大多数人认为AI应该严格限制在特定任务和数据集内，以避免信息污染和边界模糊，但作者认为AI应该能够跨渠道学习并整合不同来源的信息。这挑战了人们对AI应用范围和数据隔离的传统认知，暗示未来AI将更像是具有广泛知识背景的团队成员。

大多数人认为AI会取代人类工作，导致失业，但作者认为AI实际上改变了人类工作方式，让人们转向更高层次的任务分配和管理。这挑战了关于AI与就业关系的传统叙事，表明AI可能创造新的工作形式而非简单替代人类。

大多数人认为AI辅助编程只是辅助工具，主要用于代码补全或简单任务，但作者认为AI已经成为主要代码生产者，因为内部版本已经完成了产品团队65%的代码生成。这挑战了人们对AI在软件开发中角色的传统认知，表明AI已从辅助工具转变为核心生产力工具。

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Referee #2

Evidence, reproducibility and clarity

In their manuscript entitled " Single-molecule behavior and cell-growth regulation in human RTKs" Abe et al. demonstrate automated single-particle tracking of 52 receptor tyrosine kinases (RTKs) in both resting state and upon stimulation with the respective ligands. The approach is based on transient transfection of cells with each RTK tagged with a Halo-tag, allowing for subsequent dye labeling and live cell video recording using TIRF microscopy. Subsequently, a seemingly commercial analysis software is used to then obtain particle trajectories from single molecule localizations and analyze their properties using a hidden Markov model. The authors have previously demonstrated pioneering work in the field of single-particle tracking with respect to automation (Yasui et al, 2018) and analysis (Yanagawa et al, 2021), and in this work they scale their approach up to characterize a broad set of RTKs. The resulting observations are a powerful demonstration of the benefits of SPT in general and significantly advance our understanding of the dynamics of RTKs as a class, beyond the most prominently studied candidate EGFR, as well as promising evolutionary insights.

Comments and questions:

1. The authors picked 52 out 58 human RTKs. Why not all?
2. In contrast to the above mentioned previous publications, here a seemingly commercial software package was used (AAS by Zido). The methods part is very short on the specific parameters that were used to i) localize particles (e.g. net gradient threshold) or ii) connect localizations into trajectories (step size, allowed dark frames, min. trajectory length). Similarly a clearer explanation of the HMM calculus would significantly help to better follow the analysis approach and parameter choice. Perhaps this reviewer has missed it, but why did the authors e.g. choose 3 states for HMM?
3. The replicate experiment in Fig. S2 is appreciated, but what condition was repeated here? Also experimental details are missing: was it two repeats of: i) seeding cells in a dish, transfection, labeling, imaging? An image from cells from those repeats would be important to show, also to which degree the density of particles F varies, i.e. to which degree this is an unprecise experimental parameter itself as compared to biologically meaningful. This is especially as Fig. S2 does not contain any density comparison at all, whereas in the main figures it is indeed an experimental observable used.
4. The density raises another issue. Some of the movies show extremely dense signal. Here the authors should explain how they deal with particles whose trajectories cross. This could lead to artificial dynamics and a supplementary figure showing that their analysis is robust toward varying densities (again suggesting to include a simulation) could be helpful
5. Fig. 2C is a bit hard to understand since here localizations are colored based on their state but not from which trajectory they come. Do e.g. individual trajectories show various dynamic behaviors or are the trajectories not long enough to observe this?
6. The evolutionary aspects could use further and simpler explanations to make this passage easier to grasp

Significance

The manuscript by Abe et al. represents a significant advancement in the field of single-particle tracking (SPT) by scaling up recording 52 human receptor tyrosine kinases (RTKs), offering comprehensive insights into their dynamics beyond the traditionally studied EGFR. While the study demonstrates cutting edge single-particle tracking and provides promising evolutionary insights, it currently lacks certain methodological details that are essential for reproducibility, such as specific parameters used in particle localization and trajectory analysis. The exclusion of 6 out of 58 human RTKs without discussion also requires further explanation, but overall, the study fills a knowledge gap by providing a broad overview of RTK dynamics and their diffusion behavior. Overall, this work should have broad appeal to fields such as cell signaling as well as methods development int the area of single-particle tracking.

a decade ago or more I invented the notion of on page notation.processor that can be activated on any line in an HTML editor on demand

recently I discovered that I was 20 years behind Engelbart's work, again, reinventing another idea of his community: the notation.processor

The Command Language Interpreter that was at the heeart of the Mother of All Demos that became MetaIV, is very much in the small ball partk, the ability to define the input to a program as a language, in their case, as it was an interactive program, they gone meta too, and defined the command language interpreter using explicit meta linguistic formalism, noation, and the program becomes a command langauge interpreeter

Reviewer #1 (Public review):

Summary:

This study investigates epigenetic and three-dimensional chromatin alterations associated with primary trastuzumab resistance in HER2-positive breast cancer using integrated CUT&Tag, RNA-seq, and Micro-C analyses in JIMT1 (resistant) and SKBR3 (sensitive) cell models. The authors identify widespread remodeling of histone modification landscapes, chromatin compartment organization, and promoter-enhancer looping, highlighting SGK1 as a candidate epigenetically activated mediator associated with intrinsic resistance. The manuscript provides a technically solid and extensive multi-omic resource for the study of HER2-positive breast cancer resistance states.

Strengths:

The study integrates multiple state-of-the-art epigenomic and chromatin conformation approaches, including CUT&Tag, RNA-seq, and Micro-C, generating a comprehensive dataset that will likely be valuable to the field. The analyses are generally technically rigorous and well executed, and the manuscript is overall clearly written. The integration of chromatin architecture, enhancer activity, transcriptional regulation, and histone modification profiling provides an informative overview of large-scale epigenomic remodeling associated with resistant versus sensitive HER2-positive breast cancer states. The identification of SGK1-associated chromatin activation and enhancer rewiring is particularly interesting and supported by multiple orthogonal datasets.

The inclusion of both intrinsic and acquired trastuzumab resistance models also strengthens the study conceptually, even if the biological interpretation remains somewhat complex.

Weaknesses:

The major limitation of the study is that many of the central mechanistic conclusions remain largely correlative. Although coordinated changes in chromatin architecture, histone modifications, enhancer activity, and SGK1 expression are observed, direct evidence demonstrating that these epigenetic alterations causally drive SGK1 activation or trastuzumab resistance is currently lacking.

In addition, the interpretation of SGK1 as a broader trastuzumab-resistance driver is somewhat weakened by the analyses in the acquired resistant SKBR3_HR model, where SGK1-associated chromatin and transcriptional changes appear largely absent. This raises the possibility that SGK1 dependency may reflect a lineage- or model-specific vulnerability intrinsic to JIMT1 cells rather than a generalizable resistance mechanism.

The study also remains descriptive in several sections. Numerous chromatin interactions and compartment changes are cataloged without sufficient biological contextualization or mechanistic integration. As a result, parts of the manuscript currently read more as a comprehensive epigenomic profiling resource than a fully mechanistic study of resistance biology.

Finally, the translational impact is limited by the lack of patient-level validation linking SGK1 activation to trastuzumab response or clinical outcome in HER2-positive breast cancer cohorts.

Reviewer #2 (Public review):

Summary:

Duan, Hua et al. used CUT&Tag and Micro-C to investigate that in primary trastuzumab-resistant HER2+ breast cancer cells, promoter H3K4me3 rather than H3K27me3 is strongly correlated with transcriptional activity. Resistant cells also exhibited more abundant promoter-enhancer loops and enriched cohesin at loop anchors, accompanied by shifts in A/B compartment status. Through multi-omics integration, the authors identified SGK1 as a key gene showing elevated promoter H3K4me3 levels, enhancer activation, strengthened chromatin loops, and upregulated transcription in resistant cells, and validated SGK1 as a potential therapeutic target. These findings reveal the coordinated interplay between three-dimensional chromatin architecture and epigenetic modifications, offering important insights into trastuzumab resistance in HER2+ breast cancer.

Strengths:

Previous investigations into trastuzumab resistance have largely focused on genetic mutations or individual epigenetic modifications. In contrast, this study moves beyond genetic or single epigenetic views by integrating histone modifications and 3D chromatin architecture into a unified framework, proposing a synergistic model of promoter H3K4me3, enhancer activation, and chromatin looping that underlies non-genetic resistance. It provides a new conceptual basis for understanding non-genetic resistance mechanisms. Secondly, using high-resolution epigenomic and conformational mapping together with bidirectional in vitro and in vivo functional validation, it establishes a solid link between epigenetic changes and phenotypes, and demonstrates that SGK1 inhibition suppresses tumor growth in a xenograft model, revealing clear translational potential.

Weaknesses:

(1) All findings are based on a single pair of cell lines, JIMT1 and SKBR3, which does not allow exclusion of cell line‑specific effects. The authors did not examine SGK1 expression levels, promoter H3K4me3 status, or relevant chromatin loops in tumor tissues from patients with clinical trastuzumab resistance. Consequently, whether the conclusions can be extrapolated to actual patient populations remains unclear, which limits the clinical relevance of the findings. It is recommended that the authors directly validate the key findings using tumor samples from patients with clinical trastuzumab resistance or analyze the correlation between SGK1 expression levels and disease-free survival or pathological complete response using data from public databases for HER2+ breast cancer patients, which would help address the current limitation of lacking clinical sample validation and the uncertainty regarding the association of SGK1 with patient prognosis and treatment response.

(2) In the Discussion, the authors propose that SGK1 may assume the role of AKT to sustain mTOR activation, thereby bypassing the dependence on HER2 signaling following trastuzumab inhibition. Although this hypothesis is supported by published literature, the present study provides no direct signaling evidence, such as examining phosphorylation changes of SGK1, AKT, mTOR, or their downstream effectors.

Reviewer #3 (Public review):

Summary:

Previous work from the Cahalan lab used fluorescent Genetically Encoded Ca2+ Indicators (GECI), like GCaMP6f, tethered to the N- or C- terminus of Orai1 to monitor CRAC channel optical signals (Dynes et al., PNAS 2016 PMID: 26712003; J Gen Physiol 2020 PMID: 32589186; PNAS 2023 PMID: 37729200). In this study from the Lewis lab, the HaloTag system enables C-terminal labeling of Orai1 with a reactive JF646-BAPTA loaded into cells. The article raises two key issues with the Ca2+ indicator probe that may limit potential applications: probe loading conditions and blinking.

Making Sense of Probe Probe-lems:

This is a three-component system: the hexameric Orai1 channel, the Halo tag, and the Ca2+ indicator (four components if you count the GFP- or mCherry-tagged STIM1 in the endoplasmic reticulum membrane that activates the plasma membrane Orai1 channel). The Orai1 channel, tagged with the Halo protein, appears to function normally, judging from the characteristic inwardly rectifying Ca2+ current first observed in T lymphocytes (Lewis and Cahalan, Cell Regulation 1989 PMID: 2519622). One problem is to find a condition for indicator dye loading that results in complete and uniform labeling with the covalently linked JF646 indicator. JF646-BAPTA is a far-red fluorescent indicator related to BAPTA, with a Kd of ~150 nM. The esterified form can be loaded into cells, as is routinely done for Ca2+ indicators like fura-2 or fluo-4. Ideally, to monitor local Ca2+ in the cytosolic nanodomain of the Orai1 channel, the indicator should react with each and every Halo tag of the hexameric channel. The authors assessed published methods by varying the exposure time to the JF646-BAPTA-esterified probe. The authors then used green JF552 labeling following red JF646-BAPTA loading to assess the completeness of labeling. Even overnight incubation of Halo-tagged cells was not sufficient. The addition of Pluronic treatment for 1 hr improved labeling, and a standard condition was adopted. Under this condition, no additional labeling with the green JF552 was seen, implying complete labeling with JF646-BAPTA. However, even with complete labeling, several additional effects might reduce the effective signal-to-noise, which is lower in these studies than expected from in vitro measurements - for example, if the JF646-BAPTA molecules are incompletely de-esterified, or if there is quenching between the closely spaced probes attached to the channel hexamer.

A second, more serious problem analyzed by this article is that the JF646-BAPTA probe blinks on and off spontaneously, making it problematic to monitor true single-channel events in which the channel open state is assessed by the fluorescent probe. The authors distinguish blinking from channel-gating events by carefully noting the residual level of fluorescence in the absence of Ca2+ influx. Blinking events occur in bursts that reduce fluorescence transiently to zero, whereas the closed channel labeled with JF646-BAPTA retains a low level of fluorescence (~20%). To circumvent the blinking issue, the authors use whole-cell patch recording, in conjunction with optical recording (Patch-TIRF). This allows channel-gating events to be identified by step-wise changes in fluorescence due to Ca2+ entry upon hyperpolarization to -100 mV, above a baseline level of fluorescence at +30 mV, which the authors presume represents the closed channel level of fluorescence. Irreversible photobleaching is an additional issue, limiting the recording times to less than 1 minute.

Visualizing Orai1 Single-Channels:

With the blinking problem circumvented, at least in part, the authors uncovered a wide variety of single-channel events. Cells with low expression levels of Orai1 revealed 0-3 active Orai1 channels per STIM1 puncta. The range of gating behavior at the single-channel level is one of the revelations in this study. A substantial fraction (11%) of puncta contained "silent" channels that did not open (detected by the non-zero level of baseline fluorescence for closed channels). At the other extreme, some channels remained open for tens of seconds. On average, channels that opened and closed stochastically exhibited a bi-exponential distribution of bright states (open channels), with a major component of fast events (92 ms) and a minor component of slower ones (1190 ms), as well a single-exponential distribution of dark states (closed channels), and open probabilities >0.7. Channel open/closed times and the high open probability of active Orai1 channels seen here reinforce previous work based on analysis of CRAC current fluctuations in whole-cell recording, and optical single-channel recording using a different genetically encoded Ca2+ indicator, G-GECO1, tethered to Orai1 (Prakriya and Lewis, J Gen Physiol 2006 PMID: 16940559; Dynes et al., PNAS 2016 PMID: 26712003).

Expression levels for single-channel optical recording must be low; accordingly, puncta contained only 0-3 active channels. However, under conditions of high STIM1 and Orai1 expression, conventionally used to investigate channel function, as in Figure 1, cells with large currents express many thousands of active channels. The number of active channels per cell can be calculated by dividing the peak current (~-100 pA) by the voltage (-100 mV); this corresponds to a whole-cell conductance (G) of ~1 nS (conductance is measured in Siemens). The single channel conductance (gamma, too low to detect electrically) is estimated by noise analysis to be 20-40 fS. Thus, the number of active channels is given by G / gamma corresponding to a range of > 25,000 - 50,000 open channels per cell. Under similar conditions of high STIM1/Orai1 co-expression in HEK cells, individual Orai1 channels were visualized at high density in puncta by freeze-fracture electron microscopy (Perni et al., PNAS 2015 PMID: 26351694), revealing puncta packed with Orai1 particles corresponding to hundreds to >1000 channels per punctum. Measuring the center-to-center distances between particles in puncta revealed two peaks in a distribution of inter-particle lengths: 9 nm (consistent with the approximate width of the Orai1 channel hexamer) and 15 nm (possibly due to two adjacent Orai1 channels held together by intervening STIM1 dimers).

Strengths:

The authors do an excellent job of analyzing and discussing probe artifacts that can confound measurements at the single-channel level. On the technical side, we thank the authors for including a photon 'budget' for their imaging experiments by including: the conversion factor from camera intensity units (c.u.) to photoelectrons, cell background fluorescence levels, and nominally Ca2+ free single channel fluorescence levels. One parameter missing from the list is the size of the region of interest used for channel recording. We expect the intensity measurements provided in the channel traces to correspond to mean ROI intensity levels. Upon knowing the ROI size in pixels, the magnitude of fluorescent signals could then be calculated in photons. Taken together, these values will aid comparisons to previous work and help guide subsequent researchers doing their own optical recording.

The most important finding of this study is the ability to analyze single-channel properties of active Orai1 channels using the HaloTag approach. By direct measurement, the authors confirm previous work that there are at least two open states and that the CRAC channel open probability is greater than 0.7.

Like any good study, this work suggests opportunities for further work. At the chemistry level, one focus should be the development of new probes that don't blink and have lower affinity for Ca2+ to circumvent unwanted responses to global Ca2+ signaling. Far-red probes like JF646-BAPTA have the advantage of reduced scattering for in vivo imaging applications. At the level of channel molecular function, the results pave the way for unraveling mechanisms of channel gating, such as the requirement for STIM1 binding to activate sub-states of Orai1, and how the channel undergoes Ca2+-dependent inactivation. At the cellular physiology level, localized Ca2+ probes should help to clarify mechanisms that couple to changes in gene expression and reveal Ca2+ signaling in subcellular structures, including dendritic spines. As a nice proof of principle, Halo-tagging enabled Ca2+ signals to be measured in primary cilia (Deo et al., J Am Chem Soc 2019 PMID: 31430138). Future users of HaloTag and GECI Ca2+ indicators will need to confront the issues (probe-lems) at the single-channel level that are carefully raised and analyzed in this article.

Weaknesses:

The major confounding issue identified here is probe blinking. The authors find a way to circumvent the issue, but not to prevent it. Is it triggered by high laser light intensity? Do the six JF646-BAPTA molecules tagging a single Orai1 channel exhibit quenching or correlated blinking?

Which type of probe is better for understanding more about the CRAC channel function? It is difficult to evaluate the pros and cons of the HaloTag and GECI approaches without a side-by-side comparison under identical conditions (except for the probe, obviously). With respect to Ca2+ affinities, higher Kd values (lower affinity) are probably better. JF646-BAPTA has a relatively low Kd value (150 nm) compared to Orai1-GCaMP6f (620 nM in situ), which may account for the saturation of optical signals at potentials more negative than -75 mV in this study. In contrast, saturation did not occur at negative potentials with Orai1-GCaMP6f in the study by Dynes et al., 2020. Lower affinity also makes the probe more resistant to unwanted signals from global increases in Ca2+. With respect to response kinetics, the finding that JF646-BAPTA has faster Ca2+ binding and unbinding kinetics than GECIs in Deo et al., 2019, occurred before publication of the jGCaMP8 series indicators in Y. Zhang et al., Nature 2023. Kinetic measurement of Orai1-jGCaMP8f fusions was reported in Dynes et al., PNAS 2023, and these measurements were performed using the same patch-TIRF approach as the present manuscript. While photoinactivation of jGCaMP8f fused to Orai1 interfered with kinetic measurements, Orai1-jGCaMP8f V203Y (a mutant with greatly reduced photoinactivation) exhibited a tauon of 10 ms and tauoff of 15 ms, roughly twice as fast as the values reported for Orai1-HaloTag-JF646-BAPTA in the present manuscript. The manuscript text comparing Halo-Tag kinetics with GECI should be revised accordingly.

The authors suggest that single-channel events reported previously for Piezo1 channels (Bertaccini et al., Nat Comm 2025 PMID: 40593468) may be due to probe blinking. However, that study included two critical controls that demonstrate that signals reflect bona fide channel activity rather than blinking artifacts. Notably: (1) treatment with channel activator Yoda1 increased bright-state occupancy (Figure 3C - 3G), and (2) increasing channel open probability by administering a mechanical stimulus increased bright-state occupancy (Supplementary Figure 13).

I believe this particular form was used in Mladen, Le, Xing 2029, and then systematically studied in Estimating time-varying networks, AOAO 2010 Mladen Kolar, Le Song, Amr Ahmed, Eric P Xing

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Kashiwagi et al. undertook a population analysis of dendritic spine nanostructure applied to the objective grouping of 8 mouse models of neuropsychiatric disorders. They report that spine morphology in cultured hippocampal neurons shows a higher similarity among schizophrenia mouse models (compared with autism spectrum disorder (ASD) mouse models), and identify an effect of Ecrg4 (encoding small secretory peptides) on spine dynamics and shape in these models.

Strengths:

The study developed a method for objectively comparing spine properties in primary hippocampal neuron cultures from 8 mouse models of psychiatric disorders at the population level using high-resolution structured illumination microscopy (SIM) imaging. This novel technique identified two distinct groups of mouse models according to the population-level spine properties: those with ASD-related gene mutations and those with schizophreniarelated gene mutations. Functional studies, including gene knockdown and overexpression experiments, identified an effect of Ecrg4 on the spine phenotype of the schizophrenia model mice.

We thank the reviewer for finding our strategy novel and useful for identifying molecules associated with the spine phenotype in schizophrenia-related mouse models.

Weaknesses:

The main weakness is that the study is wholly in vitro, using cultured hippocampal neurons. The authors present this as an advantage, however, arguing that spine morphology as measured in a reduced culture system can demonstrate direct effects of gene mutations on neuronal phenotypes in the absence of indirect influences from non-neuronal cells or specific environments.

We appreciate this reviewer's concern about the limitation of cultured hippocampal neurons in extracting disease-related spine phenotypes. While we fully recognize this limitation, we consider that this in vitro system has several advantages that contribute to translational research on mental disorders.

First, our culture system has been shown to support the development of spine morphology similar to that of the hippocampal CA1 excitatory synapse in vivo. High-resolution imaging techniques confirmed that the in vitro spine structure was highly preserved compared with in vivo preparations (Kashiwagi et al., Nature Communications, 2019). The present study used the same culture system and SIM imaging. Therefore, the difference we detected in samples derived from disease models is likely to reflect impairment of molecular mechanisms underlying native structural development in vivo.

Second, super-resolution imaging of thousands of spines in tissue preparations under precisely controlled conditions cannot be practically applied using currently available techniques. The advantage of our imaging and analytical pipeline is its reproducibility, which enabled us to compare the spine population data from eight different mouse models without normalization.

Third, a reduced culture system can demonstrate the direct effects of gene mutations on synapse phenotypes, independent of environmental influences. This property is highly advantageous for screening chemical compounds that rescue spine phenotypes. Neuronal firing patterns and receptor functions can also be easily controlled in a culture system. The difference in spine structure between ASD- and schizophrenia-related mouse models is valuable information to establish a drug screening system.

Fourth, establishing an in vitro system for evaluating synapse phenotypes could reduce the need for animal experiments. Researchers should be aware of the 3Rs principles. In the future, combined with differentiation techniques for human iPS cells, our in vitro approach will enable the evaluation of disease-related spine phenotypes without the need for animal experiments. The effort to establish a reliable culture system should not be eliminated.

We modified our text to have a balanced discussion on both advantages and disadvantages of the in vitro culture system in the study of mental disorder mouse models, as follows:

"Finally, while the spine phenotype identified in the human postmortem brain undoubtedly resulted from complex interactions among genetic background, environmental influences, and regulation by non-neuronal cells, data from pure neuronal cultures are more likely to reflect the direct effects of schizophrenia-related gene mutations on synaptic functions. This property may be advantageous for identifying synaptic molecules that regulate synapse phenotypes in schizophrenia-related mouse models. However, the phenotype observed in the culture system requires confirmation using in vivo experiments of mouse models or human tissue samples. Efficient in vitro screening combined with reliable in vivo evaluation of synapses will facilitate translational research on mental disorders."

Another weakness is that CaMKIIαK42R/K42R mutant mice are presented as a schizophrenia model, the authors justifying this by saying that "CaMKII-related signaling pathway disruption has been implicated in the working memory deficits found in schizophrenia patients". Since mutations in CAMK2A cause autosomal dominant intellectual developmental disorder-53 (OMIM 617798) and autosomal recessive intellectual developmental disorder-63 (OMIM 618095), and mice carrying the CAMK2A E183V mutation exhibit ASD-related synaptic and behavioral phenotypes (PMID: 28130356), I think it's stretching credibility to refer to the CaMKIIαK42R/K42R mice as a schizophrenia model.

We agree with this reviewer that CAMK2A mutations in humans are linked to multiple mental disorders, including developmental disorders, ASD, and schizophrenia. Association of gene mutations with the categories of mental disorders is not straightforward, as the symptoms of these disorders also overlap with each other. For the CaMKIIα K42R/K42R mutant, we considered the following points in its characterization as a model of mental disorder. Analysis of CaMKIIα +/- mice in Dr. Tsuyoshi Miyakawa's lab has provided evidence for the reduced CaMKIIα in schizophrenia-related phenotypes (Yamasaki et al., Mol Brain 2008; Frankland et al., Mol Brain Editorial 2008). It is also known that the CaMKIIα R8H mutation in the kinase domain is linked to schizophrenia (Brown et al., 2021). Both CaMKIIα R8H and CaMKIIα K42R mutations are located in the N-terminal domain and eliminate kinase activity. On the other hand, the representative CaMKIIα E183V mutation identified in ASD patients exhibits unique characteristics, including reduced kinase activity, decreased protein stability and expression levels, and disrupted interactions with ASD-associated proteins such as Shank3 (Stephenson et al., 2017). Importantly, reduced dendritic spines in neurons expressing CaMKIIα E183V is a property opposite to that of the CaMKIIα K42R/K42R mutant, which showed increased spine density (Koeberle et al. 2017).

References related to this discussion.

(1) Yamasaki et al., Mol Brain. 2008 DOI: 10.1186/1756-6606-1-6

(2) Frankland et al. Mol Brain. 2008 DOI: 10.1186/1756-6606-1-5

(3) Stephenson et al., J Neurosci. 2017 DOI: 10.1523/JNEUROSCI.2068-16.2017

(4) Koeberle et al. Sci Rep. 2017 DOI: 10.1038/s41598-017-13728-y

(5) Brown et al., iScience. 2021 DOI: 10.1016/j.isci.2021.103184

We fully agree with the reviewer that different CAMK2A mutations likely cause distinct phenotypes observed in the broad spectrum of mental disorders. In the revised manuscript, we include a discussion of the relevant literature to categorize this mouse model appropriately.

"CaMKII-related signaling pathway disruption has been implicated in the working memory deficits found in schizophrenia patients [45,46]. CAMK2A mutations in humans are linked to multiple mental disorders, including developmental disorders, ASD, and schizophrenia [47]. The K42R mutation of CAMK2A does not correspond to any known human genetic variant, but the CAMK2A R8H mutation is linked to schizophrenia [48]. Both R8H and K42R mutations in the N-terminal domain of CaMKIIα eliminate kinase activity; these mutations may have a similar impact on human mental disorders."

Although the manuscript is largely well written, there are some instances of ambiguous/unspecific language. This extends to the title (Decoding Spine Nanostructure in Mental Disorders Reveals a Schizophrenia-1 Linked Role for Ecrg4), which gives no indication that the work was in vitro on cultured neurons derived from mouse models.

We appreciate the reviewer for pointing out the lack of information about the experimental system in the title of this manuscript. According to the suggestion of the reviewer, we modified the title as "Decoding spine nanostructure in cultured neurons derived from mouse models of mental disorder reveals a schizophrenia-linked role for Ecrg4".

Reviewer #2 (Public review):

Okabe and colleagues build on a super-resolution-based technique that they have previously developed in cultured hippocampal neurons, improving the pipeline and using it to analyze spine nanostructure differences across 8 different mouse lines with mutations in autism or schizophrenia (Sz) risk genes/pathways. It is a worthy goal to try to use multiple models to examine potential convergent (or not) phenotypes, and the authors have made a good selection of models. They identify some key differences between the autism versus the Sz risk gene models, primarily that dendritic spines are smaller in Sz models and (mostly) larger in autism risk gene models. They then focus on three models (2 Sz - 22q11.2 deletion, Setd1a; 1 ASD - Nlgn3) for time-lapse imaging of spine dynamics, and together with computational modelling provide a mechanistic rationale for the smaller spines in Sz risk models. Bulk RNA sequencing of all 8 model cultures identifies several differentially expressed genes, which they go on to test in cultures, finding that ecgr4 is upregulated in several Sz models and its misexpression recapitulates spine dynamics changes seen in the Sz mutants, while knockdown rescues spine dynamics changes in the Sz mutants. Overall, these have the potential to be very interesting findings and useful for the field. However, I do have a number of major concerns.

We thank the reviewer for evaluating our findings as potentially very interesting and useful.

(1) The main finding of spine nanostructure changes is done by carrying out a PCA on various structural parameters, creating spine density plots across PC1 and PC2, and then subtracting the WT density plot from the mutant. Then, spines in the areas with obvious differences only are analyzed, from which they derive the finding that, for example, spine sizes are smaller. However, this seems a circular approach. It is like first identifying where there might be a difference in the data, then only analyzing that part of the data. I welcome input from a statistician, but to me, this is at best unconventional and potentially misleading. I assume the overall means are not different (although this should be included), but could they look at the distribution of sizes and see if these are shifted?

We appreciate the reviewer's concern regarding our analysis of spine population data. The intention of pre-selecting the areas showing differences between wild-type and mutant was to make a direct comparison between two subareas (one is enriched with wild-type spines and the other is enriched with mutant spines) and clarify that the spines of schizophreniarelated mouse models were smaller than wild-type spines. Conventional methods of comparing the total spine population using simple size parameters are not useful for this purpose, as shown in Supplementary Figure 2.

To clarify the reviewer's concern, we revised the analysis of the spine population data for both Figure 3 and Figure 8.

Figure 3: We first divided the feature space projected onto PC1 and PC2 into four areas with distinct structural properties: (1) small and short, (2) small and long, (3) large and short, and (4) large and long. Next, we calculated the normalized spine counts in the four areas for both wild-type and mutant spines and obtained the relative ratio (mutant/wild-type) for each area. As we performed three independent SIM imaging experiments (in one, we imaged both wild type and mutant culture dishes prepared from the same pregnant mouse), there are three independent datasets from 8 mouse models.

We found that the spine ratio (mutant/wild-type) only in area 2 (small and long spines) differed significantly between genotypes. This result is shown in Fig. 3 and explained in the text. The spine ratios in areas 1 and 3 did not show a clear relationship to the genotypes, while the ratio in area 4 showed the opposite trend to that in area 2. The opposite trend between areas 2 and 4 indicates enrichment of both small and long spines in schizophrenia-related mouse models, consistent with our previous analysis.

Figure 8: In this analysis, we aimed to evaluate the rescue effect of Ecrg4 shRNA relative to that of control shRNA. If Ecrg4 shRNA is effective, the spine population enriched in the control shRNA condition should be reduced in the Ecrg4 shRNA condition. To confirm this point in the revised manuscript, we first defined areas in the projected PC1-PC2 plane showing either enrichment or depletion of spines in the control shRNA condition (spine numbers increasing or decreasing by more than 3 × SD). We next measured the difference in spine numbers between the control and Ecrg4 shRNA conditions in either enriched or depleted areas. The expectation is that Ecrg4 shRNA treatment reduces the extent of both enrichment and depletion. The effect was significant in both the 22qdel and Setd1a mouse models, as indicated by permutation tests. This analysis was explained in the revised manuscript.

(2) Despite extracting 64 parameters describing spine structure, only 5 of these seemed to be used for the PCA. It should be possible to use all parameters and show the same results. More information on PC1 and PC2 would be helpful, given that the rest of the paper is based on these - what features are they related to?

We thank the reviewer for the advice on providing the rationale for parameter selection in PCA. We divided spines into 160-nm segments along their long axis, and the spine segments were used to calculate the 64 parameters, which include volume of each spine segment (20 segments), convex hull volume of each spine segment (20 segments), and convex hull ratio of each spine segment (20 segments). As most spines are shorter than 0.16 × 20 =3.2 μm, these segment-related parameters contain a large fraction of zero values, which affect the proper calculation of principal components. Therefore, we selected two parameters that reflect the principal structural features (length and volume), together with three other parameters that were mutually independent and also independent from the first two parameters (pairwise correlation coefficients < 0.3). These selection criteria were described in the original manuscript. We also confirmed that PCA using all 64 parameters yields a cross correlation map similar to that shown in Fig. 2B.

Author response image 1.

We provided additional information in the Materials and Methods section of the revised manuscript.

As described previously, the pattern of four areas with distinct spine structures (1. small and short, 2. small and long, 3. large and short, 4. large and long) supports the idea that the PC1PC2 plane reflects the relationship between spine volume and length (Fig. 3A and B).

These specific features could then be analyzed in the full dataset, without doing the cherry picking above.

We provided the dataset for the relative enrichment of spine counts across four areas of the PC1-PC2 plane in Fig. 3A and B. This analysis provides a comprehensive view of spine population properties related to spine volume and length, without relying on a pre-set region of interest.

It would also be helpful to demonstrate whether PC1 and 2 differ across groups - for example, the authors could break their WT data into 2 subsets and repeat the analysis.

We noticed differences in the pattern of spine distribution across the PC1-PC2 planes in each experiment. The subtraction of the distributional data between wild-type and mutant samples effectively cancels out such differences. In general, the difference between two wild-type samples is smaller than that between wild-type and mutant samples, as shown in Author response image 2.

Author response image 2.

We added a description of variation across groups to the revised manuscript.

(3) Throughout the paper, the 'n' used for statistical analysis is often spine, which is not appropriate. At a minimum, cell should be used, but ideally a nested mixed model, which would take into account factors like cell, culture, and animal, would be preferable. Also, all of these factors should be listed, with sufficient independent cultures.

We agree that nested mixed models are more appropriate for evaluating genotype effects in most of our datasets. We confirm that the results of statistical analysis using nested mixed models were consistent with our previous conclusions in most cases.

Figure 3: We performed three independent primary cultures of embryonic hippocampal tissue with genotypes of both wild-type and mutant from the same pregnant mice for each mouse model. In our new Figure 3, each data point represents an independent culture experiment, and group comparisons were performed using one-way ANOVA followed by Tukey's post hoc test. In this analysis, statistical analysis using neurons as units of 'n' is not possible, as the number of spines measured from a single neuron is insufficient to generate the density map shown in Figure 3. The statistical analysis was described in the revised text. The details of experimental conditions related to Figure 3 are provided in Supplementary Table 1.

Figure 5A-C: We analyzed spine turnover rate using a linear mixed-effects model with genotype as a fixed effect and plate, cell, and dendrite as nested random effects. In both 22q deletion model and Setd1a model, there were significant effects of genotype (F(1,25) = 5.79, p = 0.024 for 22q deletion model and F(1,22) = 7.33, p = 0.013 for Setd1a model). In contrast, Nlgn3 mutant neurons did not show a significant difference (F(1,14) = 1.35, p = 0.26). This analysis was described in the revised text.

Figure 5D-F: Spine lifetime was analyzed using a linear mixed-effects model accounting for the hierarchical structure of the data (spines nested within dendrites, cells, and culture plates). The analysis revealed a significant effect of genotype in both 22q deletion mutant and Setd1a mutant (22qdel mutant; F(1,336) =5.33, p=0.022, Setd1a mutant; F(1,282)=6.38, p=0.012 ). The neurons of both mutants exhibited significantly longer spine lifetimes compared with wild-type neurons (22qdel mutant; ratio = 1.28, 95% CI 1.04–1.58, Setd1a mutant; ratio = 1.35, 95% CI 1.07–1.70). In contrast, Nlg3 mutation did not significantly alter spine lifetime (ratio = 0.86, 95% CI 0.61–1.22; F(1,220)=0.69, p=0.41). This analysis was described in the revised text.

Figure 5G-I: Spine volume trajectories were analyzed using linear mixed-effects models incorporating nested random effects (spine/dendrite/cell/culture plate) to account for the hierarchical structure of the data. In the 22q deletion model, newly formed spines were significantly smaller than those in wild-type neurons (genotype effect: p < 0.001). The spines in Setd1a mutant neurons also displayed significantly smaller volume than those in wild-type neurons (p < 10^-7). There were also differences in the temporal profiles of spine growth in these two mutants (p < 0.001). In contrast, newly formed spines in the Nlgn3 mutant neurons were significantly larger than those in wild-type neurons (p < 10^-4) with preserved time-course of spine growth. This analysis was described in the revised text.

Figure 5J-L: Similar analyses using linear mixed-effects models incorporating nested random effects (spine within dendrite within cell within culture plate) identified significantly smaller initial spine size in the 22q deletion model (p < 10^⁻6), while no significant differences in the initial spine volume were found for Setd1a mutants. The temporal trajectories of spine shrinkage before their loss were also not significantly altered in both 22qdel and Setd1a mutants. The Nlg3 mutant showed a significantly different time-course of spine shrinkage (p < 0.05), while the initial spine size was not altered. This analysis was described in the revised text.

Figure 7A overexpression dataset: We analyzed plate-averaged lifetime values using a linear mixed-effects model with treatment as a fixed effect. There exists a significant main effect of treatment (F(3,8) = 4.59, p = 0.038), with post hoc examination showing a significant increase in lifetime by Ecrg4 overexpression (β = 0.49 ± 0.16 SE, t(8) = 3.16, p = 0.013). Figure 7A shRNA dataset: We also applied a linear mixed-effects model for plate-averaged lifetime values with treatment as a fixed effect. The analysis revealed no significant effect of treatment (F(2,6) = 0.29, p = 0.76).

The analyses of overexpression and shRNA datasets were described in the revised text.

Figure 8: As in Figure 3, we performed three independent primary cultures of embryonic hippocampal tissue with genotypes of both wild-type and mutant from the same pregnant mice for each mouse model. The culture plates were transfected with either a control shRNA or an Ecrg4 shRNA construct. Each data point represents an independent culture experiment, and the effect of Ecrg4 shRNA relative to that of control shRNA was evaluated using a permutation test. The data analysis was described in the revised text. The details of experimental conditions related to Figure 8 are provided in Supplementary Table 1.

(4) The authors should confirm that all mutants are also on the C57BL/6J background, and clarify whether control cultures are from littermates (this would be important). Also, are control versus mutant cultures done simultaneously? There can be significant batch effects with cultures.

The mutant mice we used in this study are on C57BL/6J or C57BL/6N background. It is known that C57BL/6J or C57BL/6N mice exhibit distinct phenotypes across a range of physiological, biochemical, and behavioral systems. However, it is less likely that our analysis is affected by differences between C57BL/6J and C57BL/6N, as we compared wild-type and mutant littermates on the same genetic background. This experimental design can also reduce the batch effects with different culture preparations. This point was described in the revised text.

(5) The spine analysis uses cultures from 18-22 DIV - this is quite a large range. It would be worth checking whether age is a confounder or correlated with any parameters / principal components.

We described in the method sections that culture samples were processed for imaging at 18-22 DIV. However, all the SIM imaging experiments for eight mutant mouse models were performed on samples fixed at DIV 19. The wide range of imaging experiments (DIV 18-22) includes test samples we used to optimize imaging conditions. In the revised manuscript, we specified the timing of SIM imaging.

(6) The computational modelling is interesting, but again, I am concerned about some circularity. Parameter optimization was used to identify the best fit model that replicated the spine turnover rates, so it is somewhat circular to say that this matched the observations when one of these is the turnover rate.

We appreciate the reviewer's comment on some circularity of the argument. We agree that the turnover rate is already incorporated into the simulation model and is not an appropriate criterion for the evaluation. We modified the text accordingly.

It is more convincing for spine density and size, but why not go back and test whether parameter differences are actually seen - for example, it would be possible to extract the probability of nascent spine loss, etc.

We thank the reviewer for giving this important suggestion. The probability of nascent spine loss is an important parameter, and we initially attempted to estimate it from the original data set. However, the upper limit of our time-lapse imaging is 24 h, which is insufficient to distinguish stable and nascent spines clearly. The difficulty of extracting all the necessary parameters for spine remodeling is our motivation for starting this computational modelling.

More compelling would be to repeat the experiments and see if the model still fits the data. In the interpretation (line 314-318) it is stated that '... reduced spine maturation rate can account for the three key properties of schizophrenia-related spines...', which is interesting if true, but it has just been stated that the probability of spine destabilization is also higher in mutants (line 303) - the authors should test whether if the latter is set to be the same as controls whether all the findings are replicated.

As suggested by the reviewer, we set the probability of spine destabilization equal across wild-type and mutant models and repeated the simulations. The results indicate that this modification has small effects on spine density (0.61 vs 0.62), spine turnover rate (0.22 vs 0.21), fraction of small spines (0.21 vs 0.20), and mean spine size (0.37 vs 0.36). We described this point in the revised manuscript.

(7) No validation for overexpression or knockdown is shown, although it is mentioned in the methods - please include.

As suggested by the reviewer, we validated overexpression and knockdown. The results are summarized in Supplementary Figure 8.

Supplementary Figure 8A-C shows the immunocytochemistry of anti-Ecrg4, anti-Cip4, and anti-NPAS4 for the confirmation of overexpression of these molecules.

Supplementary Figure 8D-E shows the confirmation of the appropriate size of exogenously expressed Ecrg4, Cip4, and NPAS4 by immunoblotting. (previous Supplementary Figure 10F is now Supplementary Figure 8E).

Supplementary Figure 8F-H indicates the efficient knockdown of exogenously expressed Met-GFP, ARHGAP15-GFP, and Ecrg4-HA by respective shRNA constructs in COS-7 cells. (previous Supplementary Figure 10G is now Supplementary Figure 8H)

Also, for the knockdown, a scrambled shRNA control would be preferable.

We used Stealth RNAi Negative Control Duplexes (Invitrogen) as the shRNA control in this study. To confirm that this RNAi sequence does not affect spine turnover, we performed timelapse imaging of neurons transfected with GFP alone or with GFP and the Stealth RNAi Negative Control. No detectable change in spine turnover was observed (Supplementary Figure 8I), indicating that this RNAi control sequence is suitable for our study.

(8) The finding regarding ecgr4 is interesting, but showing that some ecgr4 is expressed at boutons and spines and some in DCVs is not enough evidence to suggest that actively involved in the regulation of synapse formation and maturation (line 356).

To reveal the active roles of Ecrg4 in spine regulation, we exogenously applied a synthetic Ecrg4 peptide to wild-type neurons and monitored both spine density and turnover rate after Ecrg4 application. The Ecrg4 application increased the spine turnover rate, whereas samples treated with the scrambled peptide did not. This result supports the active role of Ecrg4 in regulating spine turnover. The data were added as Supplementary Figures 9F and G.

(9) The same caveats that apply to the analysis also apply to the ecgr4 rescue. In addition, while for 22q the control shRNA mutant vs WT looks vaguely like Figure 2, setd1a looks completely different.

We thank the reviewer for pointing out the apparent difference in the pattern of spine population data between Figure 2 and Figure 8. We performed SIM analysis using DiI-labeled neurons in Figure 2, whereas the data in Figure 8 are derived from GFP-expressing neurons. The images of cell-surface labeling and cytoplasmic labeling cannot be analyzed in the same way, as it is necessary to adjust parameters in SIM image processing and PCA-based dimensional reduction. Consequently, the distribution of the spine population projected onto the PC1-PC2 plane differs between DiI-labeled neurons and GFP-expressing neurons. To facilitate the comparison of PCA analysis applied to GFP-expressing neurons, we replaced the weight matrix for GFP-expressing neurons with that previously calculated for the DiIlabeled neurons. This adjustment increased the similarity of the data distributions shown in Figures 2 and 8. The explanation for the different patterns in the spine population map between Figure 2 and Figure 8 was added to the revised text. The related explanation for the data processing was described in the Materials and Methods.

And if rescued, surely shRNA in the mutant should now resemble control in WT, so there shouldn't be big differences, but in fact, there are just as many differences as comparing mutant vs wild-type? Plus, for spine features, they only compare mutant rescue with mutant control, but this is not ideal - something more like a 2-way ANOVA is really needed. Maybe input from a statistician might be useful here?

We appreciate the reviewer's important comment and agree that the analytical approach used in the original manuscript was not optimal. We therefore revised our analysis to examine whether the difference observed between wild-type and mutant neurons was reduced by suppression of Ecrg4 expression.

To this end, we first identified two regions in the PC1–PC2 plane where mutant spines were either enriched or depleted relative to wild-type neurons (Areas A and B). We then counted the number of spines located in Areas A and B in control shRNA-treated mutant neurons (normalized spine counts XA and XB). Next, we quantified spine counts in the same areas using data from Ecrg4-suppressed mutant neurons (normalized spine counts YA and YB). If XA > YA and XB < YB, suppression of Ecrg4 would indicate a shift toward rescue of the phenotype observed in control shRNA-treated mutant neurons. Indeed, the datasets were consistent with this shift in relative spine counts.

To determine whether these differences exceeded those expected from random variation in spine counts, we performed a permutation test. Specifically, spine identities were randomly shuffled between the two conditions while preserving the total number of spines in each dataset. The observed differences were then compared with the distribution obtained from the permuted datasets to assess statistical significance.

We found that all three culture replicates showed statistical significance in both areas A and B for both the 22qdel and Setd1a mutations. This analysis is described in the Result section.

(10) Although this is a study entirely focused on spine changes in mouse models for Sz, there is no discussion (or citation) of the various studies that have examined this in the literature. For example, for Setd1a, smaller spines or reduced spine densities have been described in various papers (Mukai et al, Neuron 2019; Chen et al, Sci Adv 2022; Nagahama et al, Cell Rep 2020).

We appreciate the reviewer's suggestion to include a discussion of schizophrenia-related mouse models. We added more information related to the Setd1a mouse model to the Discussion section.

"Population-level spine properties were more homogeneous in schizophrenia models (those with gene mutations implicated in schizophrenia) than in the other 4 models studied, in part due to a shared tendency for smaller spines. This observation is consistent with previous studies on Setd1a mutant mice, which showed reduced spine width, decreased mushroomtype spines, and lower spine density in the prefrontal cortex [43,56,57]. In contrast to these findings, several previous studies reported reduced numbers of small spines in the postmortem cortical tissues of schizophrenia patients [22,58]. "

(11) There is a conceptual problem with the models if being used to differentiate autism risk from Sz risk genes. It is difficult to find good mouse models for Sz, so the choice of 22q11.2del and Setd1a haploinsufficiency is completely reasonable. However, these are both syndromic. 22qdel syndrome involves multiple issues, including hearing loss, delayed development, and learning disabilities, and is associated with autism (20% have autism, as compared to 25% with Sz). Similarly, Setd1a is also strongly associated with autism as well as Sz (and also involves global developmental delay and intellectual disability). While I think this is still the best we can do, and it is reasonable to say that these models show biased risk for these developmental disorders, it definitely can't be used as an explanation for the higher variability seen in the autism risk models.

We appreciate the reviewer's suggestion for more careful consideration of the interpretation of phenotypes in mouse models, with regard to their relation to clinical phenotypes in human patients. According to the suggestion of the reviewer, we modified the relevant text as follows:

"The nanoscale features of dendritic spines in ASD-associated mouse models were more variable than those in schizophrenia-associated mouse models. This difference may be related to the broader clinical spectrum of ASD, which ranges from mild impairments in social skills to severe intellectual disability. The four ASD-associated mouse models examined in this study, Nlgn3<sup>R451C/(y or R451C) , Syngap1^+/-, POGZ^Q1038R/+, and 15q11-13^dup/+, may represent subgroups with different levels of hippocampal dysfunction. Among the four ASD-associated mouse models, 15q11-13^dup/+ showed population-level spine properties closer to those of the schizophrenia models. To understand this similarity, further analysis of neural circuit changes in both ASD- and schizophrenia-associated mouse models will be necessary. Analysis of the relationships between rare genetic variants and synapse phenotypes in mouse models may contribute to their eventual categorization. This information should be useful to understand the underlying mechanisms of the broader clinical spectrum of ASD."

(12) I am not convinced that using dissociated cultures is 'more likely to reflect the direct impact of schizophrenia-related gene mutations on synaptic properties' - first, cultures do have non-neuronal cells, although here glial proliferation was arrested at 2 days, glia will be present with the protocol used (or if not, this needs demonstrating).

In our culture system, the density of non-neuronal cells is low, and most neurons are not in direct contact with non-neuronal cells. We reported this method in Nat. Neurosci. 1999, where we utilized this culture system to visualize GFP-tagged PSD-95 in neurons using recombinant adenovirus. Because recombinant adenovirus shows higher infection efficiency in glial cells, it was essential for us to establish a culture condition that isolates neurons from glial cells.

Second, activity levels will affect spine size, and activity patterns are very abnormal in dissociated cultures, so it is very possible that spine changes may not translate into in vivo scenarios. Overall, it is a weakness that the dissociated culture system has been used, which is not to say that it is not useful, and from a technical and practical perspective, there are good justifications.

We appreciate the reviewer's comment on the advantages and disadvantages of using an in vitro culture system. This comment aligns with the first reviewer's. We modified our text to have a balanced discussion on the role of the in vitro culture system in the study of mental disorder mouse models as follows:

"Finally, while the spine phenotype identified in the human postmortem brain undoubtedly resulted from complex interactions among genetic background, environmental influences, and regulation by non-neuronal cells, data from pure neuronal cultures are more likely to reflect the direct effects of schizophrenia-related gene mutations on synaptic functions. This property may be advantageous for identifying synaptic molecules that regulate synapse phenotypes in schizophrenia-related mouse models. However, the phenotype observed in the culture system requires confirmation using in vivo experiments of mouse models or human tissue samples. Efficient in vitro screening combined with reliable in vivo evaluation of synapses will facilitate translational research on mental disorders."

(13) As a minor comment, the spine time-lapse imaging is a strength of the paper. I wonder about the interpretation of Figure 5. For example, the results in Figure 5G and J look as if they may be more that the spines grow to a smaller size and start from a smaller size, rather than necessarily the rate of growth.

We thank the reviewer for the insightful comment. In the revised manuscript, we analyze the time-lapse data using linear mixed-effects models incorporating nested random effects (spine/dendrite/cell/culture plate). This analysis suggested the difference in the initial size of spines. This point is described in the revised manuscript as follows:

"Schizophrenia-associated mouse models showed higher similarity in spine morphology, driven by reduced size and growth of nascent spines."

"We further compared the initial increase in spine volume between genotypes (Figure 5G-I). Linear mixed-effects models incorporating nested random effects revealed significantly smaller initial spine volumes in both 22q11.2^del/+ and Setd1a^+/- models (genotype effect: p < 0.001 for 22q11.2^del/+ and p < 10^-7 for Setd1a^+/-). The spines in both mutants also displayed a significant reduction in spine volume increase (p < 0.001). In contrast, newly formed spines in the Nlgn3^{R451C/(y or R451C)} neurons were significantly larger than those in wild-type neurons (p < 10^-4) with preserved time-course of spine growth.”

We tested whether the initial size difference in spines can be incorporated into the computational simulation. However, due to the large variability in the initial spine size, it was difficult to perform parameter optimization in the model with additional factors. Therefore, we did not further pursue this possibility in this revision. This point is described in the revised text.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The manuscript would be strengthened if the following issues were adequately addressed:

(1) It would be helpful to know more about the in/ex vivo dendritic spine phenotype of the mouse models of neuropsychiatric disorders, to allow readers to judge whether and how the in vitro spine phenotype in hippocampal neuronal cultures overlaps with/replicates the spine phenotype within the mouse brain.

We appreciate this comment, but our currently available data is insufficient to specify the difference between in vitro and in vivo spine phenotypes. Our previous study, published in Nature. Comm. (2019), provided data showing that the overall distribution of spine size is similar between in vivo and in vitro conditions in the mouse hippocampus.

(2) Although the manuscript is largely well written, there are instances of ambiguous language, particularly when describing the spine phenotypes. For example, we are told that "ASD mouse models showed a tendency of decreasing spine subpopulation with small volumes." This description and other examples should be expressed more clearly.

Following the reviewer's suggestions, we revised the text to improve clarity. We modified the sentence "ASD mouse models showed a tendency of decreasing spine subpopulation with small volumes" to "ASD-related mouse models showed an opposite spine phenotype."To avoid possible confusion for readers, we have revised several sentences in the text to clarify the intended meaning.

Also, I question whether the word "decoding", meaning to convert (a coded message) into intelligible language, is the most appropriate for the title and abstract.

The original meaning of the word "decoding" is the conversion of a coded message into an intelligible form; however, in this study, we use the term in a broader sense, referring to the extraction of latent population-level properties of dendritic spines from multidimensional structural parameters. We believe this usage is consistent with its common use in neuroscience and systems biology, where "decoding" often refers to inferring underlying biological states or information from complex datasets.

(3) The authors should reconsider whether CaMKIIαK42R/K42R mice should be described as a schizophrenia model, when mutations in CAMK2A are known to cause autosomal dominant intellectual developmental disorder-53 (OMIM 617798) and autosomal recessive intellectual developmental disorder-63 (OMIM 618095), and mice carrying the CAMK2A E183V mutation exhibit ASD-related synaptic and behavioral phenotypes (PMID: 28130356).

We provided a detailed answer to this question in the previous part of the rebuttal.

(4) The title doesn't adequately summarise the contents of the manuscript. It should mention mice/mouse models and cultured neurons.

We also responded to this request in the previous part of the rebuttal.

Reviewer #2 (Recommendations for the authors):

(1) Please provide a supplementary table with all DEGs. Also, DEGs are listed if present in 'more than 2' models - does this mean they had to be in 3 or more? Please clarify.

According to the reviewer's suggestion, we added data on DEGs shared by >2 mouse models in Supplementary Figure 7. We also added Supplementary Tables 2 and 3 for all DEGs. The phrase "in more than 2 models" means "in 3 or 4 models".

(2) There are several references to 'schizophrenia mouse models' - it is worth rephrasing this to make clear that these are not mice with schizophrenia.

We replaced the expression "schizophrenia (or ASD) mouse models" with "schizophrenia (or ASD)-associated mouse models" or similar appropriate wording throughout the manuscript.

(3) Line 66: 'a recent...' - 2014 is not really recent.

We removed the word "recent" from the sentence.

(4) Figure S1: The legend says A-D, but they are not on the figure. Also, make clear whether this data is only WT data - it seems to be from disorder models, with 4 colors for each model - please clarify.

We changed the sentence from "shown as A to D" to "shown as A to C". The datasets in Supplementary Figure 1 are wild-type only. Each graph uses four colors to represent wildtype data from four imaging datasets obtained from different mouse models. Graphs A to C correspond to spine length, surface area, and volume, respectively.

(5) Methods, line 680-4: More detail here would be helpful.

We added more explanation for the generation of subtraction maps.

(6) Line 193: Make it clear this is hippocampal in the main text.

We added "cultures of embryonic hippocampi" to the text.

(7) Figure 5, D-F: Make clear that these are transient spines (as per main text)

We added "Lifetimes of transient spines" to both the main text and figure legend.

(8) Figure 6B: More detail is needed; no idea what this is - no axis label. D - also not clear what numbers on the y-axis mean. E - color scale??

We added details to the figure legend, the axis labels for Figures 6B and 6D, and the color scale for Figure 6E.

(9) Supplementary Figure 9 - not clear what matrices are actually showing, nor what the scale refers to - is this the number of shared DEGs? If so, please make it clearer.

The matrices show the shared DEG numbers, as shown in their titles. The scale indicates DEG numbers. We added the explanation of the color code to the figure legend.

(10) Please make clear in the main text that ecgr4 affected the turnover rate. It would be good to measure other parameters as well.

We added the phrase "a significant increase in spine turnover rate by Ecrg4 overexpression" to the main text.

(11) Figure 7: Suggest to label C on images as well, so obvious which is GFP/anti-HA overlay (and respective colors) and which is anti-HA staining.

We added the labels with respective colors to Figure 7.

(12) Ecgr4 is a precursor protein that is cleaved to produce several hormone-like peptides. Where is the HA tag - so which cleavage products will it label? Any antibodies that work in immunocytochem?

HA tag was attached to the C-terminal domain. We predict that anti-HA binds to four cleavage products (the full-length Ecrg4, Augurin, Argilin, and Δ16). Among several commercially available antibodies, only the SIGMA product could detect cells expressing Ecrg4-HA by immunocytochemistry.

(13) Supplementary Figure 10: Synaptosome would be a good addition.

We isolated the fraction of synaptosomes using Syn-PER™ Synaptic Protein Extraction Reagent in Supplementary Figure 9A. We added this explanation to the Materials and Methods section.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript entitled "Essential function reflected in the phylodynamics of a multigene family - the pir genes of malaria parasites" by Jackson and colleagues investigates the global phylogeny of pir genes across 14 Plasmodium species and one Hepatocystis species. The authors also focus on the functional characterization of the conserved ortholog pirC1 and claim that pirC1 is not the founder of the family and that it plays an essential role in blood-stage growth.

Strengths:

Overall, the manuscript is well written and interesting, as it combines comparative genomics and evolutionary analysis with functional experiments. The phylogenetic analysis is rigorous and represents a major strength of the manuscript.

Weaknesses:

The general conclusions regarding the potential function of this gene family are not fully supported by the data presented. The manuscript moves too quickly from growth phenotype and localization studies to a specific mechanistic model. The discussion argues that PIRC1 may be involved in nutrient acquisition, host sensing, or metabolic support, but the data provided do not directly support these functions, and the manuscript in its present form remains speculative. Although the manuscript includes some experimental results, it lacks direct mechanistic validation of the specific functions of the pir genes, including pirC1. In its current form, the study does not yet establish a definitive role for pirC1 in metabolic processes.

The reviewer is correct that there is no definitive proof for the function of the PIRC1 protein. We speculate that this protein is involved in a metabolic process based on mutant phenotype – small, poorly developed parasites that do not produce the same amount of DNA as wildtype parasites (and hence likely fewer merozoites). That this occurs in an in vitro culture of Plasmodium knowlesi rules out a role in the interaction with the host organism, such as sequestration or facilitating passage through the spleen. The localization of the protein outside of the parasite is consistent with a role in nutrient uptake, but we agree that additional experiments are required to determine the role of the protein definitively. We aim to look at the differences in the transcriptome and the metabolome to gain more insight into the pirC1 phenotype; this should reveal metabolic deficiencies in the mutant parasite.

Reviewer #2 (Public review):

Summary:

This is an extensive study using phylogenetic comparison across multiple plasmodium species to gain new insights in relation to their evolutionary pathways and the potential function of pir. In addition to establishing a framework to identify related orthologues across species as well as expanding paralogues families within a species, the work also focuses on understanding loss and gain of different PIRs and how this indicates a relative lack of functional constraints and essentiality for most members of the gene family.

The authors provide evidence that at least pirC has a conserved function and plays an important role in parasite growth in multiple species.

While this study represents a significant effort and does provide interesting new insights that would help our understanding of this complex gene family in the future, it has a number of limitations.

Strengths:

Extensive and thorough phylogenetic analysis that is supported by some biological validation. Provides an indication that the PIR gene family has limited biological constraints and evolved independently across different species, leading to rapid expansion and deletion of orthologous groups. Identified pirC as a functional and important member of the family that is conserved across the species.

Weaknesses:

The phylogenetic tree is based on a truncated sequence that focuses on the more conserved parts of the pir sequence. This could potentially lead to missing the key functional drivers of evolution. The biological validation of the role of pirC has some inconsistencies that need to be addressed.

The reviewer is correct. We do not use the repetitive parts of the pir gene sequences for the phylogeny. We define these as the ‘distal variable’ and ‘proximal’ domains of the protein in Fig. S1, results text and supplementary results. We remove these parts from the alignment because they are only nominally homologous (they cannot be aligned) and so break the basic assumption of phylogenetic analysis. Amino acid repeats evolve quickly and are homoplasic (their similarities do not reflect ancestry) so omitting them is correct and makes the phylogeny more reliable. While these features do not contribute to the phylogenetic estimate, we propose in the results text and Fig. S3, in agreement with the reviewer, that they are an important demonstration of how pirs have differentiated and what is different between the subfamilies. The reviewer is also correct that we have considered the whole gene sequence when comparing Alphafold predictions and in selection analyses of closely related sequences (in these cases, the repeat sequences can be aligned).

A structural prediction for the sequence used in the alignment would mostly reflect the distal conserved domain but would be misleading because the alignment combines conserved regions that are not physically attached in reality. We will clarify these points.

Reviewer #3 (Public review):

This paper aims to classify, from an evolutionary perspective, the multigene family PIR found in malaria parasites infecting rodents and Old World monkeys, and to link this classification to functional diversification. The authors also hypothesize that PIR members conserved across species play important roles in parasite survival, and seek to clarify their functions.

To achieve these aims, the authors comprehensively analyze the evolution of PIR genes using genomic and transcriptomic information from many malaria parasite species. They focus on PIRC1, a member conserved across species, and attempt to clarify its function in rodent and simian malaria parasites by examining the phenotypes of parasites in which the corresponding genetic locus has been disrupted. They also attempt to determine its localization using PIRC1 tagged with an epitope sequence. However, although the locus-disrupted parasites appear to show an approximately 50% reduction in growth rate, this effect seems to be overestimated. Another weakness is that the cause of the reduced growth rate has not been clarified. The localization analysis also remains insufficiently conclusive.

Therefore, I consider that the first half of the paper, consisting of the bioinformatics analyses, achieves the objective of comprehensively summarizing PIR and may become a reference paper for discussing the evolution and function of the PIR gene family. On the other hand, regarding the function of PIRC1, no clear conclusion can be drawn from the results presented, and several additional experiments are necessary.

My major comments are as follows.

(1) The claim that the failure of eight disruption attempts indicates that pirC1 is essential is too strong.

Lines 319-321: The authors argue that a total of eight failed attempts to disrupt the pirC1 locus using two different construct designs suggest that pirC1 is essential in P. berghei. However, the failure of these attempts could also reflect technical issues with the construct design itself, such as the length of the homologous regions used for recombination, which are approximately 650 bp. Therefore, it is an overstatement to conclude that "pirC1 is essential for P. berghei blood-stage growth." Given that parasites with disruption of the corresponding locus could be obtained in both P. chabaudi and P. knowlesi, a more appropriate statement would be that "pirC1 is important for P. berghei blood-stage growth."

It is correct that we cannot rule out that the inability to delete the pirC1 gene is Plasmodium berghei is unrelated to an essential function. We are happy to change the text to the suggested description.

(2) The data on the mCherry-expressing P. berghei line shown in Supplementary Figure 11 are insufficient.

(a) Panel C: Southern blot analysis

To conclusively identify the lower band in panel C as chromosome 1, additional probes specific to genes located on chromosomes 1 and 2 would be required. In addition, a parental parasite control should also be included. The Southern blot image of the parental parasite should show only a single band at the higher position, with no band at the lower position. Probes specific to chromosomes 1 and 2 would help demonstrate that the lower band corresponds to chromosome 1, rather than chromosome 2.

To this end, the authors could describe the result as follows:

"In the parental parasite, only a single band corresponding to chromosome 7 was detected, indicating that the smaller chromosome was genetically modified. The size of the lower band detected with the dhfr probe was identical to that of the band detected with the control chromosome 1 probe, but distinct from that detected with the chromosome 2 probe, indicating that chromosome 1 was modified."

That said, this chromosome-level Southern blot analysis is not sufficient to demonstrate that the target PBANKA_0100500 locus was specifically modified. The authors should provide more direct evidence showing that the PBANKA_0100500 locus, rather than another genomic locus, was modified. For example, Southern blot analysis after restriction enzyme digestion would provide more definitive evidence. Diagnostic PCR may also provide more specific evidence.

Although we are confident that the parasites has been modified in the expected way, we are planning to generate PCR data confirming that the mCherry tag is correctly integrated into PBANKA_010050.

(b) Panel D: Flow cytometry analysis

To allow a more accurate interpretation of the percentage of mCherry-positive cells, flow cytometry data for the parental parasite line should also be presented.

We will repeat the flow cytometry experiments and include a wildtype strain in the analysis.

(3) There are unclear points in the PCR results shown in Supplementary Figure 12.

Supplementary Figure 12: In panel B, a PCR product should also be amplified from dPCHAS_0101200 using the P1-P3 primer pair. Why is this band absent? The authors should provide the uncropped electrophoresis image so that the larger band can be seen. In addition, if labels 1 and 2 indicate independent clones, this should be stated in the figure legend.

We will gladly supply the full, uncropped electrophoresis image and we will clarify what the numbers indicate in the legend.

(4) The growth rates of P. chabaudi and P. knowlesi parasites with disruption of the PIRC1 gene locus should be quantitatively analyzed.

The growth rates of P. chabaudi and P. knowlesi are described only qualitatively, but they should be evaluated quantitatively. In Figure 4A, the parasitemia of wild-type P. chabaudi increases from approximately 6.1% on day 6 to approximately 15.6% on day 8, corresponding to a 3.8-fold increase. However, because parasite growth may already be affected by immune-mediated suppression at this stage, this value should be regarded as a minimum estimate. In contrast, the mutant increases from approximately 3.2% on day 8 to approximately 6.8% on day 10, corresponding to a 2.1-fold increase. Based on these values, the daily growth rate of the mutant appears to be reduced to at least approximately 56% of that of the wild type. Similarly, from the growth curve of P. knowlesi in Fig. 5A, the DMSO-treated group appears to increase approximately two-fold per day, whereas the rapamycin-treated group increases only approximately one-fold per day. Thus, P. knowlesi also appears to show an approximately 50% reduction in growth rate. Taken together, both P. chabaudi and P. knowlesi appear to reproducibly show an approximately 50% reduction in growth capacity. A reduction of this magnitude is difficult to describe as a "severe growth defect"; a more appropriate wording would be simply that the parasites "showed a growth defect." In addition, the terms "a severe growth defect" and "essential" appear to be overstated throughout the manuscript, and the wording should be toned down. Finally, I recommend presenting Figure 4A and Figure 5A on a logarithmic scale so that the trend in growth rates can be more intuitively appreciated from the graphs.

It should be possible to determine the growth rate of the wildtype and mutant P. knowlesi parasites. In addition, we can change the text to reflect that although there is a growth phenotype in the two species in which we obtained mutants, the parasites do have the capacity to replicate. Note that in the case of P. knowlesi, the parasites numbers in vitro do not increase, hence any additional factors that decrease the growth rate, such as immune system and spleen, will lower the reproductive rate further and render the mutant parasite unable to proliferate.

(5) The evidence that disruption of the PIRC1 gene locus in P. knowlesi does not affect erythrocyte invasion is weak.

The authors describe that "the developmental cycle of the parasites lacking PIRCl is slightly longer than that of parasites that produce PIRCl (line 383-384)," and appear to support this interpretation with data showing that "mutant parasites are significantly smaller than wild-type parasites (line 414)" and that "the DNA content in ML10-arrested parasites lacking PIRCl is lower than that of DMSO-treated parasites (line 417-418)" at 24 hours after invasion. However, a slightly longer developmental cycle alone does not seem sufficient to explain a 50% growth reduction.

I think the erythrocyte invasion capacity has not been quantitatively evaluated, and therefore, the evidence supporting the conclusion that the phenotype of P. knowlesi parasites with disruption of the PIRC1 gene locus is unrelated to erythrocyte invasion is weak. The authors should assess invasion efficiency using purified merozoites. For P. chabaudi, it should also be possible to apply an in vitro or in vivo erythrocyte invasion assay similar to that used for other rodent malaria parasites, and this should be evaluated as well.

We can further investigate the invasion phenotype of the mutant P. knowlesi parasites. The presence of a clear phenotype during the intraerythrocytic stage indicates that the protein also has a role after invasion, but we agree that determining the effect on invasion directly will be useful.

Alternatively, the reduced DNA content in ML10-arrested parasites lacking PIRC1 (lines 416-417) could suggest that the number of merozoites formed per schizont may be reduced. To clarify this point, the authors should assess whether the number of merozoites per schizont is altered in P. knowlesi (and P. chabaudi parasites lacking PIRC1).

We aim to count merozoites and the level of invasion, which will allow us to determine the reproductive rate of the mutant parasites.

(7) The authors propose the possibility that PIRC1 expressed in merozoites is released after invasion; however, the evidence that PIRC1 localizes to intracellular organelles is weak.

Line 333: "a peripheral pattern around the parasite" is indicative of parasite plasma membrane, PV, or PVM. ", indicative of a parasitophorous vacuole (PV) or parasitophorous vacuole membrane (PVM) location" should be amended to ", indicative of parasite plasma membrane, a parasitophorous vacuole (PV) or parasitophorous vacuole membrane (PVM) location". In the Figure S14 image, red signals are uniformly detected from the merozoites formed in the schizont stage parasite (not really microorganelle patterns), but not from the PVM surrounding the schizont, suggesting parasite plasma membrane localization, not PVM. I agree that the signal is detected from the compartments extending into the iRBC cytosol, which may be difficult to explain if it is located on the parasite plasma membrane, but how frequently were such images seen?

To determine the localization of the protein in the merozoite, we will image P. knowlesi merozoites.

Figure 4D. In the images of liver-stage schizonts, AMA1 does not appear to localize to the micronemes in mature merozoites, suggesting this image is an immature schizont. Although PIRC1 appears to be expressed in liver-stage schizonts, it is difficult to clearly determine whether it localizes to intracellular organelles or to the parasite plasma membrane.

This is a valuable comment. It is difficult to impossible to determine the exact localization of the protein at this stage, irrespective of the exact stage of the parasite. It is clear from the images is that the protein is not secreted at this stage. The main aim of the experiment was to determine whether the protein is produced by the parasite during the liver stage, which the results confirm.

To clarify the above points, the authors should examine whether PIRC1 is detected in intracellular organelles or around the merozoites by analyzing its localization in purified merozoites.

This we aim to do.

This reminds me a lot of a concept I heard, I believe from a video, a long time ago. It was about grooming habits between people and how it can be a reflection of comfort with physical intimacy. The video talked about the moment in a friendship or relationship when we feel comfortable enough to reach out and 'groom' the other person. That the closer a connection the more comfortable and willing we are to 'groom' the other. Someone in a newer relationship might point out something like a messy hair, lint, or a tag sticking out. As you move up levels of intimacy that moves onto just letting them know you're reaching to fix it. Finally reaching out and fixing the problem you noticed without having to say anything to them. It was supposed to be a sign to see how physically close two people were. As the person being groomed had the chance to reject the help offered and set a boundary. The person asking had a chance to express care for others appearance and or comfort. I'm not sure entirely how true observation was but this section reminded me of the video as it discusses levels of physical contact in comparison to the depth of the relationship.

Author response:

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

eLife Statement

This valuable study characterizes the emergence of the membrane-associated periodic cytoskeleton (MPS) in the axons of human motor neurons derived from induced pluripotent stem cells. Super-resolution imaging of beta-II spectrin provides convincing evidence for the patterned assembly of spectrin-poor gaps and spectrin-rich MPS in the medial region of the axons and its enhancement by the kinase inhibitor staurosporine. The data advocates against gap formation by cytoskeleton disassembly in a continuous MPS. Instead, a continuous MPS may result from nascent MPS patches and their maturation, a model that would benefit from live imaging for validation.

(R1) We thank the reviewers and editor for their constructive and thoughtful feedback. We are pleased the reviewers found our evidence to be convincing and that our study provides a valuable framework for understanding the complex dynamics of MPS assembly.

Public Reviews:

Reviewer #1 (Public review):

Summary:

Ever since the surprising discovery of the membrane-associated Periodic Skeleton (MPS) in axons, a significant body of published work has been aimed at trying to understand its assembly mechanism and function. Despite this, we still lack a mechanistic understanding of how this amazing structure is assembled in neuronal cells. In this article, the authors report a "gap-and-patch" pattern of labelled spectrin in iPSC-derived human motor neurons grown in culture. The mid-sections of these axons exhibit patches with reasonably well-organized MPS that are separated by gaps lacking any detectable MPS and having low spectrin content. Further, they report that the intensity modulation of spectrin is correlated with intensity modulations of tubulin as well. However, neurofilament fluorescence does not show any correlation. Using DIC imaging, the authors show that often the axonal diameter remains uniform across segments, showing a patch-gap pattern. Gaps are seen more abundantly in the midsection of the axon, with the proximal section showing continuous MPS and the distal segment showing continuous spectrin fluorescence but no organized MPS. The authors show that spectrin degradation by caspase/calpain is not responsible for gap formation, and the patches are nascent MPS domains. The gap and patch pattern increases with days in culture and can be enhanced by treating the cells using the general kinase inhibitor staurosporine. Treatment with the actin depolymerizing agent Latrunculin A reduces gap formation. The reasons for the last two observations are not well understood/explained.

(R2) We thank the reviewer for the detailed and accurate description of the data shown and its relevance to further our understanding of MPS assembly mechanism and function.

Strengths:

The claims made in the paper are supported by extensive imaging work and quantification of MPS. Overall, the paper is well written and the findings are interesting. Although much of the reported data are from axons treated with staurosporine, this may be a convenient system to investigate the dynamics of MPS assembly, which is still an open question.

(R3) We thank the reviewer for the positive comments on the manuscript and the convenience of the experimental system developed to further study the dynamics of MPS assembly. We hope others turn into motor neurons to explore cortical cytoskeleton biology and hopefully shed light into their susceptibility in various degenerative diseases.

Weaknesses:

Much of the analysis is on staurosporine-treated cells, and the effects of this treatment can be broad. The increase in patch-gap pattern with days in culture is intriguing, and the reason for this needs to be checked carefully. It would have been nice to have live cell data on the evolution of the patch and gap pattern using a GFP tag on spectrin. The evolution of individual patches and possible coalescence of patches can be observed even with confocal microscopy if live cell super-resolution observation is difficult.

(R4) Because staurosporine may hit various kinases relevant to the phenomenon under study we did not elaborate too deeply on the likely targets in the discussion. We have, however, included the possibility that the relevant kinase in this matter could be PKC, in light of the new study published while our manuscript was under revision (Heller et al., 2025) (see second last paragraph in the Discussion section). Staurosporine represented a convenient initial approach that allowed us to find the phenomenon, and we are now conducting new studies dissecting the molecular pathways involved. However, the extent of such studies lies beyond the scope of the present report.

See R16 regarding possible live-imaging experiments using tagged βII-spectrin constructs.

Some more comments:

(1) Axons can undergo transient beading or regularly spaced varicosity formation during media change if changes in osmolarity or chemical composition occur. Such shape modulations can induce cytoskeletal modulations as well (the authors report modulations in microtubule fluorescence). The authors mention axonal enlargements in some instances. Although they present DIC images to argue that the axons showing gaps are often tubular, possible beading artefacts need to be checked. Beading can be transient and can be checked by doing media changes while observing the axons on a microscope.

(R5) As we acknowledge this possibility, we believe that, even if they occurred, they could not contribute to our observations of gaps-and-patches phenomenon since this latter subsisted long (hours and days) after any gross manipulation of media. Moreover fixed samples, when observed under DIC, confocal or STED did not evidence such beadings. We do refer to a characteristic local enlargement that was very localized and very low in numbers (see Fig.1C and E, and Suppl. Fig1C and E), so we don't believe these are transient, and do not resemble the structure referred to as beading. Structurally, beading is essentially different since it appears in rows of consecutive “beads” in long stretches, where round, small enlargements of axonal caliber are arranged in a consecutive manner, resembling pearls on a string. As mentioned by the reviewer, the beading phenomena can occur transiently when drastically changing media osmolarity (rarely done in cell culture manipulations) or non-tranciently when axons are undergoing degeneration. Indeed, to prevent gross changes in osmolarity, our routine fixation is a 4% PFA and 4% sucrose in PBS. In any case, we did not observe signs of beading in the cultures used for this study.

(2) Why do microtubules appear patchy? One would imagine the microtubule lengths to be greater than the patch size and hence to be more uniform.

(R6) Our stainings are for tubulin protein isoforms beta-III and alpha-II. That is, they would label microtubules, but free tubulin as well. Hence we don't think this is evidence for “patchy microtubules”. The slight decrease in intensity for tubulin within gaps is indeed something to investigate, and can indicate that tubulin prefers to accumulate within patches.

(3) Why do axons with gaps increase with days in culture? If patches are nascent MPS that progressively grow, one would have expected fewer gaps with increasing days in culture. Is this indicative of some sort of degeneration of axons?

(R7) We agree with the apparent discrepancy. However, one has to take into account that these axons are still elongating even at 2 weeks in culture and beyond. Hence, at any time point, there is a new axonal compartment recently added, and hence, with low βII-spectrin and no organized MPS. Also, the dynamical evolution of the gaps-and-patches structure has to take into account the rate of βII-spectrin supply and transport. If supply is somehow lower than a given threshold, it is expected that there will be more gaps, given the new, more distant parts of the axons have a lower supply of βII-spectrin. To explore this formally, we are working on simulations of these multifactorial dynamic systems to better understand this, that together with key experimental observations would enhance our understanding into our model of MPS assembly in growing axons. However, findings for this project will be the subject of another manuscript.

(4) It is surprising that Latrunculin A reduces gap formation induced by staurosporine (also seems to increase MPS correlation) while it decreases actin filament content. How can this be understood? If the idea is to block actin dynamics, have the authors tried using Jasplakinolide to stabilize the filaments?

(R8) The results with the co-treatment with Latrunculin A and Staurosporine are indeed intriguing, and provide clear evidence that the gap-and-patch pattern arises from local assembly of the MPS, requiring newly formed actin filaments. On the other hand, the fact that F-actin within the pre-formed MPS seems unaffected is not surprising. There are many different populations of F-actin in axons (i.e. MPS rings, longitudinal filaments, actin patches, actin trails), all of which have a different rate of monomer turnover. Latrunculin A affects filaments indirectly. The target of Latrunculin A is not actin filaments, but free monomers. Monomer sequestration ultimately affects actin filaments: filaments are constantly exchanging monomers, but, devoid of free monomers, filaments get shorter and eventually disappear. The drastic decrease in global F-actin in LatA-treated axons reflects that. The fact that F-actin in the MPS is preserved shows that these filaments are stable -if they are not losing monomers in the time frame of the treatment, the filament remains unaffected. This subject is extensively covered in the 8th paragraph of the Discussion section.

We have not used Jasplakinolide. The expected outcome will not mimic that of Latrunculin A since Jasplakinolide has a different mechanism of action (i.e. it binds -and stabilizes- the actin filament).

(5) The authors speculate that the patches are formed by the condensation of free spectrins, which then leaves the immediate neighborhood depleted of these proteins. This is an interesting hypothesis, and exploring this in live cells using spectrin-GFP constructs will greatly strengthen the article. Will the patch-gap regions evolve into continuous MPS? If so, do these patches expand with time as new spectrin and actin are recruited and merge with neighboring patches, or can the entire patch "diffuse" and coalesce with neighboring patches, thus expanding the MPS region?

(R9) We agree with the reviewer's interpretation. A virtue of our experimental model and our interpretations of the observations in fixed cells is that it gives rise to informative questions such as the ones posed by the reviewer. See R16 regarding possible live-imaging experiments using tagged βII-spectrin constructs.

Reviewer #2 (Public review):

Summary:

In this manuscript, Gazal et al. describe the presence of unique gaps and patches of BetaII-spectrin in medial sections of long human motor neuron axons. BII-spectrin, along with Alpha-spectrin, forms horizontal linkers between 180nm spaced F-actin rings in axons. These F-actin rings, along with the spectrin linkers, form membrane periodic structures (MPS) which are critical for the maintenance of the integrity, size, and function of axons. The primary goal of the authors was to address whether long motor axons, particularly those carrying familial mutations associated with the neurodegenerative disorder ALS, show defects in gaps and patches of BetaII-spectrin, ultimately leading to degradation of these neurons.

(R10) We thank the reviewer for the detailed and accurate description of the data shown.

Strengths:

The experiments are well-designed, and the authors have used the right methods and cutting-edge techniques to address the questions in this manuscript. The use of human motor neurons and the use of motor neurons with different familial ALS mutations is a strength. The use of isogenic controls is a positive. The induction of gaps and patches by the kinase inhibitor staurosporine and their rescue by Latrunculin A is novel and well-executed. The use of biochemical assays to explore the role of calpains is appropriate and well-designed. The use of STED imaging to define the periodicity of MPS in the gaps and patches of spectrin is a strength.

(R11) We thank the reviewer for the positive comments on the manuscript, the techniques used and the proposed model.

Weaknesses:

The primary weakness is the lack of rigorous evaluation to validate the proposed model of spectrin capture from the gaps into adjacent patches by the use of photobleaching and live imaging. Another point is the lack of investigation into how gaps and patches change in axons carrying the familial ALS mutations as they age, since 2 weeks is not a time point when neurodegeneration is expected to start.

(R12) See R16 regarding possible live-imaging experiments using tagged βII-spectrin constructs.

We don't discard the notion that axons carrying familial ALS mutations will show defects in MPS formation and/or stability when observed at longer culture times, or under culture conditions that promote neuronal aging (Guix et al., 2021). Thus, we continue to work with these cells, but the goal of such project lies well beyond the primary message of the present manuscript, as we discuss in the second paragraph of the Discussion section.

Reviewer #3 (Public review):

Summary:

Gazal et al present convincing evidence supporting a new model of MPS formation where a gap-and-patch MPS pattern coalesces laterally to give rise to a lattice covering the entire axon shaft.

Strengths:

(1) This is a very interesting study that supports a change in paradigm in the model of MPS lattice formation.

(2) Knowledge on MPS organization is mainly derived from studies using rat hippocampal neurons. In the current manuscript, Gazal et al use human IPS-derived motor neurons, a highly relevant neuron type, to further the current knowledge on MPS biology.

(3) The quality of the images provided, specifically of those involving super-resolution, is of a high standard. This adequately supports the conclusions of the authors.

(R13) We thank the reviewer for the positive comments on the manuscript, the techniques used and the proposed model.

Weaknesses:

(1) The main concern raised by the manuscript is the assumption that staudosporine-induced gap and patch formation recapitulates the physiological assembly of gaps and patches of betaII-spectrin.

(R14) Along the project, various gaps-and-patches parameters were measured in different conditions and stainings. In all these examinations the only parameter that changed considerably was their abundance. While this suggests that the gaps-and-patches features are comparable between control and staurosporine-treated cells, we acknowledge as a general caution regarding negative data—that subtle qualitative differences cannot be entirely ruled out. We have now emphasized this possibility in the 9th paragraph of the Discussion section.

(2) One technical challenge that limits a more compelling support of the new model of MPS formation is that fixed neurons are imaged, which precludes the observation of patch coalescence.

(R15) See R16 regarding possible live-imaging experiments using tagged βII-spectrin constructs.

Recommendations for the authors:

Reviewing Editor Comments:

The reviewers all agree that the work would strongly benefit from live imaging to assess the maturation dynamics of the gap/patch pattern.

(R16) Reviewers agreed that some of the conclusions of our manuscript would benefit from live imaging for validation. Various anticipated technical and biological challenges made these approaches not to be conducted for this initial study on human motor neurons. Just to mention the most important, from previous work of our labs, these cells themselves are difficult to transfect at 2 weeks in culture. Also, ectopically expression of tagged βII-spectrin escapes normal expression control and it has been noticed that ectopic expression yields to protein localization that does not necessarily reflect the endogenous distribution, or that produces cellular responses that precludes the observation of the phenomena under study. These difficulties in studying over-expressed tagged βII-spectrin have been reported in the field, with mentions that the analysed axons were those expressing “low levels of the construct” (Boyer et al., 2026; Zhong et al., 2014; Zhou et al., 2022). Taking this into account, we did not anticipate that, for the goals of the present project, live-imaging was to be included. However, given the positive comments and reception of our conclusions, we sought to try to perform this challenging and risky approach. To that end, we used a C-terminus tagged mouse βII-spectrin-GreenLantern plasmid to transfect our cells (a kind gift from Dr. Subjohit Roy, UCSD, USA). After 3 rounds of differentiating cells and trying various combinations of plasmid quantity, lipofectimine-to-DNA ratios and times of transfection (amongst other parameters), we have got an extremely low efficiency of transfection, and the few expressing neurons showed a distribution of βII-spectrin-GreenLantern that did not match our observations of immunolocalization of endogenous βII-spectrin. Taking all these into account, the present version of the manuscript will not include live-cell imaging on expressed tagged βII-spectrin. Given that reviewers found that some statements in the initial submission would have been better supported by live-imaging, we made changes in the manuscript so as to acknowledge the limitations of concluding dynamic mechanisms from fixed samples (see for example last sentences on 5th paragraph of the Discussion section). Having said so, we hope to be able, in the future, to overcome these experimental challenges and be able to establish live-imaging of βII-spectrin in neurons. For example, to avoid unregulated transgene expression, Heller and colleagues recently generated a βII- spectrin-mNeonGreen conditional knock-in (cKI) mice, consisting of a LoxP- flanked alternative final exon of endogenous βII-spectrin with a C- terminal mNeonGreen fusion that is expressed upon Cre expression (Heller et al., 2025). The implementation and further development of such approaches will be very helpful in new studies on the dynamics of βII-spectrin and the MPS as a whole. However, the scale of work needed to accomplish those approaches represent stand-alone projects.

Reviewer #1 (Recommendations for the authors):

In the section "The MPS is absent in beta-II spectrin gaps, the authors mention that the presence of MPS in patches suggests that the axons are not undergoing degeneration. I don't think this is a good criterion to use, despite the citations they take support from.

(R17) We agree with the reviewer's suggestion: in virtue of the unlikely connection between the cited developmental axon degeneration process in sensory neurons and the possible axon degeneration of long term cultures of human-iPSCs-derived motor neurons studied here, we have eliminated the sentence of reference

The authors show that degradation by proteases does not happen in their case. In this regard, they may want to discuss the recent article by Heller et al, Science 2025 (https://doi.org/10.1126/science.adn6712) and Hofmann et al, Sci. Rep., 2022 (https://doi.org/10.1038/s41598-022-18562-5)

(R18) By western blot analysis, we did not see evident changes in proteolysis-derived fragments. However it is likely that even when finding phenotypes with protease inhibitors, protein fragments accumulation is below the sensitivity of western blots. We were expecting gross changes observable by western blot in the case proteolysis explained gap formation.

Calpain and Caspase activity has been shown to be relevant in different aspects of MPS biology. To the works cited by the reviewer, now one has to add the very recent work by Fei and colleagues (Fei et al., 2026). We have modified part of the Discussion section to analyse our results in this broader context.

Briefly, Hofmann and colleagues found that acute treatment with calpain inhibitors right before axotomy lead to an increase in percentage of periodic βII-spectrin (referred by authors as “periodicity”) in the regenerated axons in a 2-hour period. Interestingly, the βII-spectrin patches they describe at distal portions did not increase in number, but they increased in size. This indicates that in the particular situation of axonal regeneration calpain activity puts a brake into MPS formation within patches. This invited us to re-examine our own protease inhibition experiments, and measured patch length in this. The new results are shown in Supplementary Fig. 6 and and further analysed in the Discussion section. In summary, our changes were much less notable than the ones found in regenerating axons, but follow the same trend: protease inhibitors made patches longer.

On the other hand, Heller and colleagues found in live-imaging studies that calpain activity contributes to the steady-state dynamics of βII-spectrin exchange in a mature MPS lattice. More recently, Fei and colleagues found that caspase or calpain inhibition does not change the steady-state organization of a mature MPS lattice when observing treated axons after fixation samples. Fei and colleagues find a relevant role for calpains whenever massive endocytosis (of any kind) is engaged experimentally. Interestingly, all these studies, including ours, examined calpains roles in MPS in different scenarios. When looked in detail, we don’t believe that these are contradictory results among them, and a complete picture of calpains (and caspases) roles in MPS assembly, growth, maintenance and remodeling will have to take into account all the above mentioned results, including ours. All these analyses are now included in the Discussion section.

Minor comments:

(1) "Recently, it was proposed that this continuous MPS organization arises from the coalescence of discontinuous "patches" of incomplete MPS units that originate in the distal axon and migrate proximally (Zhong et al. 2014)." Please check the citation. Should it be Hoffman et al. 2022?

(R19) The reviewer is correct. The proper citation has now been included.

(2) Is there an established link between ALS and spectrin? I would suggest decreasing the emphasis on this as no clear conclusions are achieved.

(R20) As stated in the text, the study of ALS mutations is justified from two aspects: one aspect is that there are several tubulin and other cytoskeletal proteins whose mutations are linked to ALS (Castellanos-Montiel et al., 2020) and microtubules dynamics has been shown to affect the cortical skeleton (Qu et al., 2017). Second, since human motor neurons are affected in ALS, we thought that a complete characterization of the βII-spectrin cortical cytoskeleton in these cells should include ALS-related mutations. We have now included an a basic MPS description in TDP43 and SOD1 mutation (Suppl. Fig. 5).

The aspect of ALS-related mutations only occupies two short paragraphs in the main text and some panels in Supplementary information. To follow the suggestions by the Reviewer, we have downplayed the relative relevance of these results in the text, without compromising the amount of data we show.

(3) There is a typo in the approximate symbol used for 150 kDa in the section where calpain and caspase activity is reported.

(R21) Typo corrected.

(4) Please add the Latrunculin concentration used in the main text, as it makes it easier for the reader.

(R22) Done.

(5) In the Discussion, paragraph starting with "We further showed ...", there is a typo where Zhong et al is cited.

(R23) Corrected.

(6) Supplementary Figure 1B: attachment instead of 'atachment'.

(R24) Corrected.

(7) Include DIVs or time in the schematic. It is easier for the reader to understand.

(R25) We have now included time references in schematics of Suppl. Fig1B.

(8) Supplementary Figure 1C

Unable to distinguish βII-spectrin and βIII-tubulin in the merged image. Separate figure panels will help.

(R26) The merged images in the reconstructions are merely to better show the tracing individual axons at such low magnification. Relevant portions with only βII-spectrin channels are shown in C1 and C2. Separated individual channels are shown elsewhere across the manuscript.

(9) Supplementary Figure 4D

Why is there so much cleavage product for αII-spectrin across DMSO and treatment? It varied over batches as well. Doesn't this mean that αII-spectrin is going through more proteolytic cleavage? Why?

(R27) The amount of cleavage product for αII-spectrin is not a surprise to us. For instance, although calpains and caspases can potentially process both α- and β-spectrin, in in vivo scenarios where calpain activity is triggered there are much more fragments of α-spectrin being produced (Czogalla & Sikorski, 2005). On the other hand, our staining of cleaved-αII-spectrin by the SNTF antibody by immunofluorescence (Fig4C) parallels the findings by western blot -high levels of cleaved-αII-spectrin across treatments. A similar strong staining using this antibody has been recently shown in the intact axon (Heller et al., 2025). It will be interesting in the future to address if these fragments have any biological significance beyond being mere byproducts of αII-spectrin processing.

Reviewer #2 (Recommendations for the authors):

Suggestions for improving the quality of the manuscript:

(1) Live imaging in combination with FRAP assays will help define whether the capture of spectrin from gaps into patches is true. Fixed neurons only provide static information and may not reflect real-time physiological effects.

(R28) See R16 regarding possible live-imaging experiments using tagged βII-spectrin constructs.

(2) Could the presence of F-actin trails in axons facilitate the formation of patches? Will the use of formin/Arp2/3 inhibitors rescue the effect of staurosporine, similar to Latrunculin A?

(R29) Very interesting suggestion. It is likely that different pools of F-actin contribute to the dynamic of MPS formation, and actin trails are definitely worth investigating in this context.

(3) Figure 8 lacks a latrunculin A treated condition? Why is this not present?

(R30) The quantification of that treatment was excluded for space and readability. We have now included the values of group LatA + DMSO in Fig8Cand D and rearranged the whole figure.

(4) Does neuronal stimulation have any effect (KCl treatment) on gaps and patches?

(R31) Very interesting suggestion. Unfortunately, we have not examined whereas neuronal stimulation affects any parameter of the gaps-and-patches structure.

(5) Please check the manuscript for typos and reference insertion points in the text. More than a couple were noted.

(R32) We have corrected typos.

Reviewer #3 (Recommendations for the authors):

This is a very interesting study that supports a change in paradigm in the model of MPS lattice formation.

(1) One major concern is the assumption that staudosporine-induced gap and patch formation recapitulates the physiological assembly of gaps and patches of betaII-spectrin, solely based on their morphological similarity. This should be further discussed in the manuscript. Further analysis of additional cytoskeleton components, including microtubules in staurosporine-treated neurons, could also be provided.

(R33) See R14.

(2) In Figure 1E, betaIII-tubulin and NF-H seem to accumulate in betaII-spectrin-rich axonal enlargements. If these are patches, how do you reconcile this finding with Figure 2C-D, where NF-M and alphaII-tubulin are not specifically enriched in betaII-spectrin patches?

(R34) We actually show that axonal enlargements and patches are structurally unrelated, in many aspects. We mention these axonal enlargements as a way to perform an exhaustive characterization of all βII-spectrin features found in these axons.

(3) One technical challenge that limits a more compelling support of the new model of MPS formation is that fixed neurons are imaged, which precludes the observation of patch coalescence. This should be further discussed in the revised version of the manuscript.

(R35) The limitation of the experimental approach is now further discussed (see for example last sentences on 5th paragraph of the Discussion section).

(4) On a more general note, the title of some of the Results sub-sections could be revised to convey the findings of those sub-sections and not the Methods that were used (example: "Quantitave and Qualitative analyses of betII-spectrin distribution....").

(R36) According to the suggestion, we have changed the title of this subsection.

References

Boyer, N. P., Sharma, R., Wiesner, T., Parperis, C., Delamare, A., Pelletier, F., Jullien, N., Bhatt, A. M., Parra-Rivas, L. A., Kearney, P. J., Shavarebi, F., Leterrier, C., & Roy, S. (2026). Spectrin condensates provide a nidus for assembling the axonal membrane-associated periodic skeleton. iScience, 29(1), 114454. https://doi.org/10.1016/j.isci.2025.114454

Castellanos-Montiel, M. J., Chaineau, M., & Durcan, T. M. (2020). The Neglected Genes of ALS: Cytoskeletal Dynamics Impact Synaptic Degeneration in ALS. Frontiers in Cellular Neuroscience, 14, 594975. https://doi.org/10.3389/fncel.2020.594975

Czogalla, A., & Sikorski, A. F. (2005). Spectrin and calpain: A “target” and a “sniper” in the pathology of neuronal cells. Cellular and Molecular Life Sciences: CMLS, 62(17), 1913–1924. https://doi.org/10.1007/s00018-005-5097-0

Guix, F. X., Capitán, A. M., Casadomé-Perales, Á., Palomares-Pérez, I., López Del Castillo, I., Miguel, V., Goedeke, L., Martín, M. G., Lamas, S., Peinado, H., Fernández-Hernando, C., & Dotti, C. G. (2021). Increased exosome secretion in neurons aging in vitro by NPC1-mediated endosomal cholesterol buildup. Life Science Alliance, 4(8), e202101055. https://doi.org/10.26508/lsa.202101055

Heller, E., Kurup, N., & Zhuang, X. (2025). The membrane skeleton is constitutively remodeled in neurons by calcium signaling. Science (New York, N.Y.), 389(6760), eadn6712. https://doi.org/10.1126/science.adn6712

Qu, Y., Hahn, I., Webb, S. E. D., Pearce, S. P., & Prokop, A. (2017). Periodic actin structures in neuronal axons are required to maintain microtubules. Molecular Biology of the Cell, 28(2), 296–308. https://doi.org/10.1091/mbc.E16-10-0727

Zhong, G., He, J., Zhou, R., Lorenzo, D., Babcock, H. P., Bennett, V., & Zhuang, X. (2014). Developmental mechanism of the periodic membrane skeleton in axons. eLife, 3, e04581. https://doi.org/10.7554/eLife.04581

Zhou, R., Han, B., Nowak, R., Lu, Y., Heller, E., Xia, C., Chishti, A. H., Fowler, V. M., & Zhuang, X. (2022). Proteomic and functional analyses of the periodic membrane skeleton in neurons. Nature Communications, 13(1), 3196. https://doi.org/10.1038/s41467-022-30720-x

• python 3.10 的 EOL 时间是2026/03/03, 最后版本号为3.10.20，PBS的最后构建为20260303
• python 3.9 的 EOL时间是2025/10/31, 最后版本号为3.9.25，PBS的最后构建为20251031
• python 3.8 的 EOL时间是2024/09/06, 最后版本号为3.8.20，PBS的最后构建为20240909

Author response:

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

We have addressed all the reviewers’ comments through new experiments, additional analyses, or, in some cases, additional text. Below is a summary of the major changes in the manuscript.

(1) We have added a considerable amount of new characterization of the biochemical enrichment of the ribosome clusters, including EM of the ribosome clusters, UV absorbance profiles, immunoblots of additional targets, and additional replicates (new Figure 1). In summary, we provide better evidence that (i) the biochemical enrichment is working and (ii) that the loss of FMRP has no effect on this biological enrichment of ribosomal clusters.

(2) We have now reanalyzed all of the data in Figs. 5-8 using only the data after removing PCR duplicates from the RPFs. Other than the comparison between the nuclease treatments (Fig. 3), only this data is now used. Moreover, we have reanalyzed this data using suggestions from the reviewers, including providing PCA analysis (Fig S5-1), GSEA analysis (Fig 5), and normalizing for group size when comparing significance to total mRNAs, (Fig 6-7). We now also include a new analysis (Fig S7-1) to better explain how the loss of FMRP affects mainly FMRP targets defined by CLIP, but not all mRNAs resistant to run-off.

(3) We are now more conservative in our nomenclature; we use "pellet" instead of "RNA granule (RG)" and "fraction 5/6" instead of "ribosome clusters (RC)". We have added a section to the discussion about the relationship between the RNA granules measured using imaging of hippocampal neurites and the biochemical purification of ribosome clusters in the pellet, as requested by the reviewers.

(4) We have made many other minor changes to the text and analysis, which can be found in the specific response to the reviewers.

(5) One major additional requested change that was not implemented was to repeat our experiments at different time points. We have added a paragraph to the discussion outlining (i) why this was not done and (ii) the caveats of our conclusions without this data being present.

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors have investigated the role of FMRP in the formation and function of RNA granules in mouse brain/cultured hippocampal neurons. Most of their results indicate that FMRP does not have a role in the formation or function of RNA granules with specific mRNAs, but may have some role in distal RNA granules in neurons and their response to synaptic stimulation. This is an important work (though the results are mostly negative) in understanding the composition and function of neuronal RNA granules. The last part of the work in cultured neurons is disjointed from the rest of the manuscript, and the results are neither convincing nor provide any mechanistic insight.

Strengths:

(1) The study is quite thorough, the methods and analysis used are robust, and the conclusion and interpretation are diligent.

(2) The comparative study of Rat and Mouse RNA granules is very helpful for future studies.

(3) The conclusion that the absence of FMRP does not affect the RNA granule composition and many of its properties in the system the authors have chosen to study is well supported by the results.

(4) The difference in the response to DHPG stimulation concerning RNA granules described here is very interesting and could provide a basis for further studies, though it has some serious technical issues.

Thank you for these positive comments on the paper.

Weaknesses:

(1) The system used for the study (P5 mouse brain or DIV 8-10 cultured neuron) is surprising, as the majority of defects in the absence of FMRP are reported in later stages (P30+ brain and DIV 14+ neurons). It is important to test if the conclusions drawn here hold good at different developmental stages.

Unfortunately, myelin strongly interferes with the ability to use this protocol to purify ribosome clusters in older brains (See Khandjian et al., 2004). It is possible to redo the ribopuromycylation results at later times in culture, but since we cannot compare this to a comparable time in the brain, we have chosen not to do this experiment. We acknowledge this limitation in the discussion, noting that our results are only a snapshot of development and that different results may be observed at different times.

(2) The term 'distal granules' is very vague. Since there is no structural or biochemical characterization of these granules, it is difficult to understand how they are different from the proximal granules and why FMRP has an effect only on these granules.

We agree with the reviewer and have removed all references to distal granules. We clarified that we did not measure RPM puncta close to the neuron because the much stronger RPM signal made defining puncta more difficult, and thus, we cannot determine if there are differences between proximal and distal puncta.

(3) Since the manuscript does not find any effect of FMRP on neuronal RNA granules, it does not provide any new molecular insight with respect to the function of FMRP

We would respectfully disagree that the study does not provide molecular insight into the function of FMRP, as disproving that FMRP is important for stalling and determining the position of stalling would remove one of the major hypotheses about the function of FMRP, and showing that a major hypothesis in the literature is unlikely to be correct, is at least to me, providing insight. Moreover, we do show an effect of the loss of FMRP on the RPM puncta that represent neuronal RNA granules containing stalled ribosomes. This also provides insight.

Reviewer #2 (Public review):

In the present manuscript, Li et al. use biochemical fractionation of "RNA granules" from P5 wildtype and FMR1 knock-out mouse brains to analyze their protein/RNA content, determine a single particle cryo-EM structure of contained ribosomes, and perform ribo-seq analysis of ribosome-protected RNA fragments (RPFs). The authors conclude from these that neither the composition of the ribosome granules, nor the state of their contained ribosomes, nor the mRNA positions with high ribosome occupancy change significantly. Besides minor changes in mRNA occupancy, the one change the authors identified is a decrease in puromycylated punctae in distal neurites of cultured primary neurons of the same mice, and their enhanced resistance to different pharmacological treatments. These results directly build on their earlier work (Anadolu et al., 2023) using analogous preparations of rat brains; the authors now perform a very similar study using WT and FMR1-KO mouse brains. This is an important topic, aiming to identify the molecular underpinnings of the FMRP protein, which is the basis of a major neurological disease. Unfortunately, several limitations of this study prevent it from being more convincing in its present form.

In order to improve this study, our main suggestions are as follows:

(1) The authors equate their biochemically purified "RG" fraction with their imaging-based detection of puromycin-positive punctae. They claim essentially no differences in RGs, but detect differences in the latter (mostly their abundance and sensitivity to DHPG/HHT/Aniso). In the discussion the authors acknowledge the inconsistency between these two modalities: "An inconsistency in our findings is the loss of distal RPM puncta coupled with an increase in the immunoreactivity for S6 in the RG." and "Thus, it may be that the RG is not simply made up of ribosomes from the large liquid-liquid phase RNA granules."

How can the authors be sure that they are analysing the same entities in both modalities? A more parsimonious explanation of their results would be that, while there might be some overlap, two different entities are analyzed. Much of the main message rests on this equivalence, and I believe the authors should show its validity.

Thank you for your comments. We have been more conservative in the revised paper, referring to the pellet fraction as the pellet fraction rather than the RNA granule fraction to acknowledge the possibility that these two modalities differ. However, we would respectfully disagree that our main message requires RPM-labeled RNA granules in neurites and the ribosome clusters isolated by sedimentation to be “equivalent”. We do believe they are related and added a section in the discussion on this important point.

(2) The authors show that increased nuclease digestion (and magnesium concentration) led to a reduction of their RPF sizes down to levels also seen by other researchers. Analyzing these now properly digested RPFs, the authors state that the CDS coverage and periodicity drastically improved, and that spurious enrichments of secretory mRNAs, which made up one of the major fractions in their previous work, are now reduced. In my opinion, this would be more appropriately communicated as a correction to their previous work, not as a main Figure in another manuscript.

We have removed all discussion of the secretory mRNAs, as our attempts to obtain independent evidence for this finding by examining ribophorin enrichment in the pellet across different Mg²⁺ concentrations did not support this interpretation (data not shown in the paper). I understand that the change in nuclease is somewhat out of place narratively, but it is clearly relevant to this work. We would disagree with our previous work requiring a ‘correction’. We believe that the nuclease resistance of the mRNA at the entrance site is important. We reproduce our results from rats with similar nuclease treatment in mice as seen in our previous publication; thus, this work is not wrong. We have a paper in preparation that suggests the secondary structure of the mRNA at this location may be important for stalling and thus feel strongly that this result should remain in the manuscript.

(3) The fold changes reported in Figure 7 (ranging between log2(-0.2) and log2(+0.25)) are all extremely small and in my opinion should not be used to derive claims such as "The loss of FMRP significantly affected the abundance and occupancy of FMRP-Clipped mRNAs in WT and FMR1-KO RG (Fig 7A, 7B), but not their enrichment between RG and RCs".

We agree that the changes are small and indeed did not appear in the DEG analysis. However, because we are analyzing a large set of mRNAs in this analysis, the results are highly significant and remain significant when using the new statistical tests suggested by the reviewer below. We now emphasize that these are small changes and remind readers that none of the individual mRNA changes were significant in the DEG analysis.

(4) Figure 8 / S8-1 - The authors show that ~2/3 of their reads stem from PCR duplicates, but that even after removing those, the majority of peaks remain unaltered. At the same time, Figure S8-1 shows the total number of peaks to be 615 compared with 1392 before duplicate removal. Can the authors comment on this discrepancy? In addition, the dataset with properly removed artefacts should be used for their main display item instead of the current Figure 8.

We now use only the data after removing PCR duplicates for all the analyses except in Figure 3. The number of peaks observed is determined mainly by the threshold used, as stated in the methods “To be identified as a peak, the zenith of an abundance site for the reads must be 4x higher of the average of the total transcript.” Due the lower number of reads after the PCR duplicates fewer peaks reached this threshold.

(5) Figure 9 / S9-1, the density of punctae in both WT and FMR1-KO actually increases after treatment of HHT or Anisomycin (Figure S9-1 B-C). Even if a large fraction would now be "resistant to run-off", there should not be an increase. While this effect is deemed not significant, a much smaller effect in Figure 9C is deemed significant. Can the authors explain this? Given how vastly different the sample sizes are (ranging from 23 neurites in Figures S91 to 5,171 neurites in Figure 9), the authors should (randomly) sample to the same size and repeat their statistical analysis again, to improve their credibility.

The box and whisker plots emphasize the median and not the average. We now also show the averages in Figure S9-1, which indicate a slight decrease for both HHT and anisomycin.

We apologize for the typo in the figure legend in Figure 9, 171, not 5171. We now use random sampling in Figures 6 and 7, where the sample sizes differ substantially.

Reviewer #3 (Public review):

Summary:

Li et al describe a set of experiments to probe the role of FMRP in ribosome stalling and RNA granule composition. The authors are able to recapitulate findings from a previous study performed in rats (this one is in mice).

Strengths:

(1) The work addresses an important and challenging issue, investigating mechanisms that regulate stalled ribosomes that are part of stress granules, and focusing on the role of FMRP. This is a complicated problem, given the heterogeneity of the granules and the challenges related to their purification. This work is a solid attempt at addressing this issue, which is widely understudied.

(2) The interpretation of the results could be interesting if supported by solid data. The idea that FMRP could control the formation and release of stress granules, rather than the elongation by stalled ribosomes, is of high importance to the field, offering a fresh perspective into translational regulation by FMRP.

(3) The authors focused on recapitulating previous findings, published elsewhere (Anadolu et al., 2023) by the same group, but using rat tissue, rather than mouse tissue. Overall, they succeeded in doing so, demonstrating, among other findings, that stalled ribosomes are enriched in consensus mRNA motifs that are linked to FMRP. These interesting findings reinforce the role of FMRP in the formation and stabilization of RNA granules. It would be nice to see extensive characterization of the mouse granules as performed in Figure 1 of Anadolu et al., 2023.

(4) Some of the techniques incorporated aid in creating novel hypotheses, such as the ribopuromycilation assay and the cryo-EM of granule ribosomes.

Thank you for these positive comments. We have now added a more extensive characterization in Figure 1.

Weaknesses:

(1) The RNA granule characterization needs to be more rigorous. Coomassie is not proper for this type of characterization, simply because protein weight says little about its nature. The enrichment of key proteins is not robust and seems not to reach significance in multiple instances, including S6 and UPF1. Furthermore, S6 is the only proxy used for ribosome quantification. Could the authors include at least 3 other ribosomal proteins (2 from the small, 2 from the large subunit)?

We have increased N to improve the robustness of the enrichment analysis and added several additional RBPs. Along with Coomassie we now include analysis of UV absorbance and include EMs from these fractions showing the presence of 80S ribosomal clusters in the fractions we are using.

(2) Page 12-13 - The Gene Ontology analysis is performed incorrectly. First, one should not rank genes by their RPKM levels. It is well known that housekeeping genes, such as those related to actin dynamics, molecular transport, and translation, are highly enriched in sequencing datasets. It is usually more informative when significantly different genes are ranked by p-adjust or log2 Fold Change, then compared against a background to verify enrichment of specific processes. However, the authors found no DEGs. I would suggest the removal of this analysis and the incorporation of a gene set enrichment analysis (ranked by p-adjust). I further suggest that the authors incorporate a dimensionality reduction analysis to demonstrate that the lack of significance stems from biology and not experimental artifacts, such as poor reproducibility across biological replicates.

Thank you for the suggestion. We now use GSEA analysis to examine differences in gene sets between WT and FMR1- mice and find some significant changes (new Fig. 5). The old analysis is still included for comparison to our earlier paper as a supplemental figure. We have now included a PCA analysis (FigS5-1) to show reproducibility across biological replicates.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) RNA sequencing comparison between WT and FMR1 KO mice should be carried out at a later developmental stage, which may provide a better difference between these two groups

There are a number of studies that have already done this analysis and in specific brain regions 10.1016/j.neuron.2017.07.013; 10.7554/eLife.46919; 10.3389/fnmol.2017.00340; https://doi.org/10.1016/j.neuron.2023.06.009. The main goal of our RNA-seq was to standardize for the RPF studies, not to identify differences in RNA-seq between WT and FMRP. In the response to public review point 1 we explain why we do not look at later developmental timepoints.

(2) The same is true in characterizing the effect of FMRP on the RNA granules.

See response to public review point 1, which addresses this point.

(3) No evidence is provided for the effectiveness of DHPG stimulation in DIV8-10 neurons; this is needed for justification using neurons at this stage.

We have previously shown that DHPG stimulation in these neurons at this developmental time from cultures made from rat brain is sufficient to decrease the number of RPM puncta and to induce an increase in the synthesis of proteins in an initiation resistant manner (Graber et al, 2013; Graber et al, 2017). This is now more clearly stated in the manuscript. Moreover, here we replicate the result of DHPG in WT mice at reducing the number of RPM puncta.

(4) In Figure 9 B, it is not clear whether the neurites indicated are axons or dendrites. Since neurons are still in the early stages of dendritogenesis/synaptogenesis, it is important to make that distinction.

We have previously characterized RNA granules in axons and dendrites in hippocampal cultures from rats at this time (Miller et al, 2009, MCN 40:485-495)) and they are similar. While it is likely that the vast majority of the neurites at this time are dendrites, since we did not use markers, we conservatively just use the term neurites.

(5) In Figure 1 (and elsewhere), fraction 5/6 is used as a polysome or RNA cluster. The authors have not provided a UV absorption profile and only have s6 as evidence to say this polysome. In the Coomassie gel, this fraction is any different than fractions 7/7 or 9/10; what is the justification for using this fraction?

The main justification for these fractions is to be consistent with our previous paper (Anadolu et al, 2023) and the Khandian study comparing polysomes to pellet using the same fractionation protocol (El-Fatimy et al, 2016). We now provide a UV absorption profile (Fig. 1C) and EM pictures (Fig. 1D) to show the ribosome clusters in this fraction. We do not believe our results would be fundamentally different from those obtained if we had used other heavy fractions.

Minor comments

(1) The font size very small in the figures, please increase it.

We have worked hard to increase the font size in all the figures.

(2) In the result section for Figure 3B - it is written 'majority of these mRNA are non-coding mRNA' - this doesn't make sense.

Corrected

Reviewer #2 (Recommendations for the authors):

(1) There are lots of mistakes (e.g. word omissions or duplications, grammatical errors) throughout the text, too many to list here.

We have carefully edited the text to try to minimize these mistakes.

(2) In many positions related to their improved nuclease digestion protocol, samples are labelled "M ...", which apparently stands for "high magnesium and high nuclease treatment group". I would suggest switching to something more intuitive, such as "... (improved digestion)".

We have removed most of the comparisons between these samples. What remains (Figure 3), we just use Low Nuclease when we refer to the sample with low Magnesium and low nuclease.

(3) Figure 1,3 - It would be tremendously illuminating to see a polysome trace (UV260 absorbance) in addition to Coomassie-stained SDS-PAGE to underscore the interpretation of the different fractions by the authors. As it stands, there is no way of telling whether there are any polysomes present at all. This can also be done by hand using a UV absorption reader if no built-in device is available to the authors.

We have now done this (Fig. 1C) and also provided EM of this fraction to show the presence of ribosomes in this fraction.

(4) I don't understand why the authors switched from calling fraction 5/6 the "polysome fraction" in their previous work to calling it "ribosome cluster fraction" in this work. The argument given "[...] due to its structural similarity to ribosomes in RNA Granules (Anadolu et al., 2023), we conservatively call this the ribosome cluster fraction (RC)." does not instill confidence that these two fractions are indeed distinct.

We agree with the reviewer and regret this decision. We now call the pellet, the pellet and Fraction 5/6, fraction 5/6.

(5) Figure 1C - There are clear scanning or compression artefacts in the blot images (most prominently in the eEF2 lanes) that should be corrected.

We have replaced all images in Figure 1 and have increased the N of this experiment considerably.

(6) Figure 1C - The authors claim that WT mouse RG is enriched in FMRP compared to RC or starter fraction, but there is also a lot more protein loaded in the RG (especially when compared to RC). It is also hard to believe from the Coomassie staining that despite the much stronger presence of low MW bands (which is where ribosomal proteins migrate) in fraction 5/6, the s6 western blot signal is actually comparable between RC and RG. Can the authors please provide more detail on the loading of these fractions and supply quantification of FMRP in all three fractions, normalized by total protein? This might also be the source of their discrepancy, stating that contrary to their expectation, ribosomes (as measured by s6 signal / s6 signal in starter fraction) are actually increased in FMR1-KO brains.

We have repeated all of these experiments and changed our method of quantification (See methods). We no longer use the starting material in our quantification. Indeed, with the additional data and change in method, we no longer see an increase in S6 in the FMR1- pellet fraction.

(7) Figure 1 - I believe "D-F)" should only read "D-E)" based on the axis titles, and instead "FG)" should be added before the next sentence. Instead of "Staufen" it should be specified in the Figure that "Stau2" was quantified. "Staufen (59kd)" should read "Stau2 (59 kDa)" and "anti-Staufen (52kb)" should read "anti-Stau2 (52 kDa)" and the same for all other similar instances. It is further hard to believe that e.g., "Staufen2 (59kd)" (see above) is not significantly enriched with N=5, a very low spread, and over 1.5x enrichment. The authors should double-check that the appropriate statistical test was employed.

Figure 1 has been completely redone, and the two Staufen bands are enriched in this new analysis.

(8) Figure S4-2 - Most of the detail in the corresponding figure legend should be moved to the Materials and Methods section.

Details relevant to the methods in this figure legend have been now moved to the Material and Methods section.

(9) Figure 4A - The displayed/segmented tRNA densities appear unusually distorted. I would recommend displaying segmented densities of the original homogeneous reconstructions, not of separated and later fused partial maps.

Figure 4 was modified according to the suggestions of this reviewer.’

(10) Figure 9 C-D, S9-1 B-E - Are not all conditions also including puromycin as in B above? If so, it should be added to both the figure and the figure legend.

The reviewer is correct and the figure and legend has been changed to reflect this.

Reviewer #3 (Recommendations for the authors):

(1) "Loss of FMRP causes Fragile X syndrome. In humans, the loss of FMRP occurs due to the expansion of a CGG repeat in the 5' untranslated region (UTR) of the gene, leading to excessive methylation and transcriptional inhibition."

Comment: Genes don't have 5'UTR, but exons encoding 5'UTR. I suggest rephrasing this statement.

This sentence has been rephrased.

(2) "Several of these functions have been implicated in Fragile X syndrome, including FMRP's regulation of miRNA repression, splicing, translation initiation, and translational elongation".

Comment: Is this a typo? miRNA instead of mRNA?

No, this is correct. FMRP has been implicated in the regulation of microRNAs (miRNAs) in a number of studies.

(3) "elongation rates are also increased in mouse models of FMRP".

Comment: Mouse models of Fragile X?

This has been corrected.

(4) "Parts of this work were included in the Master's thesis of the first author (Li, 2024)."

This has been removed.

(5) Comment: Graphs in Figure 1 need proper y-axis labeling. What is the normalization method? What are the values presented in the y-axis?

Figure 1 has been completely changed and the Y-axes are now clear in this new version.

(6) "Thus, by looking at the percentage of puromycylation present in the presence of anisomycin, we can estimate the number of ribosomes in this state. "

Comment: Are the authors really estimating the number of ribosomes in a resistant state? One could argue that they are collecting populational information regarding resistance to anisomycin.

We have rephrased this sentence to be more conservative about what we are measuring.

(7) Comment: Page 11 - Why did the authors assume magnesium would affect the conformation state of the ribosomes? What is the rationale behind increasing the [Mg2+]?

Most preparations using ribosomes use 10 mM MgCl₂. However, most neuroscientists use physiological buffers that contain 2.5 mM MgCl₂. In bacteria, this makes a large difference, but evidence from eukaryotes is not clear. Since this is a collaboration between these two schools of thought, we decided to switch to 10 mM MgCl₂, since in the EM, there were some free 60S ribosomes (Anadolu et al, 2024).

(8) Page 11- "In other words, high Mg2+ decreased the abundance of mRNAs normally cotranslationally inserted into the ER which are unlikely to be components of transporting RNA granules containing stalled ribosomes and solidified our focus on the M protocol in the analyses below."

We have removed this from the paper, as additional experiments aimed to solidify this interpretation failed to detect an effect on secretory mRNAs.

(9) Comment: The whole "abundance", "enrichment", and "occupancy" nomenclature is hard to follow.

We have rewritten this section.

(10) Page 13 - "There were only 2 protein coding genes that were significantly different between the abundance of FMR1-KO and WT in protein coding genes - FMR1 and Wdfy1 (Extended Data Table 5-2). There were no significantly different genes between WT and FMR1-KO occupancy and enrichment. Thus, no difference rose to significance, given the large number of mRNAs used in this analysis."

Comment: It seems like this is repeating the same information three times.

This has been changed.

(11) Page 13 - "Similar to previous experiments with rats, the most abundant mRNAs resistant to run off were significantly abundant, occupied and enriched in both WT and FMRP RPFs (Fig 6)"

The Shah et al dataset we use was based on the most abundant mRNAs resistant to run-off. While we agree it is not surprising that they are also abundant in the pellet we observe, this would not necessarily be true unless the pellet is actually enriched in stalled mRNAs.

(12) Page 14 - "These mRNAs had been identified by cross-linking FMRP with mRNA, fragmenting the mRNA, immunoprecipitating the mRNA still associated with FMRP and sequencing this mRNA."

We shortened this description.

(13) Page 14 - "Interestingly, while still significant, there appeared to be a decrease in the relative abundance of these mRNAs in the FMR1-KO RG (Fig 6B)"

Comment: It is hard to observe this decrease in the boxplots. Second, the statistical tests for the bioinformatics analyses are not the most appropriate, given the large discrepancy in the number of mRNAs present in the experimental group ("All mRNAs") and the filtered groups.

We have redone the statistics using multiple random sampling of all the mRNAs such that the total number of mRNAs in the group was the same. This lowered the significance for some groups, but they are mostly still highly significant. This analysis has also been affected by switching to using the data from the PCR-subtracted RPFs. The changes we now observe are more evident in the whisker box plots due to this improvement in the data.

(14) Page 16 - "To rule out that peaks were due to amplification artifacts in the preparation of RPFs we repeated these analyses after removing PCR duplicates (Fig. S8-1; Extended Data Table S8-3) and found over 95% of the peaks identified without removing PCR duplicates were defined as a peak in at least one of the biological replicates after removing duplicates. More importantly, we found similar results with enrichment of FXS motif and enrichment of negatively charged amino acids in the FMR1-KO only, WT only and both peaks after removing PCR duplicates (Fig. S8-1; Extended Data Table S8-3)."

Comment: It is unclear why the authors needed to include the analysis without PCR duplicate removal. This is an essential step to guarantee the robustness of ribo-seq findings. I recommend removing the whole analysis from Figure 8 from the manuscript and including only the post-duplicate removal analysis.

As mentioned above, we completely agree with this statement and now show only this data and moreover have redone all the figures with only this data (except for Fig. 3).

(15) Figure 9 - I am unsure that the data is convincing enough to demonstrate reinitiation of mRNA granules induced by DHPG. I suggest a colocalization experiment with another protein well known to be localized to RNA granules, such as G3BP1. In addition, repeat the experiment with an additional group where elongation is blocked after the addition of DHPG, which presumably would prevent the reduction in the WT puncta density.

These are interesting additional experiments, but outside the scope of what we can manage. We have previously shown colocalization of Staufen, FMRP and UPF1 to these puncta (Graber et al, 2013; Graber et al, 2017) and shown that these puromycylated puncta also colocalize with nascent peptides detected using the Sun-Tag technique. While we think doing the experiment in the presence of an elongation inhibitor would be interesting, we disagree that it would prevent the reduction in WT puncta density, since we believe what is happening is the loss of the liquid-liquid phase separation of the ribosome clusters due to dephosphorylation of RBPs like FMRP and UPF1 (Graber et al, 2017), and this would reduce the puncta density whether or not the ribosomes were activated for translation.

Nevertheless, we have tried to temper the conclusions made from this result, emphasizing what we know (RPM puncta are decreased) as opposed to actual reactivation of stalled polysomes which we are not measuring.

Discussion - Page 18 - "Nevertheless, if FMRP binding was the critical determinant for presence in neuronal RNA granules, we would have expected to observe more differences." This is not true. If the data is poorly collected, you will not see differences.

This statement was removed.

(16) "A proportion of the stalled ribosomes that are not stored in large RNA granules may still be pelleted in the sucrose gradients. This fraction may be greater in the absence of FMRP."

Comment: The authors are right about this and touch on my original point that the characterization of the biochemical fractionation is not convincing enough. I'd suggest probing against more proteins that are contained in RNA granules.

We have added several proteins to the biochemical characterization shown in Figure 1. We have added a discussion about the relationship between neuronal RNA granules and the sedimented pellet fraction in the discussion section.

Reviewer #2 (Public review):

Summary:

In the manuscript by Walter-McNeill, Kruglyak and team, the authors provide solid evidence of another toxin-antidote (TA) system in C. elegans. Generally, TA systems involve selfish and linked genetic elements, one encoding a toxin that kills progeny inheriting it, unless an antidote (the second element) is also present. Currently, only two TA systems have been characterized in this species, pointing to the importance of identifying new instances of such systems to understand their transmission dynamics, prevalence, and functions in shaping worm populations.

The manuscript has been improved in some aspects upon revision. We remain enthusiastic for the overall findings and the identification of a new toxin/anti-toxin system and note that the strengths and weaknesses we detailed previously remain. We reiterate our critique regarding the strength of conclusions that can be made about small RNA pathway regulation based on meta-analysis of other datasets. While we agree that the observations presented are suggestive of small RNA regulation, likely due to piRNA targeting and subsequent 22G-RNA regulation, until these hypotheses are tested experimentally in the future by mutation of the piRNA target sites, testing ago/piRNA pathway and other 22G-RNA pathway mutants for tmrl-1 expression, etc., we think it is important to use precise language in presenting the conclusions. In particular, the abstract states:

"Multiple lines of evidence suggest that the N2 tmrl-1 allele is recognized by piRNAs, leading to MUT-16-dependent 22G siRNA production and post-transcriptional silencing of the transcript. The N2 haplotype represents the first naturally occurring unlinked toxin-antidote system where the toxin is post-transcriptionally suppressed by endogenous small RNA pathways."

We therefore recommend moderating this statement to "...is likely to be post-transcriptionally suppressed by endogenous small RNA pathways."

Previously noted strengths and weaknesses remain relevant to this revision.

Strengths:

This novel TA system (mll-1/smll-1) was identified on LGV in wild C. elegans isolates from the Hawaiian Islands, by crossing divergent strains and observing allele frequency distortions by high throughput genome sequencing after 10 generations. These allele frequency distortions were subsequently confirmed in another set of crosses with a separate divergent strain, and crosses of heterozygous males or hermaphrodites resulted in a pattern of L1 lethality in progeny (with a rod arrest phenotype) that suggested the maternal transmission of this TA system from the XZ1516 genetic background. By elegantly combining the use of near-isogenic lines, CRISPR editing to generate knock-outs, and a transgene rescue of the antidote gene, the authors identified the genes encoding the toxin and the antidote, which they refer to as mll-1 and smll-1. Moreover, the specific mll-1 isoform responsible for the production of the toxin was identified and mll-1 transcripts were observed by FISH in early and late embryos, as well as in larvae. Inducible expression of the toxin in various strains resulted in larval arrest and rod phenotypes. The authors then characterized the genetic variation of 550 wild isolates at the toxin/antidote region on LGV and distinguished three clades: 1) one with the conserved TA system, 2) one having lost the toxin and retaining a mostly functional antidote, and 3) one having lost the antidote and retaining a divergent yet coding toxin (this includes the reference strain Bristol N2, in which the homologous toxin gene has acquired mutations and is known as B0250.8). Further, the authors show that this region is under positive selection. These data are compelling and provide very strong evidence of a new TA system in this species.

Weaknesses:

The question remained as to how one clade, including N2, could retain the toxin gene but not possess a functional antidote. In the second part of the manuscript, the authors hypothesized that small RNA targeting (RNAi) of the toxin transcript could provide the necessary repression to allow worms to survive without the antidote. Through a meta-analysis of multiple small RNA datasets from the literature, the authors found evidence to support this idea, in which the toxin transcript is targeted by 22G siRNAs whose biogenesis is dependent on the Mutator foci protein, MUT-16. They note that from previous studies, mut-16 null mutants displayed a varied penetrance of larval arrest. In their own hands, mut-16 mutants displayed 15% varied larval arrest and 2% rod phenotypes. In an attempt to link B0250.8 to mut-16/siRNAs, they made a double mutant and examined body length as a proxy for developmental stage. Here, they observed a partial rescue of the mut-16 size defect by B0250.8 mutation. Finally, the authors also highlight data from further meta-analysis which predicts the recognition of B0250.8 by several piRNAs. Also based on existing data from the literature, the authors link loss of Piwi (PRG-1), which binds piRNAs, to a depletion of 22G-RNAs targeting B0250.8 and an upregulation of B0250.8 expression in gonads, suggesting that piRNAs are the primary small RNAs that target B0250.8 for down-regulation. The data in this portion of the manuscript are intriguing, but somewhat incomplete, as they are based on little primary experimentation and a collection of different datasets (which have been acquired by slightly different methods in most cases). This portion of the study would require subsequent experimentation to firmly establish this mechanistic link. For example, to be able to claim that "the N2 toxin allele has acquired mutations that enable piRNA binding to initiate MUT-16-dependent 22G small RNA amplification that targets the transcript for degradation" the identified piRNA sites should be mutated and protein and transcript levels analysed in wild-type and in the strain with mutated piRNA sites. At a minimum, the protein levels in wild-type and mut-16, prg-1, and/or wago-1 mutants should be measured by western blot and/or by live imaging (introducing a GFP or some other tag to the endogenous protein via CRISPR editing) to show that the toxin is not accumulated as a protein in wt, but increases in levels in these mutants. mRNA levels in Fig S5A suggest there is still some expression of the B0250.8 transcript in a wild type situation.

Comments on revised version.

We have no further recommendations for the authors, other than those provided above.

Author response:

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

We incorporated Reviewer #2’s suggestion to change the name of mll-1 because of overlap with a human gene. We used the updated gene names in our responses below to minimize confusion. Below are the updated gene names for the toxin-antidote system we described.

tmrl-1 - Toxin-induced Maternal Rod Lethality (formerly mll-1). After we establish that B0250.8 is also a toxin, we refer to this gene as the “N2 tmrl-1 allele”.

amrl-1 - Antidote of Maternal Rod Lethality (formerly smll-1)

Public Reviews:

Reviewer #1 (Public review):

Summary:

The article by Zdraljevic et al. reports the discovery of a third toxin-antidote (TA) element in C. elegans, composed of the genes mll-1 (toxin) and smll-1 (antidote). Unlike previously characterized TA systems in C. elegans, this element induces larval arrest rather than embryonic lethality. The study identifies three distinct haplotypes at the TA locus, including a hyper-divergent version in the standard laboratory strain N2, which retains a functional toxin but lacks a functional antidote. The authors propose that small RNA-mediated silencing mechanisms, dependent on MUT-16 and PRG-1, suppress the toxicity of the divergent toxin allele. This work provides insights into the evolutionary dynamics of TA elements and their regulation through RNA interference (RNAi).

Overall, there are many things to like about this paper and only a few small quibbles, which will not require more than a little rewriting or relatively minor analyses.

Strengths:

(1) The discovery of a maternally deposited TA element with delayed toxicity due to delayed mRNA translation of the maternally deposited toxin mRNA is a significant addition to the literature on selfish genetic elements in metazoans.

(2) Identifying three haplotypes at the TA locus provides a snapshot of potential evolutionary trajectories for these elements, which are often inferred but rarely demonstrated in naturally occurring strains. The genomic analysis of 550 wild isolates contextualizes the findings within natural populations, revealing geographic clustering and evolutionary pressures acting on the TA locus.

(3) The study employs various techniques, including CRISPR/Cas9 knockouts, FISH, long-read RNA sequencing, and population genomics. The use of inducible systems to confirm toxicity and antidote functionality is particularly robust. This multifaceted approach strengthens the validity of the findings.

(4) The authors provide compelling evidence that small RNA pathways suppress toxin activity in strains lacking a functional antidote. This highlights an alternative mechanism for neutralizing selfish genetic elements.

Weaknesses:

(1) The introduction focuses strongly (for good reason) on bacterial TA systems and then jumps to TA systems in C. elegans. It's unclear why TA systems in other eukaryotes are not discussed.

We briefly introduced bacterial TA systems because of their ubiquitousness and focused on C. elegans TA systems. We chose certain aspects of previously described Caenorhabditis TA elements that were relevant to the narrative we presented. Furthermore, we have extensively reviewed TA systems previously and have added a citation to that review in the revised manuscript (Burga et al. 2020).

(2) Similarly, there is a missed opportunity to discuss an analogy between the suppressor mechanism discovered here and the hairpin RNA suppressors of meiotic drive identified by Eric Lai and colleagues. Discussing these will provide a fuller context of the present study's findings and will not affect their novelty.

Thank you for pointing this out. We added a mention of the Stellate and Dox systems in our discussion.

(3) While the evidence for RNAi-mediated suppression is strong, the claim that positive selection drove diversification at piRNA binding sites requires further discussion and clarification. The elevated dN and dS are unusual (how unusual relative to other genes in vicinity? What is hyper-divergent statistically speaking?), but there is no a priori reason that there would be selection on piRNA binding sites within the mll-1 transcript to facilitate its recognition by endogenous RNAi machinery; what is the selective pressure for mll-1 to do so? Most TA systems would like to avoid being suppressed by the host. One cannot make the argument that this was motivated by the loss of the antidote because the loss of the antidote would be instantly suicidal, so the cadence of events described requiring hypermutation of the mll-1 transcript does not work.

We largely agree with the reviewer’s point, which we believe is based on the following sentence in the discussion: “We propose that positive selection for piRNA binding sites in the tmrl-1 transcript drove the diversification of this gene toward the N2 version.” We have removed this argument from the discussion in the revised manuscript.

Reviewer #2 (Public review):

Summary:

In the manuscript by Walter-McNeill, Kruglyak, and team, the authors provide solid evidence of another toxin-antidote (TA) system in C. elegans. Generally, TA systems involve selfish and linked genetic elements, one encoding a toxin that kills progeny inheriting it, unless an antidote (the second element) is also present. Currently, only two TA systems have been characterized in this species, pointing to the importance of identifying new instances of such systems to understand their transmission dynamics, prevalence, and functions in shaping worm populations.

Strengths:

This novel TA system (mll-1/smll-1) was identified on LGV in wild C. elegans isolates from the Hawaiian islands, by crossing divergent strains and observing allele frequency distortions by high-throughput genome sequencing after 10 generations. These allele frequency distortions were subsequently confirmed in another set of crosses with a separate divergent strain, and crosses of heterozygous males or hermaphrodites resulted in a pattern of L1 lethality in progeny (with a rod arrest phenotype) that suggested the maternal transmission of this TA system from the XZ1516 genetic background. By elegantly combining the use of near-isogenic lines, CRISPR editing to generate knock-outs, and a transgene rescue of the antidote gene, the authors identified the genes encoding the toxin and the antidote, which they refer to as mll-1 and smll-1. Moreover, the specific mll-1 isoform responsible for the production of the toxin was identified and mll-1 transcripts were observed by FISH in early and late embryos, as well as in larvae. Inducible expression of the toxin in various strains resulted in larval arrest and rod phenotypes. The authors then characterized the genetic variation of 550 wild isolates at the toxin/antidote region on LGV and distinguished three clades: (1) one with the conserved TA system, (2) one having lost the toxin and retaining a mostly functional antidote, and (3) one having lost the antidote and retaining a divergent yet coding toxin (this includes the reference strain Bristol N2, in which the homologous toxin gene has acquired mutations and is known as B0250.8). Further, the authors show that this region is under positive selection. These data are compelling and provide very strong evidence of a new TA system in this species.

Weaknesses:

The question remained as to how one clade, including N2, could retain the toxin gene but not possess a functional antidote. In the second part of the manuscript, the authors hypothesized that small RNA targeting (RNAi) of the toxin transcript could provide the necessary repression to allow worms to survive without the antidote. Through a meta-analysis of multiple small RNA datasets from the literature, the authors found evidence to support this idea, in which the toxin transcript is targeted by 22G siRNAs whose biogenesis is dependent on the Mutator foci protein, MUT-16. They note that from previous studies, mut-16 null mutants displayed a varied penetrance of larval arrest. In their own hands, mut-16 mutants displayed 15% varied larval arrest and 2% rod phenotypes. In an attempt to link B0250.8 to mut-16/siRNAs, they made a double mutant and examined body length as a proxy for developmental stage. Here, they observed a partial rescue of the mut-16 size defect by B0250.8 mutation. Finally, the authors also highlight data from further meta-analysis, which predicts the recognition of B0250.8 by several piRNAs. Also based on existing data from the literature, the authors link loss of Piwi (PRG-1), which binds piRNAs, to a depletion of 22G-RNAs targeting B0250.8 and an upregulation of B0250.8 expression in gonads, suggesting that piRNAs are the primary small RNAs that target B0250.8 for downregulation. The data in this portion of the manuscript are intriguing, but somewhat preliminary and incomplete, as they are based on little primary experimentation and a collection of different datasets (which have been acquired by slightly different methods in most cases). This portion of the study would require subsequent experimentation to firmly establish this mechanistic link. For example, to be able to claim that "the N2 toxin allele has acquired mutations that enable piRNA binding to initiate MUT-16-dependent 22G small RNA amplification that targets the transcript for degradation" the identified piRNA sites should be mutated and protein and transcript levels analysed in wild-type and in the strain with mutated piRNA sites. At a minimum, the protein levels in wild-type and mut-16, prg-1, and/or wago-1 mutants should be measured by western blot and/or by live imaging (introducing a GFP or some other tag to the endogenous protein via CRISPR editing) to show that the toxin is not accumulated as a protein in wt, but increases in levels in these mutants. mRNA levels in Figure S5A suggest there is still some expression of the B0250.8 transcript in a wild-type situation.

We thank the reviewer for their thoughtful assessment of our manuscript, and we appreciate that they recognized that the data linking the small RNA machinery to B0250.8 suppression is intriguing. While the reviewer claims our analysis is preliminary and incomplete, we believe we present an appropriate multi-faceted approach for establishing the small RNA-mediated suppression mechanism we describe. 

First, the reviewer states that we rely on “little primary experimentation”. Our primary experiments show that loss of the N2 tmrl-1 allele partially rescues ∆mut-16 developmental delay and arrest phenotypes. Therefore, we provide direct evidence that the N2 tmrl-1 functionally contributes to the ∆mut-16 phenotype. Furthermore, we overexpressed the N2 tmrl-1 allele to show that this gene is a toxin.

It is true that we use previously published datasets to establish a small RNA-mediated mechanism that likely explains our observations. The reviewer suggests that our claims are weakened by relying on a “collection of different datasets (which have been acquired by slightly different methods in most cases)”. We believe instead that evidence collected from multiple labs using an array of different techniques strengthens our conclusions. We show that N2 tmrl-1-targeting small RNAs have been identified across multiple datasets (references 26, 32, 33, 34). Taken together, these datasets support a mechanistic framework for the suppression of the N2 tmrl-1 that involves PRG-1-dependent piRNA binding, MUT-16-dependent 22G siRNA, and the secondary Ago WAGO-1 binding. 

The reviewer suggests several experiments, but we do not view them as essential to support our claims. 

(a) piRNA site mutatagenesis: we present multiple lines of evidence that the N2 tmrl-1 transcript is post-transcriptionally targeted by small RNAs in a piRNA-mediated manner, not that specific piRNA sites are necessary and sufficient for this silencing. The suggested experiment would be valuable for future work, but is beyond the scope of our study.

(b) Characterization of TMRL-1 protein levels: We agree that this experiment would provide definitive evidence of complete small RNA-mediated suppression of the N2 tmrl-1 transcript. As we explain above, however, we do show that removing the N2 tmrl-1 allele partially rescues the ∆mut-16 growth defect, demonstrating that when this gene’s regulation is disrupted, it induces toxicity. Importantly, we observed no tmrl-1-induced toxicity when we overexpressed a version of this gene with a stop codon, indicating that it acts as a protein.

Finally, the reviewer questions our claim that: "the N2 toxin allele has acquired mutations that enable piRNA binding to initiate MUT-16-dependent 22G small RNA amplification that targets the transcript for degradation."

We agree that this statement is too definitive given our current data. We have revised it to: "Multiple lines of evidence suggest that the N2 tmrl-1 allele is recognized by piRNAs, leading to MUT-16-dependent 22G siRNA production and post-transcriptional silencing of the transcript."

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The paper suggests that antidote pseudogenization occurred because RNAi replaced its function, but does not explore whether this process is ongoing or complete across all N2-like strains.

We explored this possibility, but we realize that we did not explicitly state so in the manuscript. The B0250.4 (amrl-1) gene is pseudogenized in all strains within the N2 clade. We have modified the following sentence in the results section to explicitly state this observation:

“While the previously described C. elegans TA elements are characterized by their absence in susceptible strains (2, 3), all members of the N2-like susceptible clade harbor a divergent allele of tmrl-1 with an intact coding sequence, as well as a pseudogenized version of amrl-1.”

(2) Some figures (e.g., allele frequency distortions) could benefit from additional annotations to guide interpretation. In general, the figures make the reader work harder than they need to.

We attempted to add clarity to figure captions for clarity.

Although mll-1 and smll-1 were identified as toxin and antidote genes, their molecular mechanisms remain unclear and are very interesting.

We agree that identifying the molecular mechanism associated with the toxin and antidote would be of interest, but is beyond the scope of the current paper.

Reviewer #2 (Recommendations for the authors):

(1) Because the rod phenotype was important in identifying the TA system, it seems important to include representative images of this phenotype throughout the paper.

We added a supplemental figure showing the resulting self progeny from a QX1211/XZ1516 heterozygote: Fig S1B

(2) In Figure 2A, we were confused as to why there were so few reads of mll-1. We may be misunderstanding something, so could the authors explain this to us? We would have expected more reads of mll-1, given the diagram showing that the breakpoints of the NIL were beyond (closer to the right end of) the mll-1 locus, and the phenotype correlates with the presence of the toxin (frequency of .20 L1 arrest).

The lack of sequencing depth arises because the sequence divergence between QX1211 and XZ1516 is too high to accurately map short sequencing reads derived from QX1211 to the XZ1516 genome. We added the following sentence to the figure caption to add clarity:

“The XZ1516 and QX1211 genome are so diverged that short reads derived from QX1211 don’t align to the XZ1516 genome in the 200 bp windows with no corresponding read depth, as indicated by a lack of a gray bar.”

(3) The use of TOF in Figure 4 as a proxy of animal length instead of directly indicating or measuring animal length hinders the comparison of these results with other studies (i.e., most often in the literature, we see images of worms and measurements of their sizes or use of some other morphological marker to demonstrate the proportion of worms in a particular developmental stage). Nonetheless, we think the approach is clever and certainly enables analysis of a large sample population. However, a wild-type control is missing from these experiments to give a sense of the typical distribution one would expect. Without this, one interpretation of the B0250.8 knock out data shown in B is that loss of B0250.8 results in ~10% arrested larval, which seems higher than would be expected for a wild type N2 strain, and should be explained-but again, if the wild type control showed the same pattern, that would be useful to know. The title for Figure 4 should be revised, as this figure suggests, but does not provide definitive evidence that B0250.8 is suppressed by sRNAs/sRNA pathways. See the next point for providing more definitive data to support this model.

There is a long list of publications that rely on the large particle sorter to infer how growth rate is affected in various mutants and environmental conditions (See Andersen et al. 2015, ref 28 in the manuscript, and the papers that reference this work). As the reviewer pointed out, the use of time of flight, which is simply the amount of time an object obstructs a laser at a constant flow rate, enables accurate measurement of tens of thousands of individual animals for comparison. 

The reviewer is correct to point out that without a wild type N2 control, it is impossible to tell what a typical distribution looks like. However, the experiment includes all strains necessary to make the comparisons that enable us to draw the conclusion that the N2 tmrl-1 allele contributes to larval arrest in the absence of MUT-16.

We agree with the reviewers point that this figure does not provide evidence that B0250.8 is suppressed by small RNAs and we have therefore changed the figure title.

The new figure title: The N2 tmrl-1 allele contributes to larval arrest in the absence of MUT-16

(4) To be able to claim that "the N2 toxin allele has acquired mutations that enable piRNA binding to initiate MUT-16-dependent 22G small RNA amplification that targets the transcript for degradation" the identified piRNA sites should be mutated and protein and transcript levels analysed in wild-type and in the strain with mutated piRNA sites. At a minimum, the protein levels in wild-type and mut-16, prg-1, and/or wago-1 mutants should be measured by western blot and/or by live imaging (introducing a GFP or some other tag to the endogenous protein via CRISPR editing) to show that the toxin is not accumulated as a protein in wt, but increases in levels in these mutants. mRNA levels in Figure S5A suggest there is still some expression of the B0250.8 transcript in a wild-type situation.

The reviewer makes several good suggestions for experiments to determine whether the conclusions we make from publicly available high-throughput sequencing datasets apply in our context. However, we disagree that the quoted statement “the N2 toxin allele has acquired mutations that enable piRNA binding to initiate MUT-16-dependent 22G small RNA amplification that targets the transcript for degradation” is not supported by the evidence we present from Reed et al. 2020. The data presented by Reed et al. clearly show that the N2 tmrl-1 transcript is heavily targeted by 22G siRNAs, and that the accumulation of these siRNAs depends on the presence of MUT-16 and PRG-1. The dependence on PRG-1 implicates piRNAs involvement in the mounting of a 22G response.

(5) Importantly, it is not the mll-1/B0250.8 transcript itself that was not shown to interact with WAGO-1 in the Seroussi et al. eLife paper (Lines 257-259). This study investigated sRNAs associated with every AGO, and computationally inferred the targets of each AGO using those enriched sRNA sequences. Therefore, it is the siRNAs antisense to mll-1/B0250.8 that were detected in association with WAGO-1, making it likely that WAGO-1 is the secondary AGO that targets this transcript. The argument the authors make holds true, but the authors should revise how they describe the evidence supporting that argument to accurately reflect the existing data.

Thank you for catching this mistake. We have updated the text to accurately reflect the results from the Seroussi et al 2023 publication:

“Recent work has shown that the N2 tmrl-1 transcript-derived small RNAs co-immunoprecipitated with WAGO-1, providing additional evidence that this transcript is regulated by the endogenous RNAi machinery”

(6) It seems likely that the authors explored the possibility that another antidote may be present in the third clade. Could they discuss what they did to rule out this explanation in lieu of piRNA/siRNA regulation?

We did not look for another antidote in the third clade because this clade is defined by the presence of an antidote and the absence of a toxin. Figure 3C shows the result of a cross between a third clade strain (NIC195) and XZ1516. The conclusion we draw from this experiment is that the antidote present in NIC195 provides near complete resistance to the XZ1516 toxin.

(7) Line 156, legend of Figure S3, and line 273: There was no marker used to indicate that these are the primordial germ cells. Best practices would indicate using a fluorescent marker (e.g., PIE-1 GFP or PGL-1 GFP or PRG-1 GFP, etc.) to definitively identify these as PGCs.

We agree with the reviewer’s point. As we do not have the perfect experiment, we do not definitively state that tmrl-1 transcripts localize in the primordial germ cells. 

Minor comments:

(1) A minor suggestion: incorporating some of the results now shown in the supplementary figures - Figures S1, S3, and S4 - into the main figures may make the manuscript easier to read.

We constructed the manuscript in a way we thought was straightforward. The figures listed by the reviewer are supplemental to the main conclusions of the manuscript, so we decided to leave them as supplemental figures.

(2) Line 87, Figure S1A: include numbers in the y-axis.

The numbers are included on the y-axis and we explain the x-axis tick marks in the figure caption.

(3) Figures 1B, 2B, 3C, 4B, S1B, S4: statistical analyses missing.

We have added a summary of the statistical analysis to the captions of Figures 1B, 2B, 3C, and S1B. We added more detail from the analysis of 4A, which is the figure we draw conclusions from. Figure S4 is observational data, and the only conclusion drawn from that figure is that the N2 tmrl-1 gene encodes a toxin. It is toxic in 100% of individuals we looked at and therefore doesn’t warrant statistics. 

(4) Line 100, "The rod progeny were all homozygous for QX1211 alleles at the locus on the right arm of chromosome V that displayed the allele frequency distortion in the mapping populations". Is this supported by data? While there is strong evidence to suggest it, the way it is currently written makes it seem that the rod progeny have been genotyped (by sequencing or PCR?). Is this the case? If not, the authors should revise the statement accordingly.

Yes, this is indeed the case and we have updated the text to reflect that we performed PCR of a QX1211-specific indel to verify the genotypes on the right arm of chromosome V.

(5) Figure 2A: lower panel missing x axis label.

The top panel is a cartoon representation of a NILs, and the x axis is labeled for the top panel, highlighting the mapped element. 

(6) Line 140 to 148: The authors should provide data to support these statements.

Realizing i skipped this one – these are the lines they are referring to -> Long-read RNA sequencing revealed two distinct mll-1 isoforms, a short isoform with three predicted exons and a long isoform with eight predicted exons (Fig. S2A). We constructed plasmids with inducible versions of each mll-1 isoform. When we injected susceptible strains with the short mll-1 isoform array, every F1 individual carrying the array died, with 64% of larvae exhibiting the rod phenotype, indicating that uninduced expression levels of the short mll-1 isoform are sufficient to induce lethality. By contrast, we were able to isolate susceptible strains that maintained the long mll-1 isoform array or a short mll-1 isoform array with a premature stop codon in mll-1. We observed no rod progeny upon induction of these arrays, indicating that the short isoform encodes the functional toxin, and that the toxin acts as a protein.

(7) Line 193: It would be interesting to see if there is structural conservation between mll-1 and B0250.8 using alpha-fold. Have the authors done this?

We did attempt to look for structural conservation but we found the confidence in the structural predictions to be very low, which didn’t warrant a comparison.

(8) Line 206-207: Could the authors explain why the frequency of the rod phenotype is so low when presumably over-expressing B0250.8? Does this indicate that B0250.8 is not as functional a toxin as mll-1, or is it sufficiently repressed by sRNAs and not actually overexpressed? Further, what are "abnormal" phenotypes? This should be clarified for the reader.

It is likely that the overexpression and misexpression of toxic proteins is causing the abnormal phenotypes. The rod phenotype probably manifests when the gene is expressed at the appropriate developmental stage and tissue to cause the phenotype, whereas abnormal phenotypes manifest when the expression is not in the correct stage or location. A summary of the observed phenotypes is provided in Supplementary Table 7.

(9) Line 216 and thereafter: indicate that B0250.8 is now referred to as mll-1.

We incorporated this suggestion.

(10) Line 228-231: missing to state that this is shown in Figures 4A-B.

This and the following comment suggests that we did not provide enough clarity in this section. We modified the line to the following:

Consistent with this report, in an agar plate-based preliminary assay we observed that ~15% of ∆mut-16 progeny arrest at various larval stages, and 2% of progeny are rod, which is suggestive of derepression of tmrl-1 in N2.

This lets readers know that this initial characterization of the mut-16 knockout strain is different from the data presented in figure 4.

(11) Line 230: the Figure shows ~25% of arrest for the deletion mutant of mut-16, but the text says ~15%.

The 15% the reviewer points out was obtained from a preliminary agar plate-based experiment where we attempted to characterize the mut-16 deletion strains. We turned to a more high-throughput approach to screen through more animals for each genotype, which we report in figure 4.

(12) Line 233: TOF, and not animal length, was compared. The authors should indicate that TOF is used as a proxy for animal length.

We made the suggested change. The new sentences read:

To do so, we compared time of flight (TOF) measurements—a proxy for animal length, developmental stage, and growth rate (28)—between a strain with a single knockout of mut-16 and one with a double knockout of mut-16 and the N2 tmrl-1 (a strain with a single knockout of the N2 tmrl-1 served as a negative control). We observed a reduction in TOF and an increase in the fraction of worms in larval stages in the mut-16 knockout strain, and these effects were partially rescued in the double knockout strain (Fig. 4).

(13) Line 237-239: This claim may be overstated without additional data. Consider adding a "likely" to the statement.

The line in question: 

These results indicate that the reduced growth rate observed in the mut-16 knockout strain is partially mediated by derepression of the N2 mll-1 allele.

We modified it to reflect the reviewer’s concern: 

These results indicate that the reduced growth rate observed in the mut-16 knockout strain is partially mediated by the presence of the N2 tmrl-1 allele, likely because tmrl-1 is derepressed in mut-16 knockout strains.

(14) Line 257: Figure S5C should be moved to line 259.

We made the suggested move. 

(15) Is the name mll-1 firmly established? We ask because MLL1 is a human mutation commonly associated with leukemia, and it may lead to some confusion in the field. This is a minor point, but we wanted to bring it forth.

This name was not firmly established. We modified the names to not overlap with known gene names:

tmrl-1 - Toxin-induced Maternal Rod Lethality

amrl-1 - Antidote of Maternal Rod Lethality

This study is an important contribution to our understanding of waterfowl conservation and population ecology in Europe. Recovery of marked birds, typically through harvest by waterfowl hunters, is an important means of obtaining data to assess survival and harvest probabilities in waterfowl, but the ability to differentiate between natural and harvest mortality requires a better understanding of reporting probabilities (the proportion of banded/ringed birds that are harvested by hunters that are also reported to banding authorities). In North America we have had numerous studies using reward bands to estimate this “band reporting rate”, but comparable studies have not been conducted elsewhere, until this study. I thoroughly reviewed this preprint and my overall assessment is strongly supportive. I have only a few suggestions for potential improvement.

It might be nice to bound the reporting rate estimates between 0 and 1 by formally including reporting rate in the model likelihoods rather than estimating it as a derived parameter. I can’t use the link to your code, so I’m unable to see exactly how you modeled this, but you could seemingly model reporting probability directly by including it in the likelihood anywhere that Brownie’s f or Seber’s r appears for birds marked with reward rings.

Lines 328-330: You conclude this paragraph with a statement about your results supporting additive mortality from hunting, but the rationale for this isn’t explained (I’m not disputing your claim, but you haven’t clearly articulated why you believe your results support partially additive mortality). The stark difference in estimated harvest probabilities between newly ringed and previously ringed (i.e., direct vs. indirect in North American terminology) suggests that heterogeneity in vulnerability to harvest might (also) be very important in these populations and thereby contribute to compensation of harvest. Coauthor Emilienne Grzegorczyk presented intriguing results on survival heterogeneity at the latest EURING conference and it might be worthy of a little bit of discussion here.

Minor edits: Line 77 or thereabouts: Because there is an extensive literature on reporting probabilities from North America, but quite different terminology, it might be nice to include a Methods paragraph clarifying ring/band/tag recovery as identical, young vs. adult and hatch-year vs. after-hatch-year, and define the terms direct vs. indirect recovery in terms of time since marking.

Line 154: In addition to the inscription included on reward rings, it would be helpful to indicate the exact inscription provided on standard rings. In North America we observed a pronounced increase in band reporting probabilities when band inscriptions were modified to include toll-free phone numbers and later, web addresses.

When do (most) of your recoveries occur? It would be helpful to include information on timing of harvest in France. Given that you include season of banding as a covariate on survival, subsequent estimates of survival beyond the first year will be hunting season to hunting season. It might be nice to more formally address timing of banding by including a “partial year survival” term in the first diagonal of your m-arrays. This could be a shared annual survival term, but partitioned into portions based on how much of the year an average bird would have to survive (e.g. S^(5/12) if 5 months or S^(9/12) if 9 months).

In North American ducks, we would expect to see pronounced differences in seasonal survival between sexes due to breeding risks incurred by females. For example, spring releases of female mallards would be expected to have lower survival to the first hunting season than spring releases of males. It might be nice to indicate in the methods that you ignored sex in your analysis given small sample sizes (given interactions with species, age, and timing, it might require 6-12 df to properly address), but future analyses based on additional data might wish to investigate sex differences in both survival and recovery probabilities.

You have a nice literature review, but there are a few additional papers that would be worth including: Lines 64-65: Either of the two Riecke et al. 2022 Journal of Animal Ecology papers would be good to cite for an example of how reporting probabilities can help partition annual survival into harvest and natural mortality. Koons et al. (2014, Wildfowl) would be a nice paper to cite here for life-history differences in relation to body size. The results from nasal-marked teal are intriguing, and I suspect that nasal markers might influence survival, vulnerability to harvest, and reporting probability. Arnold et al. 2016 J. Wildl. Manage., Szymanski et al. 2020 Wildl. Soc. Bull., Reinecke et al. 1992, J. Wildl. Manage., Caswell et al. 2012, J. Wildl. Manage.).

Minor changes to wording: Abstract, line 49: I think you mean “subjected” rather than “submitted”. Intro, line 57: “elaborate” rather than “elaborated”. Intro, line 83: use “to” instead of “on”. Intro, line 89: use “of” instead of “or”. Methods, line 126: use “drop-door” instead of “door-falling” Line 161: “departmental”. Line 196: “parameter” (not plural). Line 298: “a heavy predator-control program was in place”. Line 344-345: Curiosity effect has been hinted at in some other research.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

Freas and Wystrach present a computational model of steering in insects. In this model, the central complex provides an error signal indicating the animal should turn left or right; this error signal biases the function of an oscillator composed of two mutually inhibiting self-exciting units. The output of these units generates a "steering signal" that is used both to set the direction and speed of the ant. Additionally, a separate module induces pauses, and an inverse relation between forward speed and turning speed is externally imposed. Statistics of the trajectories generated by the model are compared to the measured behaviors of ants.

Strengths:

While the model is very simple compared to state-of-the-art models, that simplicity makes it a potentially useful guide to researchers studying insect navigation. Some predictions that emerge from the model appear to be experimentally testable, although a more complete description of the model and its parameters, as well as an analysis of how this model's predictions differ from previous models' predictions, would be required to design these experiments.

Weaknesses:

I found it difficult to identify evidence in the paper supporting central elements of the abstract. Hopefully, these difficulties can be resolved with a clearer presentation and the addition of supporting detail, especially in the methods.

(1) The model is not clearly described

In the Materials and Methods, there is no description of the model, just "The computational model is presented in Figure 1." (This is probably a typo and may refer to Figure 2A-C), and a link to Matlab source code. It is inappropriate to ask readers or reviewers to examine source code in lieu of providing a method, but I attempted to do so anyway. 

We have now added a full description of the model in the methods.

To my eye, the source code does not match the model presented in 2A-C. For instance, in 2C, "Steering signal" inhibits "Freeze", but I couldn't find this in the source. "Freeze" is shown to inhibit "steering signal," but as "steering signal" is a signed quantity, it's not clear what this means. Literally, since "ang_speed_raw = L-R," it would seem to indicate the "freeze" would bias towards right turns. In the code, "freeze" appears to be implemented through the boolean variable "speed_inhibition_time." The logic controlled by this variable doesn't appear to inhibit the "steering signal" but instead (depending on control parameters) either reduces the movement speed and amplifies the turning rate, or it turns the angular speed output into a temporal integral of the control signal.

We understand the confusion. Our neural implementation does not go downstream of the neural steering signal (Left and Right Descending neurons), and the way it is transformed into a movement (ang_speed_raw = L-R) is not modelled neurally (the formula is explicitly shown on the right hand side of Figure 2). Indeed, we did not attempt to put forward any assumption about neural implementation for our freezing signal (see our response to comment 2 below). To avoid confusion, we have now removed the reciprocal inhibition portion as it was previously drawn in Figure 2C, and replaced it by a non neural sign (a cross, indicating that the signal is blocked) acting between steering signal and movement.

There are a number of parameters in the source code that aren't described at all in the paper, including the internal oscillator parameters.

We now provide all the parameters in the methods, together with figures showing the dynamics of oscillations across parameter range, and a rationale for their choice (see Supplemental Figure 2).

Together, these limitations make it difficult to understand what is being simulated, what parts of the model are tied to biology, and where the model improves on or departs from previous work.

It is absolutely essential that authors fully describe the computational model, that they explain the meaning of all parameters of the model, and that they explain how the particular values of these parameters were chosen.

This is now done in the methods section under the “Model Overview” subsection.

(2) The biological inspiration is unclear

A central claim of the paper is that the model is "biologically grounded." But some elements, for instance, using a signed quantity to represent left-right steering drive, are not biologically possible; at best, these are shorthand for biologically possible implementations, e.g., opposing groups of left-right driving neurons.

The mechanism that produces fixations and saccades - the "freeze" module - is not tied to any particular anatomy of the insect brain. Initiation of a freeze occurs at a specific time coded into the model by the authors; it is not generated by an internal model signal. Release of a freeze is by drawing a random variable; there is no neural mechanism proposed to generate this signal.

We now clarified what is neural from is not from the introduction onwards, for instance:

“Because we did not want to form pre-assumptions for how such a ‘freeze signal’ could be implemented in the insect nervous system; in our model this was achieved using a simple external signal that halts forward motion at random intervals.”

In some versions of the model, instead of directly controlling the signal, during fixations, the angular drive signal is integrated into a variable "cumul_drive." No neural substrate is proposed for this integrator. In the code, if cumul_drive passes a threshold, the angular heading of the ant changes (saccades), but only if this threshold is passed before the Poisson process ends the fixation. No neural substrate is proposed for any of this logic.

This has now also be clarified in the introduction:

“During scanning, real ants display rotational saccades of variable duration and angular magnitude (Figure 1A–C). To replicate this, we introduced a threshold-based mechanism: after each fixation (i.e., zero angular and forward speed), the underlying angular steering signal accumulates until surpassing a threshold, triggering a saccade. The resulting angular magnitude of the saccade corresponds to the sum of the angular drive accumulated during the fixation. Here also we stuck to a non-neural, straight-forward algorithmic level, as we did not want to make assumptions about how such a cumulate-and-release mechanism could be neurally implemented in the insect brain (see discussion for potential implementations).”

The model steps forward in time by a fixed increment - the actual duration (in seconds) of this time step is not specified. From Figure 4F, G, it appears a simulation time step is meant to be about 10ms. This would imply an oscillator frequency of about 2 Hz (Fig 2B), that the heading oscillates at a similar frequency (2G), and that a forward crawling ant stops moving every 500 ms (2I). Are these plausible? Can they be compared to an experiment? Model parameters, including the ones that control the frequency of the oscillator, are non-dimensionalized. It is not possible to evaluate whether these parameters are biologically plausible or match experimental results.

We now added a figure showing the oscillatory dynamics of the oscillator across parameter ranges (supplemental figure 2). The step increment (i.e., and thus the sampling rate along an oscillatory cycle) necessarily varies according to the inhibition strength and self decay parameter chosen (e.g., small parameter values will lead to small step increment, and thus a high sampling rate along the oscillatory cycle). We chose oscillatory parameters to ensure that the sampling rate will be high enough to resolve multiple saccades within one oscillatory cycle and that sampling rate is small enough for computation time to remain practical.

Beyond these constraints, the oscillator parameters can be chosen arbitrarily, and a conversion of time step to actual time (ms) would be equally arbitrary and give the illusion that the model captures the data quantitatively. Because we did not model spiking neural dynamics (or brain region low field potential frequencies), we can not constrain our model through a temporal link between brain clock and behavioural speed. We thus prefer to stick to the true and non-dimensional label ‘time steps’ in our figures.

(3) Claims that behaviors emerge from the model may be overstated

The abstract claims that steering correction and fixations/saccades emerge naturally from the same model. But it appears to me that fixations/saccades are externally imposed by the specification of specific times for a "freeze." Faster angular rotation during saccades than during course correction is imposed and does not emerge naturally from neural simulations.

The abstract now clarifies that what emerges spontaneously is not scannings per se (indeed, the inhibition of movement is externally imposed) but their dynamics. Note that our model captures many aspects of scanning dynamics that are not trivial and which results from the dynamical interactions and contingencies between modules (figure 3 to 7), hence justifying the word ‘emerge’ insofar as these behavioural dynamics cannot be reduced to one module or parameter. Regarding the faster angular rotation during scanning, we agree that its cause is rather straightforward to understand: it results from the added bodily constraints of forward speed to rotational movements. Nonetheless it is not ‘imposed’ during saccades in the sense that 1.) it is biologically/physically evident rather than cherry picked and 2.) it is continuously present in our model, even during forward navigation. We believe the new version of the manuscript now conveys this message in a transparent manner.

(4) Citations to previous literature are difficult to follow, and modeling results are presented as though they are experimental data

I would ask the authors to be much clearer in their description and citation of previous work. It should be clear whether the cited work was experimental or computational. To the extent possible, the actual measurement should be described succinctly. Instead of grouping references together to support a sentence with multiple claims, references should be cited for each claim. Studies of computational models should not be presented as proving a biological result.

Indeed, This we now clearly separated citations referring to experimental evidence vs. modelling. See examples citations below

For example:

(a) Lines 141-146:

"Previous studies have established many key components of insect navigation, including .... the intrinsic oscillatory dynamics in the lateral accessory lobes (LALs) that support continuous zigzagging locomotion (Clément et al., 2023; Kanzaki, 2005; Namiki and Kanzaki, 2016;

Steinbeck et al., 2020)."

The first reference is to one author's previous modeling work - it hypothesizes that oscillations in the LAL support zigzagging but includes no data that would "establish" the fact. Kanzaki et al. 2005 describes numerical modeling and simulation with a physical robot. Namiki and Kanzaki, 2016 is a review article that links the LAL to zigzagging behavior. It describes the LAL as a winner-take-all bistable network but does not describe or hypothesize that the LAL has intrinsic oscillatory dynamics. Steinbeck et al. 2020 is a more comprehensive review; it reinforces that the LAL is a winner-take-all bistable network that drives left-right steering, including during zig-zagging behavior. But in my reading, I could not find a statement that the LAL has intrinsic oscillatory dynamics (the closest is Steinbeck et al. saying the activity pattern switches regularly, as does the behavior; this doesn't imply that the LAL is intrinsically oscillatory.)

It now reads:

“Previous studies have established many key components of insect navigation, notably, how goal headings are set in the central complex (CX) (Fisher, 2022; Green and Maimon, 2018). Modelling efforts have shown that the CX circuitry can naturally accommodate innate and learnt guidance such as path integration, learn vectors, visual route following or homing as observed in ants and bees. In parallel, oscillatory dynamics in the lateral accessory lobes (LALs) - produced by reciprocal inhibition across both hemispheres and conveyed by so-called descending flip-flopping neurons - were shown to drive the spontaneous zigzags displayed by moths upon losing their pheromone plume (Kanzaki and Mishima, 1996; Mishima and Kanzaki, 1998, 1999; Wada and Kanzaki, 2005; Kanzaki et al., 2005; Iwano et al., 2010). Here also, subsequent modelling efforts have shown how these circuits can equally support the continuous lateral oscillations displayed by a wide range of insect species, including ants.”

(b) Lines 701-703:

"In plume-tracking moths, CX output has been shown to modulate LAL flip-flop neurons driving zigzagging (Adden et al., 2022)."

This reads as though an experimental measurement was made, but in fact, this is modeling work.

Yes, this could be clearer, it now reads: 

“In moths, descending neurons in the LALs exhibit characteristic 'flip-flop' activity patterns that correlate with zigzagging maneuvers (Olberg, 1983; Kanzaki and Ikeda, 1994). Computational models suggest that having these LAL neurons modulated by the CX output can explain aspects of the moths’ plume-tracking behaviour (Adden et al., 2022).”

(c) Lines 703-706:

"In ants, strong goal signals in the CX - whether elicited by the path integrator or visual familiarity (Wehner et al., 2016; Wystrach et al., 2020b, 2015) do not only sharpen directional accuracy but also increase oscillation frequency (Clément et al., 2023)."

Here again, modeling results are presented as though they were experimental data.

Here, we are referring to the experimental part of these works, although this comment demonstrates that our statement should be more clear in stating what are biological results. It now reads: 

“In ants, behavioural studies show that strong directional drives elicited by the path integrator or visual familiarity do not only gain behavioural weights and sharpen directional accuracy (Wehner et al., 2016; Wystrach et al. 2015, Legge et al. 2014) but also increase the ants’ oscillation frequency (Clément et al., 2023). Assuming that path integrator and visual familiarity modulate goal signals in the CX, as modelled here and elsewhere (Wystrach et al., 2020b, Stone et al., 2017) and that the intrinsic oscillator is in the LAL (Clément et al., 2023, Steinbeck et al., 2020), it suggests that CX output modulates the intrinsic oscillatory activity of the LAL”

Reviewer #2 (Public review):

Summary:

The paper by Freas and Wystrach is an interesting computational study, exploring the detailed mechanisms of how simple neural circuits could explain complex behavioral patterns observed in navigating ants. The authors compare detailed, high-speed video recordings of Australian desert ants (Melophorus bagoti) with predictions made by their new computational model and find convincing similarities between the model and the behavioral data, at a level of detail not previously studied. Particularly interesting are emerging properties of the model, yielding behavioral motifs it was not designed to reproduce, but which occur in natural ant behavior.

Strengths:

A strength of the study is that the model is based on previous models, without making major novel explicit assumptions. It combines existing models of the insect central complex with a model of the lateral accessory lobe and adds a stochastic inhibition of forward velocity to the interaction of central complex and lateral accessory lobes. The central complex provides corrective steering signals when the goal direction and the current heading of an insect are not aligned, while the lateral accessory lobes provide an intrinsic oscillator underlying the behavioral oscillations shown by walking ants at all times. These background oscillations are modulated by the steering signals from the central complex. Depending on which phase of the intrinsic oscillations coincides with the corrective signals, and how fast the ant is moving forward during this time, a complex set of behaviors emerges. Most prominently, scanning behaviors, which are regularly carried out by the ants, are recapitulated in great detail by the model. Additionally, other behaviors, such as full loops, emerge naturally from the model. While computational models are not to be seen as definite evidence for any biological reality, they can provide strong support for particular neural implementations. The current study is an excellent example in that it provides evidence for a serial arrangement of central complex circuits upstream of the lateral accessory lobe circuits, modulated by speed-regulating input. While the latter is hypothetical, it yields a clear hypothesis that can be validated by connectomics studies and functional work in the future.

The study shows that even complex behavioral motifs do not require dedicated neural modules, but can rather emerge from the interplay of already known circuits - highlighting the efficiency of insect brains and possibly providing the path towards embodied hardware solutions of such circuits in autonomous agents.

Weaknesses:

There are several weaknesses in the paper as it is.

Firstly, the model is not described in the methods, but only found when following the link to the authors' GitHub repository. This is clearly not sufficient and prevents readers from evaluating the model's assumptions directly. Most importantly, how natural do the emerging properties indeed emerge from the model? What parameters need to be tuned to generate a match between data and model?

We have now added a full description of the model in the Methods section.

These include:

Mathematical equations for model components

Complete parameter table along with justifications

Description of what is fitted vs. what emerges 

Key assumptions and limitations

Regarding the emergence of scanning properties: The model has two types of parameters:

Parameters tuned to match general navigation behavior (independent of scanning):

Motor gains (g_ang, g_fwd, k): adjusted to produce realistic continuous walking paths and species differences between desert ants and Myrmecia

CX gain (g_CX = 0.5): set to produce appropriate corrective steering strength during continuous navigation

Oscillator parameters (α, β, s): are taken from Clément et al. (2023)

Parameters tuned to match scanning behavior:

CPG angular threshold (θ_CPG = 2.0): adjusted to generate realistic saccade timing Scan termination probability (p_stop = 0.5/timestep): matched to the Poisson-like distribution of scan durations in M. bagoti

Properties that emerge without specific tuning:

Fixation-saccade alternation structure (emerges from angular drive accumulation mechanism)

Directional reversals (arise from oscillator dynamics competing with CX steering)

Corrective saccade amplitude increasing with angular deviation (Figure 3)

Rare full-loop scans (emerge from CX signal shifting oscillator phase)

The behavioral continuum from straight paths → oscillations → voltes → scans (Figure 8)

We have clarified this distinction in the Methods section and emphasized that our goal was qualitative demonstration of emergence rather than quantitative parameter optimization.

Second, it is often not entirely clear what is biological data and what is a computational model. This relates to figures, text, and references. As a reader, this makes it difficult to clearly judge what is new in the current paper, how it adds to previous models, and what the predictions and assumptions are for biology.

Indeed, we have now clarified the manuscript, clearly separating when we refer to behavioural data, neurobiological data and modelling. In the figures, each panel now clearly indicates if it is model data or biological data so that any reader can immediately tell the data type.

Third, while neural data from bees and flies are taken to motivate and design the computational model, the discussion and interpretation revolve almost exclusively around ants. For the most part, this is justified, as the behavioral data used to benchmark the model are taken from ants. Nevertheless, more broadly discussing the newly defined circuit in the context of flying insects would give a better idea of the broad relevance of the neural circuits predicted by the model.

To address this suggestion we have now added two paragraphs in the discussion called: “Scanning in flying hymenopterans”.

Also happy to add more to this section if requested.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

As mentioned in the public review, I suggest fixing the two concerns I have regarding methods and discussion.

(1) Include a full description of the model in the methods, so that the model remains reproducible even if the GitHub repo is deleted in the future.

True, the code’s internal explanations could indeed be removed from GitHub later. The model component overview are now included in text.

(2) Include the relevance of the model for flying insects in the discussion more prominently. This seems to be an implicit assumption in the model, as neural data from bees and, more prominently, from Drosophila are used to motivate the model to explain ant data.

Add an “Expression in flying hymenopterans” section at ~line 834.

Minor points:

(1) Line 207: I suggest adding the recent review by Collett, Graham, and Heinze (2025, Current Biology), as it proposes interactions between LAL and CX as well.

Added

(2) Figure 4: I'm interested in the conversion from steps in the model to real units (ms) in the ants. In Figures 4F and G, it seems that 5 model steps represent circa 100ms. Does this allow us to define the neuronal time constants of the model neurons? If so, are the resulting values biologically plausible? This seems important when describing real-world dynamics being created by a model circuit.

No the model is time agnostic.

(3) Figure 7: Font sizes of axis labels are much too small. Also applies to other figures. Please ensure that when printed, labels can be read.

Enlarged axis labels in all figures. 

(4) Line 645: proprieties -> properties?

Fixed. Thanks!

(5) Figure 7: The figure heading states: "Slow forward speed (Myrmecia) example". This sounds as if real data from ants are shown here, while these are modeling data. It is clear after reading the text and caption in detail, but I was taken off course briefly here. Please make sure that there is no possibility of being misled here.

We have altered the subtitle to “Slow forward speed (Myrmecia Model) example”. 

Additionally, we have added a Model tag under each of the model image labels so classification can be done at a glance.

(6) General discussion: What about search dynamics, i.e., increasing loops when not finding the nest entrance after homing? Are those emerging from this circuit as well? Or would that need to be a separate module? There have been discussions about search emerging from the PI circuit, but as far as I know, this is not settled, and it would be good to know if the current circuit adds something useful to this aspect.

Because we kept a fixed goal heading, our model does not bring insight about overall trajectories such as search pattern. We now mention in the discussion:

“In our simulations, the CX goal representation remained fixed in both direction and strength throughout each trial. This simplification allowed us to isolate and compare the effects of different CX strengths on scanning behaviour (Figure 6). However, goal headings in the CX are likely to be updated continuously, including during scans, by novel input from visual recognition in the MB (ref). This would in turn bias saccades direction and duration. Exploring such dynamics lies beyond the scope of the present study but would represent an interesting direction for future work. Notably, our proposed CX-LAL-Body relationship could be implemented downstream of an existing path integration or visual-based model (or both) to form predictions about the occurrence and dynamic of scans along the path, as well as their impact on the emerging trajectories.”

(7) Line 690: The modulation of PFL3 by PFL2 was presented as a hypothesis in Westeinde et al., consistent with the data, but as far as I know, this is not an established fact.

You are correct. We have now softened the text, which now reads: “In Drosophila, it has been proposed that PFL2 neurons, which respond maximally when the fly faces away from the goal, modulate steering gain by converging with PFL3 neurons (which drive left or right turns) onto downstream descending neurons (Westeinde et al., 2024).”

(8) Please ensure that Drosophila is consistently spelled with a capital D and in italics.

Fixed throughout the text.

(9) Line 702: Reference Adden et al 2022: This reference is a modeling paper; it sounds as if you are referring to an experimental moth paper, though. Rephrase to clarify.

You are correct, this could be unpacked much better regarding what is modelled and what has been experimentally shown. Changed to:

Descending neurons in the LALs exhibit characteristic 'flip-flop' activity patterns that correlate with the zigzagging maneuvers of plume-tracking moths (Olberg, 1983; Kanzaki and Ikeda, 1994). Recent computational models suggest that CX output directly modulates these LAL circuits to coordinate orientation (Adden et al., 2022). 

(10) Line 761: I would assume that during scans, information is acquired that would decrease uncertainty and thus, as a result change the amplitude of the CX steering signal. Maybe I missed this, but is this closed-loop interaction integrated in the model?

In our simulation the CX goal representation remains stable in direction and strength throughout the trial. This enabled us to compare neatly the effect of different CX strengths on scanning. However, we fully agree with you that goal headings in the CX might well be continuously updated, both during scans and between scans! The goal heading novel strength or direction may thus bias the scan further left, right, in front or in the back, and also up or down regulate scan duration in both directions. 

Modelling this would require adding a layer of complexity to determine how the goal heading is updated, which is beyond the scope of the current work, but would form a remarkable project for the future. We now mention this in a dedicated paragraph in the discussion section “Model limitations and future directions”

(11) Line 814: Please add 'fly' in front of larva. Other insect larvae have a fully developed CX.

Corrected. Added fly to this sentence 

(12) Line 815: Maybe add the recent review, Heinze 2025.

Added this one (Heinze 2024) which seems to fit the best and the 2025 Curr Biol Review doesn't quite fit this line (cited elsewhere though): 

Heinze, S. (2024). Variations on an ancient theme—the central complex across insects. Current Opinion in Behavioral Sciences, 57, 101390.

(13) Methods: Subheading formatting should start with capital letters.

Ah yes, the second level of subheadings got formatted weirdly. Fixed now.

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

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Reply to the reviewers

The authors adapt MemPrep, a protocol they originally developed to purify organelle membranes from yeast, for use in human cell lines. To this end, they established immuno-isolation strategies based on tagged versions of the ER sheet protein SEC61β and the ER tubular protein REEP5 in HEK293T cells. Their purification strategy allowed them to generate highly pure ER sheet- and tubule-enriched fractions, which were then subjected to quantitative lipidomic and proteomic analyses.

Overall, this manuscript is well written and presents a careful interpretation of the data. It introduces MemPrep in mammalian cells as a method that will be useful for studying the membrane lipid and protein composition of organelles, with a particular focus on the ER. As such, the manuscript provides sufficient information and controls to assess the experiments in terms of reproducibility and clarity.

We thank the reviewer for a positive, thorough assessment and for raising important points that helped us to improve the manuscript.

Major comments:

1. Based on the immunofluorescence images in Figure 1, it is not clear that the tagged and slightly overexpressed versions of SEC61β and REEP5 localize specifically to ER sheets and tubules, respectively, or that these proteins are enriched in these distinct ER subdomains. Perhaps reducing the fixation time, for example to a maximum of 2 minutes, or using PFA fixation, could help to better preserve ER sheet and tubular domains.

To address the localization of the bait proteins in the ER membrane network, we added new co-localization microscopy data and quantifications to the revised manuscript (new Figure 1E,F; new Supplementary Figure S1C,D). Despite its low level of overexpression (new Figure 1C; new Suppl. Fig. S1A), SEC61β localizes to the entire ER membrane network including ER tubules and the nuclear envelope (new Fig. 1E,F).

Considering the new data, we have carefully rephrased all sections regarding the subcellular localization of bait-SEC61β. In the revised manuscript, we use SEC61β as a general ER marker.

Intriguingly, quantitative proteomics of the SEC61β MemPrep isolate demonstrates a selective enrichment of ER sheet-associated proteins compared to the REEP5 MemPrep, which selectively enriches proteins associated with ER tubules (Fig. 5). While we do not claim to 'isolate' ER subdomains, we enrich ER subdomains.

We have performed additional microscopy experiments and adjusted our fixation protocol as suggested by the reviewer (Revision Fig. 1). Shortening the fixation time has no apparent impact on the ER structure, while any PFA fixation seems to largely disrupt the ER.

Does expression of tagged SEC61β or REEP5 influence the ER sheet:tubule ratio? In addition, does expression of these constructs affect the lipidome or proteome of the cells?

The reviewer raises an important point, which is experimentally not easy to address. Our imaging modality is not sufficient to make a firm statement about the sheet:tubule ratio in HEK293T cells. We are not aware of any study that firmly quantifies the relative content of sheets and tubules in HEK293T cells. Imaging the ER in HEK293T cells is challenging and most studies on the ER membrane networks use other cell types to study the impact of ER-shaping protein on the ER membrane network.

In the revised manuscript we state: 'We found no evidence that the expression of the bait constructs disrupts the tubule-to-sheet ratio or other aspects of the ER architecture, but distinguishing ER sheets and ER tubules is challenging in HEK293T cells.'

Furthermore, we have studied if the expression of the bait constructs affects the cellular proteome (new Suppl. Fig. S1A,B) and lipidome (new Suppl. Fig. S4A-H (previously Suppl. Fig. S3)). The expression of the bait constructs has no substantial impact of the cellular proteome. Most importantly, we find no evidence that proteins characteristic for ER sheets or ER tubules (other than the bait proteins) change their expression level (new Suppl. Fig. S1A,B). In the revised manuscript we state:

' We decided to go one step further and compared the proteomes of wildtype HEK293T cells with the two cell lines using TMT multiplexed, untargeted protein mass spectrometry (Suppl. Fig. S1A, B). This experiment revealed that bait proteins have only a minimal, neglectable impact on the cellular proteome (Suppl. Fig. S1A, B). We did not find evidence for a systematic deregulation of proteins known to localize exclusively to ER tubules or other ER subdomains. Furthermore, quantitative proteomics validated the results from immunoblotting (Fig. 1B, C): Expression of bait-SEC61β has barely any impact on the total cellular level of SEC61β (Suppl. Fig. S1A) while the expression of the REEP5-bait results in a 1.8-fold overabundance of REEP5 (Suppl. Fig. S1B).'

Likewise, the expression of the bait constructs has little to no effect on the cellular lipidome as shown in Suppl. Fig. S4A-J. In the revised manuscript we state:

'As a control, we also tested the impact of the bait constructs on the HEK293T whole cell lipidome (Suppl. Fig. 4A-J). Overall, the lipid composition of the virally transduced cells was indistinguishable from HEK293T cells with only minor impact on the level of CL and lysolipids (Suppl. Fig. 4A-J).'

Apart from hypotonic swelling and douncing, could the authors use alternative methods for cell disruption to exclude the possibility that mechanical stress confounds the interpretation of the data?

Thanks to the reviewer's comment, we became aware of a mistake. Our cell lysis buffer is hypertonic and not hypotonic (15% sucrose w/v, 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid)(HEPES) pH 7.4, 300 mM NaCl, 1 mM EDTA freshly supplemented with protease inhibitor cocktail from Roche). We have corrected all relevant sections in the revised manuscript.

The reviewer is right that different means of mechanical lysis, and/or the incubation of the cells in hypo/hypertonic buffer are likely to have impact on the structure of the ER and to affect the isolation procedure. Changing such critical parameters will likely affect the purity of the preparation. Performing additional MemPrep isolations using different means of cell disruptions goes beyond the scope of this manuscript.

Upon establishing the MemPrep protocol, we have explored various mechanical cell disruptions: Different cannula, Dounce homogenizers, and a ball-bearing device. We experimented with both hypo- and hypertonic buffers. Given the costs and work associated with lipidomic and proteomic analyses, we have tried to find a suitable conditions for cell disruption without performing a full analysis each time. Therefore, we performed differential centrifugations as exemplary shown in Fig. 2B of the manuscript. Critical factors for our decision whether to further persue a certain condition was 1) the depletion of the mitochondrial TOM22 marker, 2) the enrichment of the ER markers, and 3) the total protein yield in the P100,000 fraction.

In the revised manuscript we state: 'Compared to the MemPrep procedure in yeast, we tested various means of cell disruption and optimized the differential centrifugation protocol.'

and

'Mild cell disruption by Dounce homogenization in a hypertonic buffer is crucial for cracking cells open, but these procedures can disrupt normal ER architecture and might facilitate the undesired mixing of previously well-defined ER subdomains. Despite these limitations, our data underscore the purity of our ER membrane preparations, demonstrate a differential enrichment of ER subdomains (Fig. 5), and establish the lipid composition of the ER membrane (Fig. 6)'.

What is the total amount of lipids and proteins isolated with REEP5- or SEC61β-based MemPrep? Are there differences in the total lipid:protein ratio between these isolates, and could this reflect differences in the ER sheet:tubule ratio?

In response to the reviewers' question, we have included a new Supplementary table 1 to the manuscript outlining the yield of total protein and total lipid of MemPrep.

The mammlian MemPrep protocol is not yet optimized for determining the lipid:protein ratio in the membrane. At this moment, we do not want to make a statement about the protein-to-lipid ratio in the ER or its subdomains. The isolates still contain material originating from the ER lumen.

The combined analysis of lipid and protein composition demonstrates the capacity of the method. To test that MemPrep can capture changes in ER membrane architecture, it would be useful to compare ER protein and lipid composition across different cellular states, such as stressed versus unstressed cells, or growing versus resting cells.

We agree with the reviewer that a comparison of the ER under different conditions would be extremely interesting. Currently, we see it beyond the scope of this study.

Minor comment:

1. In line 335, the authors state: "To address this possibility, we performed a new round of REEP5 and SEC61β MemPreps for a direct comparison of the isolates (Fig. 5A, B)." It is unclear whether the MemPrep protocol was altered or whether this refers simply to an additional round of purification. Please clarify.

Thank you. This point was also raised by reviewer 2 and 3. We have clarified our statements. In the revised manuscript we state:

'Hence, we performed a new round of REEP5 and SEC61β MemPreps in triplicates for a direct comparison of the isolates (Fig. 5A, B) rather than comparing the changes in abundance relative to the respective cell lysates as performed in Figure 3. Knowing that non-ER proteins are less efficiently enriched by the MemPrep procedure than ER proteins (Fig. 3C, D) and that the sensitivity and comprehensiveness of mass spectrometry-based proteomics experiments are reduced with increasing sample complexity (Ting et al, 2011; Beck et al, 2011) , we were hoping to gain a better insight into the distribution of low abundant and challenging to quantify proteins in the two MemPrep isolates'.

Reviewer #1 (Significance (Required)):

General assessment:

The manuscript establishes MemPrep for mammalian cells as an important discovery tool to investigate how cells coordinate membrane lipid composition with membrane protein composition, and vice versa. This is a rapidly growing research field, which attracts a lot of interest.

MemPrep is based on an immuno-isolation strategy using tagged versions of the ER sheet protein SEC61β and the ER tubular protein REEP5 in HEK293T cells. The purification strategy allowed to generate highly pure ER sheet- and tubule-enriched fractions, which were then subjected to quantitative lipidomic and proteomic analyses.

The results show that the protein composition differs between the SEC61β- and REEP5-enriched fractions. Yet the lipid composition of ER sheets and tubules is largely indistinguishable. Both fractions are dominated by PC alongside other monounsaturated GPL, and hydroxylated ceramides. These physicochemical properties of the ER lipid bilayer are matched by ER-resident membrane proteins.

Thorough bioinformatic analysis of a subset of ER membrane proteins further revealed that their transmembrane domains have reduced hydrophobicity and increased polarity compared with those of plasma membrane proteins, matching the ER lipidome.

Hence the combined analysis of lipid and protein composition demonstrates the capacity of the method. Many variations of this approach will be possible in the future to understand on the molecular level how cells assemble and control their membranes.

Advance: Other immuno-isolation methods, or "organelle immunoprecipitation" approaches, have been established for lysosomes, the Golgi apparatus, and other organelles.

MemPrep is an important and complementary addition to the technical toolbox for organelle isolation, with a particular focus on the analysis of membrane lipid and protein content.

Audience: The manuscript will be of broad interest to researchers in basic biology as well as clinical and translational research.

Reviewer's field of expertise:

Molecular membrane biology.

__Reviewer #2 __

Jain and colleagues develop a biochemical fractionation procedure in which ER microsomes are enriched through small epitope tags. The manuscript is pitched around the concept that there are ER sheets and tubules and ER proteins differentially localise to them. The authors use REEP5 as a 'tubule' bait and SEC61beta as a 'sheet' bait. These baits are immuoisolated after a sensible membrane fractionation and ER membraned purified. There is a convincing ER proteome as a result, and this is used to compare the TMD properties of the organelles resident membrane proteins. The authors make the interesting observation that the transmembrane domains are more polar in the ER. They then compare the two sheet and tubule preparations and see a different in the proteome, before comparing the lipidome. There is no difference observed between the lipidome of the sheet and tubule preps, however they see a difference in the whole cell lysate and use that to compare the ER lipidome against the whole cell.

Overall the manuscript has an interesting premise and the data is well presented, the experiments well performed and the interpretations appropriate. I think there are some issues with the mechanistic insight and novelty, and essentially although the premise is with regards to sheets and tubules there is limited progress in that direction in terms of results. I am reluctant to be to critical overall as there are certainly interesting observations that may be insightful for future studies in the field. I have some more specific comments below:

We thank the reviewer for a thorough, constructive assessment and for highlighting important points that helped us improve the manuscript.

1) The authors cite nixon-abell, but they do not mention the major point of that manuscript which is that the 'sheets' in the cellular periphery are instead dense tubular networks. I think this is quite an omission for the introduction, as it points to the premise not being as clear as stated.

In the revised manuscript we refer to the Nixon-Abell study and two additional studies from the Jokitalo lab. Notably, the Nixon-Abell study does not rule out the existence of ER sheets.

In the revised manuscript we state: ' [...] dense tubular networks in the cell periphery can appear like ER sheets in diffraction-limited microscopy (Nixon-Abell et al, 2016). Furthermore, the edges of ER sheets are populated by curvature-stabilizing proteins also found in ER tubules (Shibata et al, 2010; Shemesh et al, 2014), and ER sheets show different degrees of fenestration dependent on the cell type and the cell cycle phase (Puhka et al, 2007, 2012; Nixon-Abell et al, 2016). Consistent with our microscopic data (Fig. 1E, F) and because ER sheets may be biochemically inseparable from ER tubules, we use SEC61β as a general ER marker.'

We performed additional co-localization studies of the bait proteins with RTN4 and CLIMP63 (new Fig. 1E,F) suggesting that SEC61B can localize across many ER subdomains including ER tubules and the nuclear envelope.

We have carefully revised our manuscript accordingly and shifting the focus of our discussion away from a molecular description of discrete ER subdomains.

2) The first section when the protocol is discussed essentially relies on looking at other papers to understand. As the manuscript is centrally about this protocol, I think a brief but clear description is more appropriate.

We agree with the reviewer. We added a short section to the results section providing an overview over the MemPrep procedure. We now state:

'To this end, we adapted the MemPrep procedure originally developed for the isolation of organelle membranes from Saccaromyces cerevisiae (S. cerevisiae) (Reinhard et al, 2023, 2024). Mammalian MemPrep relies on a gentle, detergent-free, mechanical lysis of the cells in a hypertonic buffer followed by differential centrifugation to separate ER-derived microsomes from mitochondria-derived membranes. Next, larger organelle fragments are disrupted by brief pulses of sonication, and the resulting vesicles are subjected to affinity purification using magnetic dynabead-coupled antibodies directed against the cleavable tag of the bait protein. Specifically bound, ER-derived membrane vesicles are washed with harsh, urea-containing buffers and selectively released by proteolytically cleaving the bait tag.'

3) In figure 1C the two markers are supposed to localise to sheets and tubules differentially. To me they look very similar. This, of course, is a major concern. Have the authors co-expressed them (at the same levels in these lines) and seen that indeed they do differentially localise?

The reviewer raises an important point regarding the localzation of the bait proteins. While we have not co-expressed the bait proteins in cells, we have performed additional co-localization experiments with RTN4 and CLIMP63 as markers for ER tubules and ER sheets, respectively (new Figure 1E,F; new Suppl. Fig. S1C,D). The implications of these data are discussed in the manuscript.

In light of these new data, we do not refer to SEC61β as an ER sheet marker any longer, instead we refer to SEC61β as a general ER marker. We carefully revised our discussion of the data throughout the manuscript along the line suggested by the reviewer in point 8.

4) I found the TMD polarity section very interesting, but it was not clear to me why they needed their proteomics for this? Could this not be done with annotated ER membrane proteins?

The reviewer is correct. The same type of analysis could have been performed with an even bigger dataset of all ER annotated proteins. One of the co-authors, Joseph Lorent, has performed such analysis at this larger scale (PMID: 40326394). The study by Lorent et al. addressed TMH length and side chain bulkiness (PMID: 40326394) in the ER, Golgi apparatus, and the PM. This work is referenced in the manuscript.

We focused our analysis on the smaller dataset of 83 single-pass proteins found in our proteomics experiments, because we initially planned to perform a comparative analysis of ER proteins in either of the two isolates.

In line of the reviewers' suggestion, we validate our new finding on the TMH hydrophobicity in the ER using a larger dataset covering all single pass TMHs of ER proteins (215 instead of 83), Golgi apparatus proteins (260), and plasma membrane proteins (1322) (Suppl. Fig. S3D).

5) It was not clear to me based on the results section text the difference between the figure 5 proteomics and the previous runs.

This point was also raised by reviewer 1 and 3. We clarified our statement in the revised manuscript:

'Hence, we performed a new round of REEP5 and SEC61β MemPreps in triplicates for a direct comparison of the isolates (Fig. 5A, B) rather than comparing the changes in abundance relative to the respective cell lysates as performed in Figure 3. Knowing that non-ER proteins are less efficiently enriched by the MemPrep procedure than ER proteins (Fig. 3C, D) and that the sensitivity and comprehensiveness of mass spectrometry-based proteomics experiments are reduced with increasing sample complexity (Ting et al, 2011; Beck et al, 2011) , we were hoping to gain a better insight into the distribution of low abundant and challenging to quantify proteins in the two MemPrep isolates.'

6) Again in figure 5- are the authors sure that the difference was not due to the over-expression (albeit mild) of their protein.

After performing an important control experiment, we are sure that the mild over-expression of the bait proteins has no impact.

We have compared HEK293T WT cells with the bait protein expressing cell lines by quantitative proteomics (new Suppl. Fig. S1A,B). The bait proteins have no impact of the cellular proteome and do not affect the abundance of proteins known to be enriched in ER sheets or ER tubules. Hence, the enrichment of these proteins in our MemPrep isolates as shown in Fig. 5 suggests that some of the identity of ER sheets and ER tubules is maintained in our preparations even though they are not resolved by our microscopy experiments (Fig. 1). In the revised manuscript, we carefully discuss the implications of these findings.

7) There were no differences in the ER lipidome between the two baits. This may be because there is no difference between the lipid profile of sheets and tubules, but it is very hard to conclude that.

The reviewer has a point. Even though our findings suggest that we can differentially enrich for ER subdomains (the proteomics data in Fig. 5 on MemPrep isolates can be regarded as a golded standard for this statement), we do not have any knowledge about their biochemical purity. Hence, we have carefully toned down our statements on the basis of new imaging data (Fig. 1E,F; Suppl. Fig. S1C,D) and new proteomics data (Suppl. Fig. S1A,B).

Along the reasoning of the reviewer, we also rephrased our statements on the difference/similarity of ER subdomains.

8) I do not see it as my job as a reviewer to propose reorganisations and rewrites, so I encourage the authors to feel free to ignore this comment. To me the lipidome and TMD polar observations are the key manuscript findings, and there is very limited insight into the tubules and sheets line of inquiry. I wonder if it would be worth changing the focus of the manuscript overall to rather be about the ER, and not the tubules and sheets.

Again, the reviewer raises an important point that we did not want 'to ignore'. We have carefully revised the manuscript and toned down our interpretations. In the revised manuscript we put more emphasis on the ER lipidome and less so on the composition of specific ER subdomains.

__Reviewer #2 (Significance (Required)): __

Overall the manuscript has an interesting premise and the data is well presented, the experiments well performed and the interpretations appropriate. I think there are some issues with the mechanistic insight and novelty, and essentially although the premise is with regards to sheets and tubules there is limited progress in that direction in terms of results. I am reluctant to be to critical overall as there are certainly interesting observations that may be insightful for future studies in the field.

Reviewer #3

Summary: Jain et al., provide a clear and thorough manuscript that extends their prior biochemical analysis of the yeast ER-lipidome (MEMPREP) to mammalian cells. They use detergent free lysis and differential speed centrifugation from 293T cells bearing reporters with affinity handles targeted to sheet-like or tubular-like subdomains of the ER and enrich membranes and membrane-embedded proteins from these sites. The lipidomics reveals a distinct ER-lipidome, heavily enriched in PC and PI, contains predominantly mono-unsaturated phospholipids and is surprisingly invariant across sheet-like and tubule-like domains. Additional hydrophobicity analysis suggests that ER-localised TMDs are more polar and shorter than PM-resident TMDs, and the authors speculate about co-evolution of the lipidome and proteome to ensure targeting.

Major comments:

I think the data are solid, clear and convincing. The similarity of the lipidomes from sheet and tubule regions of the ER give good indication of the robustness of the technique. Whilst the yield is low, the authors go to good lengths to demonstrate purity of ER capture and de-enrichment of other cellular membranes. There is good discussion of the limitations of the technique and good comparison to recent data from other labs, most notably, a recent preprint and I think the manuscripts support eachother well. There's a fair amount of speculation in the manuscript, e.g., about lipid headgroup charge density being inferred by the charge distribution on the -1 position, but the speculation is clearly acknowledged.

1. I think that blotting for SEC61B would really help. A clear comparison to endogenous SEC61B would be helpful. I appreciate that the authors lacked an antibody here, but there are several on CiteAb that seem to detect endogenous protein.

Following the reviewers' advice, we added new data using a commercial antibody directed against SEC61β (new Fig. 1C). We also added proteomics data comparing HEK293T WT cells with the bait expressing cell lines (new Suppl. Fig. S1A,B).

We also characterized the commercial Proteintech (15087-1-AP) antibody to make sure it recognizes the same epitopes in the tagged and untagged variant of SEC61β.

It's not brilliantly easy to see the 'sharp decline' in relative frequency of hydrophobic amino acids at 21 aa for ER and Golgi; whilst the individual amino acid information is interesting (and some comment could be made about the favouring of Leucines in ER and Golgi TMDs), would this be clearer if the relative frequencies were binned into hydrophobic/aromatic, polar, positive, negative?

The reviewer is right. We have removed our statement regarding a 'sharp decline'. In fact, the decline is rather gradual for ER and Golgi TMHs, but more clear for PM TMHs. This is also reflected in the data shown in Suppl. Fig. S3D and discussed in the revised manuscript.

We state: Confirming our expectations based on the predicted TMH length (Suppl. Fig. S3A), we observed a gradual decline in the relative frequency of hydrophobic and aromatic resides at about 21 amino acids for ER (Fig. 4E) and Golgi-associated TMHs (Fig. 4F). Such decline was more clearly defined for plasma membrane TMHs but only after 24 aa or more (Fig. 4G).'

We also state: 'We therefore challenged our finding and performed an additional analysis using this larger dataset of all annotated human single-pass TMHs (Fig. S3D) and compared the hydrophobicity profiles of TMHs from the ER (215), the Golgi apparatus (260), and the PM (1322) (Lorent et al, 2025). This analysis further substantiated our finding that the ER and the Golgi apparatus host less hydrophobic TMHs compared to the plasma membrane. Furthermore, we observed that the ER and Golgi profiles display a conical shape with hydrophobic maxima at the center of the membrane's hydrophobic core, while the PM TMH's possess higher hydrophobicity in the cytoplasmic part of the membrane, compared to the exoplasmic part (Fig. S3D).'

We decided to keep the Fig. 4 with its single amino acid 'resolution' was it was in the original manuscript, because we feel that this representation still has its value. It helps connecting physicochemical parameters of an average TMH in an organelle (Fig. 4A-D; Suppl. Fig. S3A-D) with the preferred amino acid composition and distribution (Fig. 4E-G). Nevertheless, some 'noise' in inherent to the data and we hope that the adaptations to the text avoids any possible confusion of the reader.

The frequency of leucine residues in TMHs from the PM (24.5%) is comparable to the frequency of TMHs from the ER (24.1%) and from the Golgi apparatus (26.3%). Our attempts to identify an organelle-selective usage of certain amino acids did not yield robust and significant results.

Related to this point, it's hard to correlate the degree of polar amino acid incorporation in the TMDs of Golgi, ER, PM proteins (which don't appear to vary in 4E, 4F and 4G) with the variance described in 4C. Is there a better way of displaying this data, or are the polarity measurements calculated by some other metric in 4C?

The reviewer is right. Figure 4A-D and Figure 4E-G are based on different metrics. Figure 4A-D considers different physicochemical parameters of the amino acid sidechains (Fig. 4C: Kyte-Dolittle scale). Figure 4E-G only represents the relative frequencies. We believe that both representations can be useful.

Notably, the relative incorporation of polar and apolar amino acids is significantly different between TMHs from the ER and the Golgi versus the TMHs from the PM (Suppl. Fig. S3B,C).

In the revised manuscript we state: 'Our new finding that the TMHs of ER proteins are more polar than the TMHs in the plasma membrane (Fig. 4C) is also reflected by the significantly different number of apolar and polar residues in the TMHs from ER-, Golgi apparatus-, and PM-derived proteins (Suppl. S3B, C)'.

Indeed, the polarity in Fig. 4A and Fig. 4C is calculated via the Kyte-Dolittle scale, while only the normalized frequency of the amino acid is color-coded in Fig. 4E-G.

Minor comments:

1. Panel 2D isn't labelled on the figure

We represented both MemPreps in a single Panel 2C because we aimed to label in the immunoblots only a single time to avoid redundancies. We are open to change our strategy of panel labeling if our current representation is confusing.

There is limited co-enrichment of non-ER proteins in the ER-affinity preps, and the authors have done well to deal with misannotated GO terms. It might be worthwhile adding to the discussion that all TMD proteins that localise at steady-state to post-ER compartments must necessarily pass through the ER during biosynthesis. As such, detection of non-ER proteins in ER fractions is not inherently unexpected.

This is of course correct. In the revised manuscript we state: 'Finding non-ER proteins in an ER proteome is not surprising, because a very large number of proteins are first delivered to the ER, before they are sent to other cellular destinations.'

I didn't understand the line on L377 about the new round of extraction featureing inherently less complex proteomes.

This point was also raised by reviewer 1 and 2. We clarified our statement in the revised manuscript:

'Hence, we performed a new round of REEP5 and SEC61β MemPreps in triplicates for a direct comparison of the isolates (Fig. 5A, B) rather than comparing the changes in abundance relative to the respective cell lysates as performed in Figure 3. Knowing that non-ER proteins are less efficiently enriched by the MemPrep procedure than ER proteins (Fig. 3C, D) and that the sensitivity and comprehensiveness of mass spectrometry-based proteomics experiments are reduced with increasing sample complexity (Ting et al, 2011; Beck et al, 2011) , we were hoping to gain a better insight into the distribution of low abundant and challenging to quantify proteins in the two MemPrep isolates.'

For line L390-391, in the speculation about progressively more unsaturation as you move ER-Golgi-postGolgi, is there any (published) data from ER-FLIPPR that could inform about the degree of membrane fluidity/packing as you traverse the secretory pathway?

We agree that mentioning evidence on the biophysical changes along the secretory pathway is helpful in this section. In the revised manuscript we state:

'These changes of the lipid acyl chains are associated with biophysical changes of the membrane properties along the secretory pathway as observed by molecular probes reporting on lipid packing and membrane tension (Goujon et al, 2019; López-Andarias et al, 2021, 2022; Wong & Budin, 2024).'

Reviewer #3 (Significance (Required)):

The strengths of the study are the conceptual novelty and information provided - I think this is the first comprehensive reporting of the ER lipidome. This is a major organelle and I think as the lipid biology field develops, resources like this are really important. Moreover, the MEMPREP protocol is applicable for protein extraction from these domains, which will help with functional characterisation of ER subdomains and is a strong technical advance.

Weaknesses relate to the single cell type and overexpression (albeit mild) methodologies. I'm not hugely fussed about this as this manuscript describes an important 1st step.

I'm a cell biologist studying the ER

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

This paper addresses a very interesting problem of non-centrosomal microtubule organization in developing Drosophila oocytes. Using genetics and imaging experiments, the authors reveal an interplay between the activity of kinesin-1, together with its essential cofactor Ensconsin, and microtubule organization at the cell cortex by the spectraplakin Shot, minus-end binding protein Patronin and Ninein, a protein implicated in microtubule minus end anchoring. The authors demonstrate that the loss of Ensconsin affects the cortical accumulation non-centrosomal microtubule organizing center (ncMTOC) proteins, microtubule length and vesicle motility in the oocyte, and show that this phenotype can be rescued by constitutively active kinesin-1 mutant, but not by Ensconsin mutants deficient in microtubule or kinesin binding. The functional connection between Ensconsin, kinesin-1 and ncMTOCs is further supported by a rescue experiment with Shot overexpression. Genetics and imaging experiments further implicate Ninein in the same pathway. These data are a clear strength of the paper; they represent a very interesting and useful addition to the field.

The weaknesses of the study are two-fold. First, the paper seems to lack a clear molecular model, uniting the observed phenomenology with the molecular functions of the studied proteins. Most importantly, it is not clear how kinesin-based plus-end directed transport contributes to cortical localization of ncMTOCs and regulation of microtubule length.

Second, not all conclusions and interpretations in the paper are supported by the presented data.

We thank the reviewer for recognizing the impact of this work. In response to the insightful suggestions, we performed extensive new experiments that establish a well-supported cellular and molecular model (Figure 7). The discussion has been restructured to directly link each conclusion to its corresponding experimental evidence, significantly strengthening the manuscript.

Below is a list of specific comments, outlining the concerns, in the order of appearance in the paper/figures.

Figure 1. The statement: "Ens loading on MTs in NCs and their subsequent transport by Dynein toward ring canals promotes the spatial enrichment of the Khc activator Ens in the oocyte" is not supported by data. The authors do not demonstrate that Ens is actually transported from the nurse cells to the oocyte while being attached to microtubules. They do show that the intensity of Ensconsin correlates with the intensity of microtubules, that the distribution of Ensconsin depends on its affinity to microtubules and that an Ensconsin pool locally photoactivated in a nurse cell can redistribute to the oocyte (and throughout the nurse cell) by what seems to be diffusion. The provided images suggest that Ensconsin passively diffuses into the oocyte and accumulates there because of higher microtubule density, which depends on dynein. To prove that Ensconsin is indeed transported by dynein in the microtubule-bound form, one would need to measure the residence time of Ensconsin on microtubules and demonstrate that it is longer than the time needed to transport microtubules by dynein into the oocyte; ideally, one would like to see movement of individual microtubules labelled with photoconverted Ensconsin from a nurse cell into the oocyte. Since microtubules are not enriched in the oocyte of the dynein mutant, analysis of Ensconsin intensity in this mutant is not informative and does not reveal the mechanism of Ensconsin accumulation.

As noted by Reviewer 3, the directional movement of microtubules traveling at ~140 nm/s from nurse cells toward the oocyte through Ring Canals was previously reported using a tagged Ens-MT binding domain reporter line by Lu et al. (2022). We have therefore added the citation of this crucial work in the novel version of the manuscript (lane 155-157) and removed the photo-conversion panel.

Critically, however, our study provides mechanistic insight that was missing from this earlier work: this mechanism is also crucial to enrich MAPs in the oocyte. The fact that Dynein mutants fail to enrich Ensconsin is a crucial piece of evidence: it supports a model of Ensconsin-loaded MT transport (Figure 1D-1F).

Figure 2. According to the abstract, this figure shows that Ensconsin is "maintained at the oocyte cortex by Ninein". However, the figure doesn't seem to prove it - it shows that oocyte enrichment of Ensonsin is partially dependent on Ninein, but this applies to the whole cell and not just to the cell cortex. Furthermore, it is not clear whether Ninein mutation affects microtubule density, which in turn would affect Ensconsin enrichment, and therefore, it is not clear whether the effect of Ninein loss on Ensconsin distribution is direct or indirect.

Ninein plays a critical role in Ensconsin enrichment and microtubule organization in the oocyte (new Figure 2, Figure 3, Figure S3). Quantification of total Tubulin signal shows no difference between control and Nin mutant oocytes (new Figure S3 panels A, B). We found decreased Ens enrichment in the oocyte, and Ens localization on MTs and to the cell cortex (Figure 2E, 2F, and Figure S3C and S3D).

Novel quantitative analyses of microtubule orientation at the anterior cortex, where MTs are normally preferentially oriented toward the posterior pole (Parton et al. 2011), demonstrate that Nin mutants exhibit randomized MT orientation compared to wild-type oocytes (new Figure 3C-3E).These findings establish that Ninein (although not essential) favors Ensconsin localization on MTs, Ens enrichment in the oocyte, ncMTOC cortical localization, and more robust MT orientation toward the posterior cortex. It also suggests that Ens levels in the oocyte acts as a rheostat to control Khc activation.

The observation that the aggregates formed by overexpressed Ninein accumulate other proteins, including Ensconsin, supports, though does not prove their interactions. Furthermore, there is absolutely no proof that Ninein aggregates are "ncMTOCs". Unless the authors demonstrate that these aggregates nucleate or anchor microtubules (for example, by detailed imaging of microtubules and EB1 comets), the text and labels in the figure would need to be altered.

We have modified the manuscript, we now refer to an accumulation of these components in large puncta, rather than aggregates, consistent with previous observations (Rosen et al., 2000). We acknowledge in the revised version that these puncta recruit Shot, Patronin and Ens without mentioning direct interaction (lane 218).

Importantly, we conducted a more detailed characterization of these Ninein/Shot/Patronin/Ens-containing puncta in a novel Figure S4. To rigorously assess their nucleation capacity, we analyzed Eb1-GFP-labeled MT comets, a robust readout of MT nucleation (Parton et al., 2011, Nashchekin et al., 2016). While few Eb1-positive comets occasionally emanate from these structures, confirming their identity as putative ncMTOCs, these puncta function as surprisingly weak nucleation centers (new Figure S4 E, Video S1) and, their presence does not alter overall MT architecture (new Figure S4 F). Moreover, these puncta disappear over time, are barely visible at stage 10B, they do not impair oocyte development or fertility (Figure S4 G and Table 1).

Minor comment: Note that a "ratio" (Figure 2C) is just a ratio, and should not be expressed in arbitrary units.

We have amended this point in all the figures.

Figure 3B: immunoprecipitation results cannot be interpreted because the immunoprecipitated proteins (GFP, Ens-GFP, Shot-YFP) are not shown. It is also not clear that this biochemical experiment is useful. If the authors would like to suggest that Ensconsin directly binds to Patronin, the interaction would need to be properly mapped at the protein domain level.

This is a good point: the GFP and Ens-GFP immunoprecipitated proteins are now much clearly identified on the blots and in the figure legend (new Figure 4G). Shot-YFP IP, was used as a positive control but is difficult to be detected by Western blot due to its large size (>106 Da) using conventional acrylamide gels (Nashchekin et al., 2016).

We now explicitly state that immunoprecipitations were performed at 4{degree sign}C, where microtubules are fully depolymerized, thereby excluding undirect microtubule-mediated interactions. We agree with this reviewer: we cannot formally rule out interactions through bridging by other protein components. This is stated in the revised manuscript (lane 238-239).

One of the major phenotypes observed by the authors in Ens mutant is the loss of long microtubules. The authors make strong conclusions about the independence of this phenotype from the parameters of microtubule plus-end growth, but in fact, the quality of their data does not allow to make such a conclusion, because they only measured the number of EB1 comets and their growth rate but not the catastrophe, rescue or pausing frequency."Note that kinesin-1 has been implicated in promoting microtubule damage and rescue (doi: 10.1016/j.devcel.2021).In the absence of such measurements, one cannot conclude whether short microtubules arise through defects in the minus-end, plus-end or microtubule shaft regulation pathways.

We thank the reviewer for raising this important point. Our data demonstrate that microtubule (MT) nucleation and polymerization rates remain unaffected under Khc RNAi and ens mutant conditions, indicating that MT dynamics alterations must arise through alternative mechanisms.

As the reviewer suggested, recent studies on Kinesin activity and MT network regulation are indeed highly relevant. Two key studies from the Verhey and Aumeier laboratories examined Kinesin-1 gain-of-function conditions and revealed that constitutively active Kinesin-1 induces MT lattice damage (Budaitis et al., 2022). While damaged MTs can undergo self-repair, Aumeier and colleagues demonstrated that GTP-tubulin incorporation generates "rescue shafts" that promote MT rescue events (Andreu-Carbo et al., 2022). Extrapolating from these findings, loss of Kinesin-1 activity could plausibly reduce rescue shaft formation, thereby decreasing MT rescue frequency and stability. Although this hypothesis is challenging to test directly in our system, it provides a mechanistic framework for the observed reduction in MT number and stability.

Additionally, the reviewer highlighted the role of Khc in transporting the dynactin complex, an anti-catastrophe factor, to MT plus ends (Nieuwburg et al., 2017), which could further contribute to MT stabilization. This crucial reference is now incorporated into the revised Discussion.

Importantly, our work also demonstrates the contribution of Ens/Khc to ncMTOC targeting to the cell cortex. Our new quantitative analyses of MT organization (new Figure 5 B) reveal a defective anteroposterior orientation of cortical MTs in mutant conditions, pointing to a critical role for cortical ncMTOCs in organizing the MT network.

Taken together, we propose that the observed MT reduction and disorganization result from multiple interconnected mechanisms: (1) reduced rescue shaft formation affecting MT stability; (2) impaired transport of anti-catastrophe factors to MT plus ends; and (3) loss of cortical ncMTOCs, which are essential for minus-end MT stabilization and network organization. The Discussion has been revised to reflect this integrated model in a dedicated paragraph ("A possible regulation of MT dynamics in the oocyte at both plus end minus MT ends by Ens and Khc" lane 415-432).

It is important to note in that a spectraplakin, like Shot, can potentially affect different pathways, particularly when overexpressed.

We agree that Shot harbors multiple functional domains and acts as a key organizer of both actin and microtubule cytoskeletons. Overexpression of such a cytoskeletal cross-linker could indeed perturb both networks, making interpretation of Ens phenotype rescue challenging due to potential indirect effects.

To address this concern, we selected an appropriate Shot isoform for our rescue experiments that displayed similar localization to "endogenous" Shot-YFP (a genomic construct harboring shot regulatory sequences) and importantly that was not overexpressed.

Elevated expression of the Shot.L(A) isoform (see Western Blot Figure S8 A), considered as the wild-type form with two CH1 and CH2 actin-binding motifs (Lee and Kolodziej, 2002), showed abnormal localization such as strong binding to the microtubules in nurse cells and oocyte confirming the risk of gain-of-function artifacts and inappropriate conclusions (Figure S8 B, arrows).

By contrast, our rescue experiments using the Shot.L(C) isoform (that only harbors the CH2 motif) provide strong evidence against such artifacts for three reasons. First, Shot-L(C) is expressed at slightly lower levels than a Shot-YFP genomic construct (not overexpressed), and at much lower levels than Shot-L(A), despite using the same driver (Figure S8 A). Second, Shot-L(C) localization in the oocyte is similar to that of endogenous Shot-YFP, concentrating at the cell cortex (Figure S8 B, compare lower and top panels). Taken together, these controls rather suggest our rescue with the Shot-L(C) is specific.

Note that this Shot-L(C) isoform is sufficient to complement the absence of the shot gene in other cell contexts (Lee and Kolodziej, 2002).

Unjustified conclusions should be removed: the authors do not provide sufficient data to conclude that "ens and Khc oocytes MT organizational defects are caused by decreased ncMTOC cortical anchoring", because the actual cortical microtubule anchoring was not measured.

This is a valid point. We acknowledge that we did not directly measure microtubule anchoring in this study. In response, we have revised the discussion to more accurately reflect our observations. Throughout the manuscript, we now refer to "cortical microtubule organization" rather than "cortical microtubule anchoring," which better aligns with the data presented.

Minor comment: Microtubule growth velocity must be expressed in units of length per time, to enable evaluating the quality of the data, and not as a normalized value.

This is now amended in the revised version (modified Figure S7).

A significant part of the Discussion is dedicated to the potential role of Ensconsin in cortical microtubule anchoring and potential transport of ncMTOCs by kinesin. It is obviously fine that the authors discuss different theories, but it would be very helpful if the authors would first state what has been directly measured and established by their data, and what are the putative, currently speculative explanations of these data.

We have carefully considered the reviewer's constructive comments and are confident that this revised version fully addresses their concerns.

First, we have substantially strengthened the connection between the Results and Discussion sections, ensuring that our interpretations are more directly anchored in the experimental data. This restructuring significantly improves the overall clarity and logical flow of the manuscript.

Second, we have added a new comprehensive figure presenting a molecular-scale model of Kinesin-1 activation upon release of autoinhibition by Ensconsin (new Figure 7D). Critically, this figure also illustrates our proposed positive feedback loop mechanism: Khc-dependent cytoplasmic advection promotes cortical recruitment of additional ncMTOCs, which generates new cortical microtubules and further accelerates cytoplasmic transport (Figure 7 A-C). This self-amplifying cycle provides a mechanistic framework consistent with emerging evidence that cytoplasmic flows are essential for efficient intracellular transport in both insect and mammalian oocytes.

Minor comment: The writing and particularly the grammar need to be significantly improved throughout, which should be very easy with current language tools. Examples: "ncMTOCs recruitment" should be "ncMTOC recruitment"; "Vesicles speed" should be "Vesicle speed", "Nin oocytes harbored a WT growth,"- unclear what this means, etc. Many paragraphs are very long and difficult to read. Making shorter paragraphs would make the authors' line of thought more accessible to the reader.

We have amended and shortened the manuscript according to this reviewer feed-back. We have specifically built more focused paragraphs to facilitates the reading.

Significance

This paper represents significant advance in understanding non-centrosomal microtubule organization in general and in developing Drosophila oocytes in particular by connecting the microtubule minus-end regulation pathway to the Kinesin-1 and Ensconsin/MAP7-dependent transport. The genetics and imaging data are of good quality, are appropriately presented and quantified. These are clear strengths of the study which will make it interesting to researchers studying the cytoskeleton, microtubule-associated proteins and motors, and fly development.

The weaknesses of this study are due to the lack of clarity of the overall molecular model, which would limit the impact of the study on the field. Some interpretations are not sufficiently supported by data, but this can be solved by more precise and careful writing, without extensive additional experimentation.

We thank the reviewer for raising these important concerns regarding clarity and data interpretation. We have thoroughly revised the manuscript to address these issues on multiple fronts. First, we have substantially rewritten key sections to ensure that our conclusions are clearly articulated and directly supported by the data. Second, we have performed several new experiments that now allow us to propose a robust mechanistic model, presented in new figures. These additions significantly strengthen the manuscript and directly address the reviewer's concerns.

My expertise is cell biology and biochemistry of the microtubule cytoskeleton, including both microtubule-associated proteins and microtubule motors.

Reviewer #2

Evidence, reproducibility and clarity

In this manuscript, Berisha et al. investigate how microtubule (MT) organization is spatially regulated during Drosophila oogenesis. The authors identify a mechanism in which the Kinesin-1 activator Ensconsin/MAP7 is transported by dynein and anchored at the oocyte cortex via Ninein, enabling localized activation of Kinesin-1. Disruption of this pathway impairs ncMTOC recruitment and MT anchoring at the cortex. The authors combine genetic manipulation with high-resolution microscopy and use three key readouts to assess MT organization during mid-to-late oogenesis: cortical MT formation, localization of posterior determinants, and ooplasmic streaming. Notably, Kinesin-1, in concert with its activator Ens/MAP7, contributes to organizing the microtubule network it travels along. Overall, the study presents interesting findings, though we have several concerns we would like the authors to address. Ensconsin enrichment in the oocyte 1. Enrichment in the oocyte • Ensconsin is a MAP that binds MTs. Given that microtubule density in the oocyte significantly exceeds that in the nurse cells, its enrichment may passively reflect this difference. To assess whether the enrichment is specific, could the authors express a non-Drosophila MAP (e.g., mammalian MAP1B) to determine whether it also preferentially localizes to the oocyte?

To address this point, we performed a new series of experiments analyzing the enrichment of other Drosophila and non-Drosophila MAPs, including Jupiter-GFP, Eb1-GFP, and bovine Tau-GFP, all widely used markers of the microtubule cytoskeleton in flies (see new Figure S2). Our results reveal that Jupiter-GFP, Eb1-GFP, and bovine Tau-GFP all exhibit significantly weaker enrichment in the oocyte compared to Ens-GFP. Khc-GFP also shows lower enrichment. These findings indicate that MAP enrichment in the oocyte is MAP-dependent, rather than solely reflecting microtubule density or organization. Of note, we cannot exclude that microtubule post-translational modifications contribute to differential MAP binding between nurse cells and the oocyte, but this remains a question for future investigation.

The ability of ens-wt and ens-LowMT to induce tubulin polymerization according to the light scattering data (Fig. S1J) is minimal and does not reflect dramatic differences in localization. The authors should verify that, in all cases, the polymerization product in their in vitro assays is microtubules rather than other light-scattering aggregates. What is the control in these experiments? If it is just purified tubulin, it should not form polymers at physiological concentrations.

The critical concentration Cr for microtubule self-assembly in classical BRB80 buffer found by us and others is around 20 µM (see Fig. 2c in Weiss et al., 2010). Here, microtubules were assembled at 40 µM tubulin concentration, i.e., largely above the Cr. As stated in the materials and methods section, we systematically induced cooling at 4{degree sign}C after assembly to assess the presence of aggregates, since those do not fall apart upon cooling. The decrease in optical density upon cooling is a direct control that the initial increase in DO is due to the formation of microtubules. Finally, aggregation and polymerization curves are widely different, the former displaying an exponential shape and the latter a sigmoid assembly phase (see Fig. 3A and 3B in Weiss et al., 2010).

Photoconversion caveatsMAPs are known to dynamically associate and dissociate from microtubules. Therefore, interpretation of the Ens photoconversion data should be made with caution. The expanding red signal from the nurse cells to the oocyte may reflect a any combination of dynein-mediated MT transport and passive diffusion of unbound Ensconsin. Notably, photoconversion of a soluble protein in the nurse cells would also result in a gradual increase in red signal in the oocyte, independent of active transport. We encourage the authors to more thoroughly discuss these caveats. It may also help to present the green and red channels side by side rather than as merged images, to allow readers to assess signal movement and spatial patterns better.

This is a valid point that mirrors the comment of Reviewers 1 and 3. The directional movement of microtubules traveling at ~140 nm/s from nurse cells toward the oocyte via the ring canals was previously reported by Lu et al. (2022) with excellent spatial resolution. Notably, this MT transport was measured using a fusion protein containing the Ens MT-binding domain. We now cite this relevant study in our revised manuscript and have removed this redundant panel in Figure 1.

Reduction of Shot at the anterior cortex• Shot is known to bind strongly to F-actin, and in the Drosophila ovary, its localization typically correlates more closely with F-actin structures than with microtubules, despite being an MT-actin crosslinker. Therefore, the observed reduction of cortical Shot in ens, nin mutants, and Khc-RNAi oocytes is unexpected. It would be important to determine whether cortical F-actin is also disrupted in these conditions, which should be straightforward to assess via phalloidin staining.

As requested by the reviewer, we performed actin staining experiments, which are now presented in a new Figure S5. These data demonstrate that the cortical actin network remains intact in all mutant backgrounds analyzed, ruling out any indirect effect of actin cytoskeleton disruption on the observed phenotypes.

MTs are barely visible in Fig. 3A, which is meant to demonstrate Ens-GFP colocalization with tubulin. Higher-quality images are needed.

The revised version now provides significantly improved images to show the different components examined. Our data show that Ens and Ninein localize at the cell cortex where they co-localize with Shot and Patronin (Figure 2 A-C). In addition, novel images show that Ens extends along microtubules (new Figure 4 A).

MT gradient in stage 9 oocytesIn ens-/-, nin-/-, and Khc-RNAi oocytes, is there any global defect in the stage 9 microtubule gradient? This information would help clarify the extent to which cortical localization defects reflect broader disruptions in microtubule polarity.

We now provide quantitative analysis of microtubule (MT) array organization in novel figures (Figure 3D and Figure 5B). Our data reveal that both Khc RNAi and ens mutant oocytes exhibit severe disruption of MT orientation toward the posterior (new Figure 5B). Importantly, this defect is significantly less pronounced in Nin-/- oocytes, which retain residual ncMTOCs at the cortex (new Figure 3D). This differential phenotype supports our model that cortical ncMTOCs are critical for maintaining proper MT orientation toward the posterior side of the oocyte.

Role of Ninein in cortical anchoringThe requirement for Ninein in cortical anchorage is the least convincing aspect of the manuscript and somewhat disrupts the narrative flow. First, it is unclear whether Ninein exhibits the same oocyte-enriched localization pattern as Ensconsin. Is Ninein detectable in nurse cells? Second, the Ninein antibody signal appears concentrated in a small area of the anterior-lateral oocyte cortex (Fig. 2A), yet Ninein loss leads to reduced Shot signal along a much larger portion of the anterior cortex (Fig. 2F)-a spatial mismatch that weakens the proposed functional relationship. Third, Ninein overexpression results in cortical aggregates that co-localize with Shot, Patronin, and Ensconsin. Are these aggregates functional ncMTOCs? Do microtubules emanate from these foci?

We now provide a more comprehensive analysis of Ninein localization. Similar to Ensconsin (Ens), endogenous Ninein is enriched in the oocyte during the early stages of oocyte development but is also detected in NCs (see modified Figure 2 A and Lasko et al., 2016). Improved imaging of Ninein further shows that the protein partially co-localizes with Ens, and ncMTOCs at the anterior cortex and with Ens-bound MTs (Figure 2B, 2C).

Importantly, loss of Ninein (Nin) only partially reduces the enrichment of Ens in the oocyte (Figure 2E). Both Ens and Kinesin heavy chain (Khc) remain partially functional and continue to target non-centrosomal microtubule-organizing centers (ncMTOCs) to the cortex (Figure 3A). In Nin-/- mutants, a subset of long cortical microtubules (MTs) is present, thereby generating cytoplasmic streaming, although less efficiently than under wild-type (WT) conditions (Figure 3F and 3G). As a non-essential gene, we envisage Ninein as a facilitator of MT organization during oocyte development.

Finally, our new analyses demonstrate that large puncta containing Ninein, Shot, Patronin, and despite their size, appear to be relatively weak nucleation centers (revised Figure S4 E and Video 1). In addition, their presence does not bias overall MT architecture (Figure S4 F) nor impair oocyte development and fertility (Figure S4 G and Table 1).

Inconsistency of Khc^MutEns rescueThe Khc^MutEns variant partially rescues cortical MT formation and restores a slow but measurable cytoplasmic flow yet it fails to rescue Staufen localization (Fig. 5). This raises questions about the consistency and completeness of the rescue. Could the authors clarify this discrepancy or propose a mechanistic rationale?

This is a good point. The cytoplasmic flows (the consequence of cargo transport by Khc on MTs) generated by a constitutively active KhcMutEns in an ens mutant condition, are less efficient than those driven by Khc activated by Ens in a control condition (Figure 6C). The rescued flow is probably not efficient enough to completely rescue the Staufen localization at stage 10.

Additionally, this KhcMutEns variant rescues the viability of embryos from Khc27 mutant germline clones oocytes but not from ens mutants (Table1). One hypothesis is that Ens harbors additional functions beyond Khc activation.

This incomplete rescue of Ens by an active Khc variant could also be the consequence of the "paradox of co-dependence": Kinesin-1 also transport the antagonizing motor Dynein that promotes cargo transport in opposite directions (Hancock et al., 2016). The phenotype of a gain of function variant is therefore complex to interpret. Consistent with this, both KhcMutEns-GFP and KhcDhinge2 two active Khc only rescues partially centrosome transport in ens mutant Neural Stem Cells (Figure S10).

Minor points: 1. The pUbi-attB-Khc-GFP vector was used to generate the Khc^MutEns transgenic line, presumably under control of the ubiquitous ubi promoter. Could the authors specify which attP landing site was used? Additionally, are the transgenic flies viable and fertile, given that Kinesin-1 is hyperactive in this construct?

All transgenic constructs were integrated at defined genomic landing sites to ensure controlled expression levels. Specifically, both GFP-tagged KhcWT and KhcMutEns were inserted at the VK05 (attP9A) site using PhiC31-mediated integration. Full details of the landing sites are provided in the Materials and Methods section. Both transgenic flies are homozygous lethal and the transgenes are maintained over TM6B balancers.

On page 11 (Discussion, section titled "A dual Ensconsin oocyte enrichment mechanism achieves spatial relief of Khc inhibition"), the statement "many mutations in Kif5A are causal of human diseases" would benefit from a brief clarification. Since not all readers may be familiar with kinesin gene nomenclature, please indicate that KIF5A is one of the three human homologs of Kinesin heavy chain.

We clarified this point in the revised version (lane 465-466).

On page 16 (Materials and Methods, "Immunofluorescence in fly ovaries"), the sentence "Ovaries were mounted on a slide with ProlonGold medium with DAPI (Invitrogen)" should be corrected to "ProLong Gold."

This is corrected.

Significance

This study shows that enrichment of MAP7/ensconsin in the oocyte is the mechanism of kinesin-1 activation there and is important for cytoplasmic streaming and localization non-centrosomal microtubule-organizing centers to the oocyte cortex

We thank the reviewers for the accurate review of our manuscript and their positive feed-back.

Reviewer #3

Evidence, reproducibility and clarity

The manuscript of Berisha et al., investigates the role of Ensconsin (Ens), Kinesin-1 and Ninein in organisation of microtubules (MT) in Drosophila oocyte. At stage 9 oocytes Kinesin-1 transports oskar mRNA, a posterior determinant, along MT that are organised by ncMTOCs. At stage 10b, Kinesin-1 induces cytoplasmic advection to mix the contents of the oocyte. Ensconsin/Map7 is a MT associated protein (MAP) that uses its MT-binding domain (MBD) and kinesin binding domain (KBD) to recruit Kinesin-1 to the microtubules and to stimulate the motility of MT-bound Kinesin-1. Using various new Ens transgenes, the authors demonstrate the requirement of Ens MBD and Ninein in Ens localisation to the oocyte where Ens activates Kinesin-1 using its KBD. The authors also claim that Ens, Kinesin-1 and Ninein are required for the accumulation of ncMTOCs at the oocyte cortex and argue that the detachment of the ncMTOCs from the cortex accounts for the reduced localisation of oskar mRNA at stage 9 and the lack of cytoplasmic streaming at stage 10b. Although the manuscript contains several interesting observations, the authors' conclusions are not sufficiently supported by their data. The structure function analysis of Ensconsin (Ens) is potentially publishable, but the conclusions on ncMTOC anchoring and cytoplasmic streaming not convincing.

We are grateful that the regulation of Khc activity by MAP7 was well received by all reviewers. While our study focuses on Drosophila oogenesis, we believe this mechanism may have broader implications for understanding kinesin regulation across biological systems.

For the novel function of the MAP7/Khc complex in organizing its own microtubule networks through ncMTOC recruitment, we have carefully considered the reviewers' constructive recommendations. We now provide additional experimental evidence supporting a model of flux self-amplification in which ncMTOC recruitment plays a key role. It is well established that cytoplasmic flows are essential for posterior localization of cell fate determinants at stage 10B. Slow flows have also been described at earlier oogenesis stages by the groups of Saxton and St Johnston. Building on these early publications and our new experiments, we propose that these flows are essential to promote a positive feedback loop that reinforces ncMTOC recruitment and MT organization (Figure 7).

1) The main conclusion of the manuscript is that "MT advection failure in Khc and ens in late oogenesis stems from defective cortical ncMTOCs recruitment". This completely overlooks the abundant evidence that Kinesin-1 directly drives cytoplasmic streaming by transporting vesicles and microtubules along microtubules, which then move the cytoplasm by advection (Palacios et al., 2002; Serbus et al, 2005; Lu et al, 2016). Since Kinesin-1 generates the flows, one cannot conclude that the effect of khc and ens mutants on cortical ncMTOC positioning has any direct effect on these flows, which do not occur in these mutants.

We regret the lack of clarity of the first version of the manuscript and some missing references. We propose a model in which the Kinesin-1- dependent slow flows (described by Serbus/Saxton and Palacios/StJohnston) play a central role in amplifying ncMTOC anchoring and cortical MT network formation (see model in the new Figure 7).

2) The authors claim that streaming phenotypes of ens and khs mutants are due to a decrease in microtubule length caused by the defective localisation of ncMTOCs. In addition to the problem raised above, However, I am not convinced that they can make accurate measurements of microtubule length from confocal images like those shown in Figure 4. Firstly, they are measuring the length of bundles of microtubules and cannot resolve individual microtubules. This problem is compounded by the fact that the microtubules do not align into parallel bundles in the mutants. This will make the "microtubules" appear shorter in the mutants. In addition, the alignment of the microtubules in wild-type allows one to choose images in which the microtubule lie in the imaging plane, whereas the more disorganized arrangement of the microtubules in the mutants means that most microtubules will cross the imaging plane, which precludes accurate measurements of their length.

As mentioned by Reviewer 4, we have been transparent with the methodology, and the limitations that were fully described in the material and methods section.

Cortical microtubules in oocytes are highly dynamic and move rapidly, making it technically impossible to capture their entire length using standard Z-stack acquisitions. We therefore adopted a compromise approach: measuring microtubules within a single focal plane positioned just below the oocyte cortex. This strategy is consistent with established methods in the field, such as those used by Parton et al. (2011) to track microtubule plus-end directionality. To avoid overinterpretation, we explicitly refer to these measurements as "minimum detectable MT length," acknowledging that microtubules may extend beyond the focal plane, particularly at stage 10, where long, tortuous bundles frequently exit the plane of focus. These methodological considerations and potential biases are clearly described in the Materials and Methods section and the text now mentions the possible disorganization of the MT network in the mutant conditions (lane 272-273).

In this revised version, we now provide complementary analyses of MT network organization.Beyond length measurements (and the mentioned limitations), we also quantified microtubule network orientation at stage 9, assessing whether cortical microtubules are preferentially oriented toward the posterior axis as observed in controls (revised Figure 3D and Figure 5B). While this analysis is also subject to the same technical limitations, it reveals a clear biological difference: microtubules exhibit posterior-biased orientation in control oocytes similar to a previous study (Parton et al., 2011) but adopt a randomized orientation in Nin-/-, ens, and Khc RNAi-depleted oocytes (revised Figure 3D and Figure 5B).

Taken together, these complementary approaches, despite their technical constraints, provide convergent evidence for the role of the Khc/Ens complex in organizing cortical microtubule networks during oogenesis.

3) "To investigate whether the presence of these short microtubules in ens and Khc RNAi oocytes is due to defects in microtubule anchoring or is also associated with a decrease in microtubule polymerization at their plus ends, we quantified the velocity and number of EB1comets, which label growing microtubule plus ends (Figure S3)." I do not understand how the anchoring or not of microtubule minus ends to the cortex determines how far their plus ends grow, and these measurements fall short of showing that plus end growth is unaffected. It has already been shown that the Kinesin-1-dependent transport of Dynactin to growing microtubule plus ends increases the length of microtubules in the oocyte because Dynactin acts as an anti-catastrophe factor at the plus ends. Thus, khc mutants should have shorter microtubules independently of any effects on ncMTOC anchoring. The measurements of EB1 comet speed and frequency in FigS2 will not detect this change and are not relevant for their claims about microtubule length. Furthermore, the authors measured EB1 comets at stage 9 (where they did not observe short MT) rather than at stage 10b. The authors' argument would be better supported if they performed the measurements at stage 10b.

We thank the reviewer for raising this important point. The short microtubule (MT) length observed at stage 10B could indeed result from limited plus-end growth. Unfortunately, we were unable to test this hypothesis directly: strong endogenous yolk autofluorescence at this stage prevented reliable detection of Eb1-GFP comets, precluding velocity measurements.

At least during stage 9, our data demonstrate that MT nucleation and polymerization rates are not reduced in both KhcRNAi and ens mutant conditions, indicating that the observed MT alterations must arise through alternative mechanisms.

In the discussion, we propose the following interconnected explanations, supported by recent literature and the reviewers' suggestions:

1- Reduced MT rescue events. Two seminal studies from the Verhey and Aumeier laboratories have shown that constitutively active Kinesin-1 induces MT lattice damage (Budaitis et al., 2022), which can be repaired through GTP-tubulin incorporation into "rescue shafts" that promote MT rescue (Andreu-Carbo et al., 2022). Extrapolating from these findings, loss of Kinesin-1 activity could plausibly reduce rescue shaft formation, thereby decreasing MT stability. While challenging to test directly in our system, this mechanism provides a plausible framework for the observed phenotype.

2- Impaired transport of stabilizing factors. As that reviewer astutely points out, Khc transports the dynactin complex, an anti-catastrophe factor, to MT plus ends (Nieuwburg et al., 2017). Loss of this transport could further compromise MT plus end stability. We now discuss this important mechanism in the revised manuscript.

3- Loss of cortical ncMTOCs. Critically, our new quantitative analyses (revised Figure 3 and Figure 5) also reveal defective anteroposterior orientation of cortical MTs in mutant conditions. These experiments suggest that Ens/Khc-mediated localization of ncMTOCs to the cortex is essential for proper MT network organization, and possibly minus-end stabilization as suggested in several studies (Feng et al., 2019, Goodwin and Vale, 2011, Nashchekin et al., 2016).

Altogether, we now propose an integrated model in which MT reduction and disorganization may result from multiple complementary mechanisms operating downstream of Kinesin-1/Ensconsin loss. While some aspects remain difficult to test directly in our in vivo system, the convergence of our data with recent mechanistic studies provides an interesting conceptual framework. The Discussion has been revised to reflect this comprehensive view in a dedicated paragraph ("A possible regulation of MT dynamics in the oocyte at both plus end minus MT ends by Ens and Khc" lane 415-432).

4) The Shot overexpression experiments presented in Fig.3 E-F, Fig.4D and TableS1 are very confusing. Originally , the authors used Shot-GFP overexpression at stage 9 to show that there is a decrease of ncMTOCs at the cortex in ens mutants (Fig.3 E-F) and speculated that this caused the defects in MT length and cytoplasmic advection at stage 10B. However the authors later state on page 8 that : "Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14), resembling the patterns observed in controls (Figures 4B right panel and 4D). Moreover, while ens females were fully sterile, overexpression of Shot was sufficient to restore that loss of fertility (Table S1)". Is this the same UAS Shot-GFP and VP16 Gal4 used in both experiments? If so, this contradictions puts the authors conclusions in question.

This is an important point that requires clarification regarding our experimental design.

The Shot-YFP construct is a genomic insertion on chromosome 3. The ens mutation is also located on chromosome 3 and we were unable to recombine this transgene with the ens mutant for live quantification of cortical Shot. To circumvent this technical limitation, we used a UAS-Shot.L(C)-GFP transgenic construct driven by a maternal driver, expressed in both wild-type (control) and ens mutant oocytes. We validated that the expression level and subcellular localization of UAS-Shot.L(C)-GFP were comparable to those of the genomic Shot-YFP (new Figure S8 A and B).

From these experiments, we drew two key conclusions. First, cortical Shot.L(C)-GFP is less abundant in ens mutant oocytes compared to wild-type (the quantification has been removed from this version). Second, despite this reduced cortical accumulation, Shot.L(C)-GFP expression partially rescues ooplasmic flows and microtubule streaming in stage 10B ens mutant oocytes, and restores fertility to ens mutant females.

5) The authors based they conclusions about the involvement of Ens, Kinesin-1 and Ninein in ncMTOC anchoring on the decrease in cortical fluorescence intensity of Shot-YFP and Patronin-YFP in the corresponding mutant backgrounds. However, there is a large variation in average Shot-YFP intensity between control oocytes in different experiments. In Fig. 2F-G the average level of Shot-YFP in the control sis 130 AU while in Fig.3 G-H it is only 55 AU. This makes me worry about reliability of such measurements and the conclusions drawn from them.

To clarify this point, we have harmonized the method used to quantify the Shot-YFP signals in Figure 4E with the methodology used in Figure 3B, based on the original images. The levels are not strictly identical (Control Figure 2 B: 132.7+/-36.2 versus Control Figure 4 E: 164.0+/- 37.7). These differences are usual when experiments are performed at several-month intervals and by different users.

6) The decrease in the intensity of Shot-YFP and Patronin-YFP cortical fluorescence in ens mutant oocytes could be because of problems with ncMTOC anchoring or with ncMTOCs formation. The authors should find a way to distinguish between these two possibilities. The authors could express Ens-Mut (described in Sung et al 2008), which localises at the oocyte posterior and test whether it recruits Shot/Patronin ncMTOCs to the posterior.

We tried to obtain the fly stocks described in the 2008 paper by contacting former members of Pernille Rørth's laboratory. Unfortunately, we learned that the lab no longer exists and that all reagents, including the requested stocks, were either discarded or lost over time. To our knowledge, these materials are no longer available from any source. We regret that this limitation prevented us from performing the straightforward experiments suggested by the reviewer using these specific tools.

7) According to the Materials and Methods, the Shot-GFP used in Fig.3 E-F and Fig.4 was the BDSC line 29042. This is Shot L(C), a full-length version of Shot missing the CH1 actin-binding domain that is crucial for Shot anchoring to the cortex. If the authors indeed used this version of Shot-GFP, the interpretation of the above experiments is very difficult.

The Shot.L(C) isoform lacks the CH1 domain but retains the CH2 actin-binding motif. Truncated proteins with this domain and fused to GST retains a weak ability to bind actin in vitro. Importantly, the function of this isoform is context-dependent: it cannot rescue shot loss-of-function in neuron morphogenesis but fully restores Shot-dependent tracheal cell remodeling (Lee and Kolodziej, 2002).

In our experiments, when the Shot.L(C) isoform was expressed under the control of a maternal driver, its localization to the oocyte cortex was comparable to that of the genomic Shot-YFP construct (new Figure S8). This demonstrates unambiguously that the CH1 domain is dispensable for Shot cortical localization in oocytes, and that CH2-mediated actin binding is sufficient for this localization. Of note, a recent study showed that actin network are not equivalent highlighting the need for specific Shot isoforms harboring specialized actin-binding domain (Nashchekin et al., 2024).

We note that the expression level of Shot.L(C)-GFP in the oocyte appeared slightly lower than that of Shot-YFP (expressed under endogenous Shot regulatory sequences), as assessed by Western blot (Figure S8 A).

Critically, Shot.L(C)-GFP expression was substantially lower than that of Shot.L(A)-GFP (that harbored both the CH1 and CH2 domain). Shot.L(A)-GFP was overexpressed (Figure 8 A) and ectopically localized on MTs in both nurse cells and the ooplasm (Figure S8 B middle panel and arrow). These observations are in agreement that the Shot.L(C)-GFP rescue experiment was performed at near-physiological expression levels, strengthening the validity of our conclusions.

8) Page 6 "converted in NCs, in a region adjacent to the ring canals, Dendra-Ens-labeled MTs were found in the oocyte compartment indicating they are able to travel from NC toward the oocyte through ring canals". I have difficulty seeing the translocation of MT through the ring canals. Perhaps it would be more obvious with a movie/picture showing only one channel. Considering that f Dendra-Ens appears in the oocyte much faster than MT transport through ring canals (140nm/s, Lu et al 2022), the authors are most probably observing the translocation of free Ens rather than Ens bound to MT. The authors should also mention that Ens movement from the NC to the oocyte has been shown before with Ens MBD in Lu et al 2022 with better resolution.

We fully agree on the caveat mentioned by this reviewer: we may observe the translocation of free Dendra-Ensconsin. The experiment, was removed and replaced by referring to the work of the Gelfand lab. The movement of MTs that travel at ~140 nm/s between nurse cells toward the oocyte through the Ring Canals was reported before by Lu et al. (2022) with a very good resolution. Notably, this directional directed movement of MTs was measured using a fusion protein encompassing Ens MT-binding domain. We decided to remove this inclusive experiment and rather refer to this relevant study.

9) Page 6: The co-localization of Ninein with Ens and Shot at the oocyte cortex (Figure 2A). I have difficulty seeing this co-localisation. Perhaps it would be more obvious in merged images of only two channels and with higher resolution images

10) "a pool of the Ens-GFP co-localized with Ch-Patronin at cortical ncMTOCs at the anterior cortex (Figure 3A)". I also have difficulty seeing this.

We have performed new high-resolution acquisitions that provide clearer and more convincing evidence for the localization cortical distribution of these proteins (revised Figure 2A-2C and Figure 4A). These improved images demonstrate that Ens, Ninein, Shot, and Patronin partially colocalize at cortical ncMTOCs, as initially proposed. Importantly, the new data also reveal a spatial distinction: while Ens localizes along microtubules extending from these cortical sites, Ninein appears confined to small cytoplasmic puncta adjacent but also present on cortical microtubules.

11) "Ninein co-localizes with Ens at the oocyte cortex and partially along cortical microtubules, contributing to the maintenance of high Ens protein levels in the oocyte and its proper cortical targeting". I could not find any data showing the involvement of Ninein in the cortical targeting of Ens.

We found decreased Ens localization to MTs and to the cell cortex region (new Figure S3 A-B).

12) "our MT network analyses reveal the presence of numerous short MTs cytoplasmic clustered in an anterior pattern." "This low cortical recruitment of ncMTOCs is consistent with poor MT anchoring and their cytoplasmic accumulation." I could not find any data showing that short cortical MT observed at stage 10b in ens mutant and Khc RNAi were cytoplasmic and poorly anchored.

The sentence was removed from the revised manuscript.

13) "The egg chamber consists of interconnected cells where Dynein and Khc activities are spatially separated. Dynein facilitates transport from NCs to the oocyte, while Khc mediates both transport and advection within the oocyte." Dynein is involved in various activities in the oocyte. It anchors the oocyte nucleus and transports bcd and grk mRNA to mention a few.

The text was amended to reflect Dynein involvement in transport activities in the oocyte, with the appropriate references (lane 105-107).

14) The cartoons in Fig.2H and 3I exaggerate the effect of Ninein and Ens on cortical ncMTOCs. According to the corresponding graphs, there is a 20 and 50% decrease in each case.

New cartoons (now revised Figure 3E and 4F), are amended to reflect the ncMTOC values but also MT orientation (Figure 3E).

Significance

Given the important concerns raised, the significance of the findings is difficult to assess at this stage.

We sincerely thank the reviewer for their thorough evaluation of our manuscript. We have carefully addressed their concerns through substantial new experiments and analyses. We hope that the revised manuscript, in its current form, now provides the clarifications and additional evidence requested, and that our responses demonstrate the significance of our findings.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Summary: This manuscript presents an investigation into the molecular mechanisms governing spatial activation of Kinesin-1 motor protein during Drosophila oogenesis, revealing a regulatory network that controls microtubule organization and cytoplasmic transport. The authors demonstrate that Ensconsin, a MAP7 family protein and Kinesin-1 activator, is spatially enriched in the oocyte through a dual mechanism involving Dynein-mediated transport from nurse cells and cortical maintenance by Ninein. This spatial enrichment of Ens is crucial for locally relieving Kinesin-1 auto-inhibition. The Ens/Khc complex promotes cortical recruitment of non-centrosomal microtubule organizing centers (ncMTOCs), which are essential for anchoring microtubules at the cortex, enabling the formation of long, parallel microtubule streams or "twisters" that drive cytoplasmic advection during late oogenesis. This work establishes a paradigm where motor protein activation is spatially controlled through targeted localization of regulatory cofactors, with the activated motor then participating in building its own transport infrastructure through ncMTOC recruitment and microtubule network organization.

There's a lot to like about this paper! The data are generally lovely and nicely presented. The authors also use a combination of experimental approaches, combining genetics, live and fixed imaging, and protein biochemistry.

We thank the reviewer for this enthusiastic and supportive review, which helped us further strengthen the manuscript.

Concerns: Page 6: "to assay if elevation of Ninein levels was able to mis-regulate Ens localization, we overexpressed a tagged Ninein-RFP protein in the oocyte. At stage 9 the overexpressed Ninein accumulated at the anterior cortex of the oocyte and also generated large cortical aggregates able to recruit high levels of Ens (Figures 2D and 2H)... The examination of Ninein/Ens cortical aggregates obtained after Ninein overexpression showed that these aggregates were also able to recruit high levels of Patronin and Shot (Figures 2E and 2H)." Firstly, I'm not crazy about the use of "overexpressed" here, since there isn't normally any Ninein-RFP in the oocyte. In these experiments it has been therefore expressed, not overexpressed. Secondly, I don't understand what the reader is supposed to make of these data. Expression of a protein carrying a large fluorescent tag leads to large aggregates (they don't look cortical to me) that include multiple proteins - in fact, all the proteins examined. I don't understand this to be evidence of anything in particular, except that Ninein-RFP causes the accumulation of big multi-protein aggregates. While I can understand what the authors were trying to do here, I think that these data are inconclusive and should be de-emphasized.

We have revised the manuscript by replacing overexpressed with expressed (lanes 211 and 212). In addition, we now provide new localization data in both cortical (new Figure S4 A, top) and medial focal planes (new Figure S4 A, bottom), demonstrating that Ninein puncta (the word used in Rosen et al, 2019), rather than aggregates are located cortically. We also show that live IRP-labelled MTs do not colocalize with Ninein-RFP puncta. In light of the new experiments and the comments from the other reviewers, the corresponding text has been revised and de-emphasized accordingly.

Page 7: "Co-immunoprecipitations experiments revealed that Patronin was associated with Shot-YFP, as shown previously (Nashchekin et al., 2016), but also with EnsWT-GFP, indicating that Ens, Shot and Patronin are present in the same complex (Figure 3B)." I do not agree that association between Ens-GFP and Patronin indicates that Ens is in the same complex as Shot and Patronin. It is also very possible that there are two (or more) distinct protein complexes. This conclusion could therefore be softened. Instead of "indicating" I suggest "suggesting the possibility."

We have toned down this conclusion and indicated "suggesting the possibility" (lane 238-239).

Page 7: "During stage 9, the average subcortical MT length, taken at one focal plane in live oocytes (see methods)..." I appreciate that the authors have been careful to describe how they measured MT length, as this is a major point for interpretation. I think the reader would benefit from an explanation of why they decided to measure in only one focal plane and how that decision could impact the results.

We appreciate this helpful suggestion. Cortical microtubules are indeed highly dynamic and extend in multiple directions, including along the Z-axis. Moreover, their diameter is extremely small (approximately 25 nm), making it technically challenging to accurately measure their full length with high resolution using our Zeiss Airyscan confocal microscope (over several, microns): the acquisition of Z-stacks is relatively slow and therefore not well suited to capturing the rapid dynamics of these microtubules. Consequently, our length measurements represent a compromise and most likely underestimate the actual lengths of microtubules growing outside the focal plane. We note that other groups have encountered similar technical limitations (Parton et al., 2011).

Page 7: "... the MTs exhibited an orthogonal orientation relative to the anterior cortex (Figures 4A left panels, 4C and 4E)." This phenotype might not be obvious to readers. Can it be quantified?

We have now analyzed the orientation of microtubules (MTs) along the dorso-ventral axis. Our analysis shows that ens, Khc RNAi oocytes (new Figure 5B), and, to a lesser extent, Nin mutant oocytes (new Figure 3D), display a more random MT orientation compared to wild-type (WT) oocytes. In WT oocytes, MTs are predominantly oriented toward the posterior pole, consistent with previous findings (Parton et al., 2011).

Page 8: "Altogether, the analyses of Ens and Khc defective oocytes suggested that MT organization defects during late oogenesis (stage 10B) were caused by an initial failure of ncMTOCs to reach the cell cortex. Therefore, we hypothesized that overexpression of the ncMTOC component Shot could restore certain aspects of microtubule cortical organization in ens-deficient oocytes. Indeed, Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14)..." The data are clear, but the explanation is not. Can the authors please explain why adding in more of an ncMTOC component (Shot) rescues a defect of ncMTOC cortical localization?

We propose that cytoplasmic ncMTOCs can bind the cell cortex via the Shot subunit that is so far the only component that harbors actin-binding motifs. Therefore, we propose that elevating cytoplasmic Shot increase the possibility of Shot to encounter the cortex by diffusion when flows are absent. This is now explained lane 282-285.

I'm grateful to the authors for their inclusion of helpful diagrams, as in Figures 1G and 2H. I think the manuscript might benefit from one more of these at the end, illustrating the ultimate model.

We have carefully considered and followed the reviewer's suggestions. In response, we have included a new figure illustrating our proposed model: the recruitment of ncMTOCs to the cell cortex through low Khc-mediated flows at stage 9 enhances cortical microtubule density, which in turn promotes self-amplifying flows (new Figure 7, panels A to C). Note that this Figure also depicts activation of Khc by loss of auto-inhibition (Figure 7, panel D).

I'm sorry to say that the language could use quite a bit of polishing. There are missing and extraneous commas. There is also regular confusion between the use of plural and singular nouns. Some early instances include:

1. Page 3: thought instead of "thoughted."
2. Page 5: "A previous studies have revealed"
3. Page 5: "A significantly loss"
4. Page 6: "troughs ring canals" should be "through ring canals"
5. Page 7: lives stage 9 oocytes
6. Page 7: As ens and Khc RNAi oocytes exhibits
7. Page 7: we examined in details
8. Page 7: This average MT length was similar in Khc RNAi and ens mutant oocyte..

We apologize for errors. We made the appropriate corrections of the manuscript.

Reviewer #4 (Significance (Required)):

This work makes a nice conceptual advance by showing that motor activation controls its own transport infrastructure, a paradigm that could extend to other systems requiring spatially regulated transport.

We thank the reviewers for their evaluation of the manuscript and helpful comments.

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

Learn more at Review Commons

Referee #4

Evidence, reproducibility and clarity

Summary: This manuscript presents an investigation into the molecular mechanisms governing spatial activation of Kinesin-1 motor protein during Drosophila oogenesis, revealing a regulatory network that controls microtubule organization and cytoplasmic transport. The authors demonstrate that Ensconsin, a MAP7 family protein and Kinesin-1 activator, is spatially enriched in the oocyte through a dual mechanism involving Dynein-mediated transport from nurse cells and cortical maintenance by Ninein. This spatial enrichment of Ens is crucial for locally relieving Kinesin-1 auto-inhibition. The Ens/Khc complex promotes cortical recruitment of non-centrosomal microtubule organizing centers (ncMTOCs), which are essential for anchoring microtubules at the cortex, enabling the formation of long, parallel microtubule streams or "twisters" that drive cytoplasmic advection during late oogenesis. This work establishes a paradigm where motor protein activation is spatially controlled through targeted localization of regulatory cofactors, with the activated motor then participating in building its own transport infrastructure through ncMTOC recruitment and microtubule network organization.

There's a lot to like about this paper! The data are generally lovely and nicely presented. The authors also use a combination of experimental approaches, combining genetics, live and fixed imaging, and protein biochemistry.

Concerns:

Page 6: "to assay if elevation of Ninein levels was able to mis-regulate Ens localization, we overexpressed a tagged Ninein-RFP protein in the oocyte. At stage 9 the overexpressed Ninein accumulated at the anterior cortex of the oocyte and also generated large cortical aggregates able to recruit high levels of Ens (Figures 2D and 2H)... The examination of Ninein/Ens cortical aggregates obtained after Ninein overexpression showed that these aggregates were also able to recruit high levels of Patronin and Shot (Figures 2E and 2H)." Firstly, I'm not crazy about the use of "overexpressed" here, since there isn't normally any Ninein-RFP in the oocyte. In these experiments it has been therefore expressed, not overexpressed. Secondly, I don't understand what the reader is supposed to make of these data. Expression of a protein carrying a large fluorescent tag leads to large aggregates (they don't look cortical to me) that include multiple proteins - in fact, all the proteins examined. I don't understand this to be evidence of anything in particular, except that Ninein-RFP causes the accumulation of big multi-protein aggregates. While I can understand what the authors were trying to do here, I think that these data are inconclusive and should be de-emphasized.

Page 7: "Co-immunoprecipitations experiments revealed that Patronin was associated with Shot-YFP, as shown previously (Nashchekin et al., 2016), but also with EnsWT-GFP, indicating that Ens, Shot and Patronin are present in the same complex (Figure 3B)." I do not agree that association between Ens-GFP and Patronin indicates that Ens is in the same complex as Shot and Patronin. It is also very possible that there are two (or more) distinct protein complexes. This conclusion could therefore be softened. Instead of "indicating" I suggest "suggesting the possibility."

Page 7: "During stage 9, the average subcortical MT length, taken at one focal plane in live oocytes (see methods)..." I appreciate that the authors have been careful to describe how they measured MT length, as this is a major point for interpretation. I think the reader would benefit from an explanation of why they decided to measure in only one focal plane and how that decision could impact the results.

Page 7: "... the MTs exhibited an orthogonal orientation relative to the anterior cortex (Figures 4A left panels, 4C and 4E)." This phenotype might not be obvious to readers. Can it be quantified?

Page 8: "Altogether, the analyses of Ens and Khc defective oocytes suggested that MT organization defects during late oogenesis (stage 10B) were caused by an initial failure of ncMTOCs to reach the cell cortex. Therefore, we hypothesized that overexpression of the ncMTOC component Shot could restore certain aspects of microtubule cortical organization in ens-deficient oocytes. Indeed, Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14)..." The data are clear, but the explanation is not. Can the authors please explain why adding in more of an ncMTOC component (Shot) rescues a defect of ncMTOC cortical localization?

I'm grateful to the authors for their inclusion of helpful diagrams, as in Figures 1G and 2H. I think the manuscript might benefit from one more of these at the end, illustrating the ultimate model.

I'm sorry to say that the language could use quite a bit of polishing. There are missing and extraneous commas. There is also regular confusion between the use of plural and singular nouns. Some early instances include:

1. Page 3: thought instead of "thoughted."
2. Page 5: "A previous studies have revealed"
3. Page 5: "A significantly loss"
4. Page 6: "troughs ring canals" should be "through ring canals"
5. Page 7: lives stage 9 oocytes
6. Page 7: As ens and Khc RNAi oocytes exhibits
7. Page 7: we examined in details
8. Page 7: This average MT length was similar in Khc RNAi and ens mutant oocyte..

Significance

This work makes a nice conceptual advance by showing that motor activation controls its own transport infrastructure, a paradigm that could extend to other systems requiring spatially regulated transport.

eLife Assessment

This paper demonstrates that a genetic code expansion to tag two amyotrophic lateral sclerosis (ALS) proteins associated with stress granules is useful in an experimental context. The data are solid and demonstrate the feasibility of using ANAP-fluorescence for live cell imaging.

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells and primary mouse cortical neurons. By incorporating the intrinsically fluorescent noncanonical amino acid Anap at carefully selected sites, the authors enable live-cell and neuronal visualization of protein localization, stress-induced redistribution, and dynamic behavior without the structural and functional compromises often associated with large fluorescent protein tags. The work provides technical framework that will be useful for live imaging of tagged proteins.

Strengths:

A key strength is the demonstration of the specificity of the Anap fluorescence signal through appropriate controls and the agreement between Anap labeling and antibody-based detection across multiple cell types, including primary neurons. The ability to visualize stress-induced redistribution of both G3BP1 and TDP 43 in living cells highlights the practical value of this approach.

The functional validation of TDP 43-Anap is compelling. The rescue of both cell viability and RNA splicing defects in TDP 43 knockout models provides evidence that Anap incorporation preserves core protein functions. This is important, as functional disruption is a central concern for any alternative tagging strategy applied to aggregation-prone or RNA-binding proteins.

Weaknesses:

While some inherent limitations of genetic code expansion remain (e.g., variable amber suppression efficiency and the inability to directly assess endogenous protein behavior), these are acknowledged and discussed appropriately. Importantly, these limitations do not undermine the central contributions of the study.

Author response:

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

eLife Assessment

Amyotrophic lateral sclerosis (ALS) affects nerve cells in the brain and spinal cord. The authors' approach to use genetic code expansion to tag two ALS proteins associated with stress granules has value and should be useful in the ALS field. Parts of the work are well done, but there are concerns that the evidence is incomplete overall, and additional controls would strengthen the study.

We thank the editors and reviewers for their thoughtful assessment and for highlighting the potential value of applying genetic code expansion (GCE) to study ALSassociated proteins involved in stress granule biology. Our goal in this work was to establish and validate a minimally perturbative labeling strategy using the noncanonical amino acid Anap to monitor the localization and stress-dependent behavior of TDP-43 and G3BP1.

We agree that additional controls can further strengthen the conclusions. In the revised manuscript, we have clarified the experimental design and added essential controls to better support the reliability of the Anap labeling approach (Supplementary Fig. 1).

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors utilize genetic code expansion to tag TDP-43 and G3BP1, and evaluate this protein tagging system (ANAP) compared to antibodies, and evaluate protein trafficking and stress granule formation in response to stress with sodium arsenite treatment. They find similar staining to antibodies in HeLa cells, mouse embryonic stem cells, and primary mouse cortical neurons. This is a useful study that demonstrates the utility of ANAP tagging to evaluate ALS proteins.

We sincerely thank the reviewer for the positive assessment of our work and for recognizing the utility of the Anap-based GCE system for studying ALS-associated proteins.

Strengths:

Rescue of cell survival by ANAP-tagged TDP-43 is compelling

We appreciate the reviewer’s highlighting of this point. Demonstrating that TDP43-Anap can rescue cell survival was an important validation in our study, as it indicates that incorporation of the noncanonical amino acid does not substantially disrupt the biological function of TDP-43. Additionally, we also tested the RNA splicing function recovery potency of TDP-43-Anap. As shown in Fig. 1K and 1L, a recovery of expression of PFKP, a protein undergoing cryptic exon when TDP-43 lost its function [1], was observed when expressing TDP-43-Anap in TDP-43 knockout Hela cells.

Weaknesses:

While the ANAP-tagged proteins had similar distributions to antibody staining, there were some discrepancies that may be more explained by the technique than by novel findings, as the authors suggested. The inclusion of additional controls to evaluate this would be helpful.

This is a helpful suggestion. To ensure that the fluorescence signal observed in our experiments was specifically derived from site-specific Anap incorporation rather than background fluorescence, we performed three control conditions. Specifically, we tested: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. These control experiments were performed for both TDP-43 and G3BP1, and no observable fluorescence signal was detected under any of these conditions (Supplementary Fig. 1). We have clarified this control experiment in the revised manuscript.

Reviewer #2 (Public review):

Summary:

In this manuscript, Chen and colleagues describe a novel means of labeling two RNAbinding proteins, G3BP1 and TDP-43, using genetic code expansion. Overexpressed constructs that incorporate the intrinsically fluorescent non-canonical amino acid Anap redistribute to cytoplasmic granules upon application of external stressors such as sodium arsenite. Similar labeling and redistribution of overexpressed G3BP1 and TDP43 were observed in cultures of mouse primary neurons.

We are grateful for the reviewer’s accurate summary of our study and recognition of the value of GCE strategy for labeling the RNA-binding proteins G3BP1 and TDP-43.

Strengths:

Genetic code expansion and non-canonical amino acid labeling have quite a few advantages over traditional fusion proteins for tracking protein redistribution in living cells. The authors show that they are able to label exogenous G3BP1 and TDP-43 with the non-canonical amino acid Anap and follow labeled proteins in living cells with and without stress.

We acknowledge the reviewer’s comment on the advantages of GCE-based noncanonical amino acid labeling for studying protein dynamics in living cells.

Weaknesses:

The authors do not convincingly leverage the advantages of genetic code expansion in the current study. There is no specific question posed by the authors that can be or is answered using this approach, and several of the experiments lack critical controls. This is also not the first example of TDP-43 labeling by genetic code expansion (see PMID: 38290242). As a result, the study as a whole adds little to our understanding of protein trafficking and behavior under stress.

We thank the reviewer for raising these important points. Although as reviewer mentioned, genetic code expansion has previously been applied to TDP-43 [2], it mainly employed the photocaged lysine incorporation system to optogenetic control of TDP-43 translocation, and the protein was still labeled by mRubby. Our paper has totally different goal, to establish and validate a minimally perturbative labeling strategy using the intrinsically fluorescent noncanonical amino acid Anap to monitor the localization and stress-dependent behavior of both TDP-43 and G3BP1. And our work extends this approach in several important ways.

First, we demonstrate that Anap incorporation enables visualization of stress-dependent redistribution of both TDP-43 and G3BP1, two key proteins involved in stress granule biology. Importantly, we validate this approach across multiple cellular systems, including HeLa cells, mouse embryonic stem cells, and primary mouse cortical neurons, which broadens the applicability of this labeling strategy.

Second, we provide functional validation of the Anap-tagged protein, showing that TDP43-Anap rescues both cell survival and RNA splicing activity in TDP-43 knockout cells, including restoration of PFKP expression, a known cryptic exon target of TDP-43. These results support that Anap incorporation does not substantially disrupt protein function.

We performed additional control experiments to ensure the specificity of the labeling system. Specifically, we tested three control conditions: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. These control experiments were performed for both TDP-43 and G3BP1, and no observable fluorescence signal was detected under any of these conditions (Supplementary Fig. 1).

We agree that the manuscript would benefit from clearer articulation of the advantages of genetic code expansion in this context. Accordingly, we have revised the manuscript to more explicitly emphasize how Anap labeling provides a minimally perturbative alternative to large fluorescent protein fusions, which can alter the phase behavior and localization of stress granule proteins.

“Conventional fluorescent protein tags have enabled visualization of TDP-43 and G3BP1 in living cells; however, these approaches can perturb the native biophysical properties of the proteins being studied. For example, GFP or other fluorescently tagged TDP-43 usually requires additional modifications, such as deletion of the nuclear localization signal (NLS) [3, 4], to induce cytoplasmic inclusion formation. Such manipulations introduce non-physiological conditions that may alter the native trafficking and aggregation behavior of TDP-43. As for G3BP1, tags like GFP may also cause unexpected effects on the phase separation or other dynamics of the protein. In contrast, Anap based GCE strategy allows the minimally perturbative labeling and visualization of protein localization and stress-induced redistribution while preserving native protein architecture and function of both proteins. Importantly, the approach provides a generalizable genetically encoded platform for quantitatively examining the behavior of ALS-associated proteins in living cells. By enabling faithful monitoring of protein trafficking and stressgranule dynamics without extensive protein engineering, Anap-based GCE can offer a powerful strategy for probing molecular-scale mechanisms underlying ALS-linked proteinopathies”.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 1A

The authors report that the nuclear staining of G3BP1 by ANAP labeling shows the presence of nuclear pools of G3BP1 that aren't detected with antibody staining. However, unspecific nuclear staining by aminoacylated tRNAs bound to synthetases has been described. It would be important to have a control to evaluate for this possibility.

This is an important point. We agree that the nuclear ANAP signal should be carefully controlled to exclude the possibility of nonspecific staining arising from the Anap incorporation machinery itself, such as aminoacylated tRNAs and/or synthetases.

To address this concern, in methods and material part, we note that after DPBS washes to remove excess Anap, cells were incubated in fresh medium for 2 hours to allow sufficient time for the decay of unstable aminoacylated tRNAs, which are generally cleared within minutes to tens of munites [5].

Also, we performed three control conditions for both TDP-43 and G3BP1: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. Under all three conditions, we observed no detectable fluorescence signal (Supplementary Fig. 1).

In addition, as shown in Fig. 1I, the nuclear signal of G3BP1-Anap partially colocalizes with the nuclear signal of TIA-1 in several condensate-like structures. This observation further supports that the nuclear Anap signal reflects protein-associated localization rather than nonspecific fluorescence, as it overlaps with a known RNA-binding protein that can form nuclear condensates under certain conditions.

(2) Figure 1A, 1B

Anap labeling appears to stain fewer cytoplasmic structures compared to antibody staining for both G3BP1 and TDP-43 after sodium arsenite treatment. Quantification would be useful to address whether this is the case. If so, might this be due to unincorporated/truncated proteins competing with Anap-labeled proteins?

We appreciate the reviewer’s helpful suggestion. To address this point, we performed quantitative colocalization analysis using Fiji/ImageJ, calculating the Pearson correlation coefficient (R) for regions of interest between the Anap signal and antibody staining. These analyses indicate a strong overall agreement between the two detection methods under stress conditions, supporting that Anap labeling reliably reports the localization of both G3BP1 and TDP-43 (see Fig1. A, B).

Regarding the possibility that truncated or unincorporated proteins could influence the observed signal, we note that fluorescence from Anap depends on successful amber suppression and incorporation of Anap at the engineered TAG site. Proteins that fail to incorporate Anap, such as truncated products generated by premature termination, would not produce fluorescence, and therefore would not contribute to the Anap signal. Thus, the Anap fluorescence selectively reports the population of successfully labeled full-length proteins, whereas antibody staining detects both labeled and unlabeled protein pools. This difference may partially explain why antibody staining appears to label a larger number of cytoplasmic structures.

(3) Figure 1F

FRAP of G3BP1-GFP in stress granules is slower than in previous publications. The underlying reasons for this should also be addressed.

We thank the reviewer for this important observation. Differences in FRAP recovery kinetics of G3BP1 in stress granules may arise from several experimental variables that are known to influence stress granule dynamics. These include differences in cell type, expression levels of G3BP1-GFP, and imaging or photobleaching parameters. In our experiments, FRAP measurements were performed under specific conditions optimized for our experimental system, which may lead to recovery kinetics that differ from those reported in previous studies.

(4) Figure 1H

A full-size Western blot would be useful to evaluate for amount of truncated protein for G3BP1 and TDP-43. Could truncated proteins be competing with and altering ANAPtagged G3BP1 and TDP-43 localization in response to stress? This should be addressed.

We acknowledge this important point. Full-size Western blotting can provide information on the overall presence of truncated species in the transfected population; however, it represents a bulk measurement and does not capture cell-to-cell variability in amber suppression efficiency at the single-cell level. We therefore cannot exclude the possibility that truncated products are present at varying levels in individual cells and may contribute, directly or indirectly, to differences between antibody staining and Anap fluorescence.

Importantly, we observe that cells with successful Anap incorporation consistently exhibit strong antibody staining for TDP-43 or G3BP1, indicating that full-length protein is the predominant species in these cells. Because Anap fluorescence depends on successful amber suppression, it selectively reports the full-length protein population, whereas truncated products are not detected in the imaging assay. The concordance between Anap fluorescence and antibody staining therefore argues against a major contribution of truncated species to the observed localization patterns (Supplementary Fig. 1).

Accordingly, we interpret the Anap signal as reflecting the localization of successfully labeled full-length protein, while acknowledging that heterogeneity in suppression efficiency is an important limitation of the current approach.

(5) Figure 3

This is a well-designed diagram.

We are grateful for the reviewer’s positive feedback on the diagram and are pleased that the schematic effectively illustrates the experimental design and the principles of the genetic code expansion strategy used in this study.

Reviewer #2 (Recommendations for the authors):

The authors present a one-sided viewpoint concerning the connection between stress granules and disease (lines 45-46). A more balanced discussion is recommended, including data arguing against a role for abnormal stress granules in neurodegeneration.

This is an important suggestion. We agree that the relationship between stress granules and neurodegeneration remains an active area of investigation and that evidence both supporting and questioning a causal role of stress granules in disease has been reported. In the revised manuscript, we have modified the Introduction to provide a more balanced discussion of this topic.

“Altered stress-granule dynamics have been associated with ALS/FTD [6, 7]; however, whether stress granules directly drive neurodegeneration remains debated, as several studies suggest that stress granules primarily function as protective stress responses [8].”

(1) A central rationale for the study is missing. The authors state only that G3BP1 and TDP-43 'undergo dynamic stress-dependent redistribution, making them ideal candidates for minimally invasive, site-specific fluorescent labeling.' Is there a controversy or question that can be resolved using these approaches?

We thank the reviewer for raising this important point. The central motivation of this study is that the dynamic behavior and phase separation properties of stressgranule proteins are highly sensitive to protein modifications and tagging strategies.

“Conventional fluorescent protein tags have enabled visualization of TDP-43 and G3BP1 in living cells; however, these approaches can perturb the native biophysical properties of the proteins being studied. For example, GFP or other fluorescently tagged TDP-43 usually requires additional modifications, such as deletion of the nuclear localization signal (NLS) [3, 4], to induce cytoplasmic inclusion formation. Such manipulations introduce non-physiological conditions that may alter the native trafficking and aggregation behavior of TDP-43. As for G3BP1, tags like GFP may also cause unexpected effects on the phase separation or other dynamics of the protein.”

(2) Related to this, there is little context for how or why genetic code expansion is utilized for these studies

We agree that the rationale for using genetic code expansion should be more clearly explained. In this study, genetic code expansion was employed to enable sitespecific incorporation of the small fluorescent noncanonical amino acid Anap, allowing minimally perturbative labeling of proteins of interest.

“Anap based GCE strategy allows the minimally perturbative labeling and visualization of protein localization and stress-induced redistribution while preserving native protein architecture and function of both proteins. Importantly, the approach provides a generalizable genetically encoded platform for quantitatively examining the behavior of ALS-associated proteins in living cells. By enabling faithful monitoring of protein trafficking and stress-granule dynamics without extensive protein engineering, Anapbased GCE can offer a powerful strategy for probing molecular-scale mechanisms underlying ALS-linked proteinopathies.”

(3) The justification for the criteria for selecting the site for incorporation of non-canonical amino acids in G3BP1 or TDP-43 is missing.

We acknowledge this important comment and agree that the rationale for selecting the incorporation sites should be stated more clearly.

“For TDP-43, the incorporation site was selected to avoid the major functional domains involved in RNA binding, nuclear localization, and aggregation-related behavior, thereby reducing the likelihood that Anap incorporation would perturb its native trafficking or function. For G3BP1, the selected site was chosen to minimize interference with domains important for stress granule assembly, RNA binding, and protein-protein interactions. More generally, we aimed to place the ncAA at positions likely to be solventaccessible and tolerant of substitution, while avoiding highly conserved or functionally essential residues.”

(4) Studies in Figures 1 and 2 lack essential controls, including background signal from Anap in non-transfected cells, or those transfected with plasmids lacking the tRNA or tRS.

This is an important point, also raised by Reviewer 1. To evaluate potential background fluorescence arising from Anap or the labeling system, we performed several control experiments. Specifically, we examined three conditions: (1) cells cultured with Anap supplement, (2) cells expressing the Anap incorporation system with the addition of Anap, and (3) cells expressing both the TAG-mutated protein plasmid and the Anap incorporation system but without the addition of Anap. Under all three conditions, we observed no detectable fluorescence signal (Supplementary Fig. 1).

(5) Another marker of stress granules should be used for confirming the identity of G3BP1-Anap (+) or TDP-43-Anap (+) structures, including TIA1, TAF15, or polyA RNA.

We appreciate this helpful suggestion. To further confirm the identity of the stress granule structures observed in our experiments, we performed colocalization analysis with TIA-1, a well-established marker of stress granules. The results have been included in revised manuscript.

“Additionally, we examined the colocalization of G3BP1-Anap with TIA-1, another established stress granule marker. Under stress conditions, G3BP1-Anap largely colocalized with TIA-1 within stress granules. Interestingly, under basal conditions, the nuclear signal of G3BP1-Anap, which was not detected by antibody staining, appeared to partially colocalize with TIA-1 in several condensate-like structures. (Fig. 1I).”

(6) There is no information on the number of granules bleached or the number of cells selected for FRAP studies. There is no information on the shaded areas in Figure 1F or 1G, and no information on statistical comparisons between regressions in Figure 1F.

We thank the reviewer for pointing out these omissions. We have revised the figure legends to clarify these details.

“One granule from each of three independent cells was selected and photobleached for FRAP analysis.”

“Here, error bars with filled area are used for better data presentation. FRAP recovery curves were compared using two-way ANOVA.”

(7) Protein dynamics measured by FRAP are highly dependent on the concentration and/or expression level of each protein. Because of this, the authors need to control for expression level in all FRAP studies.

We agree that protein concentration and expression level can influence FRAP recovery kinetics. Since Anap incorporation is based on amber suppression, and the suppression rate in each cell varies, so it is difficult to control the expression of Anap labeled proteins, however, to minimize this potential effect, we performed FRAP measurements on cells exhibiting comparable fluorescence intensities, which served as a proxy for similar expression levels of the labeled proteins. In addition, FRAP analyses were conducted on individual granules within cells expressing moderate levels of the protein, avoiding cells with unusually high fluorescence intensity that might reflect overexpression.

Furthermore, fluorescence recovery was normalized to the pre-bleach intensity of the selected granules, which reduces variability arising from differences in overall expression levels between cells.

(8) There is no point of reference for TDP-43-Anap FRAP results in Figure 1G. Additional studies using variants harboring a mutated NLS (mNLS) can be used in place of TDP43-YFP.

This is a helpful suggestion. In response, we have performed additional FRAP experiments using TDP-43^ΔNLS, a commonly used construct that promotes cytoplasmic localization and facilitates analysis of TDP-43 granules. The results from TDP-43^ΔNLS have now been included as a reference for the FRAP measurements of TDP-43-Anap in the revised manuscript (Fig. 1D, 1G).

“We then used YFP-tagged nuclear localization signal (NLS)-deleted TDP-43 (TDP43^ΔNLS-YFP) as a reference and performed FRAP analysis to compare the mobility of TDP-43-Anap and TDP-43^ΔNLS-YFP. Fluorescence recovery of TDP-43-Anap reached ~45% within 20 s after photobleaching, consistent with liquid-like dynamics. In contrast, TDP-43^ΔNLS-YFP showed only ~22% recovery, suggesting more solid-like dynamics (Fig. 1D, 1G). These results are consistent with previous reports describing relatively immobile aggregates formed by TDP-43^ΔNLS4and illustrate the advantage of Anap-based labeling, which preserves native protein properties and enables real-time assessment of protein dynamics without introducing disruptive mutations.”

(9) There is no point of reference for comparing FRAP results from G3BP1-GFP to G3BP1-Anap. What is the 'gold standard'? Without this, it is difficult to conclude that "... Anap labeling better preserved the native mobility and biophysical properties of G3BP1 than the conventional GFP tag."

We acknowledge this important point and agree that there is currently no definitive gold standard for measuring the native mobility of endogenous G3BP1 within stress granules in living cells. Our intention was not to claim that the Anap-labeled protein definitively represents the native state, but rather to compare the relative effects of different labeling strategies.

Thus, we rewrite the sentence as “These results suggest that G3BP1-Anap displays higher mobility compared with G3BP1-GFP, indicating that Anap labeling may provide a less perturbative approach for monitoring G3BP1 dynamics.”

(10) The WB in Figure 1H is overexposed, making it difficult to compare expression levels between WT and V100Anap-transfected cells. In addition, there is no similar assay for confirming G3BP1-Anap expression.

Thank you for pointing this out. In the revised manuscript, we have replaced the image with a properly exposed Western blot to allow clearer comparison of protein expression levels.

In addition, we have now included a corresponding western blot analysis to confirm the expression of G3BP1-Anap in G3BP knockout U2OS cell (Fig. 1H). These results verify that the Anap-labeled proteins are expressed at detectable levels and support the interpretation of the imaging and FRAP experiments.

(11) Although survival studies in Figures 1I and J are promising, a more convincing demonstration of functional replacement of TDP-43 would involve an assessment of cryptic exon splicing, comparing WT to TDP-43 KO, V100Stop- and V100Anaptransfected cells.

This is a valuable suggestion.

“We also evaluated TDP-43-dependent RNA splicing activity by examining the expression of PFKP, a well-established target that undergoes cryptic exon inclusion upon loss of TDP-43 function17. As shown in Figures 1K and 1L, expression of TDP-43Anap in TDP-43 knockout HeLa cells restored PFKP expression, indicating that the Anap-labeled protein retains functional RNA splicing activity. These results demonstrate that TDP-43-Anap is capable of functionally compensating for endogenous TDP-43, supporting that the incorporation of Anap does not substantially disrupt the protein’s biological function.”

(12) Tuj1 staining in Figure 2 is inconsistent and often fails to confirm neuronal identity.

We thank the reviewer for this important comment. We acknowledge that Tuj1 staining in Figure 2 is variable and, in some cases, does not clearly delineate neuronal identity. Notably, the reduced Tuj1 signal is primarily observed in neurons that express Anap-labeled proteins under sodium arsenite treatment, which likely reflects the combined effects of transfection-associated stress and oxidative stress on neuronal morphology and cytoskeletal integrity.

In addition, transfection efficiency in primary neurons is inherently low and variable, and cells that successfully express the constructs may represent a more stress-sensitive subpopulation, further contributing to variability in staining quality. Despite optimization efforts, these technical constraints limit the consistency of Tuj1 labeling under these experimental conditions.

(13) Close-up images and correlation scatter plots in Figures 1 and 2 do not add very much information.

We thank the reviewer for this comment. To address the reviewer’s concern, we have revised the figure legends to better clarify the purpose of these panels and how they support the quantitative analysis presented in the manuscript.

For scatter plot, “Colocalization threshold analysis was performed in Fiji/ImageJ to calculate the Pearson correlation coefficient (R) for each region of interest (A, B, I, J). The X- and Y-axes represent the fluorescence intensity values of the red and green channels, respectively. When signals are colocalized, pixels with high intensity in one channel correspond to high intensity in the other, forming a diagonal distribution. In contrast, non-colocalized signals cluster along the axes. A higher R value indicates a greater degree of colocalization. Scale bar, 3 μm.”

Same information was added to figure legend of figure 2.

For the scheme, please see line 412-413 in the revised manuscript.

Reference:

(1) Rothstein, J.D. et al. Sporadic ALS induced pluripotent stem cell derived neurons reveal hallmarks of TDP-43 loss of function. Nature Communications 16, 7092 (2025).

(2) Shadish, J.A. & Lee, J.C. Genetically encoded lysine photocage for spatiotemporal control of TDP-43 nuclear import. Biophys Chem 307, 107191 (2024).

(3) Gasset-Rosa, F. et al. Cytoplasmic TDP-43 De-mixing Independent of Stress Granules Drives Inhibition of Nuclear Import, Loss of Nuclear TDP-43, and Cell Death. Neuron 102, 339–357.e337 (2019).

(4) Yan, X. et al. Intra-condensate demixing of TDP-43 inside stress granules generates pathological aggregates. Cell 188, 4123–4140.e4118 (2025).

(5) Walker, S.E. & Fredrick, K. Preparation and evaluation of acylated tRNAs. Methods 44, 81–86 (2008).

(6) Kassouf, T. et al. Targeting the NEDP1 enzyme to ameliorate ALS phenotypes through stress granule disassembly. Science Advances 9, eabq7585 (2023).

(7) Van Nerom, M. et al. C9orf72-linked arginine-rich dipeptide repeats aggravate pathological phase separation of G3BP1. Proceedings of the National Academy of Sciences 121, e2402847121 (2024).

(8) Wolozin, B. & Ivanov, P. Stress granules and neurodegeneration. Nat Rev Neurosci 20, 649–666 (2019).

Reviewer #3 (Public review):

The Sustar et al. manuscript catalogs glutamate receptor composition across distinct Drosophila NMJs: larval and adult abdominal NMJs, as well as NMJs on adult leg and flight muscles. This work is important and probably overdue. The larval NMJ is the exemplar NMJ in this system, and the identity of "essential" and "alternative" subunits at this stage is assumed by many to hold across developmental stages and NMJ types. Here, the authors show that there is surprising diversification among NMJ types and that the notion of essential/alternative subunits only holds true at larval NMJs.

The study will generate interest in the Clumsy GluR subunit, which has not been well-characterized at all, but is widely expressed at adult NMJs. They also find striking extrasynaptic expression of glutamate-gated chloride channel GluRClalpha in adult leg and flight muscles, raising questions about its role. The study is interesting, logical, and well-written. The figures are clear, and the discussion was particularly thoughtful. I have a couple of comments that the authors could consider.

(1) They cite Rivlin et al., (2004) in the Introduction as the sole previous study to investigate the molecular composition of adult NMJs, but do not mention this work again. In the Discussion, it would be helpful to compare/contrast their finding with those of the earlier work.

(2) Were these analyses done in adults of consistent ages? It seems possible that the GluR subunit composition could be different in very young adults or in aged flies. The age of the animals should be mentioned in the Methods.

(3) The broad expression of GluCl:V5 in adult leg and flight muscles is surprisingly robust and appears to light up the edges of all muscle fibers. Would the authors comment on the controls that were done to ensure that this staining is real and specific to animals carrying that V5 endogenous tag?

(4) The snRNAseq data in Figure S12 differ a bit from the IHC/GAL4 data summarized in the table in Figure 2. In particular, the data suggests that Ukar and Grik are widely expressed in adult muscles. Is there a reason not to include an "snRNA seq" column in Figure 2 alongside the data from GAL4 lines and IHC? To my mind, it is about as reliable as GAL4 lines that often capture only a subset of the full expression pattern. In this case, the snRNAseq data suggest that Ukar/Grik are likely at adult flight muscle NMJs, which might be important since NMJ was negative for everything except Neto-beta by IHC.

make this: Die Oberfläche ist funktional, verrät aber ihre Desktop-Herkunft. Die Skalierung über einige hundert Bestellungen pro Tag hinaus verlangt meist kostenpflichtige Erweiterungen aus dem Extension Store.

reply to u/Beloved-21 at https://old.reddit.com/r/Zettelkasten/comments/1u2bw2s/index_cards_vs_digital_note_app/

There are a handful of affordances you get with paper over digital.

• Most in the space of embodied cognition would indicate that you will have better retention by writing things down physically versus typing them out.
• studies indicate that the presence of screens/phones reduces the level and quality of the conversation in the room, even when the device is sitting on the table nearby
• people react more at ease with paper note taking, especially in interviews where they tend to be more guarded if you're recording everything
• The act of filing your notes forces you to engage with them multiple times. It's not just re-reading the current note to decide where to place it, but re-reading older notes to decide where the current one fits in. This gives you the benefits of spaced repetition as well as encountering the value of serendipity, synergy, and syzygy
• you're forced to be more concise and selective about what you capture versus digital where it's easier to be a hoarder of material you don't "own" or even understand.
• index cards are just as easy to carry in your pockets as any other device
• physical cards are easier to layout, arrange, and re-arrange in various orders than any of the clunky methods for doing this in the digital space where solid user interface for this sort of affordance is almost entirely lacking.
• paper forces you to slow down and engage with notes in ways that digital notes typically don't
• physical cards actually "get in your way" in a sense while digital cards are always "hidden"

I'm sure you'll find various others hiding in a digital version of my notes: https://hypothes.is/users/chrisaldrich?q=tag%3A%22note+taking+affordances%22

The real question at the end of the day is what works best for you?!? Try them both out for a few weeks or a month or more and chose the version that works best for your modes of thinking. Experimenting is the only way to answer this question for yourself. You may find other affordances that don't apply to others' work.

https://marvac.com/search?type=product%2Carticle&q=NOT+tag%3A__gift+AND+vcr+belts*&product_type=

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 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 efficiency of CRISPR in C. elegans, while inserting two fluorescent proteins and a co-CRISPR marker into three loci, and Paix et al 2015 demonstrated simultaneous insertion of two fluorescent tags. The current work is valuable and incremental advance. In general, I applaud the authors' willingness to strategize about how whole proteome tagging might be accomplished. I predict that the advance here will be one of many small advances that will get the field to that goal. The title oversells the advance presented, in my view, since seems like one among many key advances, and the first sentence of the Discussion seems a more apt summary of the key advance here.

Some injections targeted genes on the same chromosome together, which will create unnecessary issues when doing crossing that will be useful for some future experiments. This made me wonder if injecting 3 together really is helpful vs targeting each gene separately, since only 5 worms need to be injected. It cuts time down by 2/3, but perhaps avoiding targeting the same chromosome with two tags would be useful.

The limited utility of current blue fluorescent proteins makes me wonder if it's worth using at this stage, before there are better blue fluorescent proteins, or better yet, far red, to avoid issues with live imaging under phototoxic UV or near-UV illumination.

Reviewer #4 (Public review):

Summary:

Tagging the entire proteome of a metazoan would be a landmark achievement, providing a powerful complement and extension to existing "omic" catalogs in model systems. Here, Eroglu and Hobert argue that efficiently tagging multiple loci in a single "batch" would make the community-based achievement of this goal realistic. They provide rigorous evidence that such an approach is indeed feasible, exploring issues related to efficiency, design and screening strategies, disruption of gene function, and the potential for endogenously tagged alleles to reveal unexpected aspects of protein expression and localization. While the work has some minor gaps that are important to rigorously assess the feasibility of the proposed effort, the detailed and valuable insights that emerge should provide impetus to the community to coordinate efforts to make this ambitious goal a reality.

Strengths:

The work has numerous strengths. The authors provide compelling evidence that:

- three distinct loci can be efficiently targeted with three distinct fluorescent tags in a single injection.

- thoughtful targeting design can reduce the likelihood of disruption of function by the tag.

- systematic design principles based on expression level and predicted localization/function can be used to optimize tagging strategies.

- the resulting tags can provide unexpected insight into patterns of protein production and subcellular localization.

Not all of these advances are novel in themselves, but taken together, they represent an important technical and conceptual advance. The most important strength comes from the exceptionally high value of the goal itself, in that the work is that it has the potential to spur a community-wide effort toward achieving the ambitious goal of proteome-wide tagging.

Weaknesses:

The work's shortcomings are minor.

- One concern has to do with the feasibility of the proposed screening strategies. The experimental design cleverly coinjects tags for three loci in different gene expression 'zones'; this expression level determines which tag will be used. As the authors allude to, there is an important distinction between genes with the same overall FKPM value between those that are expressed broadly and those focally expressed in a specific tissue. The proposed strategy claims that there are a sufficient number of highly expressed genes "to be used as visible markers" for recovering successfully edited animals. It would be useful for the authors to discuss the issue of broad vs focused expression among this set of genes a bit more thoroughly, with an eye toward the issue of how likely it is that these genes could indeed consistently be used as visible markers, particularly for those at the low end of this limit.

- What fraction of the proteome (on a per-gene basis) is secreted proteins? How difficult will it be to screen these for successful tags? Are there specific tags that would be more optimal for secreted proteins? (The authors mention the use of an SL2 or T2A cassette to label the cells in which these proteins are expressed but note that there are technical challenges associated with doing this at scale.)

- For secreted and/or weakly expressed genes, it would be useful for the authors to estimate for what fraction of these would successful insertions need to be screened by PCR, and what resources (time and money) this would likely entail.

- For how many genes would a single tag not capture all predicted isoforms?

- Finally, some readers might object to the authors' assertion in the abstract that this work is "a first step in this direction" (presumably referring to designing a strategy for whole-proteome tagging). There is no concern that the authors are disregarding the extensive work of other groups, as they explicitly mention the contributions of other groups to the foundation that enables the present work. However, the spirit of the abstract could be misinterpreted by a well-intentioned reader.

Author response:

The following is the authors’ response to the previous 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 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 efficiency of CRISPR in C. elegans, while inserting two fluorescent proteins and a co-CRISPR marker into three loci, and Paix et al 2015 demonstrated simultaneous insertion of two fluorescent tags. The current work is valuable and incremental advance. In general, I applaud the authors' willingness to strategize about how whole proteome tagging might be accomplished. I predict that the advance here will be one of many small advances that will get the field to that goal. The title oversells the advance presented, in my view, since seems like one among many key advances, and the first sentence of the Discussion seems a more apt summary of the key advance here.

Some injections targeted genes on the same chromosome together, which will create unnecessary issues when doing crossing that will be useful for some future experiments. This made me wonder if injecting 3 together really is helpful vs targeting each gene separately, since only 5 worms need to be injected. It cuts time down by 2/3, but perhaps avoiding targeting the same chromosome with two tags would be useful.

The limited utility of current blue fluorescent proteins makes me wonder if it's worth using at this stage, before there are better blue fluorescent proteins, or better yet, far red, to avoid issues with live imaging under phototoxic UV or near-UV illumination.

These comments are a repeat of the original comments, and we refer the reader to our response to the original comments.

Reviewer #2 (Public review):

Original 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 leaves much to be desired. A Table with all the observations must be included and can replace the tedious (and often wrong) descriptions of the observations with the different lines. It would be too much to point out every mistaken expectation of protein expression. Two examples include:

The expectation that ACDH-10 is enriched in the intestine and epidermal tissues (hypodermis) is naïve - there are multiple paralogs of this protein (look at 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 expectation that HXK-1 is ubiquitously expressed is similarly naïve. There are three paralogous enzymes that 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 shows 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.

Other points:

(1) We would encourage the authors to provide systematic validation of the reported insertions. The manuscript reports that 24 of 30 tags were isolated and visible but does not clearly state whether each isolated line was confirmed by sequence‑level validation to be correctly in‑frame and free of unintended mutations at the target locus.

(2) The manuscript presents aggregated success counts (e.g., 8/10 mTagBFP2 tags, 9/10 mStayGold, 7/10 mScarlet3) and useful narrative descriptions of injection outcomes. We suggest also to include per‑locus success rates.

(3) For pools that required re‑injection after initial failures, we would like to see a description of the specific changes that were made to the injection mixes or procedures (e.g., new repair template prep, different Cas9 reagent lot, guide redesign). This will be useful troubleshooting information for others.

(4) The authors states that the fluorophore sequences are codon-optimized for C. elegans. We suggest they provide the exact donor/tag sequences used specifically state whether the fluorophore sequences contain any synthetic/artificial introns or other sequence modifications (e.g., silent PAM‑disrupting mutations) were included in the donor templates.

(5) Page 3: Include a reference for "The C. elegans genome encodes around 20,000 genes"

We hope these comments are useful.

Comments on Revised Version:

Overall, we found the responses to be quite recalcitrant.

We have one remaining composite concern about the comparison between observed expression patterns with the new strains versus published data.

First, the authors only report patterns for one stage while it should be not too much effort to image the different life stages. However, since this is a revision, we are not formally requesting they do this.

Second, in the now provided Table (thank you) 'observed expression' (last column) is lacking for 9 of the 30 proteins, and for 6 of these the procedure was not successful. Why not report patterns for the other three? It is confusing also because on page 5, the authors say that "overall, 24 of 30 tags ...all of which were visible with fluorescence stereomicroscopy" - are we missing something? Also, they then said that they "obtained 6/9 of the originally failed tags"; why are the corresponding patterns not included in table 1, and are 9 proteins still labeled as "no" in the "success?" Column?

We appreciate the chance to clarify this matter: There are only 6 “no” in the “success” column. In two cases, HAT-1 and CBP-1, expression was dim at F1 but still sufficient to pick positive worms and quantify success rate at the locus. We noted these as “dim” on the table to indicate that if expression was lower, we likely would not have been able to isolate them at F1. In one case, COX-6B, expression was too dim at F1 to be isolated but was sufficient at F2 to be visualized and isolated from parents that were positive for the other two tags. We now clarified this distinction in the table and accompanying text: “Fluorescent signals of HAT-1::mScarlet3 and CBP-1::mScarlet3 in F1 progeny were dim but still sufficiently visible for quantification of knock-in efficiency, indicating that they are at the lower end of detectability for mScarlet3.”

We imaged worms that had multiple tags as proof of principle and are happy to provide strains to those who would like to image/study them. At this point we are not convinced that imaging more worms would add to the conceptual framework.

Third, we strongly feel that the response to our comments about expression patterns is not adequate. On page 5 the authors say that "all proteins were expected to be ubiquitously expressed" and that "scRNA-seq indicated that transcript abundance was ubiquitous and without strong tissue-specific enrichment with few exceptions". However, in their rebuttal, the authors now argue for tissue-specific expression for proteins with paralogs, turning around their own argument! Moreover, their Table indicates that many genes show tissue-enriched expression by RNA-seq while many of their tagged proteins exhibit ubiquitous expression.

We respectfully disagree that there is contradiction. In our response, the discussion on paralogs was added as a clarification in response to the referee’s original comments (e.g., regarding ACDH-10): “There is also no reason to assume that fatty acid metabolism does not occur in other tissues (including the germline).” We wanted to make it clear that we were not concluding fatty acid metabolism (or other processes) does not occur in other tissues.

We wish to stress that we never argued that paralogs could not fulfil the same essential function across tissues. The proteins were selected because their biological functions (e.g., glycolysis, fatty acid β-oxidation, translation) are broadly required, and that scRNA seq generally predicted broad expression with few exceptions as detailed in the text. Paralogs with similar activities (e.g., hxk-1, -2, -3) may overlap broadly in expression, or individual paralogs may carry out the process in different tissues provided one carries out the reaction in each tissue. For acdh-10 and hxk-1 specifically, both appear broadly expressed across tissues by scRNA-seq, with no consistent enrichment or depletion across datasets. So, our central point is that: for a specific gene involved in an essential process, transcript data alone are not sufficient to accurately predict tissue specific enrichment. Not that the processes do not occur in tissues where one paralog is absent. The possibility that a paralog may compensate for lack of expression is in no way contradictory with our conclusion.

The table does not generally show tissue-enriched expression: it simply lists three tissues with the highest quantitative value in the respective dataset. For instance, taking the first gene from the list (Y82E9BR.3) and looking at the Ghaddar dataset, the top 3 tissues (log2(TPM)) are: pharyngeal muscle (13.4), gonadal sheath (12.9), marginal cells (12.9). The next 3 tissues are: body wall muscle (12.9), pharyngeal epithelium (12.8), and intestine (12.3). Even when there were apparent enrichments among the top 3 tissues, there were significant disagreements between datasets, and beyond top 3 even greater disagreements (the datasets agreed on the top tissue only 4 times over the 30 genes). These indicate that much of the variation is attributable to experimental noise rather than true predicted enrichment. The referee points to HXK-1 being correctly gonadal sheath enriched in one scRNA dataset; however, the other two datasets actually show different sites as being highest, and the same dataset misses effects in other cases. This is precisely why protein level data is needed.

We further clarified this issue in the text: “We thus selected 30 genes across a variety of bulk transcript expression ranges which are generally predicted to be broadly expressed based on molecular function or, where molecular function was unknown (e.g., ZK632.9), single cell RNA sequencing (scRNA-seq) data (Table 1, Fig. 2A, B) (Gao et al., 2024; Ghaddar et al., 2023; Taylor et al., 2021).”

Overall, this indicates that both the overall accomplishment of generating tagged protein strains and analyzing their expression is oversold.

We have tried to make clear that our contribution is not a handful of new tagged strains added to the many that already exist. Rather, as stated in the abstract and elsewhere, we propose a strategy and provide proof-of-concept for scaling up tagging efforts. We believe the importance of this cannot be oversold.

Reviewer #3 (Public review):

Summary:

The authors argue that establishing the expression pattern and sub-cellular localisation of an animal's proteome will highlight hypotheses for further study. This claim is probably accepted by many in the community. This manuscript seeks to confirm the feasibility of establishing such a resource, by using current transgenic methods to knock in DNA encoding different colored fluorescent tags into C. elegans genes.

Strengths:

The authors make the points above. For example, they provide evidence that the C. elegans germline harbors two populations of mitochondria that differ qualitatively in the proteins they express. They also confirm that labelling the whole proteome is an achievable goal with relatively limited resources and time.

Weaknesses:

The work is somewhat incremental in that it uses existing transgenic technology. 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. However, they do not use these technologies to characterize their transgenic strains.

Reviewer #4 (Public review):

Summary:

Tagging the entire proteome of a metazoan would be a landmark achievement, providing a powerful complement and extension to existing "omic" catalogs in model systems. Here, Eroglu and Hobert argue that efficiently tagging multiple loci in a single "batch" would make the community-based achievement of this goal realistic. They provide rigorous evidence that such an approach is indeed feasible, exploring issues related to efficiency, design and screening strategies, disruption of gene function, and the potential for endogenously tagged alleles to reveal unexpected aspects of protein expression and localization. While the work has some minor gaps that are important to rigorously assess the feasibility of the proposed effort, the detailed and valuable insights that emerge should provide impetus to the community to coordinate efforts to make this ambitious goal a reality.

Strengths:

The work has numerous strengths. The authors provide compelling evidence that:

Three distinct loci can be efficiently targeted with three distinct fluorescent tags in a single injection.

Thoughtful targeting design can reduce the likelihood of disruption of function by the tag.

Systematic design principles based on expression level and predicted localization/function can be used to optimize tagging strategies.

The resulting tags can provide unexpected insight into patterns of protein production and subcellular localization.

Not all of these advances are novel in themselves, but taken together, they represent an important technical and conceptual advance. The most important strength comes from the exceptionally high value of the goal itself, in that the work is that it has the potential to spur a community-wide effort toward achieving the ambitious goal of proteome-wide tagging.

We appreciate the referee’s enthusiasm and hope that this will engage members of the community in a collective effort.

Weaknesses:

The work's shortcomings are minor.

One concern has to do with the feasibility of the proposed screening strategies. The experimental design cleverly coinjects tags for three loci in different gene expression 'zones'; this expression level determines which tag will be used. As the authors allude to, there is an important distinction between genes with the same overall FKPM value between those that are expressed broadly and those focally expressed in a specific tissue. The proposed strategy claims that there are a sufficient number of highly expressed genes "to be used as visible markers" for recovering successfully edited animals. It would be useful for the authors to discuss the issue of broad vs focused expression among this set of genes a bit more thoroughly, with an eye toward the issue of how likely it is that these genes could indeed consistently be used as visible markers, particularly for those at the low end of this limit.

To give two examples, this principle aided us with screening F54C8.1 and HAT-1. We added additional discussion on this to the first paragraph of the discussion: “For instance, we could clearly visualize F54C8.1::mScarlet3 in adult sperm by fluorescence stereomicroscopy despite a bulk FPKM of 16. Similarly, nuclear localized proteins will likely be easier to detect even at low expression levels, given the concentration of signal in small subcellular compartments. Indeed, this helped us detect HAT-1::mScarlet3 (56 bulk FPKM), which may have been too dim if distributed more broadly within cells.”

What fraction of the proteome (on a per-gene basis) is secreted proteins? How difficult will it be to screen these for successful tags? Are there specific tags that would be more optimal for secreted proteins? (The authors mention the use of an SL2 or T2A cassette to label the cells in which these proteins are expressed but note that there are technical challenges associated with doing this at scale.)

We added some of these points to the discussion: “Moreover, around 17% of the C. elegans genome (3,484 genes) may encode for secreted proteins (Suh and Hutter, 2012). Endogenous tagging of a substantial fraction of these proteins could reveal spatial patterns of secretion, distinguishing components that remain near their cell of origin from those that disperse to distal sites (Keeley et al., 2020). Tagging secreted proteins can also reveal sites of secretion – such as apical or basolateral membranes, or neurites – as has been observed for specific insulins (Sural et al., 2025) and for neuropeptides that localize selectively to synaptic regions (Toker et al., 2025).”

Various tags have been used for secreted proteins including Venus, TagRFP, and mNeonGreen. The pH of secretory vesicles is ~5.0-5.5, so chosen FPs should have a pKa below this range to avoid denaturation. All 3 fluorophores used here (mStayGold, mScarlet3 and mTagBFP2) have pKa’s below this range and would likely be fluorescent within secretory vesicles.

For secreted and/or weakly expressed genes, it would be useful for the authors to estimate for what fraction of these would successful insertions need to be screened by PCR, and what resources (time and money) this would likely entail. 

We think that the bulk of ECM proteins would likely be visualizable without PCR due to their broad and stable expression, and as mentioned a good portion of these have been already tagged. However, it is likely that most of the secreted small peptides will have to be screened by PCR. We use homemade Taq, which makes material cost of the reagents minimal. A pair of genotyping primers costs ~$8 (~$27,872 for all secreted genes).

Hands on time for lysis of 48-96 worms is approximately 20-30 minutes, with time to set up PCR around 5-10 minutes per target, and time to load a gel of 10 mins. In a given pool, 2/3 could be a putative secreted protein; thus, the same lysed population would enable screening for two targets at once. Collectively, around 40-60 mins of hands-on time would be required for two genes (around 20-30 mins per gene). Given 18 targets are injected per day, if 12 are screened by PCR, the screening could be done in 6 hours per day without affecting throughput. Most of the time spent on PCR would be replacing fluorescence screening time and would not overlap with the rate limiting injection step, performed by a separate specialist.

For how many genes would a single tag not capture all predicted isoforms?

Around 25% of C. elegans genes are thought to undergo alternative splicing (PMID: 21177968), with on average, ~2 isoforms per transcript. Among our selected genes, we only had one case where a single tag would not capture all isoforms (flad-1). We examined an additional 30 random genes and found no more examples by chance. So, in our view, this will be rare though we recognize in some cases a practical decision will need to be made, which could involve consideration of expression levels of each terminal exon.

Finally, some readers might object to the authors' assertion in the abstract that this work is "a first step in this direction" (presumably referring to designing a strategy for whole-proteome tagging). There is no concern that the authors are disregarding the extensive work of other groups, as they explicitly mention the contributions of other groups to the foundation that enables the present work. However, the spirit of the abstract could be misinterpreted by a well-intentioned reader.

We appreciate the referee’s perspective and have reworded this phrase in the abstract to: “As proof-of-principle for scalable pooled tagging, we undertook a pilot study in the nematode C. elegans, in which we set out to tag 30 different genetic loci with three different fluorophores, with 3 tags being introduced at a time.”

Author response:

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

eLife Assessment

This study uses the yeast two-hybrid assay to identify proteins that may interact with yeast Set1 and other subunits of COMPASS/Set1C, the histone H3K4 methyltransferase, providing also some evidence for Set1 sumoylation and a role of SET1C methylating other factors in vitro. The results are valuable, and they should contribute to understanding the functions of the conserved SET1C complex, as they suggest potential functional connections with RNA biogenesis, chromatin remodeling, and non-histone methylation, whose implications would yet need to be explored. Nevertheless, apart from the fact that only a small subset of the Y2H interactions is further examined, the validating experiments are only partial or inconclusive, the strength of evidence being at this point incomplete.

We present a systematic SET1C interaction map that provides a structured resource for generating and testing new hypotheses on SET1C function. We emphasise that these interactions represent a hypothesis generating resource rather than a set of validated protein–protein interactions. To reflect this, the manuscript has been carefully revised to distinguish clearly between observation and interpretation, and to avoid overstatement of the data. Accordingly, we have revised the title and the abstract. Selected examples are explored further to illustrate how candidates from the dataset can be followed up, but the primary contribution of this work is to provide a structured framework and resource that can guide future mechanistic studies of SET1C function.

We thank the reviewers for their thoughtful comments. We have followed their recommendations by modifying the structure of the manuscript, removing distracting results and relocating some figures to the supplementary materials to improve the readability of the manuscript. At the same time, the reviewers acknowledge that the dataset is extensive and that aspects of the validation work are valuable.

The changes made to the manuscript's structure in accordance with the reviewers' recommendations are as follows:

(1) Figure 1 is accompanied by a table (Table S2) with the raw data describing all the interactions from the ten 2H screens. This table also lists common interactors found in the independent screens. I'm afraid Table S2 was omitted from the initial submission of the manuscript

(2) Figure 2 has been modified to include an AlphaFold modeling of a seven-subunit Set1C complex (Set1– Bre2–Sdc1₂–Swd1–Swd3–Spp1) together with Kap104. Figure 2D has been moved to a new Figure S2

(3) The initial figure S2, which was problematic, has been removed, along with the accompanying text.

(4) Figure 3 of the original paper has been moved to the supplementary material and is now shown as a new Figure S3.

(5) Figure 5 in the original paper becomes Figure 3 in the revised version

(6) Figure S3 (Co-IP between Set1 and Prp22), which serves as validation data, has been moved to the main figures and is now presented as Figure 4.

(7) Figure 6 in the original paper becomes Figure 5 in the revised version

(8) Figure 4 from the original paper has been repositioned as the first figure (new Figure 6) of the biochemical characterization of the interaction between Snf2 and Set1C.

(9) Figure 7 has been removed from the manuscript. We have kept the original Figure 7E as a new Figure S6.

(10) Figures 8, 9, 10 become Figures 7, 8, 9.

Public Reviews:

Reviewer #1 (Public review):

We thank Reviewer 1 for the careful and thoughtful evaluation of our manuscript. We fully agree that yeast two hybrid screening provides candidate interactions that require cautious interpretation, and we recognise that our original version did not always make this sufficiently explicit.

In the revised manuscript, we have made substantial changes to address this central concern. All Y2H interactions are now consistently presented as candidate or potential interactions, and speculative statements have been either removed or explicitly framed as hypotheses. Our intention is that the reader can clearly separate the dataset itself from any proposed biological implications.

Second, we have refocused the manuscript to better reflect its primary contribution. We now present the Y2H screens as a comprehensive resource that defines a set of candidate interactions for SET1C, rather than as a set of validated functional relationships. In line with this, we have reduced the emphasis on speculative models and removed sections where the connection to experimental evidence was not sufficiently strong. This includes the removal of Fig. S2 and Fig. 7 and the associated text, as well as the relocation of several figures to the supplementary material. Where appropriate, we have added statements highlighting the limitations of the approaches used and the need for future work to establish physiological relevance.

More generally, we agree with the reviewer that the value of Y2H data lies in generating testable hypotheses rather than establishing conclusions. We have therefore revised the manuscript throughout to ensure that the interpretation remains proportionate to the strength of the evidence.

We hope that these changes address the reviewer’s concerns and result in a clearer and more appropriately balanced presentation of the data.

The manuscript by Luciano et al is a collection of experiments about the yeast histone 3 lysine 4 methyltransferase, Set1, starting with 10 yeast two-hybrid screens (Y2H). Y2H screens were briefly popular 20+ years ago, but the persistently unfavourable false-to-true positive ratios limited their utility, and the conclusion emerged that Y2H is an unreliable approach for gathering protein-protein interaction data. Y2H outcomes are candidate interaction lists at best, strongly contaminated by false positives. Here, the authors employed a company (Hybridomics) to perform the Y2H screens.

The primary data is not presented, and the outcomes are summarized using the Hybridomics in-house quality scoring system in Figure 1A. It is not possible to evaluate these data, and the manuscript presents cartoon summaries that the reader must accept as valuable.

Hybrigenics brings extensive experience from conducting numerous screens, enabling the team to recognize recurring false positives that commonly arise in screening assays. In their detailed analysis, Hybrigenics reports the number of clones recovered and the extent of overlap among interaction regions, both of which contribute to the confidence scores they assign. Table S2, provided in the revised version, more accurately reflects the raw data obtained by Hybrigenics. Nevertheless, we agree that false positives contaminate the list of potential interactors. Some interactions may also be indirect through a common interactor and do not reflect a physiological interaction.

(1) Based on the extensive knowledge about Set1C/COMPASS acquired from genetics and biochemistry by many labs (including the Geli lab), the results presented here from the 10 Y2H screens are notably patchy. Of the 7 subunits of this complex, only one (Spp1) was identified using Set1 as bait. Conversely, as baits, Swd2, Spp1, Shg1, captured Set1, and the Bre2-Sdc1 interaction was reciprocally identified. These interactions were scored at the highest confidence level, which lends some confidence to the screens. However, the missing interactions, even at the third confidence level, indicate that any Y2H conclusions using these data must be qualified with caution. The authors do not appear to be cautious in their lengthy evaluations of these candidate interactions, which are illustrated with cartoons in Figures 2 and 3, with some support from the literature but almost without additional evidence. Snf2 is a particularly interesting candidate, which the authors support with pull-down experiments after mixing the two proteins in vitro (Figure 4). After Y2H, this is the least convincing evidence for a protein-protein interaction, and no further, more reliable evidence is supplied.

We thank the reviewer for raising this important point regarding the strength of the evidence supporting the Set1– Snf2 interaction. We agree that the current data do not establish a definitive physiological interaction. In the discussion, we explicitly note the limitations of the current data.

For Figure 2, as recommended by referee 2, we performed AlphaFold modeling of a seven-subunit Set1C complex (Set1–Bre2–Sdc1₂–Swd1–Swd3–Spp1) together with Kap104. Consistent with the Y2H data, the model recapitulates binding of the Kap104 SID to the PY-NLS region of Set1 (residues 40–90).

We have moved Figure 3 in the supplementary materials.

(2) Figure 5 continues the cartoon summary of extrapolations from the Y2H screens, again without supporting evidence, except that the authors state.

Figure 5 is now Figure 3. We have added the statement in the text: “It is not feasible to validate all of these interactions within the limits of this manuscript, and their validity should therefore be interpreted with caution. Nonetheless, these findings provide a useful basis for future research”.

"We have refined the interaction region between Set1, Prp8 and Prp22, showing that Prp8 and Prp22 interact strongly with Set1-F4 (n-SET). Prp22 interacts in addition with Set1-F1 (Figure S2)." However, Figure S2 does not show this evidence and is incoherent.

When we say that we have refined the interaction region between Set1, Prp8, and Prp22, we mean that we have restricted the interaction regions according to Y2H criteria. Indeed, we have not shown the spots illustrating the results. This statement has been deleted as well as Fig. S2

The figure legends for Figure S2B and C do not correspond to the figure.

(B) Expression of the F1-F5 fragments in yeast cells. Fusion proteins were detected with an anti-GAL4 monoclonal antibody. TOTO yeast cells (Hybrigenics) were transformed with the different pB66-Set1-F1 to F5 plasmids and subsequently with either P6, pP6-Snf2 762-968, pP6-Prp8 37-250, or pP6-Prp22 379-763 that were identified in the Y2H screens. Transformed cells were incubated 3 days at 30{degree sign}C on SD-LEU-TRP and then restreaked on SD-LEU-TRP-HIS with 3AT. Cell growth was monitored after 2 days at 30{degree sign}C.

(C) Solid and dotted arrows indicate that transformed TOTO cells transformed with pB66-Set1-F1 to F5 and the indicated prey (Snf2, Prp8, and Prp22) are growing in the presence of 20 mM and 5 mM of AT, respectively.

Figure S2D is two almost featureless dark grey panels accompanied by the figure legend D) Control experiment showing that TOTO cells transformed with p6 and pB66-Set1-F4 are not gowing (sic) in the presence of 5 mM or 20 mM AT.

We agree that the legend for Figure S2 was unclear and does not accurately describe the panels shown in the figure. Fig; S2 has been deleted in the revised version. The results shown in the original Fig. S2 add limited information and may detract from the clarity of the main points.

In the revised version, we have moved the CoIP analysis demonstrating the interaction between Set1 and Prp22 (previously shown in Figure S3) into the main figures (now Figure 4) to further support and validate the two-hybrid screening results presented there.

Line 343. Interestingly, the two-hybrid screens reveal that Set1 1-754 interacted with Gag capsid-like proteins of Ty1 (Figure S5), raising the possibility that Set1 binding to Ty1 mRNA is linked to the interaction of Set1 1-754 with Gag.

This is another example of the primary mistake repeatedly made by the authors -Y2H interactions are candidate results and not conclusive evidence.

This statement is supported by our previous findings showing that Set1 binds Ty1 mRNA independently of its dRRM domain and represses Ty1 mobility at a post-transcriptional stage (Luciano et al., Cell Discovery, 2017; PMID: 29071121). One possible explanation for Set1 association with Ty1 mRNA is its interaction with the Gag capsidlike protein. In this context, the observed interaction between Set1(1–754) and Gag capsid-like proteins is consistent with this model.

To further illustrate this point, the authors highlight the candidate interaction between Nis1 and 3 Set1C subunits.

While we agree that the Nis1-Set1C interaction has not been demonstrated beyond doubt, we feel that our Y2H and in vitro binding experiments provide reasonable evidence that the interactions may be relevant. It is important to consider that any interaction assay can provide negative (and false positive) results, this includes Y2H, in vitro binding and mass-spec analysis of purified complexes from cells. We feel that it is not appropriate to only trust protein interactions that are strong and stable enough to be demonstrated via purified complexes. It is clear that some protein interactions do occur in transient and weak manner and therefore are not compatible with biochemical purification approach. This indeed is the strength of alternative methods like Y2H and in vitro binding assays, that interactions can be identified and tested even if the physiological context of the interaction may be more complex.

(3) After multiple speculations based on the Y2H candidates, the authors changed to focus on sumoylation of Set1, which has previously reported to be sumoylated. Evidence identifying two sumoylation sites in Set1, in the N-SET and SET domains, is valuable and adds important progress to the role of sumoylation in the regulation of H3K4 methyltransferase, relevant for all eukaryotes. This illuminating part of the manuscript is only tenuously connected to the preceding Y2H screens and concomitant speculations.

We thank Referee 1 for their comment. While it is true that there is only a modest connection between Set1 interactors involved in direct or indirect sumoylation and the characterization of Set1 SUMOylation sites, we believe that this does not constitute a weakness of the manuscript.

(4) The manuscript then describes a red herring exercise involving Set1 methylation of Nrm1. In an already speculative and difficult manuscript, it is exasperating to read a paragraph about a failed idea. Apart from panel E, Figure 7 is a distraction, and I believe it should not be shared.

(5) However, despite the failure with Nrm1, Line 443 - The H3K4-like domain in Nrm1 raised our attention to other yeast proteins that carry such sequences.

This line of thinking is even less connected to the Y2H screens than the sumoylation work.

However, the authors present a reasonable evaluation of the yeast proteome screened for six amino acids similar to the known H3K4 motif ARTKQT (Figure 7e).

(6) However, this evaluation goes nowhere and has no connection with the next section of the manuscript, which is entirely speculation about the regulation of metabolism and stress responses based on the Y2H results and selected evidence from the literature.

In response to comments 4 and 5, we have removed Fig. 7 and the paragraph titled “The transcriptional corepressor Nrm1 interacts with SET1C.” Part of this paragraph and the section describing the screen of the yeast proteome for six–amino acid sequences resembling the H3K4 motif (ARTKQT) has been kept as Fig. S6.

In the abstract, we have removed the sentence: We demonstrate that the transcriptional corepressor Nrm1 is methylated by SET1C in vitro suggesting that H3K4-like domains may represent a class of non-histone substrates for SET1C.

At the end of the introduction, we have deleted “the transcriptional corepressor Nrm1” in the sentence: In addition, we demonstrate that the transcriptional corepressor Nrm1 and the Snf2 AT-hook are both methylated by SET1C in vitro

(7) The manuscript then describes more failed experiments regarding lysine methylation of Snf2 by Set1C, which unexpectedly reports arginine methylation rather than lysine. The manuscript does not currently meet the standard expected for this type of paper - the composition is somewhat incoherent and there are no previous reports of arginine methylation by SET domain proteins.

We have integrated extensive in vitro reconstruction experiments with complementary in vivo studies, all conducted according to the rigorous standards expected by leading journals. These approaches have allowed us to reach the conclusions presented in this manuscript. While some of these findings are unexpected, they are supported by the data. We have carefully discussed the results and their limitations to provide a comprehensive interpretation.

The manuscript presents a very experienced grasp of the literature and a sophisticated appreciation of the forefront issues, but a surprising failure to eliminate uninformative failures and peripheral distractions. The over interpretation of Y2H results is a dominating failure. There are some valuable parts within this manuscript, and hopefully, the authors can reformat to eliminate the defects and appropriately qualify the candidate data.

We thank Referee 1 for these insightful comments. In the revised version, we have followed the advice to remove non-informative failures and peripheral distractions. Additionally, we exercise greater caution to avoid over-interpreting the Y2H results.

Reviewer #2 (Public review):

Summary:

This paper starts with a large-scale yeast two-hybrid (Y2H) screen using Set1 (full-length and smaller parts) and other Set1C/COMPASS subunits as bait. There are hundreds of possible interactions identified, but only a small number are given any follow-up. While it's useful to document all the possible interactions, the unfocused and preliminary nature of the results makes the paper feel scattered and incomplete.

Strengths:

The Y2H screen was very comprehensive, producing lots of interesting possible leads for further experiments.

Weaknesses:

The results are useful but incomplete because only a small subset of the Y2H interactions is further examined. Even in the case of those that were further tested, the validating experiments are only partial or inconclusive.

Referee 2’s comments align in some respects with those of Referee 1. In the revised version, we have followed the detailed Referee 2 suggestions to reduce the scattered nature of the manuscript. In addition, we include an AlphaFold model of the interaction between the Set1 N-term 1-754 with the SID domain of Kap104 that involves the proposed Set1 PY-NLS sequence.

Reviewer #3 (Public review):

The SET1C/COMPASS complex is the histone H3K4 methyltransferase in Saccharomyces cerevisiae, where it plays pivotal roles in transcriptional regulation, DNA repair, and chromatin dynamics. While its canonical function in histone methylation is well-established, its full interactome remains poorly defined. Moreover, whether SET1C methylates non-histone substrates has been an open question. In this study, Luciano et al. employ systematic yeast two-hybrid (Y2H) screening to uncover novel interactors and functions of SET1C. Their findings reveal potential functional connections to RNA biogenesis, chromatin remodeling, and non-histone methylation.

The authors performed multiple Y2H screens using Set1 (full-length, N-terminal, and C-terminal fragments) and each of its seven subunits as baits. They identified high-confidence interactors that link SET1C to diverse cellular processes, including chromatin regulation (e.g., the SWI/SNF complex via Snf2), DNA replication (e.g., Mcm2, Orc6), RNA biogenesis (e.g., spliceosome components Prp8 and Prp22; polyadenylation factors Pta1 and Ref2), tRNA processing (e.g., Trm1, Trm732), and nuclear import/export (e.g., importins Kap104 and Kap123). Some of these interactions were further validated by immunoprecipitation or in vitro assays.

Given the interaction of Set1 with Slx5 and Wss1 - proteins involved in SUMO-dependent processes - the authors investigated and convincingly demonstrated that Set1 is sumoylated. This modification may influence the function and regulation of the SET1C complex.

Finally, the authors provide evidence that SET1C methylates proteins beyond histone H3K4, notably Nrm1, a transcriptional corepressor, and Snf2, the catalytic subunit of the SWI/SNF chromatin remodeling complex. Although Nrm1 contains a domain resembling the H3K4-methylated sequence (H3K4-like domain), this region does not appear to be required for its methylation. The search for other proteins containing similar domains as potential methylation candidates (p.12, first paragraph) seems less justified, given the lack of evidence supporting the requirement for the H3K4-like domain in methylation.

This study offers valuable insights into the interactome of SET1C, suggesting potential links between the complex and a wide range of cellular processes. However, the functional implications of the Y2H interactions remain to be explored further. Additionally, the study provides intriguing information on the possible regulation of Set1 by sumoylation. The discovery of Nrm1 and Snf2 as methylation substrates could significantly expand the known targets and functions of SET1C.

The results are supported by high-quality data.

We thank referee 3 for their positive comments

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Restructure the manuscript into at least two papers.

We thank the reviewer for this suggestion. In the revised manuscript, we have addressed this concern by substantially restructuring and streamlining the presentation. We consider the dataset, validation experiments, and functional observations to be closely integrated, and we believe that presenting them together provides the most coherent and impactful account of the work.

Minor points

There are several basic flaws in the manuscript that I feel indicate the co-authors have not proofread the manuscript sufficiently - 4 examples from early in the manuscript are listed below.

(1) The reference for Hybridomics is (73) - obviously from an earlier version that used a different referencing system that has not been corrected.

Thank you. This has been corrected.

(2) Line 194 - 197. These screens have proven their power and effectiveness. In particular, they identified ...... the CTD of Rpb1 as an interactor of the N-terminal region of Set1 (Bae et al, 2020) (Figure S1). Rbp1 interaction is not identified in the screens presented here, and Figure S1 is a cartoon and not primary evidence.

The interaction between the CTD of Rpb1 (Rpo21) and Set1 is reported in Table S2. The detailed characterization presented in Bae et al. (2020) was subsequently carried out as a direct follow-up to this screen.

(3) Line 205-211. The highly confident interactors of the seven SET1C subunits are shown in Figure 1C-E. We found that Spp1, Shg1 and Swd2 interact alone with Set1 (Figure 1C). The minimum Set1 region for which an interaction is found for each of these 3 subunits is shown in Figure 1C. The high confidence interactors of the seven SET1C subunits are shown in Figure 1C-E. We found that Spp1, Shg1 and Swd2 display Y2H interactions with Set1 (Figure 1C). The high confidence interactors of Spp1, Shg1 and Swd2 are indicated in Figure 1D (see also Table S2).

It is possible that Table S2 was omitted from the original submission, as it was requested during the production stage.

(4) Line 335. We have classified all Set1 and subunit interactors according to these SET1C roles (Figure S5). However, this refers to Figure S4 - many further references to Figure S5 are also to Figure S4.

Thank you. This has been corrected.

Reviewer #2 (Recommendations for the authors):

General recommendations:

(1) Figures 1, 2, 3, and 5 and their associated main text are essentially just lists of interactors, put in graphic form and grouped to allow speculation about possible biological functions for the interactions. But almost none of the ideas are tested, so these sections take much more space than warranted. Having so much preliminary Y2H data actually distracts attention from the follow-up experiments that are shown. I would move most or all of this to the supplement, consolidating the Y2H results into fewer figures (or even just the Table).

As mentioned earlier, the manuscript has been reorganized and Table S2 is provided.

(2) The Snf2 interaction gets the most follow-up, so separating Figure 4 from Figures 8-10 broke the flow of that story. I would group these figures together since all are related to the Snf2 AT hook story.

This was done accordingly.

(3) I understand that it's impossible to validate all the possible interactions, particularly if resources are limited. However, at least for the interactions that get further attention, it could be very useful to try some AlphaFold multimer predictions. A high confidence AlphaFold score would provide a second orthogonal piece of evidence to support the Y2H results.

We generated an AlphaFold model (Figure 2C) that recapitulates the key predictions for the Set1-Kap104 Y2H interaction.

Comments on specific sections:

(1) Y2H results. The text says Figure 1 shows all the high-confidence interactors. But the Set1 NTD interaction with the Rpb1 CTD is not shown here (it's in the supplement).

In Table S2, an interaction is observed between full-length Set1 and the Rpb1-CTD (14 repeats), where Rpb1 is referred to as Rpo21.

Figure 2 shows additional high-confidence interactors that do not appear in Figure 1, while others (like the Shg1Mog1 interaction) are shown in both Figures 1 and 2. It's confusing to scatter the data like this, which is why I recommend consolidating into a single figure or table.

In Figure 2, the high-confidence interactors of Set1 (1–754) are highlighted in red and green (Snf2, Gbp2, and Kap104), and all are also present in Figure 1. Dbp1, identified as a high-confidence interactor of Spp1, likewise appears in Figure 1. Table S2 summarizes all of these interactions.

(2) Line 219. How does a "high confidence" Set1-Kap104 Y2H interaction suggest the interaction is direct? Couldn't an indirect interaction also be tight and reproducible? This is an example where it would be worth seeing if AlphaFold also predicts an interaction and, if so, whether it involves the proposed NLS sequences.

Y2H screening indicated that Kap104 binds to the N-terminal region (aa 1–754) of Set1 via its Set1 interaction domain (SID). To validate this, we used AlphaFold to model the seven-subunit Set1C complex (Set1-Bre2-Sdc1(x2) Swd1-Swd3-Spp1) with Kap104. The resulting model showed borderline confidence for the overall fold (pTM = 0.53) and low confidence in subunit positioning (ipTM = 0.5). Visualization in PyMOL confirmed Kap104 SID binding to Set1(1–754), consistent with Y2H results. The structure highlights Kap104 SID interaction with Set1’s PY-NLS at residues 40–90; the second PY-NLS is neither visible nor engaged in this model.

(3) In the discussion of nuclear import interactors, what does it mean to say the Shg1-Mog1 interaction is "along the same line" as Set1-Kap104?

We meant that the interaction between Shg1 and Mog1 represents another example of an interaction between a Set1C subunit and a protein involved in nuclear import. Along the same line has been deleted in the revised version.

(4) To follow up on the Swd1-Nrm1 Y2H interaction, the paper shows that Nrm1 is methylated by Set1 in vitro (Figure 7), but it's not clear whether this has any biological significance. Without any in vivo follow-up, this figure is probably more appropriate for the Supplement.

As noted above, Figure 7 has been removed, only panel E of Figure 7 is retained in the revised version.

(5) Figures 6 and S8 show that Set1 is SUMOylated. Although it's not clear what this does to Set1 function or which E3 is responsible, the modification data looks convincing. The legend to Figures 6A and B says the Elutes samples are purified on nickel columns. Why are the Myc-Set1 and GB-Set1 proteins without the his-SUMO modification also binding to the nickel column? That's not happening in panels C and D. In the blots on the right for his-SUMO, is there any way to show that one of those bands is Set1? Maybe IP for MYC and then probe for the His tag?

We thank the reviewer for this observation. His-SUMO purification using Nickel beads was used to purify HisSUMOylated proteins. Purified proteins were analyzed by Western blot using anti-MYC or anti-GAL4 antibodies to detect SET1-His-SUMO, as well as anti-His antibodies to confirm the presence of purified His-SUMOylated proteins. As mentioned by the reviewer, we detected unmodified MYC-Set1 and GAL4-Set1 in both the (-) and (+) His-SUMO eluates. This phenomenon is most likely due to the stickiness of unmodified Set1 to the beads. This is a commonly observed phenomenon in this type of biochemical assay, particularly when analyzing large proteins such as Set1 (124 kDa). This stickiness behavior has been observed in similar SUMOylation assays, e.g., for Hpr1 (88 kDa) (Bretes H, 2014. PMID: 24500206), Nup1 (114 kDa), and Nup2 (78 kDa) (Folz H, 2019. PMID: 30837289). This stickiness was not observed when using Set1 fragments (panels C and D), most likely because the fragments lost the stickiness to the beads, a characteristic belonging only to the full-length Set1. We mention this point in the legend of the new figure 5.

(6) The Snf2 interaction gets the most follow-up. The GST pulldown validation of Set1 interaction with Snf2 AThook looks pretty good. However, the RGG repeats are necessary for the Set1 interaction with recombinant Snf2 proteins, but not for the co-IP of in vivo material. Again, AlphaFold could lend further support here.

Thank you for this helpful suggestion. We agree that structural modelling could, in principle, provide an additional and orthogonal line of support for the Set1-Snf2 interaction. We did explore this using AlphaFold. However, both Set1 and Snf2 contain extensive intrinsically disordered regions, including the regions implicated in the interaction, and none of the models we obtained provided interpretable structural insight into the interaction interface. In particular, the predicted complexes showed low confidence in relative domain positioning, which limits their usefulness for supporting or refining the interaction model. One possible explanation is that additional components are required to stabilise a meaningful interaction in silico. While we modelled Set1 within a seven-subunit Set1C complex, Snf2 was necessarily included in isolation from its native context. Given that Snf2 functions as part of multiple, heterogeneous chromatin remodelling complexes, the absence of its physiological binding partners may prevent AlphaFold from resolving a relevant interaction interface. In light of these limitations, we have not included the AlphaFold models in the manuscript, as we felt they would not provide reliable or informative support. Instead, we have focused on the experimental evidence presented. We have clarified this point in the revised discussion to acknowledge both the potential and the current limitations of structural prediction approaches in this context.

(7) The Snf2 methylation by Set1 is less convincing, and its biological significance is still unclear. I think it's pretty unlikely that Set1 could methylate arginine. The mass spectrometry is used for in vivo validation (mass spec), but mutating the lysines (Figure S11, S12) or Set1 deletion (Figure S14) doesn't seem to affect the signal. Could there be quantitative differences? Is there any way to quantitate the mass spec data to estimate the modified/unmodified ratio?

We thank the reviewer for highlighting the unexpected nature of the methylation results. We agree that the observation of arginine methylation in this context is surprising, particularly given that SET domain proteins are classically associated with lysine methylation. This is why we performed multiple in vitro and in vivo experiments, and careful interpretation data that were clear led us to conclude that Set1C methylates the arginines within the ARTSTRGR motif of the AT-hook. We agree that the biological significance of this modification remains unclear. We obtained data showing that deletion of the SID domain of Snf2 impairs yeast growth on lactate, whereas this mutant grows normally on glucose and galactose, in contrast to the Snf2Δ mutant, which exhibits poor growth on both glucose and galactose. In comparison, deletion of the RG motif of Snf2 does not affect growth on lactate. These results provide insight into the interaction between Set1 and Snf2 but do not shed light on the potential importance of methylation of the RG motif. We therefore chose not to include them. In the discussion, we acknowledge the limitations of the current evidence. Our intention is to retain these findings as potentially interesting observations while ensuring that their interpretation remains appropriately cautious.

Minor comments:

(1) Lines 153 and 163: Stress response is listed twice, but with different references. Maybe these need to be further defined or else combined?

We have deleted stress response line 163 and moved the references “Deshpande et al, 2022 and Nadal-Ribelles et al, 2015” line 153.

(2) Line 193: better to say the proteins were fused to the C- or N-terminus (rather than upstream/downstream). It would be worth mentioning if there was a reason why Swd2 was fused to the N-terminus, unlike all the others.

This has been done accordingly. In our hands, C-terminal fusions of Swd2 are not functional.

(3) Is the scoring scheme (highest, high, good) that produces the colors in Figure 1 shown in the table? It doesn't say what the tan color (two of the Bre2 interactors) means.

It is a mistake, Tea1 should be blue and Swi1 should not appear here. This has been fixed.

(4) Line 206. It's not clear what it means to say that three of the subunits "interact alone with Set1". It can't mean they only interact with Set1, since other interactors are shown in Figure 1B. If it meant to say the interactions don't require other COMPASS subunits? I don't see how you can tell that from the Y2H assay. Please clarify.

It means that these 3 subunits interact directly with Set1 without the need of another subunit, unlike of the other subunits.

(5) Line 252. While discussing the Set1 - Snf2 interaction, the paper cites Hirschhorn et al. That paper talks about Swi-Snf, but doesn't mention Set1 anywhere. Maybe the authors meant to cite a different paper?

We agree, this reference is not appropriated. It has been deleted.

(6) Figure S2 panels A and C are redundant and could easily be combined.

Figure S2 has been deleted.

(7) Figure S4: Should the green category also include transcription? Ssl1 is a TFIIH subunit, which could be involved in either transcription initiation or NER. Sen1 and Nrd1 are transcription termination factors, although Sen1 may also function in R-loop resolution.

We agree but it is already complicated as it is.

can we defend this claim? based on what are we saying this?

Truff wants people to be more willing to buy their products since it's cheaper. But, how much truffle really is in their product? Someone should do the math. I'm sure they are still making a bunch of profit off of their product.

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Reply to the reviewers

Reviewer #1

Minor comments 1) The authors suggest that the weak 4th protomer in the HCMV UL52 3-mer map is a consequence of flexibility. This may be the case, but it may also be the case that the class is polluted with 4-mer particles leading to reduced occupancy. Erasing the weak density and running a multi-model 3D classification providing the erased 3-mer and a 4-mer starting map may separate these.

We performed additional analysis (i.e., 3-mer and 4-mer particles were combined into a multi-class ab initio reconstruction followed by multi-class heterogenous refinement) and found that the original 3-mer map was a mixture of 3-mer and 4-mer states.

We have updated Fig. 2a, Supplementary Fig. 2, Supplementary Fig. 3, Supplementary Table 1, Supplementary Movie 1, and removed the discussion of the weak protomer in the 3-mer map from the results section. We have updated our EMDB and PDB depositions accordingly.

• 2) I found the supplemental figure to show the DNA in the tripentamer map too small, this is an interesting finding and should be shown more clearly.*

We have increased the size of Supplementary Fig. 6 and moved the figure caption to another page to accommodate this enlargement.

Reviewer #2

*Major issues 1) There is a high probability that the tripentamer is an artifact of the cross-linking. Because of this, it'd be great to know more about the cross-linking reaction, ideally mass spec identification and quantification of cross-links. This would also address the authors' speculation of contacts that stabilize the tripentamer. *

Crosslinking is a commonly used technique to stabilize complexes that are observed through other means but do not survive the cryo-EM vitrification process. In an EMSA experiment (Supplementary Fig. 4a), UL32 binds 30 bp DNA and migrates slower than when bound to a 10 bp probe, consistent with formation of a supra-pentameric complex. The samples in the EMSA gels are not crosslinked. Additionally, an SDS-PAGE gel of the crosslinked product used for cryo-EM showed tight bands at molecular weights expected for oligomers, supporting specific crosslinking (Supplementary Fig. 4b). These results suggest that crosslinking stabilizes a species that can form but is relatively unstable in solution.

Moreover, the author's claim "However, mutation of K532A/C535A reduced infectious virion production by half (Fig. 4b), suggesting that the tripentamer interface may play a role in the viral life cycle." Seems to be an overreach. Perhaps this is semantics but the data just show that these residues play a role in viral replication (albeit not a huge role based on the modest effect).

We have modified the title of the results section (Line 216-217) to state that "Residues at the tripentamer interfaces contribute to infectious virion production in HSV-1" as well as Line 234 and 241 to indicate that the residues play a role in the viral life cycle.

2) The density for the potential DNA does not look very convincing, although it still remains the strongest hypothesis. The authors should try to strengthen their argument. Does this putative DNA contact residues that they show are necessary for viral replication? Showing seq conservation on the structure could help their argument for the shared function of DNA-binding.

The DNA likely contacts conserved residues at the base and midsection of the central channel (residues R302, R301, R293, K289, R580, R579, R572; see Fig. 6a). We have shown that these residues are important for the production of infectious virions (Fig. 6c): even a single point mutation (R572A) decreased production of infectious virus particles by more than 90%, and double and triple point mutants (R579A/R580A, K289A/R293A/R301A) eliminated production of infectious virus. Sequence conservation of these charged residues in the central channel regions is shown in Supplementary Fig. 1d, f.

3) My last major issue is stylistic and concerns the descriptions of cryoEM structures. I found that the paper was a bit of challenge to read when the authors would introduce each structure. It was a bit of a slog to get through. Descriptions of the structures veered off into overly detailed comparisons that required constant comparison with the figure and didn't really advance my understanding past "the outer surfaces of the three orthologs are different." This masked the more interesting aspects of the authors' findings. Perhaps this could be summarized in supplementary figures or a table. Because this is a stylistic suggestion, the authors should feel free to ignore this request.

We appreciate the reviewer's concerns about accessibility, but we are excited that these structures allowed us to thoroughly describe the convergent and divergent structural features across the Herpesviridae and hope that our in-depth analysis will allow for detailed mechanistic follow-up.

*Minor comments 1) The descriptions of structure determination in the text were often unclear. For example, "In the 3-mer map, a poorly-resolved fourth protomer is visible at low contour levels, suggesting that an additional protomer is present but highly flexible in this class (Supplementary Fig. 3a)." Alternatively, it could be that the classification algorithm wasn't able to fully separate particles that were 3-mers from the 4mers. *

The reviewer is correct. As described above (Reviewer #1 comment 1), we performed additional analysis and found that the original 3-mer map was a mixture of 3-mer and 4-mer states. We have updated Fig. 2a, Supplementary Fig. 2, Supplementary Fig. 3, Supplementary Table 1, Supplementary Movie 1, the EMDB and PDB depositions, and removed the discussion of the weak protomer in the 3-mer map from the results section.

*When describing the structure determination of the HSV1 accessory factor, the authors describe no other particles other than the tripentamer. Were there other particles observed? It'd be a bit surprising that all of the protein adopted the tripentamer state. *

We agree that this result is striking. We picked particles using a 'blob picker' to avoid introducing template bias and found that the tripentamer is the predominant species. Below we show the results of 2D classification of blob picked particles (classes sorted by particle number; obvious junk classes excluded for clarity). There is one class that suggests a pentamer, but template picking with a pentamer template (based on ORF68) did not yield a pentamer class.

Additionally, as we describe in the results section and show in Supplementary Fig. 6a, further processing of the consensus UL32 map showed that 60% of particles formed a complete tripentamer (i.e., 15-mer) while other the remaining 40% formed incomplete tripentamers, missing one or more protomers (e.g., 17% of particles formed a 14-mer).

Was symmetry applied, particularly for the tripentamer that appears to have C-3 symmetry? This is in materials and methods but not clear why it isn't mentioned when describing the structure determination and results.

No symmetry was applied in the reconstruction for either UL32 or UL52. While we previously noted this in the methods section and in Supplementary Table 1, we have added this information to the results section (Line 169-170), the Fig. 3 legend, and cryo-EM processing figures (Supplementary Figures 2, 5, 6) for clarity.

2) Throughout the paper, the authors use the word "remodel" to describe structural differences between orthologs. However, this word usually carries the implication of conformational rearrangement within a protein, and not across orthologs. Please consider a different description.

We agree with the reviewer and have removed the term "remodel" throughout the manuscript text (i.e., Lines 116, 118, 120, 122, 302, 306) and from Supplementary Figures 1, 3, and 5.

3) Figure 2F is confusing and difficult to interpret. It seems that the main point is that these interfaces are conserved, which might be more easily displayed as a standard sequence conservation score mapped onto the structure. I'm also not sure that this figure is necessary as a main figure and could be supplemental.

We agree that the conservation could also be shown this way and have added labels to universally conserved residues of the protomer interface to Supplementary Fig. 1b, c. We have also moved Fig. 2f to the supplement (now Supplementary Fig. 2g).

• 4) The authors write "UL32 bound to the shortest probe tested (10 bp, Supplementary Fig. 4a)." This implies that ONLY the shortest probe is bound and that others are not bound. Consider rephrasing.*

We have rephrased to clarify at all probes tested, included the shortest, bound DNA (Line 153).

• 5) Frustum is misspellt. ;)*

Thank you. Spelling has been corrected (Line 185).

6) In the discussion, the authors speculate that the variability of the outer surface is due to "virus- or host-specific interactions". I'm confused by "host-specific interactions", because the host is the same for all three viruses. Perhaps the authors mean that the different accessory factors could interact with different host factors? If so, are the authors making a Red Queen argument? If so, it'd be pretty cool to do dN/dS analysis to test that hypothesis.

The reviewer is correct in that all three viruses (HSV-1, HCMV, KSHV) infect the same host; however, they replicate in different cell types, which could potentially express different host factors. We have no evidence to support this hypothesis and intended to propose that UL32 and UL52 may be interacting/co-evolving with other viral factors required for genome packaging. We have clarified Line 308 to generalize that "these regions are involved in virus-specific interactions".

To me, this window into evolution of this factor is the biggest advance of the work, and tbh I felt that the authors could lean into this a bit more in the discussion section. Are there any differences in the packaging mechanisms of the different herpes families that can be related to their different behavior? Any other molecular evolution analyses (e.g. dN/dS ratio analysis) that could inform their study?

We agree that understanding the evolution of the packaging accessory factor is an interesting future area of research. There are differences in capsid structure and occupancy of capsid-associated factors across the herpesvirus family (PMID: 34696343). However, we lack a mechanistic (or structural) understanding of viral genome packaging components across the herpesviruses, raising the possibility that there are differences in packaging mechanisms.

Interestingly, the further diverged alloherpesviruses and malacoherpesviruses (other families in the order Herpesvirales) do not appear to encode a factor with similar predicted structure to the Herpesviridae packaging accessory factor (PMID: 41902279). It is unclear how the mechanism of packaging differs in the Orthoherpesviridae and whether replication in mammalian/avian/reptilian cells places additional evolutionary pressure on the viral genome packaging mechanism.

Reviewer #3

Major comments

*1) [I]t is not clear whether the structures presented in the manuscript reflect those produced during HCMV or HSV-1 infection. *

We agree with the reviewer that it is important to consider to what extent purified biomolecules resemble their in vivo counterparts. This criticism can be applied to any ex situ structural analysis. However, our experimental structures allowed us to make testable observations, including the correct assignment of structurally important zinc fingers and the identification of functionally important residues in the central channel.

2) HCMV UL52 was presented to form two distinct structures, a 3-mer and a 4-mer (Fig. 2a). However, the authors acknowledge that the 3-mer is actually a 4-mer when the threshold for the cryo-EM map is lowered. The density is also visible in the PDB validation report for the 3-mer; EMD-74418.

Reviewers #1 and #2 were also curious about the 3-mer. As described above, we performed additional analysis that showed that the original 3-mer map was a mixture of 3-mer and 4-mer states. We have updated Fig. 2a, Supplementary Fig. 2, Supplementary Fig. 3, Supplementary Table 1, Supplementary Movie 1, EMDB and PDB depositions, and removed the discussion of the weak protomer in the 3-mer map from the results section.

*Given that ORF68, BFLF1, and UL32 (Didychuk et al., 2021) form complete pentamer rings, with BFLF1 forming stacked rings, it would seem odd for a protein with conserved function to deviate from a pentamer configuration, suggesting that the structures reported do not reflect the natively produced and functional protein. *

We agree that this is a surprising finding; we initially anticipated that UL32 and UL52 would also form stable pentameric rings. While this study does not resolve a complete mechanism for this factor, it does provide the first structural evidence for the implications of their poor sequence conservation and lack of complementarity.

Furthermore, this is not the first example of a conserved herpesvirus factor that possesses different oligomeric states across different subfamily homologs. As mentioned in the discussion, herpesvirus encode a sliding clamp processivity factor (HSV-1 UL42/HCMV UL44/KSHV ORF59) that shares a common PCNA-like fold, but which has varied oligomeric state across these herpesviruses.

*3) Unlike ORF68 (Didychuk et al., 2021) and UL32 (Suppl. Fig. 4), dsDNA binding experiments were not performed with UL52. Could the partial pentamers simply be poorly formed due to expression in insect cells (mammalian cells were used for protein purification in Didychuk et al., 2021), absence of dsDNA, or inappropriate buffer conditions? Moreover, were the EM grid and vitrification parameters optimized? Grid geometries and chemistries can have profound effects of protein stability especially in the context of the air-water interface, leading to degradation of protein complexes (Glaeser, 2018; D'Imprima et al., 2019). Does UL52 form complexes with dsDNA? Data are shown for the HSV-1 packaging accessory factor. Perhaps dsDNA would stabilize the UL52 pentamer. *

We have purified ORF68 and homologs from both human and insect cell expression systems, and do not observe changes in oligomeric behavior. We find that ORF68 purified as a stable pentamer from human cells (Didychuk eLife 2021) and from insect cells (this work). We have also recombinantly expressed and purified UL32 from human cells. UL32 was largely monomeric after strep affinity purification (chromatogram below, unpublished), as we report from insect cells (this work, Fig. 1c). We switched to insect cell expression systems because of the easier scalability.

Our SEC-MALS data (Fig. 1d) shows that purified UL52 does not oligomerize into a pentamer in solution, so the observed sub-pentameric (3-mer/4-mer) assemblies are unlikely to be an artifact of cryo-EM freezing conditions or the air-water interface. We have not tested if UL52 forms complexes with dsDNA, although it likely does; it is possible that this interaction would stabilize a pentamer.

4) In Didychuk et al., 2021, HSV UL32 is shown to form pentameric rings; negative stained 2D class averages were generated from tagged protein (twin strep tag), produced in mammalian cells (HEK293T), and not purified using size exclusion chromatography. In the present study HSV UL32 was not observed to form pentameric complexes "We first attempted to visualize the pentameric species by negative stain electron microscopy but were unable to identify particles of the expected dimensions." However, it is not clear why this was the case. If the pentameric structures were readily produced in previous experiments, why was cross-linking needed in the current study? As such, the tripentamer complexes seem artifactual in nature.

While a sufficient number of particles were observed in a pentameric state to do 2D class averages in the eLife paper, this was not the dominant state. The results we report in this work are consistent with those reported in the eLife paper. Reviewer #2 (comment #1) was also concerned about the possibility of a crosslinking artifact: we reproduce our response below:

"Crosslinking is a commonly used technique to stabilize complexes that are observed through other means but do not survive the cryo-EM vitrification process. In an EMSA experiment (Supplementary Fig. 4a), UL32 binds 30 bp DNA and migrates slower than when bound to a 10 bp probe, consistent with formation of a supra-pentameric complex. The samples in the EMSA gels are not crosslinked. Additionally, an SDS-PAGE gel of the crosslinked product used for EM showed tight bands, supporting specific crosslinking (Supplementary Fig. 4b). These results suggest that crosslinking stabilizes a species that can form but is relatively unstable in solution."

We have updated Line 148 to clarify this. We have also included a negative stain micrograph, below, in which UL32 pentamers (purified from insect cells) are visible in the absence of crosslinking.

5) Although the data presented in Fig. 4b suggest that interface residues, K532 and C535, might play a role in the formation of the tripentamer and have a minor role in HSV-1 replication, these experiments are incomplete. Single mutations are needed for each residue to assess their individual contribution to tripentamer formation, evidence for a loss of tripentamer formation is needed, and evidence for protein expression is needed.

We agree that we have not unambiguously defined the role of the tripentamer, the precise contributions of residues K532 and C535, or defined the contribution of the tripentamer to HSV-1 viral replication. We seek to report this novel structure to lay the basis for future mechanistic work. Reviewer #2 (comment 1) also questioned the role of these residues in HSV-1 replication, and we addressed this by modifying the title of the results section (Line 216) to state that "Residues at the tripentamer interfaces contribute to infectious virion production in HSV-1" as well as Line 246 and 253 to indicate that the residues play a role in the viral life cycle.

Please refer to Supplementary Fig. 7e for a western blot showing that these mutants do not impact UL32 expression. We included explicit references to UL32 expression on Lines 239 and 288.

*6) In the previous negative stain electron micrographs reported by Didychuk et al., 2021, were the higher order tripentamer complexes seen? *

We did not observed tripentamers in the Didychuk et al. 2021 negative dataset. Tripentamer formation may be concentration dependent. Negative stain EM carried out at nanomolar concentrations would likely cause dissociation of tripentamers, but cryo-EM and EMSA in our work were carried out at micromolar concentrations and were able to capture the higher order tripentamer.

• 7) Formation of disulphide bonds between cysteine residues in vitro is not indicative of complexes forming in vivo during replication. What evidence is there for disulphide bond formation between packaging accessory factor pentamers for KSHV, EBV, and LCMV? In the present study, the disulphide bond could form due to proximity as a result of the cross-linking and the presence of molecular oxygen rather than a bona fide enzyme catalysed reaction during herpesvirus replication to generate packaging accessory factor tripentamers. *

We agree that it is unlikely that disulfide bonds form during infection and have removed this speculation from the manuscript (Line 343-346).

8) The DNA densities in Suppl. Fig. 6e to 6g are curious. As noted by the authors, the 30mer dsDNAs do not traverse through the central cavity of the pentamer. They appear to make contact with neighboring pentamers, again suggesting that these complexes are artefacts from cross-linking. This should be discussed more thoroughly.

Please refer to above discussion of crosslinking and Supplementary Fig. 4.

9) Previously proposed functional roles for ORF68 include a scaffold for terminase assembly, association of the terminase with the portal, generation of initial free ends, or coordination with other replication machinery (Didychuk et al., 2021). Presuming that the new structures for HCMV UL52 and HSV-1 UL32 occur naturally, how do they fit with the previously proposed functional roles of the herpesvirus packaging accessory factor? A more in-depth discussion of this would be valuable.

The common core fold and pentamer/pentamer-like assemble are common features, as is the conserved, positively-charged central channel. We have added additional discussion of this.

*Minor comments A lack of page numbers and line numbers made reviewing this manuscript more challenging than necessary. *

We have included page numbers and line numbers in the revised manuscript.

*As noted in the 'General comments' section above, ORF68 (3.37Å) and BFLF1 (3.60Å) both form pentamers (Didychuk et al., 2021) and were produced in mammalian systems HEK293T cells. Protein purification in the present study was performed in insect (SF9 or High Five) cells. Does this affect complex stability. Also, the tag was retained for UL32 in Didychuk et al., 2021; could this provide stability of the pentamer in the original studies? *

As discussed above, we have no evidence to suggest that expression in human vs. insect cell expression systems dramatically changes oligomerization behavior (Reviewer #3, comment 3). N-terminal purification tags were also retained in this study for structural work but were removed for SEC-MALS, which shows that UL32 is likely in concentration dependent equilibrium between (unstable) pentamers and monomers.

Suppl. Fig. 3 is missing.

We apologize for this oversight and have included Supplementary Fig. 3.

*"UL52 has two regions remodeled" The use of the word 'remodeled' is not appropriate in this context as it implies a single protein can form two shapes under different conditions rather than distinct structures between two disparate proteins; UL52 compared to ORF68. This should be rephrased. *

This was also noted by reviewer 2, and we have removed the term "remodel" throughout the manuscript text (i.e., Lines 134, 138, 140, 337, 341) and from Supplementary Figures 1, 3, and 5.

*What is the density in the central core of UL52 (Fig. 2a; Suppl. Fig. 2e)? Was any form of focused classification performed to establish the identity of the density within the central pseudocavity? *

As noted in the manuscript, this density could be which could be attributed to co-purified protein or nucleic acid, or part of the unresolved, negatively charged loop (residues 82-181) interacting with the positively charged central channel. We have done additional analysis of the central channel density (3D classification with a focus mask) and do not resolve any distinct densities, suggesting that the density is very dynamic.

*Does UL52 bind to dsDNA? To support the hypothesis that the herpesvirus packaging accessory factor has conserved functions across the three subfamilies dsDNA binding experiments should be performed. *

We have not done this experiment. We think that demonstrating this finding for two of the three herpesvirus subfamilies is sufficient.

There is no discussion about how these data relate to the previous functional model for ORF68 presented in Didychuk et al., 2021. Do the new data alter the previous functional models?

The precise mechanistic contribution of the packaging accessory factor remains unknown, and our data do not delineate between the proposed potential roles described in Didychuk et al. 2021. Importantly, our structural information, demonstration of pentameric ring formation, and significance of the positively charged central channel show that the core function of this factor is likely conserved across the virus family. This was not known before our work.

*There are some interesting grammatical phrases; please address throughout the manuscript. One example - "...a notable shared aspiration..." Proteins do not have aspirations. Please use a more formal scientific statement. *

We have updated the language on Line 327.

*Fig. 4b - Statistical analyses missing. Please provide. *

Fig. 6c - Statistical analyses are missing. Please provide. Protein folding/expression data missing; see Fig. 5C showing mutations that result in poor protein expression.

Suppl. Fig. 7f - Statistical analyses absent.

Statistical analysis of the viral complementation in Figs. 4b and 6c has been included. Note that the viral yields reported in Supplementary Fig. 7f were used to calculate complementation efficiency in Figs. 4b and 6c. Protein expression of mutants shown in Fig. 6c was previously included in Supplementary Fig. 7e and is referenced on Lines 288 and 293.

*Suppl. Fig. 2 and 5 - FSC curves have oddities, especially in the corrected curves. The cryo-EM resolution estimates calculated by CryoSPARC for the UL52 '3-mer' and 4-mer, and UL32 tripentamer are likely overestimated. In the PDB validation files each of the deposited structures has a warning for the resolution estimate "The value from deposited half-maps intersecting FSC 0.143 CUT-OFF 4.31 differs from the reported value 3.32 by more than 10 %", suggesting that the resolution estimates are inaccurate. The authors should provide a resolution estimate using loose masks and generate FSC curves using another software program such as RELION's postprocess to provide resolution estimates. *

Thank you for bringing this to our attention. The differences in the resolution estimates are a known issue and are highly influenced by the tightness of the mask. In the revised manuscript we have updated the FSC curves to not include auto-tightened masks and revised our resolution estimates. This slightly changed the resolution to 3.29 Å for both UL52 3-mer and 4-mer and to 3.09 Å for the UL32 consensus map. Please also see the local resolution estimation maps in Supplementary Figures 2e and 5e for an illustration of the range of resolutions in each map.

Suppl. Fig. 6f and 6g - Is there any visible density that might resemble the EGS crosslinking reagent?

We do not expect to observe density for EGS due to the long flexible linker (~16 Å) between the two reactive groups.

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Referee #3

Evidence, reproducibility and clarity

Summary.

The manuscript describes the cryo-EM structures of a conserved, necessary, herpesvirus genome packaging accessory factor for human cytomegalovirus (HCMV), UL52, and herpes simplex virus type-1 (HSV-1), UL32. Herpesvirus packaging accessory factors have unknown function but bind dsDNA. The UL52 and UL32 structures revealed a 5-fold symmetry similar to the previous X-ray crystallography structure for Kaposi's Sarcoma-associated herpesvirus (KSHV) ORF68 and the cryo-EM structure of Epstein-Barr virus (EBV) BFLF1. However, HCMV UL52 was reported to form two structures, a 3-mer and 4-mer whereas, HSV UL32 formed a supercomplex of trimeric pentamers (tripentamer) produced by dsDNA binding and crosslinking. Similar to previous studies with ORF68, mutagenesis of HSV-1 UL32 demonstrated the importance of zinc finger residues C297, C308, C544, and H581 for core fold stability and positively charged residues H563, R572 in the central channel in the pentamer for HSV-1 recovery in virus complementation assays. In addition, mutagenesis of K532 and C535 at the tripentamer interface helix reduced virus complementation by 50%. These findings have significant overlap and similarities to previously published experiments and confirm the properties of ORF68 and BFLF1, demonstrating the conserved nature of the required packaging accessory factor for herpesviruses.

Major comments.

The manuscript is generally well written with beautifully presented cryo-EM figures. Unfortunately, the new data seem to muddy the water rather than provide clarification about the role or function of the herpesvirus packaging accessory factor. Furthermore, it is not clear whether the structures presented in the manuscript reflect those produced during HCMV or HSV-1 infection. HCMV UL52 was presented to form two distinct structures, a 3-mer and a 4-mer (Fig. 2a). However, the authors acknowledge that the 3-mer is actually a 4-mer when the threshold for the cryo-EM map is lowered. The density is also visible in the PDB validation report for the 3-mer; EMD-74418. Given that ORF68, BFLF1, and UL32 (Didychuk et al., 2021) form complete pentamer rings, with BFLF1 forming stacked rings, it would seem odd for a protein with conserved function to deviate from a pentamer configuration, suggesting that the structures reported do not reflect the natively produced and functional protein. Unlike ORF68 (Didychuk et al., 2021) and UL32 (Suppl. Fig. 4), dsDNA binding experiments were not performed with UL52. Could the partial pentamers simply be poorly formed due to expression in insect cells (mammalian cells were used for protein purification in Didychuk et al., 2021), absence of dsDNA, or inappropriate buffer conditions? Moreover, were the EM grid and vitrification parameters optimized? Grid geometries and chemistries can have profound effects of protein stability especially in the context of the air-water interface, leading to degradation of protein complexes (Glaeser, 2018; D'Imprima et al., 2019). Does UL52 form complexes with dsDNA? Data are shown for the HSV-1 packaging accessory factor. Perhaps dsDNA would stabilize the UL52 pentamer.

In Didychuk et al., 2021, HSV UL32 is shown to form pentameric rings; negative stained 2D class averages were generated from tagged protein (twin strep tag), produced in mammalian cells (HEK293T), and not purified using size exclusion chromatography. In the present study HSV UL32 was not observed to form pentameric complexes "We first attempted to visualize the pentameric species by negative stain electron microscopy but were unable to identify particles of the expected dimensions." However, it is not clear why this was the case. If the pentameric structures were readily produced in previous experiments, why was cross-linking needed in the current study? As such, the tripentamer complexes seem artifactual in nature. Although the data presented in Fig. 4b suggest that interface residues, K532 and C535, might play a role in the formation of the tripentamer and have a minor role in HSV-1 replication, these experiments are incomplete. Single mutations are needed for each residue to assess their individual contribution to tripentamer formation, evidence for a loss of tripentamer formation is needed, and evidence for protein expression is needed. In the previous negative stain electron micrographs reported by Didychuk et al., 2021, were the higher order tripentamer complexes seen?

Formation of disulphide bonds between cysteine residues in vitro is not indicative of complexes forming in vivo during replication. What evidence is there for disulphide bond formation between packaging accessory factor pentamers for KSHV, EBV, and LCMV? In the present study, the disulphide bond could form due to proximity as a result of the cross-linking and the presence of molecular oxygen rather than a bona fide enzyme catalysed reaction during herpesvirus replication to generate packaging accessory factor tripentamers.

The DNA densities in Suppl. Fig. 6e to 6g are curious. As noted by the authors, the 30mer dsDNAs do not traverse through the central cavity of the pentamer. They appear to make contact with neighboring pentamers, again suggesting that these complexes are artefacts from cross-linking. This should be discussed more thoroughly.

Previously proposed functional roles for ORF68 include a scaffold for terminase assembly, association of the terminase with the portal, generation of initial free ends, or coordination with other replication machinery (Didychuk et al., 2021). Presuming that the new structures for HCMV UL52 and HSV-1 UL32 occur naturally, how do they fit with the previously proposed functional roles of the herpesvirus packaging accessory factor? A more in-depth discussion of this would be valuable.

Minor comments.

A lack of page numbers and line numbers made reviewing this manuscript more challenging than necessary.

As noted in the 'General comments' section above, ORF68 (3.37Å) and BFLF1 (3.60Å) both form pentamers (Didychuk et al., 2021) and were produced in mammalian systems HEK293T cells. Protein purification in the present study was performed in insect (SF9 or High Five) cells. Does this affect complex stability. Also, the tag was retained for UL32 in Didychuk et al., 2021; could this provide stability of the pentamer in the original studies?

Suppl. Fig. 3 is missing.

"UL52 has two regions remodeled" The use of the word 'remodeled' is not appropriate in this context as it implies a single protein can form two shapes under different conditions rather than distinct structures between two disparate proteins; UL52 compared to ORF68. This should be rephrased.

What is the density in the central core of UL52 (Fig. 2a; Suppl. Fig. 2e)? Was any form of focused classification performed to establish the identity of the density within the central pseudocavity?

Does UL52 bind to dsDNA? To support the hypothesis that the herpesvirus packaging accessory factor has conserved functions across the three subfamilies dsDNA binding experiments should be performed. There is no discussion about how these data relate to the previous functional model for ORF68 presented in Didychuk et al., 2021. Do the new data alter the previous functional models?

There are some interesting grammatical phrases; please address throughout the manuscript. One example - "...a notable shared aspiration..." Proteins do not have aspirations. Please use a more formal scientific statement.

Fig. 4b - Statistical analyses missing. Please provide.

Fig. 6c - Statistical analyses are missing. Please provide. Protein folding/expression data missing; see Fig. 5C showing mutations that result in poor protein expression.

Suppl. Fig. 2 and 5 - FSC curves have oddities, especially in the corrected curves. The cryo-EM resolution estimates calculated by CryoSPARC for the UL52 '3-mer' and 4-mer, and UL32 tripentamer are likely overestimated. In the PDB validation files each of the deposited structures has a warning for the resolution estimate "The value from deposited half-maps intersecting FSC 0.143 CUT-OFF 4.31 differs from the reported value 3.32 by more than 10 %", suggesting that the resolution estimates are inaccurate. The authors should provide a resolution estimate using loose masks and generate FSC curves using another software program such as RELION's postprocess to provide resolution estimates.

Suppl. Fig. 6f and 6g - Is there any visible density that might resemble the EGS crosslinking reagent?

Suppl. Fig. 7f - Statistical analyses absent.

References.

Didychuk AL, Gates SN, Gardner MR, Strong LM, Martin A, Glaunsinger BA. A pentameric protein ring with novel architecture is required for herpesviral packaging. Elife. 2021 Feb 8;10:e62261. doi: 10.7554/eLife.62261. PMID: 33554858; PMCID: PMC7889075.

D'Imprima E, Floris D, Joppe M, Sánchez R, Grininger M, Kühlbrandt W. Protein denaturation at the air-water interface and how to prevent it. Elife. 2019 Apr 1;8:e42747. doi: 10.7554/eLife.42747. PMID: 30932812; PMCID: PMC6443348.

Gardner MR, Glaunsinger BA. Kaposi's Sarcoma-Associated Herpesvirus ORF68 Is a DNA Binding Protein Required for Viral Genome Cleavage and Packaging. J Virol. 2018 Jul 31;92(16):e00840-18. doi: 10.1128/JVI.00840-18. PMID: 29875246; PMCID: PMC6069193.

Glaeser RM. PROTEINS, INTERFACES, AND CRYO-EM GRIDS. Curr Opin Colloid Interface Sci. 2018 Mar;34:1-8. doi: 10.1016/j.cocis.2017.12.009. Epub 2017 Dec 22. PMID: 29867291; PMCID: PMC5983355.

Significance

General assessment: The strengths of this manuscript are the structural information provide by the cryo-EM maps for the HCMV UL52 and HSV-1 UL32 and the mutagenesis studies that corroborate previous studies for the packaging accessory factor for gammaherpesviruses KSHV and EBV. However, there are limitations. These are centered on whether the structures are representative of UL52 and UL32 complexes produced during replication rather than over expression in insect cells and stabilization using chemical cross-linking.

There is a lack of novelty in the context of the herpesvirus packaging factor. The pentameric architecture, DNA binding, zinc fingers (4), and charged residues required for DNA binding were conclusively demonstrated in previous studies (Gardner and Glaunsinger, 2018; Didychuk et al., 2021). Thus, the novelty comes from the different pentameric structures; UL52 4-mer and UL32 tripentamer. However, if these are artefactual structures due to the expression system (mammalian versus insect) used, air-liquid interface induced protein instability, or cross-linking, the novelty is lost. That's not to say the data are not informative for the herpesvirus community.

Advance: The advance in this manuscript is the new structural information for the UL52 and UL32. Even if the higher order complexes are potential artefacts, high resolution structure information for the subunit is especially informative. The mutagenesis data for UL32 are also informative in that the provide important information about a conserved and necessary protein needed for herpesvirus replication and has the potential to be used as a novel druggable target.

Audience: The manuscript will appeal to specialized and broad audiences and could influence research into antiviral therapies for herpesviruses. My field of expertise is herpesvirology, structural biology, and cryogenic electron microscopy modalities,

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

In this manuscript, the authors investigate the relationship between genetic codes and their robustness to single-point mutations. They construct ten alternative genetic codes by reassigning nine codons to Leu, Ser, or Ala, and assess mutational robustness using three reporter proteins subjected to error-prone PCR. This represents an interesting experimental approach to addressing the hypothesis that the standard genetic code is optimized for mutational robustness.

We sincerely thank the reviewer for the positive evaluation of our experimental approach. We are encouraged that the reviewer recognizes the value of constructing multiple non-standard genetic codes in vitro and using them to experimentally examine the relationship between genetic code arrangement and mutational robustness. In the revised manuscript, we have further clarified the scope of our experimental system and the interpretation of the results, particularly emphasizing that our conclusions concern the mutational robustness of individual reporter protein activity measured in an in vitro translation system.

Major comment:

While I find the experimental design valuable, I am not fully convinced by the authors' conclusion that "alterations of the genetic code within the ranges explored in this study have no significant effect on mutational robustness". The current analysis is based on the functional output of three individual reporter proteins. Given that cellular systems involve far more complex interactions, it would be more appropriate to limit this conclusion to mutational robustness at the level of individual protein activity, rather than making broader generalizations.

We thank the reviewer for this important comment. We agree that our original wording was broader than what can be directly supported by the present experiments. Because our analysis is based on the functional outputs of three individual reporter proteins translated in a reconstituted in vitro system, the results do not directly address mutational robustness at the level of the cellular system, protein interaction networks, or organismal fitness.

Accordingly, we have revised the manuscript to limit our conclusion to the mutational robustness of individual reporter protein activity. In the revised Abstract, Results, and Discussion, we now state that within the experimentally tested range of non-standard genetic codes, we did not detect a dependence of the mutation-induced decrease in reporter protein activity on mutational cost. We have also added a statement in the Discussion noting that cellular systems involve many additional layers, including protein–protein interactions, metabolic networks, quality-control systems, and growth selection, and that whether genetic code arrangement affects robustness at these higher biological levels remains an important question for future work.

Specifically, we have added this explanation and the new experiment to the revised manuscript as follows.

Abstract

“This result provides direct experimental evidence that mutational robustness does not significantly change in individual reporter protein activity when the genetic code is altered within the range of mutational cost tested in this study…”

Introduction

“Random mutations decreased reporter protein function at similar levels across all genetic codes examined, implying that alterations of the genetic code within the ranges explored in this study have no significant effect on mutational robustness of individual protein activity.”

Result

“Taken together, these results indicate that mutational robustness of individual reporter protein function did not substantially differ among the genetic codes…”

Discussion

“…suggesting that mutational robustness of protein activity remained largely unchanged within at least the ranges of mutational cost tested in this study. It should be noted that this conclusion is limited to the activity of individual reporter proteins translated in a reconstituted in vitro system. Therefore, whether similar trends would be observed at the level of cellular fitness or long-term evolution remains an open question.”

Specific comments

(1) tRNA modification and expression efficiency (Page 5, line 131)

The authors attribute the observed inefficiency to the lack of chemical modifications in the tRNAs used. However, gene expression efficiency can also be strongly influenced by DNA sequence design. To better support this claim, it would be helpful to compare luciferase activity when expressed using native E. coli tRNAs. This comparison could clarify whether the observed effects are due to tRNA modification status or other sequence-dependent factors.

We thank the reviewer for this important suggestion. We agree that the translation efficiency of NanoLuc templates with 21-, 32-, and 46-codons may be affected not only by the chemical modification of tRNAs but also by sequence-dependent factors, such as codon context and mRNA structure.

To examine this possibility, we performed an additional comparison using native E. coli tRNAs in the tfPURE system. When the NanoLuc templates encoded with 21, 32, or 46 codons were translated using native E. coli tRNAs, the observed luminescence values were 1.2 × 10¹⁰, 0.78 × 10¹⁰, and 0.60 × 10¹⁰, respectively. Thus, the 46-codon NanoLuc template showed lower activity than the 21- and 32-codon templates even with native tRNAs, indicating that sequence-dependent effects indeed contribute to translation efficiency.

However, the difference among these templates with native E. coli tRNAs was within approximately two-fold. This effect was much smaller than the marked decrease observed when the 46-codon template was translated using the in vitro prepared 46 tRNAs SGC system. Therefore, while sequence-dependent effects cannot be excluded, the inefficient translation in the reconstructed 46 tRNAs SGC is likely to be mainly attributable to the limited functionality of unmodified tRNAs decoding NNA codons.

We have revised the manuscript to clarify this interpretation and have added the new comparison using native E. coli tRNAs.

“We also examined whether the lower translation efficiency of the 46-codon NanoLuc template could be explained by sequence-dependent effects, such as codon context or mRNA structure. When the 21-, 32-, and 46-codon NanoLuc templates were translated using native E. coli tRNAs in the tfPURE system (Figure 1–figure supplement 2), the 46-codon template showed lower activity than the 21- and 32-codon templates; however, this difference was within approximately two-fold. Accordingly, we decided to use only the 32 codons used in near-SGC (i.e., excluding NNA codons) in the subsequent construction of non-standard genetic codes.”

(2) Discrepancy between expression level and activity (Figure S7 vs Figure S8).

Although GAL expression levels appear similar across different genetic codes (Figure S7), their activities differ substantially (Figure S8), even in the low-mutation library. This discrepancy warrants further investigation. Possible explanations include differences in protein folding efficiency or translational error rates, as mentioned by the authors in the main text.

To address this, the authors could analyze the protein products using mass spectrometry. If this is not feasible due to low expression levels, alternative approaches such as SDS-PAGE (e.g., with radiolabeling or Western blotting) would still provide valuable information. Additionally, comparing activity after in vitro refolding could help distinguish between folding defects and sequence-level errors. While I understand that the primary aim of this study is to compare mutational robustness across genetic codes, discussing these observations would significantly enhance the mechanistic insight of the work.

We agree that the discrepancy between similar GAL expression levels and different GAL activities across genetic codes is important for interpreting the results.

In our experiment, GAL protein amounts were quantified using a C-terminal HiBiT tag. Because the HiBiT tag was fused to the C-terminus of GAL, this assay indicates that the amount of C-terminally completed GAL products did not differ substantially among genetic codes. However, we agree that this assay does not evaluate the sequence fidelity, amino acid misincorporation patterns, or folding state of the translated products. Therefore, the observed differences in GAL activity despite similar HiBiT signals may reflect genetic code-dependent differences in translational error rates, amino acid misincorporation, protein folding efficiency, or other effects on the fraction of catalytically active protein.

We have revised the Discussion to explicitly describe this interpretation and to clarify that detailed mechanistic dissection of these baseline activity differences, for example by mass spectrometry, SDS-PAGE/Western blotting, or refolding analysis, is an important future direction but beyond the scope of the present study. We also clarified that the main analysis in this study uses the ratio of activity from the high-mutation library to that from the corresponding low-mutation library within each genetic code.

We have added this explanation to the revised manuscript as follows.

“Although protein amounts quantified by the HiBiT tag were comparable among genetic codes, GAL activities differed substantially. This indicates that the activity differences among genetic codes were not primarily attributable to differences in the amount of C-terminally completed translation products. The HiBiT assay does not provide information on the fraction of catalytically active protein, including sequence fidelity or folding state, and therefore cannot distinguish among these possibilities. Detailed characterization of translated products by mass spectrometry would provide further mechanistic insight into how individual non-SGCs affect protein quality. However, the primary objective of the present study was to compare mutation-dependent activity loss across genetic codes. Therefore, we evaluated this effect by normalizing the activity of the high-mutation library to that of the corresponding low-mutation library within each genetic code.”

(3) Protein expression analysis for additional reporters.

Since protein expression levels are critical for interpreting reporter activity, similar analyses should also be performed for luciferase (Luc) and mSG in both high- and low-mutation libraries. This would ensure that differences in activity are not confounded by variations in protein abundance.

We agree that protein abundance is an important factor for interpreting reporter activity. In this study, we performed HiBiT-based protein quantification for GAL because GAL showed the largest variation in absolute activity among genetic codes, even in the low-mutation library. This analysis showed that the amount of C-terminally completed GAL products was broadly comparable among genetic codes and between low- and high-mutation libraries, indicating that the observed GAL activity differences were not primarily attributable to differences in total protein abundance.

For all three reporters, our main analysis was based on the ratio of activity from the high-mutation library to that from the corresponding low-mutation library within each genetic code. This normalization was intended to evaluate mutation-dependent activity loss while reducing the influence of code-specific baseline differences in expression level or protein quality. We believe that the data are sufficient to evaluate the effect of mutations on protein activities. Nevertheless, we agree that protein quantification for Luc and mSG would provide useful information regarding variation in the baseline levels of reporter activity, and this is an important direction for future work.

Reviewer #2 (Public review):

Summary:

The study addresses the long-standing question in molecular biology and genetics: why has nature selected the current genetic code (SGC, or standard genetic code)? The authors have tested 'error minimization theory', one of the prevailing hypotheses to explain this. Their approach is to create a minimum genetic code (MGC) and its variants (3^9 theoretical possible codes). Using three parameters to quantify the effect of mutations (Polarity, volume, and hydropathy), they computationally test the cost of these genetic codes (3^9) by simulations. Finally, they test this cost experimentally using an in vitro translation system with 10 select genetic code variants with a range of costs (low to high). They use three randomly mutated reporter genes for this purpose - beta-galactosidase, luciferase, and mSG. They find no correlation between the cost of the genetic code and the reporters' output. Based on these observations, they suggest that error-minimization theory may not explain the current egocentric code.

The question they are asking is very exciting, and their approach is solid. The authors are very careful in their analyses and conclusions.

We sincerely thank the reviewer for the positive assessment of our study and for the helpful suggestions. We are encouraged that the reviewer found the question exciting and the approach solid. In the revised manuscript, we have clarified the rationale for using the MGC/near-SGC framework, added further analyses and explanations of the mutational cost calculations, and revised the wording of our conclusions to more explicitly define the scope and limitations of the present experimental system.

(1) The rationale for using MGC instead of SGC: It is unclear why the authors rely on the MGC for this analysis when the central question concerns the SGC. If the goal is to evaluate whether the SGC minimizes mutational cost, a more direct approach would be to generate alternative variants of the SGC itself and compare their mutational cost distributions. At present, it is difficult to assess whether conclusions drawn from this comparison are fully relevant to the stated biological question.

We thank the reviewer for this important comment. We agree that directly constructing alternative variants of the SGC by changing amino acid assignment from SGC would be the most straightforward approach to testing whether the SGC minimizes mutational cost. However, this approach is currently not feasible in our reconstituted translation system for two reasons.

First, our attempt to construct a 46-tRNA SGC-like system revealed that translation using the 46-codon NanoLuc template was approximately 100-fold less efficient than translation using the MGC or near-SGC (Fig. 1). This low activity likely reflects inefficient decoding of NNA codons by in vitro-prepared tRNAs, which lack native post-transcriptional modifications. Because this system did not provide sufficient translational activity for systematic reporter assays, we restricted subsequent experiments to the 32-codon near-SGC framework, excluding NNA codons. We now describe this technical limitation more explicitly in the revised manuscript.

Second, the MGC framework provides vacant codons that can be reassigned by adding anticodon-variant tRNAs. This feature is essential for constructing multiple genetic code variants in parallel under controlled in vitro conditions. We, therefore, constructed the near-SGC-based non-SGC by adding each tRNA variant to the MGC as an experimentally tractable model system to verify whether differences in genetic code arrangement affect mutation-induced decreases in reporter protein activity.

We have added this explanation to the revised manuscript as follows.

“We first established a minimal genetic code, composed of 21 tRNAs with vacant codons, which allows multiple alternative codon assignments to be introduced under otherwise comparable translation conditions.”

Despite this technical limitation, we believe that the central conclusion of this study—that mutational robustness in individual reporter protein activity does not change significantly when the genetic code is altered within the range of mutational costs tested here—remains well-supported by the present results.

(2) The mutational cost analysis appears biologically oversimplified because all amino acid substitutions are treated equivalently. The analysis assumes that all mutations contribute equally to fitness consequences, which does not reflect biological reality. In natural proteins, the impact of an amino acid substitution depends strongly on its structural and functional context. For example, substitutions affecting catalytic residues, ligand-binding interfaces, phosphorylation sites, or other regulatory motifs can severely impair protein function even when associated changes in polarity, hydropathy, or volume are minimal. Conversely, substitutions in structurally permissive or functionally dispensable regions may have little or no measurable effect despite larger physicochemical differences. Therefore, changes in polarity, hydropathy, and volume alone do not necessarily predict functional consequences.

We agree that the mutational cost used in this study is a simplified measure and does not capture the full biological complexity of amino acid substitutions. As the reviewer pointed out, the functional consequence of a substitution depends strongly on its structural and functional context, including whether the affected residue is involved in catalysis, ligand binding, protein–protein interactions, regulatory motifs, folding, or structurally permissive regions.

In this study, we used physicochemical-property-based mutational costs because this type of definition has been widely used in classical formulations of the error minimization theory. Our aim was therefore not to construct a comprehensive predictor of protein fitness effects, but to experimentally test whether the conventional theoretical cost metrics used to discuss genetic code optimality are reflected in the average mutation-induced decrease in reporter protein activity. We have now clarified this rationale in the revised manuscript.

“It should be noted that this conclusion is limited to the activity of individual reporter proteins translated in a reconstituted in vitro system. Therefore, whether similar trends would be observed at the level of cellular fitness or long-term evolution remains an open question.”

(3) It is not clear why they increased the concentration of the two tRNAs in near-SGC. Have they maintained the same tRNA concentrations in experiments explained in Fig 5 for all 10 genetic codes tested?

We apologize that the rationale for increasing the concentrations of tRNA^Val_CAC and tRNA^Arg_CCU was not sufficiently clear in the original manuscript. As we wrote in the previous manuscript, “To improve translation efficiency with near-SGC, we focused on two tRNA concentrations (tRNA^Val_CAC and tRNA^Arg_CCU), which were suggested to have low activities in a previous study (Iwane et al., 2016),” we tested whether increasing their concentrations would improve translation efficiency. As shown in Figure 1–figure supplement 1, NanoLuc activity increased as the concentrations of these two tRNAs were raised and used at 100 ng/µL for tRNA^Val_CAC and tRNA^Arg_CCU in the optimized near-SGC, referred to as near-SGC (RV), and in all subsequent experiments. Additional anticodon-variant tRNAs required for each non-SGC were used at optimized concentrations determined from Figure 2–figure supplement 1. For each genetic code, the same tRNA composition and concentrations were used for the low- and high-mutation libraries (See Supplementary Table S7). To clarify this point, we added the sentence, “The increased concentrations of these two tRNAs were used in all the subsequent experiments,” in the corresponding part.

Reviewer #3 (Public review):

In this manuscript, Miyachi and Ichihashi investigate whether the arrangement of the genetic code affects mutational robustness. Using an in vitro minimal genetic code with vacant codons, they constructed 10 non-standard genetic codes by reassigning Ala, Ser, and Leu, generating codes with replacement costs that were generally higher than those of the standard genetic code across several amino acid property measures. They then tested how random mutations affected the activity of reporter proteins translated under these altered codes. Although error minimization theory predicts that higher-cost codes should make mutations more harmful, the authors report that protein function declined to a similar extent across all codes examined, suggesting that mutational robustness remains largely unchanged within the range of genetic code alterations tested here.

Strengths:

This is an interesting study that investigates one of the most fundamental and intriguing questions in molecular evolution: the emergence of the genetic code, which is nearly universal across nature. The in vitro approach is a powerful aspect of the work and provides an opportunity to examine this phenomenon experimentally at a depth that has previously been inaccessible.

Weaknesses:

However, the authors' use of random mutation libraries has certain limitations that prevent the study from realizing its full potential to uncover the mechanisms governing the molecular evolution of the genetic code.

We sincerely thank the reviewer for the positive evaluation of our study and for recognizing the strength of the in vitro approach. We are encouraged that the reviewer considers this system a powerful way to experimentally address the emergence of the genetic code.

We also appreciate the reviewer’s constructive comments regarding the limitations of random mutation libraries. We agree that pooled random libraries do not allow us to assign functional effects to individual mutations or to fully uncover the molecular mechanisms underlying mutational robustness. In the revised manuscript, we therefore clarify that our conclusions concern the library-averaged effects of random mutations on individual reporter protein activity, rather than the effects of specific mutations or cellular-level fitness. To address this limitation, we have added explanations of the scope and limitations of the present approach.

(1) Statistical analyses are missing for several of the manuscript's main claims. This issue applies throughout the paper, including, but not limited to, Figures 1D, 2B, 4B-D, and 5B.

We thank the reviewer for this important comment. We agree that statistical analyses are necessary to support the major claims of the manuscript. We have therefore added statistical analyses appropriate for the purpose and experimental design of each figure.

For Fig. 1D, we performed one-way ANOVA followed by Tukey’s post hoc test on NanoLuc activity to compare translation efficiencies among the MGC, near-SGC, near-SGC (RV), and SGC conditions. This analysis showed a significant overall difference among conditions (one-way ANOVA, p < 0.0001). Tukey’s post hoc test showed that near-SGC was significantly lower than MGC, that near-SGC (RV) significantly improved near-SGC translation, and that near-SGC (RV) was not significantly different from MGC. In contrast, the 46-tRNA SGC remained significantly less efficient than near-SGC (RV). We have summarized the major comparisons in Supplementary Table S8.

For Fig. 2B, we compared NanoLuc activity between the 21-code control and the corresponding 21+1-code condition for each codon reassignment using Welch’s t-test on luminescence. This analysis was added to statistically support whether each anticodon-variant tRNA increased NanoLuc translation from the corresponding reassigned template. The statistical results are summarized in Supplementary Table S9.

For Fig. 4B–D, we converted mutation rates per base to estimated numbers of mutations per gene and performed Spearman’s rank correlation analysis to evaluate whether reporter activity decreased monotonically with increasing mutational load. This analysis showed strong negative monotonic trends between mutation rate (estimated mutation number) and reporter activity for all three reporters (ρ = −0.90 to −1.00), supporting that the random mutation libraries reduced protein activity in a mutation-load-dependent manner.

For Fig. 5B, replicate-level data were available for GAL, and we therefore performed two-way ANOVA using genetic code and mutation level as factors. This analysis detected significant main effects of genetic code and mutation level, indicating that GAL activity differed among genetic codes and decreased in the high-mutation library. However, no significant interaction between genetic code and mutation level was detected, indicating that the magnitude of mutation-induced activity reduction was not strongly code-dependent under the conditions examined.

Finally, because the central claim of Fig. 5C, 5E, and 5G is that mutational cost does not systematically predict mutation-induced activity loss, we performed Spearman’s rank correlation analysis between each mutational cost metric and the high-/low-mutation activity ratio. No significant correlations were detected for any reporter or cost metric (Spearman’s ρ = −0.23 to 0.25), supporting the conclusion that mutational cost did not show a detectable monotonic relationship with mutation-induced activity loss within the tested range.

We have added these statistical analyses to the revised manuscript. The following sentences were added to the figure legends:

Fig. 1

“Statistical comparisons in (D) were performed using one-way ANOVA followed by Tukey’s post hoc test on NanoLuc activity; major comparisons are summarized in Table S8.”

Fig. 2

“For each template, NanoLuc activity in the 21-code and corresponding 21+1-code conditions was compared using Welch’s t-test on luminescence. Statistical results are summarized in Table S9.”

Fig. 4

“Spearman’s rank correlation coefficients were ρ = −0.90 for GAL, ρ = −1.00 for Luc, and ρ = −1.00 for mSG”

Fig. 5

“For GAL activity in (B), two-way ANOVA was performed using genetic code and mutation level as factors. Significant main effects of genetic code and mutation level were detected (both p < 0.0001), whereas their interaction was not significant. For (C), (E), and (G), Spearman’s rank correlation analysis was performed between each mutational cost metric and the high-/low-mutation activity ratio. Statistical details are summarized in Table S10.”

(2) In Figure 2A, the authors modify the NanoLuc gene by reassigning Ala, Leu, or Ser to new codons and elegantly show that the in vitro availability of the corresponding tRNAs is important for protein function. However, the functional importance of the specific modified positions within NanoLuc is not clear. As a result, it is difficult to determine what the expected consequences of these codon changes should be, which in turn limits the interpretation of the observed changes in protein activity. To improve the interpretability of this experiment, the authors should report exactly how many codons were modified in each variant and, ideally, examine the effect of progressively increasing the number of reassigned codons.

We agree that the exact positions and numbers of codon replacements should be clearly reported. In the revised manuscript, we have added a list of the modified amino acid positions. In brief, two Ala codons, three Ser codons, or four Leu codons were replaced with the target vacant codon; the modified positions were Ala16 and Ala120, Ser31, Ser49, and Ser150, and Leu32, Leu67, Leu144, and Leu170, respectively.

We also agree that progressively increasing the number of reassigned codons would provide additional mechanistic insight. However, the purpose of Fig. 2 was to test whether each vacant codon could be decoded by the corresponding anticodon-variant tRNA to produce functional NanoLuc, rather than to analyze the positional contribution of each replacement. We previously performed such progressive codon replacement analysis for one reassigned codon, ACG, in a related study (Miyachi et al., 2025), and the results supported the same qualitative interpretation. Although we did not repeat this progressive analysis for all codons in the present study, we expect that the qualitative interpretation of Fig. 2 would not be substantially changed.

We have revised the figure text to clarify the scope of the experiment and added the detailed codon replacement information.

“(A) Schematic illustration of reassignment experiments. Translation with the original MGC and NanoLuc template is shown at the top for comparison. An example of Ala reassignment to the UUG codon is shown at the bottom. In this example, three Ala codons in the NanoLuc sequence were replaced with one type of vacant codon (e.g., UUG), generating a 21 + 1 (UUG-Ala) codon set. Similar reassignment experiments were performed for three amino acids (Ala, Ser, and Leu) and nine vacant codons. Specifically, two Ala codons (Ala16 and Ala120), three Ser codons (Ser31, Ser49, and Ser150), or four Leu codons (Leu32, Leu67, Leu144, and Leu170) were replaced.”

(3) The calculations presented in Figure 3 raise an interesting conceptual question: why does the near-standard genetic code not exhibit the lowest cost? One possible explanation is that the standard genetic code evolved under multiple competing constraints and is therefore not expected to be optimal for any single cost metric, while still achieving strong overall performance. In this context, it would be informative if the authors combined the three cost measures into a single integrated index and examined whether the near-SGC performs more favorably when all three dimensions are considered together. Such an analysis could add important depth to the study.

We agree that the near-SGC is not necessarily expected to minimize each individual cost metric, because the standard genetic code may reflect multiple competing physicochemical, translational, biosynthetic, and evolutionary constraints rather than optimization of a single property.

To address this point, we added an integrated cost analysis combining the three physicochemical cost metrics, Cost_PR, Cost_MV, and Cost_HI. Because these three metrics have different numerical scales, we normalized each metric before integration. We used two types of integrated indices.

First, for each metric m 𝛜 {PR, MV, HI}, we calculated a min–max normalized cost,

Where G denotes the set of 19,683 candidate non-SGCs generated by assigning Ala, Ser, or Leu to the nine vacant codon boxes. We then defined the integrated min–max cost as

Second, we calculated a z-score-normalized cost for each metric,

Where µ_m,G and 𝜎_m,G are the mean and standard deviation of Cost_{m_norm} across the candidate non-SGCs. The integrated z-score cost was then defined as

Using both integrated indices, the near-SGC ranked first when compared with all 19,683 candidate non-SGCs; in other words, no candidate non-SGC showed a lower integrated cost than the near-SGC. The integrated min–max cost of the near-SGC was 0.01525, whereas the lowest value among candidate non-SGCs was 0.12301. Similarly, the integrated z-score cost of the near-SGC was −2.47947, whereas the lowest candidate value was −1.90838.

We have added this integrated cost analysis as Supplementary Figure 5–figure supplement 7. We have also revised the Discussion to note that the near-SGC does not necessarily minimize every individual physicochemical cost, but performs most favorably when PR, MV, and HI are considered comprehensively. This result is consistent with the idea that the standard genetic code may represent a compromise among multiple constraints rather than optimization of a single physicochemical property.

“We consider that the cost ranges examined in this study represent substantial fractions, especially for MV and HI. Although the near-SGC did not necessarily exhibit the lowest cost for each individual physicochemical metric, this does not mean that it is unfavorable in the multidimensional cost space. Because the SGC may reflect a balance among multiple physicochemical constraints rather than optimization of a single property, we also calculated integrated cost indices by combining Cost_PR, Cost_MV, and Cost_HI after min–max normalization or z-score normalization. In both integrated indices, the near-SGC showed the lowest overall cost when compared with all 19,683 candidate non-SGCs (Figure 5–figure supplement 7), indicating that no candidate non-SGC exhibited a lower combined cost than the near-SGC when the three physicochemical properties were considered comprehensively.”

(4) It is difficult to assess the consequences of the random mutations presented in Figure 4 on reporter gene function based solely on the reported "error rate/base" parameter. In particular, the x-axis in Figure 4B should be converted into the estimated number of mutations per gene. This would make the results more intuitive and would allow the reader to better evaluate the expected degree of disruption to protein function.

We agree that the mutation rate per base alone does not provide an intuitive sense of the expected mutational burden for each reporter gene. We therefore added a second x-axis to Fig. 4B–D showing the estimated number of mutations per gene. This value was calculated by multiplying the mutation rate per base by the coding sequence length of each reporter gene.

We retained the original mutation rate per base axis to preserve the direct link to the sequencing-based mutation rate measurement, while adding the estimated mutations per gene axis to improve interpretability. We have revised the figure and figure 4 legend accordingly.

“The lower x-axis indicates the estimated number of mutations per gene, calculated by multiplying the mutation rate per base by the coding sequence length of each reporter gene.”

(5) A central limitation of the random mutagenesis libraries used in Figure 5, which also underlie one of the manuscript's main claims, is that the exact mutations and their distribution across the reporter genes are not reported. In addition, protein activity is measured only at the level of the entire library, without directly linking individual mutations to their functional consequences. This substantially limits mechanistic interpretation. In my view, this issue can only be addressed convincingly if the authors test a set of defined variants carrying specific mutations and directly evaluate their functional effects.

(6) Related to the previous point, in Figures 5C, 5E, and 5G, the authors present the ratio between low-mutation-rate and high-mutation-rate libraries. However, because each library contains a different collection of mutations, it is unclear what can be inferred from these comparisons. To overcome this limitation, the authors should assess the effects of altered genetic codes on specific, defined mutations rather than on heterogeneous mutation pools alone.

(7) Along the same lines, in Figures 5C, 5E, and 5G, it is unclear why the effects of random mutations would be expected to correlate with the three calculated cost metrics, given that the positions, identities, and functional relevance of the mutations within the genes are not known. Without this information, the biological meaning of these correlations remains difficult to evaluate.

We agree that using pooled random mutation libraries does not allow us to directly link individual mutations to their functional consequences. We also agree that testing defined variants carrying specific mutations would provide a more direct and mechanistic understanding of how each genetic code affects the functional impact of particular amino acid substitutions. However, the purpose of the present study was different from such a defined-variant analysis. Our aim was to experimentally test whether the conventional mutational cost metrics used in error minimization theory predict the average effect of random mutational loads on protein activity. Because these theoretical costs are themselves defined as average expected physicochemical effects over many possible single-nucleotide substitutions, we reasoned that pooled random mutation libraries provide an appropriate first experimental framework to evaluate whether such average-cost metrics are reflected in the average functional output of translated proteins.

We agree that low- and high-mutation libraries do not contain identical sets of mutations. Therefore, the high-/low-mutation activity ratio should not be interpreted as the effect of the same individual variants before and after additional mutations. Rather, it represents the relative reduction in average activity caused by increasing the mutational burden in a heterogeneous mutation pool under each genetic code. We have revised the text to clarify this interpretation.

We also agree that the positions, identities, and functional relevance of individual mutations are not resolved in this pooled assay. This limitation prevents us from assigning mechanistic effects to specific substitutions. At the same time, using a small set of defined variants would introduce its own selection bias, because the conclusions could strongly depend on which mutations and which protein positions were chosen. Therefore, we consider the random-library approach to be a useful first step for testing library-averaged effects, whereas systematically defined variant analysis or genotype-resolved activity assays will be necessary to reveal mutation-specific mechanisms in future studies.

In response to the reviewer’s concern, we have revised the Discussion to explicitly limit our conclusion to library-averaged effects on individual reporter protein activity. We now state that this approach does not identify the functional effects of individual mutations and that future studies using defined variants or high-throughput genotype–phenotype mapping will be required to determine how specific substitutions contribute to genetic code-dependent mutational robustness.

Result

“To estimate the average activity reduction associated with increased mutational burden under each genetic code, we calculated the ratio of activity obtained from the high-mutation library to that from the corresponding low-mutation library and plotted this ratio against each of the three mutational costs (Fig. 5C).”

Discussion

“A further limitation of this study is that the reporter activities were measured at the level of pooled random mutation libraries. Therefore, the high-/low-mutation activity ratio used in this study should be interpreted as the relative reduction in average activity caused by increasing the mutational burden in a heterogeneous mutation pool, rather than as the effect of identical variants before and after additional mutations. This library-averaged approach was chosen because the mutational costs considered here are also defined as average expected physicochemical effects over many possible single-nucleotide substitutions. In addition, because the non-SGCs constructed in this study were generated by reassigning only Ala, Ser, and Leu, the detectable effects may depend on how frequently mutations involving these amino acids occur in each reporter gene and whether the affected positions are functionally important. If genetic code dependent effects are restricted to a small subset of deleterious variants, such effects may be masked in pooled activity measurements. Future studies using defined variants or high-throughput genotype–phenotype mapping assays will be required to determine the mutation-specific and position-specific mechanisms underlying genetic code dependent effects on protein function (Rozhoňová et al., 2024).”

(8) For each mutagenesis library, the number of variants, the average number of mutations per variant, and the distribution of mutation positions should be reported clearly and transparently. These details are important for evaluating the strength of the conclusions.

We agree that a more transparent characterization of the random mutagenesis libraries is necessary for evaluating the strength and limitations of our conclusions.

In the revised manuscript, we have added the estimated number of mutations per gene to the Results section. This value was calculated by multiplying the mutation rate per base by the coding sequence length of each reporter gene. For the high-mutation libraries used in Fig. 5, the estimated numbers of mutations per gene were approximately 8.0 for GAL, 4.5 for Luc, and 3.3 for mSG. We also added position-wise mutation profiles along each reporter gene (Figure 4–figure supplement 2), in addition to the heatmap shown in the original manuscript. These analyses clarify the mutational burden of each library and show that mutations were broadly distributed across the analyzed regions (approximately 300 nt in the middle of each gene) of the reporter genes.

Regarding the number of variants, the translation reactions were performed using 5 nM DNA template in a 5 µL reaction, corresponding to approximately 1.5 × 10¹⁰ DNA molecules. However, this value represents the total number of DNA molecules introduced into the reaction and does not directly indicate the number of unique full-length sequence variants, because multiple molecules can share the same genotype, and our sequencing analysis was designed to quantify mutation frequencies and positional distributions rather than to reconstruct full-length genotypes of individual library members. Therefore, we do not infer the exact number of unique variants in each library. Instead, we report the average mutation burden and position-wise non-reference rate distributions.

We have revised the Results and added Supplementary Figure 4–figure supplement 2 accordingly.

“For this experiment, two random mutation libraries were used: a low-mutation library prepared using the high-fidelity polymerase and a high-mutation library prepared using Taq DNA polymerase at a Mn²⁺ concentration that yields mutation rates of 0.002 – 0.005 per base (0.0026 for GAL, 0.0027 for Luc, and 0.0048 for mSG, corresponding to approximately 8.0, 4.5, and 3.3 mutations per gene). We also plotted position-wise non-reference rates along the analyzed regions of each reporter gene, confirming that mutations were broadly distributed across the amplicons (Figure 4–figure supplement 2).”

(9) Because only three amino acids were manipulated in the non-standard genetic codes, it remains unclear whether these particular amino acids occupy positions in the reporter proteins that are especially important for function and therefore likely to generate strong phenotypic effects. More broadly, it is not clear whether the assay is sufficiently sensitive to detect the effects of only a subset of deleterious variants within a pooled library. This point should be addressed more explicitly.

We agree that this is an important limitation of the present study. Because our non-SGCs were constructed by reassigning only Ala, Ser, and Leu, the mutation-dependent effects that can differ among genetic codes are limited to mutations involving these reassigned codons or amino acid substitutions affected by these assignments. Therefore, the sensitivity of the assay depends on how frequently such substitutions occur in the reporter genes and whether the affected Ala, Ser, and Leu-related positions are functionally important.

We have revised the Discussion to address this point more explicitly. In the revised manuscript, we now state that the absence of a detectable cost-dependent effect may reflect not only the limited cost range examined, but also the limited set of reassigned amino acids, the position-dependent importance of Ala/Ser/Leu residues in the reporter proteins, and the sensitivity limit of pooled activity measurements. We further note that future studies using genotype-resolved activity assays (defined variants) will be required to determine whether specific amino acid substitutions or specific protein positions exhibit stronger genetic code-dependent effects.

“A further limitation of this study is that the reporter activities were measured at the level of pooled random mutation libraries. Therefore, the high-/low-mutation activity ratio used in this study should be interpreted as the relative reduction in average activity caused by increasing the mutational burden in a heterogeneous mutation pool, rather than as the effect of identical variants before and after additional mutations. This library-averaged approach was chosen because the mutational costs considered here are also defined as average expected physicochemical effects over many possible single-nucleotide substitutions. In addition, because the non-SGCs constructed in this study were generated by reassigning only Ala, Ser, and Leu, the detectable effects may depend on how frequently mutations involving these amino acids occur in each reporter gene and whether the affected positions are functionally important. If genetic code-dependent effects are restricted to a small subset of deleterious variants, such effects may be masked in pooled activity measurements. Future studies using defined variants or high-throughput genotype–phenotype mapping assays will be required to determine the mutation-specific and position-specific mechanisms underlying genetic code-dependent effects on protein function (Rozhoňová et al., 2024).”

Recommendations for the authors:

Reviewing Editor Comments:

While we suggest that you address all the technical points raised by the reviewers, you may specifically want to limit the conclusion of the study to mutational robustness at the level of individual protein activity, rather than making broader generalizations. Also, the statistical analysis needs to be strengthened, as indicated in the reviews.

We thank the Reviewing Editor for these important suggestions. We agree that the conclusion of the original manuscript was broader than what can be directly supported by the present experiments. In the revised manuscript, we have therefore limited our conclusion to mutational robustness at the level of individual reporter protein activity measured in a reconstituted in vitro translation system. We now explicitly state that our results do not directly address robustness at the level of cellular fitness, protein interaction networks, or long-term evolution.

We have also strengthened the statistical analyses throughout the manuscript. Specifically, we added one-way ANOVA followed by Tukey’s post hoc test for Fig. 1D, Welch’s t-tests for Fig. 2B, Spearman’s rank correlation analyses for Fig. 4B–D and Fig. 5C/E/G, and two-way ANOVA for GAL activity in Fig. 5B. These analyses have been incorporated into the revised Results, figure legends, and supplementary information.

Reviewer #2 (Recommendations for the authors):

(1) Discuss other alternative hypotheses if the error minimization theory is unlikely.

We thank the reviewer for this helpful suggestion. We think that the absence of a detectable relationship between mutational cost and reporter protein activity in our assay should not be interpreted as excluding all possible roles of error minimization in the evolution of the genetic code. Our results specifically address one aspect of the error minimization theory: whether physicochemical-property-based mutational cost predicts the average effect of random point mutations on individual reporter protein activity within the experimentally accessible range of non-SGCs tested here.

In the revised Discussion, we have clarified that the organization of the SGC may have been shaped by multiple factors, including robustness to translational errors, historical constraints associated with genetic code expansion, biosynthetic or coevolutionary processes, stereochemical interactions, and the evolvability of proteins. Our results suggest that the contribution of mutational robustness at the level of individual protein activity may be limited within the range examined here, but they do not exclude the possibility that the SGC provides advantages under other forms of error, at the level of translation fidelity, cellular fitness, or long-term evolution.

We have added a short discussion to clarify this point without expanding the scope of the manuscript beyond the present experimental results.

“It should be noted that this conclusion is limited to the activity of individual reporter proteins translated in a reconstituted in vitro system. Therefore, whether similar trends would be observed at the level of cellular fitness or long-term evolution remains an open question. Moreover, our results do not exclude other possible roles of SGC organization. The SGC may have been shaped by multiple factors, including robustness to translational errors, historical constraints during genetic code expansion, biosynthetic or coevolutionary relationships among amino acids, stereochemical interactions, and effects on protein evolvability (Katoh and Suga, 2023; Koonin and Novozhilov, 2017, 2009; Novozhilov et al., 2007; Wong, 2005).”

(2) A brief description of the PURE translation system can be provided for people from outside the field.

We have added a brief description of the PURE system in the Introduction to make the experimental platform more accessible to readers outside the field. Specifically, we now explain that the PURE system is a reconstituted cell-free translation system composed of purified translation factors, ribosomes, aminoacyl-tRNA synthetases, tRNAs, amino acids, and energy-regeneration components. We also clarify that, in this study, we used a tRNA-free version of the PURE system, in which defined synthetic tRNA sets were supplied externally to reconstruct each genetic code.

Introduction

“A representative platform for such reconstitution is the PURE system (Shimizu et al., 2001), a reconstituted cell-free translation system composed of purified translation components, including ribosomes, translation factors, aaRSs, amino acids, and energy-regeneration components. In particular, a tRNA-free PURE system (Miyachi et al., 2022), in which endogenous tRNA activity is minimized and defined tRNA sets are supplied externally, enables genetic codes to be reconstructed by controlling the supplied tRNAs.”

(3) Figure 5D and F - Technical replicates are provided only for GAL. A similar approach should be taken for LUC and mSG.

We agree that replicate-level measurements for Luc and mSG would further improve reliability. However, repeating the full translation experiments for these reporters was not feasible in the current revision, as each experiment requires large amounts of freshly prepared tRNA-free PURE system and multiple defined tRNA mixtures for every genetic code variant tested. Given these material and technical constraints, we were unable to perform additional biological replicates within the scope of this revision. We would like to emphasize, however, that the GAL replicates shown in Fig. 5D and F are fully consistent across independent experiments, providing direct evidence for the reproducibility of the assay itself. Furthermore, the key metric in our analysis, the activity ratio between high- and low-mutation groups within each genetic code, is an internally normalized measure that is inherently less sensitive to between-experiment variability than absolute activity values. The correlation analyses further showed no significant relationship between mutational cost and this ratio across all three reporters, and this conclusion is consistent regardless of which reporter is examined. Together, we believe these results provide a robust basis for the conclusions drawn, even in the absence of full replication for Luc and mSG.

(4) Provide statistical analysis wherever it is relevant (e.g, to support a lack of correlation).

We have strengthened the statistical analyses throughout the revised manuscript. In particular, to support the lack of detectable correlation between mutational cost and mutation-induced activity loss, we performed Spearman’s rank correlation analyses between each mutational cost metric and the high-/low-mutation activity ratio for all three reporters. No significant correlations were detected for any reporter or cost metric. In addition, we added statistical analyses for other relevant figures, including one-way ANOVA followed by Tukey’s post hoc test for Fig. 1D, Welch’s t-tests for Fig. 2B, Spearman’s rank correlation analyses for Fig. 4B–D, and two-way ANOVA for GAL activity in Fig. 5B.

Reviewer #3 (Recommendations for the authors):

(1) In line 122, the phrase "as evenly as possible" is ambiguous and should be explained more precisely.

We thank the reviewer for pointing this out. We have revised the phrase “as evenly as possible” to describe the codon design more precisely. Specifically, we now state that the NanoLuc coding sequences were designed so that the codons available in each genetic code were used with minimal differences in codon counts, while preserving the amino acid sequence of NanoLuc.

“For near-SGC and SGC, the NanoLuc coding sequences were designed so that the codons available in each genetic code were used with minimal differences in codon counts, while preserving the amino acid sequence (Fig. 1B, 32 codons and 46 codons).”

(2) For Figure 1D, a Western blot or another protein gel-based assay would be helpful to exclude the possibility that the observed differences arise from variation in translation efficiency rather than differences in protein activity.

We agree that a protein gel-based assay such as Western blotting would in principle allow us to distinguish differences in translated protein amount from differences in specific activity, and we understand why such data would be informative. However, we would like to clarify that the primary purpose of Fig. 1D was to evaluate the overall functional translation output of each reconstructed genetic code, rather than to determine the mechanistic basis of any observed differences. In this context, NanoLuc luminescence serves as an integrated readout of the entire translation process, encompassing both translational efficiency and protein folding/activity. Crucially, regardless of whether the observed differences in NanoLuc luminescence reflect lower protein yield, reduced specific activity, or a combination of both, the conclusion of Fig. 1D remains the same. Although we did not perform Western blotting in this study, we believe that such an analysis would not change this interpretation and that the current data are sufficient to support this conclusion.

(3) The number 3^9 is not immediately intuitive. It would be helpful if the authors also stated that this corresponds to approximately 20,000 possible non-standard genetic codes.

We have revised the text to state both the exact number and the approximate value: 3⁹ = 19,683, approximately 20,000 possible non-standard genetic codes.

(4) The rationale for using the three cost parameters (PR, MV, and HI) should be explained in greater detail. Because these parameters are central to the manuscript, a citation alone is not sufficient. A concise explanation of their biological relevance would improve the clarity and accessibility of the study.

We agree that the biological relevance of the three cost parameters should be explained more clearly. In the revised manuscript, we have added a concise explanation of why polar requirement (PR), molecular volume (MV), and hydropathy index (HI) were used.

These parameters were selected because they have been widely used in theoretical studies of genetic code optimality and represent distinct physicochemical aspects of amino acid substitutions. PR reflects polarity-related interactions and has been a classical metric in error minimization analyses of the genetic code. MV represents side-chain size and steric volume, which could influence packing and structural stability in proteins. HI reflects hydrophobicity, which is closely related to protein folding and hydrophobic core formation. We have also clarified that these metrics are simplified descriptors and do not capture residue-specific structural or functional context, which we now discuss as a limitation of the study.

“PR reflects polarity-related interactions of amino acids and has been used as a classical measure of amino acid similarity in error minimization analyses. MV represents side-chain size and steric volume, which could affect protein packing and structural stability, whereas HI reflects hydrophobicity, which could be closely related to protein folding or hydrophobic core formation.”

(5) In Figure 3, the experimental framework would be easier to follow if the authors included a schematic and data for one representative non-SGC, explicitly illustrating how it differs from the near-SGC with respect to each of the three cost measures.

We agree that showing one representative non-SGC would make the experimental framework and cost calculation more intuitive.

In the revised manuscript, we added a new panel to Fig. 3 comparing the near-SGC with a representative non-SGC. We selected the PR_max code as the representative example because it clearly illustrates how reassignment of vacant codon boxes can increase one mutational cost metric relative to the near-SGC. In this panel, we first show the codon assignment schemes of the near-SGC and PR_max code in the same genetic-code format used in Fig. 1. We then show the corresponding heatmap representations for the three physicochemical properties used in the cost calculation: polar requirement, molecular volume, and hydropathy index. The Cost_PR, Cost_MV, and Cost_HI values are shown for each code.

This new panel illustrates how changes in codon assignment are translated into different physicochemical cost landscapes and clarifies how the representative non-SGC differs from the near-SGC with respect to each of the three cost measures.

“To make the design of non-SGCs more explicit, we show one representative non-SGC together with the near-SGC in Fig. 3B. This comparison illustrates how assignment of Ala, Ser, or Leu to the vacant codon boxes changes the three mutational cost metrics, Cost_PR, Cost_MV, and Cost_HI.”

(6) In line 329, the phrase "similar pattern" is ambiguous and should be explained more explicitly.

We have revised the ambiguous phrase “similar pattern” to describe the observation more explicitly. Specifically, we now state that the relative differences in GAL activity among genetic codes observed in the low-mutation library were broadly retained in the high-mutation library, although overall activity decreased.

“For the high-mutation library, GAL activity decreased overall, while the relative differences in activity among genetic codes observed in the low-mutation library were broadly retained.”

(7) Figure S7 appears to be an important control for the experiments shown in Figure 5, and I recommend moving it to the main figures.

We thank the reviewer for this helpful suggestion. We agree that the HiBiT-based quantification of GAL protein amount is an important control for interpreting the GAL activity measurements in Fig. 5, and we appreciate the recommendation to increase its visibility. This analysis shows that the amount of C-terminally completed GAL products was broadly comparable among genetic codes, indicating that the large differences in GAL activity were not primarily attributable to differences in total translated protein amount.

After careful consideration, we have opted to retain this analysis in the supplementary figures because the main focus of Fig. 5 is the relationship between mutational cost and mutation-induced activity loss, quantified by the high-/low-mutation activity ratio. The HiBiT experiment addresses a related but distinct question: whether differences in absolute GAL activity among genetic codes can be explained by differences in protein abundance, and we felt that including it in the main figures might shift the emphasis away from the central message of Fig. 5. Nevertheless, we have added a clear reference to Figure 4–figure supplement 1 in the main text and the figure legend to ensure that readers are directed to this control when interpreting Fig. 5.

Author response:

Public Reviews:

Reviewer #1 (Public review):

This manuscript presents a tunable Bessel-beam two-photon fluorescence microscopy (tBessel-TPFM) platform that enables high-speed volumetric imaging with stable axial focus. The work is technically strong and broadly significant, as it substantially improves the flexibility and practicality of Bessel-beam-based two-photon microscopy. The demonstrations are generally strong and bridge a wide range of neuroimaging applications, namely vascular dynamics, neurovascular coupling, optogenetic perturbation, and microglial responses. These convincingly show that the approach enables biological measurements that are difficult or impractical with existing methods.

The evidence supporting the technical and biological claims is generally strong. The optical design is carefully motivated, clearly described, and validated through a combination of simulations and experimental characterization. The biological applications are diverse and well chosen to highlight the strengths of the proposed method, and the data are of high quality, with appropriate controls and comparative measurements where relevant.

Strengths:

(1) The optical innovation addresses a well-recognized limitation of existing Bessel-TPFM implementations, namely axial focus drift during tuning, and does so using a relatively simple, light-efficient, and cost-effective design.

(2) The manuscript provides convincing experimental evidence for this being a versatile platform to map flow dynamics across diverse vessel sizes and orientations in both healthy and pathological states.

(3) Biological demonstrations are comprehensive and span multiple domains such as hemodynamics, neurovascular coupling, and neuroimmune responses.

(4) Quantitative analyses of blood flow across vessel sizes and orientations, including kilohertz line scanning, are particularly compelling and clearly beyond the reach of standard Gaussian TPFM.

(5) Particular advantages are that higher blood slow speeds become measurable up to 23mm/sec (20x more than conventional frame scanning), and that simultaneous (Bessel-)imaging and (Gaussian-)perturbation are possible because of the stable axial focus.

We thank the reviewer for this thoughtful and encouraging evaluation of our work. We are particularly grateful for the recognition of both the technical rigor and the broad applicability of the tBessel-TPFM platform, as well as the assessment that our approach enables biological measurements that are difficult or impractical with existing methods. We appreciate the reviewer’s detailed summary of the strengths of the manuscript, including the identification of axial focus drift as a major limitation in prior Bessel-TPFM implementations, and the value of our center-stable, light-efficient, and accessible solution. We thank the reviewer for the encouraging comment that our biological demonstrations to be compelling and well supported by quantitative analysis.

Weaknesses:

(1) At present, the paper does not properly position the new Bessel-beam method against previous work, and fails to compare it to alternative fast volumetric imaging methods without Bessel beams.

We thank the reviewer for this important point. We agree that a more explicit comparison with existing fast volumetric imaging methods helps clarify the unique advantages of our system. Alternative fast volumetric imaging methods without Bessel beams include remote focusing (Sofroniew et al., 2016), acousto-optic deflectors (AOD) (Villette et al., 2019), piezoelectric objective stages (Göbel and Helmchen, 2007), tunable acoustic gradient lenses (TAG lens) (Huang et al., 2019), electrically tunable lenses (ETLs) (Grewe et al., 2011; Yang et al., 2018), and light beads microscopy (Demas et al., 2021). These methods have each enabled important forms of rapid volumetric imaging, but they differ in their speed, resolution, axial range, and optical complexity. For example, remote focusing can provide rapid axial refocusing while preserving high-resolution imaging but has limited defocus range and requires a carefully aligned relay system and aberration control to maintain image quality. AOD-based approaches enable fast random-access sampling, but introduce optical and calibration complexity associated with dispersion, and suffer light loss with limited diffractive efficiency. Piezoelectric objective scanning is comparatively simple and broadly accessible, but its mechanical inertia limits volume rate and can introduce artifacts during rapid or large axial motion. TAG lenses and ETLs provide compact non-mechanical axial scanning, but pose challenges on aberration control and synchronization. Light-beads microscopy achieves high volumetric throughput by near-simultaneously sampling multiple axial positions, but faces intrinsic compromise among axial coverage, number of sampling planes, and lateral sampling density, which limit lateral resolution when imaging over large depth ranges.        

Previous Bessel-beam TPFM approaches address some of these limitations by converting volumetric imaging into two-dimensional scanning with an axially extended focus. However, many existing implementations either rely on a fixed Bessel beam profile, which limits the ability to adapt spatial resolution and axial coverage to different biological applications, or use spatial light modulators, which provide tunability but introduce higher cost, increased optical complexity, reduced light efficiency, and sequential rather than simultaneous multi-wavelength operation. Other axicon or lens based tunable Bessel approaches have also been reported, but these designs generally introduce axial displacement of the Bessel focus during tuning.

In contrast, our tBessel-TPFM design provides full tunability comparable with SLM based methods, maintaining a stable axial beam center, at the same time low cost, easy to implement, intrinsically high light efficiency and support simultaneous multi-color imaging. Therefore, tBessel-TPFM provides a unique solution for applications where axial projection is acceptable and where high-speed volumetric monitoring, tunable axial coverage, motion robustness, optical simplicity, and compatibility with simultaneous perturbation are valuable.

(2) The cost-effectiveness of the proposed method is not well described or supported by evidence; it would be useful to include more detail or remove this claim.

We thank the reviewer for requesting clarification and supporting evidence regarding the cost-effectiveness of our method. We now provide a detailed cost breakdown of the tBessel module. Briefly, the module consists of three axicons, three lenses, and one iris that together enable independent control of the NA and ΔNA of the generated Bessel beam. Based on the specified components, the three axicons (AX252B and AX255B, Thorlabs) cost $635 each, the three lenses (AC254-125-B×2 and AC254-150-B, Thorlabs) cost $110 each, and the iris (SM2D25D, Thorlabs) costs $105, resulting in a total system cost of approximately $2,340. For comparison, spatial light modulator (SLM)-based implementations that offer comparable tunability typically require an SLM module costing on the order of $20,000 USD, in addition to more complex optical alignment and reduced optical efficiency.

(3) Some biological conclusions, e.g., regarding novel features of microglial dynamics (i.e., the observed two-wave responses and coordinated extension-retraction), are based on relatively limited sample size and would benefit from clearer discussion of variability across animals and fields of view.

We thank the reviewer for this important comment regarding the limited sample size of the microglial dynamics study. We agree that a more comprehensive assessment across animals would be required to establish the generality of these biological findings. In the current study, our intent is not to draw broad biological conclusions, but rather to report observations enabled by the tBessel-TPFM platform. As noted in the manuscript, we have deliberately used descriptive language (e.g., “two distinct waves of process extension were observed” “process dynamics revealed…” and “advancing processes displayed…”) to avoid over claim of the biological findings beyond the data presented.

(4) The use of neural network-based denoising for microglial imaging is reasonable but introduces potential concerns about trustworthiness; additional clarification of validation or failure modes would strengthen confidence in these results.

We thank the reviewer for raising this important point regarding the reliability of neural network-based denoising. We agree that additional validation and discussion of potential failure modes are essential to build confidence in these results. To assess the fidelity of the CARE-denoised data, we performed several additional analyses (Author response image 1). First, we compared normalized raw and denoised images averaged over 10 frames. The difference between the two images was spatially uniform and primarily reflected residual noise present in the raw data, rather than structured discrepancies (Author response image 1a). As expected, brighter features like microglial somata exhibited smaller differences due to their intrinsically higher signal-to-noise ratio, whereas weaker processes showed larger noise-related differences. Second, we extended this comparison across the full time-lapse sequence by applying consistent color mapping to both raw and denoised videos and computing frame-by-frame difference maps. These analyses show that the observed differences are consistent with noise suppression, without introducing coherent structural features or altering the apparent microglial dynamics (Author response image 1b).

Author response image 1.

Validation of CARE-based denoising for microglial imaging. (a) Comparison of 10-frame averaged normalized raw (left), CARE-denoised (middle), and their pixel-wise difference (right) images. The second row shows a zoomed-in view of the boxed region. (b) Color-coded time-lapse projections over a 10-minutes imaging session for the raw (left) and CARE-denoised (middle) data, along with their pixel-wise difference (right).

To conclude, most of the authors' claims are well supported by the data. The central conclusion, namely that tBessel-TPFM provides tunable volumetric imaging enabling experiments not feasible with existing two-photon approaches, is justified. Some biological interpretations would benefit from a more cautious framing, but they do not undermine the main technical and methodological contributions of the study. This is a strong and technically rigorous manuscript that makes a substantial methodological advance with clear relevance to neuroscience and intravital imaging. Minor clarifications and a slightly more measured discussion of certain biological findings are recommended.

We thank the reviewer for this thoughtful and encouraging summary of our work. We greatly appreciate the recognition that tBessel-TPFM provides a meaningful methodological advance and enables volumetric imaging experiments that are difficult or impractical with existing two-photon approaches.

Reviewer #2 (Public review):

The authors describe a tunable Bessel beam two-photon microscope (tBessel-TPFM) designed to overcome a common limitation of Bessel-based volumetric imaging: axial shifts of the effective focus during Bessel beam parameter tuning. Their optical design allows independent control of axial beam length and resolution while keeping the axial center fixed. This is extensively validated through simulations and experiments.<br /> Strengths:

A major strength of the work is the breadth of validation combined with the level of technical detail provided. The authors carefully characterize the optical performance of the system and clearly explain the design choices and underlying derivations, which will make it easier for others to understand and implement. The authors demonstrate the utility of the method across several in vivo applications, including neurovascular imaging, blood flow measurements, optogenetic stimulation, and microglial dynamics.

We thank the reviewer for their thoughtful and encouraging comments. We greatly appreciate the recognition of the technical rigor, breadth of validation, and clarity of explanation presented in our work.

Weaknesses:

In the in vivo demonstrations, the authors employ different Bessel beam configurations across experiments, but the beam parameters are not dynamically tuned during live imaging. A video example showing continuous or interactive tuning of the Bessel beam within a single in vivo imaging sequence would further highlight the practical advantages of this platform and strengthen the case for its potential applications.

We thank the reviewer for their suggestion. While we agree that continuous or interactive tuning of the Bessel beam during imaging would further highlight the practical flexibility of the platform, and changing the Bessel beam parameters during imaging session is feasible in our tBessel-TPFM implementation, for the in vivo applications presented in this manuscript, dynamic tuning during the actual recording is generally not required. In practice, the Bessel beam parameters are selected before data acquisition based on the biological target, desired axial coverage, spatial resolution, and acceptable level of projection overlap.

In addition, while excitation powers are reported, the manuscript does not place these values in the broader context of known photodamage thresholds for two-photon microscopy, which would be helpful to the readers.

We thank the reviewer for bringing up this important point. It is known that multiphoton imaging relies on relatively high illumination power, which causes brain heating and thus photodamage. Previous studies have reported that continuous illumination with a 920-nm laser beam at 0.8 NA over 1000s results in a peak temperature increase of ~1.73 °C/100 mW in the brain, with power above 300 mW observed to cause cellular damage. Power levels below 250 mW were considered to be safe for long-term imaging. (Podgorski and Ranganathan, 2016) In our experiments, the measured post-objective powers range from 20 mW to 149 mW, which are well below the established safe threshold.

Denoising/image restoration are applied in one of the in vivo examples, but it is unclear why this step was used specifically for this dataset and whether it was necessary to achieve adequate SNR or primarily included as an additional demonstration.

We thank the reviewer for requesting clarification on the usage of the CARE denoising model. The CARE-based denoising was applied only in Figure 5, the microglial imaging example, and was primarily included as an additional demonstration of how neural network–based image restoration can be used to enhance low-SNR volumetric datasets acquired with tBessel-TPFM. All other images and analyses in the manuscript were performed on raw data without any denoising. To assess the reliability of the CARE denoising method, we further compared raw and denoised data using 10-frame averages and color-mapped the full 10-minute time-lapse video, both showed minimal differences (Response Fig 1). These analyses confirm that the CARE denoising model did not introduce structural artifacts or affect the biological dynamics observations in our dataset.

Reviewer #3 (Public review):

The manuscript presents an elegant and cost-effective approach for generating a tunable Bessel beam on a conventional two-photon microscope. The authors assemble a compact optical module comprising three axicons and a series of lenses that permits rapid adjustment of both lateral resolution and axial extent without modifying the focal plane. This flexibility enables the system to be readily adapted to a variety of biological preparations. As a proof of concept, the authors employ the device to record blood flow velocities in cortical microcapillaries, arterioles, and venules, thereby directly visualizing vasodilatation and vasoconstriction dynamics and permitting quantitative analysis of neurovascular coupling across cortical layers in awake mice.

The authors demonstrate that the tunability of the Bessel beam can be exploited to match the numerical aperture to the vessel type: a high NA configuration, albeit slower scan, is optimal for resolving flow in capillaries, whereas a low NA setting provides faster acquisition suitable for arterioles and venules. By implementing a one-dimensional line scan with the Bessel beam, they achieve an imaging speed that is twentyfold faster than conventional frame-by-frame scanning, which proves sufficient to capture hemodynamic transients before and after an induced ischemic stroke.

In addition to pure observation, the authors integrate a co-propagating Gaussian line to the system, allowing simultaneous imaging and photostimulation within the same focal plane. This capability addresses a common limitation of other Bessel beam implementations, in which the observation and perturbation planes often become misaligned when the Bessel beam is altered. The manuscript also emphasizes the advantage of Bessel beam excitation for calcium imaging after a perturbation, because it captures neuronal activity in planes both above and below the nominal focal plane, signals that would be missed with a standard Gaussian focus. Finally, the authors apply the technique to investigate the neuroimmune response following targeted microglial ablation; they report that adjacent microglia extend processes toward the injury site while retracting processes in the opposite direction.

Overall, the work offers a technically straightforward yet powerful extension to existing two-photon platforms, providing high-speed, volumetric imaging and stimulation capabilities that are well-suited to a broad range of neurovascular and neuroimmune studies. The experimental validation is quite thorough, and the presented data convincingly illustrates the benefits of the approach.

Strengths:

The authors present a truly clever and inexpensive optical module that can be integrated into almost any two-photon microscope, providing a tunable Bessel beam with a minimal modification of the existing system. The experimental data and accompanying quantitative analysis convincingly demonstrate that the system can reveal physiological events, such as capillary flow, calcium transients across multiple axial planes, and microglial process dynamics, that are difficult or impossible to capture with a conventional Gaussian beam. The breadth of experiments chosen for the manuscript illustrates the practical utility of the device and supports the authors' conclusions that it extends the functional repertoire of standard two-photon microscopy.

We sincerely thank the reviewer for the thoughtful and encouraging feedback. We're glad that the technical design and broad applicability of the tBessel module came through clearly, and we appreciate the recognition of its ease of integration and ability to capture dynamic physiological processes.

Weaknesses:

The manuscript would benefit from a more detailed contextualisation of the claimed speed advantage. Although the authors mention other techniques in the introduction, they do not provide any direct comparison with other state-of-the-art high-speed two-photon approaches such as light beads microscopy (Demas et al., Nat. Methods 2021), temporal multiplexing schemes (Weisenburger et al., Cell 2019), or random access microscopy (Villette et al., Cell 2019). A brief comparison of imaging speed, spatial resolution, and instrumental complexity would enable readers to assess the relative merits of the present method.

We thank the reviewer for this important suggestion. We agree that a more explicit comparison with other high-speed two-photon imaging methods helps clarify the speed advantages of our system. Several existing approaches, including light-beads microscopy (LBM), temporal multiplexing, and AOD-based random-access microscopy, have demonstrated impressive high-speed volumetric imaging capabilities. Light-beads microscopy (Demas et al., 2021) reported imaging over a large volume of 5.4 × 6 × 0.5 mm³ at 2 Hz. However, this large-volume acquisition used 5-μm lateral pixel sampling, corresponding to an effective lateral resolution of approximately 10 μm. In a more comparable mesoscopic volume, LBM imaged 0.6 × 0.6 × 0.5 mm³ at 9.6 Hz with 1-μm lateral pixel sampling. In addition, the LBM module uses off-axis reflective concave mirrors, which require careful alignment, and the axial sampling range is not readily tunable. Temporal multiplexing approaches (Weisenburger et al., 2019), reported imaging over approximately 1 × 1 × 0.6 mm³ at 17 Hz. However, this volume rate was achieved with relatively coarse spatial resolution of approximately 5 μm, together with a more complex optical design involving multiplexed excitation, detection, and synchronization. AOD-based random-access microscopy (Nadella et al., 2016; Villette et al., 2019) provides very fast point or region sampling, and reported 250 × 250 μm² imaging with 512 × 512 pixels and a 50-ns pixel dwell time, corresponding to ~0.5-μm pixel sampling and ~76 frames/s for two-dimensional imaging. However, volumetric imaging requires additional axial sampling, which lowers the effective 3D acquisition rate. In addition, AOD-based systems rely on diffractive beam steering, which introduces light loss due to finite diffraction efficiency and increases optical and calibration complexity. In comparison, tBessel-TPFM imaged a 0.4 × 0.4 × 0.12 mm³ volume at 58 Hz with 0.2-μm lateral pixel sampling. Our largest demonstrated imaging volume reached 2.5 × 2.5 × 0.45 mm³ while maintaining diffraction-limited lateral resolution. Therefore, compared with these high-speed volumetric approaches, tBessel-TPFM provides a distinct balance of volume rate and spatial sampling, and easier implementation simplicity.

A second limitation that warrants discussion is the inherent trade off between volumetric coverage and image specificity. Because the Bessel beam excites fluorescence throughout an extended axial range, the detector inevitably integrates signal from a three dimensional volume into a two dimensional image. In densely labelled tissue, this can lead to significant signal crosstalk, reducing contrast and complicating quantitative interpretation. A brief analysis of how labeling density affects the fidelity of flow or calcium measurements, or suggestions for mitigating crosstalk (e.g., computational deconvolution, adaptive excitation shaping, or combinatorial sparse labeling), would broaden the applicability of the technique.

We thank the reviewer for highlighting this important trade-off between volumetric coverage and image specificity in Bessel beam imaging. As Bessel beams project fluorescence from multiple features along the z-axis onto the same x–y plane, longer beams expand depth coverage at the same acquisition speed but can confound signals from axially spaced structures (Line 119-121 in manuscript). For densely labeled samples, the probability of having structures overlap in their x-y locations is high, and thus a shorter beam should be used. In sparsely labeled samples, structures have a lower probability of overlapping, and thus longer foci can be used (Line 166-168 in manuscript). Additionally, at the same NA, longer Bessel beam have more energy in the side rings surrounding the central peak, which may lead to higher background signal (Line 121-123 in manuscript) (Lu et al., 2017). These reasons necessitate to have not only NA tuning, but also independent length tuning (ΔNA tuning) to optimize imaging Bessel length to provide a balance between structural overlap that obscures signal localization, and the volumetric speedup, in any given sample based on labeling density and imaging goals, which are realized in our tBessel design.

Reference:

Demas, J., Manley, J., Tejera, F., Barber, K., Kim, H., Traub, F.M., Chen, B., Vaziri, A., 2021. High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy. Nat Methods 18, 1103–1111. https://doi.org/10.1038/s41592-021-01239-8

Göbel, W., Helmchen, F., 2007. In Vivo Calcium Imaging of Neural Network Function. Physiology 22, 358–365. https://doi.org/10.1152/physiol.00032.2007

Grewe, B.F., Voigt, F.F., van ’t Hoff, M., Helmchen, F., 2011. Fast two-layer two-photon imaging of neuronal cell populations using an electrically tunable lens. Biomed Opt Express 2, 2035–2046. https://doi.org/10.1364/BOE.2.002035

Huang, C., Tai, C.-Y., Yang, K.-P., Chang, W.-K., Hsu, K.-J., Hsiao, C.-C., Wu, S.-C., Lin, Y.-Y., Chiang, A.-S., Chu, S.-W., 2019. All-Optical Volumetric Physiology for Connectomics in Dense Neuronal Structures. iScience 22, 133–146. https://doi.org/10.1016/j.isci.2019.11.011

Lu, R., Sun, W., Liang, Y., Kerlin, A., Bierfeld, J., Seelig, J.D., Wilson, D.E., Scholl, B., Mohar, B., Tanimoto, M., Koyama, M., Fitzpatrick, D., Orger, M.B., Ji, N., 2017. Video-rate volumetric functional imaging of the brain at synaptic resolution. Nat Neurosci 20, 620–628. https://doi.org/10.1038/nn.4516

Nadella, K.M.N.S., Roš, H., Baragli, C., Griffiths, V.A., Konstantinou, G., Koimtzis, T., Evans, G.J., Kirkby, P.A., Silver, R.A., 2016. Random-access scanning microscopy for 3D imaging in awake behaving animals. Nat Methods 13, 1001–1004. https://doi.org/10.1038/nmeth.4033

Podgorski, K., Ranganathan, G., 2016. Brain heating induced by near-infrared lasers during multiphoton microscopy. Journal of Neurophysiology 116, 1012–1023. https://doi.org/10.1152/jn.00275.2016

Sofroniew, N.J., Flickinger, D., King, J., Svoboda, K., 2016. A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging [WWW Document]. eLife. https://doi.org/10.7554/eLife.14472

Villette, V., Chavarha, M., Dimov, I.K., Bradley, J., Pradhan, L., Mathieu, B., Evans, S.W., Chamberland, S., Shi, D., Yang, R., Kim, B.B., Ayon, A., Jalil, A., St-Pierre, F., Schnitzer, M.J., Bi, G., Toth, K., Ding, J., Dieudonné, S., Lin, M.Z., 2019. Ultrafast Two-Photon Imaging of a High-Gain Voltage Indicator in Awake Behaving Mice. Cell 179, 1590-1608.e23. https://doi.org/10.1016/j.cell.2019.11.004

Weisenburger, S., Tejera, F., Demas, J., Chen, B., Manley, J., Sparks, F.T., Traub, F.M., Daigle, T., Zeng, H., Losonczy, A., Vaziri, A., 2019. Volumetric Ca2+ Imaging in the Mouse Brain Using Hybrid Multiplexed Sculpted Light Microscopy. Cell 177, 1050-1066.e14. https://doi.org/10.1016/j.cell.2019.03.011

Yang, W., Carrillo-Reid, L., Bando, Y., Peterka, D.S., Yuste, R., 2018. Simultaneous two-photon imaging and two-photon optogenetics of cortical circuits in three dimensions. eLife 7, e32671. https://doi.org/10.7554/eLife.32671

Dit deel is nog wat complex en vraagt nogal veel aanpassingen in zowel voorzieningen als applicaties.: 1. Het zetten van de DELETE_PENDING tag mag niet gezien worden als een wijziging op de Patient omdat daarmee Patient-resources die 2 jaar bestaan en waarvoor nog nooit een Task is aangemaakt of Launch is uitgevoerd weer terug komt in een status dat die gewijzigd is en dus altijd blijft bestaan. Dit vraagt over verduidelijking van de verwachtingen ten aanzien van $meta-add. Specifiek: * Geen wijziging van meta.lastUpdated en meta.version * Geen REST Audit event * Daarom ook geen notificatie op basis van de standaard Patient-subscription (want de Patient is volgens bovenstaande niet gewijzigd) 2. De te versturen AuditEvents zijn nog niet voldoende gespecificeerd 3. Wat betreft de Notificaties: * Het is mijns inziens niet gewenst dat applicaties op de bestaande Patient subscription ook de delete_pending notificaties krijgen, dat zal alleen maar lijden tot verwarring * Omdat het gebruik van de noodrem feitelijk ongewenst isen als een advanced usecase moet worden gezien is het geen enkel probleem als applicaties geen subscriptie hebben * Al het bovenstaande pleit voor een specifieke subscriptie- en notificatie-endpoint voor alle notificaties rond het opschonen van patientdata * Ik ben van mening dat juist een subscriptie op de relevante AuditEvents het meest duidelijk en krachtig is.

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Reviewer #3 (Public review):

Summary:

The primary objective of this study was to establish a practical and functional framework for the propagation of stable transgenic cell lines of Blastocystis, a common animal gut microeukaryote. Although the work focused on Blastocystis ST7-B, a subtype with relatively low prevalence in humans, this choice is justified by its association with more frequent negative health effects. Beyond their relevance to the medical field, the methodological advances described here have the potential to also expand cell biology studies of this anaerobic organism, including its unusual mitochondria and redox metabolism.

Strengths:

Prior to this work, genetic tools for Blastocystis were very limited, relying on a single strong promoter-terminator combination. The authors successfully expanded the available promoter set across a range of expression strengths by testing two dozen variants in luciferase-based assays. Critically, they developed an integrated workflow from a modular transgenic construct design, to an expanded inventory of molecular components (promoters, reporters), optimized DNA delivery, stepwise antibiotic resistance-mediated clonal selection and propagation, and to reporter validation. The evaluation of several anaerobiosis-compatible labeling strategies for live (and fixed) cell optical imaging will be particularly useful, with the SNAP-tag system appearing especially promising for Blastocystis.

Weaknesses:

The presented data generally provide solid support for the conclusions that the work reached, but clarification of reasoning and several inconsistencies, as well as amendments to the visual presentation of the data, would be highly beneficial, as detailed below.

(1) Episomal persistence of the construct:<br /> The manuscript repeatedly assumes, including in its title, that constructs persist in Blastocystis in their episomal form, but no direct evidence is provided. Although this interpretation is plausible, it should be identified more clearly as provisional. Nuclear genomic integration (e.g., via NHEJ) remains a possible explanation unless supporting evidence or rationale is provided to exclude it. Testing whether the phenotype persists without drug-mediated selection in the generated transgenic cell lines would help strengthen the case for episomal maintenance.

(2) Promoters and terminators:<br /> 2.1) There is a discrepancy between the claimed number of loci (14), from which promoters used to drive luciferase expression were derived, and those detailed as having been actually generated in Table 1 (11). This inconsistency should be corrected or explained, as it creates uncertainty around the accuracy of the dataset.<br /> 2.2) Based on the presented evidence, constructs benchmarked in bioluminescence assays differed only in their promoter composition. Although terminator selection is mentioned in the Methods section, no additional details are provided; for instance, Table 1 and Figure 2 only list 23 promoters in total. Figure 2A likewise shows only promoter-dependent variation. If the terminator was held constant (LeguP1?), this should be stated explicitly. The authors may then consider revising the wording of having tested "23 promoter-terminator pairs" to better reflect that only promoters varied.<br /> 2.3) Promoter benchmarking was done with a plasmid lacking a selection marker, so it is unclear how the maintenance of the luciferase construct was ensured. Without selection, the observed reporter intensity could reflect differential or stochastic plasmid retention rather than promoter strength alone. The luminescence assay was performed 16-18 hours after transfection, but the rationale for this particular timeframe should be explained. In this context, the authors should explicitly state whether the experiments shown in Fig.2A represent biological triplicates or technical triplicates from a single transfection.

(3) Figure 2:<br /> 3.1) Several aspects of the current design may lead to ambiguity for the reader. The boxplots are colour-coded, but it is unclear whether the colours carry meaning or are purely decorative. Because the data are already spatially separated into bins, additional random colouring is redundant and may suggest distinctions that are not intended. In addition, part A of Figure 2 is split into two panels, with the scale for the left panel shown in the right panel and some of the boxplot colours falling in the range of the scale, but not in line with their counterparts in the left panel. Because the colour use is not consistent, it is difficult to tell whether the same scale should be applied to both panels or how it should be interpreted.<br /> 3.2) The left panel of part A uses a diverging blue-white-red colour scheme, which is most appropriate when the midpoint represents a meaningful central value such as zero. Because the values shown in this graph are only positive, a non-diverging 2-colour scale or a colour palette such as 'viridis' would make the plot easier to interpret.<br /> 3.3) A black background should be avoided: 'B' and 'C' labels are invisible, and it draws attention to a distracting design feature rather than the data themselves.

(4) Figure 3:<br /> 4.1) Individual snapshots should be separated more clearly, either by using a white background or by adding visible borders to make the overall composition clearer. As currently displayed, some boundaries between fluorescent channels resemble image artifacts rather than intentional panel divisions.<br /> 4.2) In parts B-D, the legend should explain more clearly what each image shows, and the figure itself would benefit from annotations. There seem to be three sub-panels in each 'condition' of part B (as well as C and D): while the middle and rightmost panel can be easily inferred to represent the fluorescent protein and bright-field image, what the leftmost panels represent is not specified. If DAPI was used to dye DNA, an explanation why mostly multiple labelled regions are visible should be provided.<br /> 4.3) Cell morphology and appearance differ markedly between UnaG/smURFP and SNAP-tag images, which should be explained. A microscope issue is mentioned in the main text, but if that was the cause, the authors should consider replacing the images, as the current distortions complicate interpretation.

DOI: 10.64898/2026.05.20.726613

Resource: RRID:Addgene_57819

Curator: @scibot

SciCrunch record: RRID:Addgene_57819

What is this?

eLife Assessment

This important study combines single-molecule imaging and CUT&TAG to address the molecular mechanism underlying the differentiation process that initiates the formation of red blood cells in the bone marrow. The authors provide evidence that the transcription factor GATA2 transiently associates with a new set of genomic loci early in the differentiation process before it is replaced by GATA1. Together, the experiments across three biological systems are solid, but they could benefit from additional details and controls to strengthen the conclusions.

Reviewer #1 (Public review):

Summary:

During erythroid differentiation, hematopoietic progenitors relinquish multipotency and activate lineage programs. The switch from GATA2 to GATA1 is particularly important in this process, yet GATA2 chromatin‑binding kinetics remain undefined. The authors investigated GATA2-chromatin interaction dynamics during erythroid differentiation in three different cell systems using single‑molecule live‑cell imaging, and they also used CUT&Tag to profile GATA2 chromatin occupancy.

By single‑molecule imaging, the authors report two interaction modes for GATA2: short‑lived (<1 s) and long‑lived (>5 s) binding. The proportion of long‑lived molecules, the number of binding events, and the duration of long‑lived binding change (or are maintained) during differentiation. Notably, long‑lived chromatin engagement by GATA2 increases during early erythroid differentiation and decreases at the late stage. CUT&Tag identifies regulatory elements selectively occupied by GATA2 during the early transition stage. Together, these results support a model in which transcription factor kinetics form a dynamic chromatin‑engagement profile that characterizes the GATA2‑to‑GATA1 transition.

Strengths:

(1) Characterizing transcription‑factor binding kinetics during the GATA2->GATA1 transition addresses a fundamental mechanism in erythroid differentiation.

(2) Combining single‑molecule live imaging with CUT&Tag provides both dynamic and locus‑specific perspectives.

(3) Single-molecule analysis across three different cell systems strengthens the potential generalizability of the findings and highlights biological variability.

Weaknesses:

I agree that single‑molecule imaging is a powerful approach for investigating GATA2 kinetics, but the single‑molecule data are the most important part of the paper and need improvement. The analyses focus on three measures: (i) duration of long binding, (ii) proportion of short‑ and long‑binding molecules, and (iii) total binding events. However, several methodological and control issues limit confidence in the kinetic interpretations. The authors should address the following major concerns.

(1) Two binding states: justification and controls

The authors propose two states of GATA2 binding. Are there only two states? Studies that separate short‑ and long‑lived binding (e.g., Chen et al., 2014, PMID: 25342811) address two states of transcriptional factors very carefully. Some long‑binding duration distributions here are very long‑tailed (e.g., Figure 2D middle), suggesting a possible third state. The authors must explain how they determined that two states provide the "best fit" to the data and how they classified "short" versus "long" binding.

Controls should be included for long‑lived and short‑lived binding (e.g., histone proteins, HaloTag‑NLS, or a binding‑deficient GATA2 mutant) as in other studies. These controls are essential to exclude alternative explanations (see points below).

(2) Exclude photophysical and focal‑plane artifacts

The authors should exclude contributions from (i) photobleaching, (ii) blinking, and (iii) Z‑axis motion (disappearance from the focal plane). Although photobleaching correction is mentioned in the Methods, no details are provided. Describe and quantify the photobleaching correction and demonstrate that it was applied across all cell types and conditions.

Some spots in the supplementary movies appear to blink or to move substantially between frames. Provide analyses or controls that distinguish true dissociation events from photophysical blinking/bleaching or axial motion.

(3) HILO illumination and nuclear region sampled

HILO is powerful but sensitive to illumination angle: slight changes sample different nuclear regions (e.g., nuclear interior versus periphery). The nuclear periphery is enriched in heterochromatin and may bias binding statistics. Explain how the authors controlled the HILO angle and confirmed that comparable nuclear regions were imaged across cells and conditions.

(4) Quantification of event counts and long‑binding durations

The number of binding events and measured long‑binding durations are strongly affected by imaging conditions (labeling/staining, bleaching, nucleus size, cell cycle state, focal plane, spot detectability, etc.). Imaging clarity appears to differ among cells/conditions in the supplementary movie. Provide more careful analysis describing how these variables were controlled or corrected for, and assess the sensitivity of results to choices in detection and tracking parameters.

(5) Evidence that spots are single molecules

The authors state that spots represent single molecules but do not provide supporting evidence. Spot brightness varies considerably in the movies. Brightness differences may reflect axial position. Provide evidence supporting single‑molecule assignment (e.g., single‑step photobleaching traces, brightness distributions compared to a known single‑molecule control, or photon count analysis).

(6) Description of spot‑analysis pipeline

The manuscript lacks a sufficient description of the spot‑analysis method. I reviewed the STRAP pipeline paper cited (Haque and Coleman 2025 bioRxiv) and the GitHub code, but the Methods in the current manuscript should include a detailed STRAP pipeline. This would enable readers to evaluate and reproduce the analyses.

(7) Differences among cell systems

The three cell systems yield notably different results (e.g., Figure 2C vs 4C and Figure 2D/3D vs 4D). Provide a more detailed explanation for these differences and discuss how biological variability, technical differences, or imaging biases might account for the discrepancies.

Reviewer #3 (Public review):

Hobbs et al. use live-cell single-molecule tracking (SMT) of HaloTag- and SNAP-tagged GATA2 combined with CUT&Tag chromatin profiling to examine how GATA2 chromatin engagement evolves during erythroid differentiation. Across three complementary systems, G1E-ER4 cells, HPC7 cells, and primary bone marrow progenitors from a new Gata2-SNAP knock-in mouse, they report a transient strengthening of long-lived GATA2 chromatin binding at the "Early" (2 h) erythroid stage, manifested either as increased residence time (G1E-ER4) or expansion of the long-lived bound fraction (HPC7, primary cells). CUT&Tag identifies 1,167 Early-restricted GATA2 peaks partitioning into GATA2-only (promoter-proximal, GATA/RUNX motifs) and GATA2+GATA1 co-bound (distal, GATA/E-box motifs) subclasses. The authors propose that this kinetic phase represents a previously unappreciated dimension of the GATA switch.

This is a strong study with a genuinely novel finding-the non-monotonic kinetic behavior of GATA2 during erythroid priming, supported by complementary measurements in three biological systems. The issues below are largely clarifications, additional analyses of existing data, and modest refinements to the discussion. With these addressed, the manuscript will make a valuable contribution. I recommend a minor revision.

Specific points:

(1) Clarify the photobleaching correction and report per-cell bleach lifetimes.

The long-lived residence time claim in G1E-ER4 cells depends on careful accounting for photobleaching, which the Methods indicate was done via a right-censoring model. For reviewer and reader confidence, the authors should report the per-stage (or per-cell distribution of) photobleaching lifetimes and the photobleach-corrected residence time values alongside the apparent values in Figure 2D. If feasible, including a brief supplementary analysis with an H2B-Halo or similar long-lived control under matched conditions would further solidify the quantitative claims. This is an analysis of existing data and should not require new imaging.

(2) Unify or explicitly discuss the mechanistic differences across systems.

The three systems show qualitatively different signatures: residence time change in G1E-ER4, bound fraction expansion in HPC7, and primary cells. The authors currently group these under "enhanced engagement," but these signatures imply different underlying mechanisms (koff decrease vs. increased kon or increased specific-binding-competent pool). The Discussion partially addresses this by noting engineered vs. native differences, but a more explicit framing in both Results and Discussion would help readers. Specifically, reporting an on-rate proxy (for example, binding events per unit time normalized to detectable molecule number) alongside koff would let readers see how the mechanistic pieces fit together. I do not think this changes the central message; it sharpens it.

(3) Per-cell GATA2 concentration would strengthen the "uncoupling" claim.

A central claim of the Figure 6 model is that chromatin engagement is uncoupled from protein abundance. The ectopic Shield-1 stabilization system is a reasonable design choice, but quantifying total nuclear GATA2-Halo signal (for example, from the pre-bleach frame or a brief high-power acquisition) on a per-cell basis across stages would directly support the interpretation. For the primary cells, where the biological claim is strongest, a western blot or quantitative immunofluorescence on the flow-sorted populations would make the uncoupling argument much more defensible. I recognize this may be one additional experiment, but it is a high-value one.

(4) Additional single-cell distribution analysis.

Figure 1E and Figures 2 to 4 show substantial cell-to-cell heterogeneity, and the Early populations in particular look potentially bimodal. Given that the authors cite Wheat et al. and Palii et al. on probabilistic hematopoietic transitions, a brief supplementary analysis using distribution-based statistics (K-S test, or mixture model) rather than, or alongside, mean-based ANOVA would align the analysis with this conceptual framing and may reveal whether the Early state represents a subpopulation transition rather than a uniform shift. This is purely an analysis of existing data.

(5) Quantitative integration of CUT&Tag with SMT.

The manuscript presents SMT and CUT&Tag as complementary but does not attempt to quantitatively connect them. A back-of-the-envelope calculation of whether a 21% increase in residence time (G1E-ER4), or the fraction expansion in other systems, is consistent with the acquisition of the 1,167 Early-restricted sites, given plausible site affinities, would substantially strengthen integration. Even if the calculation is approximate, framing it explicitly would help readers appreciate that the two datasets reinforce each other.

(6) Short-lived kinetic interpretation and tracking parameters.

The 1.5 s gap allowance in tracking is long relative to the 0.55 to 0.73 s short-lived residence times reported in primary cells (Figure Supplement 1F), which could affect the interpretation of the "slowing of target search" claim. A brief sensitivity analysis with tighter gap parameters in the supplement would reassure readers that this effect is robust. Additionally, please clarify how the inferred slowing of search, which should reduce kon, is reconciled with the increased number of binding events per cell at the Early stage.

(7) CUT&Tag peak definition.

The Early-restricted peak set is defined by presence and absence at q less than 0.01, which can be sensitive to near-threshold peaks. Please report either (a) the CUT&Tag signal intensity distribution at the 1,167 sites across all three stages as a quantitative scatter or density plot, beyond the heatmap in Figure 5C, or (b) the result of a differential binding analysis (for example, DESeq2 on read counts in a union peak set) as a supplementary confirmation. Please also state the number of CUT&Tag replicates per stage and the overlap of Early-restricted sets across replicates.

(8) Knock-in mouse validation.

The Gata2-SNAP allele is a valuable new tool, and it would benefit from slightly more quantitative validation in the supplement. A brief characterization of basic hematopoietic parameters in homozygotes (CBC, LSK/HSPC frequencies, or colony assays) would confirm that the tagged allele is truly physiological and would serve the community that will want to use this mouse going forward. If this has been done, please include it; if not, a statement about what was checked would suffice.

Author response:

We are writing to provide our provisional response to the public reviews. We note that the reviewers’ comments focus primarily on strengthening technical rigor and quantitative interpretation. We have designed the planned revisions to directly address the reviewers’ major concerns and to strengthen the study’s evidentiary basis. We plan to submit a revised manuscript for the final Version of Record.

For clarity, we summarize below the major new experiments and analyses that address the reviewers’ primary concerns:

(1)Validation of Tracking Parameters (Reviewers 1 & 3): We will re-analyze our single molecule tracking data with tighter gap-time allowances (0 seconds) to demonstrate the robustness of our interpretations of short- and long-lived kinetics. We will also generate a supplementary movie with binding trajectories superimposed directly on detected molecules to visually confirm tracking robustness.

(2) Photobleaching & Two-State Controls (Reviewers 1 & 3): We will report per-cell photobleaching lifetimes derived from our global fluorescence decay. To strengthen this analysis, we will include supplementary measurements using a H2B-HaloTag control under matched imaging conditions and perform single-molecule tracking of GATA2 zinc-finger deletion mutants (N-terminal, C-terminal, and double) as a binding-deficient functional control.

(3) Protein Expression & Labeling Efficiency (Reviewers 1 & 2): To address concerns about transgene expression and competition with endogenous proteins, we will quantify Halo-GATA2 levels in G1E-ER4 and HPC7 cells and SNAP-GATA2 levels in primary cells using standardized titration methods with established Halo-CTCF and SNAP-RPB1 reference systems.

(4) Integration of SMT and CUT&Tag (Reviewer 3): We have conducted a quantitative foldchange analysis of our existing CUT&Tag dataset to complement our single-molecule kinetics.

However, as detailed in our specific response below (R3 point 5), we emphasize that directly integrating population-level genomic occupancy measurements with single-cell kinetic measurements is not straightforward. We will therefore frame the relationship between these datasets as a conceptual consistency check rather than a strict quantitative integration. This quantitative analysis supports and refines the Early-restricted peak set, identifying a high confidence strict subset consistent with the broader presence/absence-defined set described in Figure 5 of the manuscript (see Author response images 1–3 and our response to R3 point 7).

(5) Characterization of the GATA2-SNAP Mouse (Reviewer 3): We have characterized hematopoietic populations in the homozygous knock-in mouse, including lymphoid (CD3⁺/CD4⁺/CD8⁺/B220⁺/CD19⁺), myeloid (CD11b⁺/Gr1⁺), and erythroid (Ter119⁺) compartments. These data, presented in Author response image 4, indicate that normal mature hematopoietic output is preserved across genotypes. Statistical caveats are described in the corresponding figure legend and in our response to R3 point 8.

Public Reviews:

Reviewer 1 (Public review):

(1) Two binding states: justification and controls

The authors propose two states of GATA2 binding. Are there only two states? Some longbinding duration distributions here are very long-tailed (e.g., Figure 2D middle), suggesting a possible third state. The authors must explain how they determined that two states provide the best fit and how they classified short versus long binding. Controls should be included for long-lived and short-lived binding (e.g., histone proteins, HaloTag-NLS, or a binding-deficient GATA2 mutant).

Agreed in part; we will attempt the requested binding-deficient control using existing GATA2 deletion constructs, complemented by GRID and H2B-HaloTag controls.

We will clarify that the two-state framework is an operational model rather than a claim that GATA2 can occupy only two physical states. This approach is widely used in SMT studies of chromatin-associated transcription factors and transcription machinery (Gebhardt et al., 2013; Liu et al., 2014; Hansen et al., 2017; Kenworthy et al., 2022). In particular, Ling et al. (Science, 2026) recently used two-exponential survival-probability fitting across 58 Halotagged transcription-associated proteins to distinguish transient and stable chromatin-binding populations, while explicitly noting that the simplified two-state model provides a tractable framework even when the underlying physical behavior may be more heterogeneous.

We agree that our current two-state model may under-represent the diversity of GATA2 chromatin-binding populations in single cells. However, even within this simplified framework, the existing analysis already indicates increased upper-tail dispersion of kinetic measurements (e.g., residence time and/or percentage of stable events) at the single-cell level in early erythroid cells. To support the goodness-of-fit metrics from our two-state fitting, as Reviewer 3 recommends, we will provide a supplementary table containing confidence intervals for the rate parameters and an F-test metric describing the differences between one- and two-state fits.

To determine whether additional binding states exist, we will perform GRID (Genuine Rate Identification from Distributions), which does not bias the model toward a particular number of states and, in our experience across multiple proteins, yields fits with 3-5 binding populations. However, we have found that in many cases, GRID requires aggregating binding events from multiple cells to achieve consistently robust fits for the populations of relatively rare, long-lived (>~30 sec) binding events. Therefore, GRID will assess whether additional populations exist, but we will lose the ability to analyze changes in the cell populations at the single-cell level.

We will include the multi-state analysis as a new supplementary figure. We will additionally clarify in the Results and Methods exactly how short- and long-lived binding events are classified (1-second threshold consistent with prior single-molecule frameworks for transcription-factor chromatin interactions; Gebhardt et al., 2013; Liu et al., 2014; Kenworthy et al., 2022) and direct the reviewer to these passages.

For the requested controls, we will include H2B-HaloTag imaging under matched conditions as a long-lived reference for both photobleaching correction and as a positive control for stable chromatin association, addressing R1 point 2 and R3 point 1 simultaneously.

We will also attempt to address the reviewer’s request for a binding-deficient control. We have lentiviral constructs in hand that encode GATA2 with a C-terminal zinc-finger deletion (which removes the primary DNA-binding domain), an N-terminal zinc-finger deletion, and a double deletion. We will perform single-molecule tracking of these mutants in the engineered cell systems and test whether removing GATA2’s specific DNA-binding capacity produces the predicted reduction in long-lived chromatin engagement, providing a functional perturbation control. The interpretation of these experiments will depend on the mutants expressing and localizing appropriately, which we will validate before drawing kinetic conclusions. We note that an analogous binding-deficient mutant cannot be examined in the physiological context of the Gata2SNAP knock-in mouse, and we will frame the cell-line mutant analyses accordingly. Together with GRID and the H2B-HaloTag control, these mutants provide complementary lines of validation for the two-state kinetic framework.

(2) Photophysical and focal-plane artifacts

The authors should exclude contributions from (i) photobleaching, (ii) blinking, and (iii) Z-axis motion. Describe and quantify the photobleaching correction. Provide analyses or controls that distinguish true dissociation events from photophysical blinking/bleaching or axial motion.

Agreed.

We will substantially expand the methodological description and provide three new pieces of supplementary analysis:

- Photobleaching: A per-cell photobleaching-rate distribution will be plotted for each cell type and differentiation stage, and photobleach-corrected residence-time values will be reported alongside apparent values in the relevant figures. We will also perform H2B-HaloTag imaging under matched illumination, exposure, and dye conditions in each cell line as a longlived chromatin-bound reference, establishing per-cell-type bleach lifetimes to which the GATA2 measurements can be referenced. This approach follows recent SMT precedent in which H2B decay was used to correct residence-time measurements for photobleaching, chromatin and nuclear motion, microscope drift, defocalization, and dye photophysics (Ling et al., Science 2026). The right-censoring photobleach-correction model used in our analysis will be described in detail in the revised Methods, including parameter values and per-cell handling.

- Blinking: The STRAP single-particle tracking pipeline already accommodates fluorophore blinking when linking trajectories across successive frames, following the multiple-targettracing framework of Sergé et al. (Nature Methods, 2008). This use of short gap-frame allowances to avoid artificially splitting trajectories due to fluorophore blinking or transient defocalization is consistent with recent live-cell SMT studies of chromatin-associated factors (Ling et al., Science 2026). We will add an explicit statement to the Methods describing how blinking-tolerant linkage parameters are set, and we will reanalyze representative datasets

with stricter maximum off-frame settings to ensure this parameter does not drive our conclusions (also addressing R3 point 6).

- Z-axis motion: Given our 500-ms exposure and the ~500-nm axial detection range of the HiLo configuration, axial loss is expected to be a minor contributor. We will quantify this indirectly by plotting, as a supplementary analysis, the maximum in-plane 2D spatial exploration of each binding trajectory, defined as the long-axis diameter of the 2D trajectory envelope. Although this does not directly measure z-position, it serves as a control for large apparent displacements that could reflect molecules moving out of the HiLo detection volume and demonstrates that observed dissociation events are not dominated by axial drift.

Representative photobleaching traces from individual cells (lowest, highest, and median bleach rates) will be included to support the single-molecule interpretation (also addresses R1 point 5).

(3) HILO illumination and nuclear region sampled

HiLo is sensitive to illumination angle: slight changes sample different nuclear regions. Explain how the HiLo angle was controlled and confirmed comparable across cells and conditions.

Agreed.

We will add a Methods subsection describing our HiLo illumination procedure. In brief, we started at a TIRF-supercritical angle and reduced it toward epifluorescence just enough to achieve high imaging depth while minimizing out-of-focus background signal. Within each biological system (cell line or primary cells), the TIRF angle was held constant across Basal, Early, and Late conditions to ensure direct comparability of kinetic measurements across stages.

(4) Quantification of event counts and long-binding durations

The number of binding events and the duration of long-binding events are influenced by imaging conditions. Provide a more detailed analysis of how these variables were controlled and assess the sensitivity of the results to detection and tracking parameters.

Agreed.

We will (i) normalize per-cell binding-event counts to nuclear cross-sectional area (extracted from the segmented nuclear masks already in the STRAP pipeline) to control for differences in nuclear size; (ii) report the tracking-parameter sensitivity sweep described above; and (iii) confirm in the revised Methods that all imaging conditions (laser power, exposure, dye concentration, sample preparation) were held constant across stages and cell types, consistent with the existing manuscript text. Per the Reviewing Editor’s guidance, the planned labeling-efficiency and absolute-molecule-quantification experiments will further constrain the interpretation of binding-event counts across conditions.

(5) Evidence that spots are single molecules

Provide evidence that spots represent single molecules.

Agreed.

We will include a small number of per-event intensity traces from our STRAP tracking output, selected to illustrate the single-step photobleaching behavior characteristic of single-molecule emission (intensity remains approximately constant during the binding event and then drops to background in a single step). The nuclear-fluorescence measurements from the planned labeling-titration experiment will also allow us to confirm that bound-spot densities are consistent with single-molecule occupancy at the labeled fraction used for tracking.

(6) Description of the spot-analysis pipeline

The Methods should include a detailed STRAP pipeline description.

Partially agreed; the existing STRAP reference is appropriate, but the Methods will be expanded.

STRAP (Haque & Coleman, 2025) is a consolidated, automated implementation of two well-established, previously published frameworks: SLIMfast / multipletarget tracing (Sergé et al., 2008) and evalSPT (Normanno et al., 2015), both of which are cited in the original manuscript. We will expand the Methods to describe the parameter set used in our analysis (detection thresholds, linking radii, gap-frame allowance, photobleaching correction model) so that readers can assess the analysis without referring exclusively to the STRAP manuscript and code repository, while preserving the cited STRAP reference for the full algorithmic description. We respectfully suggest that a complete pipeline description duplicating Haque & Coleman (2025) would not be appropriate in a primary research article.

(7) Differences among cell systems

The three cell systems yield notably different results. Provide a more detailed explanation for these differences.

Agreed.

We will also explicitly describe the caveats of the engineered systems versus the native GATA2-SNAP primary-cell system, in which endogenous GATA2-SNAP remains under physiological regulation. Specifically, we will discuss how variables such as the GATA1null background, ectopic forced nuclear import of GATA1-ERT, and ectopic GATA2-Halo in G1E-ER4 cells, as well as ectopic GATA2-Halo, endogenous GATA1, and cytokine signaling in HPC7 cells, likely contribute to the observed differences in signatures.

Reviewer 2 (Public review):

(1) Expression levels of the GATA2-HaloTag transgene

Determine the expression levels of the GATA2-HaloTag transgene over the course of differentiation under the conditions used for single-molecule imaging.

Agreed.

This is the central concern flagged by the Reviewing Editor. For each cell line (G1E-ER4 and HPC7), we will (i) measure total nuclear GATA2-Halo fluorescence per cell under matched acquisition conditions and (ii) convert this fluorescence intensity to absolute molecules per cell using a Halo-CTCF/U2OS reference standard (Cattoglio et al., 2019; absolute CTCF abundance quantification applied previously by our group). This will provide per-cell GATA2Halo molecule counts at each differentiation stage (Basal, Early, Late). For the primary GATA2SNAP cells, we will perform the analogous comparison against a SNAP-RPB1/U2OS standard.

(2) Fraction of molecules labeled

Carry out a titration of the HaloTag ligand and compare the amount of labeled protein under single-molecule imaging conditions to that of saturating labeling.

Agreed.

We will perform HaloTag-ligand and SNAP-tag-ligand titrations in each cell type, comparing nuclear fluorescence under the limiting-label conditions used for single-molecule tracking with that under saturating labeling. This will yield a per-cell-type labeled fraction and allow us to confirm that comparisons of binding-event counts across conditions are not confounded by differences in labeling efficiency. The labeled-fraction values will be reported in a new supplementary figure and incorporated into our quantification of binding-event rates.

(3) Robust single-particle tracking

Show images of particle trajectories or movies superimposing trajectories on imaging data.

Agreed.

We will generate visualizations of selected long-lived binding events with single-particle trajectories overlaid on the imaging data — using a multi-frame color overlay (e.g., five sequential frames in distinct colors superimposed) so that linkage of the spot across frames is visually unambiguous — and include them as a new supplementary figure or movie. Examples will be drawn from each cell system to demonstrate consistent tracking quality.

Reviewer 3 (Public review):

(1) Photobleaching correction; per-cell bleach lifetimes

Report the per-stage (or per-cell) photobleaching lifetimes and the photobleachcorrected residence time values alongside apparent values, ideally with an H2B-Halo control.

Agreed.

Addressed by the photobleach-rate distribution and H2B-HaloTag control analyses described under R1 point 2. The supplementary figure will explicitly compare per-cell bleach lifetimes across stages, report photobleach-corrected residence-time values alongside apparent values and include H2B-HaloTag controls under matched conditions in each cell line.

(2) Mechanistic differences across systems

The three systems show qualitatively different signatures: residence time change in G1EER4, bound fraction expansion in HPC7 and primary cells. Reporting an on-rate proxy alongside k_off would help.

Agreed.

Addressed by the cross-system kinetic framing described under R1 point 7 and by the GRID state-spectrum analysis described under R1 point 1. We will explicitly frame the three systems in terms of underlying kinetic mechanism in both Results and Discussion, following the conceptual distinction emphasized by Ling et al. (Science 2026) in which residence time reports binding stability once engaged, whereas changes in bound fraction or event frequency can indicate altered association/recruitment efficiency. In this framework, the G1E-ER4 residencetime signature is consistent with reduced dissociation (a longer-lived bound state), while the longlived-fraction expansion in HPC7 and primary cells is consistent with an increased target-search efficiency or specific-binding-competent pool. Alongside the GRID-derived state-spectrum analysis, we will report an apparent engagement-rate proxy calculated as binding events per unit imaging time normalized to detectable molecule number; this proxy is an approximation, not a direct k_on measurement, as accurate determination of k_on from single-molecule tracking requires concentration-dependent on-rate experiments that are outside the scope of the present study. We thank the reviewer for this suggestion, which we agree sharpens rather than alters the central message.

(3) Per-cell GATA2 concentration and the uncoupling claim

Quantify total nuclear GATA2-Halo signal per cell across stages; for primary cells, a western blot or quantitative immunofluorescence on flow-sorted populations would make the uncoupling argument more defensible.

Agreed.

For the cell lines, the per-cell nuclear GATA2-Halo quantification described in our response to R2 point 1 addresses this point.

For primary cells, where the biological claim is strongest, we will exploit the endogenous Gata2SNAP knock-in itself as a quantitative reporter of total GATA2 protein. Specifically, we will label flow-sorted CD71/Ter119 populations from Gata2-SNAP mouse bone marrow with SNAP-Cell 647-SiR at saturating concentration in a parallel acquisition to the limiting-label single-molecule tracking experiment. Total nuclear SNAP-GATA2 fluorescence at saturating labeling provides a measure of endogenous GATA2 abundance per cell at each erythroid stage, in the same chemistry used for our single-molecule measurements, and will be benchmarked against a SNAPRPB1/U2OS reference standard for absolute molecule counting. This approach (i) measures the protein of interest in the labeling chemistry already established in this study; (ii) avoids reliance on quantitative immunofluorescence, which we have not been able to validate under our flowsorted-cell conditions; and (iii) extends the same analytical framework — saturating versus limiting labeling, with U2OS reference standards — across cell lines and primary cells. Quantitative western blotting on flow-sorted populations remains an alternative we will consider if specifically requested by the reviewers.

(4) Single-cell distribution analysis

Distribution-based statistics (K-S test, mixture model) rather than (or alongside) meanbased ANOVA, particularly for the Early populations, which look potentially bimodal.

Agreed.

We will perform Kolmogorov–Smirnov and Gaussian mixture model analyses of the single-cell long-lived fraction and residence-time distributions across stages, reporting these alongside the existing Welch ANOVA results in a new supplementary figure. This analysis is consistent with the conceptual framework cited in the manuscript (Wheat et al., 2020; Palii et al., 2019) for probabilistic hematopoietic transitions and may reveal subpopulation structure underlying the Early-stage signal. The GRID analysis further complements this by formally testing whether multi-state mixture models are statistically preferred at each stage. However, GRID analysis requires aggregating binding events across cells, which limits our ability to monitor changes in population dispersion at the single-cell level.

(5) Quantitative integration of CUT&Tag with SMT

Attempt a back-of-the-envelope calculation of whether the residence-time or fraction changes are quantitatively consistent with the acquisition of the 1,167 Early-restricted sites.

Partially agreed; will attempt an order-of-magnitude framing.

We thank the reviewer for this thoughtful suggestion. We agree that more explicit framing of the quantitative relationship between the two datasets will strengthen the integration. We will add a paragraph to the Discussion presenting an order-of-magnitude calculation linking the observed residence-time and long-lived-fraction changes to the steady-state occupancy increase predicted at competent regulatory sites, with explicit caveats regarding (i) the inherently semi-quantitative nature of CUT&Tag signal and (ii) the assumptions required to translate population-averaged occupancy into the genome-wide site count observed. For the G1EER4 cells, we observe relatively minor shifts in population-mean behavior as single-cell dispersion increases. Therefore, it may be difficult to directly link population-based measurements (e.g. CUT&Tag) with single-cell kinetic measurements (SPT). This distinction between occupancy and dynamics is consistent with recent systematic SMT analysis of the eukaryotic transcription machinery, in which factors appearing persistently associated in ensemble genomic assays were shown to exchange on second-scale timescales in living cells (Ling et al., Science 2026), emphasizing that population genomic occupancy and single-molecule residence time are complementary but not directly interchangeable measurements. Closing this gap rigorously is a major hurdle for the field and will require substantial technology development on quantitative single-cell CUT&Tag occupancy measurements. We will therefore frame our analysis as a consistency check rather than a strict quantitative integration. The reviewer notes that this analysis “does not change the central message; it sharpens it,” and we agree.

(6) Short-lived kinetic interpretation and tracking parameters

The 1.5 s gap allowance is long relative to the short-lived residence times in primary cells. A sensitivity analysis with tighter gap parameters would help. Also clarify how slowing of search reconciles with increased binding events at Early.

Agreed.

Addressed by the tracking-parameter sensitivity analysis described under R1 point 2. We apologize for the lack of clarity in our original description of the gap allowance. Our current maximum off-frame parameter is set to 2 frames, corresponding to a 0.5-s gap allowance. We will rerun the tracking analysis on representative datasets using a maximum off-frame parameter of 1, corresponding to no missed frames, and will report the resulting residence-time distributions alongside the original analysis to demonstrate robustness. We will also clarify in the Results and Discussion how changes in short-lived binding kinetics are reconciled with the increase in detectable binding events at the Early stage, drawing on the apparent engagement-rate proxy interpreted alongside the GRID-derived state-spectrum analysis.

(7) CUT&Tag peak definition and quantitative analysis

Report (a) signal intensity distribution at the 1,167 sites across stages (scatter or density plot beyond the heatmap) or (b) differential binding analysis (e.g., DESeq2). State replicate count and overlap of Early-restricted sets across replicates.

Agreed; normalized fold-change analysis completed, with replicate-aware differential binding analysis planned if additional replicates are generated.

We have performed a normalized count-based fold-change analysis of the union peak set from the existing GATA2 CUT&Tag dataset (14,468 peaks) using the goodpeaks framework previously used in our group, yielding per-peak log2 fold-change values and discrete dynamicstatus calls (Gained / Lost / Unchanged at |log2FC| ≥ 2) for each of the two transitions (Basal → Early at 0 vs 2 h, and Early → Late at 2 vs 24 h). This provides a conservative quantitative complement to the presence/absence peak-calling analysis presented in Figure 5; if additional replicate data are generated, we will perform replicate-aware differential binding analysis (DiffBind/DESeq2; Love et al., 2014; Stark & Brown, 2011) and report replicate overlap. This analysis addresses option (b) of the reviewer’s request and also enables the visualization requested in option (a) as a cross-stage scatter (Author response image 1). We present the quantitative analysis as a supplement to the presence/absence-defined Early-restricted set in Figure 5 of the manuscript, providing two orthogonal lines of evidence for the same biology. We note that the CUT&Tag experiments were initially performed as a validation step to confirm that the tagged GATA2-Halo constructs recapitulate endogenous chromatin-binding behavior, including appropriate genomic localization and expected GATA switch dynamics. This validation supports the conclusion that the observed single-molecule kinetics reflect physiologically relevant GATA2 engagement. Having established this, we subsequently extended the dataset to perform the quantitative analyses presented here.

Quantitative findings.

- 384 peaks were Gained (|log2FC| ≥ 2) at the Basal → Early transition.

- 1,006 peaks were Lost over the same transition.

- 178 peaks were Gained at Basal → Early and subsequently Lost at Early → Late, defining the strict differentially-restricted Early set (Author response image 1, red points). This set represents the higher-confidence subset of the manuscript’s broader presence/absence-defined Earlyrestricted set (n = 1,167; defined as MACS2 peaks at q < 0.01 present at Early but absent at Basal and Late).

- 200 peaks were Gained at Early and retained at Late, indicating stable acquisition.

- 49 peaks were acquired only at the Late stage.

The discrepancy between the broader presence/absence set (1,167) and the strict differential set (178) reflects the analytical choice the reviewer raised: presence/absence calls based on a peaksignificance threshold are sensitive to near-threshold peaks, whereas differential analysis with a fold-change cutoff captures only sites with quantitatively pronounced stage-restricted enrichment. We interpret these as two complementary definitions: the broader set captures all peaks meeting a stage-specific peak-calling criterion, and the strict subset isolates the most quantitatively dynamic core of that population.

Importantly, the three named example loci shown in Figure 5D of the manuscript — Nono (promoter-proximal), Nr3c1 (intron 2), and Gata3 (distal intergenic) — all survive the strict differential criterion (each shows |log<sup>2</sub>FC| ≥ 2 in both transitions, consistent with a clean Gainedthen-Lost signature). The published example panel therefore represents the high-confidence intersection of both definitions, supporting the robustness of the manuscript’s selected illustrative cases.

We will explicitly state the number of CUT&Tag replicates per stage in the revised Methods and figure legends. Where the differential analysis is currently based on a single replicate per stage, we will explicitly note this and treat the strict subset as a conservative confirmatory analysis. An additional replicate is under consideration for the full revision, and if performed, overlap of Earlyrestricted calls across replicates will be reported.

Motif cross-validation against a matched-GC background using HOMER and/or MEME-ChIP is planned for the strict differential subset and will be reported alongside the original SeqPos analysis in the revised Figure 5F or its supplement.

Author response image 1.

Cross-stage log₂ fold-change scatter for GATA2 CUT&Tag peaks. Each point represents a single peak in the union peak set (n = 14,468). The x-axis shows the log2 fold change from Basal (0 h) to Early (2 h); the y-axis shows the log2 fold change from Early (2 h) to Late (24 h). The sign convention follows the field-standard direction (positive log2FC = increased signal at the later time point). Peaks are colored by dynamic-status classification: unchanged/other (gray; n = 9,794); Lost at Early (blue; n = 109); Gained at Early and retained at Late (orange; n = 200); acquired only at Late (teal; n = 49); and Early-restricted, defined as Gained at Early and Lost at Late with |log2FC| ≥ 2 in both transitions (red; n = 178). The Early-restricted population occupies the lower-right quadrant, consistent with a transient kinetic peak of GATA2 binding.

Author response image 2.

Density representation of GATA2 CUT&Tag peak dynamics with Early-restricted peaks highlighted.

Author response image 2 is shown for illustrative reference and is not annotated with a separate legend; it presents the same data as Author response image 1 in a hexbin density format to emphasize the bulk of unchanged peaks at the origin and the spatial separation of the Early-restricted set.

Author response image 3.

Genomic-annotation comparison of newly acquired GATA2 binding at Early. Stacked-bar comparison of genomic annotations (ChIPseeker classification) for two definitions of the newly acquired GATA2 peaks at the Early erythroid stage: all peaks Gained at Basal → Early (orange; n = 384) and the strict Early-restricted subset (Gained then Lost; red; n = 178). Annotation categories shown: Promoter (≤1 kb of TSS), Intron, Distal Intergenic, and Other (Exon, 5′/3′ UTR, Downstream). Both peak sets contain substantial promoter-proximal and distal/intronic components, consistent with the two-subclass model described in Figure 5E–G of the manuscript (GATA2-only promoter-proximal peaks with GATA/RUNX motifs, and GATA2/GATA1 cobound distal peaks with composite GATA/E-box motifs). The strict subset shows a higher proportion of intronic and distal-intergenic sites and a lower proportion of promoter-proximal sites than the full Gained set; this difference will be discussed transparently in the revised Results. Motif analysis (HOMER/MEME-ChIP, planned for the full revision) will be performed on both peak sets to confirm that the GATA/RUNX and GATA/E-box subclass signatures are preserved.

(8) Knock-in mouse hematopoietic validation

A brief characterization of basic hematopoietic parameters in homozygotes (CBC, LSK/HSPC frequencies, or colony assays) would confirm the tagged allele is physiological.

Agreed; data acquired and analyzed.

We have characterized mature trilineage hematopoietic populations in whole bone marrow from wild-type, heterozygous (Gata2Het), and homozygous (Gata2Homo) Gata2-SNAP knock-in mice (n = 5 per genotype). Bone marrow cells were stained for myeloid (CD11b⁺ Gr1⁺), lymphoid (CD3⁺/CD4⁺/CD8⁺/B220⁺/CD19⁺), and erythroid (Ter119⁺) markers and analyzed by flow cytometry. Lineage frequencies are shown as percentages of live bone marrow cells in a new Figure Supplement in the revised manuscript.

For myeloid and erythroid populations, omnibus one-way ANOVA detected no significant differences across genotypes (Myeloid: F(2,12) = 2.616, P = 0.1140; Erythroid: F(2,12) = 0.4943, P = 0.6219). Dunnett’s multiple-comparisons test against the WT control did not detect significant pairwise differences for either knock-in genotype (Myeloid: WT vs Het P = 0.1351, WT vs Homo P = 0.9926; Erythroid: WT vs Het P = 0.7017, WT vs Homo P = 0.9602).

For the lymphoid compartment, although the omnibus ANOVA reached significance (F(2,12) = 6.690, P = 0.0112), no pairwise comparison against WT remained significant after multiplecomparisons correction (Dunnett’s adjusted P values: WT vs Het = 0.1217; WT vs Homo = 0.2078). We therefore interpret this result conservatively. Brown-Forsythe and Bartlett’s tests showed no significant differences in variance across genotypes (P = 0.1423 and P = 0.0908), so the result is not attributable to unequal variances. We do not interpret these data as indicating an unambiguous lymphoid phenotype in either heterozygous or homozygous Gata2-SNAP mice; this interpretation is consistent with the broader pattern across all three lineages, in which no pairwise comparison against WT survives multiple-comparisons correction. We will note in the figure legend and in the Results text that more granular HSPC-compartment analysis (LSK, MPP, lineage-restricted progenitor frequencies) and a complete blood count (CBC) remain valuable directions for future characterization of this resource and will be considered for the full revision if specifically requested.

Author response image 4.

Bone marrow trilineage frequencies in Gata2-SNAP knock-in mice. Bone marrow was harvested from the femurs and tibias of wild-type (WT), heterozygous (Gata2Het), and homozygous (Gata2Homo) Gata2-SNAP knock-in mice (n = 5 per genotype; mixed sex; 12–14 weeks). After ACK lysis, cells were stained for myeloid (CD11b⁺ Gr1⁺), lymphoid (CD3⁺/CD4⁺/CD8⁺/B220⁺/CD19⁺), and erythroid (Ter119⁺) markers and analyzed by flow cytometry. Each dot represents one mouse, and horizontal bars indicate genotype means. Statistical results: Myeloid: ANOVA F(2,12) = 2.616, P = 0.1140; Dunnett’s adjusted P values WT vs Het = 0.1351, WT vs Homo = 0.9926. Lymphoid: ANOVA F(2,12) = 6.690, P = 0.0112 (omnibus); Dunnett’s adjusted P values WT vs Het = 0.1217, WT vs Homo = 0.2078. Erythroid: ANOVA F(2,12) = 0.4943, P = 0.6219; Dunnett’s adjusted P values WT vs Het = 0.7017, WT vs Homo = 0.9602. Brown-Forsythe and Bartlett’s tests for unequal variance were non-significant in all three lineages. Although the lymphoid omnibus ANOVA reached nominal significance, no pairwise comparison with WT remained significant after multiple-comparison correction; we therefore interpret this result conservatively (see response to R3 point 8).

Summary

We thank the editors and the three reviewers for the constructive and detailed assessment. The planned revisions consist of:

- Four new experiments [planned] (HaloTag/SNAP labeling efficiency and absolute molecule counts via U2OS reference standards; H2B-HaloTag photobleaching reference; percell quantification of total endogenous GATA2 in flow-sorted primary Gata2-SNAP populations via saturating SNAP-tag labeling, benchmarked against a SNAP-RPB1/U2OS reference standard; single-molecule tracking of GATA2 N-terminal, C-terminal, and double zinc-finger deletion mutants in the engineered cell systems as a binding-deficient functional control).

- Six analyses of existing data (GRID multi-state fitting [planned]; per-cell bleach-rate distributions and photobleach-corrected residence times [planned]; tracking-parameter sensitivity [planned]; nuclear-area normalization and total-displacement controls [planned]; normalized fold-change CUT&Tag analysis [completed; motif cross-validation planned], presented in Author response images 1–3; distribution-based single-cell statistics [planned]).

- One previously-acquired dataset [completed] (trilineage hematopoietic flow cytometry of homozygous Gata2-SNAP knock-in mice; presented in Author response image 4 with full statistical detail).

- Substantial revisions to text and figures [planned] to address statistical reporting, methodological description, mechanistic framing of cross-system differences, and refinement of the Figure 6 schematic.

With respect to the requested binding-deficient single-molecule control, we will attempt to address this directly using sequence-validated lentiviral constructs in hand encoding GATA2 mutants lacking the C-terminal zinc finger, the N-terminal zinc finger, or both. These mutant analyses will be complemented by GRID multi-state analysis and H2B-HaloTag controls, providing converging lines of validation for the two-state kinetic framework. We note that an analogous mutant cannot be examined in the physiological context of the Gata2-SNAP knock-in mouse, and we will frame the cell-line mutant analyses accordingly.

We believe these revisions directly address the editors’ specific guidance regarding labeling efficiency and methodological clarification. We thank the editors and reviewers for their time and look forward to submitting the revised manuscript.

References cited in this response:

References listed below are cited in this provisional response in support of the planned analyses and methodology.

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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 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 efficiency of CRISPR in C. elegans, while inserting two fluorescent proteins and a co-CRISPR marker into three loci, and Paix et al 2015 demonstrated simultaneous insertion of two fluorescent tags. The current work is valuable and incremental advance. In general, I applaud the authors' willingness to strategize about how whole proteome tagging might be accomplished. I predict that the advance here will be one of many small advances that will get the field to that goal. The title oversells the advance presented, in my view, since seems like one among many key advances, and the first sentence of the Discussion seems a more apt summary of the key advance here.

Some injections targeted genes on the same chromosome together, which will create unnecessary issues when doing crossing that will be useful for some future experiments. This made me wonder if injecting 3 together really is helpful vs targeting each gene separately, since only 5 worms need to be injected. It cuts time down by 2/3, but perhaps avoiding targeting the same chromosome with two tags would be useful.

The limited utility of current blue fluorescent proteins makes me wonder if it's worth using at this stage, before there are better blue fluorescent proteins, or better yet, far red, to avoid issues with live imaging under phototoxic UV or near-UV illumination.

Reviewer #2 (Public review):

Original 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 leaves much to be desired. A Table with all the observations must be included and can replace the tedious (and often wrong) descriptions of the observations with the different lines. It would be too much to point out every mistaken expectation of protein expression. Two examples include:

The expectation that ACDH-10 is enriched in the intestine and epidermal tissues (hypodermis) is naïve - there are multiple paralogs of this protein (look at 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 expectation that HXK-1 is ubiquitously expressed is similarly naïve. There are three paralogous enzymes that 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 shows 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.

Other points:

(1) We would encourage the authors to provide systematic validation of the reported insertions. The manuscript reports that 24 of 30 tags were isolated and visible but does not clearly state whether each isolated line was confirmed by sequence‑level validation to be correctly in‑frame and free of unintended mutations at the target locus.

(2) The manuscript presents aggregated success counts (e.g., 8/10 mTagBFP2 tags, 9/10 mStayGold, 7/10 mScarlet3) and useful narrative descriptions of injection outcomes. We suggest also to include per‑locus success rates.

(3) For pools that required re‑injection after initial failures, we would like to see a description of the specific changes that were made to the injection mixes or procedures (e.g., new repair template prep, different Cas9 reagent lot, guide redesign). This will be useful troubleshooting information for others.

(4) The authors states that the fluorophore sequences are codon-optimized for C. elegans. We suggest they provide the exact donor/tag sequences used specifically state whether the fluorophore sequences contain any synthetic/artificial introns or other sequence modifications (e.g., silent PAM‑disrupting mutations) were included in the donor templates.

(5) Page 3: Include a reference for "The C. elegans genome encodes around 20,000 genes"

We hope these comments are useful.

Comments on Revised Version:

Overall, we found the responses to be quite recalcitrant.

We have one remaining composite concern about the comparison between observed expression patterns with the new strains versus published data.

First, the authors only report patterns for one stage while it should be not too much effort to image the different life stages. However, since this is a revision, we are not formally requesting they do this.

Second, in the now provided Table (thank you) 'observed expression' (last column) is lacking for 9 of the 30 proteins, and for 6 of these the procedure was not successful. Why not report patterns for the other three? It is confusing also because on page 5, the authors say that "overall, 24 of 30 tags ...all of which were visible with fluorescence stereomicroscopy" - are we missing something? Also, they then said that they "obtained 6/9 of the originally failed tags"; why are the corresponding patterns not included in table 1, and are 9 proteins still labeled as "no" in the "success?" Column?

Third, we strongly feel that the response to our comments about expression patterns is not adequate. On page 5 the authors say that "all proteins were expected to be ubiquitously expressed" and that "scRNA-seq indicated that transcript abundance was ubiquitous and without strong tissue-specific enrichment with few exceptions". However, in their rebuttal, the authors now argue for tissue-specific expression for proteins with paralogs, turning around their own argument! Moreover, their Table indicates that many genes show tissue-enriched expression by RNA-seq while many of their tagged proteins exhibit ubiquitous expression.

Overall, this indicates that both the overall accomplishment of generating tagged protein strains and analyzing their expression is oversold.

Reviewer #4 (Public review):

Summary:

Tagging the entire proteome of a metazoan would be a landmark achievement, providing a powerful complement and extension to existing "omic" catalogs in model systems. Here, Eroglu and Hobert argue that efficiently tagging multiple loci in a single "batch" would make the community-based achievement of this goal realistic. They provide rigorous evidence that such an approach is indeed feasible, exploring issues related to efficiency, design and screening strategies, disruption of gene function, and the potential for endogenously tagged alleles to reveal unexpected aspects of protein expression and localization. While the work has some minor gaps that are important to rigorously assess the feasibility of the proposed effort, the detailed and valuable insights that emerge should provide impetus to the community to coordinate efforts to make this ambitious goal a reality.

Strengths:

The work has numerous strengths. The authors provide compelling evidence that:

- three distinct loci can be efficiently targeted with three distinct fluorescent tags in a single injection.

- thoughtful targeting design can reduce the likelihood of disruption of function by the tag.

- systematic design principles based on expression level and predicted localization/function can be used to optimize tagging strategies.

- the resulting tags can provide unexpected insight into patterns of protein production and subcellular localization.

Not all of these advances are novel in themselves, but taken together, they represent an important technical and conceptual advance. The most important strength comes from the exceptionally high value of the goal itself, in that the work is that it has the potential to spur a community-wide effort toward achieving the ambitious goal of proteome-wide tagging.

Weaknesses:

The work's shortcomings are minor.

- One concern has to do with the feasibility of the proposed screening strategies. The experimental design cleverly coinjects tags for three loci in different gene expression 'zones'; this expression level determines which tag will be used. As the authors allude to, there is an important distinction between genes with the same overall FKPM value between those that are expressed broadly and those focally expressed in a specific tissue. The proposed strategy claims that there are a sufficient number of highly expressed genes "to be used as visible markers" for recovering successfully edited animals. It would be useful for the authors to discuss the issue of broad vs focused expression among this set of genes a bit more thoroughly, with an eye toward the issue of how likely it is that these genes could indeed consistently be used as visible markers, particularly for those at the low end of this limit.

- What fraction of the proteome (on a per-gene basis) is secreted proteins? How difficult will it be to screen these for successful tags? Are there specific tags that would be more optimal for secreted proteins? (The authors mention the use of an SL2 or T2A cassette to label the cells in which these proteins are expressed but note that there are technical challenges associated with doing this at scale.)

- For secreted and/or weakly expressed genes, it would be useful for the authors to estimate for what fraction of these would successful insertions need to be screened by PCR, and what resources (time and money) this would likely entail.

- For how many genes would a single tag not capture all predicted isoforms?

- Finally, some readers might object to the authors' assertion in the abstract that this work is "a first step in this direction" (presumably referring to designing a strategy for whole-proteome tagging). There is no concern that the authors are disregarding the extensive work of other groups, as they explicitly mention the contributions of other groups to the foundation that enables the present work. However, the spirit of the abstract could be misinterpreted by a well-intentioned reader.

Author response:

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

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. Take the yeast proteome-wide GFP tagging project (Huh et al., Nature 2003). It 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) and labs already tagging entire gene classes (PMID: 40463100), 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, most critically, 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). 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 really does want to backcross, 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. Why would one do single tagging at a time, rather than triple tagging if the whole point is to scale up 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 that limiting. At least 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. 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 have gladly expanded 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 now discuss some of these points in the introduction section:

“Currently, around 1554 proteins representing 8% of the proteome are estimated to have been endogenously tagged (Leyhr et al., 2025). However, at current rates, tagging the proteome is projected to take around 100 years and likely involve numerous duplicate attempts on a small number of commonly studied proteins (Leyhr et al., 2025). It will thus be crucial for the field to coordinate tagging efforts and scale up tagging protocols to enable coverage of the entire genome at a reasonable timescale and cost. Given the number of injections is a major time-limiting factor, pooling multiple injections into one would at minimum cut tagging time by a factor of 3. In C. elegans, screening for novel CRISPR/Cas9-induced genomic edits is already 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). Recent methods for CRISPR/Cas9 mediated genomic insertions have pushed efficiencies to sufficient levels to simultaneously insert multiple fluorophores (e.g., mNeonGreen and mScarlet) as well as a co-CRISPR marker (dpy-10) at three independent loci in a single injection (Eroglu et al., 2023; Paix et al., 2015). 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 to yield functional tags”

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 our 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 (CeNGEN/Taylor et al. 2021, highest in epidermis; Ghaddar et al 2023 highest in intestine). 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.

The Ghaddar et al. and CeNGEN/Taylor et al. datasets do not show this. The scRNA paper cited (PMID: 38816550) also shows enrichment in neurons, pharynx, coelomocyte and germ cells 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 added 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 added some of this information such as annotated expression levels in young adults from various scRNA 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 appreciate the referee’s recognition 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.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Merged figures appear saturated, and use colors that won't work for red-green colorblind viewers. 

For all figures, we also show individual channels separately, which is common practice for making fluorescence images accessible to colorblind readers (PMID: 33788834). Figures highlighting non-overlap like 6B and C are already in accessible colors when merged (blue/green) and include a numerical quantification. 3-color RGB images preserve the greatest information for the highest number of individuals.

(2) Targeting ubiquitously expressed genes as a proof of concept gives me some concern that this might underestimate the challenges that may be experienced with less widely expressed genes.

While the genes were predicted to be ubiquitously expressed, many were not in practice, like HXK-1 and F54C8.1, which were also among the lower expressed genes on our list and highly cell type restricted. As discussed, the more tissue restricted a gene, the likelier that bulk RNA levels underestimate expression. Such genes are therefore more likely to be detected in a specific tissue. We routinely isolate tissue restricted endogenous tags, including those expressed in only a few neurons, with bulk FPKMs lower than the ranges tested in this manuscript.

(3) Some results are not shown or referenced (autofluorescence, for example, is shown using a schematic in Figure 1C).

We now provide representative images alongside what would be expected to be observed by eye during screening.

(4) It would be useful to describe how to recover worms from what is shown in Figure 1A. 

In the revised version, we added the following in the caption for Fig. 1A:

“Selected worms expressing the brighter tag can be screened for dimmer tags by higher magnification and long exposure imaging. Worms can be recovered directly from slides if immobilized by levamisole as described (Ghanta et al., 2021). Alternatively, single hermaphrodite worms can be isolated, allowed to lay eggs, then screened.”

(5) A blue bar of data must be missing from Figure 3B injection pool 5.

As stated in the text, “All but one tag (cox-6B::mTagBFP2) was visible in the F1 generation of injected P0 animals, and these were subsequently isolated among F2 worms positive for the other tags in the pool.”

To clarify that data points are not unintentionally omitted, we added the following text to the caption of Fig. 3B:

“For group 5 including cox-6B::mTagBFP2, worms with detectable levels of mTagBFP2 fluorescence were not recovered in the F1 generation but were isolated among progeny of F1s positive for mStayGold and mScarlet3; we were thus unable to quantify efficiency for this locus at F1.”

(6) Some expression or localization patterns were unexpected, but complications like germline silencing and protein mislocalization, with a small fraction localizing normally and rescuing function, were not presented as possibilities. Viability is used to confirm function, but without presenting whether this means 100% viability, less, or just the ability to maintain a strain.

We already do discuss mislocalization and functionality issues in the Discussion, as well as tradeoffs of alternate methods. Any existing method to observe biological molecules, be it protein, RNA or DNA, has multiple drawbacks and sources of artifacts, which are unlikely to be fully eliminated in the foreseeable future.

In regard to germline silencing of endogenously tagged genes in C. elegans, there is actually very little evidence for this. Collectively, various labs have now generated over 200 reporter alleles of germline-expressed genes (WormTagDB), with robust expression throughout the germline and retention of function. Likewise, numerous of our tags across fluorophores showed robust germline expressions including EEF-1A.1::mTagBFP2, Y22D7AL.10::mStayGold, and HAT-1::mScarlet3. In fact, overall transcript levels generally tended to underestimate germline enrichment at the protein level. We note that single-copy transgenes driven by eef-1A.1/eft-3 promoter by itself are frequently not expressed in the germline (PMID: 31064766); that we could detect EEF-1A.1 robustly in the germline when tagged endogenously is evidence that silencing is unlikely to be a widespread concern, and at the least less of a concern than single copy transgenes. We appreciate that for a transgene, presence/absence of specific sequence elements and genomic loci play a role in expression, but an endogenous tag captures all such information at a given locus.

Indeed, we found only two reports of endogenous tags being silenced in the germline, the first being a novel tag (not fluorophore) which initially prevented expression at the tagged locus (PMID: 30109984), but after making changes to the sequence to avoid silencing signals the authors could rescue expression and thereafter saw robust expression in various novel contexts with this tag. The second example (PMID: 34547227) leaves open the possibility that germline repression of that particular gene might be a part of its endogenous regulation.

Nevertheless, given it is probably rare if occurs at all, it will likely take a large scale tagging effort to uncover such cases at sufficient numbers to study. In our view, this further justifies tagging at large, ideally genomic, scales. If we do discover that there are numerous annotated germline proteins which we don’t observe by tagging, that would be interesting to study on its own.

(7) Halotag is presented in the Discussion as a small tag, but it is bigger than GFP.

Thank you for catching this. We have removed the discussion of Halotag. Given the comparable size to FPs, it would be unlikely to alleviate issues of tag functionality.

(8) It would be useful to include FPKMs and viability percentages in Table 1.

FPKM is included in column 6, but the title for this column is cut off. In the revised table FPKM values are now shown more clearly across stages.

We did not quantify viability percentage. In our view it does not yield an informative metric when there is little information about the protein’s required dosage for function, which was the case for most proteins here. A haplosufficient gene might yield a full brood size even if 50% of protein function is lost; conversely, a highly dose sensitive protein could yield penetrant and severe inviability with mild perturbation of function. It also is not actionable information at this stage if there is no alternate tagging strategy as a baseline of comparison. The worms we picked to image all have viable embryos as adults, so in those individuals the genes were likely to be sufficiently expressed and functional.

(9) Because establishing that a guide works well is a limiting step for many CRISPR experiments (once a guide works well, it's easy to inject 5 worms and get lines), I wondered if testing that for many genes is what is really needed in the field at this stage. 

Guide quality is rarely an issue in C. elegans, as for all the genes here we tried only one guide, all of which were previously untested. We now clarified this in the discussion section:

“Notably, we find that previously untested guide RNAs and homology arms perform exceptionally well at novel loci, as we only tested one set of reagents for each locus which yielded satisfactory tagging rates.”

(10) For a manuscript where the injection is so central to what was done, I was surprised to read in the Acknowledgments that all of the injections were done by someone who is not included as an author.

We are likewise surprised by such a comment but gladly clarify: Chi Chen has been with us as an expert microinjection specialist for more than 25 years and her very important technical contributions have been acknowledged in many dozen papers. Multiple authorship guidelines, including COPE’s and ICMJE’s, state that technical contributions alone do not qualify for authorship.

Reviewer #2 (Recommendations for the authors):

(1) We would encourage the authors to provide systematic validation of the reported insertions. The manuscript reports that 24 of 30 tags were isolated and visible, but does not clearly state whether each isolated line was confirmed by sequence‑level validation to be correctly in‑frame and free of unintended mutations at the target locus.

We appreciate the reviewer’s concerns on fidelity. These parameters have been assessed in prior published work (e.g., PMID: 30504364, PMID: 34748534) and in our hands are in the range of 80% whenever we sequence non-fluorescent tags of similar sizes. The efficiencies we observed are high enough that one can expect to recover numerous worms with the exact intended sequence for each target, though we would argue mutations within the FP reporter are less likely to matter if it retains high fluorescence.

(2) The manuscript presents aggregated success counts (e.g., 8/10 mTagBFP2 tags, 9/10 mStayGold, 7/10 mScarlet3) and useful narrative descriptions of injection outcomes. We also suggest including per‑locus success rates.

Figure 3B shows per locus success rate and source data is provided for this figure. Each dot is an individual injection and the Y axis is per locus rate. We now worded this more clearly in the figure’s caption.

“Total insertion efficiencies per locus for the indicated targets across injection pools.”

(3) For pools that required re‑injection after initial failures, we would like to see a description of the specific changes that were made to the injection mixes or procedures (e.g., new repair template prep, different Cas9 reagent lot, guide redesign). This will be useful troubleshooting information for others.

We re-made the exact same injection mix but with nanodrop to ensure the purity of the repair templates as assessed by absorbance ratios (A260/230 and A260/280) were sufficient after each purification step. No other changes were made. This is now specified in the methods section in the following way:

“For re-runs of pools 4, 6 and 10 which failed initially, we regenerated the repair templates and ensured that after each column purification, the A260/230 ratio of the purified DNA was ≥2.2 and A260/280 was 1.8 ± 0.05 when measured with a Nanodrop spectrophotometer.”

(4) The authors state that the fluorophore sequences are codon-optimized for C. elegans. We suggest they provide the exact donor/tag sequences, specifically state whether the fluorophore sequences contain any synthetic/artificial introns, or whether other sequence modifications (e.g., silent PAM‑disrupting mutations) were included in the donor templates. 

This information is provided in Supplementary Table 1.

(5) Page 3: Include a reference for "The C. elegans genome encodes around 20,000 genes" 

We added a reference to the most recent release of the genome (WS237, May 2013). Spieth et al., 2014.

диаграмма:

Речь идет об отправке данных с одного клиента многим клиентам https://flashphoner.com/docs/api/WCS5/rest_api/latest/v3/#tag/Data/operation/sendData

1. От клиента к серверу POST /data/send
2. Рисуем второго клиента, к нему OnDataEvent
3. Дальше POST /apps/DefaultApp/DataStatusEvent на бэкенд и т.д.

reply to u/deleted at https://old.reddit.com/r/typewriters/comments/1te4u1i/state_of_the_typosphere/

Two or three typewriter repair shops have opened up in the past couple of years, though probably not enough to offset the retirements or deaths which include Tom Furrier (Cambridge Typewriter) and Duane Jensen (Phoenix Typewriter) respectively. Lucas Dul opened up a brick-and-mortar typewriter shop in Chicago.

Philly Typewriter and Bremerton Typewriter Company have started up typewriter repair schools/apprenticeships to expand on the trade.

Tom Hanks has continued donating typewriters to typewriter repair shops over the past few years, ostensibly to encourage the space as well as to slim down his own collection.

Richard Polt recently downsized his collection significantly. (His blog is generally a good source of the news of what's new in the past few years.)

Prices are up somewhat in general, but especially for Hermes 3000s, Olympias, Smith-Corona Silent Supers, and Olivetti Letteras even in poor condition.

Historical updates: https://typewriterdatabase.com/twdb.0.news-media

Type Pals has started up monthly meetups again: https://www.typepals.com/events

Lou Spirito designed a baseball scorecard for typewriters which was unveiled by Tom Hanks on March 29, 2025.

Qwertyfest seems to be going strong: https://www.qwertyfest.com/

Atlanta, Albuquerque, and Los Angeles have bee hosting type-ins a few times a year.

I've fleshed out some details and examples on typecasting for those interested in trying it out: https://indieweb.org/typecast

Circulation v. Reserve

1. A Tascam Recorder with a green tag (circulation)
2. A hig-end lighting kit with a blue tag (reserve)
3. 3D printing pass that requires prior training (reserve)
4. A PA system that you can book in advance (reserve)
5. A USB to USB-C adapter that a student checked out at the desk without booking in advance (circulation)
6. A SM58 microphone, circulated though Workflows (LibCal)
7. A Canon RP camera, circulated through LibCal (reserve)
8. A Canon Vixia camera, with a UVA barcode and green tag (circulation)

Reserve Equipment

Reserve equipment are all the pieces in the vault with a BLUE tag. These items rarely have a UVA barcode, and tend to be higher-end.

The library information system we use to circulate reserve equipment is LibCal

Circulation Equipment Circulation equipment are all the pieces in the vault with a GREEN tag. All of these items have a UVA barcode that is found in the equipment's case or on the largest piece of the equipment. That's it! That's the neat trick for identifying Circulation equipment.

Circulation equipment is labeled in GREEN and has a UVA BARCODE.

The library information system we use for circulation equipment is Workflows

Author response:

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

We would like to express our deep appreciation to the editor and reviewers for their constructive comments and suggestions, which have significantly improved the quality of our manuscript. In response, we have carefully revised the manuscript, addressed all comments, and performed additional experiments and analyses to strengthen our findings.

(1) We repeated retrograde tracing using CTB-647 to verify precise targeting of SPN and DGC neurons, as shown in the new Figure 7.

(2) We performed dual retrograde tracing combined with fiber photometry or optogenetic activation to investigate the role of PMC dual-projecting neurons in the control of urination, as shown in Figure supplements 11 and 12.

(3) We conducted new experiments activating PMC^ESR1+ neurons after PDNx to assess their role in urination, as shown in new Figure 6.

(4) We added a more detailed analysis of the dynamics of neural responses in PMC^ESR1+ neurons in Figure supplements 3F-3G.

(5) We analyzed peak Ca²⁺ signals in the PMC during and after the onset of EMG bursting, as shown in Figure supplement 4F.

(6) We added a comparison of spontaneous and light-induced spikes in PMC^ESR1+ neurons, as shown in Figure supplements 3B–3C.

(7) We expanded the Discussion to address how PMC^ESR1+ neurons coordinate bladder contraction and sphincter relaxation to control both the initiation and suspension of urination.

We hope these revisions meet the reviewers' expectations and contribute to the improvement of our manuscript.

Reviewer #1 (Public review):

Summary:

Urination requires precise coordination between the bladder and external urethral sphincter (EUS), while the neural substrates controlling this coordination remain poorly understood. In this study, Li et al. identify estrogen receptor 1-expressing neurons (ESR1+) in Barrington's nucleus as key regulators that faithfully initiate or suspend urination. Results from peripheral nerve lesions suggest that BarEsr1 neurons play independent roles in controlling bladder contraction and relaxation of the EUS. Finally, the authors performed region-specific retrograde tracing, claiming that distinct populations of BarEsr1 neurons target specific spinal nuclei involved in regulating the bladder and EUS, respectively.

Strengths:

Overall, the work is of high quality. The authors integrate several cutting-edge technologies and sophisticated, thorough analyses, including opto-tagged single unit recordings, combined optogenetics, and urodynamics, particularly those following distinct peripheral nerve lesions.

We are grateful for your insightful and constructive comments, which affirmed the importance and technical depth of our work. Thank you for dedicating your expertise and time to reviewing our manuscript. Guided by your suggestions, we have revised the paper as detailed below.

Weaknesses:

(1) My major concern is the novelty of this study. Keller et al. 2018 have shown that BarEsr1 neurons are active during urination and play an essential role in relaxing the external urethral sphincter (EUS). Minimally, substantial content that merely confirms previous findings (e.g. Figures 1A-E; Figures 3A-E) should be move to the supplementary datasets.

Thank you for this valuable and constructive comment. We fully agree that the novelty of our study relative to Keller et al., 2018 must be made explicit. Keller et al. established that PMC^ESR1+ neurons are active during socially evoked urine-marking behavior (voluntary urination) and demonstrated their essential role in relaxing the EUS. Their study mainly focused on behavioral context and EUS relaxation. In contrast, our work addresses a distinct, mechanistic question: how these same neurons participate in reflexive, physiological urination and coordinate both bladder detrusor contraction and EUS relaxation.

Novel aspects of the present study:

(1) Temporal dynamics of PMC^ESR1+ neurons during reflexive micturition.

Using opto-tagging and single-unit recordings, we reveal the precise firing pattern of PMC^ESR1+ neurons during reflexive voiding. Simultaneous fiber photometry, cystometry, and EUS-EMG recordings demonstrate that population-level activity of PMC^ESR1+ neurons precedes and tightly correlates with both bladder contraction and EUS relaxation a coordination not previously demonstrated.

(2) Causal role in reflexive urination.

Manual closed-loop optogenetic inhibition at the onset of reflexive voiding acutely terminates EUS bursting and bladder contraction, immediately halting urine release.

(3) Dual control of bladder and EUS.

Optogenetic activation combined with selective pelvic or pudendal nerve transection shows that PMC^ESR1+ neurons drive both bladder contraction and EUS relaxation, revealing a coordinating role beyond EUS relaxation alone.

(4) Anatomical substrate for coordinated control of bladder contraction and EUS relaxation in reflexive urination.

Retrograde tracing identifies three spinal-projecting sub-populations: SPN-only, DGC-only, and dual-targeting neurons, providing a circuit-level explanation for the simultaneous control of bladder and EUS.

Following your suggestion, panels that merely replicate Keller et al. (former Figures 1A–1E and Figures 3A–3E) have been moved to new Figure Supplements 1 and 7, respectively, so that the main figures now emphasize the new mechanistic findings.

(2) I also have concerns regarding the results showing that the inactivation of BarEsr1 neurons led to the cessation of EUS muscle firing (Figures 2G and S5C). As shown in the cartoon illustration of Figure 8, spinal projections of BarEsr1 neurons contact interneurons (presumably inhibitory) that innervate motor neurons, which in turn excite the EUS. I would therefore expect that the inactivation of BarEsr1 should shift the EUS firing pattern from phasic (as relaxation) to tonic (removal of relaxation), rather than stopping their firing entirely. Could the authors comment on this and provide potential reasons or mechanisms for this finding?

Thank you for this crucial comment. We apologize that the representative EUS-EMG traces in Figures 2G and S5C were too small to be clearly seen and that the corresponding results description was not sufficiently accurate. We have now replaced these EMG traces with enlarged versions (revised Figures 2G and S5C) and revised the corresponding Results section (lines 184, 197, 340-341). Based on the enlarged traces, we found that acute photoinhibition of PMC^ESR1+ neurons at the onset of phasic EUS-EMG bursting shifted the EUS firing pattern from large-amplitude phasic bursts to low-amplitude tonic firing. This suggests that ongoing activity of PMC^ESR1+ neurons is required to maintain phasic EUS bursting. A similar shift from phasic to tonic EUS-EMG activity during optogenetic silencing of PMC^ESR1+ neurons was reported by Keller et al., 2018 (Figure supplement 8C), confirming the reproducibility of the phenotype. We propose that the potential mechanism of this low-amplitude tonic activity may be mediated in part by a spinal reflex pathway (the guarding reflex) for preventing urination, whereby the loss of PMC^ESR1+ neurons-mediated supraspinal facilitation reduces inhibition of spinal interneurons, leading to enhanced baseline excitability of EUS motor neurons in response to bladder afferent input during bladder distension (William C. de Groat et al., Comprehensive Physiology. 2015, PMID: 25589273).

(3) Current evidence is insufficient to support the claim that the majority of BarEsr1 neurons innervate the SPN but not DGC. The current spinal images are uninformative, as the fluorescence reflects the distribution of Esr1- or Crh-expressing neurons in the spinal cord, along with descending BarEsr1 or BarCrh axons. Given the close anatomical proximity of these two nuclei, a more thorough histological analysis is required to demonstrate that the spinal injections were accurately confined to either the SPN or the DGC.

Thank you for raising this important concern. To rigorously verify that our spinal injections were confined to either the SPN or the DGC, we performed new retrograde-tracing experiments in ESR1-Cre and CRH-Cre mice. We injected a mixture of AAV-Retro-DIO-mCherry or AAV-Retro-DIO-EGFP with the retrograde tracer CTB-647 specifically into the SPN or DGC (Methods, lines 465-466). Only animals in which CTB-647 fluorescence was strictly limited to the target nucleus, without detectable spread to the adjacent region, were included in the analysis (new Figures 7A and 7E). These results confirm our original observation that PMC^ESR1+ neurons comprise three distinct spinal-projection subpopulations: one (19.0%) targeting the SPN, one (52.2%) innervating the DGC, and a third (28.8%) projecting to both regions (Results, lines 304–306; new Figures 7F–7H). In addition, the majority of PMC^CRH+ neurons project to the SPN but not the DGC (new Figures 7B–7D; Results, lines 297–301). We have assembled new Figure 7 using the newly acquired spinal images and the validated data.

Reviewer #1 (Recommendations for the authors):

From the abstract: "Anatomically, PMCESR1+ cells possess two subpopulations projecting to either the pelvic or pudendal nerve". I don't think these neurons directly project to either nerve.

Thank you for this precise comment. We apologize for incorrectly stating that PMC^ESR1+ cells project directly to the pelvic or pudendal nerves. In the revised Abstract (lines 32–36) we have rephrased the sentence to clarify the actual anatomy: “Anatomically, PMC^ESR1+ neurons consist of three distinct spinal-projection-based subpopulations: one targeting the sacral parasympathetic nucleus (SPN), one innervating the dorsal gray commissure (DGC), and a third that projects to both regions, thereby enforcing the coordination of bladder contraction and sphincter relaxation in a rigid temporal sequence.”. We trust this revision now accurately reflects the anatomical findings.

Reviewer #2 (Public review):

Summary:

The authors have performed a rigorous study to assess the role of ESR1+ neurons in the PMC to control the coordination of bladder and sphincter muscles during urination. This is an important extension of previous work defining the role of these brainstem neurons, and convincingly adds to the understanding of their role as master regulators of urination. This is a thorough, well-done study that clarifies how the Pontine micturition center coordinates different muscle groups for efficient urination, but there are some questions and considerations that remain.

Strengths:

These data are thorough and convincing in showing that ESR1+PMC neurons exert coordinated control over both the bladder and sphincter activity, which is essential for efficient urination. The anatomical distinctions in pelvic versus pudendal control are clear, and it's an advance to understand how this coordination occurs. This work offers a clearer picture of how micturition is driven.

We sincerely thank you for highlighting the rigor of our study and for recognizing the advance in understanding how PMC^ESR1+ neurons exert coordinated, anatomically segregated control over bladder and sphincter. We also appreciate the constructive suggestions that helped us further improve clarity, which we address point-by-point below.

Weaknesses:

The dynamics of how this population of ESR1+ neurons is engaged in natural urination events remains unclear. Not all ESR1+ neurons are always engaged, and it is not measured whether this is simply variation in population activity, or if more neurons are engaged during more intense starting bladder pressures, for instance. In particular, the response dynamics of single and doubly-projecting neurons are not defined. Additionally, the model for how these neurons coordinate with CRH+ neuron activity in the PMC is not addressed, although these cell types seem to be engaged at the same time. Lastly, it would be interesting to know how sensory input can likely modulate the activity of these neurons, but this is perhaps a future direction.

Thank you for this insightful comment. First, we agree that not all ESR1+ neurons are consistently engaged during urination (Figure 1B). Because bladder pressure was not measured during the opto-tagging experiments, we cannot determine whether this reflects trial-to-trial variability in population activity or pressure-dependent recruitment of additional neurons. We speculate that stronger starting bladder pressures may recruit a larger subset of ESR1+ neurons, analogous to graded, pressure-dependent recruitment observed in peripheral sensory neurons (Bruns et al., J Neural Eng. 2011, PMID: 21878706; Marshall et al., Nature. 2020, PMID: 33057202).

Second, using fiber photometry recording and optogenetic activation, we examined the dynamics of dual-projecting neurons in the PMC that were retrogradely labeled from the SPN and DGC. Their activity correlated with bladder contraction and sphincter relaxation, and optogenetic activation sequentially induced these events to trigger urination (see Recommendation #8). Although retrograde labeling captured only a subset of dual-projecting neurons, the results indicate that they coordinate bladder and sphincter activity.

Third, previous studies suggest that PMC^CRH+ cells are associated with bladder contraction and likely serve as an integration center for context-dependent micturition behavior (Hou et al., Cell. 2016, PMID: 27662084; Ito et al., Elife. 2020, PMID: 32347794). We therefore propose that PMC^CRH+ cells establish the baseline conditions and contextual readiness for voiding, whereas PMC^ESR1+ cells act as the executive command to reliably initiate and execute the event.

Finally, we agree that sensory inputs likely modulate PMC^ESR1+ neuron activity. Although this falls beyond the scope of the present study, it represents an important avenue for future investigation.

Reviewer #2 (Recommendations for the authors):

(1) In the introduction, the authors write that Keller 2018 only showed this ESR1 population to induce EUS relaxation, but those results also do show bladder contraction with photostimulation of this population. While the authors' work extends this finding in important ways, this should be acknowledged (line 60).

Thank you for this important correction. We have now revised the Introduction to explicitly acknowledge that stimulation of neurons expressing estrogen receptor 1 (ESR1) in the PMC (PMC^ESR1+) contributes to sphincter relaxation and increased bladder pressure (Introduction, lines 60-62), as originally reported by Keller et al., 2018.

(2) I think a more detailed analysis of the dynamics of neural responses in the PMC ESR1 neurons would be valuable. For example: are the same cells always engaged before micturition, or do different populations activate on different trials? Can the authors comment on the half of the opto-tagged ESR1 population that is not firing during urination? Do they ever fire? A cell-by-cell analysis of which neurons are engaged over multiple trials would be very valuable to understand the dynamics of population activity. Figure 1H shows cumulative sessions, but what do single sessions look like?

Thank you for these valuable comments. In response, we have performed refined single-trial analyses of neuronal activity, as detailed in the point-by-point replies below.

For example: are the same cells always engaged before micturition, or do different populations activate on different trials?

Among 11 PMC^ESR1+ units that showed urination-related excitation, 8 units exhibited a consistent firing increase in every voiding trial, whereas the remaining 3 increased their discharge in >78 % of trials (Figure 1B; new Figure supplement 3F). Thus, the same PMC^ESR1+ cells are recruited repeatedly, rather than distinct populations being activated on different trials. We have added this clarification to Results (lines 106–108).

Can the authors comment on the half of the opto-tagged ESR1 population that is not firing during urination? Do they ever fire? A cell-by-cell analysis of which neurons are engaged over multiple trials would be very valuable to understand the dynamics of population activity.

Approximately half of the opto-tagged PMC^ESR1+ cells showed no increase in firing rate during urination, yet exhibited spontaneous spikes at other times (new Figure supplement 3G), confirming their electrical competence. Because the PMC also participates in defecation, uterine activity, and other pelvic functions (Rouzade-Dominguez et al., Eur J Neurosci. 2003, PMID: 14686905; Schellino et al., Frontiers in Neuroanatomy. 2020, PMID: 33013330; Quaghebeur et al., Auton Neurosci. 2021, PMID: 34391125), these ESR1+ neurons may serve functions other than urination. We have now added this cell-by-cell analysis and discussion to the manuscript (Results, lines 108-112).

Figure 1 H shows cumulative sessions, but what do single sessions look like?

As shown in new Figure supplements 3F–3G, single-session raster plots reveal that PMC^ESR1+ neurons display consistent firing patterns across individual trials. Neurons whose firing rate increased during urination did so in most trials (Figure supplement 3F), whereas neurons unrelated to voiding remained silent or showed no discernible rate change during voiding across trials (Figure supplement 3G). These single-session observations are consistent with the cumulative population analysis shown in Figure 1H (new Figure 1B).

(3) Supplemental Figure 4: It seems clear from this figure that NVCs are only occurring when the sphincter fails to engage. Can the authors quantify how often this is the case?

Thank you for this important point. We have now quantified the occurrence of non-voiding contractions (NVCs) across all 229 bladder contraction events from 3 mice shown in Supplemental Figure 4. NVCs were observed exclusively when the external urethral sphincter failed to relax, accounting for 62/229 events (27.1 %), whereas coordinated voiding contractions (VCs) occurred in the remaining 167 events (72.9 %). These new data are presented in Figure supplement 4C.

(4) Continuing from the above point: the authors say that the insufficient top-down drive or strength of activity from PMC ESR1 neurons is why NVCs occur. In looking closely, it also seems there is a small hump and subsequent increase in the calcium signal when the EUS bursting begins (particularly clear in Supplementary Figure 4). Could this instead mean that the bursting/urethral activity itself is feeding back onto the PMC to continue/enhance its activity, and it is instead the lack of sphincter bursting that results in the NVC? Could the authors analyze the signal during and after bursting starts? This model is consistent with one of the classic reflexes defined by Barrington, in which urethral fluid flow/activation enhances bladder contraction. The Figure 4 transection experiments do not fully answer this, as the authors are driving activity in the PMC at this time, but they could test this using PDN transection with fiber photometry recording.

Thank you for this important point. We fully agree that EUS bursting may provide excitatory feedback to the PMC that sustains or even amplifies its activity, and that the absence of such feedback could underlie NVCs. To test this possibility, we re-analyzed the fiber-photometry traces aligned to the onset and offset of each EUS bursting (new Figure supplement 4). A small but consistent hump in the Ca²⁺ signal appeared before bursting onset and the Ca²⁺ signal continued to rise throughout the bursting (Figure supplement 4B, yellow arrow). The amplitude at bursting offset was significantly higher than both the NVC peak and the level recorded at bursting onset. These observations support the interpretation that urethral fluid flow/activation supplies excitatory feedback that reinforces PMC activity and bladder contraction, consistent with Barrington’s classic reflex. We have incorporated these new analyses into the revised manuscript (lines 145–155 and Figure supplement 4F).

We agree that the positive-feedback loop described by Barrington’s classic urethra-to-bladder reflex is an intriguing mechanism. However, the PDN-transection experiment in Figure 4 was designed to determine if bladder contractions triggered by PMC^ESR1+ cells can proceed in the absence of sphincter bursting, not to evaluate this reflex. Incorporating simultaneous fiber-photometry recording into the PDN-transection experiment would therefore go beyond the scope of the present study. In future work we are keen to combine PDN transection with fiber photometry to further determine whether the urethra-to-bladder reflex contributes to the sustained PMC activity observed in our paradigm.

(5) In Figure 4, is the timing of sphincter engagement different with ChR2 stimulation from what normally occurs? It appears that the bursting happens immediately upon activation whereas bladder contraction is a bit delayed.

Thank you for this important observation. We have carefully re-examined the EMG traces from all animals shown in Figure 4. We confirm that the onset of sphincter bursting activity during ChR2 stimulation is indeed more rapid than during natural reflex voiding; nevertheless, the onset of phasic sphincter bursting during ChR2 stimulation remained delayed relative to the intravesical pressure rise (see Figure 8B).

The immediate sphincter discharge visible in some trials was tonic EUS discharge or rare irregular bursting, not the typical EUS bursting. This tonic pattern corresponds to the spinal guarding reflex that suppresses urine leakage (Fowler et al., Nature Reviews Neuroscience. 2008, PMID: 18490916; Keller et al., Nature Neuroscience. 2018, PMID: 30104734). These segments were identified by their amplitude and spectral content and excluded from burst-onset analysis. Our analysis protocol therefore distinguishes tonic guarding activity from true phasic bursting, ensuring that only the latter was used to determine burst timing.

(6) The explanation on line 299 about how spinal reflexes are impinging on this circuit is confusing. I agree that the bladder contraction stopping later than the EUS signal likely has something to do with spinal reflexes, but it seems this could instead be feedback from the urethral fluid flow, which continues bladder contractions (urethra-destrusor facilitative reflex). Could the authors clarify their thoughts here?

Thank you for highlighting this ambiguity. We agree that the delayed cessation of bladder contraction could equally reflect either (1) the urethra-to-bladder facilitative reflex driven by ongoing urethral fluid flow or (2) spinal reflexes that we described. In the revised manuscript (Results, lines 343–349), we have re-worded the paragraph to make this dual possibility explicit, thereby avoiding an overly strong emphasis on spinal mechanisms alone.

(7) A note on phrasing: the authors frequently say PMCESR1 cells drive sphincter relaxation, but then show an effect on sphincter bursting. Experienced readers might realize that relaxation and bursting are connected, but this might be confusing for readers and should be clarified in the text.

Thank you for highlighting the potential ambiguity. We agree that the sentence “PMC^ESR1 cells drive sphincter relaxation” can seem paradoxical when our data show increased EUS bursting. In adult mice, the EUS does not remain continuously relaxed during voiding; instead, it generates rhythmic bursting composed of high-frequency spike clusters (active periods) alternating with low tonic activity (silent periods), resulting in rhythmic contractions and relaxations of EUS. This phasic activity acts as a pump that facilitates urine flow through the narrow rodent urethra (Kadekawa et al., Am J Physiol Regul Integr Comp Physiol, 2016, PMID: 26818058). The EUS bursting activity we recorded is consistent with the results reported in previous studies (Keller et al., Nat Neurosci, 2018, PMID:30104734; Ito et al., Elife, 2020, PMID:32347794).

Consequently, when PMC^ESR1 neurons initiate bursting, they simultaneously generate the relaxation phases that separate the spikes. To make this explicit we have replaced the phrase “PMC^ESR1+ cells drive sphincter relaxation” with “PMC^ESR1 neurons trigger EUS bursting, which generates rhythmic sphincter contractions and relaxations.” (Results, page 7, lines 219-221). We have applied similar clarifications throughout the revised manuscript (Results, lines 125-129). We hope this revision eliminates any apparent contradiction.

(8) The question remains as to which neurons (dual projecting, single projecting, or all?) are active in natural urination. This is possible to do through dual injection of retrograde virus in SPN and DGC that could coordinately turn on Gcamp, but this challenging experiment is perhaps beyond the scope of this paper. Even still, the authors could discuss their model for whether the dual- and single-projecting neurons are all engaged at once in a natural urination event. Do the authors have any data that could provide insight as to when these sub-populations are active? Results from the opto-tagging in Figure 1 (and comment #2 about single neuron firing properties) might provide a foundation for hypotheses or insights.

Thank you for this valuable suggestion. We have now performed the experiment you proposed: dual injection of retrograde virus (AAV-Retro-Cre and AAV-Retro-DIO-GCaMP6s) in SPN and DGC were used to selectively label PMC dual-projecting neurons, and a 200-µm optic fiber was implanted above the PMC to record their Ca²⁺ dynamics during natural urination (Figure supplement 11A and Methods, lines 470–474, 652-655). Dual-projecting neurons exhibited robust activation throughout the entire voiding phase that was tightly correlated with intravesical pressure rise and EUS bursting (Figure supplements 11A–11H). However, technical limits of current retrograde tools preclude selective isolation of single-projecting (SPN-only or DGC-only) subsets for independent fiber-photometry recordings and injection restricted to one target unavoidably labels both single- and dual-projecting cells. We now state this technical limitation explicitly (Discussion, lines 426-430).

Accordingly, in the revised Discussion (lines 389-406), we integrate fiber-photometry Ca²⁺ signals with single-unit data from opto-tagged recordings to propose several testable, non-mutually-exclusive models for how dual- and single-projecting PMC^ESR1+ neurons are engaged during natural urination: “Based on population dynamics obtained by fiber photometry (Figures 1D-1H, Figure supplements 1A-1F, and Figure supplements 11A-11H) and single-neuron firing properties recorded via optrode (Figures 1A-1C), we propose several mechanistic models for the engagement of dual- and single-projecting PMC^ESR1+ neurons during natural micturition. One possibility is that all three populations (dual-projecting, SPN-projecting and DGC-projecting neurons) are co-activated, with the dual-projecting subset acting as a “bridging amplifier” that sustains rising bladder pressure while coordinating EUS relaxation. Alternatively, SPN-projecting neurons may be recruited first to initiate bladder contraction, followed by DGC-projecting neurons that evoke EUS bursting and facilitate urine entry into the urethra; once flow begins, the urethro-detrusor facilitative reflex could recruit dual-projecting neurons to further enhance voiding efficiency. In addition, contextual or state-dependent urination—such as scent-marking behavior characterized by multiple voiding events with smaller volumes than reflexive urination—may predominantly rely on sequential and cooperative activation of single-projecting neurons. Other recruitment sequences remain conceivable. Future studies combining diverse urination-related behavioral paradigms with simultaneous recordings from projection-specifically labeled PMC neurons will be required to validate and refine these models.”

Reviewer #3 (Public review):

Summary:

The paper by Li et al explored the role of Estrogen receptor 1 (Esr1) expressing neurons in the pontine micturition center (PMC), a brainstem region also known as Barrington's nucleus (Hou et al 2016, Keller et al 2018). First, the author conducted bulk Ca2+ imaging/unit recording from PMCESR1 to investigate the correlations of PMCESR1 neural activity to voiding behavior in conscious mice and bladder pressure/external urethral muscle activity in urethane anesthetized mice. Next, the authors conducted optogenetics inactivation/activation of PMCESR1 to confirm the contribution to the voiding behavior also conducted peripheral nerve transection together with optogenetics activation to confirm the independent control of bladder pressure and urethral sphincter muscle.

We sincerely thank you for providing a thoughtful summary and insightful comments on our study.

Weaknesses:

(1) The study demonstrates that pelvic nerve transection reduces urinary volume triggered by PMC ESR1+ cell photoactivation in freely moving mice. Could the role of pudendal nerve transection also be examined in awake mice to provide a more comprehensive understanding of neural involvement?

Thank you for this valuable suggestion. We conducted an additional experiment to determine the contribution of the pudendal nerve to PMC^ESR1+ neuron-driven voiding in awake mice. Bilateral pudendal nerve transection (PDNx) reduced the optogenetically evoked urine volume compared with sham-operated controls, yet photoactivation of PMC^ESR1+ neurons still reliably induced urination after PDNx (new Figure 6). Thus, bilateral integrity of the pudendal nerve is required for efficient PMC^ESR1+ neuron-driven voiding, most likely by transmitting the signals that entrain rhythmic EUS bursting. These data and experimental details have been incorporated into Figure 6, Results (lines 272–276), and Methods (lines 542–545).

(2) While the paper primarily focuses on PMCESR1+ cells in bladder-sphincter coordination, the analysis of PMCESR1+-DGC/SPN neural circuits - given their distinct anatomical projections in the sacral spinal cord - feels underexplored. How do these circuits influence bladder and sphincter function when activated or inhibited? Also, do you have any tracing data to confirm whether bladder-sphincter innervation comes from distinct spinal nuclei?

Thank you for this critical comment. To determine how PMC^ESR1+ neurons that target distinct sacral nuclei influence bladder–sphincter coordination, we first focused on the dual-projecting subset in a new experiment (Figures supplement 11 and Methods, lines 470–477, 652-655, 669-673). Dual retrograde virus injections into SPN and DGC selectively labelled PMC dual-projecting neurons, a subset of which are ESR1+. Fiber-photometry recordings showed that these cells were active during bladder contraction and sphincter relaxation (Figure supplements 11E-11H), whereas optogenetic activation reliably initiated urination: bladder pressure rose immediately and was followed by rhythmic EUS bursting (Figure supplements 11I-11N and 12B; Results, lines 309-313, 332-335). Thus, the dual-projecting sub-population is sufficient to coordinate bladder contraction with sphincter relaxation. Current retrograde tools do not allow selective isolation of single-projecting (SPN-only or DGC-only) subsets; injecting only one target unavoidably labels both single- and dual-projecting cells. Consequently, we cannot yet compare the functional impact of pure SPN-only versus DGC-only PMC populations. This limitation is now stated explicitly in the revised Discussion (lines 426–430).

In our 2025 paper (Yan et al., Commun Biol, 2025, PMID: 40259086), we used PRV-based retrograde tracing to show that SPN and DGC constitute two separate spinal nuclei controlling the bladder and the EUS, respectively. Classic studies have reached the same conclusion (Yao et al., Nat Neurosci, 2018, PMID: 30361547; Karnup & De Groat, IBRO Reports, 2020, PMID: 32775758; Karnup, Auton Neurosci, 2021, PMID: 34391124). These citations and a concise summary have been added to the Results (lines 289–294).

(3) Although the paper successfully identifies the physiological role of PMCESR1+ cells in bladder-sphincter coordination, the study falls short in examining the electrophysiological properties of PMC ESR1+-DGC/SPN cells. A deeper investigation here would strengthen the findings.

Thank you for this thoughtful suggestion. While a detailed electrophysiological characterization of PMC^{ESR1+-DGC/SPN} neurons would provide complementary information, the primary goal of the present study was to define the in vivo functional dynamics and behavioral role of these neurons during natural urination. As you suggested, further electrophysiological analysis of PMC^{ESR1+-DGC/SPN} neurons will be an important direction for our future work.

(4) The parameters for photoactivation (blue light pulses delivered at 25 Hz for 15 ms, every 30 s) and photoinhibition (pulses at 50 Hz for 20 ms) vary. What drove the selection of these specific parameters? Moreover, for photoactivation experiments, the change in pressure (ΔP = P5 sec - P0 sec) is calculated differently from photoinhibition (Δpressure = Ppeak - Pmin). Can you clarify the reasoning behind these differing approaches?

Thank you for this opportunity to clarify our experimental design. The photoactivation protocol (25 Hz, 15 ms pulses) was chosen because PMC^ESR1+ neurons faithfully follow this frequency without depolarisation block and it reliably triggers voiding (Keller et al., Nat Neurosci, 2018, PMID:30104734). For photoinhibition we originally stated “50 Hz, 20 ms pulses”, but this was an error. Consistent with the same study (Keller et al., Nat Neurosci, 2018, PMID:30104734), we used continuous light (constant illumination) to maintain sustained suppression. The Methods section has been corrected (lines 659-661, 690-691).

The ΔP formula was tailored to the temporal profile of each manipulation. For activation, ΔP (P_{5 sec} - P_{0 sec}) captures the rapid pressure rise after light onset; the same window was used in (Hou et al., Cell. 2016, PMID: 27662084). For inhibition, because saline infusion produces rhythmic reflex voiding, we delivered light at the onset of EUS bursting (i.e. when pressure was already at ~peak). Inhibition abruptly stops the bladder contraction, so the bladder cannot return to its pre-void baseline. The Δpressure (P_peak – P_min) was therefore used to quantify the extent to which the ongoing pressure wave was aborted by photoinhibition. P_min is the lowest value reached before the next infusion-driven upswing, making the metric insensitive to the slow baseline drift produced by continuous infusion. These clarifications have been added to the Methods (Methods, lines 676-677, 679-680, 692-693).

(5) The discussion could further emphasize how PMCESR1+ cells coordinate bladder contraction and sphincter relaxation to control urination, highlighting their central role in the initiation and suspension of this process.

Thank you for this valuable comment. We have revised the Discussion to emphasize that PMC^ESR1+ neurons coordinate urination by sequentially driving bladder contraction followed by sphincter relaxation through their dual projections to the SPN and DGC. We also emphasized that this coordination is essential for the initiation and effective execution of voiding (Discussion, lines 369-388). In addition, in the revised Discussion (Discussion, lines 389-406), we integrate fiber-photometry Ca²⁺ signals with single-unit data from opto-tagged recordings to propose several testable, non-mutually-exclusive models for how PMC^ESR1+ cells are engaged during natural urination.

(6) In Figure 8, The authors analyze the temporal sequence of bladder pressure and EUS bursting during natural voiding and PMC activation-induced voiding. It would be acceptable to consider the existence of a lower spinal reflex circuit, however, the interpretation of the data contains speculation. Bladder pressure measurement is hard to say reflecting efferent pelvic nerve activity in real time. (As a biological system, bladder contraction is mediated by smooth muscle, and does not reflect real-time efferent pelvic nerve activity. As an experimental set-up, bladder pressure measurement has some delays to reflect bladder pressure because of tubing, but EUS bursting has no delay.) Especially for the inactivation experiment, these factors would contribute to the interpretation of data. This reviewer recommends a rewrite of the section considering these limitations. Most of the section is suitable for the results.

We agree with the reviewer that bladder pressure, mediated by smooth muscle contraction, provides an indirect measure of efferent pelvic nerve activity and is subject to both physiological and experimental delays. Regarding potential delay from the tubing system, pressure propagates in fluid at approximately 1000 m/s (Kela & Pekka, Proceedings of World Academy of Science Engineering & Technology, 2009, DOI: 10.5281/zenodo.1080526). Given that the total tubing length in our setup is 0.5-1 meter, this gives an estimated transmission delay of only 0.5-1 ms. However, this delay is negligible compared with the observed time difference (~700 ms) between the cessation of EUS bursting and the termination of bladder contraction. Theoretically, pressure transmission is not expected to introduce a temporal delay. However, we cannot exclude the possibility that the pressure measurement itself may impose such a delay, because bladder pressure does not necessarily reflect efferent pelvic nerve activity in real time. Future studies using simultaneous recordings of bladder pressure and pelvic nerve discharges will help clarify whether a true temporal delay exists. Nevertheless, we agree that additional physiological or peripheral factors may also contribute to this difference in timing. As suggested by the reviewer, we have revised the discussion to consider the potential influence of other factors, such as urethra-detrusor facilitative reflex (Results, lines 343-349).

Reviewer #3 (Recommendations for the authors):

(1) In opto-tag experiments, a comparison of average AP waveform during behavior and during light stimulation should be included as criteria. It should be mostly the same waveform.

Thank you for bringing this to our attention. We have now added this comparison as an inclusion criterion in the revised manuscript. Figure supplement 3B shows representative examples of the average waveforms, and Figure supplement 3C displays the distribution of correlation coefficients between spontaneous and light-evoked spikes for all recorded PMC^ESR1+ units, all of which exhibited r > 0.8.

(2) Optical fiber implantation seems to be done in two different methods. In Figure 1 and Figure 2, the fiber tip is positioned just above PMC but in Figure 3 it seems to be angled. The information should be included in the Methods section.

Thank you for this important comment. We have now clarified in the Methods that for Figures 1 and 2, the optical fibers were implanted vertically above the PMC, whereas for Figure 3, the left optical fiber was implanted at a 33° lateral angle targeting the PMC (Methods, lines 499-503).

(3) In the closed-loop inhibition experiments of Figure 2, the parameters to start closed-loop photo-inactivation were not described in the method. If it is a manual closed loop, it should be described clearly.

Thank you for raising this important point. We apologize for omitting these details in the original Methods. We have now added a complete description of the manual closed-loop photo-inhibition protocol, including the triggering criteria and operator-controlled timing, in the revised Methods section (lines 602–605).

(4) In Figure 7A/E the authors provide a spinal cord image to show the injection site, but the image is misleading. The figure only shows AAV-infected CRH/ESR1 neurons in the spinal cord section. It does not indicate the AAV injection site or the terminal distribution.

Thank you for your important comment. We apologize for providing a spinal cord image that did not accurately depict the injection site. To rigorously verify that our spinal injections were confined to SPN or DGC, we performed new retrograde-tracing experiments in ESR1-Cre and CRH-Cre mice. A mixture of AAV-Retro-DIO-mCherry or AAV-Retro-DIO-EGFP with the retrograde tracer CTB-647 was injected specifically into SPN or DGC. Only animals in which CTB-647 fluorescence was strictly limited to the target nucleus, without spread to the adjacent region, were included (new Figures 7A and 7E). These data confirmed our original observations and have been pooled in Figure 7. The manuscript and figure have been updated accordingly (Results, lines 297-301, 304-306; Methods, lines 465–466).

Author response:

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

eLife Assessment

This is a useful study presenting solid data indicating that the bacterial GTPases EngA and ObgE enable single-step reconstitution of functional 50S ribosomal subunits under near-physiological conditions. The study elegantly bridges the gap between the non-physiological aspects of the previous two-step reconstitution method and the extract-dependent iSAT system to enable ribosome assembly under translation-compatible conditions; however, it is limited by reliance on rRNA and proteins extracted from native ribosomes and does not achieve a true bottom-up reconstruction from all synthetic components. The evidence is incomplete in not characterizing the spectrum of reporter polypeptides produced and not comparing their rate and yield of synthesis from reconstituted ribosomes to that obtained with pure native ribosomes; and the impact of the study is limited by not including reporters to examine the fidelity of initiation, elongation or termination achieved with the reconstituted ribosomes.

As described below, based on the comments from the public reviewers, we have summarized at the end of the Discussion how this study contributes toward true bottom-up reconstruction from fully synthetic components, as well as the aspects that will require further development. In addition, we have newly provided data characterizing the reporter polypeptides from multiple perspectives, demonstrating that the assembled ribosomes do not exhibit issues such as reduced fidelity (Fig. 6, 7, Supplementary Data 2, 3). We believe that these data adequately address the limitations that were pointed out in the eLife Assessment.

Public Reviews:

Reviewer #1 (Public review):

This study presents evidence that the addition of the two GTPases EngA and ObgE to reactions comprised of rRNAs and total ribosomal proteins purified from native bacterial ribosomes can bypass the requirements for non-physiological temperature shifts and Mg+2 ion concentrations for in vitro reconstitution of functional E. coli ribosomes.

Strengths:

This advance allows ribosome reconstitution in a fully reconstituted protein synthesis system containing individually purified recombinant translation factors, with the reconstituted ribosomes substituting for native purified ribosomes to support protein synthesis. This work potentially represents an important development in the long-term effort to produce synthetic cells.

Weaknesses:

While much of the evidence is solid, the analysis is incomplete in certain respects that detract from the scientific quality and significance of the findings:

(1) The authors do not describe how the native ribosomal proteins (RPs) were purified, and it is unclear whether all subassemblies of RPs have been disrupted in the purification procedure. If not, additional chaperones might be required beyond the two GTPases described here for functional ribosome assembly from individual RPs.

Native ribosomal proteins (RPs) were prepared from native ribosomes, according to the well-established protocol described by Dr. Knud H. Nierhaus [Nierhaus, K. H. Reconstitution of ribosomes in Ribosomes and protein synthesis: A Practical Approach (Spedding G. eds.) 161-189, IRL Press at Oxford University Press, New York (1990)]. In this method, ribosome proteins are subjected to dialysis in 6 M urea buffer, a strong denaturing condition that may completely disrupt ribosomal structure and dissociate all ribosomal protein subassemblies. To make this point clear, we described the detailed ribosomal protein (RP) preparation procedure in the manuscript, rather than merely referring to the book.

In addition, we would like to clarify one point related to this comment. The focus of the present study is to show that the presence of two factors is required for single-step ribosome reconstitution under translation-compatible, cell-free conditions. We do not intend to claim that these two factors are absolutely sufficient for ribosome reconstitution. Hence, we have revised the manuscript to more explicitly state what this work does and does not conclude.

(2) Reconstitution studies in the past have succeeded by using all recombinant, individually purified RPs, which would clearly address the issue in the preceding comment and also eliminate the possibility that an unknown ribosome assembly factor that co-purifies with native ribosomes has been added to the reconstitution reactions along with the RPs.

As noted in the response to the Comment (1), the focus of the present study is the requirement of the two factors for functional ribosome assembly. Therefore, we consider that it is not necessary to completely exclude the possibility that unknown ribosome assembly factors are present in the RP preparation. Nevertheless, we agree that it is important to clarify what factors, if any, are co-present in the RP fraction. To address this, we performed proteomic analysis of the TP70 preparation (Supplementary Data 3) and stated the possibility of other factors’ inclusion.

We also agree that additional, as-yet-unidentified components, including factors involved in rRNA modification, could plausibly further improve assembly efficiency. We also consider that such studies may contribute to extending the system to the use of in vitro-transcribed rRNA and fully recombinant ribosomal proteins, which could be essentially a next step of this study. We noted the possibility of as-yet-unidentified components and the future perspectives in the Discussion.

(3) They never compared the efficiency of the reconstituted ribosomes to native ribosomes added to the "PURE" in vitro protein synthesis system, making it unclear what proportion of the reconstituted ribosomes are functional, and how protein yield per mRNA molecule compares to that given by the PURE system programmed with purified native ribosomes.

According to this suggestion, we measured the sfGFP synthesis rate from the increase in fluorescence over time under conditions where the template mRNA is in excess, and compared this rate directly between reconstituted and native ribosomes. We consider that this comparison provides insight into what fraction of ribosomes reconstituted in our system are functionally active (Fig. 6).

As noted in the provisional responses, quantifying protein yield per mRNA molecule is substantially more challenging. The translation system is complex, and the apparent yield per mRNA can vary depending on factors such as differences in polysome formation efficiency. In addition, the PURE system is a coupled transcription–translation setup that starts from DNA templates, which further complicates rigorous normalization on a per-mRNA basis. Because the main focus of this study is to determine how many functionally active ribosomes can be reconstituted under translation-compatible conditions, we addressed this comment by just carrying out the experiment comparing sfGFP synthesis rate.

(4) They also have not examined the synthesized GFP protein by SDS-PAGE to determine what proportion is full-length.

We have added an affinity tag to the sfGFP reporter, and then, purified the synthesized products from the reaction mixture and analyzed it by SDS–PAGE (Fig. 7a).

(5) The previous development of the PURE system included examinations of the synthesis of multiple proteins, one of which was an enzyme whose specific activity could be compared to that of the native enzyme. This would be a significant improvement to the current study. They could also have programmed the translation reactions containing reconstituted ribosomes with (i) total native mRNA and compared the products in SDS-PAGE to those obtained with the control PURE system containing native ribosomes; (ii) with specifc reporter mRNAs designed to examine dependence on a Shine-Dalgarno sequence and the impact of an in-frame stop codon in prematurely terminating translation to assess the fidelity of initiation and termination events; and (iii) an mRNA with a programmed frameshift site to assess elongation fidelity displayed by their reconstituted ribosomes.

Following the recommendation, we selected DHFR as an enzymatically active protein and used it as a reporter, confirming that it exhibited enzymatic activity comparable to that observed when synthesized by native ribosomes (Fig. 7c). In addition, MS analysis of the purified sfGFP used for SDS-PAGE analysis showed that nearly all peptide fragments were detected, covering almost the entire sequence from the initiator amino acid to the amino acid immediately preceding the stop codon (Fig. 7b, Supplementary Data 2. These results suggest that protein synthesis by the newly assembled ribosomes proceeds smoothly from initiation to termination, with no apparent problem in fidelity, and therefore indicate that functional ribosomes were successfully reconstituted.

Reviewer #2 (Public review):

This study presents a significant advance in the field of in vitro ribosome assembly by demonstrating that the bacterial GTPases EngA and ObgE enable single-step reconstitution of functional 50S ribosomal subunits under near-physiological conditions-specifically at 37 {degree sign}C and with total Mg²⁺ concentrations below 10 mM.

This achievement directly addresses a long-standing limitation of the traditional two-step in vitro assembly protocol (Nierhaus & Dohme, PNAS 1974), which requires non-physiological temperatures (44-50 {degree sign}C), and high Mg²⁺ concentrations (~20 mM). Inspired by the integrated Synthesis, Assembly, and Translation (iSAT) platform (Jewett et al., Mol Syst Biol 2013), leveraging E. coli S150 crude extract, which supplies essential assembly factors, the authors hypothesize that specific ribosome biogenesis factors-particularly GTPases present in such extracts-may be responsible for enabling assembly under mild conditions. Through systematic screening, they identify EngA and ObgE as the minimal pair sufficient to replace the need for temperature and Mg²⁺ shifts when using phenol-extracted (i.e., mature, modified) rRNA and purified TP70 proteins.

However, several important concerns remain:

(1) Dependence on Native rRNA Limits Generalizability

The current system relies on rRNA extracted from native ribosomes via phenol, which retains natural post-transcriptional modifications. As the authors note (lines 302-304), attempts to assemble active 50S subunits using in vitro transcribed rRNA, even in the presence of EngA and ObgE, failed. This contrasts with iSAT, where in vitro transcribed rRNA can yield functional (though reduced-activity, ~20% of native) ribosomes, presumably due to the presence of rRNA modification enzymes and additional chaperones in the S150 extract. Thus, while this study successfully isolates two key GTPase factors that mimic part of iSAT's functionality, it does not fully recapitulate iSAT's capacity for de novo assembly from unmodified RNA. The manuscript should clarify that the in vitro assembly demonstrated here is contingent on using native rRNA and does not yet achieve true bottom-up reconstruction from synthetic parts. Moreover, given iSAT's success with transcribed rRNA, could a similar systematic omission approach (e.g., adding individual factors) help identify the additional components required to support unmodified rRNA folding?

We fully recognize the reviewer’s point that our current system has not yet achieved a true bottom-up reconstruction. Although we intended to state this clearly in the manuscript, the fact that this concern remains indicates that our description was not sufficiently explicit. We therefore added the paragraph to ensure that this limitation is clearly communicated to readers.

(2) Imprecise Use of "Physiological Mg²⁺ Concentration"

The abstract states that assembly occurs at "physiological Mg²⁺ concentration" (<10 mM). However, while this total Mg²⁺ level aligns with optimized in vitro translation buffers (e.g., in PURE or iSAT systems), it exceeds estimates of free cytosolic [Mg²⁺] in E. coli (~1-2 mM). The authors should clarify that they refer to total Mg²⁺ concentrations compatible with cell-free protein synthesis, not necessarily intracellular free ion levels, to avoid misleading readers about true physiological relevance.

We agree that this is a very reasonable point and revised the manuscript to clarify that we are referring to the total Mg²⁺ concentration compatible with cell-free protein synthesis, rather than the intracellular free Mg²⁺ level under physiological conditions. We also changed the term “physiological” to “near-physiological” to avoid the misunderstanding.

In summary, this work elegantly bridges the gap between the two-step method and the extract-dependent iSAT system by identifying two defined GTPases that capture a core functionality of cellular extracts: enabling ribosome assembly under translation-compatible conditions. However, the reliance on native rRNA underscores that additional factors - likely present in iSAT's S150 extract - are still needed for full de novo reconstitution from unmodified transcripts. Future work combining the precision of this defined system with the completeness of iSAT may ultimately realize truly autonomous synthetic ribosome biogenesis.

Recommendations for the authors:

Reviewing Editor Comments:

Recommendations for improvement:

(1) Assess the length distribution of GFP polypeptides being produced using SDS-PAGE.

SDS-PAGE was performed according to the comment 4 of the Reviewer #1 (Fig. 7b). Please refer to our response addressing the comment.

(2) Compare the rate and yield of GFP synthesized per mRNA using their reconstituted ribosomes to that obtained with pure native ribosomes.

The efficiency of the reconstituted ribosomes was compared to native ribosomes according to the comment 3 of the Reviewer #1 (Fig. 6). Please refer to our response addressing the comment.

(3) Expand the panel of reporter mRNAs being examined to compare the fidelity of initiation, elongation or termination achieved with reconstituted ribosomes to that obtained using native ribosomes.

DHFR synthesis was addressed and also MS analysis of synthesized sfGFP was performed according to the comment 5 of the Reviewer #1 (Fig. 7b, c). Please refer to our response addressing the comment.

(4) Revise the manuscript to clarify that the in vitro assembly demonstrated here is contingent on using native rRNA and thus does not achieve a true bottom-up reconstruction from synthetic parts.

We added to the Discussion a paragraph summarizing the findings of this study, limitations, and future perspectives according to the comment 1 and 2 of the Reviewer #1 and the comment 1 of the Reviewer #2. Please refer to our responses addressing these comments.

(5) Revise the manuscript to clarify that they are referring to total Mg2+ concentrations compatible with cell-free protein synthesis, not necessarily intracellular free ion levels, to avoid misleading readers about the physiological relevance of the reconstitution.

We revised the manuscript to clarify this point according to the comment 2 of the Reviewer #2. Please refer to our response addressing the comment.

(6) Revise the text to fully describe how the native ribosomal proteins (RPs) were purified and indicate whether all subassemblies of RPs were disrupted in the purification procedure.

We revised the Methods section to clarify how the native RPs were purified and that all subassemblies of RPs were disrupted according to the comment 1 of the Reviewer #1.

(7) Revise the text to indicate that achieving ribosome reconstitutions using all recombinant, individually purified RPs is required to achieve a true bottom-up reconstruction from all synthetic components.

As with our response to the comment 4, we have added the point at the end of the Discussion as a future perspective toward true bottom-up reconstruction from all synthetic components.

(8) Consider conducting a similar systematic omission approach (e.g., adding individual factors) to help identify the additional components required to support unmodified rRNA folding.

As with our response to the comment 4 and 7, we have added the point at the end of the Discussion as a future perspective toward identification of additional essential factors for true bottom-up reconstruction.

Reviewer #1 (Recommendations for the authors):

(1) Assessing the spectrum of GFP polypeptides being produced by SDS-PAGE and comparing the rate and yield of GFP produced to that obtained with pure native ribosomes would seem to be essential additional measurements needed to bolster the evidence supporting the main conclusions of the work.

SDS-PAGE and MS analysis of the synthesized sfGFP were performed (Fig. 7a, b). Comparison of the assembled ribosomes and native ones were also performed (Fig. 6).

(2) Examining translation of other reporter mRNAs designed to compare the fidelity of initiation, elongation or termination achieved with reconstituted ribosomes to that produced by native ribosomes in the PURE system would be required to elevate the scientific quality of the work and its significance to the field.

DHFR synthesis and its activity measurement were performed (Fig. 7c). Also, MS analysis of the purified sfGFP showed that nearly all peptide fragments were detected, covering almost the entire sequence from the initiator amino acid to the amino acid immediately preceding the stop codon (Fig. 7b). We consider that these findings indicate that there is no apparent problem with fidelity.

Reviewer #2 (Public review):

Summary:

Chapman, Determan et al. investigate how pathogenic mutations in DNMT3A, which cause Tatton-Brown-Rahman Syndrome (TBRS), disrupt human cortical developmental processes using a comprehensive panel of human pluripotent stem cell models spanning DNMT3A loss-of-function severity. The authors aim to identify the cellular and molecular mechanisms underlying TBRS-associated brain overgrowth and intellectual disability, and to test whether mechanistic convergence exists between TBRS and other overgrowth-intellectual disability disorders (OGIDs) caused by mutations in EZH2 (Weaver syndrome) or PIK3CA pathway components. Their central conclusion is that GABAergic interneuron development is selectively vulnerable to DNMT3A mutation, where reduced DNA methylation causes premature de-repression of neuronal and synaptic genes, driving precocious neuronal maturation and hyperactivity sufficient to disrupt neuronal network synchrony. This report adds to a growing literature supporting the vulnerability of GABAergic interneurons in NDDs and further provides a mechanistic view of this vulnerability, potentially convergent across OGIDs. The mechanistic claims around H3K27me3 compensation and mTOR-based therapeutic convergence, while promising, rest on more preliminary evidence and would benefit from the distinction between correlation and mechanism being made more explicit in the text. Overall, this is a compelling study with a rigorous experimental design and novel findings with a potential impact on a better understanding of the OGID pathophysiology.

Strengths:

(1) A major strength of this work is the breadth and rigor of the disease modeling approach. Four independent TBRS model systems are used in tandem: a patient-derived iPSC line with isogenic CRISPR-corrected control (R882H), a knock-in hESC model (P904L) with its wild-type isogenic, patient deletion iPSC lines (Del1/2), and CRISPRi knockdown models (G1/G2), collectively spanning a range of DNMT3A loss-of-function that correlates with phenotypic severity. This allelic series design substantially strengthens causal inference beyond what any single isogenic pair could provide.

(2) The multi-omic integration across matched developmental stages provides a strong mechanistic foundation for the cellular phenotyping and provides significantly enhanced novelty. RNA-seq, whole-genome bisulfite sequencing, and H3K27me3 CUT&Tag are combined in the same cell types, and timepoints show that DNMT3A loss reduces CG methylation at neuronal and synaptic gene loci, leading to premature transcriptional activation.

(3) The selective vulnerability of ventral (GABAergic) versus dorsal (glutamatergic) progenitors is one of the study's most important findings. This lineage specificity is consistently observed across all model systems and in both 2D and organoid formats, where ventral NPCs show increased proliferation, premature neuronal gene expression, and increased neurogenesis, while dorsal NPCs are largely unaffected at the transcriptomic and cellular level despite exhibiting comparable DNA methylation changes. This adds to a body of emerging work showing GABAergic interneuron vulnerability in NDDs where ubiquitously expressed genes such as chromatin modifiers are perturbed, and provides additional molecular insights into potential mechanisms of "resilience" of dorsal populations.

(4) The functional characterization follows a logical progression from single-neuron electrophysiology (demonstrating GABAergic hyperactivity with increased action potential amplitude and firing rate) to network-level analysis using high-density multi-electrode arrays. The HD-MEA experimental design - pairing TBRS or control GABAergic neurons with a constant background of control iGlut neurons - cleanly isolates GABAergic dysfunction as the driver of network hypersynchrony.

Weaknesses:

(1) The concomitant induction of proliferation and differentiation in TBRS V-NPCs is conceptually striking, since these are generally considered antagonistic developmental programs. The authors partially address this tension by noting that DNMT3A LOF alone is insufficient to initiate neuronal differentiation, i.e., V-NPCs upregulate neuronal and synaptic genes while retaining progenitor identity, implying that transcriptomic priming and commitment to differentiation are decoupled. However, the relationship between the proliferative phenotype and the epigenetic priming phenotype remains mechanistically unresolved. The manuscript documents mTOR pathway upregulation at the protein level and identifies shared DEGs that include proliferative regulators, but it does not establish whether mTOR-driven proliferation and mCG-loss-driven neuronal gene de-repression/enhanced differentiation are causally linked or represent two independent consequences of DNMT3A LOF.

(2) Relatedly, the rapamycin rescue experiment is a valuable proof-of-concept for the PIK3/AKT/mTOR convergence but is limited to a single dose in a single model (882) with a single readout (Ki67+ proliferation). Given the prominence of mTOR pathway convergence in the manuscript as a potential shared therapeutic avenue across OGIDs, the data supporting this claim are somewhat preliminary. It remains unknown whether mTOR inhibition rescues downstream phenotypes (neurogenesis, gene expression, neuronal maturation) or whether less severe TBRS models respond similarly. This might also help tackle the first comment above. e.g., if mTOR inhibition rescued proliferation but not the transcriptomic priming, that would support two independent mechanisms.

(3) The claim that H3K27me3 compensates for mCG loss is an important mechanistic point, but the current data do not distinguish between active compensation, in which EZH2 is recruited in response to methylation loss, and functional redundancy, in which H3K27me3 is independently established and becomes the dominant repressive mark once DNA methylation is reduced. The EZH2 knockdown/inhibition experiments show that H3K27me3 is sufficient to maintain repression at hypo-DMR sites, but they do not establish that H3K27me3 gain is itself a response to methylation loss. Because H3K27me3 profiling was performed only in the severe 882 model, it is also unclear whether H3K27me3 gain scales with DNMT3A LOF severity, as a compensatory model would predict. Finally, the EZH2 overexpression rescue is performed in V-NPCs, whereas the compensation model is developed primarily in D-NPCs, making it difficult to assess whether the same mechanism operates in the lineage where it was originally inferred.

(4) The narrative framing of dorsal neuron development as unaffected by DNMT3A LOF is somewhat at odds with the data presented. The 882 D-NPCs show substantial DNA methylation changes, and TBRS D-INs exhibit what the authors describe as "substantive transcriptomic differences" involving persistent expression of pluripotency and progenitor genes, which seems to be a distinct but potentially significant phenotype. The impact of DNMT3A loss between ventral and dorsal lineages might be more accurately framed as divergent in nature rather than specific to a certain population.

(5) SST stainings are not entirely convincing. They appear mostly nuclear, and some instances localized to rosettes in organoids, whereas the protein is largely confined to processes and is expected to be found outside progenitor-rich zones like rosettes.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This is an important study that describes the consequences of the DNMT3A mutation in human neuronal development for the first time. The selective impact of DNMT3A function on GABAergic interneurons is interesting and an important feature of future therapeutics. The claims made in that manuscript are supported by strong evidence for the most part. And the data are of high quality in general and presented well.

Strengths:

The strengths of the work include: Characterization of multiple DNMT3A loss-of-function alleles, including two misense variants, R882H, P904L, and a deletion allele. The missense mutation lines both include an ideal control with the same genetic background. The CRISPRi-mediated DNMT3A knockdown has also been included. The study identifies the mTOR-PI3K pathway as a factor of overgrowth issues found in the mutant organoid. In bulk mRNA sequencing and whole-genome bisulfite sequencing, identify hypomethylated genomic regions associated with gene expression repression. Again, this is more pronounced in the ventral organoid compared to the dorsal organoid. In addition, the extensive electrophysiological characterizations with a high-density microelectrode array support the more mature status of mutant interneurons.

Weaknesses:

Although a strong study overall, some weaknesses are noted. These include:

(1) The lack of validation data for the generated iPSCs and hESCs, such as the chromosomal contents, ploidy, and pluripotency states.

We thank the reviewer for their constructive feedback. We previously validated our 882 models with whole genome sequencing and teratoma formation upon mouse fat pad injection, while the parental human embryonic stem cell line (WA01 hESCs) used for P904L variant knock-in was validated by our Genome Engineering Stem Cell (GESC) core upon derivation of that variant knock-in model. We have now added both karyotyping and pluripotency staining (SOX2/OCT4) for all other hPSC lines as (new) Supplementary Figure S17 and included further description in our Methods section under “hPSC Model Generation and Culture”.

New Data: Supplemental Figure S17 (SOX2/OCT4 staining in hPSCs and karyotyping of all lines used)

Text edits: Additional language confirming hPSC line validation will be added to the Methods section under “hPSC Model Generation and Culture” on page 18.

(2) Other weaknesses relate to data interpretation and insufficient discussion of related matters, as detailed in the recommendations to the authors.

We thank the reviewer for their insightful suggestions and have detailed our responses in the “recommendations to the authors” section.

(3) Also, some errors are noted and detailed in the recommendation section.

We thank the reviewer for catching these errors and have since corrected them, with detailed responses below.

Reviewer #2 (Public review):

Summary:

Chapman, Determan et al. investigate how pathogenic mutations in DNMT3A, which cause Tatton-Brown-Rahman Syndrome (TBRS), disrupt human cortical developmental processes using a comprehensive panel of human pluripotent stem cell models spanning DNMT3A loss-of-function severity. The authors aim to identify the cellular and molecular mechanisms underlying TBRS-associated brain overgrowth and intellectual disability, and to test whether mechanistic convergence exists between TBRS and other overgrowth-intellectual disability disorders (OGIDs) caused by mutations in EZH2 (Weaver syndrome) or PIK3CA pathway components. Their central conclusion is that GABAergic interneuron development is selectively vulnerable to DNMT3A mutation, where reduced DNA methylation causes premature de-repression of neuronal and synaptic genes, driving precocious neuronal maturation and hyperactivity sufficient to disrupt neuronal network synchrony. This report adds to a growing literature supporting the vulnerability of GABAergic interneurons in NDDs and further provides a mechanistic view of this vulnerability, potentially convergent across OGIDs. The mechanistic claims around H3K27me3 compensation and mTOR-based therapeutic convergence, while promising, rest on more preliminary evidence and would benefit from the distinction between correlation and mechanism being made more explicit in the text. Overall, this is a compelling study with a rigorous experimental design and novel findings with a potential impact on a better understanding of the OGID pathophysiology.

Strengths:

(1) A major strength of this work is the breadth and rigor of the disease modeling approach. Four independent TBRS model systems are used in tandem: a patient-derived iPSC line with isogenic CRISPR-corrected control (R882H), a knock-in hESC model (P904L) with its wild-type isogenic, patient deletion iPSC lines (Del1/2), and CRISPRi knockdown models (G1/G2), collectively spanning a range of DNMT3A loss-of-function that correlates with phenotypic severity. This allelic series design substantially strengthens causal inference beyond what any single isogenic pair could provide.

(2) The multi-omic integration across matched developmental stages provides a strong mechanistic foundation for the cellular phenotyping and provides significantly enhanced novelty. RNA-seq, whole-genome bisulfite sequencing, and H3K27me3 CUT&Tag are combined in the same cell types, and timepoints show that DNMT3A loss reduces CG methylation at neuronal and synaptic gene loci, leading to premature transcriptional activation.

(3) The selective vulnerability of ventral (GABAergic) versus dorsal (glutamatergic) progenitors is one of the study's most important findings. This lineage specificity is consistently observed across all model systems and in both 2D and organoid formats, where ventral NPCs show increased proliferation, premature neuronal gene expression, and increased neurogenesis, while dorsal NPCs are largely unaffected at the transcriptomic and cellular level despite exhibiting comparable DNA methylation changes. This adds to a body of emerging work showing GABAergic interneuron vulnerability in NDDs where ubiquitously expressed genes such as chromatin modifiers are perturbed, and provides additional molecular insights into potential mechanisms of "resilience" of dorsal populations.

(4) The functional characterization follows a logical progression from single-neuron electrophysiology (demonstrating GABAergic hyperactivity with increased action potential amplitude and firing rate) to network-level analysis using high-density multi-electrode arrays. The HD-MEA experimental design - pairing TBRS or control GABAergic neurons with a constant background of control iGlut neurons - cleanly isolates GABAergic dysfunction as the driver of network hypersynchrony.

Weaknesses:

(1) The concomitant induction of proliferation and differentiation in TBRS V-NPCs is conceptually striking, since these are generally considered antagonistic developmental programs. The authors partially address this tension by noting that DNMT3A LOF alone is insufficient to initiate neuronal differentiation, i.e., V-NPCs upregulate neuronal and synaptic genes while retaining progenitor identity, implying that transcriptomic priming and commitment to differentiation are decoupled. However, the relationship between the proliferative phenotype and the epigenetic priming phenotype remains mechanistically unresolved. The manuscript documents mTOR pathway upregulation at the protein level and identifies shared DEGs that include proliferative regulators, but it does not establish whether mTOR-driven proliferation and mCG-loss-driven neuronal gene de-repression/enhanced differentiation are causally linked or represent two independent consequences of DNMT3A LOF.

We thank the reviewer for their comment and agree that this phenotype, whereby progenitors exhibited both increased proliferation and hallmarks of gene expression associated with neuronal differentiation is striking and interesting, given that these are typically antagonistic paradigms during normal development.

We documented that these phenotypes involve upregulated expression of both neuronal/synaptic and proliferative genes in V-NPCs (Figure 2d), with concomitant loss of repressive DNA methylation at regulatory elements associated with these genes (Figure 2f, Supplemental Data 5). In this work, DNMT3A mutation had a more prominent role in de-repressing neuronal and synaptic gene expression to promote hallmarks of neuron differentiation, while playing a relatively less central role in direct regulation of proliferation genes, as seen from the relative prominence of neuronal/synaptic- versus proliferation-related GO terms in our Supplemental Data 5 table.

To examine the mechanisms underlying increased V-NPC proliferation in our TBRS models, we assessed a potential relationship with the PIK3/AKT/mTOR pathway, as this is implicated in increased proliferation resulting from DNMT3A-associated mutation in myeloid leukemia (Dai et al., 2017, PMID: 28461508). In our work, DNMT3A mutation increased the expression and/or phosphorylation of mTOR signaling pathway targets specifically in V-NPCs (Figure 1q-r, Supplemental Figure S3a-d). However, while TBRS mutation directly affected repressive DNA methylation at a suite of cell proliferation-related genes, these did not include the PIK3/AKT/mTOR pathway genes themselves, suggesting an indirect relationship between altered DNA methylation and increased mTOR signaling.

Text Edits: We will incorporate further discussion of how DNMT3A-mediated gene repression and levels of PIK3/AKT/mTOR pathway signaling may be interacting, providing a framework for future studies to identify how these related OGID gene mutations may converge mechanistically.

(2) Relatedly, the rapamycin rescue experiment is a valuable proof-of-concept for the PIK3/AKT/mTOR convergence but is limited to a single dose in a single model (882) with a single readout (Ki67+ proliferation). Given the prominence of mTOR pathway convergence in the manuscript as a potential shared therapeutic avenue across OGIDs, the data supporting this claim are somewhat preliminary. It remains unknown whether mTOR inhibition rescues downstream phenotypes (neurogenesis, gene expression, neuronal maturation) or whether less severe TBRS models respond similarly. This might also help tackle the first comment above. e.g., if mTOR inhibition rescued proliferation but not the transcriptomic priming, that would support two independent mechanisms.

We thank the reviewer for their comment. We explored both the overall levels and phosphorylation of proteins involved in PIK3/AKT/mTOR signaling in the 882, 904, Del1, Del2, and KO V-NPC models (Figure 1q-r, Supplementary Figure S3a-d), finding specific increases of all proteins. We showed that rapamycin addition reversed the increased proportion of KI67+ proliferating cell nuclei resulting from 882 mutation in V-NPCs in main Figure 1s, while demonstrating that rapamycin also reduced the proportion of KI67+ nuclei observed in both less severe 904 and Del1 V-NPC models (Supplementary Figure S3e-f).

We agree that understanding whether rapamycin treatment can rescue TBRS neuronal phenotypes would be very interesting, as previous work on Tuberous Sclerosis Complex has utilized rapamycin and other mTOR inhibitors to effectively reverse TSC-related alterations of neuronal morphology and neuronal hyperexcitability (Buttermore et al., 2025, PMID: 40792287). Future studies examining convergent mechanisms and therapeutics for OGIDs should examine how similarly targeting this and related pathways rescues altered neuronal morphology, maturation, and function, as we have demonstrated that TBRS mutation has subsequent consequences for V-IN differentiation, maturation, and function. This point has been detailed in the discussion section on pages 15-16.

(3) The claim that H3K27me3 compensates for mCG loss is an important mechanistic point, but the current data do not distinguish between active compensation, in which EZH2 is recruited in response to methylation loss, and functional redundancy, in which H3K27me3 is independently established and becomes the dominant repressive mark once DNA methylation is reduced. The EZH2 knockdown/inhibition experiments show that H3K27me3 is sufficient to maintain repression at hypo-DMR sites, but they do not establish that H3K27me3 gain is itself a response to methylation loss. Because H3K27me3 profiling was performed only in the severe 882 model, it is also unclear whether H3K27me3 gain scales with DNMT3A LOF severity, as a compensatory model would predict. Finally, the EZH2 overexpression rescue is performed in V-NPCs, whereas the compensation model is developed primarily in D-NPCs, making it difficult to assess whether the same mechanism operates in the lineage where it was originally inferred.

We thank the reviewer for the opportunity to clarify our findings and experimental reasoning. A previous study using a conditional Dnmt3a knockout mouse model (Li et al., 2022, PMID: 35604009) demonstrated increased expression of multiple PRC2 components following the loss of Dnmt3a. This study demonstrated that sites which lost DNA methylation gained H3K27me3 in postnatal neurons upon Dnmt3a loss. Therefore, we hypothesize that the gain of H3K27me3 likely occurs in response to loss of DNMT3A methylation.

While we did not perform CUT&Tag for H3K27me3 in our less severe models, we did validate gene expression changes following EZH2 knockdown and inhibition in both the R882H (Figure 4g-h) and P904L (Supplementary Figure S8b) models, finding that gene expression was unchanged in the model with the less severe DNMT3A mutation (P904L). Based upon these findings, we hypothesized that compensatory H3K27me3 may occur only upon severe DNMT3A loss, as seen in the dominant-negative R882H model. Furthermore, as H3K27me3 compensation was more prominent in D-NPCs, we hypothesized that this might be sufficient to prevent de-repression and aberrant neuronal gene repression upon loss of DNMT3A-mediated repression in D-NPCs. However, since TBRS mutation caused the most prominent de-repression of neuronal gene expression in V-NPCs, we also tested whether EZH2 overexpression could reverse this, finding that it partially suppressed this dysregulated neuronal gene expression. To better clarify this logic and the findings, we will make text edits to this results section.

Text edits: We will clarify the reasoning for performing the EZH2 overexpression experiments in V-NPCs and reference Li et al., 2022 in both the results (pg. 9-10) and discussion.

(4) The narrative framing of dorsal neuron development as unaffected by DNMT3A LOF is somewhat at odds with the data presented. The 882 D-NPCs show substantial DNA methylation changes, and TBRS D-INs exhibit what the authors describe as "substantive transcriptomic differences" involving persistent expression of pluripotency and progenitor genes, which seems to be a distinct but potentially significant phenotype. The impact of DNMT3A loss between ventral and dorsal lineages might be more accurately framed as divergent in nature rather than specific to a certain population.

We thank the reviewer for their comment. While TBRS mutations appear to have a significantly stronger effect on V-NPCs and subsequently V-INs, both transcriptomic and methylation alterations do also occur upon TBRS mutation in D-NPCs and D-INs, as noted in Supplemental Figure S4d, S11, and Supplemental Data 2. However, we observed substantially greater molecular alterations in V-NPCs/V-INs, a lack of overt cellular phenotypes in D-NPCs where assayed, and a lack of functional consequences in matured D-INs, suggesting a more significant requirement for DNMT3A in regulating the differentiation and subsequent maturation of cortical inhibitory interneurons during embryonic and early pre-natal development, the developmental periods that we can readily model in hPSC-derived neurons.

It should also be noted that these hPSC differentiation models do not recapitulate post-natal deposition of non-CpG (mCA) DNA methylation, a mechanism disrupted postnatally by TBRS-associated mutations in our prior work in murine models (Harrison Gabel; e.g. Beard et al., 2023, PMID: 37952155). Therefore, we hypothesize that if we could sufficiently mature D-INs to a state that modeled postnatal development and recapitulated this non-CpG methylation, we might be able to detect cellular and functional phenotypes in later stage D-INs. To avoid misinterpretation, we will alter the language in the results section to confirm that there are both transcriptomic and methylation changes in our D-NPCs/D-INs, but that these are not accompanied by cellular phenotypes or neuronal dysfunction.

Text edits: We will better clarify that there are transcriptomic and methylation changes in D-NPCs/D-INs, but that these changes are minimal compared to those in V-NPCs/V-INs, as supported by the lack of cellular and functional phenotypes seen in D-NPCs/D-INs.

(5) SST stainings are not entirely convincing. They appear mostly nuclear, and some instances localized to rosettes in organoids, whereas the protein is largely confined to processes and is expected to be found outside progenitor-rich zones like rosettes.

We agree that the perinuclear SST staining detected in these young ventral telencephalic-patterned organoids at day 30 differs somewhat from the more process-localized and cytosolic signal seen in later stage organoids in other studies. This may be related to the use of different commercial SST antibodies across studies but also likely reflects SST immunoreactivity in newborn neurons near the onset of SST expression. For example, immature SST-immunoreactive neurons in the early postnatal rat cortex exhibit predominant SST staining in perinuclear cytoplasm and short processes (e.g. Fig. 3 in Lee et al, PMID: 9664223) while acquiring more cytosolic and process-localized staining as postnatal neuron maturation occurs. Evaluation of immunopositivity for other markers of neurogenesis (ASCL1) and immature neurons (TUJ1) is also congruent with these findings for SST, with TBRS-associated mutations increasing in the fraction of cells in V-NPCs/V-ORGs that express these three markers.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors investigated TBRS etiology by using new human pluripotent stem cell models, modeling varying levels of TBRS-associated loss of DNMT3A function. They identified increased lineage-specific proliferation of precursors in TBRS ventral MGE-like progenitors, which they propose was related to increased signaling through the PIK3/AKT/mTOR pathway. Furthermore, they show that reduced DNA methylation during MGE-like progenitor differentiation into GABAergic interneurons can cause a premature expression of neuronal and synaptic genes, triggering precocious neuronal maturation. In conclusion, they propose that TBRS-derived GABAergic neurons exhibit hyperactivity that can alters the development and structure of neuronal networks.

Strengths:

Overall, the data presented is convincing, from an early developmental point of view, given that the iPSC-derived 2D cultures or organoids used do not get to reach a mature state. Nonetheless, the data clearly show the effects that deleterious mutations in TBRS can cause during the period of neurogenesis, which was missing in the field.

Weaknesses:

(1) Li et al., 2022 (referred to in the manuscript) seems to already show the interplay between H3K27me3 and Dnmt3a discussed in this study i.e., that in the absence of DNA methylation, there is an expansion of polycomb-like repression. These data should be better acknowledged in the paragraph 'Repressive H3K27me3 compensates for severe loss of DNA methylation' (page 9), given it supports the data presented in this manuscript and suggests this as a common mechanism in the interplay between these two repressive marks, as it is well established in the literature.

We thank the reviewer for this suggestion and will incorporate this reference into both the results and the discussion when discussing the respective roles of DNMT3A and PCR2-mediated repression.

Text edits: We will add Li et al., 2022 to both the results section (pg. 9-10) and our discussion section.

(2) The authors should acknowledge that the omics data come from a mixed population of cells.

We thank the reviewer for their comment. We have validated that the established 2-D differentiation methods we used in this study generate cell populations with >85-90% enrichment for the desired progenitor and neuronal cell type, based upon marker expression, but acknowledge that these are bulk -omics data obtained from cells that may represent a mixed population and have now detailed this in the methods section under “Sequencing”.

Text edits: we will add language acknowledging that our omics data (bulk) was generated from mixed populations of cells.

(3) The authors are encouraged to further discuss whether the overgrowth observed in ventral GABAergic cultures or organoids compares to the overgrowth observed in diseased patients. One expects MRIs to have been performed in patients and that these could be harnessed to discern if overgrowth occurs in the cortex or ventral regions of the brain.

We thank the reviewer for their suggestion and do note that at least one published study documents increased cortical thickness in the MRIs of TBRS patients (Jiménez de la Peña et al., 2024, PMID: 37795572); however, to our knowledge studies have not examined regional or cell type-selective overgrowth of cortical tissue in TBRS patients. Future clinical studies examining the nature of the neuronal progenitor overgrowth and resulting consequences for patient brain imaging would be of interest to better understand TBRS-associated etiology of brain overgrowth and its manifestations.

Author response:

[Editors' note: The authors included an author response to reviews from another journal]

Reviewer #1 (Comments to the Authors):

In this manuscript the authors describe that cells in collective movements adopt a superdiffusive behavior to out pace individual cells. This behavior is regulated by cell-cell junctional stability and force transmission. The authors state that speed is regulated by vinculin through mechanosensitivity.

While is makes intuitive sense that cells may move more efficiently collectively as it reduces their exploratory space and therefore increases their efficiency of movement,

We agree that this is an intuitive explanation. However, previous literature had shown that confluent cells may or may not migrate depending on conditions that do not solely depend on the space available per cell, but also involve the intrinsic activity of the cell, its cortical tension, and its adhesion with its neighbors, with sometimes counterintuitive effects (doi: 10.1016/J.CEB.2021.07.011). This was the reason that motivated us to investigate how these various ingredients affected space exploration efficiency on different time scales.

Our results indeed refute the intuition that cells move more efficiently when their exploratory space is reduced by showing that the outcome depends on the time scale considered (Fig. S3B). Specifically, on short time scales (less than 3 hours), the area explored by individual MDCK cells is larger than that explored by MDCK cells at confluence. On a longer time scale (greater than 3 hours), however, the area explored by confluent MDCK cells is larger. This switch is a direct consequence of the change in migratory behavior from persistent random walk to superdiffusion, Moreover, its position in time depends on the cell line: extrapolation of our results on RPE-1 cells suggests that it should theoretically occur after approximately 300hrs, if this time scale was experimentally accessible (Fig. S3F).

…the role of junctions specifically is less clear.

We are sorry that we were not able to clearly convey the roles of junctions. We have substantially rewritten our text to address this and all the changes are highlighted in orange. As summarized in Fig. 6F, junctions have three roles. The first role is on persistence, through velocity coordination between neighbors, the second is on speed, through the stability of junctions, and the third role is on directionality, through the sensitivity of the monolayer to the wound edge.

The first role is evidenced thanks to the comparison of the MSD between single cell and confluent migration assays and the use of the alpha-catenin KD cell line. Alpha-catenin depletion is known to be the most potent disruptor of adherens junctions (DOI:10.1091/mbc.e06-05-0471, , DOI:10.1126/science.aaf7119, (DOI:10.1073/pnas.1002662107, DOI:10.1073/pnas.1119313109), and we show that it significantly alters the superdiffusive behavior that emerges in the confluent migration assay (Fig. 3E,F, 5C). Therefore, junction integrity is critical for the control of cell persistence.

Moreover, alpha-catenin depletion induces a loss of velocity coordination between neighbors (Fig. S3E), which we show through numerical simulations to induce superdiffusion (Fig. 3G). By contrast, E-cadherin KO and vinculin mutants have no effect on the superdiffusion of confluent cells (Fig. 3E, 4A). Therefore, the critical molecular ingredient is the link provided by alpha-catenin to the cytoskeleton that provides junction integrity.

The second role of junctions is evidenced thanks to the comparison of cell speeds between single and confluent migration assays with the vinculin mutants (Fig. S4A). Results show that cell speed is reduced of about 10µm/h by confluence, regardless of the mutant except for YE, whose only difference with other mutants is its lower stability (Fig. 4F). This supports that junction stability, and not the other effects of mutants, controls cell speed (we provide a detailed demonstration in the response to the following question). As expected, junction integrity is required as well, as seen from the higher cell speed of the alpha-catenin KD cell line compared to WT (first MSD point in Fig. 3B, E).

The third role of junctions is evidenced thanks to the comparison between confluent and directed migration assays (Fig. 6A). Results show that the wound healing rate is proportional to cell speed at confluence, regardless of the mutant except for YE, which displays no tension gradient in junctions from front to back cells (Fig. 6C). This supports that such gradient is required for cells to identify on which side is the wound edge. As expected, junction integrity is required as well, as seen from the loss of directional bias of the alpha-catenin KD cell line (Fig. 5F).

The authors chose vinculin as the basis by which to manipulate tensions at cell-cell junctions, but this comes with considerable drawbacks. Namely, since vinculin appears at both cell-cell and cell-matrix junctions, its role and the role of its mutations is not clear here. The authors state that the collective migration speed is related to junctional stability, but because vinculin is also at FA, how can this be concluded?

We apologize for the lack of clarity. We hope that the highlighted changes in the revised manuscript will improve this point. As exemplified above, comparing cell migration between isolated cells and confluent cells is essential to enable us to distinguish between the contributions of AJs and FAs. Indeed, since isolated cells lack AJs, the impact of vinculin mutants on single cell migration can only be explained by their effects on FAs. This is how we first determine the effects of vinculin mutants on migration that depend on FAs. Because confluent cells also have FAs, we expect that the effects of vinculin mutants on the migration of isolated cells will still be present in confluent cells, to which will be added the effects of these mutants on AJs and their consequences on migration, if any.

Therefore, when compared to WT cells, if a given mutant decreases or increases migration speed in individual cells, and does so in confluent cells in the same proportion, then its effects at confluence can be entirely explained by its effects in individual cells, and there are no additional effects of that mutant from AJs. This is indeed what we observe for all mutants except the YE mutant (Fig. S4C), leading us to conclude that none of the vinculin mutants, except the YE mutant, have an effect on migration at confluence that results from AJs. In contrast, the YE mutant has effects on migration at confluence that cannot be explained by its effect on individual cell migration. Therefore, the effects of YE at confluence depend on AJs, whether they result from alterations in AJs, FAs, or both. To distinguish between these scenarios, we proceed by elimination, comparing the effects of YE to those of other mutants on force transmission and adhesion stability, and how these two factors associate with migration speed, as explained below. In FAs, YE alters force transmission differently in individual cells and at confluence, but we already know from Fig. 2 that force transmission in FAs cannot alone explain the speed of migration. This result rules out an indirect effect of AJs on cell migration at confluence through FAs. Furthermore, in AJs, YE affects stability and force transmission, but TL has the same effect on force transmission as YE and we already know that none of the effects of TL on migration depend on AJs (Fig. 3, S4C). This result rules out an effect of force transmission in AJs on migration speed at confluence. We conclude that stability at the AJ level, which is the remaining property specifically impaired by YE, is what regulates migration speed at confluence.

The manuscript's logic and flow are not clear in some places, making the story hard to follow. As one example, the FRAP data, which the authors suggest is used to investigate vinculin's combined role does not help in this capacity as the interpretation and its connection to the bigger story are not clear.

We are sorry again for the lack of clarity. We used FRAP data to evaluate the effects of vinculin mutants on adhesion stability. Indeed, mutants have different effects on adhesion stability (Fig. 2E, 4F). In addition, they also have different effects on force transmission (Fig. 2D, 4D,E). The partial overlap in functional alterations caused by the mutants allows us to test the involvement of the overlapping function (here stability) in the overall migration outcome. For example, if two mutants have a similar effect on adhesion stability but different effects on migration speed (such as TL and T12), we can then rule out that speed results from adhesion stability. Similarly, if two mutants have different effects on stability but a similar effect on speed (such as TL and YE), we can also rule out that speed results from stability. We applied the same reasoning to force transmission to conclude that neither adhesion stability nor force transmission alone is sufficient for cells to migrate rapidly. However, the combination of the two enables rapid migration.

As another example, the information derived from the use of the mutants is not clear in the context of the message in the manuscript since they affect cell-cell and cell-matrix junctions and in some places show results that are counter intuitive and not well-explained, to which the authors admit they are surprising but then do not explain their meaning.

As such, it is very hard to follow the logic with regard to the information resulting from the mutant experiments.

We provide above a detailed break-down of our strategy to analyze the results. We regret that our manuscript did not adequately convey our conclusions and we hope that the new version of the manuscript improves this point.

Proliferation has been shown to play a role in wound healing. Does proliferation change in the various conditions?

This is an important point. The average speed of cells at confluence is approximately 20 µm/h (Fig. 4B), which means that each cell moves approximately its own size in one hour. During this time, assuming a 16-hour cell cycle, 6% of the cells would have divided, each of them likely pushing one of its neighbors a distance equivalent to the size of a cell. Therefore, cell proliferation accounts for at most a few percent of the total cell movement. For this reason, we can assume that growth does not account for a large part of the movement we observe. This is consistent with previous work showing that proliferation does not contribute significantly to wound healing (DOI: 10.1073/pnas.0705062104, DOI: 10.1083/jcb.201207148).

Minor comments:

The authors should provide a better description of the mutants: what does a tailless mutant not bind, or bind differently? More context is needed to help interpret the results. While the mutants have all been published on before, it would be helpful to have more information here so that the manuscript is easier to follow.

We are sorry that the information we provided was insufficient. We have now detailed the mutations to help the reader understand how interactions are altered.

Figure 1A is not necessary. Figure 1 overall is fairly predictable as there have been many papers using the persistent random walk as the best model to single cell migration (dating back to the early 1990's). The authors define a new term, angular memory, which they show decreases with increasing delta t as one would predict.

We acknowledge that persistent random walks have already been observed for individual cells, as in references 3-4 cited in the introduction. Nevertheless, we believe that Figure 1 is important because not all cells migrate as persistent random walkers when isolated. Some migrate in a more exotic manner, resulting in superdiffusive behavior, as in references 5-8 cited in the introduction. Since we observe superdiffusive behavior at confluence (Figure 2), it was therefore necessary to show whether or not single cells were superdiffusive too. We also use this figure to introduce angular memory, a measure that, to our knowledge, has never been used before. According to intuition, it decreases to 0 for persistent random walkers, just as another resembling measure, velocity autocorrelation, would do. However, the angular memory of fractional Brownian walkers does not vanish with increasing delta t (Fig. 3D), while velocity correlation would, just as that of persistent random walkers. This difference makes angular memory much more appropriate for distinguishing between the two migration behaviors, and prompted us to introduce it in the first figure as a reference.

In the wound healing assay, which cells were measured? Leading edge or interior, and does it matter?

Figure 5A shows that cells behave differently depending on their distance from the wound. This is because the traces shown correspond to the first few hours of the movie, during which the cells at the front begin to move first. Figure S5A shows the speed of the cells over time after the wound and indicates that the cells reach a stable speed after approximately 3 to 4 hours. Figure S5B shows the speed of the cells as a function of distance from the wound at steady state. These results show that the speed of the cells no longer depends on the distance from the wound at this stage. As indicated in the “Materials and Methods” section, we only considered time points beyond this stage for subsequent analyses of population-averaged MSD and velocity presented in Figure 5, so the location of cells at the front or rear was irrelevant.

Reviewer #2 (Comments to the Authors):

To migrate cells must spatially explore their environments, a process that is guided by intrinsic signals (adhesive and mechanical properties, etc) and extrinsic (gradient cues) signals. This exploration can occur on the single or multicellular level. In this study, the authors examine the effect of cell-cell interactions, guidance cues, and cell mechanics in the exploratory capacity of MDCK cells. The authors show that cell-cell adhesion provides a "infinite directional memory for migration" and cell speed is dependent upon the focal adhesion stability, cell mechanics, and the mobility of adherens junctions-these processes are modulated by vinculin.

My three major concerns with the manuscript are as follows:

(1) While there is potential new information about the role cell-cell junctions and guidance cues play in cell migration, there is not enough NEW insight presented. Rather the role of vinculin in these processes is expected given what is already known about its ability to control focal adhesion stability, mechanics, and adherens junctions.

We agree that our cell migration results make sense based on the effects of vinculin mutants on the stability and force transmission of adhesions. Nevertheless, we argue that this was not the only possible scenario. Indeed, we find that none of the effects of vinculin mutants on AJs (except YE) have an impact on cell migration (Fig. S4C). One might have expected that the increased stability provided by the TL and T12 mutants would reduce the speed of collective cell migration, just as the YE mutant increased cell speed due to its altered stability. This is not what we found, and this reveals a nonlinear relationship between AJ stability and migration speed that could be investigated more thoroughly in future studies. Another example is that the effects of the mutants on force transmission in AJs do not impact migration speed at confluence but do impact directed collective migration (Fig. 6). One might have expected that vinculin-mediated force transmission in AJs would impact collective migration, whether directed or not.

More importantly, we show that the role of intercellular adhesion in cell migration is more complex than expected. Indeed, it depends on the timescale considered: intercellular adhesion is detrimental to short-term spatial exploration and beneficial in the long term (Fig. S3B). Such a timescale-dependent behavior is impossible to predict from previously known effects of the mutants or other molecular considerations. Furthermore, we show that this behavior can be fully explained by the coordination of velocities between neighbors, which depends on intact connections between AJs and the cytoskeleton via alpha-catenin, but is independent of vinculin mutants that connect AJs to the cytoskeleton in parallel with alpha-catenin. One might have expected these connections to also have an impact on velocity coordination, and thus on spatial exploration, but we show that this is not the case (Fig. 3). Finally, we show that directed collective migration has a negligible impact on cell exploration at our experimental timescale (Fig. 5), whereas we initially expected the wound to make migration more ballistic. This reveals that such a directional signal affects spatial exploration at much longer timescales than expected.

Overall, our results quantify the outcome of competing effects and provide timescales at which one effect outweighs the other in influencing cell migration. We believe this is an original approach that provides substantial new insights into collective cell migration.

(2) The phenotypes of the cells expressing the mutant vinculins varying greatly. These phenotypes are not addressed despite the fact that they could potentially complicate the analyses. For example, there are dramatic differences between focal adhesion numbers and sizes in the cells expressing the different vinculin mutants; cell spreading is also dramatically altered. Likewise, the T12 mutant vinculin has previously been reported to have increased adhesive strength, increased traction forces, and cell spreading. How does this knowledge change the interpretation?

We agree that vinculin mutants may have effects on the size and number of FAs, cell spreading, and traction forces that we do not examine here. These consequences can be explained by the effects of these mutants on force transmission in FAs and on their stability, which we report in our work. They do not affect our interpretations. Here, we provide a predictive model of migration speed based on the combination of two consequences of vinculin function, namely stability and force transmission. An interesting avenue for future research would be to assess whether these combinations can be reduced to a single higherlevel effect of vinculin on the cellular phenotype that would be sufficient to predict migration speed. This work remains to be done, as neither FA size and number, cell spreading, adhesion force, nor traction forces alone are sufficient to predict migration speed.

Along the same lines, it has previously been established that tagged version of vinculin do not efficiently integrate into adherens junctions. Published work from the Nelson laboratory suggests that GFP-vinculins do not localize to cell-cell junctions and work from other laboratories suggests localization occurs only when the endogenous vinculin is silenced.

We are aware that some GFP-vinculin constructs may not localize as well as the endogenous protein at AJs. This is due to the localization of the GFP tag on the head of vinculin and depends on the length of the linker between GFP and the head of vinculin. The longer the linker, the easier the interaction with AJ partners. Unlike these constructs, the vinculinTSMod sensors we use in our work do not carry a GFP on the head and do not suffer from the same limitations.

Furthermore, vinculin recruitment to AJs depends on force, with little or no recruitment when tension on the AJs is relaxed (DOI: 10.1038/ncb2055). Vinculin recruitment has in fact already been used as an indicator of AJ tension in Drosophila (DOI: 10.1038/s41467-01807448-8). Consequently, the amount of vinculin visible at the AJs varies depending on the tension exerted on the AJs, which our results confirm: vinculin is more difficult to detect at the AJs in cells located at the front of a wound than in those located at the back (Fig. 6B), which is consistent with the difference in vinculin tension between front and back cells (Fig. 6C) and to the E-cadherin tension gradient between front and back cells (DOI: 10.1083/jcb.201706013). Overall, these results show that vinculin is not always easy to detect at AJs, but this is due to the properties of vinculin, which the constructs we use reproduce better than previous constructs (see also below).

The images in figure S2 and the prebleach images in figure S4 do not show convincing localization of the mutant vinculins to cell-cell adhesions. This is of special concern given that YE mutant protein hardly has any discernable localization to cell-cell junctions; additionally, none of the mutant proteins were tested for their ability to co-localize with adherens junction components. This raises the question if the parameters being examined and the conclusions drawn from them are affected by a difference in localization.

We agree that the recruitment of vinculin at intercellular contacts may be difficult to see.

Besides force-dependent effects mentioned above, other factors are involved. The images shown in Figures S2 and S4 are from live cells in which cytoplasmic vinculin is still present, and its level proportional to the mobility of vinculin. Indeed, the TL and T12 mutants show a more marked contrast between intercellular contacts and the cytoplasm, which is consistent with their greater stability at AJs (Fig. 4F). Conversely, YE shows lower contrast, which is consistent with the lower stability of this construct at AJs (Fig. 4F). The FL construct lies between the two. As a result, the cytoplasmic content can variably mask vinculin recruitment at the AJs depending on the mutant.

We have now performed additional quantifications of mutant recruitment at intercellular contacts as a function of distance from the basal surface of the cells and relative to their recruitment in FAs, in live cells. Results are shown in the new Fig. S4F. We find that all the constructs are recruited to intercellular contacts with a density that is at most half of that in FAs and that varies along the height. FL shows the highest density, localized more apically, consistent with the localization of an AJ-bound actin belt. The mutants appear to be more homogenously distributed along the height of the lateral surface, which may be explained by their impaired autoinhibition (TL, T12), or mechanosensitivity (YE). This variability also contributes to the difficulty in seeing vinculin recruitment in all cells in a single z-slice.

To confirm the proper recruitment of vinculin constructs to AJs we have performed immunofluorescence against alpha-catenin and phalloidin on each of the stable cell lines. Results are shown in the new Fig. S4D and E. In these experiments, cell permeabilization allows for the release of some of the cytoplasmic pool of vinculin, which highlights the recruitment of all vinculin constructs to intercellular contacts. There, all vinculin constructs colocalize with alpha-catenin and F-actin, as expected. Additionally, images displayed are maximum intensity projections to mitigate recruitment variability along the height.

Overall, our results clearly support the localization of vinculin at intercellular contacts, and the differences between the constructs are consistent with the effects of their mutations.

(3) There is a lack of new mechanistic insight. Conclusions are made about a role of vinculin dimerization. This conclusion appears to be based upon the usage of the mutant version of vinculin Y1065. Did the authors directly measure the ability of this mutant protein to dimerize? Is actin binding also affected.

The binding properties of the Y1065E mutant, including its dimerization and binding to actin, have already been characterized by other researchers (ref. 40 in our manuscript, as well as DOI:10.1111/j.1432-1033. 1997.01136.x or DOI: 10.1016/j.febslet.2013.02.042). We assumed that these properties are now well established and can be used to explain higher-level phenotypes that we show for the first time, to our knowledge.

Reviewer #3 (Comments to the Authors):

Canever et al. tracked two epithelial cell lines on collagen coated glass and showed that isolated cells (non confluent) move as persistent random walkers, whereas confluent monolayers migrate super diffusive, with long range directional memory. By systematically perturbing adhesion machinery they found that focal adhesion mutations mainly tune the speed of single cell tracks, but cannot create long range memory, while force bearing adherens junctions are essential for the super diffusive regime-genetically perturbing them collapses collective memory. These interesting results identify junctional tension as important to switch epithelial cells/sheets between individual and collective search modes - an important quantitative insight that is of clear relevance to cell biologists.

- The presented data is nicely quantitative and convincing, but I have subtle concerns about the generality of the findings. While the authors show that the differential behavior, they describe is not cell-line specific (MDCK, RPE), there are no experiments evaluating the generality of their conclusions across different matrix conditions. How are the measured migration parameters affected by matrix stiffness? Cell migration on collagen coated glass coverslips is a relatively narrow and artificial condition. How is the collective directional memory expected to behave on softer substrates? The generality of the conclusions could be strengthened by repeating measurements using hydrogels of varying stiffness. Further, it should be discussed to which tissues in the body the selected matrix conditions and migration modes plausibly apply.

We agree that the generality of our results and the relevance of glass-rigid substrates is an important point. In vivo, epithelial cells rest on a basement membrane with a typical stiffness of approximately 10 MPa, as demonstrated by experimental evaluations on various tissue explants, including renal glomeruli and Bruch's membrane, which are relevant to MDCK and RPE-1 cells (DOI: 10.1111/j.1742-4658.2007.05823.x, DOI: 10.1172/JCI106898, DOI:10.1038/eye.1987.35), we have added these references in the manuscript to support our experimental strategy. In vitro, the most significant effects of substrate stiffness on FAs and cell migration generally occur at much lower stiffnesses, between 0.2 and 100 kPa, and cell phenotypes generally plateau at levels comparable to those observed on glass, even below 100 kPa (DOI: 10.1242/jcs.133645, DOI: 10.1038/ncb3268, DOI:10.1039/c5ib00307e, DOI: 10.1039/c9sm01893j). Furthermore, substrate stiffness has much more moderate effects on confluent cells than on isolated cells. For example, it has been previously demonstrated that confluent layers of MCF10A epithelium showed no change in velocity coordination in the range of 3 to 65 kPa (DOI: 10.1083/jcb.201207148). Therefore, collagen-coated glass appears to be a reasonable model for the basement membrane. Overall, we believe that we have conducted our experiments under physiological conditions, and that our results apply to a wide range of substrate stiffnesses.

- It would be nice to see how long it takes confluent cell layers to close rectangular wounds of defined size when cells migrate as individual (adherens junctions perturbation) versus collective (wt) (on substrates of different stiffness). Presumably, there should be faster wound closure under the collective regime, at least for simple shaped wounds.

This is an interesting question, which our results indirectly address. In our study, we measured the wound healing speed of the WT MDCK cell line as well as lines expressing mutant vinculin constructs (Fig. 6A). These results show that this speed ranges from 5 to 15 µm/h depending on the construct expressed (and for reasons that we explain in the manuscript). These values make it easy to estimate the time required to close a wound based on its width. For example, it would take 5 hours to close a 100 µm wide wound for the WT cell line, which has a rate of 10 µm/h (on both sides of the wound).

Wound closure for cells with disrupted adhesive junctions has already been documented (DOI: 10.1083/jcb.200910041). The results show that wound closure is indeed slower than with WT cells. Although this previous study does not reveal the underlying causes, our work now shows that there are two factors: weaker directional memory due to impaired intercellular coordination and, in the longer term, an additional lack of sensitivity to the guidance signal provided by the wound.

- Akin to substrate stiffness variation, I am missing experiments that test the effect of cytoskeletal tension on these migration modes. Experiments with Rho kinase or myosin inhibitors could meaningfully broaden the scope of this study.

Rho kinase or myosin inhibitors applied to cells during the time required to assess migration patterns (a movie recorded overnight is necessary to obtain a statistically reliable calculation of MSD over 3 to 4 hours) are likely to affect many other cellular processes in addition to the cytoskeletal tension directly involved in migration. We believe that the accumulation of these effects will make interpretation of the results very difficult. For example, it has been shown that inhibition of ROCK by Y27 promotes healing of corneal endothelial lesions by affecting proliferation through cyclin D and p27 (DOI: 10.1167/iovs.13-12225), or by improving respiration, which would provide the energy necessary for migration (DOI: 10.1096/fj.202101442RR). Consistently, another study on HaCaT epidermal cells confirms that myosin phosphatase accelerates wound healing through proliferation (DOI: 10.1016/j.bbadis.2018.07.013). In contrast, in HUVEC cells, ROCK inhibition significantly impaired the proliferation and migration of vascular endothelial cells in vitro in a dose-dependent manner (DOI: 10.1097/ICO.0000000000000493).

Furthermore, previous studies have highlighted that differential contractility at the subcellular level is important for collective migration (DOI: 10.1038/ncb2133, DOI: 10.1083/jcb.201706013), which is not possible to examine with global activation or inhibition of contractility. This prompts the development of more refined and specific measurement and disruption strategies to assess the respective impact of cytoskeletal tension on cell-cell and cell-matrix adhesion mechanisms. Our work, which uses biosensors to assess how this tension differentially affects cell-cell and cell-matrix adhesions, is a step in this direction. The localized spatio-temporal activation or inhibition of myosin subtypes or Rho GTPase regulators specific to these adhesion structures will likely answer these questions in the future, but we believe that the development and application of these approaches will require a substantial amount of work that goes beyond the scope of our study.

Take this out - we don't have worker tags on the 3rd floor anymore

This is not RMC policy but we can discuss.

This statement raises transparency issues because consumers may assume the cotton products are fully sustainable, even though the material can still be mixed with conventional cotton. Companies should clearly explain the limitations of sustainability claims to avoid confusion and misleading marketing.

Streichen

I find it interesting how we use the terms power users and lurkers for crowdsourcing sectors specfically. When raising money, you would think that you would not want to tag any donor, of any kind, of any donation, with any negative label. The term lurker makes it seem like a negative term to the person who is a lurker- like someone who didn't do all that they could to support. Meanwhile power users have that positive connotation, which makes sense, but its the dicotomy between the two labels that I find interesting. Not all donors are equal, but any donor is surely one you would want to tag with a good label? I understand for social media posts etc, this concept changes, but for crowdsourcing specifically I find this interesting.

1. Many of the alt tag descriptions for these products exceed 125 characters. Also, if you were to click on one of these items, you would see that the normal descriptions are also extremely long.

The use of signal phrases is often taught merely as citation tools, ignoring their role in creating a cohesive writing structure. These examples demonstrate how signal phrases can enhance the flow and readability of academic writing.

Author response:

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

This manuscript reports the identification of putative orthologues of mitochondrial contact site and cristae organizing system (MICOS) proteins in Plasmodium falciparum - an organism that unusually shows an acristate mitochondrion during the asexual part of its life cycle and then this develops cristae as it enters the sexual stage of its life cycle and beyond into the mosquito. The authors identify PfMIC60 and PfMIC19 as putative members and study these in detail. The authors at HA tags to both proteins and look for timing of expression during the parasite life cycle and attempt (unsuccessfully) to localise them within the parasite. They also genetically deleted both gene singly and in parallel and phenotyped the effect on parasite development. They show that both proteins are expressed in gametocytes and not asexuals, suggesting they are present at the same time as cristae development. They also show that the proteins are dispensible for the entire parasite life cycle investigated (asexuals through to sporozoites), however there is some reduction in mosquito transmission. Using EM techniques they show that the morphology of gametocyte mitochondria is abnormal in the knockout lines, although there is great variation.

Major comments:

The manuscript is interesting and is an intriguing use of a well studied organism of medical importance to answer fundamental biological questions. My main comments are that there should be greater detail in areas around methodology and statistical tests used. Also, the mosquito transmission assays (which are notoriously difficult to perform) show substantial variation between replicates and the statistical tests and data presentation are not clear enough to conclude the reduction in transmission that is claimed. Perhaps this could be improved with clearer text?

We would like to thank the reviewer for taking the time to review our manuscript. We are happy to hear the reviewer thinks the manuscript is interesting and thank the reviewer for their constructive feedback.

To clarify the statistical analyses used, we included a new supplementary dataset with all statistical analyses and p-values indicated per graph. Furthermore, figure legends now include the information on the exact statistical test used in each case.

Regarding mosquito experiments, while we indeed reported a reduction in transmission and oocysts numbers, we are aware that this effect might be due to the high variability in mosquito feeding assays. To highlight this point, we deleted the sentence “with the transmission reduction of [numbers]….” and we included the sentence “The high variability encountered in the standard membrane feeding assays, though, partially obstructs a clear conclusion on the biological relevance of the observed reduction in oocyst numbers“

More specific comments to address:

Line 101/Fig1E (and figure legend) - What is this heatmap showing. It would be helpful to have a sentence or two linking it to a specific methodology. I could not find details in the M+M section and "specialized, high molecular mass gels" does not adequately explain what experiments were performed. The reference to Supplementary Information 1 also did not provide information.

We added the information “high molecular mass gels with lower acrylamide percentage” to clarify methodology in the text. Furthermore, we extended the figure legend to include all relevant information. Further experimental details can be found in the study cited in this context, where the dataset originates from (Evers et al., 2021).

Line 115 and Supplementary Figure 2C + D - The main text says that the transgenic parasites contained a mitochondrially localized mScarlet for visualization and localization, but in the supplementary figure 2 it shows mitotracker labelling rather than mScarlet. This is very confusing. The figure legend also mentions both mScarlet and MitoTracker. I assume that mScarlet was used to view in regular IFAs (Fig S2C) and the MitoTracker was used for the expansion microscopy (Fig S2D)?

Please clarify.

We thank the reviewer for pointing this out – this was indeed incorrectly annotated. We used the endogenous mito-mScarlet signal in IFA and mitoTracker in U-ExM. The figure annotation has now been corrected.

Figure 2C - what is the statistical test being used (the methods say "Mean oocysts per midgut and statistical significance were calculated using a generalized linear mixed effect model with a random experiment effect under a negative binomial distribution." but what test is this?)?

The statistic test is now included in the material and method section with the sentence “The fitted model was used to obtain estimated means and contrasts and were evaluated using Wald Statistics”. The test is now also mentioned in the figure legend.

Also the choice of a log10 scale for oocyst intensity is an unusual choice - how are the mosquitoes with 0 oocysts being represented on this graph? It looks like they are being plotted at 10^-1 (which would be 0.1 oocysts in a mosquito which would be impossible).

As the data spans three orders of magnitude with low values being biologically meaningful, we decided that a log scale would best facilitate readability of the graph. As the 0 values are also important to show, we went with a standard approach to handle 0s in log transformed data and substituted the 0s with a small value (0.001). We apologize for not mentioning this transformation in the manuscript. To make this transformation transparent, we added a break at the lower end of the log-scaled y-axis and relabelled the lowest tick as ‘0’. This ensures that mosquitoes with zero oocysts are shown along the x-axis without being assigned an artificial value on the log scale. We would furthermore like to highlight that for statistics we used the true value 0 and not 0.001.

Figure 2D - it is great that the data from all feeding replicates has been shared, however it is difficult to conclude any meaningful impact in transmission with the knock-out lines when there is so much variation and so few mosquitoes dissected for some datapoints (10 mosquitoes are very small sample sizes). For example, Exp1 shows a clear decrease in mic19- transmission, but then Exp2 does not really show as great effect. Similarly, why does the double knock out have better transmission than the single knockouts? Sure there would be a greater effect?

We agree with the reviewer and with the new sentence added, as per major point, we hope we clarified the concept. Note that original Figure 2D has been moved to the supplementary information, as per minor comment of another reviewer.

Figure 3 legend - Please add which statistical test was used and the number of replicates.

Done

Figure 4 legend - Please add which statistical test was used and the number of replicates.

Done. Regarding replicates, note that while we measured over 100 cristae from over 30 mitochondria, these all stem from the same parasite culture.

Figure 5C - the 3D reconstructions are very nice, but what does the red and yellow coloring show?

Indeed, the information was missing. We added it to the figure legend.

Line 352 - "Still, it is striking that, despite the pronounced morphological phenotype, and the possibly high mitochondrial stress levels, the parasites appeared mostly unaffected in life cycle propagation, raising questions about the functional relevance of mitochondria at these stages."

How do the authors reconcile this statement with the proven fact that mitochondria-targeted antimalarials (such as atovaquone) are very potent inhibitors of parasite mosquito transmission?

Our original sentence was reductive. What we wanted to state was related to the functional relevance of crista architecture and overall mitochondrial morphology rather than the general functional relevance of the mitochondria. We changed the sentence accordingly.

Furthermore, even though we do not discuss this in the article, we are aware of mitochondria targeting drugs that are known to block mosquito transmission. We want to point out that it is difficult to discern the disruption of ETC and therefore an impact on energy conversion with the impact on the essential pathway of pyrimidine synthesis, highly relevant in microgamete formation. Still, a recent paper from Sparkes et al. 2024 showed the essentiality of mitochondrial ATP synthesis during gametogenesis so it is very likely that the mitochondrial energy conversion is highly relevant for transmission to the mosquito.

Reviewer #1 (Significance):

This manuscript is a novel approach to studying mitochondrial biology and does open a lot of unanswered questions for further research directions. Currently there are limitations in the use of statistical tests and detail of methodology, but these could be easily be addressed with a bit more analysis/better explanation in the text.

This manuscript could be of interest to readers with a general interest in mitochondrial cell biology and those within the specific field of Plasmodium research.

My expertise is in Plasmodium cell biology.

We thank the reviewer for the praise.

Reviewer #2 (Evidence, reproducibility and clarity):

Major comments:

(1) In my opinion, the authors tend to sensationalize or overinterpret their results. The title of the manuscript is very misleading. While MICOS is certainly important for crista formation, it is not the only factor, as ATP synthase dimer rows make a highly significant contribution to crista morphology. Thus, one can argue with equal validity that ATP synthase should be considered the 'architect', as it's the conformation of the dimers and rows modulate positive curvature. Secondly, while cristae are still formed upon mic60/mic19 gene knockout (KO), they are severely deformed, and likely dysfunctional (see below). Thus, I do not agree with the title that MICOS is dispensable for crista formation, because the authors results show that it clearly is essential. So, the title should be changed.

We thank the reviewer for taking the time to review our manuscript.

Based on the reviewers’ interpretation we conclude the title does not come across as intended. We have changed the title to: “The role of MICOS in organizing mitochondrial cristae in malaria parasites”

The Discussion section starting from line 373 also suffers from overinterpretation as well as being repetitive and hard to understand. The authors infer that MICOS stability is compromised less in the single KOs (sKO) in compared to the mic60/mic19 double KO (dKO). MICOS stability was never directly addressed here and the composition of the MICOS complex is unaddressed, so it does not make sense to speculate by such tenuous connections. The data suggest to me that mic60 and mic19 are equally important for crista formation and crista junction (CJ) stabilization, and the dKO has a more severe phenotype than either KO, further demonstrating neither is epistatic.

We do agree with the reviewer’s notion that we did not address complex stability, and our wording did not make this sufficiently clear. We shortened and rephrased the paragraph in question.

The following paragraphs (line 387 to 422) continues with such unnecessary overinterpretation to the point that it is confusing and contradictory. Line 387 mentions an 'almost complete loss of CJs' and then line 411 mentions an increase in CJ diameter, both upon Mic60 ablation. I do not think this discussion brings any added value to the manuscript and should be shortened. Yes, maybe there are other putative MICOS subunits that may linger in the KOS that are further destabilized in the dKO, or maybe Mic60 remains in the mic19 KO (and vice versa) to somehow salvage more CJs, which is not possible in the dKO. It is impossible to say with confidence how ATP synthase behaves in the KOs with the current data.

We shortened this paragraph.

(2) While the authors went through impressive lengths to detect any effect on lifecycle progression, none was found except for a reduction in oocyte count. However, the authors did not address any direct effect on mitochondria, such as OXPHOS complex assembly, respiration, membrane potential. This seems like a missed opportunity, given the team's previous and very nice work mapping these complexes by complexome profiling. However, I think there are some experiments the authors can still do to address any mitochondrial defects using what they have and not resorting to complexome profiling (although this would be definitive if it is feasible):

i) Quantification of MitoTracker Red staining in WT and KOs. The authors used this dye to visualize mitochondria to assay their gross morphology, but unfortunately not to assay membrane potential in the mutants. The authors can compare relative intensities of the different mitochondria types they categorized in Fig. 3A in 20-30 cells to determine if membrane potential is affected when the cristae are deformed in the mutants. One would predict they are affected.

Interesting suggestion. As our staining and imaging conditions are suitable for such analysis (as demonstrated by Sarazin et al., 2025, https://www.biorxiv.org/content/10.1101/2025.11.27.690934v1), we performed the measurements on the same dataset which we collected for Figure 3. We did, however, not detect any difference in mitotracker intensity between the different lines. The result of this analysis is included in the new version of Supplementary figure S6.

ii) Sporozoites are shown in Fig S5. The authors can use the same set up to track their motion, with the hypothesis that they will be slower in the mutants compared to WT due to less ATP. This assumes that sporozoite mitochondria are active as in gametocytes.

While theoretically plausible and informative, we currently do not know the relevance of mitochondrial energy conversion for general sporozoite biology or specifically features of sporozoite movement. Given the required resources and time to set this experiment up and the uncertainty whether it is a relevant proxy for mitochondrial functioning, we argue it is out of scope for this manuscript.

iii) Shotgun proteomics to compare protein levels in mutants compared to WT, with the hypothesis that OXPHOS complex subunits will be destabilized in the mutants with deformed cristae. This could be indirect evidence that OXPHOS assembly is affected, resulting in destabilized subunits that fail to incorporate into their respective complexes.

While this experiment could potentially further our understanding of the interaction between MICOS and levels of OXPHOS complex subunits we argue that the indirect nature of the evidence does not justify the required investments.

To expedite resubmission, the authors can restrict the cell lines to WT and the dKO, as the latter has a stronger phenotype that the individual KOs and conclusions from this cell line are valid for overall conclusions about Plasmodium MICOS.

I will also conclude that complexome/shotgun proteomics may be a useful tool also for identifying other putative MICOS subunits by determining if proteins sharing the same complexome profile as PfMic60 and Mic19 are affected. This would address the overinterpretation problem of point 1.

(3) I am aware of the authors previous work in which they were not able to detect cristae in ABS, and thus have concluded that these are truly acristate. This can very well be true, or there can be immature cristae forms that evaded detection at the resolution they used in their volumetric EM acquisitions. The mitochondria and gametocyte cristae are pretty small anyway, so it not unreasonable to assume that putative rudimentary cristae in ABS may be even smaller still. Minute levels of sampled complex III and IV plus complex V dimers in ABS that were detected previously by the authors by complexome profiling would argue for the presence of miniscule and/or very few cristae.

I think that authors should hedge their claim that ABS is acristate by briefly stating that there still is a possibility that miniscule cristae may have been overlooked previously.

We acknowledge that we cannot demonstrate the absolute absence of any membrane irregularities along the inner mitochondrial membrane. At the same time, if such structures were present, they would be extremely small and unlikely to contain the full set of proteins characteristic of mature cristae. For this reason, we consider it appropriate to classify ABS mitochondria as acristate. To reflect the reviewer’s point while maintaining clarity for readers, we have slightly adjusted our wording in the manuscript, changing ‘fully acristate’ to ‘acristate’.

This brings me to the claim that Mic19 and Mic60 proteins are not expressed in ABS. This is based on the lack of signal from the epitope tag; a weak signal is detected in gametocytes. Thus, one can counter that Mic19 and Mic60 are also expressed, but below the expression limits of the assay, as the protein exhibits low expression levels when mitochondrial activity is upregulated.

We agree with the reviewer that the absence of a detectable epitope-tag signal does not definitively exclude low-level expression, and we have therefore replaced the term ‘absent’ with ‘undetectable’ throughout the manuscript. In context with previous findings of low-level transcripts of the proteins in a study by Lopez-Berragan et al. and Otto et al., we also added the sentence “The apparent absence could indicate that transcripts are not translated in ABS or that the proteins’ expression was below detection limits of western blot analysis.” to the discussion. At the same time, we would like to clarify that transcript levels for both genes fall within the <25th percentile, suggesting that these low values likely represent background signal rather than biologically meaningful expression. This interpretation is further supported by proteomic datasets in PlasmoDB, which report PfMIC19 and PfMIC60 expression in gametocyte and mosquito stages, but not in asexual blood stages.”

To address this point, the authors should determine of mature mic60 and mic19 mRNAs are detected in ABS in comparison to the dKO, which will lack either transcript. RT-qPCR using polyT primers can be employed to detect these transcripts. If the level of these mRNAs are equivalent to dKO in WT ABS, the authors can make a pretty strong case for the absence of cristae in ABS.

We appreciate the reviewer’s suggestion. As noted in the Discussion, existing transcriptomic datasets already show detectable MIC19 and MIC60 mRNAs in ABS. For this reason, we expect RT-qPCR to reveal low (but not absent) levels of both transcripts, unlike the true loss expected to be observed in the dKO. Because such residual signals have been reported previously and their biological relevance remains uncertain, we do not believe transcript levels alone can serve as a definitive indicator of cristae absence in ABS.

They should highlight the twin CX9C motifs that are a hallmark of Mic19 and other proteins that undergo oxidative folding via the MIA pathway. Interestingly, the Mia40 oxidoreductase that is central to MIA in yeast and animals, is absent in apicomplexans (DOI: 10.1080/19420889.2015.1094593).

Searching for the CX9C motifs is a valuable suggestion. In response to the reviewer´s suggestion we analysed the conservation of the motif in PfMIC19 and included this in a new figure panel (Figure 1 F).

Did the authors try to align Plasmodium Mic19 orthologs with conventional Mic19s? This may reveal some conserved residues within and outside of the CHCH domain.

In response to this comment we made Figure 1 F, where we show conserved residues within the CHCH domains of a broad range of MIC19 annotated sequences across the opisthokonts, and show that the Cx9C motifs are conserved also in PfMIC19. Outside the CHCH domain, we did not find any meaningful conservation, as PfMIC19 heavily diverges from opisthokont MIC19.

(5) Statistical significance. Sometimes my eyes see population differences that are considered insignificant by the statistical methods employed by the authors, eg Fig. 4E, mutants compared to WT, especially the dKO. Have the authors considered using other methods such as student t-test for pairwise comparisons?

The graphs in figures 3, 4 and 5 got a makeover, such that they now are in linear scale and violin plots (also following a suggestion from further down in the reviewer’s comments). We believe that this improves interpretability. ANOVA was kept as statistical testing to assure the correction for multiple comparisons that cannot be performed with standard t-test. A full overview of statistics and exact pvalues can also be found in the newly added supplementary information 2.

Minor comments:

Line 33. Anaerobes (eg Giardia) have mitochondria that do produce ATP, unlike aerobic mitochondria

We acknowledge that producing ATP via OXPHOS is not a characteristic of all mitochondria-like organelles (e.g. mitosomes), which is why these are typically classified separately from canonical mitochondria. When not considering mitochondria-like organelles, energy conversion is the function that the mitochondrion is most well-known for and the one associated with cristae.

Line 56: Unclear what authors mean by "canonical model of mitochondria"

To clarify we changed this to “yeast or human” model of mitochondria.

Lines 75-76: This applies to Mic10 only

We removed the “high degree of conservation in other cristate eukaryotes” statement.

Line 80: Cite DOI: 10.1016/j.cub.2020.02.053

Done

Fig 2D: I find this table difficult to read. If authors keep table format, at least get rid of 'mean' column' as this data is better depicted in 2C. I suggest depicted this data either like in 3B depicting portion of infected vs unaffected flies in all experiments, then move modified Table to supplement. Important to point out experiment 5 appears to be an outlier with reduced infectivity across all cell lines, including WT.

To clarify: the mean reported in the table indicates the mean per replicate while the mean reported in figure 2C is the overall mean for a given genotype that corrects for variability within experiments. We agree that moving the table to the supplementary data is a good idea. We decided to not include a graph for infected and non-infected mosquitoes as this information would be partially misleading, highlighting a phenotype we argue to be influenced by the strong variability.

Fig. 3C-G: I feel like these data repeatedly lead to same conclusions. These are all different ways of showing what is depicted in Fig 2B: mitochondria gross morphology is affected upon ablation of MICOS. I suggest that these graphs be moved to supplement and replaced by the beautiful images.

Thank you for the nice comment on our images. We have now moved part of the graphs to supplementary figure 6 and only kept the Relative Frequency, Sphericity and total mitochondria volume per cell in the main figure.

Line 180: Be more specific with which tubulin isoform is used as a male marker and state why this marker was used in supplemental Fig S6.

We have now specified the exact tubulin isoform used as the male gametocyte marker, both in the main text and in Supplementary Fig. S6. This is a commercial antibody previously known to work as an effective male marker, which is why we selected it for this experiment. This is now clearly stated in the manuscript.

Line 196 and Fig 3C: the word 'intensities' in this context is very ambiguous. Please choose a different term (puncta, elements, parts?). This is related to major point 2i above.

To clarify the biological effect that we can conclude form the measurement, we added an explanation about it in the respective section of the results, and we decided to replace the raw results of the plug-in readout with the deduced relative dispersion.

Line 222: Report male/female crista measurements

We added Supplementary information 2, which contains exact statistical test and outcomes on all presented quantifications as well as a per-sex statistical analysis of the data from figure 4. Correspondingly, we extended supplementary information 2 by a per-sex colour code for the thin section TEM data.

Fig. 4B-E: depict data as violin plots or scatter plots like Fig. 2C to get a better grasp of how the crista coverage is distributed. It seems like the data spread is wider in the double KO. This would also solve the problem with the standard deviation extending beyond 0%.

We changed this accordingly.

Lines 331-333: Please clarify that this applies for some, but not all MICOS subunits. Please also see major point 1 above. Also, the authors should point out that despite their structural divergence, trypanosomal cryptic mitofilins Mic34 and Mic40 are essential for parasite growth, in contrast to their findings with PfMic60 (DOI: https://doi.org/10.1101/2025.01.31.635831).

This has been changed accordingly.

Line 320: incorrect citation. Related to point 1above.

Correct citation is now included in the text.

Lines 333-335. This is related to the above. Again, some subunits appear to affect cell growth under lab conditions, and some do not. This and the previous sentence should be rewritten to reflect this.

This has been changed accordingly.

Line 343-345: The sentence and citation 45 are strange. Regarding the former, it is about CHCHD10, whose status as a bona fide MICOS subunit is very tenuous, so I would omit this. About the phenomenon observed, I think it makes more sense to write that Mic60 ablation results in partially fragmented mitochondria in yeast (Rabl et al., 2009 J Cell Biol. 185: 1047-63). A fragmented mitochondria is often a physiological response to stress. I would just rewrite as not to imply that mitochondrial fission (or fusion) is impaired in these KOs, or at least this could be one of several possibilities.

The sentence has been substituted following the indication of the reviewer. Though we still include the data of the human cells as this has also been shown in Stephens et al. 2020.

Line 373: 'This indicates' is too strong. I would say 'may suggest' as you have no proof that any of the KOs disrupts MICOS. This hypothesis can be tested by other means, but not by penetrance of a phenotype.

Done

Line 376-377; 'deplete functionality' does not make sense, especially in the context of talking about MICOS subunit stability. In my opinion, this paragraph overinterprets the KO effects on MICOS stability. None of the experiments address this phenomenon, and thus the authors should not try to interpret their results in this context. See major point 1.

We removed the sentence. Also, the entire paragraph has been shortened, restructured and wording was changed to address major point 1.

Other suggestions for added value

(1) Does Plasmodium Sam50 co-fractionate with Mic60 and Mic19 in BN PAGE (Fig. 1E)

While we did identify SAMM50 in our BN PAGE, the protein does not co-migrate with the MICOS components but instead comigrates with other components of a putative sorting and assembly machinery (SAM) complex. As SAMM50, the SAM complex and the overarching putative mitochondrial membrane space bridging (MIB) complex are not mentioned in the manuscript, we decided to not include the information in Author response image 1.

Author response image 1.

Reviewer #2 (Significance):

The manuscript by Tassan-Lugrezin is predicated on the idea that Plasmodium represents the only system in which de novo crista formation can be studied. They leverage this system to ask the question whether MICOS is essential for this process. They conclude based on their data that the answer is no, which the authors consider unprecedented. But even if their claim is true that ABS is acristate, this supposed advantage does not really bring any meaningful insight into how MICOS works in Plasmodium.

First the positives of this manuscript. As has been the case with this research team, the manuscript is very sophisticated in the experimental approaches that are made. The highlights are the beautiful and often conclusive microscopy performed by the authors. Only the localization of Mic60 and Mic19 was inconclusive due to their very low expression unfortunately.

The examination of the MICOS mutants during in vitro life cycle of Plasmodium falciparum is extremely impressive and yields convincing results. Mitochondrial deformation is tolerated by life cycle stage differentiation, with a modest but significant reduction of oocyte production, being observed.

However, despite the herculean efforts of the authors, the manuscript as it currently stands represents only a minor advance in our understanding of the evolution of MICOS, which from the title and focus of the manuscript, is the main goal of the authors.

In its current form, the manuscript reports some potentially important findings:

(1) Mic60 is verified to play a role in crista formation, as is predicted by its orthology to other characterized Mic60 orthologs.

(2) The discovery of a novel Mic19 analog (since the authors maintain there is no significant sequence homology), which exhibits a similar (or the same?) complexome profile with Mic60. This protein was upregulated in gametocytes like Mic60 and phenocopies Mic60 KO.

(3) Both of these MICOS subunits are essential (not dispensable) for proper crista formation

(4) Surprisingly, neither MICOS subunit is essential for in vitro growth or differentiation from ABS to sexual stages, and from the latter to sporozoites. This says more about the biology of plasmodium itself than anything about the essentiality of Mic60, i.e. plasmodium life cycle progression tolerates defects to mitochondrial morphology. But yes, I agree with the authors that Mic60's apparent insignificance for cell growth in examined conditions does differ with its essentiality in other eukaryotes. But fitness costs were not assayed (e.g. by competition between mutants and WT in infection of mosquitoes)

(5) Decreased fitness of the mutants is implied by a reduction of oocyte formation.

While interesting in their own way, collectively they do not represent a major advance in our understanding of MICOS evolution. Furthermore, the findings bifurcate into categories informing MICOS or Plasmodium biology. Both aspects are somewhat underdeveloped in their current form.

This is unfortunate because there seem to be many missed opportunities in the manuscript that could, with additional experiments, lead to a manuscript with much wider impact. For me, what is remarkable about Plasmodium MICOS that sets it apart from other iterations is the apparent absence of the Mic10 subunit. Purification of plasmodium MICOS via the epitope tagged Mic60 and Mic19 could have verified that MICOS is assembled without this core subunit. Perhaps Mic60 and Mic19 are the vestiges of the complex, and thus operate alone in shaping cristae. Such a reduction may also suggest the declining importance of mitochondria in plasmodium.

Another missed opportunity was to assay the impact of MICOS-depletion of OXPHOS in plasmodium.

This is a salient issue as maybe crista morphology is decoupled from OXPHOS capacity in Plasmodium, which links to the apparent tolerance of mitochondrial morphology in cell growth and differentiation. I suggested in section A experiments to address this deficit.

Finally, the authors could assay fitness costs of MICOS-ablation and associated phenotypes by assaying whether mosquito infectivity is reduced in the mutants when they are directly competing with WT plasmodium. Like the authors, I am also surprised that MICOS mutants can pass population bottlenecks represented by differentiation events. Perhaps the apparent robustness of differentiation may contribute plasmodium's remarkable ability to adapt.

I realize that the authors put a lot of efforts into their study and again, I am very impressed by the sophistication of the methods employed. Nevertheless, I think there is still better ways to increase the impact of the study aside from overinterpreting the conclusions from the data. But this would require more experiments along the lines I suggest in Section A and here.

We thank the reviewer for their extensive analysis of the significance of our findings, including the compliments on our microscopy images and the sophisticated experimental approaches. We hope we have convincingly argued why we could or could not include some of the additional analyses suggested by the reviewer in section 1 above.

With regard to the significance statement, we want to point out that our finding that PfMICOS is not needed for initial formation of cristae (as opposed to organization thereof), is a confirmation of something that has been assumed by the field, without being the actual focus of studies. We argue that the distinction between formation and organization of cristae is important and deserves some attention within the manuscript. The result of MICOS not being involved in the initial formation of cristae, we argue to be relevant in Plasmodium biology and beyond. As for the insights into how MICOS works in Plasmodium we have confirmed that the previously annotated PfMIC60 is indeed involved in the organization of cristae. Furthermore, we have identified and characterized PfMIC19. These findings, we argue, are indeed meaningful insights into PfMICOS.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary:

MICOS is a conserved mitochondrial protein complex responsible for organising the mitochondrial inner membrane and the maintenance of cristae junctions. This study sheds first light on the role of two MICOS subunits (Mic60 and the newly annotated Mic19) in the malaria parasite Plasmodium falciparum, which forms cristae de novo during sexual development, as demonstrated by EM of thin section and electron tomography. By generating knockout lines (including a double knockout), the authors demonstrate that knockout of both MICOS subunits leads to defects in cristae morphology and a partial loss of cristae junctions. With a formidable set of parasitological assays, the authors show that despite the metabolically important role of mitochondria for gametocytes, the knockout lines can progress through the life stages and form sporozoites, albeit with diminished infection efficiency.

We thank the reviewer for their time and compliment.

Major comments:

(1) The authors should improve to present their findings in the right context, in particular by:

i) giving a clearer description in the introduction of what is already known about the role of MICOS. This starts in the introduction, where one main finding is missing: loss of MICOS leads to loss of cristae junctions and the detachment of cristae membranes, which are nevertheless formed, but become membrane vesicles. This needs to be clearly stated in the introduction to allow the reader to understand the consistency of the authors' findings in P. falciparum with previous reports in the literature.

We extended the introduction to include this information.

iii) at the end to the introduction, the motivating hypothesis is formulated ad hoc "conclusive evidence about its involvement in the initial formation of cristae is still lacking" (line 83). If there is evidence in the literature that MICOS is strictly required for cristae formation in any organism, then this should be explained, because the bona fide role of MICOS is maintenance of cristae junctions (the hypothesis is still plausible and its testing important).

To clarify we rephrased the sentence to: “Although MICOS has been described as an organizer of crista junctions, its role during the initial formation of nascent cristae has not been investigated.”

(2) Line 96-97: "Interestingly, PfMIC60 is much larger than the human MICOS counterpart, with a large, poorly predicted N-terminal extension." This statement is lacking a reference and presumably refers to annotated ORFs. The authors should clarify if the true N-terminus is definitely known - a 120kDa size is shown for the P. falciparum but this is not compared to the expected length or the size in S. cerevisiae.

To solve the reference issue, we added the uniprot IDs we compared to see that the annotated ORF is bigger in Plasmodium. We also changed the comparison to yeast instead of human, because we realized it is confusing to compare to yeast all throughout the figure, but then talk about human in this specific sentence.

Regarding whether the true N-terminus is known. Short answer: No, not exactly.

However, we do know that the Pf version is about double the size of the yeast protein.

As the reviewer correctly states, we show the size of 120kDa for the tagged protein in Figure 1G. Considering that we tagged the protein C-terminally, and observed a 120kDa product on western blot, it is safe to conclude that the true N-terminus does not deviate massively from the annotated ORF, and hence, that there is a considerable extension of the protein beyond a 60kDa protein. We do not directly compare to yeast MIC60 on our western blots, however, that comparison can be drawn from literature: Tarasenko et al., 2017 showed that purified MIC60 running at ~60kDa on SDS-PAGE actively bends membranes, suggesting that in its active form, the monomer of yeast MIC60 is indeed 60kDa in size.

To clarify, we now emphasize that we ran the Alphafold prediction on the annotated open reading frame (annotated and sequenced by Bohme et al. and Chapell et al. now cited in the manuscript), and revised the wording to make clear what we are comparing in which sentence.

(3) lines 244-245: "Furthermore, our data indicates the effect size increases with simultaneous ablation of both proteins?". The authors should explain which data they are referring to, as some of the data in Fig 3 and 4 look similar and all significance tests relate to the wild type, not between the different mutants, so it is not clear if any overserved differences are significant. The authors repeat this claim in the discussion in lines 368-369 without referring to a specific significance test. This needs to be clarified.

As a reply to this and other comments from the reviewers we added the multiple testing within all samples. In addition, to clarify statistics used we included a supplementary dataset with all p-values and statistical tests used.

(4) lines 304-306: "Though well established as the cristae organizing system, the role of MICOS in initial formation of cristae remains hidden in model organisms that constitutively display cristae.". This sentence is misleading since even in organisms that display numerous cristae throughout their life cycle, new cristae are being formed as the cells proliferate. Thus, failure to produce cristae in MICOS knockout lines would have been observable but has apparently not been reported in the literature. Thus, the concerted process in P. falciparum makes it a great model organism, but not fundamentally different to what has been studied before in other organisms.

We deleted this statement.

(5) lines 373-378. "where ablation of just MIC60 is sufficient to deplete functionality of the entire MICOS (11, 15),". The authors' claim appears to be contrary to what is actually stated in ref 15, which they cite:

"MICOS subunits have non-redundant functions as the absence of both MICOS subcomplexes results in more severe morphological and respiratory growth defects than deletion of single MICOS subunits or subcomplexes."

This seems in line with what the authors show, rather than "different".

This sentence has been removed.

(6) lines 380-385: "... thus suggesting that membrane invaginations still arise, but are not properly arranged in these knockout lines. This suggests that MICOS either isn't fully depleted,...". These conclusions are incompatible with findings from ref. 15, which the authors cite. In that study, the authors generated a ∆MICOS line which still forms membrane invaginations, showing that MICOS is not required at all for this process in yeast. Hence the authors' implication that MICOS needs to be fully depleted before membrane invaginations cease to occur is not supported by the literature.

This sentence has been deleted in the revised version of the manuscript.

Minor comments:

(1) The authors should consider if the first part of their title could be seen as misleading: It suggests that MICOS is "the architect" in cristae formation, but this is not consistent with the literature nor their own findings.

Title is changed accordingly

- Line 43, of the three seminal papers describing the discovery of MICOS in 2011, the authors only cite two (refs 6 and 7), but miss the third paper, Hoppins et al, PMID: 21987634, which should probably be corrected.

Done, the paper is now cited

- Page 2, line 58: for a more complete picture the authors should also cite the work of others here which shows that although at very low levels, e.g. complex III (a drug target) and ATP synthase do assemble (Nina et al, 2011, JBC).

Done

- Page 3, line 80: "Irrespective of the shape of an organism's cristae, the crista junctions have been described as tubular channels that connect the cristae membrane to the inner boundary membrane (22, 24)." This omits the slit-shaped cristae junctions found in yeast (Davies et al, 2011, PNAS), which the authors should include.

The paper and concept have been added to the manuscript, though the sentence has been moved up in the introduction, when crista junctions are first introduced.

- Line 97: "poorly predicted N-terminal extension", as there is no experimental structure, we don't know if the prediction is poor. Presumably the authors mean either poorly ordered or the absence of secondary structure elements, or the poor confidence score for that region in the prediction? This should be clarified or corrected.

We were referring to the poor confidence score. To address this comment as well as major point 2, we rewrote the respective paragraph. It now clearly states that confidence of the prediction is low, and we mention the tool that was used to identify conserved domains (Topology-based Evolutionary Domains).

- Line 98: "an antiparallel array of ten β-sheets". They are actually two parallel beta-sheets stacked together. The authors could find out the name of this fold, but the confidence of the prediction is marked a low/very low. So, its existence is unknown, not just its "function".

We adapted the domain description to “a stack of two parallel beta-sheets" and replaced the statement on unknown function by the statement “Because this domain is predicted solely from computational analysis, both its actual existence in the native protein and its biological function remain unknown.”

- Fig 1B: The authors show two alphafold predictions of S. cerevisiae and P. falciparum Mic60 structures. There is however an experimental Mic60/19 (fragment) structure from the former organism (PMID: 36044574), which should be included if possible.

We appreciate the reviewer’s suggestion and note that the available structural data indeed provides valuable insight into how MIC60 and MIC19 interact. However, these structures represent fusion constructs of limited protein fragments and therefore capture only a small portion of each protein, specifically the interaction interface. Because our aim in Fig. 1B is to compare the overall domain architecture of the full-length proteins, we believe that including fragment-based structures would be less informative in this context.

- Line: 318-321: "The same trend was observed for PfMIC19 and PfMIC60. Although transcriptomic data suggested that low-level transcripts of PfMIC19 and PfMIC60 are present in ABS (38), we did not detect either of the proteins in ABS by western blot analysis. While this statement is true, the authors should comment on the sensitivity of the respective methods - how well was the antibody working in their hands and how do they interpret the absence of a WB band compared to transcriptomics data?

The HA antibody used in our experiments is a standard commercial reagent that performs reliably in both WB and IFA, although it shows a low background signal in gametocytes. We agree that the sensitivity of the method and the interpretation of weak or absent bands should be addressed explicitly. Transcript levels for both PfMIC19 and PfMIC60 in asexual blood stages fall within the <25 percentile, suggesting that these signals likely represent background. Nevertheless, we acknowledge that low-level protein expression below the detection limit of western blot analysis cannot be excluded. To reflect these considerations, we added the sentence: ‘The apparent absence could indicate that transcripts are not translated in ABS or that the proteins’ expression was below detection limits of western blot analysis.

- Lines 322-323: would the authors not typically have expected an IFA signal given the strength of the band in Western blot? If possible, the authors should comment if the negative fluorescence outcome can indeed be explained with the low abundance or if technical challenges are an equally good explanation.

Considering the nature of the investigated proteins (embedded in the IMM and spread throughout the mitochondria) difficulties in achieving a clear signal in IFA or U-ExM are not very surprizing. While epitopes may remain buried in IFA, U-ExM usually increases accessibility for the antibodies. However, U-ExM comes at the cost of being prone to dotty background signals, therefore potentially hiding low abundance, naturally dotty signals such as the signal of MICOS proteins that localize to distinct foci (at the CJ) along the mitochondrion. Current literature suggests that, in both human and yeast, STED is the preferred method for accurate spatial resolution of MICOS proteins (https://www.ncbi.nlm.nih.gov/pubmed/32567732,https://www.ncbi.nlm.nih.gov/pubmed/3206734 4). Unfortunately, we do not have experience with, nor access to, this particular technique/method.

- Lines 357-365: the authors describe limitations of the applied methods adequately. Perhaps it would be helpful to make a similar statement about the analysis of 3D objects like mitochondria and cristae from 2D sections. E.g. the apparent cristae length depends on whether cristae are straight (e.g. coiled structures do not display long cross sections despite their true length in 3D).

The limitations of other methods are described in the respective results section.

We added a clarifying sentence in the results section of Figure 4:

“Note that such measurements do not indicate the true total length or width of cristae, as the data is two-dimensional. The recorded values are to be considered indicative of possible trends, rather than absolute dimensions of cristae.“

This statement refers to the length/width measurements of cristae.

In the context of Figure 4D we mention the following (see preprint lines 229 – 230): “We expect this effect to translate into the third dimension and thus conclude that the mean crista volume increases with the loss of either PfMIC19, PfMIC60, or both.”

For Figure 5, we included a clarifying statement in the results section of the preprint (lines 269 – 273): “Note that these mitochondrial volumes are not full mitochondria, but large segments thereof. As a result of the incompleteness of the mitochondria within the section, and the tomography specific artefact of the missing wedge, we were unable to confirm whether cristae were in fact fully detached from the boundary membrane, or just too long to fit within the observable z-range.”

- Line 404: perhaps undetected or similar would be a better description than "hidden"?

The sentence does not exist in the revised manuscript.

Reviewer #3 (Significance):

The main strength of the study is that it provides the first characterisation of the MICOS complex in P. falciparum, a human parasite in which the mitochondrion has been shown to be a drug target. Mic60 and the newly annotated Mic19 are confirmed to be essential for proper cristae formation and morphology, as well as overall mitochondrial morphology. Furthermore, the mutant lines are characterised for their ability to complete the parasite life cycle and defects in infection effectivity are observed. This work is an important first step for deciphering the role of MICOS in the malaria parasite and the composition and function of this complex in this organism. The limitation of the study stems from what is already known about MICOS and its subunits in great detail in yeast and humans with similar findings regarding loss of cristae and cristae defects. The findings of this study do not provide dramatic new insight on MICOS function or go substantially beyond the vast existing literature in terms of the extent of the study, which focuses on parasitological assays and morphological analysis. Exploring the role of MICOS in an early-divergent organism and human parasite is however important given the divergence found in mitochondrial biology and P. falciparum is a uniquely suited model system. One aspect that would increase the impact of the paper would be if the authors could mechanistically link the observed morphological defects to the decreased infection efficiency, e.g. by probing effects on mitochondrial function. This will likely be challenging as the morphological defects are diverse and the fitness defects appear moderate/mild.

As suggested by Reviewer 2, we examined mitochondrial membrane potential in gametocytes using MitoTracker staining and did not observe any obvious differences associated with the morphological defects. At present, additional assays to probe mitochondrial function in P. falciparum gametocytes are not sufficiently established, and developing and validating such methods would require substantial work before they could be applied to our mutant lines. For these reasons, a more detailed mechanistic link between the observed morphological changes and the reduced infection efficiency is currently beyond reach.

The advance presented in this study is to pioneer the study of MICOS in P. falciparum, thus widening our understanding of the role of this complex to different model organism. This study will likely be mainly of interest for specialised audiences such as basic research parasitologists and mitochondrial biologists. My own field of expertise is mitochondrial biology and structural biology.

Nope. You could just use the traditional approach where most tokenizers only have a generic tag/discriminant for all number tokens. The every-non-text-token-is-one-character constraint is arbitrary and unnecessary.

(Perceivable Principle) I noticed this big promotional banner right away, but it made me wonder how it translates for someone using a screen reader. According to the Perceivable principle, the image next to this text needs a concise <alt> tag of 125 characters or less so visually impaired users don't miss out on the information. If it is just named something random like "IMG_098.jpg" in the code, the site is failing to make this content truly presentable to everyone.

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Referee #1

Evidence, reproducibility and clarity

This study investigates the roles of Rab32 and Rab38 in hepatic lipid droplet metabolism. The authors propose that Rab32/38-positive lysosome-related organelles (LROs) mediate lipid droplet degradation through a mechanism independent of conventional macroautophagy. While the study addresses an interesting question, several conceptual and technical issues need to be addressed before the conclusions can be fully supported.

Major Concerns

1.The authors primarily define the Rab32/38-positive ring-like structures as "lysosome-related organelles (LROs)" based on their morphological characteristics and co-localization with LAMP1. However, this classification lacks biochemical validation. Would it be more appropriate to include a Lyso-IP assay to provide additional supporting evidence? 2.In hepatocytes, what is the operational definition of LROs? Beyond being "larger in size," how are these structures functionally distinguished from conventional lysosomes? If Rab32/38 defines LRO identity, why does GFP-Rab32/38 not co-localize with all LAMP1-positive structures (Figure S1A)? 3.In Figure 2A, the dextran pulse-chase experiment shows fluid-phase uptake into large vacuoles; however, dextran can enter any endocytic compartment after prolonged chase periods. What evidence supports that these structures are bona fide LROs rather than enlarged late endosomes or lysosomes resulting from long-term culture? What determines why only certain lysosomes become Rab32/38-positive? This heterogeneity is not explained. Does it imply that pre-existing lysosomes convert into LROs, or that LROs are newly formed under high-density stress? The developmental trajectory of these structures has not been explored. 4.The authors propose a microautophagy mechanism based on the "invagination-like" structures observed by light microscopy (Figure 3A). However, the resolution of light microscopy is insufficient to distinguish true membrane invaginations from lipid droplets that are closely apposed to, or partially wrapped by, the outer membrane of LROs in three-dimensional space. Would a CLEM experiment be necessary to confirm that lipid droplets are indeed located within the lumen of LROs, rather than in deep invaginations that remain connected to the cytosol? In addition, multilamellar membrane structures were observed after Bafilomycin A1 treatment (Figure 3A). Have these structures been validated by electron microscopy, or could they simply represent complex membrane infoldings within swollen lysosomes? The conclusions drawn from light microscopy alone appear somewhat insufficient. 5.The authors use ATG4B C74A overexpression to claim macroautophagy independence. However, while this mutant blocks LC3 lipidation, the study still lacks genetic evidence, such as ATG knockouts. In Figure S2B, the authors state that the "majority" of Rab38-positive LRO-associated lipid droplets are LC3-negative, but no quantitative data are provided. 6.The manuscript does not clearly distinguish the functions of Rab32 and Rab38. Although the authors describe these proteins as paralogs with overlapping roles, multiple data points indicate that they have differential effects on lipid droplet (LD) metabolism. Notably, Rab38-but not Rab32-significantly affects LD delivery to acidic compartments, exerts a stronger influence on LRO size, and responds more robustly to VPS4B perturbation. These observations suggest that Rab32 and Rab38 regulate distinct steps of LD metabolism rather than functioning redundantly. However, the manuscript does not clearly highlight these functional differences and lacks mechanistic validation. 7.Figure 5A shows that the PI3P probe (2×FYVE) forms ring-like structures inside or near the LRO membrane. However, LROs themselves are Rab5-negative (Figures 1C-E), and PI3P is typically generated by Vps34 on early endosomes. Where do these PI3P signals originate? Are they transported from other organelles, or is there a local PI3P-generating mechanism on the LRO membrane? If the latter, which kinase is responsible, and is Vps34 recruited to the LRO membrane? This issue is not discussed. If PI3P is indeed locally generated on LROs, it could represent a key feature distinguishing LROs from classical lysosomes.

Minor Concerns

1.The double-knockout mice exhibit obesity and fatty liver; however, Rab32 and Rab38 are expressed in multiple tissues. A whole-body knockout model cannot distinguish whether these effects are hepatocyte-autonomous or arise from contributions by adipose tissue or macrophages, emphasizing the need for liver-specific knockout animals or cell models. Serum TAG levels were unchanged, and the authors speculate that VLDL secretion may be impaired, but this was not directly tested. Furthermore, the authors do not address the observed sex-specific effects, which appear to be male-specific. 2.The concentration of Orlistat used is relatively high (50-200 μM) and may cause non-specific effects. Have dose-response experiments been performed, or have other LAL inhibitors (e.g., Lalistat) been tested? 3.LysoTracker reflects acidity rather than lysosome identity, and reduced acidification in DKD cells may affect co-localization analysis.

Significance

Assessment of Significance Overall Assessment

Strengths:

Conceptual novelty: Introduces lysosome-related organelles (LROs) into hepatic lipid metabolism, expanding the functional repertoire of Rab32/38 beyond pigment cells and macrophages.

Mechanistic exploration: Links LD uptake to PI3P/PI(3,5)P2 signaling and VPS4B, providing molecular handles for future studies.

In vivo validation: DKO mice show age-dependent obesity and HFD sensitivity, establishing physiological relevance.

Weaknesses:

Rab32 vs. Rab38 functions remain blurred: Data suggest differential roles (Rab38 in LD delivery, Rab32 in LD size regulation), but authors default to "redundancy" narrative.

Microautophagy evidence incomplete: Relies on light microscopy; EM/CLEM needed to confirm true internalization.

Model relevance unclear: High-confluence AML12 vacuoles lack clear physiological correlate in healthy liver.

Audience

Primary:

Lysosome biologists

Autophagy researchers

Lipid metabolism researchers

Secondary:

Cell biologists

Metabolic disease researchers

Geneticists

这个7250亿美元的AI投资规模数据表明AI领域正在经历前所未有的资本投入。这一数字相当于许多中等规模国家的GDP，反映了市场对AI技术的极高期望。然而，文章质疑这种巨额投资是否能获得相应回报，暗示可能存在AI泡沫风险。

reply to https://www.facebook.com/groups/TypewriterCollectors/posts/10161712887224678/

to Steve Clancy Zach Hubbird Jean Brunet

I'm curious what the sourcing is on your differentiation of the two models? Are there manuals, advertising, or other details to back up the differences? From what I can see, the phrase "Rhythm Touch" seems to have been an advertising tag for the Underwood SS which started a few months after production of the SS began and there wasn't any difference in them other than the advertising tag.

Robert Messenger has some scant history on the machine and the differences, primarily due to a redesign at the time, at https://oztypewriter.blogspot.com/2012/11/on-this-day-in-typewriter-history_25.html. The primary change from the S to the SS seems to have been a move from a carriage shift to a basket shift and so it seems somewhat fitting that Underwood uses the phrase "Rhythm Touch" as an advertising gimmick much like Smith-Corona were doing with their "Floating Shift" marketing.

Generally standards at the time were not differentiated by different trim lines as standards had all the bells and whistles for office use (potentially aside from custom use cases like decimal tabulators or extra wide carriage). Meanwhile all the trim variations were generally seen in the portable market geared toward home use rather than office. This would seem to support the idea that there's only the SS and "Rhythm Touch" is only an advertising tag line as the SS was newly introduced in January of '46 and "Rhythm Touch" appears around July '46.

There's also some discussion on the TWdB in the commentary at https://typewriterdatabase.com/1950-underwood-ss.23202.typewriter which may add to the question.

I'm curious to hear everyone's thoughts on the idea/thesis that the only model is the Underwood SS which is being marketed as the "Rhythm Touch" or evidence to the contrary to refute the claim.

Is it just me or are there only two of them visible on the picture?