult to separate local intracrine effects from systemic hormonal changes, and key controls and hormone measurements are lacking.

LPS-induced inflammation may not reflect the chronic nature of MGD.

We thank the reviewer for the thoughtful evaluation and for highlighting the importance of distinguishing intracrine mechanisms from systemic hormonal effects.

We agree that, as currently presented, the use of a global Hsd3b6 knockout model makes it difficult to fully separate local intracrine effects from systemic hormonal changes. This point is also consistent with the major concern raised in the editorial assessment regarding the need to more clearly establish the proposed intracrine mechanism. To address this issue, we will strengthen the evidence for intracrine regulation by incorporating additional analyses. Specifically, we will assess systemic testosterone levels in Hsd3b6 knockout mice and include appropriate controls using orchidectomized (ORX) mice. These analyses will help to better distinguish local intracrine mechanisms from systemic hormonal influences.

eLife Assessment

This study provides valuable insights into aged muscle stem cell biology by revealing phenotypic and functional heterogeneity within the geriatric MuSC pool and proposing a VCam-low/negative subpopulation that may account for the reported decline in MuSC numbers with age. These findings have implications for understanding aging-related changes in stem cell maintenance and for improving strategies to isolate or rejuvenate aged MuSCs. However, the evidence supporting the main claims is incomplete, key analyses such as absolute MuSC quantification, fate assessment of VCam-low/negative cells, inclusion of standard aged cohorts, and validation of proposed surface markers are still needed to confirm that overall MuSC abundance is maintained and that a distinct subpopulation has been identified.

Reviewer #1 (Public review):

It is widely accepted that the number of muscle stem cells (MuSCs) declines with aging, leading to diminished regenerative capacity. In this study, when MuSCs were labeled with YFP at a young age, the authors found that the YFP-positive MuSC population remained stable with aging. However, VCAM1 and Pax7 expression levels were reduced in the YFP-positive MuSCs. These VCAM1-negative/low cells exhibited limited proliferative potential and reduced regenerative ability upon transplantation into MuSC-depleted mice. Furthermore, Vcam1-/low MuSCs were highly sensitive to senolysis and represented the population in which Vcam1 expression could be restored by DHT. Finally, the authors identified CD200 and CD63 as markers capable of detecting the entire geriatric MuSC population, including Vcam1-/low cells. Although numerous studies have reported an age-related decline in MuSC numbers, this study challenges that consensus. Therefore, the conclusions require further careful validation.

Major comments:

(1) As mentioned above, numerous studies have reported that the number of MuSCs declines with aging. The authors' claim is valid, as Pax7 and Vcam1 were widely used for these observations. However, age-related differences have also been reported even when using these markers (Porpiglia et al., Cell Stem Cell 2022; Liu et al., Cell Rep 2013). When comparing geriatric Vcam1⁺ MuSCs with young MuSCs in this study, did the authors observe any of the previously reported differences? Furthermore, would increasing the sample size in Figure 1 reveal a statistically significant difference? The lack of significance appears to result from variation within the young group. In addition, this reviewer requests the presentation of data on MuSC frequency in geriatric control mice using CD200 and CD63 in the final figure.

(2) Can the authors identify any unique characteristics of Pax7-VCAM-1 GER1-MuSCs using only the data generated in this study, without relying on public databases? For example, reduced expression of Vcam1 and Pax7. The results of such analyses should be presented.

(3) In the senolysis experiment, the authors state that GER1-MuSCs were depleted. However, no data are provided to support this conclusion. Quantitative cell count data would directly address this concern. In addition, the FACS profile corresponding to Figure 4D should be included.

(4) Figure S4: It remains unclear whether DHT enhances regenerative ability through restoration of the VCAM1 expression in GER1-MuSCs, as DHT also acts on non-MuSC populations. Analyses of the regenerative ability of Senolysis+DHT mice may help to clarify this issue.

(5) Why are there so many myonuclear transcripts detected in the single-cell RNA-seq data? Was this dataset actually generated using single-nucleus RNA-seq? This reviewer considers it inappropriate to directly compare scRNA-seq and snRNA-seq results.

Comments on revisions:

Related to Comment#3: The percentage is also influenced by the number of other cell types. Therefore, to demonstrate cell removal, it is necessary to present the absolute number of cells. If the cells were removed and were not replenished from Vcam1+ cells, the absolute number of cells should be reduced.

Related to Comment#4: Without the DHT+Senolysis experiment proposed by this reviewer or related experiments, there is no evidence demonstrating that GERI-MuSCs functionally rejuvenate. The current data only show that VCAM1 expression is restored.

Related to Comment#8: Individual results from 3-4 biological replicates should be shown in Figure 4. It will help readers to recognize the variation of each sample.

Reviewer #2 (Public review):

Kim et al. investigate heterogeneity in aged muscle stem cells using a model that enables lifelong lineage tracing. The questions addressed in the paper are highly relevant to the fields of aging and stem cell biology, and the experimental approach overcomes some of the limitations of previous studies.

The study provides evidence for phenotypic and functional heterogeneity within the lineage-traced aged MuSC pool. However, the data as presented do not yet support the broader conclusions that MuSC abundance is maintained with age or that a previously unrecognized aged MuSC subpopulation has been identified. These claims would require stronger age-matched cohorts, absolute cell counts normalized to tissue mass, and direct comparison to previously described aged muscle stem cell states.

If the core observations were experimentally reinforced, this study could prompt the field to reassess muscle stem cell loss, heterogeneity, and age-associated changes in canonical marker expression in geriatric mice. However, because several of the central claims depend on analyses that are currently incomplete, the conceptual impact should be treated as provisional. The deposited bulk RNA-seq and scRNA-seq datasets should be useful for mapping these states to existing atlases and for re-analysis by groups interested in quiescent and senescent programs in geriatric muscle stem cells.

Reviewer #3 (Public review):

Summary:

The manuscript by Kim et al. describes a MuSC subpopulation that loses VCam expression in geriatric muscle and shows reduced ability to contribute to muscle regeneration. They propose that this population underlies the reported decline of MuSCs in aged mice, suggesting that these cells remain present in geriatric muscle but are overlooked due to low or absent VCam expression. The identification of a subpopulation that changes with aging would be compelling and of interest to the field.

Strengths:

The authors employ a wide range of assays, from in vitro to in vivo systems, to characterize Vcam-low/negative cells from geriatric muscle. The loss of Vcam appears strong in geriatric mice. They further identify CD63 and CD200 as potential surface markers that remain stable with age, thereby enabling the isolation of MuSCs across different age groups.

Weaknesses:

Some issues remain before establishing whether this population represents a true functional subset or explains the reported decline in MuSC numbers in aged mice. Stronger fate assessment of Vcam-low/negative cells is needed to assess their propensity for cell death and whether this contributes to the conclusions. Comparisons include young, middle-aged, and geriatric mice, but not aged (~24 months) mice, which would help comparisons to previous reports of age-related MuSC decline. The suggestion that the Vcam-low/negative population reflects a senescence-like state remains unclear, as these cells display limited canonical senescence markers, exhibit reversible cell-cycle exit, and yet are reported to be sensitive to senolytic treatment. Validation of CD63 and CD200 as reliable age-independent MuSC markers requires further testing, specifically using the Pax7-YFP tracing model and co-labeling in geriatric mice. Finally, the grouping patterns in some analyses suggest that the Vcam-low/negative fraction may be present in only a subset of geriatric mice, raising the possibility that it reflects health status or pathology rather than a consistent aging-associated phenotype.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

(1) As mentioned above, numerous studies have reported that the number of MuSCs declines with aging. The authors' claim is valid, as Pax7 and Vcam1 were widely used for these observations. However, age-related differences have also been reported even when using these markers (Porpiglia et al., Cell Stem Cell 2022; Liu et al., Cell Rep 2013). (a) When comparing geriatric Vcam1⁺ MuSCs with young MuSCs in this study, did the authors observe any of the previously reported differences? (b) Furthermore, would increasing the sample size in Figure 1 reveal a statistically significant difference? The lack of significance appears to result from variation within the young group. (c) In addition, this reviewer requests the presentation of data on MuSC frequency in geriatric control mice using CD200 and CD63 in the final figure.

(a) When comparing geriatric Vcam1⁺ MuSCs with middle aged MuSCs, we found 1,428 DEGs, where 701 genes were downregulated and 727 genes were upregulated (Fig. S3E). Some of the pathways altered were similar to previously reported differences, such as alterations in the autophagy-lysosome related genes and PI3K-Akt Pathways. However, these alterations did not affect the functional integrity of geriatric Vcam1⁺ MuSCs (Fig. 3 A-F). On the other hand, greater alterations were observed in geriatric Vcam1^- MuSCs, accompanied by functional impairment. We have added further elaborations in the manuscript to reflect the comment from the reviewer (pg. 17, lines 369-379).

(b) Thank you for this helpful comment. We understand the reviewer’s concern that the variability within the young group may contribute to the absence of statistical significance. We respectfully note that the variance observed in the young cohort could be biologically expected rather than technical noise. Multiple studies have shown that young adult MuSCs display great transcriptional and functional heterogeneity from undergoing post-natal myogenic maturation (e.g., Biressi et al., 2010; Tierney & Sacco, 2016; Motohashi & Asakura, 2014). This broader heterogeneity naturally increases variance in marker distribution within young samples. We would also like to clarify that our main conclusions are not solely based on differences in the overall proportion of YFP⁺ and Lin⁻ cells among age groups. Instead, we also rely on the functional and phenotypic heterogeneity that specifically emerges in geriatric MuSCs.

Although the young group shows greater biological variation, the mean values are relatively similar among the groups. Multiple independent datasets in our study including functional performance and molecular profiles consistently show that the total MuSC frequency does not markedly decline with aging. For these reasons, even if the sample size is increased, we do not expect a change in the overall interpretation of this result. We have revised the Results section to acknowledge the variability observed in the young group and to emphasize that total MuSC frequency is not central to the conclusions of this study (pg. 6, lines 129-134).

(c) MuSC frequency in geriatric control mice using CD200 and CD63 in the final figure are in the figure legend of Fig. 5F (pg. 39, line 825-828).

(2) Can the authors identify any unique characteristics of Pax7-VCAM-1 GERI-MuSCs using only the data generated in this study, without relying on public databases? For example, reduced expression of Vcam1 and Pax7. The results of such analyses should be presented.

In Fig S2C, using the bulk-RNA sequencing data generated in this study, we observe reduced expression of both Pax7 and Vcam1 in Pax7-VCAM-1 GERI-MuSCs population. To better highlight this finding, we have added text in the Results section that explicitly describes the reduced Pax7 expression and Vcam1 loss as distinguishing features of Pax7-VCAM-1 GERI-MuSCs in our dataset (pg. 9, lines 199-200).

(3) In the senolysis experiment, the authors state that GER1-MuSCs were depleted. However, no data are provided to support this conclusion. Quantitative cell count data would directly address this concern. In addition, the FACS profile corresponding to Figure 4D should be included.

In Figure 4D we quantified the frequency of VCAM1 Low YFP positive Lin negative MuSCs after senolysis treatment. This analysis shows a clear trend toward a decrease in the GERI subpopulation, although the difference did not reach conventional statistical significance in this experiment (t test p = 0.0596). We have therefore revised the text to describe this as a reduction trend rather than complete depletion, and we now explicitly report the p value in the results section (pg. 12, line 270-272). Furthermore, representative FACS profiles for Figure 4D is now included with the quantification (pg. 38, line 811-814).

(4) Figure S4: It remains unclear whether DHT enhances regenerative ability through restoration of the VCAM1 expression in GER1-MuSCs, as DHT also acts on non-MuSC populations. Analyses of the regenerative ability of Senolysis+DHT mice may help to clarify this issue.

We thank the reviewer for this important insight. We agree that DHT can act on non-stem cell populations in the muscle environment and therefore we cannot conclusively attribute the improved regenerative performance solely to restoration of VCAM1 expression in GERI-MuSCs. To address this concern, we have revised the discussion to explicitly state this limitation and to clarify that DHT may influence multiple cell types that contribute to muscle regeneration. We also indicate that combined senolysis plus DHT treatment would be an informative future approach, although additional animal experiments were not feasible within the scope of the current study (pg. 18, line 382-390).

(5) Why are there so many myonuclear transcripts detected in the single-cell RNA-seq data? Was this dataset actually generated using single-nucleus RNA-seq? This reviewer considers it inappropriate to directly compare scRNA-seq and snRNA-seq results.

Regarding the question of why many myonuclear transcripts were detected and whether this dataset was generated using single nucleus RNA sequencing, we confirm that the experiments were performed using single cell RNA sequencing. The presence of myonuclear transcripts likely reflects partial nuclear leakage or fragmentation during the enzymatic dissociation of aged muscle tissue. This is a known technical issue when preparing single cell suspensions from adult or geriatric skeletal muscle.

To avoid inappropriate interpretation, we identified the myonuclear transcript enriched cluster and excluded it from all downstream analyses that involve MuSC comparison. Therefore, our major conclusions do not rely on this cluster. We have revised the Results text to clearly state that the dataset was generated using single cell RNA sequencing and to explain how myonuclear transcript-positive cells were handled (pg. 8, lines 176-181).

Reviewer #2 (Public review):

In this study, Kim et al. explore the heterogeneity within the aged MuSC population using a mouse model that enables lineage tracing of MuSCs throughout life. The questions addressed in the manuscript are highly relevant to the fields of aging and stem cell biology, and the experimental approach overcomes limitations of earlier studies. However, some of the claims would benefit from additional data analysis, and the central claim of the identification of a "previously unrecognized subpopulation" of aged MuSCs should be evaluated in light of prior work that has also examined MuSC heterogeneity in aging.

Specific points:

(1) As a general comment that is transversal to multiple figures, several experiments should include a direct comparison to a young cohort. Previous studies have shown that the depletion of subpopulations with aging is observed early in the aging process, for example, the loss of Pax7-high MuSCs is observed already in 18‐month‐old mice (Li, 2019, doi: 10.15252/embj.2019102154). Using only mice at 12-14 months as the control group is therefore insufficient to claim that no changes occur with aging.

We thank the reviewer’s suggestion for comparing the aged mice to a young cohort and we acknowledge that previous studies have observed depletion of subpopulations is observed early in the aging process. However, this study is specifically designed to delineate the transition from middle aged to geriatric stages, rather than to characterize differences that are already well established in young versus geriatric comparisons. Previous studies have extensively documented the decline in MuSC function between young and aged animals, whereas the process and timing by which these changes emerge remain unclear. Our results show that major alterations in MuSC phenotype and identity are detected predominantly in the geriatric stage rather than at the middle aged stage. To avoid any misunderstanding, we have revised the text to clearly state that the primary objective of this work is to define the critical shift that occurs from middle aged to geriatric muscle stem cells (page 3-4, line 67-71).

(2) One of the central claims of the manuscript is a challenge to the notion that MuSCs number declines with age. However, the data analysis associated with the quantification of YFP+ cells needs to be expanded to support this conclusion. The authors present YFP+ cells only as a proportion of Lin-neg cells. Since FAP numbers are known to decrease with aging, a stable proportion of YFP+ cells would simply indicate that MuSCs decline at the same rate as FAPs. To more accurately assess changes in MuSC abundance, the authors should report absolute numbers of YFP+ cells normalized to tissue mass (cells/ mg of muscle).

We thank the reviewer for this helpful suggestion. We agree that a proportion based analysis alone does not fully exclude the possibility that MuSCs and FAPs decrease at similar rates during aging. At the time of isolation, muscle mass was not recorded, so we are unable to report YFP⁺ cell numbers normalized to tissue weight as requested. To partially address this limitation, we have now clarified our gating strategy in the methods and Figure 1 to explicitly indicate Sca1⁺ FAP exclusion (pg. 6, line 121-122, pg. 22, lines 460-463). These analyses do not support a major selective loss of MuSCs relative to other mesenchymal populations with aging.

(3) The authors emphasize that several studies use VCAM1 as a surface marker to identify MuSCs. However, many other groups rely on α7-integrin, and according to Figure 1D, the decline in ITGA7 expression within the YFP+ population is not significant. Therefore, the suggestion that MuSC numbers have been misquantified with aging would apply only to a subset of studies. If the authors can demonstrate that YFP+ cell numbers (normalized per milligram of tissue) remain unchanged in geriatric mice, the discussion should directly address the discrepancies with studies that quantify MuSCs using the Lin−/α7-integrin+ strategy.

We thank the reviewer for this important comment. We agree that VCAM1 is only one of several commonly used surface markers for MuSC identification and that many studies quantify MuSCs using the Lin negative and ITGA7 positive strategy. That is why in our study, in addition to VCAM1, we also examined ITGA7 expression within the YFP positive population. Although the mean ITGA7 level did not significantly decline, the variance among geriatric MuSCs was significantly increased based on the F test. This supports the idea that aging does not uniformly reduce marker expression but instead increases phenotypic instability, which could lead to under detection of a subset of MuSCs even when ITGA7 is used as the primary marker. We have added this interpretation to the Discussion (pg. 16, lines 346-355).

(4) The authors focus their attention on a population of VCAM-low/VCAM-neg subpopulation of MuSCs that is enriched in aging. However, the functional properties of this same population in middle-aged (or young) mice are not addressed. Thus, it remains unclear whether geriatric VCAM-low/VCAM-neg MuSCs lose regenerative potential or whether this subpopulation inherently possesses low regenerative capacity and simply expands during aging.

We thank the reviewer for this comment. In young and middle aged mice, the VCAM low or VCAM negative population is extremely small, nearly absent in most samples. The emergence and expansion of this population is therefore a feature that becomes detectable only at the geriatric stage. Given that these cells are not present in appreciable numbers earlier in life, the reduced regenerative performance observed in geriatric VCAM1^low MuSCs likely reflects a phenotype that arises during aging rather than an inherent property of a pre-existing subpopulation. We have added this clarification to the Results section (pg. 7, lines 142-146).

(5) According to Figure 1F, the majority of MuSCs appear to fall within the category of VCAM-low or VCAM-neg (over 80% by visual estimate). It would be important to have an exact quantification of these data. As a result, the assays testing the proliferative and regenerative capacity of VCAM-low/negative cells are effectively assessing the performance of more than 80% of geriatric MuSCs, which unsurprisingly show reduced efficiency. Perhaps more interesting is the fact that a population of VCAM-high geriatric MuSCs retains full regenerative potential. However, the existence of MuSCs that preserve regenerative potential into old age has been reported in other studies (Garcia-Prat, 2020, doi: 10.1038/s41556-020-00593-7; Li, 2019, doi: 10.15252/embj.2019102154). At this point, the central question is whether the authors are describing the same aging-resistant subpopulations of MuSCs using a new marker (VCAM) or whether this study truly identifies a new subpopulation of MuSCs. The authors should directly compare the YFP+VCAM+ aged cells with other subpopulations that maintain regenerative potential in aging.

We thank the reviewer for this comment. First, in response to the request for precise quantification, we now provide the proportions of VCAM1-high and VCAM1-low/negative MuSCs in each age group in the figure legends for Fig.1F (pg. 34-35, lines 765-772). In geriatric mice, VCAM1 low/negative MuSCs represent approximately 44.6% ± 35.7%, whereas VCAM-high MuSCs represent 3.9% ± 1.8%. The substantial variability reflects mouse-to-mouse heterogeneity at very advanced ages.

Importantly, our conclusions do not rely solely on the observation that a large fraction of geriatric MuSCs exhibit reduced regenerative potential. Rather, the VCAM-low state represents a transcriptionally and functionally distinct subpopulation that emerges specifically in the geriatric stage, and exhibits molecular signatures not present in young or mid-aged MuSCs. We have expanded the Results and Discussion to clarify this point.

Regarding whether VCAM-high geriatric MuSCs correspond to previously reported “aging-resistant” MuSCs (e.g., Garcia-Prat 2020; Li 2019), we agree that there may be conceptual overlap, as both populations retain regenerative activity. However, those studies identified resilient MuSCs based on mitochondrial or Pax7-high properties, whereas our classification is based on surface VCAM1 intensity, and we currently lack direct evidence that these populations are equivalent. We have therefore added a statement acknowledging this possibility while clarifying that our work does not claim that VCAM1-high MuSCs represent a newly discovered resilient subset, but instead focuses on the emergence and characterization of the VCAM-low dysfunctional subpopulation (pg. 16, lines 346-355).

(6) In Figure 3F, it is unclear from the data presentation and figure legend whether the authors are considering the average of fiber sizes in each mouse as a replicate (with three data points per condition), or applied statistical analysis directly to all individual fiber measurements. The very low p-values with n=3 are surprising. It is important to account for the fact that observations from the same mouse are correlated (shared microenvironment, mouse-specific effects) and therefore cannot be considered independent.

We thank the reviewer for raising this important statistical point. We fully agree that individual myofibers from the same mouse are not independent biological replicates. In morphometric analyses of regenerated muscle, however, it is standard practice to analyze the full CSA distribution across all regenerated fibers, as the distribution itself (rather than a per-mouse mean) provides the biologically relevant measure of regeneration quality.

The original analysis therefore treated each regenerated fiber as a component of the overall CSA distribution, not as an independent biological replicate, and the statistical comparison was performed at the level of distributions rather than per-mouse replication. We agree that per-mouse averaged CSA values would also be informative, but the raw data were not archived in a format that allows reconstruction of mouse-specific fiber subsets.

Importantly, the group-level CSA distribution differences are robust and remain clearly detectable regardless of statistical approach. We have added clarification in the figure legend to explicitly describe how CSA measurements were obtained and analyzed mouse (pg. 36, lines 796-800).

(7) Regarding Figure 5, it is unclear why ITGA7, a classical surface marker for MuSCs that appears unchanged in aged YFP+ MuSCs (Fig. 1F), is considered inadequate for detecting and isolating GERI-MuSCs.

We thank the reviewer for raising this point. As shown in Figure 1F, the mean ITGA7 expression level does not significantly decline in geriatric YFP positive MuSCs. However, the variance of ITGA7 expression is significantly increased in geriatric MuSCs based on the F test, indicating instability in surface marker expression. This suggests that a fraction of MuSCs may fall below the conventional gating threshold for ITGA7 during aging. Therefore, ITGA7 remains effective for identifying a large portion of MuSCs but may under detect the subset of geriatric MuSCs with reduced marker expression. We have revised the Discussion to clarify this point (pg. 16, lines 346-355).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 3B: In the colony formation assay, the authors should specify the number of biological replicates and the number of cells analyzed per mouse.

We have now added the number of biological replicates and the number of cells analyzed per mouse in the figure legend of Figure 3B (pg. 37, lines 790-791).

(2) Figure 3F: The replication number is indicated as n = 3, which appears to refer to the number of transplanted mice. How many myofibers were analyzed in each transplanted mouse? The authors should provide a more detailed description of the methodology in the Figure legend or M&M.

We thank the reviewer for the question and clarify that n = 3 refers to three independent transplanted mice per group. For each mouse, the entire TA muscle was cryosectioned and immunostained, and all regenerated fibers containing centrally located nuclei were included in the CSA quantification. We have added clarification in the Figure legend to indicate that quantification was performed on all regenerated fibers from each mouse (pg. 37, lines 796-800).

(3) Figure 4: The RNA-seq results are presented as a single dataset per sample. If multiple experiments were performed, individual datasets should be shown. Replicated analyses are essential to ensure the reliability of the findings.

In response to the reviewer comment, we confirm that the RNA sequencing in Figure 4 was performed with 3-4 independent biological replicates for each condition. These replicates showed very consistent sequencing quality and gene expression profiles and were therefore combined for the differential expression analysis. We have revised the materials and methods to clearly describe the number of biological replicates and the analysis workflow. (pg. 25, lines 543).

(4) Line 148: If the authors examined MyoG expression, it should be described as committed myoblasts.

We have now changed the term from myoblasts to committed myoblasts (pg. 8, line 168).

(5) Typo and Referencing Errors:

(a) Line 244: The term 'Antide' appears to be a typo.

We thank the reviewer for noting this point. ‘Antide’ is not a typo but the correct name of a GnRH antagonist (Antide acetate). To avoid confusion, we have revised the text to specify ‘Antide, a GnRH antagonist’ at its first mention (pg. 13, line 289).

(b) Lines 278, 280: Please correct Figure 5H to Figure 5F.

We apologize for this error. We have fixed the figure notations accordingly (pg. 15, lines 326-330).

(c) Some references are incomplete or inappropriate (ex. line 49, line 71, line 86, line 109).

We apologize for this error. We have fixed the references accordingly (pg. 4, line 94, pg.6, line 117).

(d) Line 49: Skeletal muscle regeneration is orchestrated primarily by tissue resident stem cells, known as muscle stem cells (MuSCs) or satellite cells (Relaix et al., 2021). The following paper should be cited:

Satellite cell of skeletal muscle fibers.

MAURO A. J Biophys Biochem Cytol. 1961 Feb;9(2):493-5.

The reference has been revised (pg. 3, line 49).

(e) Line 109: Paired box protein 7 (Pax7) is a transcription factor widely recognized as a defining marker of MuSCs (Sambasivan et al., 2011). The following paper should be cited:

Pax7 is required for the specification of myogenic satellite cells.

Seale P, Sabourin LA, Girgis-Gabardo A, Mansouri A, Gruss P, Rudnicki MA. Cell. 2000 Sep 15;102(6):777-86.

The reference has been revised (pg.6, line 117).

(6) Lines 73-74: Many rejuvenation studies define 'aged' mice as 12 to 24 months old. This reviewer is not aware of any studies that have examined 12-month-old MuSCs as a model of aging.

We apologize for this error. We have fixed the numbers to 18 months accordingly (pg. 4, line 94).

Reviewer #3 (Recommendations for the authors):

(1) Geriatric versus aged mice in the MuSC subpopulation analysis. The authors use geriatric mice (>28 months) to demonstrate the loss of VCam expression in MuSCs and propose that this accounts for previous reports of decreased MuSC numbers in aged contexts. However, as noted in their introduction, most reports use "aged" mice, which are typically around 24 months old, which is biologically distinct from the geriatric stage. This distinction makes it difficult to conclude that the reported decline in MuSC numbers in aged mice can be explained by the phenomenon observed only in geriatric mice (Line 289). The authors should test whether VCam expression is altered in aged (24-month-old) mice to strengthen this argument.

We appreciate the reviewer’s thoughtful comment and agree that 24 month old mice are commonly used as an aged reference in the literature. However, prior studies using 18 to 24 month old animals have reported inconsistent results regarding whether and to what extent MuSCs decline during this period. To avoid ambiguity from intermediate aging stages, we purposefully selected geriatric mice older than 28 months, a condition under which MuSC depletion has been more consistently reported in previous studies. Notably, our data show that even at this stage MuSC abundance is not dramatically reduced, which makes it unlikely that a robust decline would already be present at 24 months. We have clarified this rationale in the revised text. Although investigating the precise timing of the emergence of these changes at earlier time points is an important future direction, it is beyond the scope of the present study.

(2) Variability and bimodal distributions.

Figure 1b: The decline in VCAM+ MuSCs in geriatric mice shows high variability - 3 of 7 replicates align more closely with young/mid-aged levels. Please clarify this variability.

We thank the reviewer for pointing out the variability. We agree that there is heterogeneity in the extent of VCAM1 reduction across geriatric mice. This variability likely reflects animal-to-animal differences in the onset and progression of aging-related phenotypes, which are known to vary at very advanced ages. Importantly, despite this variability, all geriatric samples contain a detectable VCAM1 low population that is not observed in young or middle-aged mice, and the overall trend is consistent across all replicates. We have clarified this in the revised manuscript (pg. 6, lines 125-127).

Figure 1c: While the Mid and Geriatric groups are tightly clustered, the Young group appears bimodal, which challenges the claim (Line 118) that values are "comparable across ages." Since all males were used and it is not sex related, what is driving this bimodal distribution?

We appreciate the reviewer’s observation regarding the variability in the young group. Muscle stem cells in young adult mice are known to encompass diverse transcriptional and functional substates, which contribute to greater biological heterogeneity at this stage (Biressi et al. 2010; Tierney & Sacco 2016; Motohashi & Asakura 2014). As aging progresses, these substates gradually converge toward a common functional phenotype, resulting in more uniform profiles in middle-aged and geriatric mice. Therefore the bimodal appearance in the young group likely reflects the broader developmental heterogeneity of early adult MuSCs rather than a technical discrepancy. We have added this explanation to the revised in the results section (pg.6. lines 129-134).

Figure 4D: Geriatric replicates also display a trimodal distribution. This should be addressed throughout - what is causing these types of distribution, and how does this impact significance tests and conclusions?

We appreciate the reviewer’s observation regarding the multimodal distribution. We interpret this pattern as reflecting increased individual variability that becomes more pronounced at the geriatric stage. Even though aging affects all mice, the extent and timing of age-related phenotypic changes can vary considerably across individuals at very advanced ages. This leads to broader divergence in VCAM1 expression states among geriatric mice. Therefore, when we look at the correlation between VCAM1 High and VCAM1 Low/- population, there exists a significant negative correlation between the two populations (Fig. S3F). We have clarified this interpretation in the text and note that the statistical analysis was performed using the mouse as the biological replicate, so this variability does not alter the overall conclusion (pg.12-13, lines 270-278).

(3) The fate of the Vcam-low/negative cells should be better assessed. For example, Line 180: Colony formation is low/absent in VCAM-low/- cells. Are these cells still viable? Cell death assays are needed. Is expansion capacity truly impaired, or are the cells simply non-viable? Using gene expression as the only means (Line 300) to suggest not dying is insufficient.

We thank the reviewer for this important point. As per the reviewer's analysis, there is lack of direct evidence to show that these cells are viable and apoptosis or viability assay would further strengthen our research. However, we carefully suggest that they are viable from the fact that these cells can be isolated by FACS and generate high quality RNA sequencing libraries, which would not be possible if they were undergoing cell death. Moreover, the transcriptomic data indicate upregulation of stress response and senescence associated pathways rather than apoptotic or necrotic signatures. These findings suggest that VCAM low or negative cells are alive but exhibit reduced proliferative and regenerative capacity. We have revised the text to clarify that our data reflect impaired function rather than loss of viability and that apoptosis assays represent a direction for future investigation (pg. 16, 360-366).

(4) Transplant assays are suggestive, but could use additional characterization. Lines 191 & Figure 3E-F: While representative images match quantification, areas at the edge of VCAM-low/- TAs show signs of regeneration. Please include lower-magnification images. Additionally, assess early post-transplant engraftment efficiency - do certain populations experience a higher loss rate (cell death)? YFP-tracing would also help confirm the donor contribution to fibers.

While we did not collect additional early time-point samples for new engraftment analyses, we carefully re-examined all available transplantation data, including the distribution and density of YFP⁺ donor-derived cells in early post-injury sections. We did not observe patterns suggestive of differential early cell loss between VCAM-high and VCAM-low groups. Thus, although we cannot formally quantify early engraftment efficiency, the existing evidence does not support a model in which differential donor-cell retention accounts for the observed regenerative differences.

Also, we attempted direct YFP co-staining of regenerated myofibers, but as reported by several groups, YFP signal within mature or regenerating myofibers is often diminished or inconsistent after fixation and permeabilization, making reliable fiber-level YFP detection technically challenging in our system. Therefore, instead, we confirmed donor contribution using PBS-injected control muscles, which lack donor MuSCs, and showed that PBS-injected muscles never generated YFP⁺ fibers. This demonstrates that endogenous MuSCs do not contribute to YFP⁺ myofibers in our model, and therefore indirectly supports our suggestion that any YFP⁺-regenerated fiber necessarily originates from transplanted donor cells. We hope the reviewer understands the technical limitations.

(5) Figure S3D: mRNA profiling suggests Mid-aged MuSCs are more distinct from Geriatric Vcam-hi than expected. This should be addressed or at least elaborated on in text.

We appreciate this insightful comment. We agree that mid aged VCAM high MuSCs show detectable transcriptional differences from geriatric VCAM high cells. This pattern likely reflects the fact that some aging related molecular changes begin to accumulate gradually during the middle aged stage even before overt functional decline or VCAM1 loss becomes evident. Importantly, however, these transcriptomic shifts do not lead to the emergence of the VCAM low dysfunctional phenotype that is uniquely present in geriatric muscle. We have added clarification to the text noting that molecular alterations arise progressively while the major phenotypic transition in VCAM1 expression and regenerative impairment occurs at the geriatric stage (pg.11, 238-244).

(6) The conclusion of senescence needs more support. Lines 218-226: p16 is elevated in VCAM-low/- cells, but drawing conclusions on senescence from 1-2 markers (mRNA) is insufficient. DQ Treatment: It's unclear how DQ alters cell composition in the absence of clear senescence markers (besides p16). Since DQ targets BCL-2/anti-apoptotic pathways, analyzing these signaling cascades is necessary. Line 255: The term "terminally senescent" is contradictory. These may be pre-senescent. It's also surprising DQ would target such cells, and further clarification is needed. Lines 307-313: Proposing a revised definition of senescence is premature. These cells may be pre-senescent, and multiple ways to senescence exist (replicative, stress-induced, etc.). Please clarify.

We agree with the reviewer that the term 'terminally senescent' may be premature and potentially contradictory. Although p16 is elevated in this population, we acknowledge that one or two mRNA markers are insufficient to establish bona fide senescence, and that multiple senescence programs exist, including replicative, stress-induced, and mitochondrial-associated pathways. We have revised this to 'senescent-like' throughout the manuscript to better reflect the complexity of this state. Also, although beyond the scope of this study, we now emphasize that future studies incorporating additional senescence markers, functional assays, and lineage tracing will be required to determine the precise senescence status of VCAM-low MuSCs (pg.17-18, lines 381-392).

Regarding DQ treatment, we agree that DQ is not selective for senescent cells, as it targets BCL-2–related survival pathways. The reduction of VCAM-low cells after DQ treatment therefore indicates increased dependence on survival signaling in this population rather than providing direct evidence of senescence. We have revised the text to clarify this interpretation (pg.12-13, lines 270-278).

(7) Figure 5C: The Pax7+ cells appear interstitial rather than sublaminar. This raises questions about the specificity of staining. Providing lower-magnification images with these as insets may help.

We thank the reviewer for this helpful comment. We agree that the high-magnification image in Figure 5C may give the impression that Pax7⁺ cells are interstitial due to the limited field of view. We regret to inform the reviewer that low-magnification images for this sample are not available as these images were obtained via confocal imaging where we only recorded areas of interest. Therefore, we are unable to provide an additional panel at this time and we hope the reviewer understand.

(8) CD63 and CD200 expression on Pax7-YFP traced cells. Figure 5: YFP-traced geriatric MuSCs co-stained for CD63 and CD200 are essential. Current data only show expression in Young traced cells. It's crucial to confirm whether protein/surface expression persists in geriatric YFP+ (traced) cells. The current Figure 5 F does not appear to include YFP tracing for geriatrics.

We thank the reviewer for highlighting the importance of confirming CD63 and CD200 expression specifically in Pax7-YFP traced MuSCs from geriatric muscle. The datasets shown in Figure 5F were generated from wild-type C57BL/6 mice using a standard MuSC gating strategy rather than Pax7-YFP animals. All geriatric Pax7-YFP mice available for this study were exhausted during earlier experiments, and additional tissue is not available for new co-staining or FACS analyses. We now state this technical limitation in the manuscript and clarify that the geriatric CD63/CD200 data were obtained from conventionally isolated MuSC populations rather than YFP-traced cells (pg.18-19, lines 407-416).

Minor points:

(1) Please show the outliers in addition to the concentric circles. Figures 1B, C, and F are examples, but this should be addressed throughout.

Outliers have been added where applicable.

(2) Figure 2C: Was a significance test performed between the 5 dpi and "geri" fractions?

We thank the reviewer for this important point. We have now performed the requested statistical comparison between the 5 dpi fraction and the geriatric VCAM1-defined subpopulations using the same analysis framework applied in Figure 2 (Kruskal–Wallis test followed by Dunn’s multiple comparisons).

While 5 dpi MuSCs differed significantly from young MuSCs (adjusted p = 0.0139), the comparisons between 5 dpi and each geriatric subgroup (VCAM-high, -mid, and -low) did not reach statistical significance after correction for multiple testing (adjusted p = 0.17, 0.15, and 0.17, respectively). These results have been added to the revised Figure 2C corresponding figure legend (pg. 36, lines 777-780).

Importantly, we now clarify in the text that although 5 dpi muscles display a prominent increase in VCAM1-high cells at the population level, this increase does not statistically exceed the variability observed within geriatric subpopulations under the conservative non-parametric testing framework used.

(3) Line 155: The phrase "Surprisingly, all clusters mapped to quiescent clusters" is misleading; this is expected given the population type.

We thank the reviewer for this helpful comment. We have revised the sentence to remove the misleading wording and now describe the observation more accurately (pg. 8 lines 180-181).

(4) Line 211: The figure notation should be corrected from Figure S4E to Figure S3E.

We apologize for this error. We have fixed the figure notation for Figure S4E to S3E (pg. 11, line 247).

(5) Line 216: "All of which" seems overstated. Many populations share similar profiles with minor differences.

We appreciate the reviewer’s comment. We agree that the phrase “all of which” overstated the degree of divergence among clusters. We have revised the wording to more accurately reflect the data (pg. 11-12, lines 252-253).

(6) Line 270: The notations for panels D, E, and F need to be updated to match the figure. Panel "H" is not indicated in Figure 5.

We apologize for this error. We have fixed the figure notations accordingly (pg. 15, lines 326-336).

eLife Assessment

This study illustrates a valuable application of BID-seq to bacterial RNA, allowing transcriptome-wide mapping of pseudouridine modifications across various bacterial species. The evidence presented includes solid data and analyses that would benefit from additional experimental validation. The work will interest a specialized audience involved in RNA biology.

Reviewer #1 (Public review):

Summary:

The manuscript by Xu et al. reported base-resolution mapping of RNA pseudouridylation in five bacterial species, utilizing recently developed BID-seq. They detected pseudouridine (Ψ) in bacterial rRNA, tRNA, and mRNA, and found growth phase-dependent Ψ changes in tRNA and mRNA. They then focused on mRNA and conducted comparative analysis of Ψ profiles across different bacterial species. Finally, they developed a deep learning model to predict Ψ sites based on RNA sequence and structure.

Strengths:

This is the first comprehensive Ψ map across multiple bacterial species, and systematically reveals Ψ profiles in rRNA, tRNA, and mRNA under exponential and stationary growth conditions. It provides a valuable resource for future functional studies of Ψ in bacteria.

Weaknesses:

Ψ is highly abundant on non-coding RNA such as rRNA and rRNA, while its level on mRNA is very low. The manuscript focuses primarily on Ψ on mRNA, which is prone to false positives. Many conclusions in the manuscript are speculative, based solely on the sequencing data, but not supported by additional experiments.

Reviewer #2 (Public review):

Summary:

In this study, Xu et al. present a transcriptome-wide, single-base resolution map of RNA pseudouridine modifications across evolutionarily diverse bacterial species using an adapted form of BID-Seq. By optimizing the method for bacterial RNA, the authors successfully mapped modifications in rRNA, tRNA, and, importantly, mRNA across both exponential and stationary growth phases. They uncover evolutionarily conserved Ψ motifs, dynamic Ψ regulation tied to bacterial growth state, and propose functional links between pseudouridylation and bacterial transcript stability, translation, and RNA-protein interactions. To extend these findings, they develop a deep learning model that predicts pseudouridine sites from local sequence and structural features.

Strengths:

The authors provide a valuable resource: a comprehensive Ψ atlas for bacterial systems, spanning hundreds of mRNAs and multiple species. The work addresses a gap in the field - our limited understanding of bacterial epitranscriptomics, by establishing both the method and datasets for exploring post-transcriptional modifications.

Weaknesses:

The main limitation of the study is that most functional claims (i.e. translation efficiency, mRNA stability, and RNA-binding protein interactions) are based on correlative evidence. While suggestive, these inferences would be significantly strengthened by targeted perturbation of specific Ψ synthases or direct biochemical validation of proposed RNA-protein interactions (e.g., with Hfq). Additionally, the GNN prediction model is a notable advance.

Reviewer #3 (Public review):

Summary:

This study aimed to investigate pseudouridylation across various RNA species in multiple bacterial strains using an optimized BID-seq approach. It examined both conserved and divergent modification patterns, the potential functional roles of pseudouridylation, and its dynamic regulation across different growth conditions.

Strengths:

The authors optimized the BID-seq method and applied this important technique to bacterial systems, identifying multiple pseudouridylation sites across different species. They investigated the distribution of these modifications, associated sequence motifs, their dynamics across growth phases, and potential functional roles. These data are of great interest to researchers focused on understanding the significance of RNA modifications, particularly mRNA modifications, in bacteria.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

The manuscript by Xu et al. reported base-resolution mapping of RNA pseudouridylation in five bacterial species, utilizing recently developed BID-seq. They detected pseudouridine (Ψ) in bacterial rRNA, tRNA, and mRNA, and found growth phase-dependent Ψ changes in tRNA and mRNA. They then focused on mRNA and conducted a comparative analysis of Ψ profiles across different bacterial species. Finally, they developed a deep learning model to predict Ψ sites based on RNA sequence and structure.

This is the first comprehensive Ψ map across multiple bacterial species, and systematically reveals Ψ profiles in rRNA, tRNA, and mRNA under exponential and stationary growth conditions. It provides a valuable resource for future functional studies of Ψ in bacteria.

We thank Reviewer 1 for the supportive and positive comments, particularly for highlighting the novelty and value of our comprehensive pseudouridine landscapes across multiple bacterial species as a valuable resource for the scientific community.

Ψ is highly abundant on non-coding RNA such as rRNA and tRNA, while its level on mRNA is very low. The manuscript focuses primarily on mRNA, which raises questions about the data quality and the rigor of the analysis. Many conclusions in the manuscript are speculative, based solely on the sequencing data but not supported by additional experiments.

We appreciate the insightful comments of Reviewer 1. We fully agree that Ψ is highly abundant on rRNA and tRNA, while its fractions on mRNA are generally lower. Ψ is highly conserved at specific positions in rRNA and tRNA, such as Ψ within tRNA T‑arm (position 55), where it plays essential roles in tRNA structural folding, tRNA stability, and mRNA translation, across plants, mammals, and bacteria[1–3]. However, most Ψ sites in mRNA exhibit lower fractions compared to rRNA and tRNA. This phenomenon is also widely observed in HeLa cell mRNA and plant mRNA, as evidenced by bisulfite-induced deletion sequencing and 2-bromoacrylamide-assisted cyclization sequencing[3–5]. In bacteria, the modifications on mRNA are harder to map and quantify, due to its low abundance in total RNA and difficulty in bacterial rRNA removal. This highlights the significance of our study.

To prove our data quality and analytical rigor, we first present the most convincing sites in bacteria, as benchmark sites. Specifically, we detected 9 out of 10 known conserved pseudouridine (Ψ) sites in E. coli across two biological replicates [6], displaying notable modification fraction. Ψ516 site in E. coli 16S rRNA, which serves as a benchmark site, consistently exhibited a high modification fraction (~100%) under multiple growth conditions, underscoring the robustness of our method. In other strains, we also observed conserved 16S rRNA Ψ sites.

To further demonstrate strong reproducibility and sensitivity. We selected three positive Ψ sites from two independent biological replicates for experimental validation, alongside one negative control site, using pseU‑TRACE method[6]. Ct values were first normalized to the corresponding Ct value of the negative control site, and the treated samples were then further normalized to their corresponding input controls (new Supplementary Fig. 2e).

Four Ψ sites were tested with pseU‑TRACE: Ψ site at position 944 on 23S rRNA, a negative control site located within guaA gene, a Ψ site within clpV1 gene, and an intergenic Ψ site located between guaA and guaB genes. We successfully validated these Ψ sites in P. aeruginosa. The detailed pseU‑TRACE experimental procedures and corresponding data figures have been added to the revised manuscript, in either Results or Methods sections (Line 171-175, 594–617).

Previous transcriptome-wide mapping of Ψ have primarily relied on CMC-based methods to induce RT truncation signatures at the modified sites, exhibiting a limited Ψ detection sensitivity caused by low labeling efficiency[5]. In contrast, BID-seq method used in this study provides substantially higher sensitivity of Ψ detection, particularly the low-stoichiometry Ψ sites within mRNA. The high reliability and quantitative performance of BID-seq have been extensively validated in prior work using mammalian cells and synthetic Ψ-containing oligonucleotides[4].

To further ensure robustness and minimize false positives—when identifying low-level mRNA Ψ sites through bioinformatic analysis—we have applied stringent and uniform filtration criteria to all candidate sites on mRNA (new Supplementary Table 1):

(1) Total sequencing coverage >20 reads in both ‘Treated’ (BID-seq; Σd_t > 20) and ‘Input’ libraries (Σd_i > 20);

(2) An average deletion count >5 in ‘Treated’ libraries;

(3) An average modification fraction >0.02 (2%) in ‘Treated’ libraries;

(4) A deletion ratio in ‘Treated’ libraries at least two-fold higher than that in ‘Input’ libraries.

Sites with a Ψ stoichiometry >0.5 (50%) were classified as highly modified. These filtration criteria have now been explicitly described in Methods section (Lines 739–745). We strictly adhered to these Ψ site identification standards, leading to all subsequent analysis and functional studies.

Finally, to address concerns regarding reproducibility, we calculated mRNA Ψ site overlap and correlation of Ψ fractions, between two biological replicates, which has been presented in (new Supplementary Fig. 2a,d).

Overall, we have revised the manuscript to clarify these methodological strengths, and validate mRNA Ψ detection. We also tone down all speculative conclusions, with more clear linkage to the actual sequencing data, which await future functional validation.

Reviewer #2 (Public review):

Summary:

In this study, Xu et al. present a transcriptome-wide, single-base resolution map of RNA pseudouridine modifications across evolutionarily diverse bacterial species using an adapted form of BID-Seq. By optimizing the method for bacterial RNA, the authors successfully mapped modifications in rRNA, tRNA, and, importantly, mRNA across both exponential and stationary growth phases. They uncover evolutionarily conserved Ψ motifs, dynamic Ψ regulation tied to bacterial growth state, and propose functional links between pseudouridylation and bacterial transcript stability, translation, and RNA-protein interactions. To extend these findings, they develop a deep learning model that predicts pseudouridine sites from local sequence and structural features.

Strengths:

The authors provide a valuable resource: a comprehensive Ψ atlas for bacterial systems, spanning hundreds of mRNAs and multiple species. The work addresses a gap in the field - our limited understanding of bacterial epitranscriptomics, by establishing both the method and datasets for exploring post-transcriptional modifications.

We thank Reviewer 2 for the supportive and positive comments. We appreciate the reviewer’s recognition of the novelty and value of our work in providing a comprehensive pseudouridine atlas across multiple bacterial species.

Weaknesses:

The main limitation of the study is that most functional claims (i.e., translation efficiency, mRNA stability, and RNA-binding protein interactions) are based on correlative evidence. While suggestive, these inferences would be significantly strengthened by targeted perturbation of specific Ψ synthases or direct biochemical validation of proposed RNA-protein interactions (e.g., with Hfq).

We thank Reviewer 2 for the constructive feedback. We fully agree that our functional claims regarding translation efficiency, mRNA stability, and RNA-binding protein interactions rely primarily on correlative evidence from existing datasets rather than a direct experimental validation. We agree that the perturbation of specific pseudouridine synthases and direct biochemical validation of proposed RNA-protein interactions (for instance, Hfq) would substantially strengthen the conclusions on bacterial Ψ function. In Discussion section, we have added a discussion on this limitation of our current study (Line 517–523). Considering the scope of our current work, we anticipate such validation experiments in future research.

Additionally, the GNN prediction model is a notable advance, but methodological details are insufficient to reproduce or assess its robustness.

In response to methodological concerns regarding our pseU_GNN prediction model, we have undertaken substantial improvements to address these issues comprehensively. We have updated the complete codebase on GitHub (https://github.com/Dylan-LT/pseU_NN.git) with comprehensive documentation and a user-friendly prediction tool specifically designed for Ψ site prediction across the four bacterial species examined in this study.

We further systematically evaluated multiple neural network architectures and implemented critical architectural refinements. Specifically, we incorporated bidirectional LSTM (bid-LSTM) layers upstream of the transformer block to more effectively capture sequential dependencies and contextual information in RNA sequences. This enhanced architecture demonstrates substantially improved predictive performance, achieving an AUC-ROC of 0.89 on independent test datasets using 41-nucleotide input sequences (new Figure 6).

We have revised Figure 6 and Supplementary Fig. 7, along with their corresponding content and figure legends (Lines 428-430, 434–436, 440-447, 1065-1073), to reflect these architectural improvements and performance enhancements. We have detailed the methods part (Lines 679–708), including model architecture, validation methods and evaluation score calculation. Additionally, we have provided detailed documentation of the evaluation score calculation methodology to ensure reproducibility and transparency.

Reviewer #3 (Public review):

Summary:

This study aimed to investigate pseudouridylation across various RNA species in multiple bacterial strains using an optimized BID-seq approach. It examined both conserved and divergent modification patterns, the potential functional roles of pseudouridylation, and its dynamic regulation across different growth conditions.

Strengths:

The authors optimized the BID-seq method and applied this important technique to bacterial systems, identifying multiple pseudouridylation sites across different species. They investigated the distribution of these modifications, associated sequence motifs, their dynamics across growth phases, and potential functional roles. These data are of great interest to researchers focused on understanding the significance of RNA modifications, particularly mRNA modifications, in bacteria.

We thank Reviewer 3 for the supportive and positive assessment. We are particularly grateful for the reviewer’s acknowledgment of the value of our analyses on modification distribution, sequence motifs, growth‑phase dynamics, and potential functional roles, which we hope will be of broad interest to researchers studying bacterial RNA modifications, particularly mRNA Ψ.

Weaknesses:

(1) The reliability of BID-seq data is questionable due to a lack of experimental validations.

We thank Reviewer 3 for the constructive feedback. We have undertaken comprehensive revisions to address the concerns regarding manuscript structure and information organization. We have incorporated pseU‑TRACE experiments and data quality results to provide orthogonal validation of Ψ detection, strengthening the robustness of our work.

Here we copied the response in Reviewer 1 section:

“To further demonstrate strong reproducibility and sensitivity. We selected three positive Ψ sites from two independent biological replicates for experimental validation, alongside one negative control site, using pseU‑TRACE method[6]. Ct values were first normalized to the corresponding Ct value of the negative control site, and the treated samples were then further normalized to their corresponding input controls (new Supplementary Fig. 2e ).

Four Ψ sites were tested with pseU‑TRACE: Ψ site at position 944 on 23S rRNA, a negative control site located within guaA gene, a Ψ site within clpV1 gene, and an intergenic Ψ site located between guaA and guaB genes. We successfully validated these Ψ sites in P. aeruginosa. The detailed pseU‑TRACE experimental procedures and corresponding data figures have been added to the revised manuscript, in either Results or Methods sections (Line 171-175, 594–617).”

(2) The manuscript is not well-written, and the presented work shows a major lack of scientific rigor, as several key pieces of information are missing.

We thank Reviewer 3 for the suggestion. We restructured the main text to present a clearer logical flow, with key objectives (Lines 83–96, 171–175, 428–447, 517-523) explicitly stated in Introduction section and Conclusions section, with data figures directly addressing these stated aims (Supplementary Fig. 1–7).

(3) The manuscript's organization requires significant improvement, and numerous instances of missing or inconsistent information make it difficult to understand the key objectives and conclusions of the study.

We thank Reviewer 3 for the constructive feedback. All supplementary figures have been updated with detailed figure legend, methodology description, and consistent formatting. We also systematically inspected and resolved instances of missing or inconsistent information throughout the main text and supplementary materials (Supplementary Fig. 1–7; Supplementary Table 1). To enhance computational reproducibility, we have updated our GitHub repository with well-documented code and developed user-friendly prediction tools for Ψ identification across the four bacterial species examined in this study.

(4) The rationale for selecting specific bacterial species is not clearly explained, and the manuscript lacks a systematic comparison of pseudouridylation among these species.

We thank Reviewer 3 for the constructive feedback. The bacterial species analyzed in this study were selected based on both diversity and significance. K. pneumoniae, B. cereus, and P. aeruginosa are top model human pathogens responsible for a wide range of clinically significant infections, yet transcriptome-wide pseudouridylation has not been systematically explored in these organisms[7–9]. P. syringae, the most important model plant pathogen, was included to extend our analysis beyond human pathogens and to examine Ψ modification in a distinct ecological and evolutionary context, where epitranscriptomic regulation also remains poorly characterized[10]. Importantly, the selected species represent both Gram-positive (B. cereus) and Gram-negative (K. pneumoniae, P. aeruginosa, and P. syringae) bacteria, spanning substantial differences in genome size, GC content, lifestyle, and pathogenic strategies. This diversity enables a comparative framework for examining conserved and species-specific pseudouridylation patterns across bacterial lineages.

To address the reviewer’s concern, we have revised the manuscript to more clearly articulate the rationale for species selection and have added a comparative analysis highlighting similarities and differences in Ψ site distribution and modification levels among these species (Lines 83–96). We systematically compared Ψ-carrying motif for analyzing sequence context of 10 bases flanking Ψ sites in bacterial mRNA, with Supplementary Fig. 4 added.

Reference

(1) Leppik, M., Liiv, A. & Remme, J. Random pseuoduridylation in vivo reveals critical region of Escherichia coli 23S rRNA for ribosome assembly. Nucleic Acids Res. 45, (2017).

(2) Rajan, K. S. et al. A single pseudouridine on rRNA regulates ribosome structure and function in the mammalian parasite Trypanosoma brucei. Nat. Commun. 14, (2023).

(3) Li, H. et al. Quantitative RNA pseudouridine maps reveal multilayered translation control through plant rRNA, tRNA and mRNA pseudouridylation. Nat. Plants 11, 234–247 (2025).

(4) Dai, Q. et al. Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution. Nat. Biotechnol. 41, 344–354 (2023).

(5) Xu, H. et al. Absolute quantitative and base-resolution sequencing reveals comprehensive landscape of pseudouridine across the human transcriptome. Nat. Methods 21, 2024–2033 (2024).

(6) Fang, X. et al. A bisulfite-assisted and ligation-based qPCR amplification technology for locus-specific pseudouridine detection at base resolution. Nucleic Acids Res. 52, (2024).

(7) Wyres, K. L., Lam, M. M. C. & Holt, K. E. Population genomics of Klebsiella pneumoniae. Nature Reviews Microbiology vol. 18 Preprint at https://doi.org/10.1038/s41579-019-0315-1 (2020).

(8) Kerr, K. G. & Snelling, A. M. Pseudomonas aeruginosa: a formidable and ever-present adversary. Journal of Hospital Infection vol. 73 Preprint at https://doi.org/10.1016/j.jhin.2009.04.020 (2009).

(9) Ehling-Schulz, M., Lereclus, D. & Koehler, T. M. The Bacillus cereus Group: Bacillus Species with Pathogenic Potential . Microbiol. Spectr. 7, (2019).

(10) Xin, X. F., Kvitko, B. & He, S. Y. Pseudomonas syringae: What it takes to be a pathogen. Nature Reviews Microbiology vol. 16 Preprint at https://doi.org/10.1038/nrmicro.2018.17 (2018).

eLife Assessment

The study curated a set of Liver X receptor ligands that may guide the design of future drugs that activate the Liver X receptor as potential therapeutics for cardiovascular disease, Alzheimer's and type 2 diabetes, without inducing mechanisms that promote fat/lipid production. The authors also present improved multiplexed precision CRT (coregulator TR-FRET) and cellular assays which allows measurement of ligand potencies to displace corepressors in the presence of coactivators, which cannot be achieved in a regular CRT assay. This makes the evidence presented compelling as it stretches beyond the current state-of-the-art, and these important findings are expected to have practical implications in many sub-fields and remain of interest to scientists working in cell and molecular biology, drug discovery, medicinal chemistry and pharmacology.

Reviewer #1 (Public review):

Summary:

This important study functionally profiled ligands targeting the LXR nuclear receptors using biochemical assays in order to classify ligands according to pharmacological functions. Overall, the evidence is solid, but nuances in the reconstituted biochemical assays and cellular studies and terminology of ligand pharmacology limit the potential impact of the study. This work will be of interest to scientists interested in nuclear receptor pharmacology.

Strengths:

(1) The authors rigorously tested their ligand set in CRTs for several nuclear receptors that could display ligand-dependent cross-talk with LXR cellular signaling and found that all compounds display LXR selectivity when used at ~1 µM.

(2) The authors tested the ligand set for selectivity against two LXR isoforms (alpha and beta). Most compounds were found to be LXRbeta-specific.

(3) The authors performed extensive LXR CRTs, performed correlation analysis to cellular transcription and gene expression, and classification profiling using heatmap analysis-seeking to use relatively easy-to-collect biochemical assays with purified ligand-binding domain (LBD) protein to explain the complex activity of full-length LXR-mediated transcription.

Comments on revisions:

The authors have addressed the comments from the prior round of review with care. I find the revised manuscript significantly strengthened.

Reviewer #2 (Public review):

Summary:

In this manuscript by Laham and co-workers, the authors profiled structurally diverse LXR ligands via a coregulator TR-FRET (CRT) assay for their ability to recruit coactivators and kick off corepressors, while identifying coregulator preference and LXR isoform selectivity.

The relative ligand potencies measured via CRT for the two LXR isoforms were correlated with ABCA1 induction or lipogenic activation of SRE depending on cellular contexts (i.e, astrocytoma or hepatocarcinoma cells). While these correlations are interesting, there is some leg room to improve the quantitative presentation of these correlations. Finally, the CRT signatures were correlated with the structural stabilization of the LXR: coregulator complexes. In aggregate, this study curated a set of LXR ligands with disparate agonism signatures that may guide the design of future nonlipogenic LXR agonists with potential therapeutic applications for cardiovascular disease, Alzheimer's and type 2 diabetes, without inducing mechanisms that promote fat/lipid production.

Strengths:

This study has many strengths, from curating an excellent LXR compound set, to the thoughtful design of the CRT and cellular assays. The design of a multiplexed precision CRT (pCRT) assay that detects corepressor displacement as a function of ligand-induced coactivator recruitment is quite impressive as it allows measurement of ligand potencies to displace corepressors in the presence of coactivators, which cannot be achieved in a regular CRT assay that looks at coactivator recruitment and corepressor dissociation in separate experiments.

Comments on revisions:

These weaknesses have been satisfactorily addressed by the authors in the revised preprint.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This important study functionally profiled ligands targeting the LXR nuclear receptors using biochemical assays in order to classify ligands according to pharmacological functions. Overall, the evidence is solid, but nuances in the reconstituted biochemical assays and cellular studies and terminology of ligand pharmacology limit the potential impact of the study. This work will be of interest to scientists interested in nuclear receptor pharmacology.

Strengths:

(1) The authors rigorously tested their ligand set in CRTs for several nuclear receptors that could display ligand-dependent cross-talk with LXR cellular signaling and found that all compounds display LXR selectivity when used at ~1 µM.

(2) The authors tested the ligand set for selectivity against two LXR isoforms (alpha and beta). Most compounds were found to be LXRbeta-specific.

The majority of ligands were found to be LXRβ-selective; however, examples of non-selective and LXRα-selective ligands were identified. It should be noted that this is a small compound set of literature ligands with reasonable structural diversity.

(3) The authors performed extensive LXR CRTs, performed correlation analysis to cellular transcription and gene expression, and classification profiling using heatmap analysis-seeking to use relatively easy-to-collect biochemical assays with purified ligand-binding domain (LBD) protein to explain the complex activity of full-length LXR-mediated transcription.

Weaknesses:

(1) The descriptions of some observations lack detail, which limits understanding of some key concepts.

Changes to the submitted manuscript hopefully add clarity. Several observations reinforce aspects of the literature and are a corollary of the observation that the majority of ligands with agonist activity more strongly stabilize/induce coactivator-bound complexes with LXRβ. This results in general LXRβ selectivity for agonists and also more variability in the response of LXRα to different ligand chemotypes. The most significant observations were for partial agonists that stabilize corepressor binding, in particular of the complex with LXRα.

(2) The presence of endogenous NR ligands within cells may confound the correlation of ligand activity of cellular assays to biochemical assay data.

This is generally a confounding factor for ligands with apparent antagonist activity and is a source of ambiguity in designating inverse agonists across the nuclear receptor research field. Theoretically, this could also impact weak and partial agonists; however, this requires further study.

(3) The normalization of biochemical assay data could confound the classification of graded activity ligands.

Normalization to TO (100%) and vehicle (0%) is applied to most data. It is not clear how this confounds data interpretation. TO is a very reliable and reproducible agonist without significant bias towards LXR isoforms.

(4) The presence of >1 coregulator peptide in the biplex (n=2 peptides) CRT (pCRT) format will bias the LBD conformation towards the peptide-bound form with the highest binding affinity, which will impact potency and interpretation of TR-FRET data.

Multiplex assays must be optimized to balance binding affinity of the coregulator peptides (bear in mind these are somewhat-artificial small peptide constructs that are hoped to reflect binding of the much larger coregulator protein itself). Since the dominant theory of NR tissue-selectivity is based on the cellular availability (read concentration) of coregulators, this balance exists in a cellular context.

(5) Correlation graphical plots lack sufficient statistical testing.

Correlations are now supported by statistical data and we have added hierarchical clustering analysis.

(6) Some of the proposed ligand pharmacology nomenclature is not clear and deviates from classifications used currently in the field (e.g., hard and soft antagonist; weak vs. partial agonist, definition of an inverse agonist that is not the opposite function to an agonist).

Classifications used currently in the field vary from one NR to another and the use of partial and inverse agonist, in particular, is usually qualitative, unclear, and often misleading. We expand on these classifications with respect to our use of labels to classify pCRT response to LXR ligands. In agreement with the reviewer, we have replaced IA (inverse agonist) with (RA) reverse agonist as a label specifically associated with pCRT analysis.

Reviewer #2 (Public review):

Summary:

In this manuscript by Laham and co-workers, the authors profiled structurally diverse LXR ligands via a coregulator TR-FRET (CRT) assay for their ability to recruit coactivators and kick off corepressors, while identifying coregulator preference and LXR isoform selectivity.

The relative ligand potencies measured via CRT for the two LXR isoforms were correlated with ABCA1 induction or lipogenic activation of SRE, depending on cellular contexts (i.e, astrocytoma or hepatocarcinoma cells). While these correlations are interesting, there is some leeway to improve the quantitative presentation of these correlations. Finally, the CRT signatures were correlated with the structural stabilization of the LXR: coregulator complexes. In aggregate, this study curated a set of LXR ligands with disparate agonism signatures that may guide the design of future nonlipogenic LXR agonists with potential therapeutic applications for cardiovascular disease, Alzheimer's, and type 2 diabetes, without inducing mechanisms that promote fat/lipid production.

Strengths:

This study has many strengths, from curating an excellent LXR compound set to the thoughtful design of the CRT and cellular assays. The design of a multiplexed precision CRT (pCRT) assay that detects corepressor displacement as a function of ligand-induced coactivator recruitment is quite impressive, as it allows measurement of ligand potencies to displace corepressors in the presence of coactivators, which cannot be achieved in a regular CRT assay that looks at coactivator recruitment and corepressor dissociation in separate experiments.

Weaknesses:

I did not identify any major weaknesses.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Page 2. "The endogenous ligands ... activate LXR via canonical or alternate mechanisms." What is an alternate mechanism?

Small modifications to Fig. 1 caption identify a mechanism alternative to the canonical mechanism: LXR transcriptional complexes are RXR heterodimers that can be activated by a canonical mechanism of coregulator recruitment or an alternative de-repression mechanism

(2) Page 5: "Notably, the 25 amino acid SRC-1 peptide is the only coactivator tested for LXR binding that has the fluorophore remote from the coactivator peptide." What does this mean, and could it influence the results?

The sentence has been expanded to clarify the meaning. Notably, the 25 amino acid SRC-1 peptide is the only coactivator, amongst those tested for LXR binding, which has the fluorophore remote from the coactivator peptide: i.e., the only coactivator tested that uses a fluorophore labeled anti-tag antibody to bind the tagged coactivator rather than a fluorophore-labeled coactivator. In methods based on fluorescent tags (CRT, TR-FRET, fluorescence polarization, etc.), a fluorophore that interacts directly with the receptor can generate a maximal signal that differs depending on this interaction: i.e. the identity of the coregulator used in CRT can influence the response. As seen in Figures 6 and S6, maximal response is dependent on ligand and coregulator.

(3) Page 5: "The [CRT] assay measures the EC50 for coactivator recruitment, a measure of ligand binding affinity." The dose-dependent activity in the CRT assays is more classically defined as a functional "potency", not "affinity".

The text is changed to remove “measure of affinity”: The assay measures the ligand-dependent EC₅₀ for ligand-induced coactivator recruitment to LXR; the affinity of the ligand for the LXR:coregulator complex contributes to this potency

(4) Page 5: "Perhaps surprisingly, considering the description of multiple LXR ligands as partial agonists, most agonists studied gave maximal response at the same level as T0, behaving as full agonists." Can the authors speculate as to why partial agonist activity is not observed in their CRT assays when it has been observed in CRT assays for other nuclear receptors?

This section has been reworded and please note the apparent partial agonist activity observed in CRT assays for multiple coactivators as shown in Figures 6 and S6 (also see (2) above). Although many LXR ligands have been reported to display partial agonist activity, most agonists studied in this specific biotin-SRC-1 CRT assay, gave maximal response at the same level as T0, behaving as full agonists.

(5) Page 5: "Conformational cooperativity of LBD residues beyond these two amino acids leads to different conformations of Leu274 and Ala275 that generally favor ligand binding to LXRβ." Where are these residues located? Why are they important?

We have simplified this paragraph that introduces the interesting observations and interpretation of Ding et al. to illustrate potential contributions to isoform selectivity: The ligand binding pockets of the two LXR isoforms differ by only one amino acid located in helix-3. (H3: LXRα-Val263 and LXRβ-Ile277) Interestingly, correction of this difference by mutation of these residues to alanine (V263A and I277A) was observed to lower, but not to ablate isoform selectivity in reporter assays.[108] Supported by modeling studies, this observation by Ding et al. led to the suggestion that conformational cooperativity of LBD residues beyond these two amino acids, generally favors ligand binding to LXRβ. Therefore, most reported ligands, including those examined in the current work, are LXRβ-selective or non-selective.

(6) Some correlation plots are described to show "poor" correlations without showing the underlying statistical fits. All correlation plots should show Pearson and Spearman correlation coefficients and p-values within the figures.

This section of the manuscript has been completely reworked with full correlation analysis and stats . There is no substantive change in data interpretation.

(7) The normalization of TR-FRET data could introduce undesired bias when comparing activities. The methods section should provide more details about normalization of CRT data, including stating whether the control compounds' activity data were collected on the same CRT 384-well plate on the same day, or different plates, or different days, etc.

This is now clarified in SI materials and methods section. In-plate controls are always used.

(8) The authors describe their pCRT assay as "multiplex", whereas "biplex" might be more accurate, as they only used two peptides.

Biplex is commonly used referring to qPCR. Bio-Plex is a commercial version of an antibody assay. Duplex is obviously a term used in nucleic acid research. Therefore, multiplex is a simpler, more generic term that we feel is suitable and can be extended to add a third coregulator.

(9) The pCRT assays use the same peptide concentrations (200 nM). However, the peptides will have different affinities for the LBD, which may bias ligand-dependent pCRT profiles. The peptide that binds with higher affinity in the absence of ligand will bias the LBD conformation and impact ligand affinity. Can the authors comment on any limitations of the pCRT approach vs. a normal CRT? Did the authors perform any optimization to see if increasing peptide concentrations (>200 nM) or having different concentrations (e.g., 400 nM SRC1 and 200 nM NCorR2) influences the pCRT data, extracted parameters, correlations, etc.?

As we write in the Limitations section, our assays are focused on ligand-dependence, whereas other excellent studies focus more on coregulator-dependence. The length and affinity of peptide constructs varies and therefore it is important to “balance” corepressor and coactivator concentrations. The most important conclusions from our pCRT assays concern the ability of some ligands to stabilize corepressor binding in the monoplex CRT and the universal ability of coactivator complex stabilization to eject the corepressor in the multiplex assay. Furthermore, without measurements and correlations in “natural” cellular contexts, the CRT data obtained in cell-free conditions is somewhat artificial. We evaluated a range of peptide concentrations to assess signal-to-background and overall assay performance. Each new receptor added to the panel underwent rigorous optimization to establish robust and reliable assay conditions. This included identifying a suitable positive control for each receptor, determining the optimal coregulator selection and concentration, and refining other key parameters such as buffer composition and total well volume. The concentrations reported represent the optimized balance—producing a strong, reproducible signal without oversaturation or disproportionate contribution from any individual assay component.

(10) Page 11. The authors introduce a few ligand classification terms that are not standard in the field and unclear: "soft" vs. "hard" antagonist, "weak" vs. "partial" agonist, and their definition of an inverse agonist that, in classical pharmacologic terms, should have an opposite (inverse) function to an agonist. Furthermore, the presence of endogenous LXR ligands within cells may confound the correlation of ligand activity of cellular assays to biochemical assay data. See the following paper for an example of ligand-dependent classification and activation mechanisms when there are endogenous cellular ligands at play: https://elifesciences.org/articles/47172

The paragraph discussing nomenclature went through many iterations of terminology and a further paragraph was removed that discussed problems with ligand classification in the broader field of NR pharmacology: this has now been added back. We apologise for not citing the excellent Strutzenberg et al. paper on RORa pharmacology, which is now included. In this paper, Griffin and co-workers also use terms that are not standard in the field, such as “silent agonist”, which covers, in part, ligands that we describe as “weak agonists”. A standard, definitive lexicon of terms across NRs is unfortunately problematic. We have added 2 paragraphs:

The nomenclature for NR ligands often lacks precision and differs across NR classes. SERM (a subset of selective NR modulator) is used to describe varied families of ER ligands that show tissue-selective agonist and/or antagonist actions. Unfortunately, “partial agonist” is also widely used to describe SERMs, even though its use is usually pharmacologically incorrect and biased agonist may be a more accurate label.[124] The majority of reported ER ligands are SERMs, even some that cause ER degradation, because they are transcriptionally active. Consequently, the term “pure antagonist” (PA) has been used to differentiate transcriptionally null ligands[125]; although, pure antagonist/antiestrogen was originally introduced to describe antagonism of both AF1 and AF2 functions.[90]

Elegant work by Griffin’s team on RAR-related orphan receptor C (RORɣ) is interesting, because it used a combination of HDX-MS and CRT and defined categories of RORɣ ligands.[126] In addition to full agonist, “silent agonist” was introduced to include endogenous and synthetic partial agonists; although, by definition, partial agonists should antagonize full agonists. On the antagonist side of the spectrum, “active antagonist” was used to describe ligands that reduce cellular activity to baseline; and “inverse agonist” for ligands that reduce cellular transcription below baseline and induce recruitment of corepressors. Curiously, inverse agonist has almost never been used to describe ER ligands and is used frequently for other NR ligands, mostly for ligands that reduce transcription below baseline, without any evidence for corepressor recruitment. GSK2033 and SR9238 show inverse agonist activity in cells (Figs 3, 5); however, neither is capable of recruiting SMRT2 or NCOR2 to LXR (Fig. 7).

(11) Figure 9A and Figure S8. Could hierarchical clustering analysis be used to more rigorously compare the activities of the ligands?

We have now added hierarchical clustering analysis (Figs 4 S4). It should be noted that the value of such an analysis is much higher when the number of ligands is increased.

(12) How does cellular potency correlate to pCRT vs. CRT potencies? Does pCRT better explain cellular potency?

We have added this specific correlation (multiplex CRT vs. monoplex CRT).

(13) The authors should provide an SI table of parameters (potency values) used for correlation and heatmap analyses.

Tables have been added to SI accordingly.

Reviewer #2 (Recommendations for the authors):

This manuscript has many strengths, but can still be improved by addressing the following critiques:

(1) I am surprised the team did not find a ligand with a higher efficacy than T0. Please would you explain why T0 seems to have maxed out ligand efficacy for both LXRalpha and LXRbeta?

Several ligands gave superior efficacy to T0 in cell-based reporter assays and in CRT assays shown in Figures 6 and S6: AZ876, BE1218, and MK9 gave maximal response higher than that of T0.

(2) In the subsection, "Activity and isoform selectivity of LXR ligands", you mentioned that "The assay measures the EC50 for coactivator recruitment, a measure of ligand binding affinity." This is incorrect. EC50 is a measure of ligand potency, not affinity.

See Reviewer-1 (3)

(3) In Figure 3 it is unclear what was used to normalize the antagonist responses in Panel F. Also, I recommend changing the y-axis of Panel F to -100 to 50 to get a better view of the response.

This has been clarified: zero is vehicle control. Change to y-axis is made.

(4) In Figure 4, the correlation R-squared values should be presented as a Table to have a better qualitative assessment of the correlations. It is challenging to judge which correlations are better by relying only on visual inspection. I also recommend moving the two panels from Figure S3 to Figure 4 as panels E and F.

Extensive changes to Figure 4 have been made in response to this comment and that of Reviewer 1, who wanted these values in the figures: Reviewer-1 points (6) and (12).

(5) In Figure 5, the fold changes in panels G, H, and I could better be presented as a bar graph. Also, the cytotoxicity of ligands needs to be assessed. For instance, in BE1218, there is a sharp decrease in fold change going from ~1 uM to ~10 uM. This will also confirm if the downward trends for SR9238 and GSK2033 are "real" and not as a result of cells dying off at higher ligand concentrations.

Across our many studies on potent NR ligands, at concentrations above 3 uM, cell growth inhibition is observed. This is true for ER ligands, such as tamoxifen, with explanations in the literature including membrane disruption and low-affinity cytoplasmic binding proteins. We include cell viability measurements in Supplemental as a specific response to the reviewer’s query. There is no loss of cell viability in HepG2 cells.

(6) Several ligands induce recruitment of coactivators but with minimal ability to displace corepressors. Physiologically, what would be the expected effect of these ligands on LXR activity?\

We have defined such ligands from pCRT analysis as weak agonists (WA); however, pCRT shows WA ligands induce corepressor loss in the presence of coactivator. Depending on coregulator balance and isoform expression and the importance of the derepression mechanism in a specific cell context, WA ligands might be expected to be differentiated from SA (strong agonist) ligands.

(7) In the subsection, "synchronous coregulator recruitment by multiplex, precision CRT" you mentioned that "For LXRbeta, the correlation between SRC1 recruitment in monoplex and multiplexed CRT is good," but the data is not shown. I think it would be better to show this data for transparency.

See query (4) and Reviewer-1. Done.

(8) In Figure 9, Panel A, the heat map is quantitated as 0-150. Is this fold change? If so, add this label to the figure legend.

It is Normalized Response as %, which is now added.

(9) In Figure 9, Panel B, please explain why in all cases, CoA-bound LXR resides at a higher energy level than the CoR-bound, and the apo LXR is at a lower energy level than the CoA-bound protein. A coregulator-bound (holo) protein structure is generally a lower energy (more stable) structure than the unbound (apo) protein. The binding of a coregulator stabilizes the protein's conformation and shifts the equilibrium towards a more thermodynamically favorable state. Using the same argument, it does not make sense to me that the CoR-bound LXR is on the same energy level as the apo LXR.

This schema reflects our observations in pCRT. No signal was observed for coactivator-bound (holo) protein in the absence of ligand; whereas, a signal was observed for corepressor-bound (holo) protein in the absence of ligand. Therefore, the CoA-bound LXR is higher energy than apo-LXR (+ unbound CoA). Conversely, the signal for CoR-bound LXR can be reduced or increased by ligands, requiring the CoA-bound LXR to be of similar energy to apo-LXR (+ unbound CoR).

(10) In the Figure 9b caption, "measured at 1uM" pertains to the concentration of ligand or coregulator? This is unclear. You should report the concentration of both ligand and coregulator.

Clarified in caption.

(11) In Figure S4, signal for SR9238 shoot up to ~300 units for ligand concentrations >3 uM. Please explain what could have contributed to this anomalous activation and why this was moved to the Supplementary File and not shown in the main figure (Figure 5).

The HepG2-SRE assay is a nano-luc reporter assay, unlike the CCF-ABCA1 that is a firefly luciferase assay. There is substantial anecdotal evidence that furimazine/nano-luc is susceptible to stabilization enhancement. The RT-PCR data presented in Fig. 5 confirms that this is an artifact for some biphenyl sulfones.

eLife Assessment

This study presents results supporting a model that tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the stem cell niche and inhibit the differentiation of neighboring cells. The valuable findings show that GSC tumors often contain non-mutant cells whose differentiation is suppressed by the GSC tumorous cells. However, the evidence showing that the GSC tumors produce BMP ligands to suppress differentiation of non-mutant cells is incomplete due to concerns about the new HCR data.

Reviewer #1 (Public review):

Summary:

This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Fig. 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Fig. 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Fig. 2). They present data suggesting that in 73% of SGCs BMP signaling is low (assessed by dad-lacZ) (Fig. 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Fig. 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Fig. 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Fig. 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what in seen in the ovarian stem cell niche. This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Fig. 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Fig. 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Fig. 2). They present data suggesting that in 73% of SGCs BMP signaling is low (assessed by dad-lacZ) (Fig. 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Fig. 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Fig. 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Fig. 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what in seen in the ovarian stem cell niche.

Strengths:

(1) Use of an excellent and established model for tumorous cells in a stem cell microenvironment

(2) Powerful genetics allow them to test various factors in the tumorous vs non-tumorous cells

(3) Appropriate use of quantification and statistics

Weaknesses:

(1) What is the frequency of SGCs in nos>flp; bam-mutant tumors? For example, are they seen in every germarium, or in some germaria, etc or in a few germaria.

This concern was addressed in the rebuttal. The line number is 106, not line 103.

(2) Does the breakdown in clonality vary when they induce hs-flp clones in adults as opposed to in larvae/pupae?

This concern was addressed in the rebuttal. However, these statements are no on lines 331-335 but instead starting on line 339. Please be accurate about the line numbers cited in the rebuttal. They need to match the line numbers in the revised manuscript.

(3) Approximately 20-25% of SGCs are bam+, dad-LacZ+. Firstly, how do the authors explain this? Secondly, of the 70-75% of SGCs that have no/low BMP signaling, the authors should perform additional characterization using markers that are expressed in GSCs (i.e., Sex lethal and nanos).

The authors did not perform additional staining for GSC-enriched protein like Sex lethal and nanos.

(4) All experiments except Fig. 1I (where a single germarium with no quantification) were performed with nos-Gal4, UASp-flp. Have the authors performed any of the phenotypic characterizations (i.e., figures other than figure 1) with hs-flp?

In the rebuttal, the authors stated that they used nos>flp for all figures except for Fig. 1I. It would be more convincing for them to prove in Fig. 1 than there is not phenoytpic difference between the two methods and then switch to the nos>FLP method for the rest of the paper.

(5) Does the number of SGCs change with the age of the female? The experiments were all performed in 14-day old adult females. What happens when they look at young female (like 2-day old). I assume that the nos>flp is working in larval and pupal stages and so the phenotype should be present in young females. Why did the authors choose this later age? For example, is the phenotype more robust in older females? or do you see more SGCs at later time points?

The authors did not supply any data to prove that the clones were larger in 14-day-old flies than in younger flies. Additionally, the age of "younger" flies was not specified. Therefore, the authors did not satisfactorily answer my concern.

(6) Can the authors distinguish one copy of GFP versus 2 copies of GFP in germ cells of the ovary? This is not possible in the Drosophila testis. I ask because this could impact on the clonal analyses diagrammed in Fig. 4A and 4G and in 6A and B. Additionally, in most of the figures, the GFP is saturated so it is not possible to discern one vs two copies of GFP.

In the rebuttal, the authors stated that they cannot differential one vs two copies of GFP. They used other clone labeling methods in Fig. 4 and 6. I think that the authors should make a statement in the manuscript that they cannot distinguish one vs two copies of GFP for the record.

(7) More evidence is needed to support the claim of elevated Dpp levels in bam or bgcn mutant tumors. The current results with dpp-lacZ enhancer trap in Fig 5A,B are not convincing. First, why is the dpp-lacZ so much brighter in the mosaic analysis (A) than in the no-clone analysis (B); it is expected that the level of dpp-lacZ in cap cells should be invariant between ovaries and yet LacZ is very faint in Fig. 5B. I think that if the settings in A matched those in B, the apparent expression of dpp-lacZ in the tumor would be much lower and likely not statistically significantly. Second, they should use RNA in situ hybridization with a sensitive technique like hybridization chain reactions (HCR) - an approach that has worked well in numerous Drosophila tissues including the ovary.

The HCR FISH in Fig.5 of the revised manuscript needs an explanation for how the mRNA puncta were quantified. Currently, there is no information in the methods. What is meant but relative dpp levels. I think that the authors should report in and unbiased manner "number" of dpp or gbb puncta in TFs. For the germaria, I think that they should report the number of puncta of dpp or gbb divide by the total area in square pixels counted. Additionally, the background fluorescence is noticeably much higher in bamBG/delta86 germaria, which would (falsely) increase the relative intensity of dpp and gbb in bam mutants. Although, I commend the authors for performing HCR FISH, these data are still not convincing to me.

(8) In Fig 6, the authors report results obtained with the bamBG allele. Do they obtain similar data with another bam allele (i.e., bamdelta86)?

The authors did not try any experiments with the bamdelta86 allele, despite this allele being molecularly defined, where the bamBG allele is not defined.

Reviewer #2 (Public review):

In the current version, Zhang et al. have made substantial improvements to the manuscript. It is now easier to read, and the data are more solid compared with the previous version, supporting their conclusion that tumor GSCs secrete stemness factors (BMPs and Dpp) to suppress the differentiation of neighboring wild-type GSCs. This study should benefit a broad readership across developmental biology, germ cell biology, stem cell biology, and cancer biology.

However, the following suggestions may further improve the clarity and rigor of the research content:

(1) Clarification of sample size (n).<br /> Each germarium can contain highly variable numbers of SGCs, sometimes reaching 50-100. When reporting "n" values, the authors are encouraged to also indicate the number of germaria analyzed. For example, in lines 126-128:<br /> "Notably, 74% of SGCs (n = 132) were GFP-negative, while the remaining 26% were GFP-positive (Figure 2B, C). This suggests that SGCs can be categorized into two distinct groups: those resembling GSCs (GSC-like) and those resembling cystoblasts (cystoblast-like)."<br /> Please clarify how many germaria were examined to obtain n = 132. In addition, it is unclear whether the authors intend to suggest that the GFP-negative SGCs are GSC-like or cystoblast-like; this point should be clarified.

(2) Improvement of Fig. 6 in situ hybridization images.<br /> The in situ hybridization images in Fig. 6 are not fully convincing. The control images, in particular, would benefit from higher resolution and enlarged views of the germarium region. In panel C, abundant signals are also present outside the germarium, which may complicate interpretation and should be clarified or controlled for.

Alternatively, the authors could strengthen the in situ analysis by using bam mutants or bam dpp / bam gbb double mutants as controls to better define signal specificity.

Reviewer #3 (Public review):

Zhang et al. investigated how germline tumors influence the development of neighboring wild-type (WT) germline stem cells (GSC) in the Drosophila ovary. They report that germline tumors generated by differentiation-arrested mutations (bam and bgcn) inhibit the differentiation of neighboring WT GSCs by arresting them in an undifferentiated state, resulting from reduced expression of the differentiation-promoting factor Bam. They find that these tumor cells produce low levels of the niche-associated signaling molecules Dpp and Gbb, which suppress bam expression and consequently inhibit the differentiation of neighboring WT GSCs non-cell-autonomously. Based on these findings, the authors propose that germline tumors mimic the niche to suppress the differentiation of the neighboring wild-type germline stem cells.

Strengths:

The study uses a well-established in vivo model to address an important biological question concerning the interaction between germline tumor cells and wild-type (WT) germline stem cells in the Drosophila ovary. If the findings are substantiated, this study could provide valuable insights that are applicable to other stem cell systems.

Weaknesses:

The authors have addressed some of my concerns in the revised submission. However, the data presented do not allow the authors to distinguish whether the failed differentiation of WT stem cells/germline cells results from "arrested differentiation due to the loss of the differentiation niche" or from "direct inhibition by tumor-derived expression of niche-associated molecules Dpp and Gbb". The critical supporting data, HCR in situ results, are not sufficiently convincing.

Author response:

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

eLife Assessment

This study presents results supporting a model that tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the stem cell niche and inhibit the differentiation of neighboring cells. The valuable findings show that GSC tumors often contain non-mutant cells whose differentiation is suppressed by the GSC tumorous cells. However, the evidence showing that the GSC tumors produce BMP ligands to suppress differentiation of non-mutant cells is incomplete. It could be strengthened by the use of sensitive RNA in situ hybridization approaches.

Thank you for your valuable assessment. RNA in situ hybridization evidence has been added to the revised manuscript (Figure 5A-D) to support that GSC tumors produce BMP ligands.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This preprint from Shaowei Zhao and colleagues presents results that suggest tumorous germline stem cells (GSCs) in the Drosophila ovary mimic the ovarian stem cell niche and inhibit the differentiation of neighboring non-mutant GSC-like cells. The authors use FRT-mediated clonal analysis driven by a germline-specific gene (nos-Gal4, UASp-flp) to induce GSC-like cells mutant for bam or bam's co-factor bgcn. Bam-mutant or bgcn-mutant germ cells produce tumors in the stem cell compartment (the germarium) of the ovary (Figure 1). These tumors contain non-mutant cells - termed SGC for single-germ cells. 75% of SGCs do not exhibit signs of differentiation (as assessed by bamP-GFP) (Figure 2). The authors demonstrate that block in differentiation in SGC is a result of suppression of bam expression (Figure 2). They present data suggesting that in 73% of SGCs, BMP signaling is low (assessed by dad-lacZ) (Figure 3) and proliferation is less in SGCs vs GSCs. They present genetic evidence that mutations in BMP pathway receptors and transcription factors suppress some of the non-autonomous effects exhibited by SGCs within bam-mutant tumors (Figure 4). They show data that bam-mutant cells secrete Dpp, but this data is not compelling (see below) (Figure 5). They provide genetic data that loss of BMP ligands (dpp and gbb) suppresses the appearance of SGCs in bam-mutant tumors (Figure 6). Taken together, their data support a model in which bam-mutant GSC-like cells produce BMPs that act on non-mutant cells (i.e., SGCs) to prevent their differentiation, similar to what is seen in the ovarian stem cell niche.

Strengths:

(1) Use of an excellent and established model for tumorous cells in a stem cell microenvironment.

(2) Powerful genetics allow them to test various factors in the tumorous vs non-tumorous cells.

(3) Appropriate use of quantification and statistics.

We greatly appreciate your valuable comments.

Weaknesses:

(1) What is the frequency of SGCs in nos>flp; bam-mutant tumors? For example, are they seen in every germarium, or in some germaria, etc, or in a few germaria?

This is a good question. Because the SGC phenotype depends on the presence of both germline tumor clones and out-of-niche wild-type germ cells, our quantification was restricted to germaria containing both. In 14-day-old fly ovaries, 70% of germaria (432/618) met this criterion (Line 103). Each of them contained an average of 1.5 SGCs (Figure 1K).

(2) Does the breakdown in clonality vary when they induce hs-flp clones in adults as opposed to in larvae/pupae?

Our attempts to induce ovarian hs-FLP germline clones by heat-shocking adult flies were unsuccessful, with very few clones being observed. Therefore, we shifted our approach to an earlier developmental stage. Successful induction was achieved by subjecting late-L3/early-pupal animals to a twice-daily heatshock at 37°C for 6 consecutive days (2 hours per session with a 6-hour interval, see Lines 331-335) (Zhao et al., 2018).

(3) Approximately 20-25% of SGCs are bam+, dad-LacZ+. Firstly, how do the authors explain this? Secondly, of the 70-75% of SGCs that have no/low BMP signaling, the authors should perform additional character rization using markers that are expressed in GSCs (i.e., Sex lethal and nanos).

These 20-25% of SGCs are bamP-GFP⁺ dad-lacZ^-, not bam⁺ dad-lacZ⁺ (see Figure 2C and 3D). They would be cystoblast-like cells that may have initiated a differentiation program toward forming germline cysts (see Lines 122-130). The 70-75% of SGCs that have low BMP signaling exhibit GSC-like properties, including: 1) dot-like spectrosomes; 2) dad-lacZ positivity; 3) absence of bamP-GFP expression. While additional markers would be beneficial, we think that this combination of properties is sufficient to classify these cells as GSC-like.

(4) All experiments except Figure 1I (where a single germarium with no quantification) were performed with nos-Gal4, UASp-flp. Have the authors performed any of the phenotypic characterizations (i.e., figures other than Figure 1) with hs-flp?

Yes, we initially identified the SGC phenotype through hs-FLP-mediated mosaic analysis of bam or bgcn mutant in ovaries. However, as noted in our response to Weakness (2), this approach was very labor-intensive. Therefore, we switched to using the more convenient nos>FLP system for subsequent experiments. To our observation, there was no difference in inducing the SGC phenotype by these two approaches.

(5) Does the number of SGCs change with the age of the female? The experiments were all performed in 14-day-old adult females. What happens when they look at a young female (like 2-day-old). I assume that the nos>flp is working in larval and pupal stages, and so the phenotype should be present in young females. Why did the authors choose this later age? For example, is the phenotype more robust in older females? Or do you see more SGCs at later time points?

These are very good questions. The SGC phenotype was consistent over the 14-day analysis period (Figure 1J) and was specifically dependent on the presence of germline tumor clones. In 14-day-old fly ovaries, these clones were both larger and more frequent than in younger flies. This age-dependent enhancement in clone size and frequency significantly improved our quantification efficiency (see Lines 101-112).

(6) Can the authors distinguish one copy of GFP versus 2 copies of GFP in germ cells of the ovary? This is not possible in the Drosophila testis. I ask because this could impact the clonal analyses diagrammed in Figure 4A and 4G and in 6A and B. Additionally, in most of the figures, the GFP is saturated, so it is not possible to discern one vs two copies of GFP.

Thank you for this valuable comment. It was also difficult for us to distinguish 1 and 2 copies of GFP in the Drosophila ovary. In Figure 4A-F, to resolve this problem, we used a triple-color system, in which red germ cells (RFP^+/+ GFP^-/-) are bam mutant, yellow germ cells (RFP^+/- GFP^+/-) are wild-type, and green germ cells (RFP^-/- GFP^+/+) are punt or med mutant. In Figure 4G-J, we quantified the SGC phenotype only in black germ cells (GFP^-/-), which are wild-type (control) or mad mutant. In Figure 6, we quantified the SGC phenotype only in green germ cells (both GFP^+/+ and GFP^+/-), all of which are wild-type.

(7) More evidence is needed to support the claim of elevated Dpp levels in bam or bgcn mutant tumors. The current results with the dpp-lacZ enhancer trap in Figure 5A, B are not convincing. First, why is the dpp-lacZ so much brighter in the mosaic analysis (A) than in the no-clone analysis (B)? It is expected that the level of dpp-lacZ in cap cells should be invariant between ovaries, and yet LacZ is very faint in Figure 5B. I think that if the settings in A matched those in B, the apparent expression of dpp-lacZ in the tumor would be much lower and likely not statistically significant. Second, they should use RNA in situ hybridization with a sensitive technique like hybridization chain reactions (HCR) - an approach that has worked well in numerous Drosophila tissues, including the ovary.

Thank you for this critical comment. The settings of immunofluorescent staining and confocal parameters in the original Figure 5A were the same as those in 5B. To our observation, the levels of dpp-lacZ in terminal filament and cap cells were highly variable across germaria, even within the same ovary. We have omitted these results from the revised Figure 5. Instead, the HCR-FISH data have been added (Figure 5A-D) to support that bam mutant germline tumors secret BMP ligands.

(8) In Figure 6, the authors report results obtained with the bamBG allele. Do they obtain similar data with another bam allele (i.e., bamdelta86)?

No. Given that bam^BG was functionally indistinguishable from bam^Δ86 in inducing the SGC phenotype (Figure 1J), we believe that repeating these experiments with bam^Δ86 would be redundant and would not alter the key conclusion of our study. Thank you for your understanding!

Reviewer #2 (Public review):

While the study by Zhang et al. provides valuable insights into how germline tumors can non-autonomously suppress the differentiation of neighboring wild-type germline stem cells (GSCs), several conceptual and technical issues limit the strength of the conclusions.

Major points:

(1) Naming of SGCs is confusing. In line 68, the authors state that "many wild-type germ cells located outside the niche retained a GSC-like single-germ-cell (SGC) morphology." However, bam or bgcn mutant GSCs are also referred to as "SGCs," which creates confusion when reading the text and interpreting the figures. The authors should clarify the terminology used to distinguish between wild-type SGCs and tumor (bam/bgcn mutant) SGCs, and apply consistent naming throughout the manuscript and figure legends.

We apologize for any confusion. In our manuscript, the term "SGC" is reserved specifically for wild-type germ cells that maintain a GSC-like morphology outside the niche. bam or bgcn mutant germ cells are referred to as GSC-like tumor cells (Lines 89-90), not SGCs.

(a) The same confusion appears in Figure 2. It is unclear whether the analyzed SGCs are wild-type or bam mutant cells. If the SGCs analyzed are Bam mutants, then the lack of Bam expression and failure to differentiate would be expected and not informative. However, if the SGCs are wild-type GSCs located outside the niche, then the observation would suggest that Bam expression is silenced in these wild-type cells, which is a significant finding. The authors should clarify the genotype of the SGCs analyzed in Figure 2C, as this information is not currently provided.

The SGCs analyzed in Figure 2A-C are wild-type, GSC-like cells located outside the niche. They were generated using the same genetic strategy depicted in Figures 1C and 1E (with the schematic in Figure 1B). The complete genotypes for all experiments are available in Source data 1.

(b) In Figures 4B and 4E, the analysis of SGC composition is confusing. In the control germaria (bam mutant mosaic), the authors label GFP⁺ SGCs as "wild-type," which makes interpretation unclear. Note, this is completely different from their earlier definition shown in line 68.

The strategy to generate SGCs in Figure 4B-F (with the schematic in Figure 4A) is different from that in Figure 1C-F, H, and I (with the schematic in Figure 1B). In Figure 4B-F, we needed to distinguish punt^-/- (or med^-/-) with punt^+/- (or med^+/-) germ cells. As noted in our response to Reviewer #1’s Weakness (6), it was difficult for us to distinguish 1 and 2 copies of GFP in the Drosophila ovary. Therefore, we chose to use the triple-color system to distinguish these germ cells in Figure 4B-F (see genotypes in Source data 1).

(c) Additionally, bam^+/- GSCs (the first bar in Figure 4E) should appear GFP⁺ and Red>sup>+</sup> (i.e., yellow). It would be helpful if the authors could indicate these bam^+/- germ cells directly in the image and clarify the corresponding color representation in the main text. In Figure 2A, although a color code is shown, the legend does not explain it clearly, nor does it specify the identity of bam^+/- cells alone. Figure 4F has the same issue, and in this graph, the color does not match Figure 4A.

The color-to-genotype relationships for the schematics in Figures 2A and 4E are provided in Figures 1B and 4A, respectively. Due to the high density of germ cells, it is impractical to label each genotype directly in the images. In contrast to Figure 4E, the colors in Figure 4F do not represent genotypes; instead, blue denotes the percentage of SGCs, and red denotes the percentage of germline cysts, as indicated below the bar chart.

(2) The frequencies of bam or bgcn mutant mosaic germaria carrying [wild-type] SGCs or wild-type germ cell cysts with branched fusomes, as well as the average number of wild-type SGCs per germarium and the number of days after heat shock for the representative images, are not provided when Figure 1 is first introduced. Since this is the first time the authors describe these phenotypes, including these details is essential. Without this information, it is difficult for readers to follow and evaluate the presented observations.

Thank you for this constructive suggestion. These quantification data have been added to the revised Figure 1 (Figure 1J, K).

(3) Without the information mentioned in point 2, it causes problems when reading through the section regarding [wild-type] SGCs induced by impairment of differentiation or dedifferentiation. In lines 90-97, the authors use the presence of midbodies between cystocytes as a criterion to determine whether the wild-type GSCs surrounded by tumor GSCs arise through dedifferentiation. However, the cited study (Mathieu et al., 2022) reports that midbodies can be detected between two germ cells within a cyst carrying a branched fusome upon USP8 loss.

Unlike wild-type cystocytes, which undergo incomplete cytokinesis and lack midbodies, those with USP8 loss undergo complete cell division, with the presence of midbodies (white arrow, Figure 1F’ from Mathieu et al., 2022) as a marker of the late cytokinesis stage (Mathieu et al., 2022).

(a) Are wild-type germ cell cysts with branched fusomes present in the bam mutant mosaic germaria? What is the proportion of germaria containing wild-type SGCs versus those containing wild-type germ cell cysts with branched fusomes?

(b) If all bam mutant mosaic germaria carry only wild-type GSCs outside the niche and no germaria contain wild-type germ cell cysts with branched fusomes, then examining midbodies as an indicator of dedifferentiation may not be appropriate.

We appreciate your critical comment. bam mutant mosaic germaria indeed contained wild-type germline cysts, as evidenced by an SGC frequency of ~70%, rather than 100% (see Figures 2H, 4F, 4J, 6F, 6I, and Figure 6-figure supplement 3C). Since the SGC phenotype depends on the presence of bam or bgcn mutant germline tumors, we quantified it as “the percentage of SGCs relative to the total number of SGCs and germline cysts that are surrounded by germline tumors” (see Lines 103-108). Quantifying the SGC phenotype as "the percentage of germaria with SGCs" would be imprecise. This is because the presence and number of SGCs were variable among germaria with bam or bgcn mutant germline clones, and a small number of germaria entirely lacked these clones. The data of "SGCs per germarium with both germline clones and out-of-niche wild-type germ cells" have been added to the revised Figure 1 (Figure 1K).

(c) If, however, some germaria do contain wild-type germ cell cysts with branched fusomes, the authors should provide representative images and quantify their proportion.

Such germaria could be found in Figure 2G, 3B, 3C, 6D, 6E, and 6H. The percentage of germline cysts can be calculated by “100% - SGC%”.

(d) In line 95, although the authors state that 50 germ cell cysts were analyzed for the presence of midbodies, it would be more informative to specify how many germaria these cysts were derived from and how many biological replicates were examined.

As noted in our response to points a) and b) above, the germ cells surrounded by germline tumors, rather than germarial numbers, are more precise for analyzing the phenotype. For this experiment, we examined >50 such germline cysts via confocal microscopy. As the analysis was performed on a defined cellular population, this sample size should be sufficient to support our conclusion.

(4) Note that both bam mutant GSCs and wild-type SGCs can undergo division to generate midbodies (double cells), as shown in Figure 4H. Therefore, the current description of the midbody analysis is confusing. The authors should clarify which cell types were examined and explain how midbodies were interpreted in distinguishing between cell division and differentiation.

We assayed for the presence of midbodies or not specifically within the wild-type germline cysts surrounded by bam or bgcn mutant tumors, not within the tumors themselves (Lines 96-97). As detailed in Lines 90-100, the absence of midbodies was used as a key criterion to exclude the possibility of dedifferentiation.

(5) The data in Figure 5 showing Dpp expression in bam mutant tumorous GSCs are not convincing. The Dpp-lacZ signal appears broadly distributed throughout the germarium, including in escort cells. To support the claim more clearly, the authors should present corresponding images for Figures 5D and 5E, in which dpp expression was knocked down in the germ cells of bam or bgcn mutant mosaic germaria. Showing these images would help clarify the localization and specificity of Dpp-lacZ expression relative to the tumorous GSCs.

Thank you for your constructive comment. RNA in situ hybridization data have been added to support that bam or bgcn mutant germline tumors secret BMP ligands (Figure 5A-D).

(6) While Figure 6 provides genetic evidence that bam mutant tumorous GSCs produce Dpp to inhibit the differentiation of wild-type SGCs, it should be noted that these analyses were performed in a dpp⁺/⁻ background. To strengthen the conclusion, the authors should include appropriate controls showing [dpp^+/-; bam^+/-] SGCs and [dpp^+/-; bam^+/-] germ cell cysts without heat shock (as referenced in Figures 6F and 6I).

Schematic cartoons in Figure 6A and 6B demonstrate that these analyses were performed in a dpp^+/- background. Figure 6-figure supplement 1 indicates tha dpp^+/- or gbb^+/- does not affect GSC maintenance, germ cell differentiation, and female fly fertility. Figure 6C is the control for 6D and 6E, and 6G is the control for 6H, with quantification in 6F and 6I. We used nos>FLP, not the heat shock method, to induce germline clones in these experiments (see genotypes in Source data 1).

(7) Previous studies have reported that bam mutant germ cells cause blunted escort cell protrusions (e.g., Kirilly et al., Development, 2011), which are known to contribute to germ cell differentiation (e.g., Chen et al., Frontiers in Cell and Developmental Biology, 2022). The authors should include these findings in the Discussion to provide a broader context and to acknowledge how alterations in escort cell morphology may further influence differentiation defects in their model.

Thank you for teaching us! We have included the introduction of these two papers in the revised manuscript (Lines 197-199).

(8) Since fusome morphology is an important readout of SGCs vs differentiation. All the clonal analysis should have fusome staining.

SGC is readily distinguishable from multi-cellular germline cyst based on morphology. In some clonal-analysis experiments, fusome staining was not feasible due to technical limitations such as channel saturation or antibody incompatibility. Thank you for your understanding!

(9) Figure arrangement. It is somewhat difficult to identify the figure panels cited in the text due to the current panel arrangement.

The figure panels were arranged to optimize space while ensuring that related panels are grouped in close proximity for logical comparison. We would be happy to consider any specific suggestions for an alternative layout that could improve clarity.

(10) The number of biological replicates and germaria analyzed should be clearly stated somewhere in the manuscript-ideally in the Methods section or figure legends. Providing this information is essential for assessing data reliability and reproducibility.

The detailed quantification information is labeled directly in figures or described in figure legends, and all raw quantification data are provided in Source data 2.

Reviewer #3 (Public review):

Summary:

Zhang et al. investigated how germline tumors influence the development of neighboring wild-type (WT) germline stem cells (GSC) in the Drosophila ovary. They report that germline tumors inhibit the differentiation of neighboring WT GSCs by arresting them in an undifferentiated state, resulting from reduced expression of the differentiation-promoting factor Bam. They find that these tumor cells produce low levels of the niche-associated signaling molecules Dpp and Gbb, which suppress bam expression and consequently inhibit the differentiation of neighboring WT GSCs non-cell-autonomously. Based on these findings, the authors propose that germline tumors mimic the niche to suppress the differentiation of the neighboring stem cells.

Strengths:

This study addresses an important biological question concerning the interaction between germline tumor cells and WT germline stem cells in the Drosophila ovary. If the findings are substantiated, they could provide valuable insights applicable to other stem cell systems.

We greatly appreciate your valuable comments.

Weaknesses:

Previous work from Xie's lab demonstrated that bam and bgcn mutant GSCs can outcompete WT GSCs for niche occupancy. Furthermore, a large body of literature has established that the interactions between escort cells (ECs) and GSC daughters are essential for proper and timely germline differentiation (the differentiation niche). Disruption of these interactions leads to arrest of germline cell differentiation in a status with weak BMP signaling activation and low bam expression, a phenotype virtually identical to what is reported here. Thus, it remains unclear whether the observed phenotype reflects "direct inhibition by tumor cells" or "arrested differentiation due to the loss of the differentiation niche." Because most data were collected at a very late stage (more than 10 days after clonal induction), when tumor cells already dominate the germarium, this question cannot be solved. To distinguish between these two possibilities, the authors could conduct a time-course analysis to examine the onset of the WT GSC-like single-germ-cell (SGC) phenotype and determine whether early-stage tumor clones with a few tumor cells can suppress the differentiation of neighboring WT GSCs with only a few tumor cells present. If tumor cells indeed produce Dpp and Gbb (as proposed here) to inhibit the differentiation of neighboring germline cells, a small cluster or probably even a single tumor cell generated at an early stage might prevent the differentiation of their neighboring germ cells.

Thank you for your critical comment. The revised manuscript now includes a time-course analysis of the SGC phenotype (Figure 1J). Our data in Figure 6 demonstrate that BMP ligands from germline tumors are required to inhibit SGC differentiation. Furthermore, we have incorporated into the manuscript the possibility that disruption of the differentiation niche may also contribute to the SGC phenotype (Lines 197-199).

The key evidence supporting the claim that tumor cells produce Gpp and Gbb comes from Figures 5 and 6, which suggest that tumor-derived dpp and gbb are required for this inhibition. However, interpretation of these data requires caution. In Figure 5, the authors use dpp-lacZ to support the claim that dpp is upregulated in tumor cells (Figure 5A and 5B). However, the background expression in somatic cells (ECs and pre-follicular cells) differs noticeably between these panels. In Figure 5A, dpp-lacZ expression in somatic cells in 5A is clearly higher than in 5B, and the expression level in tumor cells appears comparable to that in somatic cells (dpp-lacZ single channel). Similarly, in Figure 5B, dpp-lacZ expression in germline cells is also comparable to that in somatic cells. Providing clear evidence of upregulated dpp and gbb expression in tumor cells (for example, through single-molecular RNA in situ) would be essential.

We greatly appreciate your critical comment. In our data, the expression levels of dpp-lacZ in terminal filament and cap cells were highly variable across germaria, even within the same ovary. We have omitted these results in the revised Figure 5. RNA in situ hybridization data have been added to visualize the expression of BMP ligands within bam mutant germline tumor cells (Figure 5A-D).

Most tumor data present in this study were collected from the bam[86] null allele, whereas the data in Figure 6 were derived from a weaker bam[BG] allele. This bam[BG] allele is not molecularly defined and shows some genetic interaction with dpp mutants. As shown in Figure 6E, removal of dpp from homozygous bam[BG] mutant leads to germline differentiation (evidenced by a branched fusome connecting several cystocytes, located at the right side of the white arrowhead). In Figure 6D, fusome is likely present in some GFP-negative bam[BG]/bam[BG] cells. To strengthen their claim that the tumor produces Dpp and Gbb to inhibit WT germline cell differentiation, the authors should repeat these experiments using the bam[86] null allele.

Although a structure resembling a "branched fusome" is visible in Figure 6E (right of the white arrowhead), it is an artifact resulting from the cytoplasm of GFP-positive follicle cells, which also stain for α-Spectrin, projecting between germ cells of different clones (see the merged image). In both our previous (Zhang et al., 2023) and current studies, bam^BG was functionally indistinguishable from bam^Δ86 in its ability to block GSC differentiation and induce the SGC phenotype (Figure 1J). Given this, we believe that repeating the extensive experiments in Figure 6 with the bam^Δ86 allele would be scientifically redundant and would not change the key conclusion of our study.

It is well established that the stem niche provides multiple functional supports for maintaining resident stem cells, including physical anchorage and signaling regulation. In Drosophila, several signaling molecules produced by the niche have been identified, each with a distinct function - some promoting stemness, while others regulate differentiation. Expression of Dpp and Gbb alone does not substantiate the claim that these tumor cells have acquired the niche-like property. To support their assertion that these tumors mimic the niche, the authors should provide additional evidence showing that these tumor cells also express other niche-associated markers. Alternatively, they could revise the manuscript title to more accurately reflect their findings.

Dpp and Gbb are the key niche signals from cap cells for maintaining GSC stemness. Our work demonstrates that germline tumors can specifically mimic this signaling function, not the full suite of cap cell properties, to create a non-cell-autonomous differentiation block. The current title “Tumors mimic the niche to inhibit neighboring stem cell differentiation” reflects this precise concept: a partial, functional mimicry of the niche's most relevant activity in this context. We feel it is an appropriate and compelling summary of our main conclusion.

In the Method section, the authors need to provide details on how dpp-lacZ expression levels were quantified and normalized.

Because of the highly variable expression levels in terminal filament and cap cells, we have omitted the dpp-lacZ results in the revised manuscript.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Minor points

(1) Not all readers may be familiar with the nos>FLP/FRT or hs-FLP/FRT systems. It would be helpful if the authors could briefly introduce these genetic mosaic systems and explain how they were used in this study before presenting the results.

Thank you for this constructive suggestion. Such brief introduction has been added to the revised manuscript (Lines 64-70).

(2) Line 68-70: "Surprisingly, ...outside the niche retained a GSC-like single-germ-cell (SGC) morphology, even when encapsulated within egg chambers (Figure 1C, D, Figure 1- figure supplement 1).

(3) The figure citation is not appropriate, as Figures 1C and 1D do not show "single germ cells (SGCs) encapsulated within egg chambers." To improve clarity, the authors could revise the sentence as follows: "Surprisingly, wild-type germ cells located outside the niche retained a GSC-like single-germ-cell (SGC) morphology (Figures 1C and D), even when encapsulated within egg chambers (Figure 1-figure supplement 1)." This modification would make the description consistent with the figure content and easier for readers to follow.

Thank you for teaching us! The manuscript has been revised following this suggestion (Lines 70-73).

(4) Line 106-110. The description is confusing. The authors state, "Under normal conditions... Notably, 74% of SGCs (n = 132) were GFP-negative, while the remaining 26% were GFP-positive (Figure 2B, C). However, Figure 2B shows the bam mutant mosaic germaria, and Figure 2C does not specify the genotypes of the germaria used for the analysis of GSCs, CBs, and SGCs. The authors should clarify the experimental conditions and genotypes corresponding to each panel. In addition, it would be more informative to indicate how many germaria these quantified GSCs, CBs, and SGCs were derived from.

(5) Throughout the manuscript, the authors report the number of SGCs analyzed (e.g., Lines 149-151). However, it would be more informative to also indicate how many germaria these quantified SGCs were derived from. Providing this information would help readers assess the sampling size and variability across biological replicates.

Thank you for your suggestion. As shown in Figure 2B, these wild-type (RFP-positive) GSCs and CBs were also derived from bam mutant mosaic germaria. The phrase "under normal conditions" has been deleted from the revised manuscript to prevent any potential ambiguity. Given the specificity of the SGC phenotype, the germ cells surrounded by germline tumors, rather than germarial numbers, are more precise for its quantification (Lines 103-108). The data of “SGCs per germarium with both germline clones and out-of-niche wild-type germ cells” have been added to the revised Figure 1K.

Reviewer #3 (Recommendations for the authors):

(1) Additionally, the authors should clarify what the "red dot" signal in the GFP-positive cap cell in Figure 3 F (left panel) represents.

The “red dot” is an asterisk that is used to mark a cap cell (Line 620).

(2) Finally, on line 266, "bamP-GFP-positive" should be corrected to "bamP-GFP-negative."

It should be “bamP-GFP-positive”, not “bamP-GFP-negative” (see Figure 2B).

Reference:

Mathieu, J., Michel-Hissier, P., Boucherit, V., and Huynh, J.R. (2022). The deubiquitinase USP8 targets ESCRT-III to promote incomplete cell division. Science 376, 818-823.

Zhang, Q., Zhang, Y., Zhang, Q., Li, L., and Zhao, S. (2023). Division promotes adult stem cells to perform active niche competition. Genetics 224.

Zhao, S., Fortier, T.M., and Baehrecke, E.H. (2018). Autophagy Promotes Tumor-like Stem Cell Niche Occupancy. Curr Biol 28, 3056-3064.e3053.

eLife Assessment

The manuscript concerns a fundamental and controversial question in Trypanosoma brucei biology and the parasite life cycle, providing further evidence that slender bloodstream forms can indeed infect Tsetse flies. The study is solid in design and execution, and addresses several criticisms made of the authors' earlier work. Nevertheless, some of the main conclusions are only partially supported: one issue is how, precisely, a "slender" bloodstream form is defined, and discrepancies with some results from other laboratories remain unexplained.

Reviewer #2 (Public review):

Summary:

This paper is an exciting follow-up to two recent publications in eLife: one from the same lab, reporting that slender forms can successfully infect tsetse flies (Schuster, S et al., 2021), and another independent study claiming the opposite (Ngoune, TMJ et al., 2025). Here, the authors address four criticisms raised against their original work: the influence of N-acetyl-glucosamine (NAG), the use of teneral and male flies, and whether slender forms bypass the stumpy stage before becoming procyclic forms.

Strengths:

We applaud the authors' efforts in undertaking these experiments and contributing to a better understanding of the T. brucei life cycle. The paper is well-written and the figures are clear.

Comments on revisions:

We thank the authors for the revised manuscript and for considering our comments.

We outline below the 3 points that, in our opinion, remain to be clarified.

(1) Effect of NAG on slender-form infections in tsetse flies<br /> The conclusion that "NAG has a negligible effect on slender infections in tsetse flies" based on Figure 1, cannot be fully supported in the absence of a positive control. A relevant positive control is well established in the literature, namely that NAG promotes Tsetse infection by stumpy forms. Without such a control, it is not possible to exclude technical issues (for example, an ineffective NAG treatment), which would yield results similar to those presented in Figure 1.

(2) Infection of non-teneral flies<br /> Because the experiments shown in Figure 1 (teneral flies) and Figure 2 (non-teneral flies) were not conducted in parallel or under identical conditions, it is important that the figure legends clearly state the parasite numbers used in each case. Specifically, infections of teneral flies were performed with 200 parasites/mL (approximately 4 parasites per bloodmeal), whereas non-teneral infections used 1 × 10⁶ parasites/mL (approximately 20,000 parasites per bloodmeal?). At present, this information is scattered across the Methods and Supplementary Tables 1 and 2, making it difficult for readers to immediately appreciate that the parasite load differs by roughly 5,000-fold between these conditions.

As previously shown by the authors (Schuster et al., 2021) and in the Rotureau laboratory (Tsagmo Ngoune et al.), and as generally expected, the initial parasite dose strongly influences infection outcomes in teneral flies. In this context, it would be informative to know whether the authors have attempted infections of non-teneral flies using lower parasite numbers (noting that Tsagmo Ngoune et al. used a maximum of 10,000 parasites) and what the infection rate was.<br /> Relatedly, the statement in line 370 appears to be an overgeneralization, as fly age was not directly tested under matched experimental conditions:

Line 370 - "Here, we unambiguously show that, in the absence of immunosuppressive treatment, slender forms can establish infections in tsetse flies, irrespective of the fly's age or sex."

(3) Transcriptomic analysis<br /> Supplementary Figure 8 lacks statistical analysis, which limits its interpretability. Two types of comparisons would be particularly helpful:<br /> (i) a comparison of PAD1/2 expression levels between slender and stumpy forms at 0 h; and<br /> (ii) for each gene, a comparison of the overall change in expression (from 0 to 72 h) between infections initiated with slender versus stumpy forms.<br /> In addition, the figure legend should clarify what "expression levels" refer to. TPM? Normalized counts?

Finally, for the benefit of the field, eLife could encourage publishing a collaborative study in which the Engstler and Rotureau laboratories exchange parasite lines and culture protocols (including media with and without methylcellulose) and perform tsetse fly infections in parallel in their respective laboratories. Such an approach could help resolve the remaining discrepancies and provide a valuable reference for the community.

Author Response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work provides evidence that slender T. brucei can initiate and complete cyclical development in Glossina morsitans without GlcNAc supplementation, in both sexes, and importantly in non-teneral flies, including salivary-gland infections.

Comparative transcriptomics show early divergence between slender- and stumpy-initiated differentiation (distinct GO enrichments), with convergence by ~72 h, supporting an alternative pathway into the procyclic differentiation program.

The work addresses key methodological criticisms of earlier studies and supports the hypothesis that slender forms may contribute to transmission at low parasitaemia.

Strengths:

(1) Directly tackles prior concerns (no GlcNAc, both sexes, non-teneral flies) with positive infections through to the salivary glands.

(2) Transcriptomic time course adds some mechanistic depth.

(3) Clear relevance to the "transmission paradox"; advances an important debate in the field.

Weaknesses:

(1) Discrepancy with Ngoune et al. (2025) remains unresolved; no head-to-head control for colony/blood source or microbiome differences that could influence vector competence.

We acknowledge that a direct head-to-head comparison was not performed and that microbiome composition can affect vector competence. However, both the tsetse flies used in Ngoune et al. (2025) and those in our study originated from the same colony and were maintained under comparable standard laboratory conditions. In both cases, flies were fed on sheep blood through identical silicon membrane systems, minimizing potential differences.

(2) Lacks in vivo feeding validation (e.g., infecting flies directly on parasitaemic mice) to strengthen ecological relevance.

Our study deliberately focused on controlling experimental variables through the use of an artificial feeding system, which allows for standardization of parasite dose and exposure conditions. This approach facilitates reproducibility and direct comparison with previous studies. Also, to us it appears questionable if feeding flies on infected laboratory mice really adds ecological relevance.

(3) Mechanistic inferences are largely correlative (although not requested, there is no functional validation of genes or pathways emerging from the transcriptomics).

Functional validation of individual genes or pathways was not undertaken in this study. Instead, the aim was to identify and compare transcriptional signatures associated with slender-to-procyclic versus stumpy-to-procyclic differentiation, and to directly address previous criticism of original finding that slender bloodstream forms are capable of infecting the tsetse fly.

(4) Reliance on a single parasite clone (AnTat 1.1) and one vector species limits external validity.

Incorporating additional pleomorphic T. brucei clones and alternative tsetse species would undoubtedly broaden our understanding of parasite-vector interactions, and studies using fresh field isolates and wild-caught tsetse flies would be even more informative. However, in order to directly address the specific concerns raised against our original study (Schuster et al., 2021), it was essential to employ the same parasite clone and vector species.

We further emphasize that the pleomorphic clone used here is a well-characterized and widely employed T. brucei strain that closely reflects parasites encountered under natural conditions. Likewise, Glossina morsitans represents the standard vector species used in the majority of tsetse laboratories, thereby ensuring reproducibility and facilitating comparison with existing work in the field.

Reviewer #2 (Public review):

Summary:

This paper is an exciting follow-up to two recent publications in eLife: one from the same lab, reporting that slender forms can successfully infect tsetse flies (Schuster, S et al., 2021), and another independent study claiming the opposite (Ngoune, TMJ et al., 2025). Here, the authors address four criticisms raised against their original work: the influence of N-acetyl-glucosamine (NAG), the use of teneral and male flies, and whether slender forms bypass the stumpy stage before becoming procyclic forms.

Strengths:

We applaud the authors' efforts in undertaking these experiments and contributing to a better understanding of the T. brucei life cycle. The paper is well-written and the figures are clear.

Weaknesses:

We identified several major points that deserve attention.

(1) What is a slender form? Slender-to-stumpy differentiation is a multi-step process, and most of these steps unfortunately lack molecular markers (Larcombe et al, 2023). In this paper, it is essential that the authors explicitly define slender forms. Which parameters were used? It is implicit that slender forms are replicative and GFP::PAD1-negative. Isn't it possible that some GFP::PAD1-negative cells were already transitioning toward stumpy forms, but not yet expressing the reporter? Transcriptomically, these would be early transitional cells that, upon exposure to "tsetse conditions" (in vitro or in vivo), could differentiate into PCF through an alternative pathway, potentially bypassing the stumpy stage (as suggested in Figure 4). Given the limited knowledge of early molecular signatures of differentiation, we cannot exclude the possibility that the slender forms used here included early differentiating cells. We suggest:

(1.1) Testing the commitment of slender forms (e.g., using the plating assay in Larcombe et al., 2023), assessing cell-cycle profile, and other parameters that define slender forms.

(1.2) In the Discussion, acknowledging the uncertainty of "what is a slender?" and being explicit about the parameters and assumptions.

We appreciate the critical evaluation concerning the identity of slender forms and potential presence of intermediate forms displaying slender morphology yet exhibiting cell-cycle arrest, as proposed in Larcombe et al. (2023). Indeed, our original paper is entitled “Unexpected plasticity in the life cycle of Trypanosoma brucei.” It is precisely this phenotypic plasticity that enables slender parasites to transition directly into the procyclic insect stage. Notably, we have shown that even monomorphic trypanosome strains are capable of undergoing this transition in the fly, and such strains are not considered to represent “intermediate” or “half-stumpy” forms. Consequently, while the question “what constitutes a slender parasite?” may be of conceptual interest, it currently is, in our view, not central to the biological conclusions of this study.

Nevertheless, we now have included an additional section in our Discussion that compares the slender cells used in our study with the commitment classification introduced by Larcombe et al. Our infection experiments were conducted using cells that meet the Larcombe-criteria of “true slender cells”, characterized by the absence of PAD1 expression and the maintenance of a slender morphology (Supplementary Figure 3A, B, following FACS sorting). Moreover, these cells are not cell-cycle arrested but continue to proliferate (Supplementary Figure 3C). Accordingly, our experimental assumptions and parameters align those of previous studies, in which continuous cell division, lack of cell cycle arrest, lack of PAD1 expression, and slender morphology are still established markers defining the slender bloodstream form.

(1.3) Clarifying in the Materials and Methods how cultures were maintained in the 3-4 days prior to tsetse infections, including daily cell densities. Ideally, provide information on GFP expression, cell cycle, and morphology. While this will not fully resolve the concern, it will allow future reinterpretation of the data when early molecular events are better understood.

We thank the reviewer for this helpful suggestion. Details on the maintenance of T. brucei cultures and culture conditions, including cell density, are provided in our previous publication (Schuster et al., 2021). In the present study, cultures were routinely monitored prior to infection to ensure that the cells used were GFP-negative and exhibited the characteristic slender morphology.

For infections performed with higher cell numbers, fluorescence-activated cell sorting (FACS) was used to obtain a 100% GFP-negative population, thereby avoiding the need for daily monitoring of GFP fluorescence. This approach ensured that all infection experiments were initiated with a homogeneous population of slender bloodstream forms.

(2) Figure 1: This analysis lacks a positive control to confirm that NAG is working as expected. It would strengthen the paper if the authors showed that NAG improves stumpy infection. Once confirmed, the authors could discuss possible differences in the tsetse immune response to slender vs. stumpy forms to explain the absence of an effect on slender infections.

The enhancing effect of N-acetylglucosamine (NAG) on stumpy-form infections of T. brucei is well established and widely accepted in the field (e.g. Peacock et al., 2006, 2012). In the present Research Advance, our objective was to directly address the specific concerns raised in response to our previous publication (Schuster et al., 2021), in which NAG supplementation during stumpy infections was already included and shown to function as expected. Accordingly, the aim here was not to reiterate the established role of NAG in promoting stumpy infections, but rather to directly examine infections initiated by slender bloodstream forms in the absence of NAG, thereby approximating more natural conditions.

(3) Figure 2. To conclude that teneral flies are less infected than non-teneral flies, data from Figures 1 and 2 must be directly comparable. Were these experiments performed simultaneously? Please clarify in the figure legends. Moreover, the non-teneral flies here are still relatively young (6-7 days old), limiting comparisons with Ngoune, TMJ et al. 2025, where flies were 2-3 weeks old.

The experiments presented in Figures 1 and 2 were not performed simultaneously. Importantly, the comparison between teneral and non-teneral flies was not intended as a direct quantitative comparison across experiments, but rather to assess infection outcomes under distinct physiological states of the vector. It is well established that teneral flies are generally more susceptible to T. brucei infection than non-teneral flies, a phenomenon commonly referred to as the “teneral phenomenon.”

Our objective was to demonstrate that slender bloodstream forms are capable of establishing infections also in non-teneral flies, thereby directly addressing concerns in the comment to our original study (Schuster et al.) that the experimental set-up may have created an unnaturally permissive environment. The data presented here in fact support the conclusion that slender forms can contribute to disease transmission under more natural conditions.

A key determinant of the increased susceptibility of teneral flies is the incomplete maturation of the peritrophic matrix (PM) (Walshe et al., 2011; Haines, 2013). In Glossina morsitans morsitans, the PM reaches its full length along the midgut approximately 84 hours post-eclosion (Lehane and Msangi, 1991). In addition, teneral flies have not yet taken a bloodmeal prior to the infective one, a factor known to further increase susceptibility (Haines, 2013).

In the present paper, non-teneral flies were selected that had received two non-infectious bloodmeals prior to the infective challenge. At 6-7 days post-eclosion, these flies possessed a fully established PM, which is known to increase refractoriness to infection (Walshe et al., 2011), while still being sufficiently young to survive the time required for T. brucei to complete its developmental cycle. This is an important point, as our timing allowed robust interpretation of infection outcomes, without the substantial loss of flies (approximately 40%) that has been reported to occur prior to dissection in Ngoune et al., 2025.

(4) Figure 3. The PCA plot (A) appears to suggest the opposite of the authors' interpretation: slender differentiation seems to proceed through a transcriptome closer to stumpy profiles. Plotting DEG numbers (panel C) is informative, but how were paired conditions selected? Besides, plotting of the number of DEGs between consecutive time points within and between parasite types is also necessary. There may also be better computational tools to assess temporal relationships. Finally, how does PAD1 transcript abundance change over time in both populations? It would also be important to depict the upregulation of procyclic-specific genes.

Regarding the PCA plot (Figure 3A), we agree that slender form differentiation transiently exhibits transcriptomic similarities to stumpy form profiles. However, as discussed in the paper, this overlap specifically reflects shared early differentiation responses rather than the adoption of a full stumpy-like transcriptome. The overall trajectory and clustering pattern indicate that slender-derived parasites follow a distinct differentiation path that - as expected -ultimately converges with the procyclic stage, consistent with our interpretation.

For the DEG analysis (Figure 3C), paired conditions were selected based on biologically meaningful time points corresponding to key stages in the differentiation process, allowing for direct comparisons between slender- and stumpy-derived populations either for the same timepoints following addition of cis-aconitate (Supplementary Figure 5) or timepoints plotting close on the PCA (Supplementary Figure 6).

We also appreciate the recommendation to consider alternative computational approaches for assessing temporal relationships. While our current analysis provides robust insights into transcriptomic transitions, we agree that future studies employing different tools could further refine our observations.

Finally, we have included the expression dynamics of PAD1 and PAD2 in the Supplementary Data (Supplementary Figure 8). The expression profile for procyclic-specific genes can now be found in Supplementary Figure 9.

(5) Could methylcellulose in the medium sensitize parasites to QS-signal, leading to more frequent and/or earlier differentiation, despite low densities? If so, cultures with vs. without methylcellulose might yield different proportions of early-differentiating (yet GFP-negative) parasites. This could explain discrepancies between the Engstler and Rotureau labs despite using the same strain. The field would benefit from reciprocal testing of culture conditions. Alternatively, the authors could compare infectivity and transcriptomes of their slender forms under three conditions: (i) in vitro with methylcellulose, (ii) in vitro without methylcellulose, and (iii) directly from mouse blood.

The original description of stumpy induction factor (SIF)-mediated quorum sensing in Trypanosoma brucei was performed by the Boshart laboratory using (a) the same cell line employed in the present study and (b) an identical HMI-9 medium supplemented with the same amount of methylcellulose (Reuner et al., 1997; Vassella et al., 1997). All relevant controls were comprehensively reported in those studies in the late 1990s. There is therefore no experimental or historical basis to suggest that methylcellulose sensitises parasites to stumpy differentiation. Moreover, the viscosity of HMI-9-methylcellulose remains well below the threshold required to impose a diffusion barrier for small molecules such as peptides. Consequently, accumulation of SIF as a result of increased medium viscosity can be excluded on physical grounds.

The present Research Advance was conducted with a focused objective, namely, to directly address the specific concerns raised in response to our original publication (Schuster et al., 2021). Expanding the study to include additional experimental conditions, such as systematic comparisons of cultures grown with and without methylcellulose, or analyses of parasites freshly isolated from mouse blood, would have extended the scope well beyond what is useful for a Research Advance and would have diluted the central purpose of this contribution.

Recommendations for authors:

Reviewer #1 (Recommendations for the authors):

Thank you for your perseverance in filling the gaps flagged by others - these data strengthen the story.

Reviewer #2 (Recommendations for the authors):

(1) Figure 1: The use of teneral flies is not mentioned in the text or the legend

Thank you: we added this to the main text and figure legend (lines 103 and 140).

(2) Figure 1 legend (line 2): Typo - "with or 60 nm" should read "with or without 60 nm."

Thank you: this has been corrected (line 141).

(3) Figure 2. Please provide the FACS gating strategy and cell numbers before and after sorting

The cell number before gating is 1x10⁷ cells, and 1x10⁶ cells were collected via FACS for infection experiments. This is stated in the Materials & Methods section (lines 473 and 478).

(4) Figure 3. RNAseq data presentation could be improved:

(a) Clarify which type of differentially expressed genes are shown in panels B and C (presumably those upregulated in slender forms and those upregulated in stumpy forms).

Thank you: the information has now been added to the figure legend (lines 279 and 282).

(b) The color code in panel A is inverted relative to panels B and C.

Thank you: this has been corrected (figure 3B and C).

(c) The GO-term analysis represents an important conclusion and should be moved to the main figure.

As a Research Advance, this paper is restricted in the number of figures and therefore the decision had to be made to move the GO-term analysis to the Supplements.

(d) Provide dataset quality control in the supplement (genes detected per sample, sample consistency, replicate correlations, etc.).

Sequencing analysis is now explained in detail in the Materials & Methods section (lines 515 - 528).

(5) Figure legends: Indicate how many times each experiment was performed and the number of independent biological replicates.

The number of replicates (and flies per replicate) is stated for both infection experiments in the respective figure legends (lines 143 and 203/04). For the RNA sequencing, it is stated in the main text, and we now have also added the information to the figure legend (lines 219 and 276/77).

(6) Discussion: Despite the ongoing debate about midgut pH, could the authors also comment on other evidence suggesting that stumpy forms are better adapted to the fly?

The pH of the midgut has been determined by the Acosta-Serrano laboratory. We have cited the paper (Liniger et al. 2003) in lines 328-330 of the discussion. Furthermore, we have discussed the developing mitochondria of stumpy forms as well as expression of Krebs cycle, and the proposed higher resistance to proteolytic stress (Vickerman, 1965; Brown et al., 1973; Hamm et al., 1990; Reuner et al., 1997, Nolan et al., 2000).

eLife Assessment

This study provides a valuable contribution to the field of zebrafish immunology by demonstrating that the two TNF paralogs tnfa and tnfb show distinct cellular sources and temporal expression patterns during inflammation. These findings are potentially significant because they suggest regulatory divergence and functional specialization within the TNF signaling system in teleosts. While the evidence supporting differential expression is convincing, the work remains largely observational and would benefit from functional experiments and deeper mechanistic insight to determine whether these differences translate into distinct roles in inflammatory signaling. This work will be of interest to immunologists interested in inflammatory cytokine evolution and immune regulation in vertebrates.

Reviewer #1 (Public review):

Summary:

This study investigates the roles of the two tumor necrosis factor genes (tnfa and tnfb) in zebrafish during inflammatory responses. TNF is a central regulator of inflammation across vertebrates; however, while mammalian TNF signaling is well characterized, the functional divergence of duplicated TNF genes in teleosts remains less well understood. In this work, the authors generate novel zebrafish fluorescent reporter lines for tnfb and use them to perform comparative analyses of the spatial and temporal expression patterns of tnfa and tnfb during inflammation. They report that these paralogous genes are produced by distinct immune cell populations and exhibit different induction kinetics during inflammatory processes. Based on these observations, the authors propose that tnfa and tnfb may fulfill non-redundant roles in the zebrafish immune response.

Strengths:

The study addresses an important gap in understanding the functional divergence of TNF paralogs in teleosts. Given that gene duplication events are common in fish genomes, clarifying how duplicated cytokines partition their functions is valuable for both evolutionary immunology and zebrafish model research. The work makes effective use of the zebrafish model, which is particularly well suited for in vivo imaging of dynamic immune cell behaviors during inflammation. A key strength of the study is the integration of analyses of cell-type specificity, transcriptional regulation, and temporal expression dynamics. In particular, the live imaging experiments are compelling and provide clear visual evidence that tnfa and tnfb differ in both cellular sources and expression kinetics, which strengthens the claim that these paralogs may have diverged in their regulation and potentially their function. By distinguishing these aspects of the two cytokines, the study provides useful conceptual and methodological guidance for future investigations of inflammatory signaling in zebrafish.

Weaknesses:

(1) While the manuscript convincingly documents distinct expression patterns, the functional consequences of these differences remain unexplored. The conclusions regarding non-redundant roles would benefit from functional perturbation experiments. Relatedly, the authors propose that tnfa and tnfb may play different immunological roles, but the mechanistic basis underlying these differences is not addressed. For example, do the two cytokines engage different receptors or signaling pathways? Do they trigger distinct downstream transcriptional programs?

(2) Some imaging-based observations appear largely qualitative. Additional quantitative analyses, such as statistical comparisons of expression levels across time points or cell populations, would strengthen the robustness of the conclusions. For instance, in Figure 4, the expression levels of tnfa and tnfb reporter transgenes in immune cells should be quantitatively compared between control and amputated conditions.

(3) It would also be important to clarify whether the distinct maturation kinetics of the fluorescent reporters were taken into account when interpreting expression timing. Since GFP typically matures more rapidly than mCherry in vivo, the authors should comment on whether this difference could influence the apparent expression kinetics of tnfa versus tnfb.

Reviewer #2 (Public review):

Summary:

In this manuscript, van Dijk et al analyse the expression of the largely ignored paralogue of TNF in zebrafish, tnfb. They generate reporter transgenic lines and show that the reporter expression is consistent with endogenous mRNA expression in zebrafish larvae. Unlike its better-known paralogue tnfa, tnfb is constitutively expressed in mantle cells of neuromasts, and in a few leukocytes. It is also inducible in macrophages and some neutrophils upon wounding or detection of microbes, with faster kinetics than tnfa or il1b.

Strengths:

Generation and convincing validation of new transgenic reporter lines for tnfb with either green or red fluorescent proteins. Superb imaging and careful analysis of these lines crossed to complementary reporter transgenics, backed with in situ hybridization and qRT-PCR analysis of FACS-sorted cells. Excellent methods section.

Weaknesses:

Lack of functional analysis; these lines are a potentially valuable tool, but so far provide no clue regarding the role of tnfb. Is it a pro-inflammatory cytokine acting in synergy with tnfa, or is it an antagonist? What are its receptor(s)? What signalling pathways and downstream genes does it induce? Addressing at least some of these questions should greatly increase the impact of the paper.

Author response:

Reviewer #1 (Public review):

(1) While the manuscript convincingly documents distinct expression patterns, the functional consequences of these differences remain unexplored. The conclusions regarding non-redundant roles would benefit from functional perturbation experiments. Relatedly, the authors propose that tnfa and tnfb may play different immunological roles, but the mechanistic basis underlying these differences is not addressed. For example, do the two cytokines engage different receptors or signaling pathways? Do they trigger distinct downstream transcriptional programs?

We agree functional analysis on Tnfb is relevant to address, however, the focus of the current manuscript (Tools and Resources article type) was to report the generation and validation of the new tnfb-reporter line, we feel that functional data is better suited for a separate manuscripts. In fact, this will be part of a follow manuscript which will be forthcoming soon.

(2) Some imaging-based observations appear largely qualitative. Additional quantitative analyses, such as statistical comparisons of expression levels across time points or cell populations, would strengthen the robustness of the conclusions. For instance, in Figure 4, the expression levels of tnfa and tnfb reporter transgenes in immune cells should be quantitatively compared between control and amputated conditions.

In figure 4, we focus on which cells express either cytokine, not on when they express it nor whether the one cell expresses more or less eGFP/mCh. Also, tnfb:mCh-F and tnfa:eGFP-F expression is membrane-bound as these protein is farnesylated, whereas il1b:eGFP is not, and has a cytoplasmic distribution. Because of possible biases due to the different distribution or abundance of cytoplasmic vs farnesylated proteins within a cell, we never compared max eGFP to max mCherry within a treatment group.

(3) It would also be important to clarify whether the distinct maturation kinetics of the fluorescent reporters were taken into account when interpreting expression timing. Since GFP typically matures more rapidly than mCherry in vivo, the authors should comment on whether this difference could influence the apparent expression kinetics of tnfa versus tnfb.

In figure 5, we do count the cells expressing either of the cytokine, and use eGFP/mCherry signal to infer on how early these cells express the cytokine. We, however, do not directly compare maximum eGFP or mCherry fluorescence intensity per cell, which, especially in the early time points, could be biased by differences in protein maturation, we only score eGFP or mCherry presence in a cell. We could not really compare or account for differences in protein maturation as we do not possess Il1b and tnfa transgenic lines driving mCherry expression for comparison (and to our knowledge are not available in other laboratories). Based on the obtained results however, it appears that the earlier maturation of eGFP compared to mCherry may not influence the outcome of the analysis, as no single tnfa:eGFP-F+ cells were observed at any time point and single il1b:eGFP+ cells were observed only 6h after amputation, whereas eGFP/mCherry double positive cells could be observed as early as 2h after amputation. Any bias should influence the period between 1h and 2h, and we did not look at time lapses shorter than 1h.

Reviewer #2 (Public review):

(1) Lack of functional analysis; these lines are a potentially valuable tool, but so far provide no clue regarding the role of tnfb. Is it a pro-inflammatory cytokine acting in synergy with tnfa, or is it an antagonist? What are its receptor(s)? What signalling pathways and downstream genes does it induce? Addressing at least some of these questions should greatly increase the impact of the paper.

Please refer to response to Reviewer #1 point 1.

We will address the other recommendation to the authors as they will improve the manuscript.

eLife assessment

The study provides an important advance towards understanding how spatial and temporal transcriptional programs are integrated to regulate lineage-specific chromatin and enhancer activation. The functional evidence is currently incomplete, but the current data provide a solid correlative and conceptual foundation. Functional experiments directly linking Gsb occupancy to chromatin state and regulation of some lineage-specific targets would further strengthen the causal interpretation of the model. Clarifying the scope of conclusions and explicitly acknowledging the technical limitations of current chromatin assays would provide a more balanced interpretation of the manuscript.

Reviewer #1 (Public review):

Summary:

It has long been known that Drosophila embryonic ventral nerve cord neuroblasts incorporate both spatial and temporal transcription factor expression to generate 30 distinct neuroblasts and lineages per hemisegment. This manuscript aims to elucidate the mechanism by which this integration of spatial and temporal transcription factors occurs through "direct regulation" or "epigenetic regulation". Direct regulation is defined as both spatial and temporal factors binding to open chromatin and working together to dictate specific lineages. Epigenetic regulation is defined as a spatial factor priming the chromatin in a neuroblast-specific manner to allow for the integration of temporal factors to generate specific lineages. The authors conclude that there is a two-step model in which a spatial transcription factor code "primes" the chromatin in terms of accessibility and then recruits temporal factors to ensure lineage-specific enhancer activation.

Strengths:

The authors tested two models, "direct regulation" vs "epigenetic regulation" in a well-defined pool of neural stem cells during normal development.

Weaknesses:

The data in this study cannot clearly substantiate these two models.

Overall, there are a number of issues that are inconsistent and not supportive of the model proposed in this manuscript. Firstly, there is no evidence of pioneer factor activity in any of the NB lineages described - i.e., any changes in chromatin accessibility being shown over time. The authors must show chromatin conformation changes during the window of spatial transcription factor expression in order to convince the readers of this phenomenon. Secondly, the phenotypic data do not align with the sequencing data - the story would be more cohesive if the sequencing data and phenotypic data were in the same NB subtypes. On one hand, we are shown that Gsb misexpression induces loss of chromatin accessibility in NB 7-4, however in the widespread loss model, we are not shown a phenotype in these NB7-4 - which suggest that the chromatin accessibility at these sites (sites that have already been distinguished as SoIs for that NB subtype) does not play an important role in distinguishing NB 7-4 identity. However, the authors report loss of NB3-5 identity but have no evidence as to how the chromatin has changed (or if it has at all) in that subtype, leaving the readers to wonder how the loss of identity occurred.

Reviewer #2 (Public review):

Summary:

This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.

Strengths:

The experiments are well-executed, the interpretations are generally sound, and the figures are clear and elegant. However, some conclusions are drawn too broadly without essential functional data. Therefore, additional work is needed to more effectively convey the central message.

Weaknesses:

(1) Integration of TaDa and functional data on Gsb for the STF model

The authors demonstrate that TaDa profiling maps Gsb binding across the genome and identifies candidate chromatin-priming sites in NB5-6. Gsb LOF/GOF experiments reveal effects on NB identity. Combining TaDa data with LOF and GOF analyses indicates that Gsb influences NB5-6 specification by binding to both open and relatively closed chromatin, helping maintain NB5-6 identity while limiting NB3-5 fate.

However, the study does not establish a direct link between specific LOF/GOF phenotypes and particular genomic targets. For instance, analyzing Gsb occupancy at lineage-specific identity factors or terminal selector genes (such as Lbe, Ap, or Eya for NB5-6; and Ems, etc., for NB3-5) in wild-type and manipulated conditions (Gsb misexpression) would directly connect chromatin binding to the regulation of fate determinants. These investigations would strengthen the mechanistic connection between the correlative TaDa profiles and the observed identity changes, supporting the idea that Gsb functions as a context-dependent chromatin-priming factor within the STF code, rather than as a generic transcription factor.

(2) Gsb misexpression reveals bidirectional chromatin remodelling

Experiments with ectopic Gsb expression demonstrate bidirectional chromatin remodeling in NB7-4, showing decreases in accessibility at some binding sites and increases at others. While the authors show that Gsb can disrupt chromatin upon misexpression, interpreting its "pioneer-like" or chromatin-priming activity is complex due to several factors: the misexpression occurs in a non-native lineage, the direct versus indirect effects rely on whole-embryo Dam-Gsb peaks instead of NB7-4-specific binding, and heat-shock-induced chromatin changes are not fully accounted for. These issues make it challenging to definitively determine Gsb's role in chromatin priming.

A complementary approach would be to perform Gsb knockdown/loss-of-function in its native NB5-6 lineage and profile chromatin accessibility (TaDa or CATaDa). This would allow a cleaner, more physiologically relevant assessment of Gsb's contribution to priming, SoI establishment, and Hb recruitment. Such an experiment would strengthen the causal link between Gsb occupancy and chromatin state and clarify whether Gsb truly acts as a context-dependent pioneer in vivo, rather than producing indirect effects due to ectopic misexpression.

(3) En is not a pioneer factor

The authors conclude that Engrailed (En) is not a pioneer factor, based on the observation that En binding correlates with accessible chromatin and that En is not enriched at NB5-6-specific SOIs. However, this conclusion is not sufficiently supported by the functional data.

First, the absence of En binding at NB5-6-specific SOIs does not necessarily indicate an inability to engage closed chromatin. These regions were not selected for the presence of En consensus motifs, so their lack of occupancy may simply reflect the absence of En binding motifs rather than a lack of pioneering capacity. A systematic motif analysis at NB5-6-specific SOIs is needed to determine whether En binding sites are present but unoccupied.

Second, the claim that En lacks pioneer activity relies solely on a single steady-state TaDa/DamID occupancy assay at one developmental stage. Because pioneer factor interactions can be transient, low-affinity, and stage-specific, such binding may not be detected by TaDa, which also depends on local GATC density and methylation kinetics and may yield false negatives. Given these technical limitations, the absence of En binding at less accessible regions does not definitively rule out a priming role.

In the absence of direct functional assays (En LOF/GOF), the authors should explicitly acknowledge these technical and conceptual limitations and tone down the claim that "En lacks pioneer activity".

(4) Clarity of STF-code Model and Central Message

The manuscript begins by presenting two models, direct and epigenetic, but the central takeaway of the paper is not clear. Specifically, the nuanced roles of the spatial factors Gsb and En as chromatin-priming versus stabilizing/effector factors within an STF code, and the resulting division of labor, are not clearly illustrated. The distinction between Gsb as a chromatin-priming factor and En as a cofactor-dependent activator/stabilizer should be explicitly presented in a stepwise model for better clarity. The authors could strengthen this by providing a schematic with two sequential stages illustrating how neuroblast identity factors (STF code) change chromatin states to drive lineage-specific enhancer activation. The schematic can be shown from the neuroectoderm to individual NB lineages to make it more panoramic.

(5) Identification of Priming Factors in NB7-4

While the authors suggest that an unknown priming factor might be responsible for establishing sites of integration in NB7-4, they do not identify or explore potential candidates for this role. Further investigation into what factors might be involved in chromatin priming in NB7-4 could provide a more complete understanding of the mechanisms at play.

(6) Functional Validation of STF Code Components

The study proposes an STF code for each neuroblast lineage, but the specific components of these codes, beyond Gsb and En, are not fully explored. Identifying and validating additional factors that contribute to the STF code in each lineage could strengthen the conclusions.

Author Response:

eLife assessment:

The study provides an important advance towards understanding how spatial and temporal transcriptional programs are integrated to regulate lineage-specific chromatin and enhancer activation. The functional evidence is currently incomplete, but the current data provide a solid correlative and conceptual foundation. Functional experiments directly linking Gsb occupancy to chromatin state and regulation of some lineage-specific targets would further strengthen the causal interpretation of the model. Clarifying the scope of conclusions and explicitly acknowledging the technical limitations of current chromatin assays would provide a more balanced interpretation of the manuscript.

We thank the reviewers and editors for their comments on our manuscript. We address here the concerns raised by them.

Public Reviews:

Reviewer #1 (Public review):

Summary:

It has long been known that Drosophila embryonic ventral nerve cord neuroblasts incorporate both spatial and temporal transcription factor expression to generate 30 distinct neuroblasts and lineages per hemisegment. This manuscript aims to elucidate the mechanism by which this integration of spatial and temporal transcription factors occurs through "direct regulation" or "epigenetic regulation". Direct regulation is defined as both spatial and temporal factors binding to open chromatin and working together to dictate specific lineages. Epigenetic regulation is defined as a spatial factor priming the chromatin in a neuroblast-specific manner to allow for the integration of temporal factors to generate specific lineages. The authors conclude that there is a two-step model in which a spatial transcription factor code "primes" the chromatin in terms of accessibility and then recruits temporal factors to ensure lineage-specific enhancer activation.

We thank the reviewer for this clear and succinct summary and for accurately capturing the central idea of the model we propose. In particular, we appreciate that the reviewer highlights the distinction between the previously proposed “direct regulation” and “epigenetic regulation” models, which our work suggests may operate together within neuroblast lineages through a combinatorial spatial transcription factor code.

Strengths:

The authors tested two models, "direct regulation" vs "epigenetic regulation" in a well-defined pool of neural stem cells during normal development.

We thank the reviewer for recognizing this aspect of the study.

Weaknesses:

The data in this study cannot clearly substantiate these two models.

Overall, there are a number of issues that are inconsistent and not supportive of the model proposed in this manuscript. Firstly, there is no evidence of pioneer factor activity in any of the NB lineages described - i.e., any changes in chromatin accessibility being shown over time. The authors must show chromatin conformation changes during the window of spatial transcription factor expression in order to convince the readers of this phenomenon.

Thank you for raising this point. In most studies, pioneer or chromatin-priming activity is inferred from a transcription factor’s ability to bind regions of relatively low accessibility and to remodel chromatin upon perturbation, rather than from direct developmental time-course measurements of chromatin accessibility.

In our study we provide two lines of evidence consistent with such activity. First, TaDa profiling shows that Gsb occupies both accessible loci and regions that are relatively less accessible in NB5-6. Second, ectopic expression of Gsb in the non-cognate NB7-4 lineage results in clear chromatin remodelling, with loci both gaining and losing accessibility (Fig. 6). These perturbation experiments demonstrate that Gsb is sufficient to alter chromatin accessibility in vivo and therefore support a chromatin-priming role for it.

We agree that a developmental time-course would be very informative. The difficulty is that, in this system, the relevant sequence unfolds extremely rapidly and across two different cellular contexts. Spatial transcription factors such as Gsb are expressed in the neuroectoderm, neuroblasts are then specified and delaminate, and Hb expression begins almost immediately after NB formation — on the order of minutes to tens of minutes. Before delamination there is no neuroblast to target with NB-specific drivers, and once the NB forms the temporal program is already underway. More generally, resolving chromatin accessibility changes across this transition would require temporally precise profiling at very high resolution in vivo, likely with live or near-live methods, and is not feasible with the Dam-based lineage-restricted approaches currently available.

Secondly, the phenotypic data do not align with the sequencing data - the story would be more cohesive if the sequencing data and phenotypic data were in the same NB subtypes. On one hand, we are shown that Gsb misexpression induces loss of chromatin accessibility in NB 7-4, however in the widespread loss model, we are not shown a phenotype in these NB7-4 - which suggest that the chromatin accessibility at these sites (sites that have already been distinguished as SoIs for that NB subtype) does not play an important role in distinguishing NB 7-4 identity. However, the authors report loss of NB3-5 identity but have no evidence as to how the chromatin has changed (or if it has at all) in that subtype, leaving the readers to wonder how the loss of identity occurred

Thank you for raising this point regarding the alignment between the chromatin and phenotypic analyses. The reviewer’s comment made us realise that the rationale for these experiments may not have been sufficiently clear in the original manuscript and could therefore be perceived as misaligned. We therefore explain the logic of the experimental design here and will edit the manuscript in the revision to clarify this point for readers.

The chromatin experiments were designed to test whether Gsb is capable of remodelling chromatin when introduced into a non-cognate lineage. For this purpose, NB7-4 provided a suitable lineage with clean genetic access for TaDa/CATaDa experiments, allowing us to assess whether ectopic Gsb expression can alter chromatin accessibility in vivo.

The functional role of Gsb, however, was examined within the spatial domain in which it is normally expressed. We knocked-down Gsb broadly and early in development and assayed its effects on NB5-6. Consistent with its established role in row-5/6 patterning, reduction of Gsb disrupted the specification of NB5-6 identity. In the converse experiment, broad misexpression of Gsb led to a partial expansion of NB5-6 markers. Because spatial patterning in the ventral nerve cord is organized into mutually exclusive row identities, changes in NB5-6 specification can be accompanied by reciprocal effects in neighbouring lineages. In our experiments, this is reflected in changes in markers of adjacent identities, particularly NB3-5. For this reason, NB3-5 markers provide a sensitive and informative readout of altered NB5-6 specification in the phenotypic analyses.

We recognize that this point may not have been clear in the original manuscript. To avoid similar confusion for readers, we will make this reasoning explicitly clear in the revision.

Reviewer #2 (Public review):

Summary:

This article by Bhattacharya et al. investigates how neural stem cells (NSCs, NBs) in Drosophila integrate spatial and temporal cues to activate neuron-specific terminal selector (TS) genes. Prior to this work, it was understood that NSCs utilize spatial transcription factors (STFs) and temporal transcription factors (TTFs) to determine lineage identity and birth order, but the mechanisms of integration were not fully elucidated. The authors employed chromatin profiling techniques to analyze the binding of STFs and TTFs in two specific neuroblast lineages, NB5-6 and NB7-4. They found that Gsb (an STF) binds both accessible and less-accessible chromatin in NB5-6, while En (another STF) binds only to pre-accessible chromatin in NB7-4. The findings support an "STF code" where the combination of pioneer and non-pioneer spatial factors, along with temporal factors, triggers neuroblast-specific enhancer activation and determines lineage identity.