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Reply to the reviewers

OVERALL RESPONSE

We showed that chronic adaptation to mPOS, a new mechanism of cell stress initially discovered in yeast, induces muscle atrophy in a mouse model. We are pleased to see the overall enthusiasm from the reviewers about our work. The reviewers are unanimous in (1) that the work represents “a huge amount of work” that “has been well conducted regarding the characterization of the Ant1(TG/+) murine model that exhibits a muscle loss phenotype”, and (2) that our study linking mitochondria-induced proteostatic stress to muscle atrophy opens “a new field of investigation” and “is of interest to the scientific communities studying skeletal muscle pathophysiology and mitochondrial homeostasis”.

Mitochondrial alterations are important hallmarks in skeletal muscles under many pathological conditions. Given that bioenergetic deficiency alone is not sufficient to explain muscle wasting as shown by other groups, the reviewers commented that our work “is original and opens new perspectives in the field of mitochondrial dysfunction related to myopathies”.

Additional strengths noted by the reviewers include the demonstration of mPOS in an animal model, and the potential implication of our work for FSHD that is one of the most common muscle disease in humans.

We are also pleased to learn that the reviewers reached “cross-referee” recommendations to improve the paper. We are excited to see these recommendations. We are motivated and have the capacity to implement all the four series of experiments recommended by the reviewers as outlined below.

REVIEWERS’ COMMENTS AND OUR RESPONSE

1. It would also be useful to perform cell fractionation and measure the accumulation of Ant1 and other unimported mitochondrial precursor proteins in the cytosol (reviewer 1). Characterizing the aggregates observed in muscles, to see whether they contain ANT1, ubiquitin, p62 and unimported mitochondrial proteins (reviewer 2 & 3). It would be informative to measure the formation of soluble and insoluble protein aggregates (reviewer 1&2).

Response

        We propose to perform subcellular fractionation of muscle lysates using sucrose gradient centrifugation, coupled with western-blot. This will enable us to learn whether Ant1-induced stress increases the retention of unimported mitochondrial (pre)proteins (e.g., Ant1, Tom20, MDH2, TFAM, SDHA and Aco2) in the cytosol or extramitochondrial aggregates. We routinely practice this technique and we have all these antibodies validated in the lab.

        Our previous work showed that the giant aggresomes induced by ANT1 overexpression contain Ant1 and mitochondrial proteins in HEK293T cells (Liu et al., 2019, MBoC 30:1272-1284). However, the aggresomes we observed in the ANT1-transgenic muscles have sizes often comparable to mitochondria. Protein import stress may also lead to the accumulation and misfolding of precursors on the mitochondrial surface that are subject to ubiquitination and autophagic removal. It would be difficult to distinguish between aggresomes and mitochondria by IHC using antibodies against Ant1, ubiquitin, p62 and unimported mitochondrial proteins. To overcome this, we will take advantage of the subcellular fractionation technique described above.  This should enable us to clarify whether the cytosolic small aggregates co-fractionate with p62 and ubiquitin.

        As suggested by reviewer 1 & 2, we will determine whether NP-40 insoluble but SDS-soluble aggregates can be detected in the Ant1-transgenic muscles, using the dot blot technique as we previously published (Liu et al., 2019, MBoC 30:1272-1284). Antibodies against mitochondrial proteins, p62 and ubiquitin will be used to determine whether the aggregates are enriched in mitochondrial protein, p62 and ubiquitin.

        Collectively, the experiments proposed about will provide biochemical support for the retention, and possibly ubiquitination and p62-mediated aggregation of unimported mitochondrial proteins in the cytosol of Ant1-transgenic muscles.

2. Finalizing the characterization of the EM analysis (reviewer 2 & 3) – The reviewers suggested that we should try to quantify the different aggresomal/autophagic/vacuolar structures in the transgenic and control muscles in the TEM experiments.

Response

  Yes, we will perform the quantitation with the grids we prepared as suggested by the reviewers.

3. Evidence reduced altered protein synthesis rate (Reviewer 1 & 3) – Reviewer 1 suggested that it may be helpful to provide biochemical evidence for potential changes to protein synthesis rate, in order the validate the RNA-Seq data. This is also echoed by reviewer 3. The non-radioactive SUnSET technique (FASEB J. 2011 Mar;25(3):1028-39. doi: 10.1096/fj.10-168799) was recommended for measuring protein synthesis rate in vivo.

Response

  We appreciate reviewers’ suggestions and will be happy to set up this experiment. Briefly, we will inject the mice (n=3 for the transgenic and control mice) with puromycin to bind neosynthesized peptides. Muscle tissues will be collected and analyzed by western blot using an anti-puromycin antibody (#MABE343, Millipore Sigma). Quantitation of the western blot signals will inform whether relative protein synthesis rate is decreased in transgenic muscles compared with wild-type controls.

4. Quantifying different lysosomal markers (reviewer 2) – Reviewer 2 suggested that we should quantify the levels of LC3I/II, Lmap2 and other lysosomal markers (e.g, Beclin1).

Response

  We agree with this and the western blot experiments will be performed accordingly, using frozen muscle samples that we collected. We will also extend to the analysis using antibodies against CTSL (Abcam, #ab103574) and V-ATPase subunits such as ATP6V1H (Abcam, #ab187706) and ATP6V1G2 (Sigma, # WH0000534M2) that are upregulated in the transgenic muscles as revealed by RNA-Seq.

ADDITIONAL RECOMMENDATIONS.

The reviewers made additional minor recommendations that we found very constructive and helpful for improving the manuscript. These include providing for details of animal number and muscle types used in the experiments, statistical analysis, image analysis, and method sections for protein extraction and western blot. The reviewers also made suggestions for reorganization of some of the Figures. We have implemented some of these suggestions in the revised version of the manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this study the authors generated a transgenic mouse model of impaired mitochondrial import/loading by overexpressing ANT1, a mitochondrial carrier protein. The moderate overexpression of ANT1 was sufficient to cause progressive muscle wasting, characterized by a reduction of myofiber size. By transmission electron microscopy, the authors observed that aggresome-like structures in the cytosol of ANT1 Tg muscle cells, suggesting that occurrence of mitochondrial Precursor Over-accumulation Stress (mPOS). The authors characterized the transcriptional profile of the muscle of ANT1 Tg and WT mice by RNA-sequencing. The data supports that mPOS leads to changes in gene expression to promote: 1) mitochondrial protein import and proteostasis; 2) inhibition of protein synthesis; and 3) protein degradation. The authors speculate that this is an adaptative response to cope with the cytosolic proteostatic stress induced by ANT1 overloading but that, if chronically sustained, leads to a reduction of protein content and consequent muscle wasting.

Major comments:

The key conclusions of this work are that ANT1 overexpression induces accumulation of proteins in aggresomes in the cytosol, supporting the existence of overaccumulation of unimported mitochondrial precursor proteins (or mPOS) in an in vivo animal model. As an adaptive response, the cell's transcription profile changes to inhibit protein synthesis and promote protein degradation. This adaptation creates a protein imbalance that leads to muscle wasting.

The data and methods are clearly presented. However, this work would benefit from more evidence to strengthen the main conclusions. I have the following comments:

1) By transmission electron microscopy, the authors detected aggresome-like structures in the cytosol of ANT1Tg/+ but not in that of WT muscles. Is it possible to quantify the frequency of each type of structure in WT and ANT1Tg/+? Does this frequency increase with age or correlate with the degree of myofiber phenotype (reduction in size)?

2) mPOS is characterised by the accumulation of unimported mitochondrial precursor proteins. Is there any evidence either that the aggresome-like structures contain unimported mitochondrial proteins or that mitochondrial precursor proteins, still containing their mitochondrial targeting sequence, accumulate in the cytosol? Does unimported Ant1 precursor protein accumulate in ANT1Tg/+ mice?

3) Previous studies have shown that expression of mutant Ant1 protein causes mitochondrial morphology defects and mtDNA deletions. Do ANT1Tg/+ mice have altered mitochondrial morphology or deletions/loss of mtDNA in muscle?

4) The transcriptomic data and the amplification of the lysosomal compartment suggest an activation of multiple protein degradation processes, that could contribute to the reduced protein content in ANT1Tg/+ muscles. The increase in P-4E-BP and eIF2alpha expression in ANT1Tg/+ muscles suggest protein synthesis may be decreased. These data are consistent with unbalanced protein synthesis versus degradation. Figure 7A demonstrates a reduction in steady state protein levels in ANT1Tg/+ muscles. However, Figure 7A is insufficient to confirm a mechanistic explanation of the muscle wasting phenotype as the authors state at the end of the Results. This would require direct evidence of altered protein synthesis rates and protein degradation, which, although challenging in vivo, have not been directly demonstrated. The authors should therefore modify the final sentence in the Results.

Minor comments:

Suggestions to improve the presentation of data and conclusions: On page 7, it reads "No myofiber type grouping was observed in muscle samples stained for mitochondrial activities, suggesting the lack of chronic neuropathy." This sentence is lacking a reference to a figure (maybe Fig 2, D?).

On page 12, the authors say "These genes are known to be activated as an important regulatory circuit in the Integrated Stress Response (ISR), an elaborating signaling network that is stimulated by divers cellular stresses to decrease global protein synthesis and to activate selected genes in the benefit of cellular recovery (42)." The word diverse is misspelled.

On page 13, the authors wrote: "First, we found that the transcription of genes encoding proteasomal subunits, NFE2L1 and NFE2L2 are upregulated (Fig. 6A & 6B). NFE2L1 and NFE2L2 activate the transcription of proteasomal genes." This section could be rephrased for clarity. The first sentence suggests that NFE2L1 and NFE2L2 are proteasomal subunits. But then the authors say they activate transcription. I believe what the authors meant was that the genes encoding proteasomal subunits were upregulated (Fig. 6A), as well as NFE2L1 and NFE2L2 (Fig. 6B), that activate the transcription of proteasomal genes.

On page 13 (last paragraph), the authors mention that "numerous genes involved in autophagy, cytoskeletal organization and intracellular trafficking are upregulated in ANT1Tg/+ muscles" but they only explain what STBD1, ARHGAP33 and ARHGEF2 do. The other genes are not mentioned. If the authors want to speculate that the differences in gene expression are relevant, they should explain the role of the different genes/proteins.

On page 14, where it reads "we found that Lamp2-possitive lysosomes and/or lysosome-derived structures are amplified in the ANT1Tg/+ muscles", the word positive is misspelled.

Regarding Fig 1: in C there are two Tg bars, do they represent the two independent transgenic mice referred in the text? Or are these males and females, like in the following graphs? The authors should clarify this. What do the values and error bars represent and which statistical tests were used?

Regarding Fig 2: How was the coefficient of variability calculated? A sentence explaining this might be helpful in the methods' section. There is no scale bar in D. Which statistical test was used in I-J? The authors mention " P values were calculated by unpaired Student's t test." for E-H but no for I-J.

Fig 3. is missing a scale bar.

In Fig 5, B-C: the labelling of males and females is missing from the graphs. Authors should indicate which statistical test was performed for each graph.

In Fig S1: Why split A from B? Why only showing 1 of the Tg for each timepoint and not both? If the images are representative of both transgenics, then it should be mentioned. Suggestion: adding the timepoint info (3, 6, 17 months) above each panel makes it easier to understand the figure without reading the legend. E-H: the graphs show grouped variables, wouldn't a Two-way ANOVA be more appropriate to test for statistical significance?

Significance

mPOS has been observed in yeast (Wang and Chen 2015) and human cells (Liu et al. 2019). This works shows, for the first time, evidence for mPOS is an in vivo animal model. This work suggests that a moderate overexpression of ANT1 is sufficient to impair mitochondrial import and loading, causing mPOS and an adaptative response to cytosolic proteostatic stress. These findings are relevant to understand pathologies where ANT1 is affected, such as facioscapulohumeral muscular dystrophy (FSHD) (Laoudj-Chenivesse et al 2005). mPOS represents a novel mechanism through which mitochondrial dysfunction impacts on muscle wasting. Moreover, since impairment of the mitochondrial import and loading machinery is observed in aging and disease (MacKenzie and Payne 2007), these findings are relevant to better understand the impact of mitochondrial dysfunction in different cell types.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors investigate the role of mitochondria - other than bioenergetic or oxidative stress - in the loss of muscle mass. They hypothesize that the accumulation of immature mitochondrial proteins in the cytosol is responsible for muscle atrophy, independently to mitochondria metabolism. For this purpose, they generated a murine model that over-expresses ANT1, a mitochondrial protein. The two-fold increase in Ant1 protein level leads to an overload in mitochondria import machinery, thus an accumulation of ANT1 in the cytosol, and thus to mitochondrial Precursor Over-accumulation Stress (mPOS). Consequently, the protein degradation pathway is stimulated, leading to an imbalance between protein synthesis/degradation, and in long term in muscle wasting.

Major comments:

The authors have executed a huge amount of work. Before fully reaching the conclusion that mPOS induce an imbalance between protein degradation/protein synthesis, by mainly increasing the lysosomal pathway, the authors should test/validate few more things:

1-Quantify the increase in LAMP2 muscle (WB and/or immunolabeling quantifications

2-Quantify other lysosomal markers

3-Characterize better the aggresomes observed in ANT1Tg/+ muscles

4-Add details for the methods (details are missing and make it difficult to judge this section, see comments below) These experiments should be easily done, 3 months of work: regular WB analysis, immunohistology and analyses EM images the authors already have, cost for supply <$2000.

Results:

Figure 1, movie 1-3 and paragraph "Moderate Ant1 overexpression causes progressive muscle wasting." The authors generated two independent hemizygous transgenic mice (ANT1Tg/+) and characterized them. The authors show a greater level of ANT1 in the transgenic mice. Could they show the localization of ANT1 in ANT1Tg/+ muscles: cytosol? Near the mitochondria? Sub-sarcolemmal mitochondria or else? Does ANT1 form aggregates? If yes, do the aggregates co-localised with ubiquitin? Proteasome? Lysosomal markers?

Figure 3 and paragraph "Cytosolic aggresome formation supports mPOS." The authors show EM images of muscle section of ANT1Tg/+ muscles at 1 and 2 years old. The authors wrote that there is an increase of aggresomes: they show in figure 3Q and M structures that look like mature lysosomes, or in 3F and 3R early mitophagy ... The authors should try to classify the different structures they observed and quantify these structures (eg number of autophagic vacuole per sarcomere). They should then perform some immunostaining on muscle sections at same age to confirm an increase in lyosomal markers for example. They still should do the same analysis (quantification and immunostaining) in WT muscle tissue same age. Figure 3 B and C suggest lipid vacuoles. Can the authors check using Oil red-O staining for example (or another staining)? The accumulation of lipid drops in transgenic muscle would suggest an impact on the metabolism, and more specifically on the lipid metabolism. All these structures are classic and should be observable in WT muscle, but probably at a lower frequency. Attempting to quantify these parameters and confirm by histochemistry would help to characterise better the murine model.

Figure 4, Supplemental Figure 4, Supplemental Table1 and paragraph "Ant1 overloading activates genes involved in mitochondrial protein import and proteostasis, and those encoding small heat shock family B chaperones consistent with mPOS." The authors generated Supplemental table 2 but never mentioned it in the text. Figure 4 and supplemental Figure 4, can the authors add the stat. The authors conclude from these figures (Figure 4 and Supplemental Figure 4) that ANT1 overexpression causes a protein import stress on the mitochondria. This is based on transcriptomic analysis and RTqPCR. They should validate at the protein level: eg level of HSPBs, NACA and HsP90 by WB and localisation in muscle section by immunostaining (counterstaining with mitochondria marker)

Figure 6 and 7 and paragraph "Activation of multiple protein degradation processes and reduced protein content in ANT1Tg/+ muscles." Figure 6H: the authors should quantify LAMP2 level. Other markers of the lysosomal should be assessed at the protein level (LC3I/II, Beclin1 etc) The proteasome pathway does not seem strongly stimulated as no increase in ubiquitinylation nor in P62 are observed by Western blot. However, the authors should check whether The aggresomes observed, do they colocalise with ubiquitin and/or P62 proteins in muscle section (if yes try to find a way to quantify this if there is some colocalization). Are the aggresomes soluble or non-soluble proteins? The latter could interfere on the absence of detection n of increases in protein ubiquitinylation.

Material and methods

Paragraph describing the statistical analysis is missing. Number of mice, sample used for each experiment should be added in the Mat and Methods as well.Which muscle was used for which experiments (for histology, EM and RNAseq in Mat & Methods)? The procedure for image analysing is missing: objective used, number of images analysis per sample, how many muscle were studied? Protein extraction and Western blot procedures is missing

Minor comments:

-Figure 1I: typo in the x axe legend: quadriceps instead of quadrucep

-Figure 2 and paragraph "Mitochondrial respiration is moderately decreased in ANT1Tg/+ muscles." Figure 2A-B-C: the authors should move the supplemental figure 2A and B in the main figure, and place figure 2Band C in sup data. To confirm that there is a difference in fiber size distribution, the authors should perform a Kolmogorov Smirnov test. Can the author clarify if whether they are using minimum diameter of fibres throughout the results (figure 2c and supplemental figure 2A and B), and if this is what is meant by the term "lesser diameter"? Figure 2I-J: It would be interesting to compare the different respiratory state at different age using an ANOVA2 factor and post-hoc test.

-Page 13: full stop missing after the reference (49): "ligases respectively, are frequently upregulated (49),"

-Supplemental Figure 4: reorganise the plot: put the reference ANT1 in first position, then organise per pathway involvement (eg: put together SLC7A1, SLC7A5 and ASNS for the acid transport, MTHFD2 and PSAT1 together for the one-carbon metabolism etc).

-The authors describe figure 6E and F before A,B,C,D... they thus may need to switch them around.

Significance

Muscle loss associated with cachexia, sarcopenia, or neuromuscular disorders, if of current interest to the field, with much work ongoing to study the role of inflammation, denervation, REDOX homeostasis and proteostasis. The current paper suggests a new mechanism that could be involved in muscle atrophy: mitochondrial protein load and import. The authors generated a new murine model that would be useful to the muscle community to investigate pathways involved in muscle wasting, in different physiological and pathological context. Working on different neuromuscular disorders and muscle ageing, the existence of such a model would be an interesting tool to investigate the role of mitochondrial dysfunctions (dysfunctions other than mitochondrial metabolism) in muscle wasting.

REFEREES CROSS COMMENTING

Reading the comments from other reviewers, it seems that there is general agreement that this paper has been well conducted regarding the characterization of Ant1TG/+ murine model, and muscle loss.

Similarly, all the reviewers seem to agree that: Finalizing the characterization of the EM analysis, characterizing the aggregates observed in muscles (containing ANT1?, ub?, p62?, soluble or non-soluble aggregates), as well as quantifying the protein synthesis and different lysosomal markers would improve the paper.

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Referee #1

Evidence, reproducibility and clarity

Summary In this manuscript, Wang and colleagues show that chronic adaptation to mitochondria-dependent proteostatic stress in the cytosol induces muscle atrophy. Although mitochondrial dysfunction is known to cause muscle wasting, the underlying mechanism is unclear. The authors generated a transgenic mouse (ANT1Tg/+) in which the expression of the nuclear-encoded mitochondrial carrier protein Ant1 is increased by two-fold. These mice are characterized by the progressive loss of body weight and muscle mass. As revealed by muscle histology and immunocytochemistry analysis, ANT1Tg/+ mice have decreased myofiber size and increased myofiber size variability. Consistent with muscle wasting, ANT1Tg/+ mice are characterized by decreased home cage activities and exercise tolerance. Mechanistically, the authors found that ANT1 overexpression has a relatively mild effect on mitochondrial respiration. However, ANT1 overexpression induces cytosolic proteostasis stress (mPOS), the formation of aggresome-like structures and the activation of small heat shock proteins in the cytosol accompanied by the upregulation of the stress-activated transcriptions factors. The drastically remodeled transcriptome of ANT1Tg/+ mice muscles is indicated by the authors as an adaptive response to counteract mPOS.

Major comments

The phenotypic characterization of the ANT1Tg/+ skeletal muscle is well conducted and detailed. However, the key conclusions are mainly based on the interpretation of RNA-seq data with little experimental evidence of the underlying mechanism. For this reason, the authors should qualify some of their claims as preliminary or speculative. For instance, they find upregulation of SENS2 gene, which has been demonstrated to take part in mitophagy. In addition, they corroborate this data with electron microscopy images where mitophagy structures are present. However, these two data are not enough to state that mitophagy is involved. It would be advisable to focus on fewer genes but with a stronger validation process.

I recommend performing the following experiments:

To better characterize the effects of Ant1 overexpression on mitochondrial function, the author should address whether ANT1Tg/+ mitochondria are more prone to depolarize or not.

It would be informative to measure the formation of soluble and insoluble protein aggregates.

It would also be useful to perform cell fractionation and measure the accumulation of Ant1 and other mitochondrial proteins in the cytosol.

Finally, I would suggest to measure protein synthesis by the non-radioactive SUNSET technique (FASEB J. 2011 Mar;25(3):1028-39. doi: 10.1096/fj.10-168799).

Minor comments: The author should mention how many muscles were used for the EM studies. I would encourage discussion of the atrophic (and not dystrophic) phenotype of the ANT1Tg/+ mice related to the connection between Ant1 and FSHD.

Significance

The authors investigate a still poorly explored mechanism underlying muscle wasting based on mitochondrial import machinery dysfunction. The work is original and opens new perspectives in the field of mitochondrial dysfunction related to myopathies.

Mitochondria alterations play a key role in the context of muscle decline in many diseases and in aging. It is well known that the alteration of mitochondrial respiration and the oxidative stress increase are hallmarks of mitochondria dysfunction in skeletal muscles under pathological conditions. However, there is evidence that these features are not sufficient to explain the severe phenotype of muscle wasting. This work opens the way to the possibility that non-bioenergetics factors could take part in the pathological scenario. In detail, the involvement of the mitochondrial import mechanism, which causes a cytosolic proteostatic stress, is a new field of investigation. A few years ago, the same authors demonstrated that the mitochondrial precursor over-accumulation stress (mPOS) triggers a cytosolic proteostatic stress in yeast, however until now there was no evidence whether this phenomenon could occur in animals and which tissues would be involved. Thanks to this work, the authors demonstrated that mPOS occurs in skeletal muscle.

This study is of interest to the scientific communities studying skeletal muscle pathophysiology and mitochondrial homeostasis. My main research field is the role of mitochondrial homeostasis in skeletal muscle function in health and disease.

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Reply to the reviewers

Reviewer #1:

In this manuscript the authors explore the requirements for centromere transcription using single-molecule FISH. Previous studies have found that centromeres are transcriptionally active in a wide variety of organisms. Centromere transcription has been proposed to facilitate Cenp-A deposition through chromatin remodeling and to directly contribute to centromere/kinetochore function by producing a functional ncRNA. However, we currently know almost nothing about how transcription is initiated at the centromere or how levels of centromere transcripts are controlled. This manuscript makes several major findings that are potentially of importance to groups studying centromere transcription. 1.) Centromere RNAs are produced by RNA Polymerase II and are localized in the nucleus of a wide-range of cell types. 2.) Centromere RNAs do not localize to the centromere, which is in contrast to several recent studies. 3.) Centromere proteins are not required for transcription of alpha-satellite sequences. 4.) Localization of centromeres to the nucleolus represses centromere transcription. Overall, this is a solid manuscript and has the potential to make a significant impact in the field. Below I suggest a couple of experiments and modification to the data presentation that could improve the manuscript.

We thank this reviewer for their interest in this paper and agree with their clear articulation of the key points.

1. • All of the experiments in this manuscript rely on detection of centromere RNAs using single molecule FISH probes. These probes are validated by showing the RNase treatment removes the FISH signal. A strength of this approach is that the authors use multiple different probe sets and achieve comparable results. However, there is no orthogonal validation that the probes detect alpha satellite RNA. All of the experiments in this manuscript would be significantly improved by showing that the results presented here can be confirmed by a different approach. I suggest that the authors use Q-RT-PCR to validate the smFISH results. * The smFISH probes provide a powerful and unique strategy to detect alpha-satellite transcripts. To ensure that these experiments are carefully controlled, we analyzed multiple distinct probe sequences that recognize alpha-satellite transcripts derived from different chromosomes, as this reviewer highlights. We also conducted an in-depth computational analysis to ensure that these probes do not match genomic sequences outside of alpha-satellite regions. However, we recognize and agree that a complementary method to detect these transcripts would be a useful addition to this paper. We are currently highly constrained in our ability to conduct these experiments due to COVID-19-related laboratory closures, but if feasible our goal for a revised manuscript would be to conduct qPCR experiments for a subset of the conditions that are the most central to the key results in this paper (focusing particularly on HeLa and Rpe1 control cell lines, CENP-C iKO, Ki67 KO, and RNA Polymerase I and RNA Pol II inhibitors).

• Several results in this manuscript directly contradict results in published studies, but these discrepancies are not discussed. I believe the authors need to discuss the following discrepancies between their results and those in the literature: *

• McNulty et al. Dev. Cell. 2017. Show that alpha-satellite RNA is transcribed from all centromeres and remains localized to the site of transcription. The different results and possible explanations for the differences should be discussed. *

1. • Additionally, Rosic et al. JCB 2014, Blower Cell Reports 2016 and Bobkov et al. JCB 2018 all show that centromere RNAs localize to centromere regions. The differences between these studies and the authors results should be discussed. *

• The authors show that satellite RNA cannot be detected on mitotic chromosomes. However, Johnson et al. Elife 2017, Bobkov et al. JCB. 2018, and Perea-Resa et al. Mol. Cell. 2020 show that EU-labeled RNA can be detected at the centromere during mitosis. The authors should discuss the discrepancy between their results and these studies. Is it possible that their smFISH probes do not detect nascent, chromatin-bound transcripts? *

We believe that a strength of our paper is that it assesses alpha-satellite transcripts in individual intact cells using fixation conditions that preserve the native behaviors without disruptive and harsh extraction. As our results differ from those of other laboratories in some cases, we agree that it would be helpful to comment more directly on these differences with prior work. Points a, b, and c above all relate to the presence of alpha-satellite transcripts at centromeres. For the revised paper, we will include a discussion of these prior observations and some possible reasons for the differing results. In particular, we think that these discrepancies reflect two key differences:

1. Other strategies with harsh extraction conditions likely eliminate soluble alpha-satellite transcripts that are not tightly associated with centromeres, whereas our work preserves these.
2. It is possible that we are unable to detect nascent transcripts by smFISH as these are embedded within the RNA polymerase. Extraction conditions: An advantage of the smFISH probes used in our paper is that these require mild fixation conditions without prior extraction to better preserve cellular structures allowing us to analyzed intact cells, rather than chromosome spreads. Thus, our approach maintains the diverse alpha-satellite transcripts that are not bound to centromeres, and which may have been washed away in other studies. In contrast, some prior studies used stringent extraction conditions and primarily conducted experiments in chromosome spreads (not intact cells). Although it is not feasible to precisely determine the basis for differences without repeating this work the precise approaches and conditions from each paper and working closely with each group, we believe that these substantial technical differences explain our differing observations that reveal that the majority of alpha-satellite transcripts do not remain at centromeres..

Nascent transcripts: As suggested by this reviewer, we agree that our differing conditions may mean that we are unable to detect nascent transcripts that are closely associated with the RNA polymerase, inaccessible due to their chromatin proximity, or that are not sufficiently elongated such that they are present to hybridize to multiple copies of the smFISH probes to be detectable. The alpha-satellite transcripts must be derived from centromeric and pericentromeric regions and so must exist there at some point (as also attested to the EU signals that this reviewer mentions in the work from our collaborative the Blower lab; we have also detected EU signal at centromeres). However, our work suggests that alpha-satellite transcripts do not persist at centromeres indefinitely once generated, with mature transcripts in the nucleoplasm and liberated from chromosomes during mitosis. We believe that the combination of the relative inability of our smFISH probes to detect nascent transcripts, but stringent conditions disrupting non-centromere bound transcripts for prior work likely explain these distinctions.

• The authors show nicely that deletion of Ki-67 reduces centromere localization to the nucleolus and increases centromere transcription. However, this has no effect on centromere function. Studies from the Earnshaw lab (e.g. Nakano et al. Dev Cell 2008 and Bergmann et al. EMBO J. 2011) show that increasing or decreasing centromere transcription results in loss of kinetochore function on a human artificial chromosome. The authors should discuss the differences between their results and these studies. Is it possible that the small size of the HAC exaggerates the importance of the correct levels of centromere transcription? *

We are big fans of the Earnshaw lab work. In this case, there are a couple of possibilities to explain the strong effect that the Earnshaw lab observed on kinetochore function by perturbing centromere transcription. First, the degree of the change in centromere transcription may make a big difference. The Ki-67 results in an approximately 2-fold increase in alpha-satellite smFISH foci, which may still be within a permissive range for normal kinetochore function. Second, the experiments from the Earnshaw lab rely on targeting activating or silencing proteins to the centromere region, and it is possible that changes in centromere chromatin downstream of these factors contribute to the observed phenotypes in addition to altering the amount of centromere transcription. We will include a brief discussion of the Earnshaw work in a revised paper.

• The authors treat cells with transcriptional inhibitors for 24 hours. I am concerned that this may result in massive cell death. It would be helpful to include cell viability data from these experiments. *

We appreciate this point and agree that cell lethality is an important consideration given the essential role of the RNA polymerases. For the inhibitors, we first treated the cells for a variety of different time points to evaluate these behaviors. For example, we found that we could treat cells with RNA Polymerase II inhibitors for as much 48-72 hours without detecting noticeable cell death. Thus, at the 24 hour time point, the cells remain viable and intact, as is also visible in the images showing DNA staining for these treatments in Figure 3. We also note that this timing is consistent with prior studies that block transcription or translation. However, we did additionally conduct these experiments at earlier time points (5 hours and 12 hours post-drug addition) and obtained similar results. For example, for the Cdk7 inhibitor using the ASAT probe, we observed the following smFISH foci/cell: Control (3.4 foci/cell), 5 h (1.5 foci/cell), 12 h (1.2 foci/cell), 24 h (0.9 foci/cell). There is a clear effect even at 5 hours of treatment and a continued downward trend. Both for simplicity and because the replicates and number of cells that were quantified were lower for these conditions, we chose not to include these in the paper. We will include a statement regarding these earlier time points in the revised version.

• In Figure 3C the authors examine the effects of centromere protein knock outs on centromere transcription. To me this is the most important experiment in the manuscript and is a major step forward for the field. The authors use inducible CRISPR knock out cell lines that are not 100% penetrant. It would be helpful if the authors could describe how they ensured that cells included in the image quantification were knock out cells. *

Based on this comment and the other questions from the other reviewers, we recognize that we need to provide a much better description of the CRISPR knockout strategy, the prior validation of these cell lines, and the strategies that allow us to use these cell lines in a robust manner to ensure that we are effectively eliminating the target genes. We have systematically tested this strategy in multiple cases and find that this strategy is superior to RNAi for its efficacy and the potency of the phenotype, particularly for this type of cell biological assay.

The Cas9-based strategy is a highly effective way to conditionally eliminate essential genes. In this case, the efficiency of the Cas9 nuclease ensures that the genomic locus is cleaved in essentially 100% of cases. As this is repaired in an error prone manner and typically using non-homologous end joining, 66% of individual events result in frame shifts mutations that disrupt the coding sequence of a target gene, with ~50% of cells resulting in frame shifts in both copies of a gene. In addition, if a sgRNA targets a region of a gene that cannot tolerate mis-sense mutations, this will result in an even greater fraction of mutant cells. Thus, these inducible knockout cell lines result in robust and irreversible gene knockout, with a large fraction of cells (50% or more) displaying a clear phenotype. However, it is also true that there are a subset of cells within the population that will repair the DNA damage following Cas9 cleavage in a way that preserves protein function such that they behave similarly to control cells. Importantly, this means that there will be two classes of cells within a population – those that are unaffected, and those that are strongly affected. As we are analyzing each cell individually instead of creating a population average, this will capture this phenotypic diversity to reveal two populations of behaviors in cases where eliminating a gene results in a substantial change in smFISH foci. For example, the smFISH foci/cell data for the CENP-C inducible knockout (Fig. 3C and 3E) indicates that many cells have smFISH foci numbers that are comparable to control cells, but others that display substantial differences and highly increased numbers. An ideal control in these experiments would be to additionally analyze the levels of the target protein together with the smFISH analysis. Unfortunately, many of the antibodies are not compatible with the conditions needed for the smFISH. For CENP-C, the antibody that we have is not compatible with the conditions that we are using for the smFISH, so it is not feasible to co-stain these cells as suggested. Instead, for our analysis of the centromere-nucleoli localization (for example), we used the presence of a clear CENP-C interphase phenotype (“bag of grapes” resulting from chromosome mis-segregation) as an indication that the cells had been knocked out for CENP-C.

The majority of the Cas9-based inducible knockouts that we used for this paper were generated previously in the lab (McKinley et al. 2015; McKinley et al. 2017). For the centromere protein knockouts (McKinley et al. 2015), these were analyzed previously with respect to phenotype and monitored for the depletion of each gene target over time. For the larger collection of cell cycle and cell division inducible knockouts, for our prior work we systematically validated each of these with respect to their phenotype (see http://cellcycleknockouts.wi.mit.edu). Thus, we are confident that each of these cell lines is functional and effective for eliminating the target gene.

For conducting the experiments using the inducible Cas9 cell lines in this paper, we used the presence of these previously-defined phenotypes within the population as a validation that the strategy is working. Again, in general we find these knockouts are both penetrant and severe in their phenotypes. Importantly, for this diverse set of genes, we note that our goal was to broadly survey diverse factors to identify changes in alpha-satellite transcript levels. We intended this analysis as a “screen” where we would identify factors that resulted in a substantial change in the number of smFISH foci. As with any larger analysis, it is possible that there are false negatives where we did not detect a strong effect on transcript levels (such that they may contribute to centromere transcription). We have tried to use caution not to indicate that this data excludes any possible role for these factors in transcript levels, although in general the majority of the tested factors did not show a substantial change in smFISH foci. For the revised paper, we will make an explicit statement to this effect.

• On p8. The authors cite Quenet and Dalal. eLife 2014 for the idea that transcription during G1 is important for new Cenp-A loading. They should also cite Chen et al. Dev. Cell 2015 and Bobkov et al. JCB. 2018. *

Thank you for these helpful suggestions. We will update the text to incorporate these references.

Reviewer #2:

The study by Bury et al. investigates the formation of two different types of alpha-satellite transcripts (ASAT, SF1 and 3) in different human cell lines. Using smFISH they find that during the cell cycle these centromeric transcripts don’t stay at the centromere and are found in the cytoplasm after mitosis. Using specific inhibitors, they find that transcription is dependent on RNAPII, but not on various centromere and kinetochore proteins taking advantage of an inducible CRISPR-depletion system that the lab had previously developed. Interestingly, they find that CENP-C, a major component of the centromere and previously characterised as an RNA-binding protein, negatively regulates alpha-satellite transcript levels. Another regulator for transcript levels appears to be centromere-nucleolus interactions (as also indicated in the title) acting to suppress expression of these non-coding RNAs.

This is overall a really interesting study and indeed, transcription at the centromere is little understood at this point. Given the importance of the centromere the findings in this manuscript will be of high interest to both researchers in the field and a general audience. There are novel and interesting insights into centromeric transcripts but the study still requires some controls.

We appreciate this reviewer’s kind words and their clear description of our work.

1) The authors state that the majority of smFISH foci do not colocalise with centromeres in a combined IF/FISH experiment (some quantification and a % of that subpopulation should be given somewhere). This is a bit concerning but of course could also be true. It either means that alpha-satellite transcripts leave the centromere as suggested by the authors (although some should be visible at the centromeres during the act of transcription). Alternatively, a trivial explanation would be that there is a lot of unspecific staining, which can occur in FISH-experiments to varying degrees. The RNase treatment to control for the absence of potential DNA hybridization is convincing, but the FISH probe could also interact with non-centromeric cellular RNA. With the centromere localisation as a reference point gone, some control is needed to validate that the RNA-FISH signals are indeed recognising alpha-satellite RNA that emerged from centromeres. The authors could try competition experiments titrating unlabelled specific or unspecific DNA probes alongside their labelled specific FISH probe into their FISH experiment to see if they lose or maintain the signal and the number of foci. The specific RNA FISH probes could also be used in DNA FISH, to demonstrate they are working and recognising specific centromeres.

For understanding this behavior, we believe that an important feature of alpha-satellite transcripts is that they are relatively stable (protected from nucleases within the nucleus), but that their overall number is low, consistent with transcription of other non-coding regions across the genome. Thus, if a transcript were produced at centromeres, but subsequently diffuses away, only a small subset would be detectable at centromeres. In addition to our validation these probes using RNAse, we would like to highlight that we have analyzed multiple distinct sequences that recognize different subsets of alpha-satellite repeats. In each case, the observed behaviors are very similar. In addition, the nature of the oligo FISH method requires multiple individual probes to anneal to the same transcript such that a signal is only detected if a sufficient number of oligos bind to the same transcript. This makes nonspecific binding unlikely to contribute to a false signal. Finally, a subset of the perturbations that we tested that are relevant to centromere function (including the CENP-C inducible knockout) clearly affect the levels of these transcripts, supporting a centromere origin. The additional control experiments suggested by the reviewer could be useful, but are technically complex with their own caveats in interpretation and we do not feel that they would add substantially to the existing paper. Instead, as discussed in response to Reviewer #1, point #1, we plan to validate key results described in the paper using qRT-PCR (if possible based on current experimental constraints in the lab associated with COVD-19).

As described above in response to Reviewer #1, point #2, we also believe that some differences with prior work suggesting that alpha-satellite transcripts localize to centromeres may be due to stringent extraction conditions that eliminated non-centromere bound transcripts, while at the same time reflecting our inability to detect nascent transcripts. Quantifying “colocalization” within the nucleus is limited by the resolution in light microscopy, and we would prefer to use caution in defining which transcripts in our smFISH analysis overlap with centromeres. However, we believe that our work clearly highlights the fact that a general feature of mature alpha-satellite transcripts is that they localize throughout the nucleoplasm and are not strongly associated with mitotic chromosomes.

2) Apart from Figure 4, there is no analysis shown for statistical significance. This should be done for most if not all quantifications. Are indeed ASAT and antisense RNA Foci number not significantly different? The authors say that the levels of alpha-sat RNA in Rpe1 cells are not substantially different from other cell lines, but is it also not significant (Fig 1F)? In Figure 2D it is concluded that transcripts foci number are increased in S/G2 (from G1) and remain stable in mitosis, but it looks like there is an increase in mitosis. Again, it looks like the higher number of smFISH foci/Cell is significantly higher for both ASAT and SF1, so some statistical analysis would be required here.

For this paper, we quantified hundreds of cells for each condition, measuring the number of foci/cell in each case. Because of these large n’s, even relatively small differences between samples become statistically significant when tested using standard statistical comparisons (unpaired T test and one-way ANOVA test amongst others). For our experiments, every sample condition included an analysis of control cells, allowing us to compare the control condition to any perturbations on the same day. However, there is some variability between these different replicates, with the average number of ASAT smFISH foci/cell in HeLa cells ranging from 3.4 to 5.6. When compared relative to each other, a subset of these control samples will appear to be statistically different from each other despite the fact that this is not a substantial difference between replicates. Similarly, the majority of the tested inducible knockout cell lines are statistically different from control cells, even when the differences are relatively minor. Therefore, we have tried to use caution when applying the double-edged sword of statistics to these analyses. Instead, we have tried to consider differences with a “substantial magnitude” instead of “statistically significant” differences that may make modest, but statistically significant differences seem artificially more important. We believe that the graphs in which every data point is represented, together with listing the average number of foci/cell in each condition allow the reader to evaluate this data for themselves. Many of the trends that this reviewer highlights are indeed interesting comparisons to consider for future work.

3) Starting with the description of Figure 1E in the main text the paper equates foci count of smFISH per cell with RNA transcript levels. I'm not convinced that these are necessarily the same. You could have many weak foci or few very bright with the same amount of overall transcripts in both. The authors start out introducing smFISH as highly sensitive "for accurate characterisation of number ...of RNA transcripts". This suggests that foci intensity could be used as a read-out for transcript levels. It should be possible to measure individual intensity of the foci for a subset of images. Do foci intensity correlate or anti-correlate with foci numbers? Is the sum of the intensities of all the foci less variable than the foci number for an individual cell type?

Due to the repetitive nature of alpha-satellite sequences, an increased intensity of a smFISH foci could reflect either the close proximity of multiple separable transcripts, or a longer transcript with multiple binding sites for the smFISH probes. Because of this, throughout the paper, we have referred to these as “foci” instead of stating a specific transcript number. As part of the automated computational analysis of the smFISH images, we additional analyzed foci intensity. In general, these values were similar across a cell population and between various perturbations with the key results and findings consistent whether we measured foci number or overall foci intensity per cell. However, foci intensity can vary slightly across a coverslip (technical constraints, not biological differences), and thus we have focused on foci number as a more consistent metric that correlates with the production of alpha-satellite transcripts.

4) I really like the use of the inducible CRISPR system to remove various centromere factors. However, some validation would be required to show that the system is effective in removing the proteins of interest in these experiments. For instance it would be helpful to show in Figure 3D an additional panel with CENP-C staining. Also for a subset of factors, some antibody staining co-staining with the smFISH could be provided in the supplemental material.

We appreciate this point. However, we feel that the existing experiments appropriately consider the nature of the knockout. First, we primarily used Cas9-based inducible knockouts that were generated previously in the lab (McKinley et al. 2015 and McKinley et al. 2017). As these knockouts have been described previously and extensively validated with respect to phenotype (in every case; see http://cellcycleknockouts.wi.mit.edu for example) and antibody staining (in selected cases), we have not repeated this here for the diverse cell cycle knockouts used. In general, we find these knockouts are both penetrant and severe in their phenotypes. Given the broad number of knockouts that we tested, this is not feasible in every case. We also intended this analysis as a type of “screen” where we could validate any “hits” that were observed, and will use caution in our wording not to imply that a negative result is decisive.

The important exceptions to this are CENP-C (which we analyzed more closely) and Ki67 (for which both the inducible and stable knockouts were generated for this paper). For Ki67, the antibody staining is shown and we believe that this is clear. For CENP-C, the antibody that we have is unfortunately not compatible with the conditions that we are using for the smFISH, so it is not feasible to co-stain these cells as suggested. For the smFISH analysis in the inducible CENP-C knockout, we analyzed every single cell, including some cells that are likely to have intact CENP-C levels. Thus, if anything, the potent increase in smFISH foci underrepresents the dramatic effect of CENPC depletion. Based on our prior work (McKinley et al. 2015) we found that the CENP-C knockout results in a pervasive “bag of grapes” phenotype in which chromosomes mis-segregate during mitosis and are packaged into separable interphase nuclei. For the analysis of the nucleoli, we selected cells that displayed this clear phenotype (as shown in the figures).

5) Since none of the CRISPR iKO has a particular inhibiting phenotype it would be useful to include some positive control in the CRISPR experiment. Would it be possible to use a CRISPR iKO target that affect some factor of the transcription machinery (RNA Pol II or similar) to reduce transcript levels?

Generating additional Cas9 iKO cell lines is feasible, but would be time consuming. In this case, we are not convinced of the value of generating and validating these additional cell lines (particularly with the additional current constraints due to COVID-19). For evaluating the role of the RNA polymerases, we believe that the effect of the drug treatment is clear. For creating a positive control to assess whether the CRISPR iKO strategy is a feasible way to conduct these experiments, we would like to highlight the CENP-C iKO cell line, which has a potent effect in this assay.

6) The authors find a negative correlation between the nucleolus-centromere association and the number of alpha sat foci. This is really interesting and they suggest that the nucleolus association could negatively regulate centromere transcription. However, this correlation is rather indirect in the sense that cells with a higher-degree of nucleolus-centromere localisation have fewer smFISH foci and the inverse, disruption of the nucleolus increases smFISH foci number as a whole. A model based on physical association would suggest that a nucleolus associated centromere produces less or no transcripts. Given that this is not a population-based assay, it should be possible to address this directly by analysing the location of individual centromeres and corresponding transcripts to strengthen the hypothesis. This could be done by either analysing the smaller subset of centromere-associated foci that colocalise with the smFISH signal and test whether the majority of these signals are proximal or distal to the nucleolus (this would not work or be less meaningful if the subpopulation is very small). Or doing a combined DNA/RNA FISH experiment. The expectation would be that DNA FISH signals of centromeres close to the nucleolus would not produce an RNA FISH signal somewhere else, and vice versa.

We predict that centromere-nucleolar associations are dynamic. Thus, we anticipate that centromeres would be associated transiently with the nucleolus (perhaps for a few hours), and that a given centromere would not be associated with the nucleolus in every cell at a specific time point. Thus, we believe that analyzing these behaviors across a diverse range of cells, as we did for this paper, is appropriate. In addition, technical considerations make these suggested experiments prohibitive. Defining the relationship between a centromere RNA and its originating centromere would require combined DNA and RNA FISH. The repetitive nature of alpha-satellite repeats and the strong similarity of these sequences between chromosomes makes it highly complex to visualize an individual centromere. Even if we were able to do this, the conditions required to simultaneously detect nucleoli (immunofluorescence), RNA (smFISH), and DNA (requires denaturation and hybridization) make this such that it would be complex to correlate the localization of an individual centromere with the levels of the corresponding alpha-satellite transcripts. In addition, these RNAs are likely to persist for an extended duration (possibly throughout the course of an entire cell cycle), such that they would not necessarily correlate with the current localization behavior of the centromere from which they are derived. For future work (beyond the scope of this paper), we plan to create cell lines expressing both centromere (CENP-A) and nucleolar markers (for example, Ki67) to conduct time lapse imaging to assess the dynamic associations between these structures.

7) At the end of the abstract, the authors conclude that the control of centromere transcription might be regulated by the centromere-nucleolar contacts to modulate chromatin dynamics. What does that really mean? One possibility they give in the discussion is rejuvenating centromeric chromatin. It would be nice if they could show some effect along those lines at the centromere in one of the manipulations they did (either through inhibiting or increase transcription). At least as discussed in the paper (Supp. Fig 3 D) it appears that overall levels of CENP-A are not affected. Is this different for newly loaded CENP-A? Or some other aspect of chromatin dynamics that is modulated? I realise that this might have been difficult to detect and therefore missing in the current study.

In a separate study from our lab as part of our recent work (Swartz et al. 2018), we found that CENP-A is gradually incorporated at centromeres in non-dividing quiescent cells, including non-transformed human Rpe1 cells and starfish oocytes. In the case of oocytes, which contain a substantial pool of mRNAs such that they do not require ongoing transcription for viability, we found that inhibiting RNA Polymerase II and preventing ongoing transcription blocked the incorporation of newly synthesized histones, including both canonical histone H3 and CENP-A. We realize that our description of this prior work was not sufficient to understand our integrated model, which relies on information from both papers. For the revised paper, we will update our discussion to better describe this data and present our model.

• Page 8: The authors state that as cells entered mitosis, dissociation of smFISH foci from chromatin was observed. While the absence of co-localisation of DAPI and smFISH signals is obvious in mitotic cells, what evidence is there that smFISH foci are chromatin associated in interphase nuclei? Rephrasing this bit might avoid confusion here. *

We appreciate this point. We did not mean to imply that the smFISH foci are bound to (or associate with) chromatin in the interphase nucleus. We will reword this as suggested.

Reviewer #3:

The manuscript of Bury et al. addresses how alpha-satellite transcription around centromeres is regulated. Using smFISH to detect alpha-satellite RNA transcripts, the authors find that alpha satellites are transcribed by RNA pol II, but their transcription is independent of centromeric proteins. In addition, they present evidence that nucleolar association represses alpha-satellite transcription. The data is convincing, solid and generally supports the conclusions. The manuscript includes appropriate control experiments, such as test for the validity of the RNA FISH probes. The manuscript is well-written and easy to follow, also for someone who is not directly an expert in the field.

The authors use a single-cell technique (smFISH) to look at the localization and transcription of alpha-satellite transcription from centromeres. The technical advance of this paper is limited, as smFISH is a well-established technique by now. Nevertheless, applying this single-cell approach to these repetitive regions has resulted in new insights regarding the regulation of alpha-satellite transcription, especially their localization of centromeres to nucleoli. Regarding the significance of these insights in the context of centromere biology/regulation and its literature is hard to evaluate for me, because this is not my field of expertise (my background is in single-cell transcription regulation). As a researcher from a related research field, I think the findings of this manuscript are mostly relevant for the direct research community of centromere and alpha-satellite biology, but not for researchers outside the field.

We appreciate these comments regarding the carefully controlled nature of our paper and the value of the advances for understanding alpha-satellite transcription. We also agree that smFISH is an established technique, although it has not been applied to these repetitive alpha-satellite sequences in prior work, allowing us to make important new observations usng the studies in this paper.

1. • The description of the inducible knock out cell lines is very limited. My main concern is how is checked that the gene is actually knocked out. I went back to the referenced paper, but it is still is not clear to me whether the new knockouts are sufficiently checked. It would be more convincing if the authors could show western blots or other evidence that their knockouts are working. In any case, the description of the knockout generation should be more elaborate. * This important point was also noted by the other reviewers. Please see our responses to Reviewer #1 point 4 and Reviewer #2 point 4. As described above, for a revised paper, we will provide an improved description of these knockout cell lines, our validation of these tools, and how we conducted the experiments in this paper.

• The authors nicely show that there is an inverse correlation between nucleolar association of the centromere and alpha-satellite transcription. The data supports this claim, but given the many knockouts and cell lines that were tested, with many intermediate phenotypes (such as CENP-B), I find the correlation based on 4 points a bit sparse. I would recommend filling up figure 4C with a few more mutants, to show that the inverse correlation holds for all mutants. These experiments would be straightforward for the authors, as the knockout/cell lines and techniques are already available. *

We see a compelling general correlation between the fraction of nucleolar-localized centromeres and alpha-satellite transcript levels. Our goal for Figure 4C was to highlight this correlation for a selected subset of conditions. However, we do not believe that there will be a precise linear correlation between transcript levels and nucleolar centromeres under every condition. Indeed, it is quite possible that some perturbations would affect transcript levels without altering nucleolar associations. This is particularly true for perturbations that cause subtle phenotypes. Systematically analyzing centromere-nucleolar co-localization for each of the knockouts represents a substantial undertaking that we do not feel would contribute substantially to this existing paper.

• The nucleolar repression is also supported by the Fibrillarin and Ki67 knockout. These are nice experiments which support their findings. What I am missing is whether these data quantitatively agree with the inverse correlation. Are these mutants completely lacking nucleoli, and if so, would you not expect both mutants to show the same upregulation? Similar to my point above, where do these mutants fall in the graph of figure 4C? *

For the perturbations described in this paper, we believe that inhibiting RNA Polymerase I most closely approximates the condition where nucleolar function is eliminated. Although Ki67 is a nucleolar protein in interphase, loss of Ki67 does not cause lethality indicating that nucleolar function is largely intact. We agree that it would be a good experiment to assess nucleolar-centromere associations in the Ki67 knockout. In fact, we have tried these experiments several times. However, due to the absence of Ki67 (for which we have the best localization tools), we instead needed to use Fibrillarin to monitor nucleoli. We have found this antibody to be much more finicky and not as readily compatible with the fixation conditions needed to detect centromeres. Thus far, we have not been able to generate clear data for this behavior.

• Related to this, since their imaging techniques have single-cell resolution, I wonder if cells that contain many centromeres in the nucleolus have less alpha satellite transcripts than cell with few centromeres. *

The correlation between centromere-nucleolar associations and alpha-satellite transcript numbers is strongly supported by our data across a population. However, analyzing this in individual cells is additionally complicated by the fact that we found that transcript levels vary over the cell cycle (low in G1, higher in S/G2). In addition, monitoring each of these markers in individual cells is technically complicated. Thus, while we appreciate this suggestion, we believe that our data stands on its own.

• One claim that is a bit speculative is the suggestion that transcription itself and not the RNA may be required for the function of the alpha-satellites. This is indeed supported by the fact that most transcripts are not localized at the centromeres. However, this contrasts to the findings of the papers that increasing alpha-satellite transcription in different mutants does not appear to result in any phenotype on centromere function. For a non-expert, the function of these transcripts/transcription itself is not clear from the current manuscript, so I would recommend discussing the nuances of its functions in more detail in the discussion. *

We agree that our model is speculative, but have chosen to include this to provide our perspective on the possible roles for centromere transcription based on this paper and our other recent work (Swartz et al. 2018). We believe that our data provide a context and set of constraints for potential roles of centromere transcription, but also agree that future work is needed to resolve these. Based on this comment and those from the other reviewers, we will also provide a better description of the data in the Swartz et al. paper, which analyzed different features of centromere transcription.

• To quantify the smFISH data, the authors count the number of foci. From the images, it looks like the different foci have very different intensities. This may occur if the transcripts are different length when transcribed from different genomic regions. However, this may also occur if several RNA co-localize to the same spot, i.e. if one spot contains several RNAs. Can the authors verify that the distribution of spot intensities matches the expected intensities based on the different transcribed alpha-satellite regions? *

Please see our response to Reviewer #2, point #3.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript of Bury et al. addresses how alpha-satellite transcription around centromeres is regulated. Using smFISH to detect alpha-satellite RNA transcripts, the authors find that alpha satellites are transcribed by RNA pol II, but their transcription is independent of centromeric proteins. In addition, they present evidence that nucleolar association represses alpha-satellite transcription.

The data is convincing, solid and generally supports the conclusions. The manuscript includes appropriate control experiments, such as test for the validity of the RNA FISH probes. The manuscript is well-written and easy to follow, also for someone who is not directly an expert in the field.

Major comments:

The description of the inducible knock out cell lines is very limited. My main concern is how is checked that the gene is actually knocked out. I went back to the referenced paper, but it is still is not clear to me whether the new knockouts are sufficiently checked. It would be more convincing if the authors could show western blots or other evidence that their knockouts are working. In any case, the description of the knockout generation should be more elaborate.

The authors nicely show that there is an inverse correlation between nucleolar association of the centromere and alpha-satellite transcription. The data supports this claim, but given the many knockouts and cell lines that were tested, with many intermediate phenotypes (such as CENP-B), I find the correlation based on 4 points a bit sparse. I would recommend filling up figure 4C with a few more mutants, to show that the inverse correlation holds for all mutants. These experiments would be straightforward for the authors, as the knockout/cell lines and techniques are already available.

The nucleolar repression is also supported by the Fibrillarin and Ki67 knockout. These are nice experiments which support their findings. What I am missing is whether these data quantitatively agree with the inverse correlation. Are these mutants completely lacking nucleoli, and if so, would you not expect both mutants to show the same upregulation? Similar to my point above, where do these mutants fall in the graph of figure 4C?

Related to this, since their imaging techniques have single-cell resolution, I wonder if cells that contain many centromeres in the nucleolus have less alpha satellite transcripts than cell with few centromeres.

Minor comments

One claim that is a bit speculative is the suggestion that transcription itself and not the RNA may be required for the function of the alpha-satellites. This is indeed supported by the fact that most transcripts are not localized at the centromeres. However, this contrasts to the findings of the papers that increasing alpha-satellite transcription in different mutants does not appear to result in any phenotype on centromere function. For a non-expert, the function of these transcripts/transcription itself is not clear from the current manuscript, so I would recommend discussing the nuances of its functions in more detail in the discussion.

To quantify the smFISH data, the authors count the number of foci. From the images, it looks like the different foci have very different intensities. This may occur if the transcripts are different length when transcribed from different genomic regions. However, this may also occur if several RNA co-localize to the same spot, i.e. if one spot contains several RNAs. Can the authors verify that the distribution of spot intensities matches the expected intensities based on the different transcribed alpha-satellite regions?

Significance

The authors use a single-cell technique (smFISH) to look at the localization and transcription of alpha-satellite transcription from centromeres. The technical advance of this paper is limited, as smFISH is a well-established technique by now. Nevertheless, applying this single-cell approach to these repetitive regions has resulted in new insights regarding the regulation of alpha-satellite transcription, especially their localization of centromeres to nucleoli. Regarding the significance of these insights in the context of centromere biology/regulation and its literature is hard to evaluate for me, because this is not my field of expertise (my background is in single-cell transcription regulation). As a researcher from a related research field, I think the findings of this manuscript are mostly relevant for the direct research community of centromere and alpha-satellite biology, but not for researchers outside the field.

REFEREES CROSS COMMENTING

I agree with all the points raised. There is indeed a lot of overlap. The experiments should not take long in normal circumstances, but given the current situation, some extra time may indeed be required.

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Referee #2

Evidence, reproducibility and clarity

The study by Bury et al. investigates the formation of two different types of alpha-satellite transcripts (ASAT, SF1 and 3) in different human cell lines. Using smFISH they find that during the cell cycle these centromeric transcripts don't stay at the centromere and are found in the cytoplasm after mitosis. Using specific inhibitors, they find that transcription is dependent on RNAPII, but not on various centromere and kinetochore proteins taking advantage of an inducible CRISPR-depletion system that the lab had previously developed. Interestingly, they find that CENP-C, a major component of the centromere and previously characterised as an RNA-binding protein, negatively regulates alpha-satellite transcript levels. Another regulator for transcript levels appears to be centromere-nucleolus interactions (as also indicated in the title) acting to suppress expression of these non-coding RNAs.

Major points

1) The authors state that the majority of smFISH foci do not colocalise with centromeres in a combined IF/FISH experiment (some quantification and a % of that subpopulation should be given somewhere). This is a bit concerning but of course could also be true. It either means that alpha-satellite transcripts leave the centromere as suggested by the authors (although some should be visible at the centromeres during the act of transcription). Alternatively, a trivial explanation would be that there is a lot of unspecific staining, which can occur in FISH-experiments to varying degrees. The RNase treatment to control for the absence of potential DNA hybridization is convincing, but the FISH probe could also interact with non-centromeric cellular RNA. With the centromere localisation as a reference point gone, some control is needed to validate that the RNA-FISH signals are indeed recognising alpha-satellite RNA that emerged from centromeres.¬ The authors could try competition experiments titrating unlabelled specific or unspecific DNA probes alongside their labelled specific FISH probe into their FISH experiment to see if they lose or maintain the signal and the number of foci. The specific RNA FISH probes could also be used in DNA FISH, to demonstrate they are working and recognising specific centromeres.

2) Apart from Figure 4, there is no analysis shown for statistical significance. This should be done for most if not all quantifications. Are indeed ASAT and antisense RNA Foci number not significantly different? The authors say that the levels of alpha-sat RNA in Rpe1 cells are not substantially different from other cell lines, but is it also not significant (Fig 1F)? In Figure 2D it is concluded that transcripts foci number are increased in S/G2 (from G1) and remain stable in mitosis, but it looks like there is an increase in mitosis. Again, it looks like the higher number of smFISH foci/Cell is significantly higher for both ASAT and SF1, so some statistical analysis would be required here.

3) Starting with the description of Figure 1E in the main text the paper equates foci count of smFISH per cell with RNA transcript levels. I'm not convinced that these are necessarily the same. You could have many weak foci or few very bright with the same amount of overall transcripts in both. The authors start out introducing smFISH as highly sensitive "for accurate characterisation of number ...of RNA transcripts". This suggests that foci intensity could be used as a read-out for transcript levels. It should be possible to measure individual intensity of the foci for a subset of images. Do foci intensity correlate or anti-correlate with foci numbers? Is the sum of the intensities of all the foci less variable than the foci number for an individual cell type?

4) I really like the use of the inducible CRISPR system to remove various centromere factors. However, some validation would be required to show that the system is effective in removing the proteins of interest in these experiments. For instance it would be helpful to show in Figure 3D an additional panel with CENP-C staining. Also for a subset of factors, some antibody staining co-staining with the smFISH could be provided in the supplemental material.

5) Since none of the CRISPR iKO has a particular inhibiting phenotype it would be useful to include some positive control in the CRISPR experiment. Would it be possible to use a CRISPR iKO target that affect some factor of the transcription machinery (RNA Pol II or similar) to reduce transcript levels?

6) The authors find a negative correlation between the nucleolus-centromere association and the number of alpha sat foci. This is really interesting and they suggest that the nucleolus association could negatively regulate centromere transcription. However, this correlation is rather indirect in the sense that cells with a higher-degree of nucleolus-centromere localisation have fewer smFISH foci and the inverse, disruption of the nucleolus increases smFISH foci number as a whole. A model based on physical association would suggest that a nucleolus associated centromere produces less or no transcripts. Given that this is not a population-based assay, it should be possible to address this directly by analysing the location of individual centromeres and corresponding transcripts to strengthen the hypothesis. This could be done by either analysing the smaller subset of centromere-associated foci that colocalise with the smFISH signal and test whether the majority of these signals are proximal or distal to the nucleolus (this would not work or be less meaningful if the subpopulation is very small). Or doing a combined DNA/RNA FISH experiment. The expectation would be that DNA FISH signals of centromeres close to the nucleolus would not produce an RNA FISH signal somewhere else, and vice versa.

7) At the end of the abstract, the authors conclude that the control of centromere transcription might be regulated by the centromere-nucleolar contacts to modulate chromatin dynamics. What does that really mean? One possibility they give in the discussion is rejuvenating centromeric chromatin. It would be nice if they could show some effect along those lines at the centromere in one of the manipulations they did (either through inhibiting or increase transcription). At least as discussed in the paper (Supp. Fig 3 D) it appears that overall levels of CENP-A are not affected. Is this different for newly loaded CENP-A? Or some other aspect of chromatin dynamics that is modulated? I realise that this might have been difficult to detect and therefore missing in the current study.

Minor points

Page 8: The authors state that as cells entered mitosis, dissociation of smFISH foci from chromatin was observed. While the absence of co-localisation of DAPI and smFISH signals is obvious in mitotic cells, what evidence is there that smFISH foci are chromatin associated in interphase nuclei? Rephrasing this bit might avoid confusion here.

Significance

This is overall a really interesting study and indeed, transcription at the centromere is little understood at this point. Given the importance of the centromere the findings in this manuscript will be of high interest to both researchers in the field and a general audience. There are novel and interesting insights into centromeric transcripts but the study still requires some controls.

REFEREES CROSS COMMENTING

I agree with the comments of the other reviewers. I appears that there is a lot of overlap between the referees regarding the exciting parts and those that raise concerns. In particular I share the view of Reviewer 1 on the imporantance of validating the FISH probes and the knockouts (also raised by R3) and the concern that a 24h transcription inhibition is prone to secondary effects. I would agree with both that less time might be required to complete revisions (may be 1-3 months) but was factoring in some extra time for wet experiments which likely take longer under the current conditions.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript the authors explore the requirements for centromere transcription using single-molecule FISH. Previous studies have found that centromeres are transcriptionally active in a wide variety of organisms. Centromere transcription has been proposed to facilitate Cenp-A deposition through chromatin remodeling and to directly contribute to centromere/kinetochore function by producing a functional ncRNA. However, we currently know almost nothing about how transcription is initiated at the centromere or how levels of centromere transcripts are controlled. This manuscript makes several major findings that are potentially of importance to groups studying centromere transcription. 1.) Centromere RNAs are produced by RNA Polymerase II and are localized in the nucleus of a wide-range of cell types. 2.) Centromere RNAs do not localize to the centromere, which is in contrast to several recent studies. 3.) Centromere proteins are not required for transcription of alpha-satellite sequences. 4.) Localization of centromeres to the nucleolus represses centromere transcription. Overall, this is a solid manuscript and has the potential to make a significant impact in the field. Below I suggest a couple of experiments and modification to the data presentation that could improve the manuscript.

Major points:

1.All of the experiments in this manuscript rely on detection of centromere RNAs using single molecule FISH probes. These probes are validated by showing the RNase treatment removes the FISH signal. A strength of this approach is that the authors use multiple different probe sets and achieve comparable results. However, there is no orthogonal validation that the probes detect alpha satellite RNA. All of the experiments in this manuscript would be significantly improved by showing that the results presented here can be confirmed by a different approach. I suggest that the authors use Q-RT-PCR to validate the smFISH results.

2.Several results in this manuscript directly contradict results in published studies, but these discrepancies are not discussed. I believe the authors need to discuss the following discrepancies between their results and those in the literature:

a. NcNulty et al. Dev. Cell. 2017. Show that alpha-satellite RNA is transcribed from all centromeres and remains localized to the site of transcription. The different results and possible explanations for the differences should be discussed.

b. Additionally, Rosic et al. JCB 2014, Blower Cell Reports 2016 and Bobkov et al. JCB 2018 all show that centromere RNAs localize to centromere regions. The differences between these studies and the authors results should be discussed.

c. The authors show that satellite RNA cannot be detected on mitotic chromosomes. However, Johnson et al. Elife 2017, Bobkov et al. JCB. 2018, and Perea-Resa et al. Mol. Cell. 2020 show that EU-labeled RNA can be detected at the centromere during mitosis. The authors should discuss the discrepancy between their results and these studies. Is it possible that their smFISH probes do not detect nascent, chromatin-bound transcripts?

d. The authors show nicely that deletion of Ki-67 reduces centromere localization to the nucleolus and increases centromere transcription. However, this has no effect on centromere function. Studies from the Earnshaw lab (e.g. Nakano et al. Dev Cell 2008 and Bergmann et al. EMBO J. 2011) show that increasing or decreasing centromere transcription results in loss of kinetochore function on a human artificial chromosome. The authors should discuss the differences between their results and these studies. Is it possible that the small size of the HAC exaggerates the importance of the correct levels of centromere transcription?

Minor Points

1.The authors treat cells with transcriptional inhibitors for 24 hours. I am concerned that this may result in massive cell death. It would be helpful to include cell viability data from these experiments.

2.In Figure 3C the authors examine the effects of centromere protein knock outs on centromere transcription. To me this is the most important experiment in the manuscript and is a major step forward for the field. The authors use inducible CRISPR knock out cell lines that are not 100% penetrant. It would be helpful if the authors could describe how they ensured that cells included in the image quantification were knock out cells.

3.On p8. The authors cite Quenet and Dalal. eLife 2014 for the idea that transcription during G1 is important for new Cenp-A loading. They should also cite Chen et al. Dev. Cell 2015 and Bobkov et al. JCB. 2018.

Significance

Describes the requirements of transcription of centromere RNAs. Identifies factors that regulate centromere transcription.

Audience: centromere biologists.

REFEREES CROSS COMMENTING

I agree with all the concerns raised by the other reviewers. I think that all three reviews taken together are a fair and constructive review of this manuscript.

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Reply to the reviewers

Reply to the Reviewers

We thank the reviewers for their thoughtful comments and suggestions how to improve our manuscript. Most of the remarks are now addressed in a new version of the manuscript with modifications marked in blue. We found especially interesting the idea to explore the changes in dynamics of microtubules that make up bridging fibers, which we will do in revision. In addition, we will perform Western blot analysis of PRC1 and acquire better images of cells with SiR-DNA for Fig. 2 A.

** Major issues: **

Reviewer #1:

The use of blue light necessary to relocate opto-PRC1 from the spindle to the membrane is a concern, specially given the strongest phenotype associated with acute vs. constitutive inactivation of PRC1. While these differences may indeed reflect distinct cellular adaptation responses to each procedure, the authors must rule out that phototoxicity caused by blue light (e.g. see Douthwright S, Sluder G. Live Cell Imaging: Assessing the Phototoxicity of 488 and 546 nm Light and Methods to Alleviate it. J Cell Physiol. 2017 Sep;232(9):2461-2468. doi: 10.1002/jcp.25588. PubMed PMID:27608139) is not responsible for the observed stronger phenotypes. A control of U2OS cells expressing the centromere marker (without opto-PRC1) in metaphase after exposure to the same blue light regimen (i.e. 200 ms every 10 sec for 20 min and same laser power) should be provided.

Response: We thank the reviewer for raising this important point. We added the suggested experiments on U2OS cells without opto-PRC1 filmed with same blue light regimen, and updated Fig. S2 A-C, E to contain also the new measurements of inter-kinetochore distance (dKC), distance from equatorial plane (dEQ), corresponding time-lapse images, and angle between sister kinetochore axis and spindle long axis (αKC), respectively. We added the following text in Results: “The effects of PRC1 removal were found neither in control experiments without iLID, nor in a different set of control experiments where cells without opto-PRC1 and without iLID were exposed to the same laser illumination protocol (Fig. 2 B,E,F,H; Fig. S2, A-C, E), suggesting that the observed effects were not a consequence of laser photodamage (Douthwright and Sluder, 2017).”

Reviewer #1:

I could not find in the manuscript whether opto-PRC1 is RNAi resistant. I would assume so, as the authors are targeting the 3'-UTR of endogenous PRC1, but at least a western blot should be provided: 1) to properly ascertain depletion efficiency of the endogenous protein; and 2) the levels of opto-PRC1 after depletion.

Response: We added a note in Methods that opto-PRC1 is RNAi-resistant. We have assessed the depletion efficiency of the endogenous PRC1 and the levels of opto-PRC1 after depletion by using immunofluorescence of PRC1 on the spindle (Fig. S1 A and B). Additionally, we will perform Western blot analysis to show depletion efficiency of endogenous PRC1 and the levels of transfected opto-PRC1 after depletion of endogenous PRC1. However, as we observed that the efficiency of opto-PRC1 plasmid transfection is low, Western blot analysis may provide biased levels of PRC1 in the complete population, not specific to the analyzed opto cells.

Reviewer #1:

One aspect related with data interpretation and the proposed model: if PRC1 selectively bundles anti-parallel microtubules, how could it mechanically couple sister k-fibers that are made of parallel MTs? This should be explained in detail, ideally supported by data.

Response: This is an important issue, which we now explain in detail in Discussion: “As midzone-crossing microtubules associate with k-fibers on either side of the metaphase plate (O'Toole et al., 2020), PRC1 and probably also other microtubule-associated proteins crosslink antiparallel overlaps between k-fiber microtubules extending from one pole and bridging microtubules extending from the opposite spindle half, as well as antiparallel overlaps within the bridging fiber.”

Reviewer #1:

The author should find a way to unequivocally demonstrate that opto-PRC1 is fully functional and can rescue depletion of endogenous PRC1. The fact that recovery of PRC1 on spindles never fully rescue spindle architecture and chromosome properties might indicate that opto-PRC1 is not fully functional. For example, can it rescue anaphase or cytokinesis roles of PRC1?

Response: To demonstrate functionality of opto-PRC1, we added images of cell's progression to cytokinesis in both control and opto cells in new Fig. S1D and added the following text to Results: “Importantly, after exposure to the blue light, opto cells were able to progress to cytokinesis (Fig. S1 D)”. Furthermore, as PRC1's major binding partners, Kif4A and MKLP1 (Fig. S4A, and new Fig. S4J, respectively), which depend on its localization in the spindle midzone in anaphase, are found to co-localize with opto-PRC1 in anaphase, opto-PRC1 is fully functional and rescues depletion of endogenous PRC1. We added the following text to Results: “In anaphase, MKLP1 also co-localized with opto-PRC1 in the spindle midzone (Fig. S4 J) (Gruneberg et al., 2006; Kurasawa et al., 2004)”.

** Minor issues: **

Reviewer #1:

Abstract: the authors introduce the problem by stating that chromosome position at the spindle equator is mainly regulated by forces by kMTs. We do not know this, actually there is evidence in the literature that kif4a on chromosome arms is required to maintain chromosomes aligned by exerting forces on ipMTs (e.g. Wandke et al., JCB, 2012). Along the same line, there is evidence from the Dumont lab that sister k-fibers are not mechanically coupled. These alternative views should be discussed and taken into account when formulating the problem under investigation in the present study.

Response: We changed the sentence in Abstract to include polar ejection forces: “During metaphase, chromosome position at the spindle equator is regulated by the forces exerted by kinetochore microtubules and polar ejection forces”. When formulating the problem in Introduction, we discuss polar ejection forces and cite Wandke et al., 2012, and several other papers. We also discuss the findings about PRC1-mediated coupling of sister k-fibers from the Dumont lab in relation to our local effect of PRC1 removal on a fraction of sister kinetochore pairs: “This local effect is in line with weak mechanical coupling between neighboring k-fibers, yet strong coupling between sister k-fibers (Elting et al., 2017; Suresh et al., 2020).” In addition, we mention the Dumont lab results when we suggest that the persistent misorientation of kinetochores after PRC1 return to the spindle is due to perturbed overlap geometry during the absence of PRC1: “This is in agreement with a recent finding that PRC1 restricts pivoting of k-fibers near kinetochores by promoting tight coupling between sister k-fibers (Suresh et al., 2020).”

Reviewer #1:

The authors refer to kinetochore alignment or lagging kinetochores throughout the text. Although this is unquestionable, it might be more appropriate to refer to chromosome alignment or lagging chromosomes instead, as this is the object to me moved.

Response: We agree with the reviewer and changed this at several places throughout the text.

Reviewer #1:

page 2: "...PRC1 regulates forces acting on kinetochores". The authors should mention that this would be indirect, as PRC1 is not at kinetochores itself.

Response: This is true and therefore we added the word indirectly in this sentence.

Reviewer #1:

page 6: "PRC1 removal did not activate the spindle assembly checkpoint". Although this might be considered semantics, given that the SAC is constitutively active and needs to be satisfied, the authors might adopt a more accurate description such as "PRC1 removal did not prevent spindle assembly checkpoint satisfaction".

Response: We changed the sentence into the suggested one.

Reviewer #1:

page 13: the authors mention about the localization of Kif18a on bridging fibers. Was this known? From the images it is unclear if we are looking at bridging fibers or k-fibers. Co-localization with PRC1 would help clarifying this issue. If indeed associated with bridging fibers, this would raise an alternative interpretation of how Kif18a contributes to maintain chromosome alignment.

Response: We thank the reviewer for raising this important point. Kif18A localization in the bridge is a new observation, and to make it clearer we introduced merged images where both Kif18A and PRC1 are shown during optogenetic experiment (Fig. S4E) and four examples of enlarged regions around kinetochores with Kif18A-GFP to show its localization in the bridging fiber in mock treated cells and its lack of localization in the bridging fiber after PRC1 siRNA (Fig. S4F). We also added a discussion of a new potential role of Kif18A (and Kif4A and MKLP1) in chromosome alignment: “Interestingly, we found that Kif4A, MKLP1, and Kif18A localize in the bridging fibers in metaphase and this localization was lost after optogenetic or siRNA-mediated PRC1 removal. During anaphase, the PRC1-dependent Kif4A and MKLP1 in the bridging fibers are involved in sliding of antiparallel microtubules to elongate the spindle (Vukušić et al., 2019). Kif4A and MKLP1 may have a similar role in metaphase, and thus Kif4A removal from the bridging fibers induced by PRC1 removal may affect chromosome alignment by affecting microtubule sliding in the bridging fiber. This possibility is in agreement with previous work showing that Kif4A depletion reduces microtubule flux (Wandke et al., 2012). Similarly, Kif18A in the bridging fiber may have microtubule-sliding and crosslinking activities similar to those of the yeast kinesin-8 (Su et al., 2013), which may promote chromosome alignment. The roles of these and other motors within bridging fibers in chromosome alignment will be an intriguing topic for future studies.”

** Major concerns: **

Reviewer #2:

Regarding lagging chromosomes: Page 6 and Fig. 2F: "Kinetochore remains displaced even after opto-PRC1 return": Why is this? The reasoning in the discussion is not clear/convincing. Is it possible that these irreversible changes reflect light-induced deactivation of protein? Or, could these irreversible changes arise from a perturbation in the structure of microtubules at the end of the 'light' period? Discussion or additional supportive evidence to address this will be helpful.

Response: We thank the reviewer for raising this question. As we have not observed these effects in control cells with opto-PRC1 and without iLID that relocates opto-PRC1 to the membrane, we do not find light-induced deactivation of opto-PRC1 likely. We find the latter possibility more realistic, thus we added the following text to Discussion: “Kinetochore positions and orientations did not revert to the initial values within 10 min of PRC1 return. We speculate that upon PRC1 removal the geometry of the overlap structures is perturbed due to a change in the force balance in the spindle. When PRC1 returns to the perturbed overlaps, it likely confines the chromosomes in new positions and orientations. This is in agreement with a recent finding that PRC1 restricts pivoting of k-fibers near kinetochores by promoting tight coupling between sister k-fibers (Suresh et al., 2020).”

Reviewer #2:

The correlation between misaligned kinetochores and lagging chromosomes is not clear. Are lagging chromosomes more frequently attached to kinetochores that show high deq values (Fig. S2G) in metaphase?

Response: This is an interesting point. To clarify our results, we rewrote the text: “Opto cells that showed lagging kinetochores in anaphase had a slightly smaller inter-kinetochore distance before anaphase than opto cells without lagging kinetochores, but we did not find correlation between lagging kinetochores in anaphase and kinetochore misalignment in metaphase (Fig. S2 H).” Fig. S2 H is the old Fig. S2 G. Please note that we were not able to backtrack individual lagging kinetochores to metaphase to see if they had a higher value of d_eq. Instead, we measured mean d_eq of all kinetochores in opto cells that had a lagging chromosome and in those that did not.

Reviewer #2:

Regarding the contribution of other motors: Looking at the contributions of various other microtubule-associated proteins in accounting for effects of PRC1 removal is a good addition to the paper (Fig. 4). However, the consequences of the depletion of Kif18A and MKLP1 from the bridging fiber are not elaborated upon. Is the presence of these motors at the bridging fiber functionally important? It would be good to incorporate their known activity in the final model for how PRC1-crosslinked fibers align chromosomes. In particular, a recent biorxiv submission from this group has a thorough examination of the consequences of motor removal in anaphase, and perhaps some of their findings and other literature can be used to draw some insights into if and how the presence of these motors on PRC1-crosslinked fibers contribute to chromosome alignment.

Response: We thank the reviewer for this important idea, which we now elaborate in Discussion: “Interestingly, we found that Kif4A, MKLP1, and Kif18A localize in the bridging fibers in metaphase and this localization was lost after optogenetic or siRNA-mediated PRC1 removal. During anaphase, the PRC1-dependent Kif4A and MKLP1 in the bridging fibers are involved in sliding of antiparallel microtubules to elongate the spindle (Vukušić et al., 2019). Kif4A and MKLP1 may have a similar role in metaphase, and thus Kif4A removal from the bridging fibers induced by PRC1 removal may affect chromosome alignment by affecting microtubule sliding in the bridging fiber. This possibility is in agreement with previous work showing that Kif4A depletion reduces microtubule flux (Wandke et al., 2012). Similarly, Kif18A in the bridging fiber may have microtubule-sliding and crosslinking activities similar to those of the yeast kinesin-8 (Su et al., 2013), which may promote chromosome alignment. The roles of these and other motors within bridging fibers in chromosome alignment will be an intriguing topic for future studies.”

Reviewer #2:

Page 13 and Fig. 4A & 4B: "The localization of Kif18A in the bridge was perturbed by both acute and long-term PRC1 removal." However, this is not apparent from Figures 4A and 4B. It would be helpful to clarify how this interpretation was made from the data in the figure.

Response: We thank the reviewer for pointing this out. To clarify this issue, we added merged-channel images of the cell from Fig. 4 A to Fig. S4 E to show colocalization of Kif18A and PRC1. Moreover, we added enlargements from spindles without and with PRC1 depletion in Fig. S4 F to show presence and absence of Kif18A in the bridging fibers, respectively. To clarify how the images were evaluated, we added the following text in Methods: “Localization test of GFP-Kif4A or Kif4A-GFP, MKLP1-GFP, Kif18A-GFP, EGFP-CLASP1, and CENP-E-GFP in the bridging fibers of either opto cells or cells treated with mock siRNA or PRC1 siRNA was performed by visually inspecting the GFP signal through the z-stack, in the region where PRC1-labeled fibers were found, i.e., in the region that spans between sister kinetochores and continues ~2 µm laterally from sister kinetochores.”

Additional suggested experiment and analysis:

Reviewer #2:

One factor that could potentially contribute to the changes in chromosome alignment and increase in lagging chromosomes upon PRC1 removal, is changes in dynamics of microtubules that make up bridging fibers. This may also provide insights on the role of associated proteins. One possible experiment is to look at tubulin turnover in the bundles (example by FRAP). Another alternative possibility is to examine EB3 comets in the presence and absence of PRC1 (note: these are just some potential suggestions; the authors may have other ways of addressing the question). Examining the dynamics would help in addressing if bridging fibers is dynamically remodeled through metaphase and early anaphase and whether the loss of PRC1 causes a change in the dynamics of these microtubules.

Response: We thank the reviewer for this exciting suggestion. We will perform experiments to examine EB3 comets (their numbers and velocities) in the bridging fibers in the presence and absence of PRC1.

Reviewer #2:

Do kinetochores oscillate / fluctuate about the metaphase plate over time? Does the absence of PRC1 affect these fluctuations? Since the authors already have the data (Fig. 2), they can track the trajectories of sister kinetochore displacement from the equatorial plane as a function of time from prometaphase on, both in the presence and absence of PRC1. This analysis will be informative in understanding how kinetochores and bridging fibers act together to maintain force balance in the spindle and how misalignments are corrected.

Response: This is an interesting point. We added the following results: “Kinetochore displacement was not a result of higher oscillation amplitude because kinetochores fluctuated to a similar extent in the presence and absence of opto-PRC1, but in its absence the displaced kinetochores fluctuated within a region that was offset from the equatorial plane (Fig. S2 D).”

** Minor points: ** Reviewer #2:

Is the number of microtubules that make up the bridging fiber the same for outermost and inner kinetochores?

Response: This is an interesting question. Even though we could not measure this here, we suggest that the outermost bridges may have more microtubules: “The misaligned kinetochores were found in the inner part of the spindle, where PRC1 signal disappeared faster than on the outer part, which indicates that the inner bridging fibers were more severely affected by PRC1 removal and/or that they are made up of fewer microtubules than the outer bridges.”

Reviewer #2:

The quantity dax that is plotted in fig. S2F has not been defined in the text.

Response: We now define dAX in the caption of Fig. S2 F: “Graphs show aKC versus corresponding dEQ and the distance from the midpoint between sister kinetochores to the long spindle axis, dAX (left), ...”

Reviewer #2:

Discussion of these findings in the context of recent work from the lab of Sophie Dumont will be interesting (Suresh et al. eLife 2020;9:e53807).

Response: We thank the reviewer for reminding us to discuss this highly relevant recent paper by the Dumont lab. We included a discussion of the findings about PRC1-mediated coupling of sister k-fibers in relation to our local effect of PRC1 removal on a fraction of sister kinetochore pairs: “This local effect is in line with weak mechanical coupling between neighboring k-fibers, yet strong coupling between sister k-fibers (Elting et al., 2017; Suresh et al., 2020).” In addition, we mention these results when we suggest that the persistent misorientation of kinetochores after PRC1 return to the spindle is due to perturbed overlap geometry during the absence of PRC1: “This is in agreement with a recent finding that PRC1 restricts pivoting of k-fibers near kinetochores by promoting tight coupling between sister k-fibers (Suresh et al., 2020).”

Reviewer #3:

Some of the images are sub-optimal. For example Fig 2A, there doesn't seem to be much/any PRC1 on the spindle in the "Dark 0 min" condition, although some is visible after the reversal. Do the authors have a better example to show here? In Figures 1 and 2 we can see the removal clearly yet in later images, the spindle is zoomed such that the relocation cannot be observed.

Response: We agree that PRC1 is not properly visible in Fig. 2 A and we will do new experiments to obtain better images. Regarding the later images in Figs. 3 and 4 that are zoomed, they are displayed in this manner to show the localization of proteins in the bridging fiber and/or at the ends of kinetochore fibers. In these experiments, the removal of PRC1 was the same as in earlier images, which is visible in examples shown in Figs. S3 and S4.

Reviewer #3:

Have the authors looked at whether the cells progress normally after removal and reversal of PRC1? In the paper the authors describe how the knockdown and re-expression of opto-PRC1 does not interfere with mitotic progression, but we wondered whether cells recover after the optogenetic operation, compared to a control with similar illumination.

Response: We found that cells were able to progress to cytokinesis and added an example to Fig. S1 D and the following text to Results: “Importantly, after exposure to the blue light, opto cells were able to progress to cytokinesis (Fig. S1 D).”

Reviewer #3:

Is there a reason why no Dark-state images are shown in Fig 2C and I?

Response: We swapped those images with Dark-state images.

Reviewer #3:

For some of the plots, the y-axis is not shown scaled from 0. This is misleading because it exaggerates differences. Examples are 2E,F,G,H, 3G,J, S3E,G,H, S5C,D,E,F,G.

Response: We agree and we now show graphs with the y-axis starting at 0 for Fig. 2 F,H, Fig. S2 B,E, Fig. 3 G,J, Fig. S3 G, and Fig. S5 C,F,G. However, we did not change the graphs for d_kc, theta, spindle length and width, and widths of bridging and k-fibers, because these values span a rather narrow range, which is far from zero. Because the differences are statistically significant, we chose a scale at which they can be easily visualized.

Reviewer #3:

In the legend, formulae should be written in correct notation.

Response: Corrected: Formulae y=A*exp(-τ*x) and y=A*exp(-τ*x)+c were used for opto-PRC1 removal and return, respectively.

Reviewer #3:

In Fig 2 legend it says that a 0.5-pixel-radius Gaussian blur is applied. Doesn't the kernel for transformation result in an identity matrix?

Response: To clarify this, we replaced “0.5-pixel-radius Gaussian blur” with “0.5-pixel-sigma Gaussian blur” in figure captions and added the following to Methods: “To remove high frequency noise in displayed images a Gaussian blur filter with a 0.5-pixel sigma (radius) was applied where stated”.

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Referee #3

Evidence, reproducibility and clarity

This manuscript by Jagrić et al. shows a role for PRC1 at metaphase by using an optogenetic method to rapidly remove PRC1 from bridging fibres in the mitotic spindle. They show that this methodology (which uses light to relocate PRC1 temporarily on the plasma membrane) is superior to long-term depletion by siRNA and, because it is reversible, has advantages over chemically-induced protein translocation. They put the method to use to examine PRC1's role in bridging fibres the results are consistent with siRNA approaches but cleaner due to the acute nature of the method. Overall the paper is convincing and is likely to be of interest to cell biologists working on mitosis.

We have only minor comments that can be easily addressed during the current crisis. Note that we covered this paper in our lab journal club when it went up on bioRxiv and our comments in that pre-pandemic time were the same as now.

1.Some of the images are sub-optimal. For example Fig 2A, there doesn't seem to be much/any PRC1 on the spindle in the "Dark 0 min" condition, although some is visible after the reversal. Do the authors have a better example to show here? In Figures 1 and 2 we can see the removal clearly yet in later images, the spindle is zoomed such that the relocation cannot be observed.

2.Have the authors looked at whether the cells progress normally after removal and reversal of PRC1? In the paper the authors describe how the knockdown and re-expression of opto-PRC1 does not interfere with mitotic progression, but we wondered whether cells recover after the optogenetic operation, compared to a control with similar illumination.

3 Is there a reason why no Dark-state images are shown in Fig 2C and I?

4.For some of the plots, the y-axis is not shown scaled from 0. This is misleading because it exaggerates differences. Examples are 2E,F,G,H, 3G,J, S3E,G,H, S5C,D,E,F,G

5.In the legend, formulae should be written in correct notation.

6.In Fig 2 legend it says that a 0.5-pixel-radius Gaussian blur is applied. Doesn't the kernel for transformation result in an identity matrix?

Significance

We thought the paper is likely to be of significant interest to cell biologists working on mitosis.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Jagric and colleagues adapt an optogenetic method for acute and reversible removal of spindle associated proteins to the cell membrane. They apply this technique to deplete the microtubule crosslinking protein PRC1 from the metaphase spindle with high temporal accuracy. They establish that the spindle localization of PRC1 can be perturbed in a fast and reversible manner, on a timescale of minutes, using this method.

Next, they use this system to show that acute depletion of PRC1, which has previously been shown to localize to the bridging fibers that link kinetochore pairs. They find that PRC1 depletion modestly disrupts chromosome alignment on the metaphase plate and results in an increased frequency of lagging kinetochores during anaphase. The advantage of the optogenetic system is that they can look at the reversibility and compare the effects of acute depletion to long-time course methods such as RNAi. This comparison is well done and well presented in the paper. The authors further probe the mechanism underlying the defects associated with PRC1 depletion and find a decrease in the number of microtubules that make up a bridging fiber. The localization of other proteins to the kinetochores are not affected by the removal of PRC1, but the localization of Kif18a and MKLP1 to bridging fibers is disrupted. Together, the authors propose a model where the movement of bi-oriented chromosomes is restricted to the region containing PRC1-crosslinked bridging fibers, and this buffering is important in maintaining chromosome alignment.

Overall, the paper is well written, and the schematics, figures and descriptions of experiments are easy to follow. The microscopy experiments and data analysis are carefully performed and thorough, and the representation of data in figures and tables is very clear. The comparison between RNAi and opto-depletion has been well executed and a great addition. The advance in this paper is the establishment of an optogenetics system to selectively and reversibly perturb PRC1. While the method is not novel (Guntas et al., 2015), its development and application to a spindle protein will be of interest to researchers in the field, and I expect this work to be a major resource in that regard. I am less enthusiastic about the biological findings as the effects of PRC1-removal from the bridging fiber are modest. In addition, some effects, such as kinetochore misalignment and decrease in the number of microtubules in the bridging fiber, are not reversible, which raises some concerns about whether these effects are directly mediated by specific protein depletion. I have outlined my specific comments below:

Major concerns:

1 . Regarding lagging chromosomes:

•Page 6 and Fig. 2F: "Kinetochore remains displaced even after opto-PRC1 return": Why is this? The reasoning in the discussion is not clear/convincing. Is it possible that these irreversible changes reflect light-induced deactivation of protein? Or, could these irreversible changes arise from a perturbation in the structure of microtubules at the end of the 'light' period? Discussion or additional supportive evidence to address this will be helpful.

•The correlation between misaligned kinetochores and lagging chromosomes is not clear. Are lagging chromosomes more frequently attached to kinetochores that show high deq values (Fig. S2G) in metaphase?

2 . Regarding the contribution of other motors

•Looking at the contributions of various other microtubule-associated proteins in accounting for effects of PRC1 removal is a good addition to the paper (Fig. 4). However, the consequences of the depletion of Kif18A and MKLP1 from the bridging fiber are not elaborated upon. Is the presence of these motors at the bridging fiber functionally important? It would be good to incorporate their known activity in the final model for how PRC1-crosslinked fibers align chromosomes. In particular, a recent biorxiv submission from this group has a thorough examination of the consequences of motor removal in anaphase, and perhaps some of their findings and other literature can be used to draw some insights into if and how the presence of these motors on PRC1-crosslinked fibers contribute to chromosome alignment.

•Page 13 and Fig. 4A & 4B: "The localization of Kif18A in the bridge was perturbed by both acute and long-term PRC1 removal." However, this is not apparent from Figures 4A and 4B. It would be helpful to clarify how this interpretation was made from the data in the figure.

Additional suggested experiment and analysis:

1 . One factor that could potentially contribute to the changes in chromosome alignment and increase in lagging chromosomes upon PRC1 removal, is changes in dynamics of microtubules that make up bridging fibers. This may also provide insights on the role of associated proteins. One possible experiment is to look at tubulin turnover in the bundles (example by FRAP). Another alternative possibility is to examine EB3 comets in the presence and absence of PRC1 (note: these are just some potential suggestions; the authors may have other ways of addressing the question). Examining the dynamics would help in addressing if bridging fibers is dynamically remodeled through metaphase and early anaphase and whether the loss of PRC1 causes a change in the dynamics of these microtubules.

2 . Do kinetochores oscillate / fluctuate about the metaphase plate over time? Does the absence of PRC1 affect these fluctuations? Since the authors already have the data (Fig. 2), they can track the trajectories of sister kinetochore displacement from the equatorial plane as a function of time from prometaphase on, both in the presence and absence of PRC1. This analysis will be informative in understanding how kinetochores and bridging fibers act together to maintain force balance in the spindle and how misalignments are corrected.

Minor points:

1 . Is the number of microtubules that make up the bridging fiber the same for outermost and inner kinetochores?

2 . The quantity dax that is plotted in fig. S2F has not been defined in the text.

3 . Discussion of these findings in the context of recent work from the lab of Sophie Dumont will be interesting (Suresh et al. eLife 2020;9:e53807).

Significance

The advance in this paper is the establishment of an optogenetics system to selectively and reversibly perturb PRC1. While the method is not novel (Guntas et al., 2015), its development and application to a spindle protein will be of interest to researchers in the field, and I expect this work to be a major resource in that regard. I am less enthusiastic about the biological findings as the effects of PRC1-removal from the bridging fiber are modest.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Jagric et al. investigates the role of PRC1 in the maintenance of chromosome alignment at the spindle equator using acute inactivation by optogenetic control. This is an elegant system based on the iLID system that recruits a protein of interest to the cell membrane in a reversible way. Accordingly, acute removal of PRC1 resulted in reduction of bridging fibers and decreased inter-kinetochore distances, while widening the metaphase plate and increasing the frequency of lagging chromosomes in anaphase. The authors investigate whether acute PRC1 removal from bridging fibers compromise other proteins and conclude that PRC1 acts essentially by coupling bridging and kinetochore fibers. They propose that PRC1 uses this role to buffer kinetochore movements to promote chromosome alignment. Overall, this is a very high quality study that adds to our knowledge about the roles of PRC1 and bridging fibers in spindle mechanics and will be of interest to a specialized readership of mitosis researchers. Nevertheless, there are still few remaining issues, mostly concerning additional controls and interpretations that should be addressed prior to publication.

Major issues:

1- The use of blue light necessary to relocate opto-PRC1 from the spindle to the membrane is a concern, specially given the strongest phenotype associated with acute vs. constitutive inactivation of PRC1. While these differences may indeed reflect distinct cellular adaptation responses to each procedure, the authors must rule out that phototoxicity caused by blue light (e.g. see Douthwright S, Sluder G. Live Cell Imaging: Assessing the Phototoxicity of 488 and 546 nm Light and Methods to Alleviate it. J Cell Physiol. 2017 Sep;232(9):2461-2468. doi: 10.1002/jcp.25588. PubMed PMID:27608139) is not responsible for the observed stronger phenotypes. A control of U2OS cells expressing the centromere marker (without opto-PRC1) in metaphase after exposure to the same blue light regimen (i.e. 200 ms every 10 sec for 20 min and same laser power) should be provided.

2- I could not find in the manuscript whether opto-PRC1 is RNAi resistant. I would assume so, as the authors are targeting the 3'-UTR of endogenous PRC1, but at least a western blot should be provided: 1) to properly ascertain depletion efficiency of the endogenous protein; and 2) the levels of opto-PRC1 after depletion.

3- One aspect related with data interpretation and the proposed model: if PRC1 selectively bundles anti-parallel microtubules, how could it mechanically couple sister k-fibers that are made of parallel MTs? This should be explained in detail, ideally supported by data.

4- The author should find a way to unequivocally demonstrate that opto-PRC1 is fully functional and can rescue depletion of endogenous PRC1. The fact that recovery of PRC1 on spindles never fully rescue spindle architecture and chromosome properties might indicate that opto-PRC1 is not fully functional. For example, can it rescue anaphase or cytokinesis roles of PRC1?

Minor issues:

1- Abstract: the authors introduce the problem by stating that chromosome position at the spindle equator is mainly regulated by forces by kMTs. We do not know this, actually there is evidence in the literature that kif4a on chromosome arms is required to maintain chromosomes aligned by exerting forces on ipMTs (e.g. Wandke et al., JCB, 2012). Along the same line, there is evidence from the Dumont lab that sister k-fibers are not mechanically coupled. These alternative views should be discussed and taken into account when formulating the problem under investigation in the present study.

2- The authors refer to kinetochore alignment or lagging kinetochores throughout the text. Although this is unquestionable, it might be more appropriate to refer to chromosome alignment or lagging chromosomes instead, as this is the object to me moved.

3- page 2: "...PRC1 regulates forces acting on kinetochores". The authors should mention that this would be indirect, as PRC1 is not at kinetochores itself.

4- page 6: "PRC1 removal did not activate the spindle assembly checkpoint". Although this might be considered semantics, given that the SAC is constitutively active and needs to be satisfied, the authors might adopt a more accurate description such as "PRC1 removal did not prevent spindle assembly checkpoint satisfaction".

5- page 13: the authors mention about the localization of Kif18a on bridging fibers. Was this known? From the images it is unclear if we are looking at bridging fibers or k-fibers. Co-localization with PRC1 would help clarifying this issue. If indeed associated with bridging fibers, this would raise an alternative interpretation of how Kif18a contributes to maintain chromosome alignment.

Significance

If additional controls are provided, this manuscript represents a significant technical advance in the study of PRC1 function. The results however are just incremental relative to previous state-of-the-art and will be of interest to more specialized researchers working on mitosis and spindle architecture. The concept of buffer for kinetochore movement is interesting, but how exactly PRC1 contributes to this is not addressed in the present work. Maybe some modeling would help test some ideas.

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Reply to the reviewers

We are grateful for the reviewers’ appreciation and comments. We have tried to address all concerns, and believe that those changes have greatly ameliorated the precision and presentation of our findings. All of our responses are in green in this document, and so are the changes in the manuscript and figure legends.

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

In "An asymmetry in the frequency and position of mitosis in the epiblast precedes gastrulation and suggests a role for mitotic rounding in cell delamination during primitive streak epithelial-mesenchymal transition", Mathiah, Despin-Guitard and colleagues study divisions during mouse gastrulation. They perform ex vivo culture, live imaging and immunostaining to observe the frequency and position of mitosis within the embryo as well as the destiny of daughter cells after their divisions. The find that divisions on the posterior side of the embryo tend to be more basally located and could contribute to cell delamination into the mesodermal layer. Authors also affect antero-posterior signaling by genetically preventing the migration of the anterior visceral endoderm, which leads to mitosis away from the apical side of the epithelium on all lateral parts of the embryo.

This study tackles a key developmental process which is poorly understood in mammals due to its concomitance with the implantation phase. Therefore, any carefully-made description of this process has the capacity to be eye-opening. This is potentially the case for this report, which provides nice images that most likely required skills and important efforts to obtain. The authors have written a clear manuscript with an interesting narrative. However, the quantifications are very poorly described, which makes it impossible for anyone to reproduce these results. I describe below a number of suggestions to clarify the quantifications, which is in my opinion a prerequisite to consider the conclusions from the authors.

Fig1: Authors describe differences in the formation of rosettes between the anterior and posterior sides of the embryo. The microscopy images and movies provided are overlaid with drawings from the authors but without this visual help, I, and I assume other readers, see more rosettes than highlighted and fail to see some of the rosettes that are marked. To avoid this subjectivity, a clear methodology is required. In the methods, the authors state: "For quantification, rosettes were manually annotated and counted on Z sections located 5 μm from the basal side of the epiblast." And that is all. What defines a rosette? How many cells need to share a vertex to be considered as part of a rosette? How long do they need to persist not to be considered as occurring by chance? What about cells part of multiple rosettes? Does the rosette organization need to be apical, basal or all the way? Having those clearly defined criteria would be essential for anyone else to reproduce this quantification and would also offer a much more comprehensive description of the phenomenon and allow for more powerful conclusions.

A rosette is defined as a multicellular transient structure composed of at least 5 cells converging to a central vertex. Practically, a region of interest where cell contours are in focus is determined on the Z-section located 5 mm from the epiblast basal side, which is easily identified as the epithelial architecture changes radically when one enters the visceral endoderm. Only rosettes that are visible throughout the epiblast layer, from the basal to the apical side, are counted. To ensure this, manual segmentation of all cells (for all Z plan acquired, from the basal plane to the apical side) contributing to a rosette was performed for lightsheet imaging. This is illustrated in Video 2. For confocal imaging, segmentation was annotated only at the basal plane, but visual verification that the rosette structure is persistent throughout the layer was performed. One cell could be part of several rosettes, and rosette events were counted even when visible only on one timeframe, but this was consistent for all embryo sides. Due to the time resolution of confocal imaging, rosettes could not be followed overtime. However, the time resolution of lightsheet imaging allowed observing rosettes lifespan and resolution. The protocol for image analysis has been better detailed in the results (lines 162-163) and the methods section of the revised version of the manuscript (lines 452-467, copied hereunder).

"Rosettes: For lightsheet imaging, embryos were dissected at E5.75. Images were acquired for 10 to 12 hours. Quantification focused on the first 20 to 30 frames (around 3 hours) to capture pregastrulation events and reduce the risk of bias from imaging. The rest of the frames showed that the embryo continued growing for several hours. Z-stacks from 4 sides were fused using Zeiss plugin for lightsheet Imaging. Images were then processed using Arivis Vision4D v2.12.3 (Arivis, Germany). Embryo contours were segmented manually on each Z-slice and time point, in order to adjust for embryo rotation manually if necessary. For each side of the embryo, Z stack was cropped to an average of 30 Z slices, from the basal side (5 microns from VE layer, which can be morphologically distinguished due to cell shape and membrane Tomato distribution) to the cavity, marking the apical side. Rosettes were identified and counted on Z sections located 5 µm from the basal side of the epiblast. Practically, vertices were systematically scanned to find those in which 5 cells or more met. Cells contributing to a rosette were then manually segmented on each Z-slice and time point by highlighting cellular membranes using Wacom’s Cintiq 13HD, to create a 3D reconstruction. For confocal imaging, rosettes were identified using the same method, and counted on Z sections located 5 µm from the basal side of the epiblast after visual verification that it was present throughout the Z-stack. For both techniques, presence of associated apical rounding was assessed for each vertex. Cells could contribute to several rosettes."

In addition, the data are given as "rosette/frame" and as "rosette/mm2". What is the point of giving both data, which are essentially the same? The frame is irrelevant. It would be more interesting to know how many cells there are in this area, as cell packing could be a determinant of rosette formation. "Rosette/mm2/min" is very confusing. It should state "rosette.mm-2.min-1" or "rosette/mm2.min".

Following this comment, we indeed chose to get rid of the data expressed as “rosette/frame”. Cells were counted in the area of the epiblast in focus to present data as number of rosettes normalized by the number of cells in the region of interest for both lightsheet and confocal microscopy data (described in results section lines 140 and 164). These measurements led to a similar conclusion, confirming that rosettes are more frequent in posterior epiblast. Difference in cell packing was indeed essential to rule out. We estimated cell packing as the ratio of cell number to surface area, and found it to be similar in posterior, anterior, and lateral sides of embryos at a given stage, which indicates that cell packing is not a determinant for difference in rosette frequency in this context. We discussed packing in the Results section (lines 169-173, copied hereunder).

"The cell number per surface area was similar on all sides, which indicates that the higher number of rosettes was not due to increased cell packing. Rosettes have also been identified in the chick PS (Wagstaff, Bellett, Mogensen, & Münsterberg, 2008), where they were proposed to facilitate ingression during gastrulation."

We modified the legend to use "rosette/mm2.min”.

On a conclusive note, I fail to understand how relevant the formation of rosettes would be. The authors should clarify this point.

Epithelial rosettes have been observed as common intermediates in numerous morphogenesis events. In particular cases, such as Drosophila germ band extension, or zebrafish lateral line development, the mechanisms of formation (planar cell polarity (PCP) and apical constriction, respectively) and resolution have been very well described. In the mouse embryo, anterior visceral endoderm (AVE) migration has been linked to PCP signaling-dependent rosette formation (Trichas 2012). In primitive streak (PS) formation, rosettes with actin-rich centers were described in the chick PS and found to be Nodal dependent (Wagstaff 2008 and Yaganawa 2011). Their mode of formation or resolution is currently unknown. Our observations confirm the findings in chick and highlight the presence of rosettes at an earlier stage, before PS can be identified. Interestingly, rosettes are enriched on the posterior side at the same time when Nodal signaling becomes asymmetric, leading to posterior restriction of basal membrane perforations (Kyprianou 2020). To progress towards understanding rosettes’ significance in the mouse gastrulation context, it would be interesting to study whether the distribution of rosettes is homogenous before anterior-posterior axis specification. Additionally, it would be important to assess whether random epiblast cells delaminate before PS formation, as observed in chick (Voiculescu 2014). We could not attempt those experiments so far, as we perform most experiments by two-photon microscopy, by which only one embryo side can be recorded at a time, and have no way to distinguish embryo orientation before AVE migration. A better understanding of rosette mode of formation and resolution, including the role of Nodal, would also be necessary to assess the importance of our observations. The technical evolution in mouse embryo imaging will probably permit solving those questions in the near future, through prolonged imaging with tracking of every cell fate (McDole 2018). We have tried to improve the discussion (lines 314-326, copied hereunder), and acknowledge the limitations of our findings to a description of a phenomenon without proven significance at this stage.

"However, since we observed a marked imbalance in rosette frequency as soon as the anterior-posterior axis was specified, it is possible that rosettes reflect increased epithelium fluidity in posterior epiblast, which is exposed to a distinct mechanical context, at the very beginning of PS morphogenesis. Indeed, a posterior shift in the distribution of basement membrane perforations was identified just after AVE migration, due to an asymmetry in Nodal signaling dependent metalloproteinase activity (Kyprianou et al., 2020). To progress towards understanding rosette formation significance in this context, it would be interesting to study whether the distribution of rosettes is homogenous before anterior-posterior axis specification, and to assess whether random epiblast cells delaminate before PS formation, as observed in chick (Voiculescu, Bodenstein, Lau, & Stern, 2014). As Nodal plays a major role in PS initiation, the presence and distribution of rosettes should be studied in models in which Nodal signaling can be tuned (Kumar, Lualdi, Lewandoski, & Kuehn, 2008)."

Fig2: I have essentially the same issue for bottle cells and delamination counting as for rosettes. In this case, there is nothing in the methods section.

We have added a paragraph to describe the mosaic analysis in the Methods section (lines 472-488):

"Mosaic: Embryos were recorded in a lateral position. As the proportion of GFP positive cells varied between mosaic embryos, normalisation was performed by dividing by the number of green cells in a given embryo. Anterior and posterior halves were defined by drawing a line perpendicular to the embryonic/extraembryonic boundary and passing through the distal tip. Bottle-shaped cells were identified as having a thin attachment on the apical surface (less than a third of the larger section), and the majority of the cell body located in the basal side. Quantification was performed both on the 3D rendering, and through navigating through the Z-stack. The same criteria where used on all sides of the embryo, and quantification was verified by two independent investigators. Delamination was defined as retraction of the apical process, and displacement of the cell body in the mesoderm layer, which could be identified because of the ubiquitous membrane Tomato labelling. Cell division was characterized by cell rounding followed by the appearance of daughter cells. Cell dispersion after mitosis was defined as absence of basolateral contact between daughter cells, which implies presence of at least one epiblast cell (more often 2 or 3) between daughter cells. Mitosis was considered “non-apical” when happening at least 10 µm away from the apical pole, hence not in the first pseudo-layer of nuclei lining the apical pole."

What defines a cell as bottle shape and not bottle shape (apical vs basal width for example)?

Bottle-shaped cells were visually identified as having a thin attachment on the apical surface (less than a third of the larger section), and the majority of the cell body located in the basal side. Quantification was performed both on the 3D rendering, and by navigating through the Z-stack. Due to the large variation in shape, no systematic measurement was performed. However, the same criteria were used on all sides of the embryo, and quantification was verified by two independent investigators. As proposed by Reviewer 2, those criteria would include scutoids with smaller apical surface, which explains why we observe bottle-shaped cells both on the anterior and posterior sides. In addition to Methods, we included a better description of the methodology in the Results (lines 196-200).

"The quantification of bottle-shaped cells was performed in 3D and through Z-stack navigation and included all cells with an apical section smaller than a third of the basal section. Some cells had a round basal cell body and a thin apical extension while others resembled the recently described scutoids performing apico-basal transitions (Gómez-Gálvez et al., 2018)."

Where does a cell need to be to be counted as delaminated (a distance needs to be stated, absolute (better) or relative)?

Delamination is defined as retraction of the apical process, and displacement of the cell body in the mesoderm layer. Using the ubiquitous membrane tomato marker we could easily distinguish the epiblast, mesoderm and visceral endoderm layers, notably through cell packing, morphology and arrangement. This was described in Results (lines 200-204).

"Asymmetrical cells were present on both sides, but more frequent on the posterior side, and cell delamination (retraction of the apical process and cell body shift in the mesoderm layer) only took place on the posterior side. Cells maintained an apical attachment until their basally located cell body had begun crossing the PS/mesoderm border, and only fully detached after delamination."

What defines sister cells as dispersing after division? How far apart do they have to be? After how much time? From the movies provided, the acquisition time seems to short to assess cell dispersal.

Cell dispersion after mitosis was defined as absence of basolateral contact between daughter cells as they extend towards the basal side, which implies intercalation of at least one epiblast cell (more often 2 or 3) between daughter cells. After cytokinesis was completed, extension and separation of daughter cells was visible in the next time point (after 25 min). The time resolution was thus sufficient to note that daughter cells were not adjacent, which is consistent with other studies (Abe 2018).

We have modified the Methods (copied above) and the Results section of the revised version of the manuscript (lines 213-217).

"Upon elongation of daughter cells to reach the basal pole of the epiblast, the majority displayed no basolateral connection between each other and were instead separated by intercalating epiblast cells, which would be expected to result in daughter cells dispersion over time, as described in (Abe, Kutsuna, Kiyonari, Furuta, & Fujimori, 2018)."

Fig3: Mitotic index calculation is described in the figure legend but not in the methods section. It should also be in the methods section and made explicit that the number of mitotic cells is normalized to green cells only, not the entire cell population. The mitotic index seems higher in this population than in the entire embryo as seen in Fig4.

The mitotic index (MI) was indeed calculated differently so numbers cannot be directly compared. MI identified for anterior and posterior epiblast is not statistically different from the ones found in Figure 4 for E7 embryos. In mosaic embryos, we do not have a way to delimitate the PS. In Figure 4, measurements of MI in the PS (delimitated by the area where the basement membrane is degraded) include cells that are destined to delaminate as wells as those that won't. In the mosaic embryos, MI is measured in cells that delaminate only, and is indeed higher. This represents a small population, which likely explains why it does not reach statistical significance and manifests as a trend.

We have fixed the Methods (see above) and Results (lines 222-228) sections.

"For systematic quantification, epiblast regions were defined as anterior or posterior by tracing a line passing by the distal pole and perpendicular to the embryonic/extraembryonic border, and GFP positive cells undergoing rounding were followed overtime (Fig. 3a-c). Although the frequency of cell division (normalized to the total number of GFP positive cells) was similar in anterior and posterior epiblast, there was a trend towards a higher division rate specifically in cells undergoing delamination to become mesoderm (Fig. 3d)."

What defines an exiting cell**?

An exiting cell is characterized by morphological remodelling, apical retraction, as well as the position of the cell body across the mesoderm/epiblast border visualized by the precise membrane Tomato labelling. It is now described in Methods and in Results (lines 201-204: " cell delamination (retraction of the apical process and cell body shift in the mesoderm layer) only took place on the posterior side. Cells maintained an apical attachment until their basally located cell body had begun crossing the PS/mesoderm border, and only fully detached after delamination".

Regarding the non-apical rounding, why not calling it basal rounding? How far from the apical side does a cell need to be counted as non-apical?

The reason for that denomination is that these so-called “non-apical mitoses” are not strictly basal either. Indeed, mitosis is considered “non-apical” when happening at least 10µm away from the apical pole, meaning that these mitoses do not occur within the first pseudo-layer of nuclei lining the apical pole. This is described in Methods.

In the panel h, with the posterior division outcome, is that for all divisions or only for non-apical divisions?

The panel (Fig. 3g, there was an error in figure labelling in the previous version) has been modified to better precise cell outcomes. It represents all posterior divisions, and quantifies the outcome according to the position of mitosis along the apical-basal axis of the cell. See Results, line 230-232: "Non-apical mitosis in the posterior epiblast was preferentially associated with EMT, as it resulted in formation of one or two mesoderm cells (Fig. 3g)."

Do basal divisions give rise to more epi?

No, non-apical divisions mainly give rise to mesoderm cells. Indeed, approximatively 66% of basal divisions give rise to two mesoderm cells, and 33% to an epiblast and a mesenchymal cell (Figure 3g). We never observed a non-apical division resulting in two epiblast cells.

Is epi or meso fate only determined by location in a different layer or are fate markers used?

Epiblast or mesenchymal fate was determined by both morphological and localization criteria. Epiblast cells have an apical and a basal pole. Mesoderm cells have no apical process, and display initiation of front-rear polarity often defined by the presence of nascent migration appendix. As stated before, membrane Tomato labelling allows exact distinction of germ layers.

What happens to the non-apical mitosis on the anterior side?

On the anterior side, the very few anterior non-apical mitoses only give epiblast cells (not shown).

Fig4: Methods state "For Phospho-histone H3 quantifications, sections were chosen at least 10 μm apart to ensure that each cell was only counted once, and counting was performed using the Icy software" and legend states "The PS region is defined by the area where the basal membrane (yellow) is degraded, and the posterior region quantification excludes counts from the PS region".

What about cells at the boundary between PS and non-PS regions? This needs to be extended and brought together in the methods section. **Also, the tissue architecture in the PS is not as well defined as in the rest of the tissue.

A cell was counted as being part of the PS region if at least 50% of its cell body (visual measuring) was within the area where the basal membrane is non-ambiguously degraded, and if the cell retained its attachment to the apical pole (cell contours were determined by F-actin detection using Phalloidin). The Methods section has been completed in the revised version of the manuscript (lines 490-501).

"Phospho-histone H3: For Phh3 quantifications, sections were chosen at least 10 mm apart to ensure that each cell was only counted once, and counting was performed using the Icy software (http://icy.bioimageanalysis.org). For sagittal sections, anterior and posterior regions were defined by drawing a line perpendicular to the embryonic/extraembryonic boundary and passing through the distal tip. For transverse sections, anterior-posterior boundary was placed at mid-distance between the anterior and posterior poles. The PS region was defined by the area where the basement membrane was degraded, and the posterior region quantification excluded counts from the PS region. A cell was counted as being part of the PS region if at least 50% of its cell body was within the area where the basement membrane was non-ambiguously degraded, and if the cell retained its attachment to the apical pole (cell contours were defined by F-actin detection using Phalloidin)."

Is the epithelial polarity clear enough to be determined without AB marker in the PS?

We considered that a cell retained its AB polarity if the cell extended to both apical and basal pole. Even if the pseudostratified epithelium architecture is complex, most cell contours could be delimited when navigating through the Z-stack.

Finally, the number of cells counted is missing. This has been fixed in the Figure legend.

Supp Fig5: based on available images of the Rac1KO embryo, I am not sure that epithelial architecture is established well enough to assess the location of mitosis along the apico-basal axis.

Indeed, the architecture of the Rac1 KO mutants is vastly altered. As a consequence, only a small number of Rac1 mutants in which we could delimitate the germ layers were analysed, and only the cells we could unambiguously locate were considered. The Rac1 VE-deleted phenotype, on the other hand, was not severe enough as there is only a partial AVE migration defect in most mutants (Migeotte et al., 2010). This is why we confirmed the data on AVE migration defective embryos by using the RhoA VE-deleted mutant, which has a strong AVE migration defect but retains good tissue architecture. We tried to increase figure clarity by annotating the embryo cavity as well as the embryonic/extraembryonic boundary. We also submit a less compressed version of the figures, which we hope will facilitate image analysis.

Reviewer #1 (Significance (Required)):

Although I am not as familiar with mouse gastrulation as I would like to be, I am familiar with gastrulation, live imaging and analysis. At this point, I find it difficult to discuss the conclusions of the study since the methodology is so unclear. Nevertheless, any carefully-made description of mammalian gastrulation has the capacity to be eye-opening. This is potentially the case for this report, which provides nice images that most likely required skills and important efforts to obtain.

We hope the changes we made help better understanding the methodology, and thank Reviewer 1 for positive comments and the help in identifying the points we had failed to properly describe.

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

This manuscript, from Mathiah and colleagues, describes an in-depth analysis of differences in cell organization and division within the epiblast of the very early mouse embryo, and in particular, with the onset of gastrulation. Their data indicate a difference in the organization of cells between the anterior/lateral and posterior regions of the epiblast even before gastrulation has commenced, as well as differences in the location of mitoses relative to the apical and basal ends of the cells. The data provide new insight into the early regionalization of the epiblast. However, the authors should include reference to, and discussion of, the paper by Michael Snow on growth and regionalization of the epiblast (Snow MHL (1977) Gastrulation in the mouse: Growth and regionalization of the epiblast. J. Emb. Exp. Morph. 42: 293-303), where he did a much more fine-grained analysis of mitotic index across the entire epiblast, defining a proliferative zone in the anterior part of the primitive streak where the mitotic index was higher between E6.5 and E7.5. He also describes non-apical mitoses specifically in the primitive streak region as compared to all other regions of the epiblast. The results of the present study dovetail nicely with the results presented by Snow.

This was indeed a major oversight, and we apologize for it. The work of Snow identifies very nicely a proliferative zone in the anterior part of the PS. We did not comment on that as our study focuses on posterior PS. We included the reference in the revised version of the manuscript, and pointed the fact that he first described non-apical mitosis in the PS (lines 233-236).

"Remarkably, this concurs with the observation by Snow (Snow, 1977) that in the PS of E6.5 and E7 embryos, mitosis could be found at all levels of the tissue, including adjacent to the endoderm, while it was located at the apical surface of the pseudostratified tissue everywhere else."

Overall, this is a very nice study, but some revisions would help with clarity at certain points. The data on rosette formation are interesting, but it is not clear what an increase in rosettes in the posterior region means. The authors contend (lines 169-170) that this represents a dynamic epithelium primed for EMT, but it is not clear how rosettes facilitate or promote EMT, and especially why that would be seen at E5.75 before EMT has begun. An alternative interpretation might be that the shape of the epithelium may be changing and the packing of the epithelial cells has to change to accommodate this. We do know that the overall shape of the embryo changes from elongate medial-lateral to elongate anterior-posterior just as EMT is initiated (Perea-Gomez et al., (2004) Current Biology 14: 197-207) and it may be that changes in cell packing are required to accommodate this. The authors may want to consider whether the rosettes that they observed represent scutoids (Gomez-Galvez et al., (2018) Scutoids are a geometrical solution to three-dimensional packing of epithelia. Nat. Commun. 9:2960). An analysis of the 3-dimensional organization of the cells within the rosettes (i.e. at all Z levels) may shed some light on this.

Following on the comments by Reviewer 1 and 2, we quantified cell packing, and found it to be identical on all sides at a given stage. We have added a better description of rosette quantification (lines 169-172 and lines 452-470), a video showing 3D reconstruction of cells in a rosette (Video 2), and an extended discussion (lines 314-326) in the revised version of the manuscript. Some cells within the epiblast are indeed likely to be shaped as scutoids, some with an apical-basal asymmetry (lines 196-202). The reference was added to the manuscript (line 200).

Figure 2b,c and Figure 3a, a', b, c - Addition of dotted lines to indicate the apical and basal ends of the epiblast would be helpful in orienting the reader**.

We have added lines to indicate apical and basal ends of the epiblast.

Figure 2c' - what these graphs represent exactly is somewhat vague, and the figure legend is also very vague. In particular, the third graph on cell dispersion is not clear. Does this mean that the daughter cells are separated from one another following division? Or that they are in different compartments (epiblast/mesoderm) after division? A better description should be included in the figure legend.

Following on the comments by Reviewer 1 and 2, we have added a better description of cell dispersion in the Results (lines 213-217), Methods (lines 483-486) and figure legend.

Figure 3g would appear to show the proportion of the total number of posterior divisions that give rise to particular combinations of daughter cells (epi/epi, epi/meso, meso/meso). However, the discussion of this graph in the text (lines 215-219) suggests that it demonstrates that non-apical mitoses always result in meso/meso and epi/meso daughter cells, which it does not. That analysis would be very interesting to add to Figure 3, with the daughter cell types broken down into those coming from apical mitoses and those coming from basal mitoses.

The analysis was broken down as suggested, and has been added to the revised version of the manuscript (line 352, Figure 3g).

In Figure 4, it is not clear how anterior and posterior are defined, and what criteria were used to distinguish posterior from primitive streak. This is nicely demonstrated in Supplementary Figure 3 - maybe panels A and B could be included in Figure 4 to improve the clarity of the analysi**s.

We have better described the quantification methodology in the Methods section (lines 490-501), moved panel a from Supplementary Figure 3 to Figure 4a as suggested, and added an explanatory drawing (Figure 4b) to the revised version of the manuscript.

The data on mitotic index in Figures 2, 4, and 5 do not appear to be consistent. The mitotic index for E7.25 in Figure 2e is similar between anterior and posterior, even though the posterior includes the primitive streak, while the mitotic index presented for the three stages in Figure 4b would imply that the mitotic index for the entire posterior region should be higher than the anterior at all three stages. Similarly, in Figure 5a' and b', the mitotic index in anterior and posterior regions of E5.75 and E6.25 embryos are not significantly different despite the primitive streak being included in the posterior count, while the data presented in Figure 4 would imply that the entire posterior region including primitive streak should be much higher than the anterior. The authors should clarify this in the Results.

In Figure 2 and 3, the mitotic index (MI) is calculated as number of cell division among GFP+ cells divided by the total number of GFP+ cells, while in Figure 4 and 5 it is quantified as Phospho-histone H3+ cells per total number of cells (DAPI). We have clarified this in the revised graphs and legends of the novel version of the manuscript. Those numbers cannot be directly compared. Nonetheless, we found no statistical difference between the MI shown in Figure 3d, and the MI shown in Figure 4c third row (E7). In sagittal view, the PS area cannot be delimited, so we compared anterior and posterior regions, with the PS included in the posterior region, and saw no difference in MI. In transverse section, there was no MI difference when comparing anterior and posterior embryo halves. However, when we refined the analysis by defining the PS as the area where the basal membrane was degraded, a higher MI emerged specifically in the PS compared to anterior and posterior (not including PS) regions. This difference was thus lost by dilution when the PS area was included in the posterior region. We have also stated this distinction more clearly in the revised version of the manuscript (lines 269-271).

The data on non-apical mitoses in the RhoA-VE deleted (Figure 6) and Rac1ko embryos (Supplemental figure5) are not particularly compelling. It is hard to see the basal mitoses in the new AVE-opposed regions in the mutant embryos in the images presented. Perhaps the graphs in these two figures could have the AVE-opposed data broken down into two groups - the region that is posterior and the region that is anterior but not adjacent to AVE. Better images would improve the clarity of these data as well.

As explained in response to Reviewer 1, we have attempted to clarify the anatomy through annotation, and provide less compressed images. We agree that the embryos are altered. Nonetheless, especially in RhoA-VE deleted, the germ layers could be distinguished and non-apical mitosis identified through combining 3D analysis and navigation through the Z-stack. We honestly admit those are the best images we could get, and we believe that they allow to make the point that non-apical mitosis are only found in the area further away from the AVE.

Reviewer #2 (Significance (Required)):

The data on differential proliferation and apical vs. basal mitoses are complementary to data already published, but the present study updates the existing data by the addition of live imaging and 3-dimensional reconstruction of cell shapes, providing a more complete insight into the process. The observation that rosettes are detectable at the basal ends of the epiblast, and more so in the posterior, is novel, but the significance for embryonic development is not well rationalized.

These data are of interest to those investigating the mechanisms of early morphogenesis, as well as those interested in the cellular correlates of molecular regionalization that results from the well-described signaling pathways regulating axis specification.

My background is in early mouse embryo morphogenesis, therefore I feel that I have sufficient expertise to evaluate the data presented.

We thank Reviewer 2 for positive comments, and are grateful for the constructive criticism and important references.

\*Referees Cross Commenting***

I agree with Reviewer 1 on the lack of detail about the methods - my comments stemmed from the same confusion about how measurements were made, but Reviewer 1 more articulately addressed the key points. I agree with Reviewer 3 on the quality of the videos. It is very difficult to see how they could follow cells with a 20 minute interval.

I would like to address the comment by Reviewer 3 on the use of agarose in the imaging experiments. The methods section states that agarose was used to make the culture "chambers" used for light-sheet imaging, which was not the major approach used for imaging in this study. Only the data in Figure 1A came from those experiments, and it was validated by confocal data in 1B,C where the embryos were cultured in Ibidi chambers with culture medium and no agarose present. So I don't think agarose effects on embryo development are a major worry. Also, this same approach was used by Ryan Udan in Mary Dickinson's lab to visualize yolk sac vasculogenesis, and it did not appear to have a deleterious effect on development in that case, although the embryos imaged here were much earlier and are definitely differentially sensitive to culture conditions from those cultured at E8.

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

This is an essentially descriptive study, looking at primitive streak formation and cell ingression from the epiblast in the mouse from about E5.75 to E7.5 or so using time-lapse microscopy (light sheet and confocal) of cultured embryos. The study also takes advantage of some genetically encoded reporters, some of them inducible by tamoxifen, which allow following cells, closer examination of their shapes, and in some cases unambiguous orientation of the embryos based on expression of the reporter. Overall the study is well designed.

I have two very major concerns about this paper - first, the culture system used in most experiments uses agarose, which has been found in several labs to affect normal cell movements and other cell behaviours. It is essential to determine that embryos cultured in these conditions develop normally for much longer than the period of imaging to ensure that the findings are relevant to normal development rather than an artefact. This is particularly important because mouse embryos develop rather poorly at peri-implantation stages with any culture method, and this one could make matters even worse.

Embryos were mounted into an agarose cylinder in which a tunnel had been created with a 150 microns wide copper wire. Embryos were mounted vertically, with the cone oriented on the bottom, to avoid restriction of growth at distal tip of the embryo. As embryos had a smaller diameter than the tunnel, they could comfortably grow without being restricted (Methods, lines 413-414). Although embryos could not been recovered after the long imaging period (12h), embryos similarly mounted in the agarose cylinder but not imaged were kept in culture, and showed normal growth compared to a free-floating embryo (Methods, lines 431-433). In addition, we focused on the first hours of imaging to reduce the risk of phototoxicity-induced anomalies (Methods, lines 453-455). Moreover, although we identified the asymmetry in rosette abundance through lightsheet imaging, we confirmed the finding through confocal imaging of free-floating embryos, and found similar results (Results lines 153-167, Figure 1b and c, Video 3).

While it has been reported that agarose can affect the development of chick embryos in culture, agarose has been a widely used culture matrix for live imaging particularly for lightsheet imaging in other organisms including drosophila, zebrafish, and mouse. We thank Reviewer 2 (in cross-comments) for highlighting that in Udan et al., (2014), a report from Mary Dickinson’s lab, embryos are cultured in agarose “chambers” for lightsheet. Although some of the experiments in Udan et al., (2014) are performed at E8, this paper also focuses on pre-gastrulation mouse embryos as they culture E6.5 embryos for 24 hours, image from 5 view angles, analyze 572 z-slices representing half of the embryo (Fig5 and Fig6 Udan et al., 2014) and show no adverse effects.

The second concern is that for a paper that is almost entirely about time-lapse microscopy observations of live embryos, the movies are very poor. Although the images are generally good and the 3-d sequences/images from the light-sheet microscope sequences are quite impressive (and have good spatial resolution), the time resolution is extremely poor and the movies very short. It is largely impossible to follow cell behaviours or movements in these sequences.

Indeed, the time lapse between time points as well as the total duration of the acquisition is limited, especially when embryos are imaged by confocal microscopy. These measures were taken mainly to preserve the integrity of the embryo and thus ensure that growth conditions were the closest to optimal in vivo conditions. For rosette analysis, the 20 minutes interval was too long to follow rosette resolution, as stated in the manuscript. For mosaically labelled embryos, we quantified only the cells for which the fate and/or progeny could be identified without ambiguity, which was made easier as we chose a 4OH-tamoxifen posology that resulted in a low proportion of labelling. As both cell delamination and mitosis are relatively slow processes, this time resolution proved sufficient. Time resolution for lightsheet was 7 minutes, which is similar compared to other works on mouse gastrulation (such as Williams et al., 2012), and actually higher than most two-photon or confocal studies, including that of our previous reports (Migeotte et al., 2010, Saykali et al., 2019, Trichas et al., 2012) in which cell tracking could be efficiently performed. This high time resolution allowed following individual rosettes overtime (Sup. Fig.2c).

Reviewer #3 (Significance (Required))

The study focuses on cell shape changes and various processes that accompany ingression and reports that ingression may occur through a variety of different mechanisms that occur at the same time, including rosette formation, individual ingression of bottle-shaped cells, and larger population ingression events. This is very similar to what has been described in chick embryos (eg. Voiculescu et al. eLife 2014 - surprisingly this is not cited), although in rodents primitive streak formation occurs in the absence of large-scale movements of cell sheets. Basically there are no surprises in the findings either for mouse or in comparison with other species, but the study is OK in terms of contributing useful information about streak formation and function in mouse (if the above problems are fixed).

We thank Reviewer 3 for helpful comments and references. We respectfully disagree concerning the risk of bias due to agarose cylinder culture, as exposed above. Concerning the videos, we have provided less compressed videos to retain as much image quality as possible. Although it would evidently be better to have a higher time resolution and longer movies, we believe it is not a limitation for the events we study and describe as they can be reliably followed with the time resolution and observation length we provide. The reference to Voiculescu et al., 2014 is indeed important, we have added it to the revised version of the manuscript (line 324) and apologize for the oversight.

\*Referees Cross Commenting***

In response to reviewer 2: One issue with this is that one does not know whether there is a "deleterious" effect of the agarose on movements until one is sure that (a) one understands what the movements would look like without agarose and that there are no differences, and (b) (a serious shortcoming here) that embryos need to be shown to develop completely normally in those culture conditions WAY beyond the period of imaging. There are lots of observations by several labs (some unpublished of course, but some are published) suggesting that agar and agarose do interfere with cell movements. In chick for example the Chapman and Schoenwolf method where embryos are placed on agarose, there are always head defects due to impaired movements and the agarose interfering with tissue tensile forces.

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Referee #3

Evidence, reproducibility and clarity

This is an essentially descriptive study, looking at primitive streak formation and cell ingression from the epiblast in the mouse from about E5.75 to E7.5 or so using time-lapse microscopy (light sheet and confocal) of cultured embryos. The study also takes advantage of some genetically encoded reporters, some of them inducible by tamoxifen, which allow following cells, closer examination of their shapes, and in some cases unambiguous orientation of the embryos based on expression of the reporter. Overall the study is well designed.

I have two very major concerns about this paper - first, the culture system used in most experiments uses agarose, which has been found in several labs to affect normal cell movements and other cell behaviours. It is essential to determine that embryos cultured in these conditions develop normally for much longer than the period of imaging to ensure that the findings are relevant to normal development rather than an artefact. This is particularly important because mouse embryos develop rather poorly at peri-implantation stages with any culture method, and this one could make matters even worse.

The second concern is that for a paper that is almost entirely about time-lapse microscopy observations of live embryos, the movies are very poor. Although the images are generally good and the 3-d sequences/images from the light-sheet microscope sequences are quite impressive (and have good spatial resolution), the time resolution is extremely poor and the movies very short. It is largely impossible to follow cell behaviours or movements in these sequences.

Significance

The study focuses on cell shape changes and various processes that accompany ingression and reports that ingression may occur through a variety of different mechanisms that occur at the same time, including rosette formation, individual ingression of bottle-shaped cells, and larger population ingression events. This is very similar to what has been described in chick embryos (eg. Voiculescu et al. eLife 2014 - surprisingly this is not cited), although in rodents primitive streak formation occurs in the absence of large-scale movements of cell sheets. Basically there are no surprises in the findings either for mouse or in comparison with other species, but the study is OK in terms of contributing useful information about streak formation and function in mouse (if the above problems are fixed).

Referees Cross Commenting

In response to reviewer 2

One issue with this is that one does not know whether there is a "deleterious" effect of the agarose on movements until one is sure that (a) one understands what the movements would look like without agarose and that there are no differences, and (b) (a serious shortcoming here) that embryos need to be shown to develop completely normally in those culture conditions WAY beyond the period of imaging.

There are lots of observations by several labs (some unpublished of course, but some are published) suggesting that agar and agarose do interfere with cell movements. In chick for example the Chapman and Schoenwolf method where embryos are placed on agarose, there are always head defects due to impaired movements and the agarose interfering with tissue tensile forces.

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Referee #2

Evidence, reproducibility and clarity

This manuscript, from Mathiah and colleagues, describes an in-depth analysis of differences in cell organization and division within the epiblast of the very early mouse embryo, and in particular, with the onset of gastrulation. Their data indicate a difference in the organization of cells between the anterior/lateral and posterior regions of the epiblast even before gastrulation has commenced, as well as differences in the location of mitoses relative to the apical and basal ends of the cells. The data provide new insight into the early regionalization of the epiblast. However, the authors should include reference to, and discussion of, the paper by Michael Snow on growth and regionalization of the epiblast (Snow MHL (1977) Gastrulation in the mouse: Growth and regionalization of the epiblast. J. Emb. Exp. Morph. 42: 293-303), where he did a much more fine-grained analysis of mitotic index across the entire epiblast, defining a proliferative zone in the anterior part of the primitive streak where the mitotic index was higher between E6.5 and E7.5. He also describes non-apical mitoses specifically in the primitive streak region as compared to all other regions of the epiblast. The results of the present study dovetail nicely with the results presented by Snow.

Overall, this is a very nice study, but some revisions would help with clarity at certain points. The data on rosette formation are interesting, but it is not clear what an increase in rosettes in the posterior region means. The authors contend (lines 169-170) that this represents a dynamic epithelium primed for EMT, but it is not clear how rosettes facilitate or promote EMT, and especially why that would be seen at E5.75 before EMT has begun. An alternative interpretation might be that the shape of the epithelium may be changing and the packing of the epithelial cells has to change to accommodate this. We do know that the overall shape of the embryo changes from elongate medial-lateral to elongate anterior-posterior just as EMT is initiated (Perea-Gomez et al., (2004) Current Biology 14: 197-207) and it may be that changes in cell packing are required to accommodate this. The authors may want to consider whether the rosettes that they observed represent scutoids (Gomez-Galvez et al., (2018) Scutoids are a geometrical solution to three-dimensional packing of epithelia. Nat. Commun. 9:2960). An analysis of the 3-dimensional organization of the cells within the rosettes (i.e. at all Z levels) may shed some light on this.

Figure 2b,c and Figure 3a, a', b,c - Addition of dotted lines to indicate the apical and basal ends of the epiblast would be helpful in orienting the reader.

Figure 2c' - what these graphs represent exactly is somewhat vague, and the figure legend is also very vague. In particular, the third graph on cell dispersion is not clear. Does this mean that the daughter cells are separated from one another following division? Or that they are in different compartments (epiblast/mesoderm) after division? A better description should be included in the figure legend.

Figure 3g would appear to show the proportion of the total number of posterior divisions that give rise to particular combinations of daughter cells (epi/epi, epi/meso, meso/meso). However, the discussion of this graph in the text (lines 215-219) suggests that it demonstrates that non-apical mitoses always result in meso/meso and epi/meso daughter cells, which it does not. That analysis would be very interesting to add to Figure 3, with the daughter cell types broken down into those coming from apical mitoses and those coming from basal mitoses.

In Figure 4, it is not clear how anterior and posterior are defined, and what criteria were used to distinguish posterior from primitive streak. This is nicely demonstrated in Supplementary Figure 3 - maybe panels A and B could be included in Figure 4 to improve the clarity of the analysis.

The data on mitotic index in Figures 2, 4, and 5 do not appear to be consistent. The mitotic index for E7.25 in Figure 2e is similar between anterior and posterior, even though the posterior includes the primitive streak, while the mitotic index presented for the three stages in Figure 4b would imply that the mitotic index for the entire posterior region should be higher than the anterior at all three stages. Similarly, in Figure 5a' and b', the mitotic index in anterior and posterior regions of E5.75 and E6.25 embryos are not significantly different despite the primitive streak being included in the posterior count, while the data presented in Figure 4 would imply that the entire posterior region including primitive streak should be much higher than the anterior. The authors should clarify this in the Results.

The data on non-apical mitoses in the RhoA-VEdeleted (Figure 6) and Rac1ko embryos (Supplemental figure5) are not particularly compelling. It is hard to see the basal mitoses in the new AVE-opposed regions in the mutant embryos in the images presented. Perhaps the graphs in these two figures could have the AVE-opposed data broken down into two groups - the region that is posterior and the region that is anterior but not adjacent to AVE. Better images would improve the clarity of these data as well.

Significance

The data on differential proliferation and apical vs. basal mitoses are complementary to data already published, but the present study updates the existing data by the addition of live imaging and 3-dimensional reconstruction of cell shapes, providing a more complete insight into the process. The observation that rosettes are detectable at the basal ends of the epiblast, and moreso in the posterior, is novel, but the significance for embryonic development is not well rationalized.

These data are of interest to those investigating the mechanisms of early morphogenesis, as well as those interested in the cellular correlates of molecular regionalization that results from the well-described signaling pathways regulating axis specification.

My background is in early mouse embryo morphogenesis, therefore I feel that I have sufficient expertise to evaluate the data presented.

Referees Cross Commenting

I agree with Reviewer 1 on the lack of detail about the methods - my comments stemmed from the same confusion about how measurements were made, but Reviewer 1 more articulately addressed the key points.

I agree with Reviewer 3 on the quality of the videos. It is very difficult to see how they could follow cells with a 20 minute interval.

I would like to address the comment by Reviewer 3 on the use of agarose in the imaging experiments. The methods section states that agarose was used to make the culture "chambers" used for light-sheet imaging, which was not the major approach used for imaging in this study. Only the data in Figure 1A came from those experiments, and it was validated by confocal data in 1B,C where the embryos were cultured in Ibidi chambers with culture medium and no agarose present. So I don't think agarose effects on embryo development are a major worry. Also, this same approach was used by Ryan Udan in Mary Dickinson's lab to visualize yolk sac vasculogenesis, and it did not appear to have a deleterious effect on development in that case, although the embryos imaged here were much earlier and are definitely differentially sensitive to culture conditions from those cultured at E8.

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Referee #1

Evidence, reproducibility and clarity

In "An asymmetry in the frequency and position of mitosis in the epiblast precedes gastrulation and suggests a role for mitotic rounding in cell delamination during primitive streak epithelial-mesenchymal transition", Mathiah, Despin-Guitard and colleagues study divisions during mouse gastrulation. They perform ex vivo culture, live imaging and immunostaining to observe the frequency and position of mitosis within the embryo as well as the destiny of daughter cells after their divisions. The find that divisions on the posterior side of the embryo tend to be more basally located and could contribute to cell delamination into the mesodermal layer. Authors also affect antero-posterior signaling by genetically preventing the migration of the anterior visceral endoderm, which leads to mitosis away from the apical side of the epithelium on all lateral parts of the embryo.

This study tackles a key developmental process which is poorly understood in mammals due to its concomitance with the implantation phase. Therefore, any carefully-made description of this process has the capacity to be eye-opening. This is potentially the case for this report, which provides nice images that most likely required skills and important efforts to obtain. The authors have written a clear manuscript with an interesting narrative. However, the quantifications are very poorly described, which makes it impossible for anyone to reproduce these results. I describe below a number of suggestions to clarify the quantifications, which is in my opinion a prerequisite to consider the conclusions from the authors.

Fig1: Authors describe differences in the formation of rosettes between the anterior and posterior sides of the embryo. The microscopy images and movies provided are overlaid with drawings from the authors but without this visual help, I, and I assume other readers, see more rosettes than highlighted and fail to see some of the rosettes that are marked. To avoid this subjectivity, a clear methodology is required. In the methods, the authors state: "For quantification, rosettes were manually annotated and counted on Z sections located 5 μm from the basal side of the epiblast." And that is all. What defines a rosette? How many cells need to share a vertex to be considered as part of a rosette? How long do they need to persist not to be considered as occurring by chance? What about cells part of multiple rosettes? Does the rosette organization need to be apical, basal or all the way? Having those clearly defined criteria would be essential for anyone else to reproduce this quantification and would also offer a much more comprehensive description of the phenomenon and allow for more powerful conclusions. In addition, the data are given as "rosette/frame" and as "rosette/mm2". What is the point of giving both data, which are essentially the same? The frame is irrelevant. It would be more interesting to know how many cells there are in this area, as cell packing could be a determinant of rosette formation. "Rosette/mm2/min" is very confusing. It should state "rosette.mm-2.min-1" or "rosette/mm2.min". On a conclusive note, I fail to understand how relevant the formation of rosettes would be. The authors should clarify this point.

Fig2: I have essentially the same issue for bottle cells and delamination counting as for rosettes. In this case, there is nothing in the methods section. What defines a cell as bottle shape and not bottle shape (apical vs basal width for example)? Where does a cell need to be to be counted as delaminated (a distance needs to be stated, absolute (better) or relative)? What defines sister cells as dispersing after division? How far apart do they have to be? After how much time? From the movies provided, the acquisition time seems to short to assess cell dispersal.

Fig3: Mitotic index calculation is described in the figure legend but not in the methods section. It should also be in the methods section and made explicit that the number of mitotic cells is normalized to green cells only, not the entire cell population. The mitotic index seems higher in this population than in the entire embryo as seen in Fig4. What defines an exiting cell? Regarding the non-apical rounding, why not calling it basal rounding? How far from the apical side does a cell need to be to be counted as non-apical? In the panel h, with the posterior division outcome, is that for all divisions or only for non-apical divisions? Do basal divisions give rise to more epi? Is epi or meso fate only determined by location in a different layer or are fate markers used? What happens to the non-apical mitosis on the anterior side?

Fig4: Methods state "For Phospho-histone H3 quantifications, sections were chosen at least 10 μm apart to ensure that each cell was only counted once, and counting was performed using the Icy software" and legend states "The PS region is defined by the area where the basal membrane (yellow) is degraded, and the posterior region quantification excludes counts from the PS region". This needs to be extended (what about cells at the boundary between PS and non-PS regions?) and brought together in the methods section. Also the tissue architecture in the PS is not as well defined as in the rest of the tissue. Is the epithelial polarity clear enough to be determined without AB marker in the PS? Finally, the number of cells counted is missing.

Supp Fig5: based on available images of the Rac1KO embryo, I am not sure that epithelial architecture is established well enough to assess the location of mitosis along the apico-basal axis.

Significance

Although I am not as familiar with mouse gastrulation as I would like to be, I am familiar with gastrulation, live imaging and analysis. At this point I find it difficult to discuss the conclusions of the study since the methodology is so unclear. Nevertheless, any carefully-made description of mammalian gastrulation has the capacity to be eye-opening. This is potentially the case for this report, which provides nice images that most likely required skills and important efforts to obtain.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The work reports finding a molecular genetic basis for individual differences in behavior in different strains of outbred mice, even including individual behavioral differences between mice of the same inbred genetically isogenic strain. The authors were able to measure copy numbers for the tandemly repeated intronic snoRNA clusters SNORD115 and SNORD116 and found correlation with measures of anxiety in open-field test and elevated plus maze. Expression data for previously proposed targets of these snoRNAs are also provided.

Major comments:

1.The techniques to measure copy numbers are challenging and the authors' conclusion that ddPCR was their method of choice is convincing. They were able to obtain limited optical mapping (Bionano zephyr) data, only for SNORD116 and only in mouse, but these data are useful to corroborate those obtained with ddPCR.

2.Figure 3 reports single copy numbers for individuals that are presumably heterozygous. Do we have to assume that the numbers reported represent the larger alleles since the ddPCR method does not allow to distinguish two different size alleles, as was shown for optical mapping?

3.The analyses reported do not take into account the specific parental origin of the alleles used in the regression analyses. Since PWSCR-specific SNORDs are only expressed from the paternal chromosomes, this generates some uncertainty about the whole dataset.

4.Lines 353-365: The ankrd11 exon-specific RNAseq data are confusing and too preliminary. More work needs to be done to resolve the splice variants in this region and their relationship to SNORD116 copy numbers. Alternatively lines 356-361 should be deleted.

5.In all tested rodents, higher SNORD copy number was correlated with higher relative anxiety score. In the human samples, however, higher anxiety scores were associated with lower copy numbers. These apparently contradictory results are not mentioned in the abstract, and are not satisfactory explained in the text.

6.Extension to other species would be desirable but was limited by availability of genomic data: Results are presented for wood mouse only for SNORD115 and for the guinea pig for SNORD116.

Minor comments:

1.The authors present skull shape data related to SNORD116 copy numbers, but fail to consider how these data are relevant to the craniofacial abnormalities reported in an ankrd11 mutation. Barbaric et al (2008) reported a dominant ENU- induced mutation caused shortened snouts, wider skull, deformed nasal bones, reduced BMD, reduced osteoblast activity and reduced leptin levels. This phenotype was traced to a heterozygous missense mutation (conserved glutamate to lysine change) in an HDAC binding site. They postulated that the mutation fails to recruit HDACs to a transcription complex and to inhibit hormone-receptor activated gene transcription. What is the postulated link between this mechanism and the here reported skull shape data correlated with SNORD copy number variation?

2.The observed co-variation of copy numbers between the two SNORD clusters could indicate a duplication involving the entire region, This could be tested by determining the dosage of IPW, UBE3a and Snrpn genes.

3.Line 129 "the RNA coding region" and Line 148 "snoRNA coding parts" (and elsewhere) does seems correct, as by definition, this is non-coding RNA. The region they are referring to could be called the "processed C/D box snoRNA". The mechanism that generates these C/D box snoRNAs is well established: the "genes" are located in introns of host genes - and after transcription - the spliced out introns are exonucleolytically trimmed to the functional sizes. Both SNORD115 and 116 clusters are within a large transcript that originates from the SNRPN promoter of the paternal allele.

4.Figure 2 does not show data on skull shape as claimed in the legend.

5.S1 Figure: Snprn should be Snrpn

Significance

This provocative work proposes the regulation of behavioral variance by dosage changes of a regulatory RNA. The dosage changes are apparently caused by dynamic and frequent alteration in copy number. This is a novel concept and worthy of publicizing. Extensive data documentation is provided for others to analyze and possibly replicate. The data potentially throw light on the function of the tandemly repeated imprinted snoRNA clusters in the PWS critical region.

Novel aspects of this work include the discovery of copy number variation of these snoRNAs; and validation of a target of SNORD116: Ankrd11 is one of many potential targets of SNORD116 that was previously computationally predicted, this paper reports experimental evidence for this interaction.

The work would be of interest to researchers in behavioral evolution, non-coding RNA function, epigenetics and overall genome evolution.

Define your field of expertise with a few keyword: Molecular genetic disorders, Prader-Willi syndrome, mouse models

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Reply to the reviewers

Dear dr. Monaco,

We thank you and the reviewers for the positive and encouraging reviews on our manuscript entitled “Protective anti-prion antibodies in human immunoglobulin repertoires” and are glad to address the reviewer’s suggestions

In the following you will find a point-by-point response to the referees' critique.

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

Comment 1: Abtract: Although it is clear and direct, the last sentence where it refers to "a link to the low incidence of spontaneous prion diseases in human populations", is not easy to understand without a detailed explanation as given in the Discussion. I suggest a re-wording.

Response 1: We have reworded the sentence in the abstract and given more explanation in the discussion (see also Reviewer 2, Comment 2).

Comment 2: Results: It is clear how these Fabs act in preventing prion-induced neurotoxicity as shown in the COCS model. In addition to this effect, they also inhibit prion spreading, although this appears to be a lesser effect than inhibition of neurotoxicity. Thus, it would be interesting to discuss the possible effect of a Fab therapy, which provide a fully inhibition of the neurotoxicity but only partially inhibition of the prion propagation.

• Response 2: As suggested by the reviewer, we have added appropriate text to the discussion to comment on the option of a potential Fab therapy with a fully inhibition of neurotoxicity and partially inhibition of prion propagation. Comment 3: The therapeutic effect of the Fabs in the cell model was performed by adding the Fabs to the medium 1 h after infection and during splitting. Is there any study that evaluates the effect of Fabs added to the medium before inoculation or at later times?

Response 3: The goal of these experiments was to investigate whether the antibodies in question would counteract prion infections in principle, rather than performing a precise range-finding of the optimal therapeutic window. We have opted to not add the Fabs before inoculation, because past experience (and many papers) show that the “prophylactic” treatment rarely correlated with post-exposure efficacy. We also have not treated the cells after prion infection at later time points, because the data at later time points may be less pronounced and more variable. As for the treatment of cells with anti-PrP antibodies prior to exposure to prions, a study has been conducted in N2a cells (Pankievicz J et al., 2006, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1779824/). There, preincubation of N2a cells with mouse monoclonal anti-PrP antibodies (Mabs) before prion infection (22L) and preincubation of the inoculum with Mabs before infection of the cells led to a significant reduction in PrPSc levels as assessed by proteinase-K Western blot. This paper is now discussed in our manuscript.

Comment 4: Discussion: The authors repeatedly refer to the toxicity that antibodies against GD might have. Related to this, there is currently a therapy (experimental medicine) in humans using an antibody against this region. Perhaps it would be interesting to make a comment on this.

Response 4: Our findings (Sonati et al, Nature 2013, and several following papers) are fundamentally incompatible with those of the London lab on the toxicity of anti-GD antibodies, and elsewhere I have warned loudly against the use of such antibodies in humans. However, this discussion is peripheral to the findings presented here. We have added some text to the discussion but we would rather not expand on this specific issue.

Comment 5: Page 15. I have found the speculative comment: "Accordingly, clinically silent prion generation may occasionally occur in healthy individuals. PrPSc aggregates arising de novo may result in exposure of neoepitopes and/or epitopes occluded in cell-borne PrPC." interesting. However, some of the auto-antibodies found in healthy humans are against a region believed to be structurally unaltered in PrPSc, which it doesn't fit with the theory of exposure to neo-epitopes.

• Response 5: I still believe that my hypothesis is viable, but of course I concede that – thus far – I have no supporting data. We have therefore modified the text to alleviate this comment.

Reviewer #2:

Comment 1: This is a technically advanced and carefully executed study that clearly demonstrate the presence of natural autoantibodies to PrP, some of which show protective properties, in an unselected human population. Although this finding is interesting on its own right, its impact on issues such as incidence of sporadic prion diseases is unclear given that apparently only 0.06% of the nearly 38,000 subjects examined carried these antibodies "in high titer".

Response 1: We agree with the reviewer and have modified the statement as follows: “The frequency of high-titer anti-PrP antibody carriers (0.06%) is much lower than the occurrence of Fab71-like HCDR3 sequences in published human repertoires. This discrepancy could mean that most anti-PrP specificities exist in a dormant state, or are expressed as B-cell receptors, but do not produce circulating antibodies. It will be interesting to discover the triggers that may ignite antibody production and, possibly, afford protection against prions”. The discrepancy between the frequency of anti-PrP antibodies found in the plasma screen and by analysis of the antibody repertoires in the NGS datasets could stem from the fact that most anti-PrP specificities exist in a dormant state, or are expressed as B-cell receptors, but do not produce circulating antibodies (Joseena Iype et al., J Immunol 2019; now also included in the manuscript).

Comment 2: Furthermore, this reviewer could not locate the base of the pivotal statement made in the Abstract that these autoantibodies lack in carriers of disease-associated PRNP mutations. These two points need to be clarified.

Response 2: The statement refers to the study by Frontzek et al. (citation #48: Frontzek, K. et al. Autoantibodies against the prion protein in individuals with PRNP mutations Neurologyhttps://n.neurology.org/content/early/2020/02/25/WNL.0000000000009183?rss=1). Although listed in the references, the citation got lost in the discussion. We have inserted the reference again.

Comment 3: The manuscript suffers for the excessive amount of data that are crammed in the five figures. Combined these figures display a total of 33 panels some of which are quite complicated. The authors should be more selective and roll over some of the nonessential information i.e. that related to methodology, to the Supplement.

• Response 3: We agree with the reviewer and have moved several panels to the Supplement.

Comment 4: The use of acronyms is excessive and should be reduced (see for example COCS).

• Response 4: We have attempted to reduce the number of acronyms. We have however introduced the term COCS in Falsig et al., Nature Neuroscience 2007, and have used it regularly in more than a dozen follow-up papers. Comment 5: The legends need to be carefully checked for clarity, especially figure 4

• Response 5: We have revised the legends to improve their clairity.

Reviewer #3:

Comment 1: On page 10, the authors state that Fab71 (Figure 3e) and Fab100 (Extended data Figure 7) substantially lowered PrPSc levels in prion-infected cells. However, in both cases, only about half of the cultures tested showed less PrPSc than either the control samples or samples treated with other Fabs. This variability undercuts the conclusion that what they are observing is a substantial, reproducible effect. The authors should consider moderating their conclusion somewhat to better fit the data.

Response 1: We agree with the reviewer. The effect of Fab71 and Fab100 in reducing PrPSc levels in cells as compared to control samples and samples treated with the other Fabs is only partially present and variable among the replicates, but still statistically significant (One-way ANOVA; p

Comment 2: In figure 2, the legend to panel a does not match the figure. Fab3 and Fab71 are represented by the blue lines, not the red lines as stated in the legend.

• Response 2: We thank the referee for pointing this out. We have now corrected it.

Comment 3: In the legend to Extended data figure S4, please give the epitopes to Fab10 and Fab53.

Response 3: We have included the epitopes of these two Fabs (OR51-91 for Fab10 and CC2-HC92-120 for Fab53) in the Figure legend.

Comment 4: In Figure 3c, the lines indicating the significant groups are not well-aligned. In the left side of the panel, the lines should connect the dark gray control group squares with the Fab25 pink diamonds. Likewise, in the right side of the panel, the lower set of lines should connect the dark gray control group squares with the Fab83 dark blue triangles.

• Response 4: We have corrected this issue.

Comment 5: I agree with the comments of both reviewers. The suggestion of reviewer #2 to move methodology-related panels in the main figures to supplemental data would make it much easier for the reader to focus on the critical experimental data.

• Response 5: See response to comment 2 of reviewer 2. With all issues addressed, we hope that our revised manuscript will now be found suitable for proceeding to the next steps.

Best regards,

Adriano Aguzzi

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Senatore et al. is large scale study looking for natural human antibodies directed against prion protein (PrP). Using a synthetic human Fab phage display library, they found and characterized multiple human anti-PrP Fabs most of which recognized epitopes to a region of PrP from amino acid residues 92-120. Based on this information, they searched for and found low affinity, long-lived anti-PrP antibodies in both a repertoire of human antibodies and in 27 of almost 38,000 human clinical samples. They speculate that anti-PrP antibodies may help to protect against sporadic forms of prion disease and conclude that they may represent a source of potential immunotherapeutics against human prion infection.

Minor comments:

1) On page 10, the authors state that Fab71 (Figure 3e) and Fab100 (Extended data Figure 7) substantially lowered PrPSc levels in prion-infected cells. However, in both cases, only about half of the cultures tested showed less PrPSc than either the control samples or samples treated with other Fabs. This variability undercuts the conclusion that what they are observing is a substantial, reproducible effect. The authors should consider moderating their conclusion somewhat to better fit the data.

2) In figure 2, the legend to panel a does not match the figure. Fab3 and Fab71 are represented by the blue lines, not the red lines as stated in the legend.

3) In the legend to Extended data figure S4, please give the epitopes to Fab10 and Fab53.

4) In Figure 3c, the lines indicating the significant groups are not well-aligned. In the left side of the panel, the lines should connect the dark gray control group squares with the Fab25 pink diamonds. Likewise, in the right side of the panel, the lower set of lines should connect the dark gray control group squares with the Fab83 dark blue triangles.

Significance

This is an extensive, well-written study which provides significant data suggesting that humans can make anti-PrP antibodies. This is a novel finding that raises important questions about how the body may respond to spontaneous formation of infectious prions. Technically, the study is sound with appropriately interpreted data. Overall the study and the antibodies it characterizes, some of which are novel, will be of interest to prion researchers.

Referees cross commenting

I agree with the comments of both reviewers. The suggestion of reviewer #2 to move methodology-related panels in the main figures to supplemental data would make it much easier for the reader to focus on the critical experimental data.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors extensively and rigorously characterized a subset of antibodies to PrP identified in a human Fab phage display library. These selected antibodies were compared and found to be similar to repertoires of naturally occurring human antibodies present in circulating B cells. Profiling of antibodies harvested from an unbiased 38,000 patient population uncovered the presence of high titer anti-PrP autoantibodies in 21 individuals sharing no specific pathologies. This finding demonstrates the presence of apparently innocuous immunity to prion in an unselected population. Based also on "the reported lack of such antibodies in carriers of disease-associated PRNP mutations" the authors propose that the low incidence of "spontaneous" prion diseases may be linked to the presence of these protective antibodies in the general population.

Major comments:

This is a technically advanced and carefully executed study that clearly demonstrate the presence of natural autoantibodies to PrP, some of which show protective properties, in an unselected human population. Although this finding is interesting on its own right, its impact on issues such as incidence of sporadic prion diseases is unclear given that apparently only 0.06% of the nearly 38,000 subjects examined carried these antibodies "in high titer". Furthermore, this reviewer could not locate the base of the pivotal statement made in the Abstract that these autoantibodies lack in carriers of disease-associated PRNP mutations. These two points need to be clarified. The manuscript suffers for the excessive amount of data that are crammed in the five figures. Combined these figures display a total of 33 panels some of which are quite complicated. The authors should be more selective and roll over some of the nonessential information i.e. that related to methodology, to the Supplement.

Minor comments:

The use of acronyms is excessive and should be reduced (see for example COCS). The legends need to be carefully checked for clarity, especially figure 4

Significance

Significance

See above

Referees Cross Commenting

I agree with most of the comments by Reviewers 1 and 3. However, my queries remain.

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Referee #1

Evidence, reproducibility and clarity

This is a very interesting article with important implications in the prion field. It is extremely well detailed and exquisitely well written. The objective of the article is very clear and the results obtained are not only interesting but also have very important implications for understanding prion diseases.

I have some comments and a few minor concerns.

Abtract:

Although it is clear and direct, the last sentence where it refers to "a link to the low incidence of spontaneous prion diseases in human populations", is not easy to understand without a detailed explanation as given in the Discussion. I suggest a re-wording.

Results:

It is clear how these Fabs act in preventing prion-induced neurotoxicity as shown in the COCS model. In addition to this effect, they also inhibit prion spreading, although this appears to be a lesser effect than inhibition of neurotoxicity. Thus, it would be interesting to discuss the possible effect of a Fab therapy, which provide a fully inhibition of the neurotoxicity but only partially inhibition of the prion propagation.

The therapeutic effect of the Fabs in the cell model was performed by adding the Fabs to the medium 1 h after infection and during splitting. Is there any study that evaluates the effect of Fabs added to the medium before inoculation or at later times?

Discussion:

The authors repeatedly refer to the toxicity that antibodies against GD might have. Related to this, there is currently a therapy (experimental medicine) in humans using an antibody against this region. Perhaps it would be interesting to make a comment on this.

Page 15. I have found the speculative comment: "Accordingly, clinically silent prion generation may occasionally occur in healthy individuals. PrPSc aggregates arising de novo may result in exposure of neoepitopes and/or epitopes occluded in cell-borne PrPC." interesting. However, some of the auto-antibodies found in healthy humans are against a region believed to be structurally unaltered in PrPSc, which it doesn't fit with the theory of exposure to neo-epitopes.

Significance

The advance is highly significance for two reasons: 1) the tools that the authors have generated are really useful for the community and 2) The fact the healthy humans can generate anti-PrP antibodies is completely new and open new ways to understand the prion diseases mechanisms.

The audience is principally for those working on prion and prion-like diseases.

My expertise is in prion and prion-like diseases.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): \*Summary:** Reproducibility of genetic interactions across studies is low. The authors identify reproducible genetic interactions and ask the question of what are properties of robust genetic interactions. They find that 1. oncogene addiction tends to be more robust than synthetic lethality and 2. genetic interactions among physically interacting proteins tend to be more robust. They then use protein-protein interactions (PPIs) to guide the detection of genetic interactions involving passenger gene alterations. **Major comments:** The claims of the manuscript are clear and well supported by computational analyses. My only concern is the influence of (study) bias on the observed enrichment of physical protein interactions among genetic interactions. 1. Due to higher statistical power the here described approach favors genetic interactions involving frequently altered cancer genes (as acknowledged by the authors). 2. Also some of the libraries in the genetic screens might be biased towards better characterized screens. 3. PPI networks are highly biased towards well studied proteins (in which well studied proteins - in particular cancer-related proteins - are more likely to interact). The following tests would help to clarify if and to which extend these biases contribute to the described observations:*

Our response: We thank the reviewer for the positive assessment of our manuscript and have addressed the issue of study bias in response to the specific queries below.

* 1 . The authors should demonstrate that the PPI enrichment in reproducible vs non-reproducible genetic interactions is not solely due to the biased nature of PPI networks. One simple way of doing so would be to do the same analysis with a PPI network derived from a single screen (eg PMID: 25416956). I assume that due to the much lower coverage the effect will be largely reduced but it would be reconfirming to see a similar trend in addition to the networks on which the authors are already testing. Another way would be to use a randomized network (with the same degree distribution as the networks the authors are using and then picking degree matched random nodes) in which the observed effect should vanish.

*

Our response: We appreciate the reviewer’s point and have now assessed both of the suggested approaches.

The overlap with unbiased yeast two-hybrid (y2h) screens, even the recent HuRI dataset (Luck et al, Nature 2020), was too small in scale to draw any conclusions. Among the ~140,000 interactions tested for genetic interactions, only 51 overlap with y2h interactions. Two of the discovered genetic interactions were supported by a y2h interaction, while one of the robust genetic interactions was supported by a y2h interaction. While this is actually more than would be expected based on the overlap of interactions in the test space the numbers are not especially convincing.

We therefore focused on two alternative assessments. We first compared our results with the network derived from the systematic AP-MS mapping of protein interactions in HEK293 cells (BioPlex 3.0, Huttlin et al, Biorxiv 2020). We restricted our analysis of genetic interactions to gene pairs that could conceivably be observed in the BioPlex dataset (i.e. between baits screened and preys expressed in HEK293T). We found that although the numbers were small, the same pattern of enrichment was observed:

This analysis has now been added to the revised manuscript as Supplementary Table S4 and Figure S3E (shown below):

We next compared the results we observed with the real STRING protein-protein interaction network to 100 degree-matched randomisations of this network. We observed that the number of discovered and validated genetic interactions observed using the real STRING interaction network was greater than that observed using the randomised networks. With this in mind, we have now revised the manuscript to state:

‘Previous work has demonstrated that the protein-protein interaction networks aggregated in databases are subject to significant ascertainment bias – some genes are more widely studied than others and this can result in them having more reported protein-protein interaction partners than other genes(Rolland et al., 2014). As cancer driver genes are studied more widely than most genes, they may be especially subject to this bias. To ensure the observed enrichment of protein-protein interactions among genetically interacting pairs was not simply due to this ascertainment bias, we compared the results observed for the real STRING protein-protein interaction network with 100 degree-matched randomised networks and again found that there was a higher than expected overlap between protein-protein interactions and both discovered and validated genetic interactions (Supplemental Fig. S4).’

Supplemental Figure S4. Genetic interactions are more enriched in real protein-protein interaction networks than randomised networks. Histograms showing the overlap between 100 degree matched randomisations of the STRING medium confidence protein-protein interaction and discovered (a and b) and validated (c and d) genetic interactions. The observed overlap with the real STRING protein interaction are highlighted with the orange lines.

* 2 . What's the expected number of robust genetic interactions involving passenger gene alterations? Is it surprising to identify 11 interactions? This question could be addressed with some sort of randomization test: When selecting (multiple times) 47,781 non-interacting random pairs between the 2,972 passenger genes and 2,149 selectively lethal genes, how many of those pairs form robust genetic interactions?

*

Our response: We have now addressed this as follows:

“At an FDR of 20% we found 11 robust genetic interactions involving passenger gene alterations (Supplemental Table S6). To assess whether this is more than would be expected by chance we randomly sampled 47,781 gene pairs from the same search space 100 times. The median number of robust genetic interactions identified amongst these randomly sampled gene pairs was 1 (mean 1.27, min 0, max 6) suggesting that the 11 robust genetic interactions observed among protein-protein interacting pairs was more than would be expected by chance.”

\*Minor comments:**

Two additional analyses would add in my opinion value to the manuscript:

-The authors state that reasons for irreproducibility of genetic interactions are of technical or biological nature. Is it possible to disentangle the contribution of the two factors given the available data? Eg how many genetic interactions are reproducible in two different screening platforms using the same cell line vs how similar are results of screens from two different cell lines in the same study?

*

Our response: We are also very interested in this question, but with the available data, we are not confident that we could draw solid conclusions.

-The authors state that "some of the robust genetic dependencies could be readily interpreted using known pathway structures" and argue that they recover for example MAPK or Rb pathway relationships. Is this a general trend? Do genes forming a robust genetic interactions have a higher tendency to be in the same pathway as opposed to different pathways?

Our response: We have now systematically tested the robust genetic interactions for each driver gene for enrichment in specific pathways. Relevant text is as follows:

‘To test if this enrichment of pathway members among the robust dependencies associated with specific driver genes was a common phenomenon, for each driver gene with at least three dependencies we asked if these dependencies were enriched in specific signalling pathways (see Methods). Of the twelve driver genes tested, we found that five of these were enriched in specific pathways and in all five cases found that the driver gene itself was also annotated as a member of the most enriched pathway (Table SX). As expected RB1 (most enriched pathway ‘G1 Phase’) and BRAF (most enriched pathway ‘Negative feedback regulation of MAPK pathway’) were among the five driver genes, alongside PTEN (‘PI3K/AKT activation’), CDKN2A (‘Cell cycle’), and NRAS (‘Ras signaling pathway’).’

Details in the methods are as follows:

‘Pathway enrichment was assessed using gProfiler (Raudvere et al., 2019) with KEGG (Kanehisa et al., 2017) and Reactome (Jassal et al., 2020) as annotation databases and the selectively lethal genes as the background list.’

*I think the pathway topic could be in general better exploited: eg does pathway (relative) position play a role?**

*

Our response: We agree that pathway position, especially distance from driver gene in an ordered pathway, would be very interesting to tease out but we don’t think that current pathway annotations are reliable enough nor the set of robust genetic interactions large enough to analyse this properly.

*Reviewer #1 (Significance (Required)):**

Personalized cancer medicine aims at the identification of patient-specific vulnerabilites which allow to target cancer cells in the context of a specific genotype. Many oncogenic mutations cannot be targeted with drugs directly. The identification of genetic interactions is therefore of crucial importance. Unfortunately, genetic interactions show little reproducibility accross studies. The authors make an important contribution to understanding which factors contribute to this reproducibility and thereby providing means to also identify more reliable genetic interactions with high potential for clinical exploitation or involving passenger gene alterations (which are otherwise harder to detect for statistical reasons).

REFEREES CROSS COMMENTING

Reviewer 2 raises a few valid points, which if addressed would certainly increase the clarity of the paper. In particular addressing the first point (the self interactions of tumor suppressors) seems important to me. From what I can see all of reviewer 2's comments can be addressed easily.

*

End of Reviewer 1 comments

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

*In this manuscript, Lord et al. describe the analysis of loss-of-function (LOF) screens in cancer cell lines to identify robust (i.e., technically reproducible and shared across cell lines) genetic dependencies. The authors integrate data from 4 large-scale LOF studies (DRIVE, AVANA, DEPMAP and SCORE) to estimate the reproducibility of their individual findings and examine their agreement with other types of functional information, such as physical binding. The main conclusions from the analyses are that: a) oncogene-driven cancer cell lines are more sensitive to the inhibition of the oncogene itself than any other gene in the genome; b) robust genetic interactions (i.e., those observed in multiple datasets and cell lines driven by the same oncogene/tumour suppressor) are enriched for gene pairs encoding physically interacting proteins.

**Main comments:**

I think this study is well designed, rigorously conducted and clearly explained. The conclusions are consistent with the results and I don't have any major suggestions for improving their support. I do, however, have a few suggestions for clarifying the message.

*

Our response: We thank the reviewer for this positive assessment of our manuscript and have addressed the requests for clarity below.

-Could the authors provide some intuitive explanation (or speculation) about the 2 observed cases of tumour suppressor "addiction" (TP53 and CDKN2A)? While the oncogene addiction cases are relatively easy to interpret, the same effects on tumour suppressors are less clear. Is it basically an epistatic effect, which looks like a relative disadvantage? For example, if we measure fitness: TP53-wt = 1, TP53-wt + CRISPR-TP53 = 1.5, TP53-mut = 1.5, TP53-mut + CRISPR-TP53 = 1.5. That is, inhibiting TP53 in TP53 mutant cells appears to be disadvantageous (relative to WT) only because inhibiting TP53 in wild-type cells is advantageous?

Our response: The reviewer is correct – the TP53 / TP53 dependency is similar to an epistatic effect. In a TP53 mutant background targeting TP53 with shRNA or CRISPR has a neutral effect, while in a TP53 wild type background targeting TP53 with shRNA or CRISPR often causes an increase in cell growth. We have clarified this in the text below (new text in bold)

‘We also identified two (2/9) examples of ‘self vs. self’ dependencies involving tumour suppressors -TP53 (aka p53) and CDKN2A (aka p16/p14arf) (Supplemental Fig. S2c). This type of relationship has previously been reported for TP53: TP53 inhibition appears to offer a growth advantage to TP53 wild type cells but not to TP53 mutant cells(Giacomelli et al., 2018). Inhibiting TP53 in TP53 mutant cells has a largely neutral effect, while on average inhibiting TP53 in TP53 wild type cells actually increases fitness growth. Consequently, we observed an association between TP53 status and sensitivity to TP53 inhibition. A similar effect was observed for CDKN2A, although the growth increase resulting from inhibiting CDKN2A in wild-type cells is much lower than that seen for TP53 (Supplemental Fig. S2c).;

*-In the analysis of overlap between genetic and physical interactions, the result should be presented more precisely. Currently, the text reads "when considering the set of all gene pairs tested, gene pairs whose protein products physically interact were more likely to be identified as significant genetic interactors". However, the referenced figure (Fig. 5a) shows an orthogonal perspective: relative to all gene pairs tested, those that have a significant genetic interaction are more likely to have a physical interaction as well. In other words, in the text, we are comparing the relative abundance of genetic interactions in 2 sets: tested and physically interacting. However, in the figure, we are comparing the relative abundance of protein interactions in 2 sets -- tested and genetically interacting. The odds ratio and the p-values stay the same but the result would be more clear if the figure matched the description in the text.

*

Our response: Due to the fact that genetic interactions are rare (~1% of all gene pairs tested have a discovered genetic interaction, ~0.1% have a validated genetic interaction) it’s hard to convey the enrichment effectively. This is demonstrated in the below figure – it’s clear that there are more discovered / validated genetic interaction pairs among the protein-protein interaction pairs but the scale is hard to appreciate:

Focusing only on the discovered/validated genetic interactions makes the picture a little clearer but does not effectively show that the discovered pairs themselves are enriched among protein-protein interaction pairs

As we feel the original figures convey the main message most effectively, we have altered the text rather than the images as follows:

“We found that, when considering the set of all gene pairs tested, gene pairs identified as significant genetic interactors in at least one dataset are more likely to encode proteins that physically interact (Fig. 5a)”

\*Minor comments:**

There're a few places where the more explicit explanation would improve the readability of the manuscript.

-Page 5: The multiple regression model used to identify genetic interactions is briefly mentioned in the text (and described more extensively in the methods). I think it would be better to explicitly describe the dependent and independent variables of the model in the text, so that the reader can intuitively understand what is being estimated*.

Our response: We have added additional information to the main text as follows:

‘This model included tissue type, microsatellite instability and driver gene status as independent variables and gene sensitivity score as the dependent variable (Methods). Microsatellite instability was included as a covariate as it has previously been shown to be associated with non-driver gene specific dependencies (Behan et al., 2019), while tissue type was included to avoid confounding by tissue type.’*

-Page 5: "Using this approach, we tested 142,477 potential genetic dependencies..." -- could the authors provide a better explanation of where that number is coming from? E.g., 142,477 = ... driver genes x 2470 selectively lethal genes?*

Our response: Because not every selectively lethal gene is tested in every dataset (e.g. DRIVE only screened ~8,000 genes instead of the whole genome) the 142,477 number does not correspond to a simple multiplication of number of driver genes times number of selectively lethal gene. However, we have added additional information in bold as follows:

‘Using this approach, we tested 142,477 potential genetic dependencies between 61 driver genes and 2,421 selectively lethal genes. We identified 1,530 dependencies that were significant in at least one discovery screen (Fig. 2a, Supplemental Fig. S1). All 61 driver genes had at least one dependency that was significant in at least one discovery screen while less than half of the selectively lethal genes (1,141 / 2,421) had a significant association with a driver gene. Of the 1,530 dependencies that were significant in at least one discovery screen, only 229 could be validated in a second screen (Supplemental Table S3, Fig. 2a). For example, in the AVANA dataset TP53 mutation was associated with resistance to inhibition of both MDM4 and CENPF, but only the association with MDM4 could be validated in a second dataset (Fig. 2b, 2c). Similarly, in the DEPMAP dataset NRAS mutation was associated with increased sensitivity to the inhibition of both NRAS itself and ERP44, but only the sensitivity to inhibition of NRAS could be validated in a second dataset (Fig. 2b, 2c).

The 229 reproducible dependencies involved 31 driver genes and 204 selectively lethal genes.’

-Page 5: Repeating the number of findings of each type would help understanding the landscape of the genetic dependencies (suggested numbers in brackets): "Of the (229?) reproducible genetic dependencies nine were 'self vs self' associations". "The majority (7/9?) of these ... were oncogene addiction effects". "We also identified 2 (2/9?) examples of 'self vs self' dependencies involving tumour suppressors".

Our response: We have taken the reviewer’s advice and added these figures to the main text for clarity

* -Page 12: "Three of these interactions involve genes frequently deleted with the tumour suppressor CDKN2A (CDKN2B and MTAP) and mirror known associations with CDKN2A". It is not clear what "mirror" means -- do they recapitulate known interactions?

*

Our response: Yes, we meant to indicate that they recapitulate known CDKN2A interactions and have now replaced ‘mirror’ with ‘recapitulate’.

-Page 15: "Although we have not tested them here, other features predictive of between-species conservation may also be predictive of robustness to genetic heterogeneity" -- could the authors explicitly list the features?

Our response: We have now explicitly listed these features as follows:

“Previous work has also shown that genetic interactions between gene pairs involved in the same biological process, as indicated by annotation to the same gene ontology term, are more highly conserved across species (Ryan et al., 2012; Srivas et al., 2016). Similarly, genetic interactions that are stable across experimental conditions (e.g. that can be observed in the presence and absence of different DNA damaging agents) are more likely to be conserved across species (Srivas et al., 2016). Although we have not tested them here, these additional features predictive of between-species conservation may also be predictive of robustness to genetic heterogeneity.”

*Reviewer #2 (Significance (Required)):

The identification of a significant overlap between genetic and physical interactions in cancer cell lines is an interesting and promising observation that will help understanding known genetic dependencies and predicting new ones. However, similar observations have been made in other organisms and biological systems. These past studies should be referenced to provide a historical perspective and help define further analyses in the cancer context. In particular, studies in yeast S. cerevisiae have shown that, not only there is a general overlap between genetic interactions (both positive and negative) and physical interactions, but at least 2 additional features are informative about the relationship: a) the relative strength of genetic interactions and b) the relative density of physical interactions (i.e., isolated interaction vs protein complexes). Here's a sample of relevant studies: 1) von Mering et al., Nature, 2002; 2) Kelley & Ideker, Nat Biotechnol, 2005; 3) Bandyopadhyay et al., PLOS Comput Biol, 2008; 4) Ulitsky et al., Mol Syst Biol, 2008; 5) Baryshnikova et al., Nat Methods, 2010; 6) Costanzo et al., Science, 2010; 7) Costanzo et al., Science, 2016.

Similar observations have also been made in mammalian systems: e.g., in mouse fibroblasts (Roguev et al., Nat Methods, 2013) and K562 leukemia cells (Han et al., Nat Biotech, 2017). I don't think that past observations negate the novelty of this manuscript. The analysis presented here is more focused and more comprehensive as it is based on a large integrated dataset and is driven by a series of specific hypotheses. However, a reference to previous publications should be made.

As a frame of reference: my expertise is in high-throughput genetics of model organisms, including mapping and analyzing genetic interactions.

*

Our response: We thank the reviewer for highlighting this point.

We have attempted to provide better context for our work in the discussion as follows:

‘In budding and fission yeast, multiple studies have shown that genetic interactions are enriched among protein-protein interaction pairs and vice-versa (Costanzo et al., 2010; Kelley and Ideker, 2005; Michaut et al., 2011; Roguev et al., 2008). Pairwise genetic interaction screens in individual mammalian cell lines have also revealed an enrichment of genetic interactions among protein-protein interaction pairs (Han et al., 2017; Roguev et al., 2013). Our observation that discovered genetic interactions are enriched in protein-protein interaction pairs is consistent with these studies. However, these studies have not revealed what factors influence the conservation of genetic interactions across distinct genetic backgrounds, i.e. what predicts the robustness of a genetic interaction. In yeast, the genetic interaction mapping approach relies on mating gene deletion mutants and consequently the vast majority of reported genetic interactions are observed in a single genetic background (Tong et al., 2001). In mammalian cells, pairwise genetic interaction screens across multiple cell lines have revealed differences across cell lines but not identified what factors influence the conservation of genetic interactions across cell lines(Shen et al., 2017). While variation of genetic interactions across different strains or different genetic backgrounds has been poorly studied, previous work has analysed the conservation of genetic interactions across species and shown that genetic interactions between gene pairs whose protein products physically interact are more highly conserved (Roguev et al., 2008; Ryan et al., 2012; Srivas et al., 2016). Our analysis here suggests that the same principles may be used to identify genetic interactions conserved across genetically heterogeneous tumour cell lines.’

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Lord et al. describe the analysis of loss-of-function (LOF) screens in cancer cell lines to identify robust (i.e., technically reproducible and shared across cell lines) genetic dependencies. The authors integrate data from 4 large-scale LOF studies (DRIVE, AVANA, DEPMAP and SCORE) to estimate the reproducibility of their individual findings and examine their agreement with other types of functional information, such as physical binding. The main conclusions from the analyses are that: a) oncogene-driven cancer cell lines are more sensitive to the inhibition of the oncogene itself than any other gene in the genome; b) robust genetic interactions (i.e., those observed in multiple datasets and cell lines driven by the same oncogene/tumour suppressor) are enriched for gene pairs encoding physically interacting proteins.

Main comments:

I think this study is well designed, rigorously conducted and clearly explained. The conclusions are consistent with the results and I don't have any major suggestions for improving their support. I do, however, have a few suggestions for clarifying the message.

-Could the authors provide some intuitive explanation (or speculation) about the 2 observed cases of tumour suppressor "addiction" (TP53 and CDKN2A)? While the oncogene addiction cases are relatively easy to interpret, the same effects on tumour suppressors are less clear. Is it basically an epistatic effect, which looks like a relative disadvantage? For example, if we measure fitness: TP53-wt = 1, TP53-wt + CRISPR-TP53 = 1.5, TP53-mut = 1.5, TP53-mut + CRISPR-TP53 = 1.5. That is, inhibiting TP53 in TP53 mutant cells appears to be disadvantageous (relative to WT) only because inhibiting TP53 in wild-type cells is advantageous?

-In the analysis of overlap between genetic and physical interactions, the result should be presented more precisely. Currently, the text reads "when considering the set of all gene pairs tested, gene pairs whose protein products physically interact were more likely to be identified as significant genetic interactors". However, the referenced figure (Fig. 5a) shows an orthogonal perspective: relative to all gene pairs tested, those that have a significant genetic interaction are more likely to have a physical interaction as well. In other words, in the text, we are comparing the relative abundance of genetic interactions in 2 sets: tested and physically interacting. However, in the figure, we are comparing the relative abundance of protein interactions in 2 sets -- tested and genetically interacting. The odds ratio and the p-values stay the same but the result would be more clear if the figure matched the description in the text.

Minor comments:

There're a few places where the more explicit explanation would improve the readability of the manuscript.

-Page 5: The multiple regression model used to identify genetic interactions is briefly mentioned in the text (and described more extensively in the methods). I think it would be better to explicitly describe the dependent and independent variables of the model in the text, so that the reader can intuitively understand what is being estimated.

-Page 5: "Using this approach, we tested 142,477 potential genetic dependencies..." -- could the authors provide a better explanation of where that number is coming from? E.g., 142,477 = ... driver genes x 2470 selectively lethal genes?

-Page 5: Repeating the number of findings of each type would help understanding the landscape of the genetic dependencies (suggested numbers in brackets): "Of the (229?) reproducible genetic dependencies nine were 'self vs self' associations". "The majority (7/9?) of these ... were oncogene addiction effects". "We also identified 2 (2/9?) examples of 'self vs self' dependencies involving tumour suppressors".

-Page 12: "Three of these interactions involve genes frequently deleted with the tumour suppressor CDKN2A (CDKN2B and MTAP) and mirror known associations with CDKN2A". It is not clear what "mirror" means -- do they recapitulate known interactions?

-Page 15: "Although we have not tested them here, other features predictive of between-species conservation may also be predictive of robustness to genetic heterogeneity" -- could the authors explicitly list the features?

Significance

The identification of a significant overlap between genetic and physical interactions in cancer cell lines is an interesting and promising observation that will help understanding known genetic dependencies and predicting new ones. However, similar observations have been made in other organisms and biological systems. These past studies should be referenced to provide a historical perspective and help define further analyses in the cancer context. In particular, studies in yeast S. cerevisiae have shown that, not only there is a general overlap between genetic interactions (both positive and negative) and physical interactions, but at least 2 additional features are informative about the relationship: a) the relative strength of genetic interactions and b) the relative density of physical interactions (i.e., isolated interaction vs protein complexes). Here's a sample of relevant studies: 1) von Mering et al., Nature, 2002; 2) Kelley & Ideker, Nat Biotechnol, 2005; 3) Bandyopadhyay et al., PLOS Comput Biol, 2008; 4) Ulitsky et al., Mol Syst Biol, 2008; 5) Baryshnikova et al., Nat Methods, 2010; 6) Costanzo et al., Science, 2010; 7) Costanzo et al., Science, 2016.

Similar observations have also been made in mammalian systems: e.g., in mouse fibroblasts (Roguev et al., Nat Methods, 2013) and K562 leukemia cells (Han et al., Nat Biotech, 2017). I don't think that past observations negate the novelty of this manuscript. The analysis presented here is more focused and more comprehensive as it is based on a large integrated dataset and is driven by a series of specific hypotheses. However, a reference to previous publications should be made.

As a frame of reference: my expertise is in high-throughput genetics of model organisms, including mapping and analyzing genetic interactions.

REFEREES CROSS COMMENTING

I agree with the questions raised by reviewer #1. And I think the authors should be able to address them (either through analyses or reasoning) within 1-3 months.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Reproducibility of genetic interactions across studies is low. The authors identify reproducible genetic interactions and ask the question of what are properties of robust genetic interactions. They find that 1. oncogene addiction tends to be more robust than synthetic lethality and 2. genetic interactions among physically interacting proteins tend to be more robust. They then use protein-protein interactions (PPIs) to guide the detection of genetic interactions involving passenger gene alterations.

Major comments:

The claims of the manuscript are clear and well supported by computational analyses. My only concern is the influence of (study) bias on the observed enrichment of physical protein interactions among genetic interactions. 1. Due to higher statistical power the here described approach favors genetic interactions involving frequently altered cancer genes (as acknowledged by the authors). 2. Also some of the libraries in the genetic screens might be biased towards better characterized screens. 3. PPI networks are highly biased towards well studied proteins (in which well studied proteins - in particular cancer-related proteins - are more likely to interact). The following tests would help to clarify if and to which extend these biases contribute to the described observations:<br> 1 . The authors should demonstrate that the PPI enrichment in reproducible vs non-reproducible genetic interactions is not solely due to the biased nature of PPI networks. One simple way of doing so would be to do the same analysis with a PPI network derived from a single screen (eg PMID: 25416956). I assume that due to the much lower coverage the effect will be largely reduced but it would be reconfirming to see a similar trend in addition to the networks on which the authors are already testing. Another way would be to use a randomized network (with the same degree distribution as the networks the authors are using and then picking degree matched random nodes) in which the observed effect should vanish.

2 . What's the expected number of robust genetic interactions involving passenger gene alterations? Is it surprising to identify 11 interactions? This question could be addressed with some sort of randomization test: When selecting (multiple times) 47,781 non-interacting random pairs between the 2,972 passenger genes and 2,149 selectively lethal genes, how many of those pairs form robust genetic interactions?

Minor comments:

Two additional analyses would add in my opinion value to the manuscript:

-The authors state that reasons for irreproducibility of genetic interactions are of technical or biological nature. Is it possible to disentangle the contribution of the two factors given the available data? Eg how many genetic interactions are reproducible in two different screening platforms using the same cell line vs how similar are results of screens from two different cell lines in the same study?

-The authors state that "some of the robust genetic dependencies could be readily interpreted using known pathway structures" and argue that they recover for example MAPK or Rb pathway relationships. Is this a general trend? Do genes forming a robust genetic interactions have a higher tendency to be in the same pathway as opposed to different pathways? I think the pathway topic could be in general better exploited: eg does pathway (relative) position play a role?

Significance

Personalized cancer medicine aims at the identification of patient-specific vulnerabilites which allow to target cancer cells in the context of a specific genotype. Many oncogenic mutations cannot be targeted with drugs directly. The identification of genetic interactions is therefore of crucial importance. Unfortunately, genetic interactions show little reproducibility accross studies. The authors make an important contribution to understanding which factors contribute to this reproducibility and thereby providing means to also identify more reliable genetic interactions with high potential for clinical exploitation or involving passenger gene alterations (which are otherwise harder to detect for statistical reasons).

REFEREES CROSS COMMENTING

Reviewer 2 raises a few valid points, which if addressed would certainly increase the clarity of the paper. In particular addressing the first point (the self interactions of tumor suppressors) seems important to me. From what I can see all of reviewer 2's comments can be addressed easily.

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Reply to the reviewers

Reviewer #1

1.The stimulatory effect of LPHC diet on browning of some WATs has been previously reported (Nutrition, 42, 37-45 Oct 2017). Also, the activation of AMPK was observed in this study. However, the detailed mechanism responsible for AMPK activation by LPHC diet remains elusive in the present study, which lowers its scientific importance.

Response: OK. We will adequately mention previous study in which the AMPK activation was observed upon LPHC diet and more deeply decipher the molecular mechanisms that lead to AMPK activation by analyzing AMP- and ROS/Ca2+-dependent pathways according to the Reviewer’s suggestions (for more details, see point 2-4).

2.Different with WAT, LPHC diet increases glucose uptake and FA synthesis in BAT (Nutrition. 30 (4), 473-80 Apr 2014). __Is it possible that AMPK activation in WAT due to the lowered glucose uptake, which might increase AMP/ATP ratio? It is recommended to determine the uptake of glucose in WAT.__

Response: In the article cited by the Reviewer, authors measured glucose uptake only in BAT and found that it was significantly increased. On the contrary, no data are reported regarding glucose metabolism in WAT. Our in vitro data clearly indicate that AMPK activation occurs upon amino acid restriction (AAR) and in the presence of glucose in the culture medium (see Fig. 6C, Suppl. Fig. 5I). Moreover, glucose uptake is increased upon this condition (see Fig. 5G). Hence a decreased glucose uptake by WAT and the activation of AMPK via a decrease of AMP/ATP ratio has to be likely excluded. However, we will test AMP and ATP levels both in vivo and in vitro and this, together with experiments aimed at deciphering the contribution of mitochondrial ROS (mtROS) and CaMKK (see points 3, 4), we will hopefully clarify the mechanisms of AMPK activation upon LPHC diet.

The present study indicates that the promotional effect of LPHC diet on WAT browning is dependent on mitochondrial ROS generation. However, it is still unknown why the production of ROS increased and why ROS could activate AMPK. The authors should clarify these critical steps.

Response: Redox unbalance is widely reported to directly or indirectly stimulate AMPK activation (Shao et al., 2015, Cell Metab; Hinchi et al., 2018, J Biol Chem). Moreover, it has been demonstrated that activation of AMPK could depend on mtROS and be independent of an increase in AMP/ATP ratio (Emerlin et al., 2009, Free Radic Biol Med). Based on this evidence and our results, we believe that, upon AAR or LPHC diet, the recorded increase of mtROS concentration could not derive from an enhanced production but rather to a decrease of intracellular availability of the sulfur amino acid cysteine that represents an efficient ROS scavenger. Actually, by replenishing cysteine through N-acetyl cysteine (NAC) treatment we were able to buffer mtROS increase (see Fig. 6A), as assayed by cytofluorimetric analyses through mitoSox staining, and avoid AMPK phosphorylation (see Fig. 6C and 6J) as well as the downstream upregulation of brown fat and muscular genes (see Fig 6B and 6I). In line with this result, treatment with erastin, a cysteine depleting agent, was able to mimic the effects of AAR and LPHC diet by up-regulating the expression of brown-like and muscular genes (see Fig. 6G). Therefore, to more deeply decipher the mechanisms involved in AMPK activation and to further involve cysteine depletion in mtROS increase and AMPK activation, we could assay mtROS and AMPK levels also following erastin treatment. Of course, to involve cysteine decrease in AMPK activation, measuring intracellular cysteine levels upon AAR and LPHC diet is mandatory and will be carried out. Importantly, we have preliminary data, not included in the present manuscript, indicating that cysteine is decreased both upon AAR and LPHC diet; hence, after increasing the sample size, we will include this result in the revised version.

4.The relationship between cytosolic calcium and AMPK was not clear. In addition to the fact that AMPK regulates SERCA to increase cytosolic Ca depicted in the present study, AMPK could also be activated by increased Ca via CaMKK. A recent study indicates that the activation of AMPK requires TRPV4-mediated Ca release from ER (Cell Metabolism Volume 30, Issue 3, 3 September 2019, Pages 508-524.e12). This issue should also be clarified.

Response: Regarding the possible involvement of TRPV4-mediated Ca release from ER, through RNAseq we found that TRPV4 mRNA is slightly expressed in subcutaneous white adipose tissue and changes in its expression were not found upon LPHC diet. Moreover, TRPV4 protein was not detected in our samples by proteomic analysis. Notably, by integrating transcriptomic and proteomic data, it emerged that cell membrane intracellular calcium transporters (i.e. CACNG1, CACNA2D1), which are interconnected to the network of sarcoplasmic reticulum calcium cycle, are upregulated upon LPHC diet (see Fig. 5I). Therefore, we will evaluate the effects of a calcium channel blocker (e.g. Verapamil) and/or extracellular calcium chelator (e.g. BAPTA) on AMPK activation and its downstream gene expression cascade. In parallel, to possibly involve CAMKK in the activation of AMPK, treatment with a CAMKK inhibitor (e.g. Sto-609) will be carried out. Importantly, mtROS are upstream inducers of intracellular calcium raise (Mungai et al., 2011, Mol Cell Biol) and therefore an involvement of mtROS-Ca2+ axis could not be ruled out. In line with this hypothesis, by buffering mtROS through NAC treatment, we were able to abrogate intracellular calcium raise elicited by AAR (see Fig. 6F). Therefore, by performing the above described experiments and by evaluating CAMKK following NAC treatment, we will be hopefully able to establish whether AMPK activation is AMP-(in)dependent and/or relies on mtROS/Ca2+/CAMKK pathway.

Reviewer #2

o Interesting paper but see comments below.

Response: OK, thanks

o The relevance of the described effects for whole-body energy balance regulation is not shown. Indirect calorimetry could be interesting. The only whole-body effect (slightly improved glucose clearance in oGTT) was very small.

Response: OK. As suggested by this Reviewer we can include indirect calorimetry to give a more comprehensive view of the effects of LPHC diet on the whole-body energy balance (see also the following point).

o …1) Indirect calorimetry could be very helpful to show effects on energy metabolism. 2) Can the authors discuss why they didn't conduct the experiment also under thermoneutral conditions?

Response: OK. As stated above, we will add indirect calorimetry experiments and, as suggested by this Reviewer, we will discuss this issue in the revised version. Importantly, we already have indirect calorimetry data that were not included in the present version of the manuscript and that we will add in the revised version.

o Maybe an additional collaborator is necessary.

Response: Yes, collaborators who performed indirect calorimetry will be included as co-authors in the revised version.

o Article numbers of all diets must be added and information if the all diets were purified diets. This could have effects on the gut microbiome.

Response: OK. We will add the article numbers as well as more detailed information about all the diets.

o Sample sizes are very low. The authors should explain why only males were used in the experiments. oGTT analysis should also include calculation of area under the curve. No explicit statement if correction for multiple testing is required or other measures to reduce false positive results.

Response: We have used only male mice to avoid sex bias. We will edit the OGTT analysis graph to include calculation area under the curve. Regarding the sample size, a mistake occurred when the figure legends have been written. Actually, in materials and methods section, we clearly indicated the number of animals used (n=8 mice for WD and n=6 mice for LPHC diet and not n=3). Information regarding the statistical analyses was included in Bioinformatics and Statistical Analysis section. In this section, we described how the correction for multiple testing was carried out (i.e. one-way ANOVA followed by Dunnetts correction). In the revised version, we will dedicate a separate section for statistical analysis to avoid misreading.

**Minor comments:**

o Are prior studies referenced appropriately?* Relevant reference: Desjardins, E.M., Steinberg, G.R. Emerging Role of AMPK in Brown and Beige Adipose Tissue (BAT): Implications for Obesity, Insulin Resistance, and Type 2 Diabetes. Curr Diab Rep 18, 80 (2018). __https://doi.org/10.1007/s11892-018-1049-6____ __

Response: We thank the Reviewer for this suggestion and we will include and appropriately discuss this paper.

o *Are the text and figures clear and accurate?* YES

Response: Ok, thanks for this positive evaluation.

Reviewer #3

My main critique, coming from the perspective of a dietitian that works in human trials in the US, is that the diet called a "Western" diet is not similar to the diet that humans with metabolic problems typically eat…

Response: OK. The aim of this work was to study at molecular level the responses of white adipose tissue to changes in protein to carbohydrate ratio. We completely agree with the Reviewer that “Western” diet is not an appropriate term to describe the diet that we have used; hence, we will change “Western diet” in “Control diet” throughout the manuscript. Actually, according to the general guidelines for nutrition studies on mice, when experimental animals are fed a special diet (i.e. LPHC in our study), the control animals should be fed a diet matched in every way to the special diet, except of course for the dietary variable (i.e. P/C ratio in our study) that the researcher is studying (Pellizzon and Ricci, The common use of improper control diets in diet-induced metabolic disease research confounds data interpretation: the fiber factor (2018). Nutrition & Metabolism 15:3).

**Major comments:**

-The authors provide strong support their key findings.

Response: We thank this reviewer for this positive evaluation.

-The mice were on the LPHC diet for a short period of time (2 weeks). Ongoing amino acid deficiency has potential to promote frailty and other deleterious outcomes. No long-term diet outcomes can be inferred from this study.

Response: OK. We will discuss this issue, highlighting that this dietary regimen should be recommended on human only for a short period and that further study is needed for understanding the long-term effects of LPHC diet.

-The authors have provided no evidence that a LPHC diet improves human health, so I think they need to scale back those assertions, particularly as it relates to people shifting to a LPHC from what they currently eat, since people don't typically eat what the authors refer to as a "Western" diet as it's defined in this paper.

Response: OK. We will reference studies in which LPHC diet has been suggested to improve human health.

-As far as I can tell, no additional experiments are needed to support their claims identifying how the LPHC affects AMPK activated pathways in mice.

Response: We thank this reviewer for this positive evaluation.

-The methods are rigorous and sufficiently described to be reproducible.

Response: We thank this reviewer for this positive evaluation.

**Minor comments:**

-__Minor grammatical issues through e.g. "It is worth to notice" in last paragraph on page 12; there are font differences in the methods section __

Response: OK. We will correct these minor grammatical/font issues.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors use a mouse model to compare molecular responses to a 23% protein/57 carbohydrate/20 fat diet to a 7% protein/73% carbohydrate/20% fat diet. The authors show that the low protein diet enhanced activation of biological pathways related to fatty acid catabolism including FAO, TCA cycle and electron transport chain in sWAT but not BA, similar to cold exposure. The authors use redundant assays and experiments in cell models to validate the genes and molecular pathways involved in the sWAT response to a low protein diet in mice. The authors show that AMPK activation promotes the induction of typical brown fat and muscular genes in sWAT. The authors identify novel non-canonical pathways (Serca1 and Serca2a,) that are upregulated in sWAT browning.

My main critique, coming from the perspective of a dietitian that works in human trials in the US, is that the diet called a "Western" diet is not similar to the diet that humans with metabolic problems typically eat. The typical US diet is closer to approximately a 17% protein/50% carbohydrate/33% split (https://doi.org/10.1016/j.nut.2015.02.007, https://doi.org/10.1038/s41430-017-0031-8). This level of protein utilized for the experimental "Western" diet here is comparable to levels used for "high protein" diets in some human studies (https://doi.org/10.1111/nure.12111).

Since the experimental diet differs substantially from what metabolically sick people typically eat, the ability to speculate how the findings from this study may apply to humans with metabolic diseases is very limited. This paper is really well-done, but I think the authors should call the experimental diet a high-protein, moderate carbohydrate diet (HPMC), not a "Western" diet. There are many who argue that such a HPMC is metabolically advantageous and promotes weight loss/improved body composition, so this study lays the groundwork for refuting that guidance. It would be exciting to see a head to head comparison of the two diets in humans in the future!

Major comments:

-The authors provide strong support their key findings

-The mice were on the LPHC diet for a short period of time (2 weeks). Ongoing amino acid deficiency has potential to promote frailty and other deleterious outcomes. No long-term diet outcomes can be inferred from this study.

-The authors have provided no evidence that a LPHC diet improves human health, so I think they need to scale back those assertions, particularly as it relates to people shifting to a LPHC from what they currently eat, since people don't typically eat what the authors refer to as a "Western" diet as it's defined in this paper.

-As far as I can tell, no additional experiments are needed to support their claims identifying how the LPHC affects AMPK activated pathways in mice.

-The methods are rigorous and sufficiently described to be reproducible

Minor comments:

-Minor grammatical issues through e.g. "It is worth to notice" in last paragraph on page 12; there are font differences in the methods section

Significance

The work is significant as it describes the metabolic effects of a LPHC at the molecular level for the first time. This paper demonstrates how a low protein diet may promote longevity and improve glucose metabolism, which has been shown to some extent in humans, but hasn't had a mechanistic explanation until now.

If similar findings were supported in longer term animal and human trials, it could lay the groundwork for modifying dietary recommendations to promote metabolic health and longevity.

This paper is of interest to basic scientists studying diet and energy metabolism. The potential health implications are interesting to people in healthcare and scientists studying human metabolism.

I am a dietitian who has conducted weight loss trials in humans, emphasizing varying macronutrient ratios. I have also done whole body metabolism work in humans using metabolic chambers. I have experience in urinary proteomics, but I lack sufficient expertise to scrutinize much of the methodology of the basic work you present here.

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Referee #2

Evidence, reproducibility and clarity

Aquilano et al. submitted a manuscript investigating the effects of a low-protein/high-carbohydrate diet on AMPK-dependent thermogenic activity in subcutaneous adipose tissue in mice presumably resulting in stimulated energy dissipation. Based on the observation that LPHC diets may promote metabolic benefits the authors aimed to study the underlying molecular functions. They focused mainly on a comparison of molecular markers for thermogenesis and the related metabolic pathways in brown and subcutaneous white adipose tissue in response to feeding mice a LPHC diet for two weeks. Using a proteomics approach first, they identified 75 proteins differentially present in sWAT compared to BAT. These could be linked both to canonical as well as non-canonical (muscular) thermogenic functions as the authors state. Overall, they conclude that feeding a LPHC diet induces a white-to-brown conversion in sWAT. Deep RNA-sequencing identified 416 up and 52 down-regulated gene transcripts in sWAT. GO terms analysis showed enrichment for biological processed related to mitochondrial fatty acid catabolism, response to cold, and muscle contraction genes. Following up this rational, they conducted several experimental approaches to identify regulators in this system. For example, they tried to rule out that changes in gut microbiome composition could mediate metabolic benefits in response to LPHC diet. Finally, they hypothesized that nutrient shortage in particular amino acid lowering is responsible for sWAT browning. Here, AMPK seems to play a central role in the browning of sWAT in response to LPHC diet.

Major comments:

o Are the key conclusions convincing? YES interesting paper but see comments below.

o Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? YES - The relevance of the described effects for whole-body energy balance regulation is not shown. Indirect calorimetry could be interesting. The only whole-body effect (slightly improved glucose clearance in oGTT) was very small.

o Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation. YES - 1) Indirect calorimetry could be very helpful to show effects on energy metabolism. 2) Can the authors discuss why they didn't conduct the experiment also under thermoneutral conditions?

o Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments. Maybe an additional collaborator is necessary.

o Are the data and the methods presented in such a way that they can be reproduced? YES mostly - but article numbers of all diets must be added and information if the all diets were purified diets. This could have effects on the gut microbiome.

o Are the experiments adequately replicated and statistical analysis adequate? Sample sizes are very low. The authors should explain why only males were used in the experiments. oGTT analysis should also include calculation of area under the curve. No explicit statement if correction for multiple testing is required or other measures to reduce false positive results.

Minor comments:

o Are prior studies referenced appropriately? Relevant reference: Desjardins, E.M., Steinberg, G.R. Emerging Role of AMPK in Brown and Beige Adipose Tissue (BAT): Implications for Obesity, Insulin Resistance, and Type 2 Diabetes. Curr Diab Rep 18, 80 (2018). https://doi.org/10.1007/s11892-018-1049-6

o Are the text and figures clear and accurate? YES

Significance

My expertise: Energy metabolism in rodent models for metabolic disease, body temperature regulation, body mass regulation

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Referee #1

Evidence, reproducibility and clarity

This work found that a LPHC meal activates browning of sWAT by ROS/AMPK pathway, and tried to clarify the detailed mechanism of the beneficial effect of LPHC diet. Although the paper contains scientific novelty and is well-written, most of the results are descriptive and deeper mechanistic study seems lacking. Here listed some comments and questions.

1.The stimulatory effect of LPHC diet on browning of some WATs has been previously reported (Nutrition, 42, 37-45 Oct 2017). Also, the activation of AMPK was observed in this study. However, the detailed mechanism responsible for AMPK activation by LPHC diet remains elusive in the present study, which lowers its scientific importance.

2.Different with WAT, LPHC diet increases glucose uptake and FA synthesis in BAT (Nutrition. 30 (4), 473-80 Apr 2014). Is it possible that AMPK activation in WAT due to the lowered glucose uptake, which might increase AMP/ATP ratio? It is recommended to determine the uptake of glucose in WAT.

1. The present study indicates that the promotional effect of LPHC diet on WAT browning is dependent on mitochondrial ROS generation. However, it is still unknown why the production of ROS increased and why ROS could activate AMPK. The authors should clarify these critical steps.

4.The relationship between cytosolic calcium and AMPK was not clear. In addition to the fact that AMPK regulates SERCA to increase cytosolic Ca depicted in the present study, AMPK could also be activated by increased Ca via CaMKK. A recent study indicates that the activation of AMPK requires TRPV4-mediated Ca release from ER (Cell Metabolism Volume 30, Issue 3, 3 September 2019, Pages 508-524.e12). This issue should also be clarified.

Significance

This work indicates that LPHC diet promotes browing of WAT through activation of AMPK by elevating mitochondrial ROS production. Compared to previous studies, this work firstly found the critical importance of mitochondrial ROS in activation of AMPK through a series of works on omics data. However, they failed to clearly explain the detailed mechanism responsable for either enhanced mitochondrial ROS production by LPHC diet or activation of AMPK by mitochondrial ROS. Therefore, due to most of the conclusions have been presented in some previous published papers, the main novelty of the present work should be greatly improved by further mechanistic stidies.

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Reply to the reviewers

I thank the referees for their enthusiasm and time providing critical feedbacks to our manuscript. The novelty of our work is the identification of the importance of Mfn2 in regulating the Rac signaling and neutrophil migration& adhesion, which is significantly relevant to the mitochondrial field and cell biology in general. Below please find our point-to-point response to the comments.

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

Introduction:

"Although mitochondria-derived ATP possibly regulates neutrophil chemotaxis in vitro (Bao et al., 2015), removal of extracellular ATP improves neutrophil chemotaxis in vivo (Li et al., 2016). These conflicting reports prompted us to search for mechanisms delineating the role of mitochondria in neutrophil migration outside the realm of ATP or cellular energy (Bi et al., 2014; Schuler et al., 2017; Zanotelli et al., 2018)." This sentence is superficial and misleading: extracellular ATP may interfere with chemotaxis through various energy-independent mechanisms (see for example Zumerle et al. Cell Reports 2019) and this is not conflicting with the role of intracellular ATP in migration.*

We were not clear in the writing that Bao et al suggest that neutrophils secret ATP at the leading edge and mitochondria at the leading edge is the source of the extracellular ATP. Both studies focused on extracellular ATP. We agree that the reports are not necessarily conflicting since exogenous ATP can induce additional signaling. We rewrote this sentence emphasizing that we are looking for mechanisms in addition to ATP, which is distinct from previous studies.

Figure 1: The authors didn't show evidence of the genome edition (PCR, RFLP or Sequencing over the sgRNA target) or at least RT-PCR or WB for MFN2. In Fig 1b, 1c the scale bar is missing. "Neutrophils were sorted from both lines and their respective loci targeted by the 4 sgRNAs were deep sequenced." There are no data about sorting strategies for zebrafish neutrophils in the figure. Moreover, only 2 sgRNAs are shown and there are no sequencing data.

To show evidence of the genome edition, we have deep sequenced this loci of mfn2 and opa1 and the mutation frequencies were stated in the original text. The sorting strategies were described in Methods-Mutational Efficiency Quantification. Each mfn2 KO has 2 individual sgRNAs, and two KO (mfn2 KO and mfn2 KO#2) were shown in Fig 1b, so there are 4 sgRNAs targeting mfn2. Since each embryos have approximately 150 neutrophils, WB is not feasible. Sequencing is the standard method (Ablain et al., 2015; Zhou et al., 2018). We only stated the mutation efficiency in the manuscript because amplifying the genomic DNA from the sorted cells introduces PCR bias and the numbers are not a quantitative reflection of the degree of gene disruption. We will include the sequencing result of the sgRNA target sites in a supplemental Figure.

We used one scale bar for all the panels in Fig 1b,c. All panels are at the some magnification.

Figure 2:** In the WB, reconstitution is not obvious. In general, all WBs are not quantified (and they should be quantified). The in vivo experiment does not have proper controls. For example, can the authors exclude that in these mice there is reduced inflammation because neutrophils have defective activation? What about NETs? And cytokines/chemokines? And exocytosis? In the absence of these controls, the experiment cannot be properly interpreted.

We have quantified all WBs in our study. The results were sometimes stated in the text only. We will add the quantifications to each blot.

The mice model we chose is used to evaluate in vivo neutrophil migration. We used a neutrophil specific promoter to delete mfn2 in mice and collected data at a very early time point when the tissue inflammatory environment is determined by tissue resident sentinel cells, such as macrophages. Although our results support that mfn2 is required for neutrophil migration in mammals, we agree that we can not fully rule out that other neutrophil functions are also regulated by mfn2.

To address whether other neutrophil functions are affected by MFN2, we will performed assays to evaluate NETosis and degranulation in MFN2 KD HL-60 cells to evaluate the other neutrophil functions.

Figure 3: The conclusion of the authors "In summary, Mfn2 modulates the actin cytoskeleton and cell migration in MEFs" should be supported by experiments to distinguish between the specific role of Mfn2 and the role of mitochondrial dynamics (Opa1, Drp1, Mfn1). It is also not clear why the authors decided to use MEFs instead of other cells (more similar to neutrophils which are not adherent cells). The results obtained in MEFs may be irrelevant for neutrophils.

We agree that MEFs are very different from neutrophils. We chose MEFs since the function of Mfn2 in MEF is well characterized (Chen et al., 2003; de Brito and Scorrano, 2008; Naon et al., 2016). Both Mfn1 and Mfn2 MEF have fragmented mitochondria. Mfn1, which is very similar to Mfn2, serves as the best control. We will confirm the mitochondria structure in the KO cells.

For specificity, in addition to mfn2, we looked at Mfn1 and opa1 in different systems. We did not select Drp1 since the mitochondrial network in neutrophils is highly fused (Fig 4 and 5)(Maianski et al., 2002; Zhou et al., 2018).

We have also knocked down Opa 1 in HL-60s. We observed massive cell death in this line and cell migration is affected, possibly due to a depletion of cellular ATP as reported (Amini et al., 2018). We will include the data showing cell death, qRT to show knockdown efficiency and chemotaxis. In zebrafish neutrophils, knocking out Opa1 also reduced cell migration (Fig 1S).

Figure 4-5: Fig 5a: in ctrl and sh1 the ER seems to be larger than the phalloidin (=cytoskeleton=cell border approximately) in a few regions. Only the sh1+T seems to fit correctly.

We use the F-actin staining as an indicator of cell front. F-Actin is predominant at cell front, but much less in the cell body and uropod. Here we set the confocal laser power at a certain level to give us a good resolution of brighter signals which may not be strong enough to detect signals in the cell body. That’s why the fluorescence is very dim or even absent in the cell body. However, the majority of ER do fit in the cell border if look closer.

The TEM image (only 1 in supplementary) is not sufficient to convince that the tethering is lost. Quantification of number of contacts and distance between ER and mitochondria should be included.

Using EM method, Mfn2 ablation decreases the ER-contacting mitochondrial surface by ∼20–35% (Naon et al., 2017). Using the same cells, different groups reached different conclusions using TEM(Filadi et al., 2017). We reason that ER-mitochondria contact sites are rare events in TEM since the samples are sliced. We will try to take more TEM images to quantify the distance. However, we are not sure that we can come up with a definitive conclusion by TEM. Nevertheless, we observed significant mitochondrial structural changes using IF and observed the changes in cytosolic calcium levels, which is consistent with the known function of Mfn2 as a ER-mitochondrial tether (Naon et al., 2016).

The title of figure 5 is wrong. However, in these figures, it is clear that cells are beautifully polarized, with mitochondria accumulating at the uropod (and even more in the absence of Mfn2). When comparing these images with those published by Campello et al (JEM 2006), there are 2 observations that can be made: first of all, these data confirm that mitochondrial fission promotes cell polarity; second, they suggest that the defect is not at the level of cell polarity/chemotaxis.

We have fixed the title of figure 5.

We agree that mfn2 defective neutrophils does not have a defect in cell polarization. The defects in migration is possibly due to other reasons such as poor adhesion or regulation in the actin cytoskeleton dynamics. However, our data is not sufficient to support that mitochondrial fission promotes cell polarization and chemotaxis.

Figure 6: Calcium data are, in general, very weak. First of all, controls with ionomycin are missing. Statistical analyses of the curves should be included. As for the use of the MCU inhibitor Ru360, is there any evidence that it is cell-permeant in this context? Is it blocking MCU? Since the authors can show mitochondrial calcium upon FMLP, they should also demonstrate that Ru360 is indeed working and inhibiting mitochondrial calcium uptake. The sentence "The MCU inhibitor Ru360did not cause further reduction of chemotaxis in MFN2 knockdown dHL-60 cells (Supplementary Fig. 6c, d and Supplementary Movie. 12), indicating that MCU and MFN2 lies in the same pathway in terms of regulating chemotaxis in dHL-60 cells" is speculative. In general, there is no solid demonstration that the effect is calcium-mediated.

We will include the control of ionomycin and include statistics of the results.

Ru360 is a widely used MCU inhibitor. The fact that Ru360 itself inhibited neutrophil migration supported that the chemical enters cells. We agree that stating “indicating that MCU and MFN2 lies in the same pathway in terms of regulating chemotaxis in dHL-60 cells" is speculative. In addition, we tried to reduce cytosolic calcium levels in mfn2 KD cells either using Ca2+ chelator (BAPTA, in Fig S6) or an IP3 receptor inhibitor. In both cases we observed reduced migration blocking calcium signal alone. The mfn2 KD phenotype was not rescued. This could due to that multiple molecules/pathways are calcium dependent in cell migration. We will include all the negative data. We thus far are still unable to establish a functional link of the calcium with mfn2 regulated signaling.

We have moved the calcium data to Fig 4. The elevated calcium signal is an indirect evidence to support the loss of ER-mitochondria tether. We have modified our conclusion to leave out calcium as a relevant signal regulated by mfn2 for neutrophil migration.

As for Rac, it is surprising to see that Rac inhibition has no effect on cell migration. Rac is known to promotes migration in fibroblasts and other cell types and Rac deficiency inhibits migration (see for example Steffen et al, JCS 2013). Two sets of experiments are absolutely required: 1) verify this in fibroblasts since it has been elegantly shown that Rac is essential in these cells for migration; 2) analyse the effect of Rac inhibitors in pPak kinetics.

Rac is required for cell migration and the growth of branched actin network. The Rac inhibitors we selected here are specific to two rac GEFs, vav and Tiam. Steffen et al, JCS 2013 used Rac1 KO MEF, which is different from ours. Thus the works are not contradictory. MEFs are very different from neutrophils. We chose MEFs since there are knockout cells available and well characterized. The MFN2 KO cells display prominent lamellipodia, which is also consistent with the observation in Steffen et al, JCS 2013. We have used these two inhibitors in MEF wound closure and did not observe a strong phenotype.

We will analyze the effect of Rac inhibitors in pPak kinetics in the control and Mfn2 deficient dHL-60 cells.

*Reviewer #1 (Significance (Required)):**

As presented here, the manuscript has a modest significance. The audience would be specialised: cell migration, cell signalling. My expertise is immunology, cell activation, cell migration, cell signalling.

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

**Major comments:**

Although the results could be very interesting, and could be significantly relevant to the mitochondrial field and the cell biology one in general, major points need to be addressed to fully support conclusions of the authors. Different controls and quantification are missing, Actin dynamics analysis should be improved, effects of the artificial tether is weakly characterized and the demonstration of the specific role of mito-ER contacts via mfn2 in migration should be reinforced.

-In figure 1, quantification of circulating neutrophils is required in Mfn2 KO embryos. The authors should also show these quantified results for OPA1KO, which are just mentioned in the text. In addition, in figure 1b and d, the neutrophils from the Mfn2KO embryos seem bigger compared to control. Can the authors comment on neutrophils size and potential contribution to the phenotype? Finally, the authors propose a defect in neutrophil migration in Mfn2-KO, however neutrophils are found in the circulation. The authors should explain these results.*

Since the cells are all in circulation, we can only estimate the percentage. Overall, the phenotype is drastic, shown in movie S1. We will state how many fish embryos we have imaged and how often we observe this phenotype (only 1 or 2 in the tissue (mfn2 KO) or in circulation (control)). The bigger spots are resulted from cells outside the focal plane-zebrafish embryos are thick tissues. We agree that since neutrophils in the KO fish are all circulation, we cannot make a conclusion whether they can migrate in tissue in zebrafish. We conclude that “mfn2 regulates neutrophil tissue retention and extravasation in zebrafish”, but did not comment on chemotaxis.

-The authors need to reinforce the Mfn2 specificity for their phenotype. In particular in Fig S1, they show that loss of OPA1 significantly decreases neutrophil migration in vivo. However, they then only study the effect of Mfn1 silencing in neutrophil and MFN1 KO MEFs (Sup Fig s3). The authors should perform the same experiments in neutrophil and MEF upon loss of OPA1 (similar to Fig S3). Does loss of OPA1 and Mfn1 decrease neutrophil arrest to activated endothelial cells?

We knocked down OPA1 in HL-60 cells. The cells appear unhealthy and display a migration defect, consistent with the data in zebrafish. We are not comfortable making conclusions here since secondary affects in dying cells may cause any phenotype not directly attributed to the loss of OPA1. Nevertheless, we will include the data.

We have decided not to include Opa1 KO MEF since the cell morphology as documented in ATCC is similar to that of WT MEF. Only the MEF2 KO MEF is more circular. MFN1 KO MEF is a better specificity control which we have characterized in depth.

Since Mfn1 KD HL-60 cells migrate well on surface, they are not expected to have adhesion defects. Nevertheless, we will determine whether loss of Mfn1 decrease neutrophil arrest to activated endothelial cells and include the data.

-Using their images, the authors should also document on the directionality of the cell during cell migration. Do Mfn2 depleted cells do not migrate because they are arrested or because they are lacking directionality? Environment/chemokine sensing defects?

We will quantified the directionality of the cells. As pointed out by reviewer 1, mfn2 deficient cells can polarize and not defective in chemokine sensing. We do not expect a significant change in directionality defect.

-Actin dynamics analysis should be improved. Loss of Mfn1 and Mfn2 lead to cell shape changes. The authors should quantify this phenotype by analysing cell circularity (as well as for Opa1 loss). Stress fibres number or Phalloidin intensity quantification in cell body should also be performed.

We will quantified the circularity, stress fiber numbers and phalloidin intensity in Mfn1 and Mfn2 KO MEFs.

-Can the migration defects could be attributed to Focal adhesion protein dynamics defects? The authors shown an hyperactivation of Rac1 and an hyperphosphorylation of PAK, which can control FAP (focal adhesion proteins) dynamics. In addition, immunofluorescence analysis shows a decreased signal and cellular misdistribution of paxillin. The authors should characterize these phenotypes. FAP levels (Paxillin/Phospho-Paxillin and Vinculin) should be analysed by immunoblot, the number of FAP/cell, distribution and size should also be quantified. Their dynamics should also be analysed by live cell imaging. Finally, Paxillin level and distribution seems to be also impacted in Mfn1KO cells. Can the authors comment on that? The different quantifications would help to better understand the effect of different mitofusins in cytoskeleton dynamic.

We thank the reviewer for the great advices for our follow up work. So far our results supports Rac over activation as a relevant pathway how mfn2 regulates neutrophil migration. Although Rac can regulate focal adhesion dynamics in other cells (Rooney et al., 2010), how Rac activation regulates focal adhesion dynamics in neutrophils is not clear. Mfn2 regulated membrane tether could affect lipid trafficking, cellular metabolism and other signaling molecules. It will take substantial amount of work to make a conclusion and it is more suitable a separate report. This is one of the directions we will pursuit in our future studies.

-Please perform rescue experiments for cell migration in MFN2KO and MFN1KO MEFs. Immunoblots showing protein levels of these proteins would be appreciated. To really discriminate how Mfn2 regulates cell migration, the authors should also perform rescue experiments using a fusogenic mutant Mfn2 ((K109A). It will help to demonstrate the relevance of mito-ER contacts and not mitochondrial fusion in the phenotype.

For the reason mentioned above, we do not plan to do additional experiments in MEF cells since this work is focused on neutrophils. It is documented that Mfn2 K109A cannot restore mitochondrial fusion. However, it is not clear whether this construct can restore ER-tether. Result using this construct will be hard to interpret.

-Figure 4, the authors stipulate that Mfn2 regulates ER-mitochondria tethering. However, the authors present no evidence for this conclusion. The authors should perform manders coefficient in MFN2 KO cells and compared it to control. Also, loss of Mfn2 induces mitochondrial fragmentation, which can lead to problem for mito-er contacts quantification by light microscopy. The authors should use their TEM pictures to quantify mito-ER contacts (Number, length and % of mito perimeter), not only mitochondrial morphology. Mfn1 should be used as negative control. it would be interesting also to determine the status of the mito-ER contact in the different conditions used in the manuscript to stimulate cell migration like fMLP treatment.

We have performed manders coefficient in the mfn2 KD cells and observed no difference compared with the control. It is possibly due to the prevalent ER structure in the cells-despite the structural change, mitochondria are still mostly on top of ER when examined using IF. Using EM method, Mfn2 ablation decreases the ER-contacting mitochondrial surface by ∼20–35% (Naon et al., 2017). Using the same cells, different groups reached different conclusions using TEM(Filadi et al., 2017). We reason that ER-mitochondria contact sites are rare events in TEM since the samples are sliced. We will try to take more TEM images to quantify the distance. However, we are not sure that we will come up with a definitive conclusion by TEM. Nevertheless, we observed significant mitochondrial structural changes using IF and observed the changes in cytosolic calcium levels, which is consistent with the known function of Mfn2 as a ER-mitochondrial tether (Naon et al., 2016).

-The authors use an artificial tether to manipulate mito-ER contacts in cellulo. However, no information from its origin, or its design are documented in the manuscript. In addition, the authors should show that this tether efficiently works by analyzing mito-ER contacts upon expression by EM and mitochondrial calcium uptake. Does this tether rescue mito-ER contacts defects induced by loss of Mfn2? How the authors explain that the tether rescues mitochondrial morphology defects in MFN2KO? In these conditions, mitochondria should not be able to fuse anymore as Mfn2 is lost? This is really intriguing results. Does the tether rescue the other parameters? Mitochondrial distribution (with quantification)? Cell shape? Paxillin defects? ROS and membrane potential? These rescue experiments analyses are important to determine which parameters are really involved in cell migration defects due to the decreased tethering. Finally, it would be of great interest to analyse the effect of the tether alone on cell migration, Rac1 activity, cell shape? Gain of function? These results may reinforce the idea that contact sites regulate cell migration.

The tether is a GFP protein carrying both ER and mitochondrial localization sequences at the ends (Kornmann et al., 2009). The details are now added to the manuscript.

In HL-60 mfn2 KD cells, tether expression partially rescues mitochondrial distribution (quantified in Fig 5c), cell migration and Rac over activation. Although ROS and membrane potential are slightly affected by Mfn2 deletion in HL-60 cells, it is not clear whether they play any roles in mfn2 regulated cell adhesion or migration. We will attempt to use TEM to determine the mitochondrial structure upon tether rescue.

Despite multiple attempts, we could not obtain a line over-expressing the tether in wt HL-60 cells. We suspect that further increase in the tether is toxic to the cells.

-It is well established that a decrease of membrane potential leads to a decrease of mitochondrial calcium uptake. Calcium results obtained by the authors without any information on the roles of the tether could not lead to any conclusion. Does the tether rescues membrane potential and calcium uptake by the mitochondria? So far, the decrease of mitochondrial calcium upon stimulation in Mfn2KO cab be attributed to both mito-ER contact or membrane potential defects. It has been shown that MEFs MFN2 KO can lead to a decrease of MCU provel level leading to a decrease of mitochondrial calcium uptake (PMID: 25870285). The authors should also check MCU protein level.

We observed that mfn2 deficiency resulted in a minor reduction in membrane potential. Although Mfn2 KO MEF has reduced level of Mcu, Mfn2 silence in MEF does not affect Mcu levels (Filadi et al., 2015). Another group also concluded that Mfn2 deletion does not necessarily affect Mcu levels (Naon et al., 2016). Nevertheless, we will measure the MCU protein level in the Mfn2 knockdown HL-60 cells.

-Hyperactivation of Rac1 is only based on phosphorylation of PAK, which is quite weak. The authors should better describe the hyperactivation of RAC1 or other RhoGTPases in their Mfn2 KO MEFs. What are the levels of RAC1 and other RhoGTPases? Subcellular distribution in the cell? Kits are also available to determine RhoGTPase activity by pull down assay (Cell biolabs).

In Mfn2 KO MEFs, Rac overactivation is suggested by the increased lamellipodia formation, classical Rac readouts. Since the current manuscript focuses on neutrophils, we will performed the Rac GFP pull down experiments in HL-60 cells. We will also stain Rac GTP in HL-60 cells.

*-The references are up-to-date. The text and the figures are clear and accurate.**

**Minor comments:**

-The authors should show the efficiency of the KO generated for Mfn2 and Opa1 in zebrafish embryos. Sequencing results to highlight the position of the mutations and their consequences on the coding protein should be shown, as well as immunoblot analysis should be performed to analyse Mfn1, Mfn2 and OPA1 protein levels. The generation of a MFN1-KO transgenic line would have been appreciated to finely compare the roles of the 3 GTPases involved in mitochondrial fusion during neutrophil infiltration and migration in vivo.*

To show evidence of the genome edition, we have deep sequenced this loci of mfn2 and opa1 and the mutation frequencies were stated in the original text. Since each embryos have approximately 150 neutrophils, WB is not feasible. Sequencing is the standard method (Ablain et al., 2015; Zhou et al., 2018). We only stated the mutation efficiency in the manuscript because amplifying the genomic DNA from the sorted cells introduces PCR bias and the numbers are not a quantitative reflection of the degree of gene disruption. We will include the sequencing result of the sgRNA target sites in a supplemental Figure.

The mfn1 gene in zebrafish is duplicated. We are not sure whether we can obtain efficient disruption at both loci. We hope the results using Mfn1 KO MEF and MFN1 KD HL-60 cells are enough to show a specific role of Mfn2 in cell migration.

-MFN1, MFN2, AND OPA1 protein levels should be analysed by immunoblot in the Mfn1 and Mfn2 KO MEFs.

It is unlikely that mfn1/2 KO will affect OPA1 levels (Saita et al., 2016). Both MFN1 and MFN2 MEF display fragmented mitochondrial network which can be rescued by overexpression of MFN1 or MFN2 (Chen et al., 2003). The level of OPA1 in the cells are not relevant. We will stain mitochondria in the mfn1/2 KO MEFs to make sure that the cells have fragmented mitochondria as expected.

-In cell spreading assay, it would be great to identify cells during the process, by an asterix for example. "wt MEFs extended transient filopodia and lamellipodia and eventually elongated, whereas Mfn2-null MEFs generated extensive membrane ruffles and retained the circular shape". It would be interesting to quantify these different parameters.

We will add Asterixes to the cells. We will quantify the percentage of cells that can rearrange their cell shape in the WT and Mfn2 KO MEFs.

-For all their immunoblot analysis, the authors should use a mitochondrial marker as loading control (VDAC1, TOM20, HSP60...). In figure 5, Vinculin should not be used a loading control, with its role in focal adhesion dynamics.

Vinculin is stable in HL-60 cells under multiple conditions and selected as a control. The signal intensity correlates well with the amount of protein loaded. Using mitochondrial proteins as loading controls is not common and may be risky as the amount of mitochondria in cells can be variable.

-Legends for figures 5 and 6 are inverted.

Thanks, we have changed the heading of figure 5. The legends were correct.

-Please document in the material and methods section, how confocal images have been acquired: number of z-stacks, reconstitution, 3D analysis...

We will update in the method the parameters of imaging acquisition.

-The authors should show their results of blood cell composition quantification in ctrl vs MFn2 depletion.

We will include the results of blood cell composition in a supplemental figure.

-The authors should describe all the acronyms used throughout all the manuscript. For example, LTB4, fMLP...

We have describe all the acronyms in the updated manuscript.

*Reviewer #2 (Significance (Required)):

Beyond their role in energy production, mitochondria are involved in numerous cellular functions including cell migration. Mitochondria form a network balanced by fission and fusion events, where membrane contact sites with the endoplasmic reticulum are crucial. These contact sites are also involved in mitochondrial and cellular functions via their capacity to exchange lipids, metabolites and calcium. The role of mitochondria in cell migration has started to emerge where mitochondrial fragmentation and/or mitochondrial calcium homeostasis are acknowledged to drive cancer cell migration and to regulate actin dynamics. In this manuscript, Zhou W and colleagues proposed for the first time the role of mitochondria-ER contacts in cell migration. Mechanistically, this can be associated to the capacity of these contacts to control mitochondrial functions or mitochondrial calcium homeostasis. These findings are physiologically relevant and of particular interest to the mitochondrial and cell migration field but also to general cell biology. It represents a novel function associated to these membrane contact sites and point-out these contacts as signalling platform creating microdomains of metabolites exchanges involved in cell migration.

Keywords: Mitochondria - Membraned dynamics - calcium homeostasis - Membrane contact sites*

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

Mitofusin 2 (Mfn2) is a mitochondrial outer membrane protein that is important for mitochondrial fusion and the establishment of mitochondrial ER contacts. It has been published before that these contact sites are important for calcium signaling. Zhou et al. examined the role of Mfn2 in neutrophils. They propose a model in which mitochondrial ER contacts established via Mfn2 are crucial for regulation of Rac, which is a small GTPase driving cell migration by promoting actin polymerization. Loss of Mfn2 results in elevated cytosolic calcium, over-activation of Rac, and defects of chemotactic movements. These defects can be partially rescued by restoration of mitochondrial ER contacts through expression of an artificial tether protein.

**Major points**

1.The authors claim on p. 6 that decreased neutrophil retention is not simply due to defects in mitochondrial fusion. However, the experimental setup they used for mfn2 (Fig. 1) is different from that for opa1 (Fig. S1), and therefore the results are not directly comparable. Unfortunately, the authors don't show fragmentation of mitochondria, neither in mfn2 nor in opa1 depleted cells. To support their statement they must show that lack of Mfn2 and Opa1 causes mitochondrial fragmentation to the same extent and then examine neutrophil retention in the same assay. Also, it would make sense to include Mfn1 in this analysis, as the authors later claim that the effects they observed are specific for Mfn2.*

Since Mfn2 KO neutrophils are not in tissue, the experiment in Fig 1S to look at cell speed in tissue is not feasible. Since the cells are all in circulation, we can only estimate the percentage. Overall, the phenotype is very drastic, see movie S1. We will state how many fish embryos we have imaged and how often we observe this phenotype (only 1 or 2 in the tissue (mfn2 KO) or in circulation (control and opa1 KO)). Opa1 KO neutrophils are not in circulation.

To show evidence of the genome edition, we have deep sequenced this loci of mfn2 and opa1 and the mutation frequencies were stated in the original text. Since each embryos have approximately 150 neutrophils, WB and other biochemical assays are not feasible. Sequencing is the standard method (Ablain et al., 2015; Zhou et al., 2018). We only stated the mutation efficiency in the manuscript because amplifying the genomic DNA from the sorted cells introduces PCR bias and the numbers are not a quantitative reflection of the degree of gene disruption. We will include the sequencing result of the sgRNA target sites in a supplemental Figure.

Since Mfn2 KO neutrophils are all in circulating, we cannot observe their mitochondrial morphology. This is the reason why we used HL-60 cells for the mechanistic study. The mfn1 gene in zebrafish is duplicated. We have generated an mfn1b KO line and did not observe any phenotype. However we are not sure whether we can obtain efficient disruption at both loci. We hope the results using Mfn1 KO MEF and MFN1 KD HL-60 cells are enough to show a specific role of Mfn2 in cell migration.

We will have stained the mitochondria structure in the MEF1/2 MEF cells and the in Mfn1/2 KD dHL-60 cells. Opa1 KD HL-60 cells display extensive cell death and we are not confident interpreting any results from this line.

2.The authors should examine mitochondrial morphology in MFN2 shRNA treated cells (Fig. 2) and in mfn2-null MEFs (Fig. 3).

Mitochondrial morphology is examined in MFN2 shRNA treated cells (Fig 4c and 5a). The mitochondrial morphology in mfn2-null MEFs are published (Chen et al., 2003). We will further confirmed their results by staining mitochondrial structure in the KO MEFs.

3.The authors claim that chemotaxis defects of neutrophils are specific for MFN2 knock down, but not for MFN1. They show a Western blot of mfn1 knock down cells in Figure S3s. There is a band clearly visible, which appears to be much stronger than the MFN2 band in sh1 cells in Fig. 2a. Therefore, this conclusion is not valid.

The band intensities are dependent on the antibody quality and imaging acquisition and display. We don’t feel comfortable comparing the amount of two different proteins from two separate blots.

4.The colocalization of MFN2 with mitochondria and ER, shown in Fig. 4a, should be improved. Both mitochondria and ER appear abnormally clumped. The authors should stain mitochondria, ER and Mfn2 in the same cells. Images should be displayed much larger. The same is true for Fig. 5a. The authors claim that an artificial tether restored mitochondrial morphology in mfn2 knock down cells. They should state in the text which tether was used. Furthermore, they should explain their criteria for judgement of mitochondrial morphology. At least in my exes, mitochondria appear highly clumped in the image shown for sh1+T cells. In Fig. 5c it is not indicated how many cells were scored.

We will replace Fig 4a with a more representative image.

Neutrophils are blood cells and do not spread as well as adherent cells. We have also overexposed the images to show the smaller mitochondria, which cannot be visualized without saturating the signals. We tried to stain the cells with Mfn2 and Calnexin. However we cannot retain the mitotracker signal in fixed cells and could not do a triple label in dHL-60 cells. For this reason we have done double staining of mitochondria-ER, MFN2-mitochondria and MFN2-ER.

We have included the citation and the description of the tether. The tether is composed of a GFP protein carrying both ER and mitochondrial localization sequences at the ends, which functions independently of MFN2.

The criteria for the judgement of the mitochondrial morphology is now included in the methods, clustering analysis.

\*Minor points**

5.The Western blot shown in Fig. 5d suggests that expression of the tether construct reduced the amount of MFN2. How can this be explained?*

This Mfn2 amount is not significantly altered by the tether expression when quantified. We will add the quantification of all WB to the figures.

6.The paper is sometimes hard to digest for a reader who is not familiar with the authors' experimental systems. The description of the experiments in the main text is highly condensed.

We will elaborate on the experimental system in the results section.

7.Page 11: "5 m post stimulation" should read "5 min post stimulation".

Thank you. We have made this correction.

8.Some references are incomplete (page numbers are lacking).

We will reformat our references and checked for page numbers.

*Reviewer #3 (Significance (Required)):

Apparently, the manuscript is written for an audience with a special interest in chemotactic movements of neutrophils. I guess that the results reported in this manuscript will be of interest for this field. My background is mitochondrial biology and dynamics and I don't have the expertise to evaluate the aspects specific for neutrophils.*

It is well established that mfn2 mediates mitochondrial fusion and ER contact. Our novelty is the discovery that mfn2 suppresses Rac activation, which is essential for neutrophil adhesion and migration.

References:

Ablain, J., E.M. Durand, S. Yang, Y. Zhou, and L.I. Zon. 2015. A CRISPR/Cas9 vector system for tissue-specific gene disruption in zebrafish. Developmental cell. 32:756-764.

Amini, P., D. Stojkov, A. Felser, C.B. Jackson, C. Courage, A. Schaller, L. Gelman, M.E. Soriano, J.M. Nuoffer, L. Scorrano, C. Benarafa, S. Yousefi, and H.U. Simon. 2018. Neutrophil extracellular trap formation requires OPA1-dependent glycolytic ATP production. Nature communications. 9:2958.

Chen, H., S.A. Detmer, A.J. Ewald, E.E. Griffin, S.E. Fraser, and D.C. Chan. 2003. Mitofusins Mfn1 and Mfn2 coordinately regulate mitochondrial fusion and are essential for embryonic development. The Journal of cell biology. 160:189-200.

de Brito, O.M., and L. Scorrano. 2008. Mitofusin 2 tethers endoplasmic reticulum to mitochondria. Nature. 456:605-610.

Filadi, R., E. Greotti, G. Turacchio, A. Luini, T. Pozzan, and P. Pizzo. 2015. Mitofusin 2 ablation increases endoplasmic reticulum-mitochondria coupling. Proceedings of the National Academy of Sciences of the United States of America. 112:E2174-2181.

Filadi, R., E. Greotti, G. Turacchio, A. Luini, T. Pozzan, and P. Pizzo. 2017. On the role of Mitofusin 2 in endoplasmic reticulum-mitochondria tethering. Proceedings of the National Academy of Sciences of the United States of America. 114:E2266-E2267.

Kornmann, B., E. Currie, S.R. Collins, M. Schuldiner, J. Nunnari, J.S. Weissman, and P. Walter. 2009. An ER-mitochondria tethering complex revealed by a synthetic biology screen. Science. 325:477-481.

Maianski, N.A., F.P. Mul, J.D. van Buul, D. Roos, and T.W. Kuijpers. 2002. Granulocyte colony-stimulating factor inhibits the mitochondria-dependent activation of caspase-3 in neutrophils. Blood. 99:672-679.

Naon, D., M. Zaninello, M. Giacomello, T. Varanita, F. Grespi, S. Lakshminaranayan, A. Serafini, M. Semenzato, S. Herkenne, M.I. Hernandez-Alvarez, A. Zorzano, D. De Stefani, G.W. Dorn, 2nd, and L. Scorrano. 2016. Critical reappraisal confirms that Mitofusin 2 is an endoplasmic reticulum-mitochondria tether. Proceedings of the National Academy of Sciences of the United States of America. 113:11249-11254.

Naon, D., M. Zaninello, M. Giacomello, T. Varanita, F. Grespi, S. Lakshminaranayan, A. Serafini, M. Semenzato, S. Herkenne, M.I. Hernandez-Alvarez, A. Zorzano, D. De Stefani, G.W. Dorn, 2nd, and L. Scorrano. 2017. Reply to Filadi et al.: Does Mitofusin 2 tether or separate endoplasmic reticulum and mitochondria? Proceedings of the National Academy of Sciences of the United States of America. 114:E2268-E2269.

Rooney, C., G. White, A. Nazgiewicz, S.A. Woodcock, K.I. Anderson, C. Ballestrem, and A. Malliri. 2010. The Rac activator STEF (Tiam2) regulates cell migration by microtubule-mediated focal adhesion disassembly. EMBO reports. 11:292-298.

Saita, S., T. Ishihara, M. Maeda, S. Iemura, T. Natsume, K. Mihara, and N. Ishihara. 2016. Distinct types of protease systems are involved in homeostasis regulation of mitochondrial morphology via balanced fusion and fission. Genes to cells : devoted to molecular & cellular mechanisms. 21:408-424.

Zhou, W., L. Cao, J. Jeffries, X. Zhu, C.J. Staiger, and Q. Deng. 2018. Neutrophil-specific knockout demonstrates a role for mitochondria in regulating neutrophil motility in zebrafish. Disease models & mechanisms. 11.

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Referee #3

Evidence, reproducibility and clarity

Mitofusin 2 (Mfn2) is a mitochondrial outer membrane protein that is important for mitochondrial fusion and the establishment of mitochondrial ER contacts. It has been published before that these contact sites are important for calcium signaling. Zhou et al. examined the role of Mfn2 in neutrophils. They propose a model in which mitochondrial ER contacts established via Mfn2 are crucial for regulation of Rac, which is a small GTPase driving cell migration by promoting actin polymerization. Loss of Mfn2 results in elevated cytosolic calcium, over-activation of Rac, and defects of chemotactic movements. These defects can be partially rescued by restoration of mitochondrial ER contacts through expression of an artificial tether protein.

Major points

1.The authors claim on p. 6 that decreased neutrophil retention is not simply due to defects in mitochondrial fusion. However, the experimental setup they used for mfn2 (Fig. 1) is different from that for opa1 (Fig. S1), and therefore the results are not directly comparable. Unfortunately, the authors don't show fragmentation of mitochondria, neither in mfn2 nor in opa1 depleted cells. To support their statement they must show that lack of Mfn2 and Opa1 causes mitochondrial fragmentation to the same extent and then examine neutrophil retention in the same assay. Also, it would make sense to include Mfn1 in this analysis, as the authors later claim that the effects they observed are specific for Mfn2.

2.The authors should examine mitochondrial morphology in MFN2 shRNA treated cells (Fig. 2) and in mfn2-null MEFs (Fig. 3).

3.The authors claim that chemotaxis defects of neutrophils are specific for MFN2 knock down, but not for MFN1. They show a Western blot of mfn1 knock down cells in Figure S3s. There is a band clearly visible, which appears to be much stronger than the MFN2 band in sh1 cells in Fig. 2a. Therefore, this conclusion is not valid.

4.The colocalization of MFN2 with mitochondria and ER, shown in Fig. 4a, should be improved. Both mitochondria and ER appear abnormally clumped. The authors should stain mitochondria, ER and Mfn2 in the same cells. Images should be displayed much larger. The same is true for Fig. 5a. The authors claim that an artificial tether restored mitochondrial morphology in mfn2 knock down cells. They should state in the text which tether was used. Furthermore, they should explain their criteria for judgement of mitochondrial morphology. At least in my exes, mitochondria appear highly clumped in the image shown for sh1+T cells. In Fig. 5c it is not indicated how many cells were scored.

Minor points

5.The Western blot shown in Fig. 5d suggests that expression of the tether construct reduced the amount of MFN2. How can this be explained?

6.The paper is sometimes hard to digest for a reader who is not familiar with the authors' experimental systems. The description of the experiments in the main text is highly condensed.

7.Page 11: "5 m post stimulation" should read "5 min post stimulation".

8.Some references are incomplete (page numbers are lacking).

Significance

Apparently, the manuscript is written for an audience with a special interest in chemotactic movements of neutrophils. I guess that the results reported in this manuscript will be of interest for this field. My background is mitochondrial biology and dynamics and I don't have the expertise to evaluate the aspects specific for neutrophils.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The manuscript by Zhou W and colleagues entitled "Mitofusin 2 regulated neurophil adhesive migration and actin cytoskeleton" proposed that mitochondrial outer membrane GTPase Mitofusin 2 controls cell migration via its capacity to regulate mitochondria-endoplasmic reticulum (ER) contacts, independently of its fusogenic activity. Using transgenic Mfn2 zebrafish, they first show that Mfn2 mutant embryos exhibit circulating neutrophils and defects in neutrophil recruitment to generated wound, compared to control. Then, using a combination of in cellulo and in vivo mouse models, they show that loss of Mfn2 decreases neutrophil migration, their adhesion under sheer stress and their infiltration to the peritoneal cavity in vivo. Third, they confirm these results using Mfn2 KO MEFs, where they show migration and actin skeleton defects, in contrast to Mfn1 KO MEFs. Mechanistically, they propose that migration defects induced by Mfn2 loss are associated to a decrease of membrane contact sites between the ER and the mitochondria. Using different in cellulo cell migration assays, they show that migration defects in Mfn2-null was rescued upon an artificial mito-ER tether. Finally, they propose that the loss of Mfn2 leads to cytosolic calcium accumulation, inducing hyper-activation of the RhoGTPase, Rac1, a key regulator of actin dynamics and cell migration. Together, the authors proposed a new function of Mfn2 in regulating cell migration via mito-ER contacts tethering.

Major comments:

Although the results could be very interesting, and could be significantly relevant to the mitochondrial field and the cell biology one in general, major points need to be addressed to fully support conclusions of the authors. Different controls and quantification are missing, Actin dynamics analysis should be improved, effects of the artificial tether is weakly characterized and the demonstration of the specific role of mito-ER contacts via mfn2 in migration should be reinforced.

-In figure 1, quantification of circulating neutrophils is required in Mfn2 KO embryos. The authors should also show these quantified results for OPA1KO, which are just mentioned in the text. In addition, in figure 1b and d, the neutrophils from the Mfn2KO embryos seem bigger compared to control. Can the authors comment on neutrophils size and potential contribution to the phenotype? Finally, the authors propose a defect in neutrophil migration in Mfn2-KO, however neutrophils are found in the circulation. The authors should explain these results.

-The authors need to reinforce the Mfn2 specificity for their phenotype. In particular in Fig S1, they show that loss of OPA1 significantly decreases neutrophil migration in vivo. However, they then only study the effect of Mfn1 silencing in neutrophil and MFN1 KO MEFs (Sup Fig s3). The authors should perform the same experiments in neutrophil and MEF upon loss of OPA1 (similar to Fig S3). Does loss of OPA1 and Mfn1 decrease neutrophil arrest to activated endothelial cells?

-Using their images, the authors should also document on the directionality of the cell during cell migration. Do Mfn2 depleted cells do not migrate because they are arrested or because they are lacking directionality? Environment/chemokine sensing defects?

-Actin dynamics analysis should be improved. Loss of Mfn1 and Mfn2 lead to cell shape changes. The authors should quantify this phenotype by analysing cell circularity (as well as for Opa1 loss). Stress fibres number or Phalloidin intensity quantification in cell body should also be performed.

-Can the migration defects could be attributed to Focal adhesion protein dynamics defects? The authors shown an hyperactivation of Rac1 and an hyperphosphorylation of PAK, which can control FAP (focal adhesion proteins) dynamics. In addition, immunofluorescence analysis shows a decreased signal and cellular misdistribution of paxillin. The authors should characterize these phenotypes. FAP levels (Paxillin/Phospho-Paxillin and Vinculin) should be analysed by immunoblot, the number of FAP/cell, distribution and size should also be quantified. Their dynamics should also be analysed by live cell imaging. Finally, Paxillin level and distribution seems to be also impacted in Mfn1KO cells. Can the authors comment on that? The different quantifications would help to better understand the effect of different mitofusins in cytoskeleton dynamic.

-Please perform rescue experiments for cell migration in MFN2KO and MFN1KO MEFs. Immunoblots showing protein levels of these proteins would be appreciated. To really discriminate how Mfn2 regulates cell migration, the authors should also perform rescue experiments using a fusogenic mutant Mfn2 ((K109A). It will help to demonstrate the relevance of mito-ER contacts and not mitochondrial fusion in the phenotype.

-Figure 4, the authors stipulate that Mfn2 regulates ER-mitochondria tethering. However, the authors present no evidence for this conclusion. The authors should perform manders coefficient in MFN2 KO cells and compared it to control. Also, loss of Mfn2 induces mitochondrial fragmentation, which can lead to problem for mito-er contacts quantification by light microscopy. The authors should use their TEM pictures to quantify mito-ER contacts (Number, length and % of mito perimeter), not only mitochondrial morphology. Mfn1 should be used as negative control. it would be interesting also to determine the status of the mito-ER contact in the different conditions used in the manuscript to stimulate cell migration like fMLP treatment.

-The authors use an artificial tether to manipulate mito-ER contacts in cellulo. However, no information from its origin, or its design are documented in the manuscript. In addition, the authors should show that this tether efficiently works by analysing mito-ER contacts upon expression by EM and mitochondrial calcium uptake. Does this tether rescue mito-ER contacts defects induced by loss of Mfn2? How the authors explain that the tether rescues mitochondrial morphology defects in MFN2KO? In these conditions, mitochondria should not be able to fuse anymore as Mfn2 is lost? This is really intriguing results. Does the tether rescue the other parameters? Mitochondrial distribution (with quantification)? Cell shape? Paxillin defects? ROS and membrane potential? These rescue experiments analyses are important to determine which parameters are really involved in cell migration defects due to the decreased tethering. Finally, it would be of great interest to analyse the effect of the tether alone on cell migration, Rac1 activity, cell shape? Gain of function? These results may reinforce the idea that contact sites regulate cell migration.

-It is well established that a decrease of membrane potential leads to a decrease of mitochondrial calcium uptake. Calcium results obtained by the authors without any information on the roles of the tether could not lead to any conclusion. Does the tether rescues membrane potential and calcium uptake by the mitochondria? So far, the decrease of mitochondrial calcium upon stimulation in Mfn2KO cab be attributed to both mito-ER contact or membrane potential defects. It has been shown that MEFs MFN2 KO can lead to a decrease of MCU provel level leading to a decrease of mitochondrial calcium uptake (PMID: 25870285). The authors should also check MCU protein level.

-Hyperactivation of Rac1 is only based on phosphorylation of PAK, which is quite weak. The authors should better describe the hyperactivation of RAC1 or other RhoGTPases in their Mfn2 KO MEFs. What are the levels of RAC1 and other RhoGTPases? Subcellular distribution in the cell? Kits are also available to determine RhoGTPase activity by pull down assay (Cell biolabs).

-The references are up-to-date. The text and the figures are clear and accurate.

Minor comments:

-The authors should show the efficiency of the KO generated for Mfn2 and Opa1 in zebrafish embryos. Sequencing results to highlight the position of the mutations and their consequences on the coding protein should be shown, as well as immunoblot analysis should be performed to analyse Mfn1, Mfn2 and OPA1 protein levels. The generation of a MFN1-KO transgenic line would have been appreciated to finely compare the roles of the 3 GTPases involved in mitochondrial fusion during neutrophil infiltration and migration in vivo.

-MFN1, MFN2, AND OPA1 protein levels should be analysed by immunoblot in the Mfn1 and Mfn2 KO MEFs.

-In cell spreading assay, it would be great to identify cells during the process, by an asterix for example. "wt MEFs extended transient filopodia and lamellipodia and eventually elongated, whereas Mfn2-null MEFs generated extensive membrane ruffles and retained the circular shape". It would be interesting to quantify these different parameters.

-For all their immunoblot analysis, the authors should use a mitochondrial marker as loading control (VDAC1, TOM20, HSP60...). In figure 5, Vinculin should not be used a loading control, with its role in focal adhesion dynamics.

-Legends for figures 5 and 6 are inverted.

-Please document in the material and methods section, how confocal images have been acquired: number of z-stacks, reconstitution, 3D analysis...

-The authors should show their results of blood cell composition quantification in ctrl vs MFn2 depletion.

-The authors should describe all the acronyms used throughout all the manuscript. For example, LTB4, fMLP...

Significance

Beyond their role in energy production, mitochondria are involved in numerous cellular functions including cell migration. Mitochondria form a network balanced by fission and fusion events, where membrane contact sites with the endoplasmic reticulum are crucial. These contact sites are also involved in mitochondrial and cellular functions via their capacity to exchange lipids, metabolites and calcium. The role of mitochondria in cell migration has started to emerge where mitochondrial fragmentation and/or mitochondrial calcium homeostasis are acknowledged to drive cancer cell migration and to regulate actin dynamics. In this manuscript, Zhou W and colleagues proposed for the first time the role of mitochondria-ER contacts in cell migration. Mechanistically, this can be associated to the capacity of these contacts to control mitochondrial functions or mitochondrial calcium homeostasis. These findings are physiologically relevant and of particular interest to the mitochondrial and cell migration field but also to general cell biology. It represents a novel function associated to these membrane contact sites and point-out these contacts as signalling platform creating microdomains of metabolites exchanges involved in cell migration.

Keywords: Mitochondria - Membraned dynamics - calcium homeostasis - Membrane contact sites

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Zhou et al. describes the role of mitofusin-2 in neutrophil adhesive migration. The authors suggest that MFN-2 is required to sustain neutrophil migration and link this observation to the role of MFN-2 in maintaining mitochondria-ER contacts and suppressing Rac activation. Although some of the experiments are convincing, the authors come to conclusions that are not entirely supported by their data and a few statements appear the result of inductive reasoning. A major problem is the distinction between adhesion and migration: in several parts of the manuscript, there is confusion between these two events and the experiments are not designed (and not discussed) in order to clarify this point. For example, the fact that in zebrafish embryos lacking Opa1 there is no defect in neutrophil retention but reduced neutrophil migration should suggest that MFN-2 controls adhesion rather than migration. But this is not properly elaborated. The same problem comes with the role of Rac, which has been elegantly shown to be required for cell migration but not for cell spreading or focal adhesion formation (Steffen et al, JCS 2013). Again, it is necessary to distinguish between migration and other functions requiring the actin cytoskeleton.

Specific comments:

Introduction:

"Although mitochondria-derived ATP possibly regulates neutrophil chemotaxis in vitro (Bao et al., 2015), removal of extracellular ATP improves neutrophil chemotaxis in vivo (Li et al., 2016). These conflicting reports prompted us to search for mechanisms delineating the role of mitochondria in neutrophil migration outside the realm of ATP or cellular energy (Bi et al., 2014; Schuler et al., 2017; Zanotelli et al., 2018)." This sentence is superficial and misleading: extracellular ATP may interfere with chemotaxis through various energy-independent mechanisms (see for example Zumerle et al. Cell Reports 2019) and this is not conflicting with the role of intracellular ATP in migration.

Figure 1: The authors didn't show evidence of the genome edition (PCR, RFLP or Sequencing over the sgRNA target) or at least RT-PCR or WB for MFN2. In Fig 1b, 1c the scale bar is missing. "Neutrophils were sorted from both lines and their respective loci targeted by the 4 sgRNAs were deep sequenced." There are no data about sorting strategies for zebrafish neutrophils in the figure. Moreover, only 2 sgRNAs are shown and there are no sequencing data.

Figure 2: In the WB, reconstitution is not obvious. In general, all WBs are not quantified (and they should be quantified). The in vivo experiment does not have proper controls. For example, can the authors exclude that in these mice there is reduced inflammation because neutrophils have defective activation? What about NETs? And cytokines/chemokines? And exocytosis? In the absence of these controls, the experiment cannot be properly interpreted.

Figure 3: The conclusion of the authors "In summary, Mfn2 modulates the actin cytoskeleton and cell migration in MEFs" should be supported by experiments to distinguish between the specific role of Mfn2 and the role of mitochondrial dynamics (Opa1, Drp1, Mfn1). It is also not clear why the authors decided to use MEFs instead of other cells (more similar to neutrophils which are not adherent cells). The results obtained in MEFs may be irrelevant for neutrophils.

Figure 4-5: Fig 5a: in ctrl and sh1 the ER seems to be larger than the phalloidin (=cytoskeleton=cell border approximately) in a few regions. Only the sh1+T seems to fit correctly. The TEM image (only 1 in supplementary) is not sufficient to convince that the tethering is lost. Quantification of number of contacts and distance between ER and mitochondria should be included. The title of figure 5 is wrong. However, in these figures, it is clear that cells are beautifully polarized, with mitochondria accumulating at the uropod (and even more in the absence of Mfn2). When comparing these images with those published by Campello et al (JEM 2006), there are 2 observations that can be made: first of all, these data confirm that mitochondrial fission promotes cell polarity; second, they suggest that the defect is not at the level of cell polarity/chemotaxis.

Figure 6: Calcium data are, in general, very weak. First of all, controls with ionomycin are missing. Statistical analyses of the curves should be included. As for the use of the MCU inhibitor Ru360, is there any evidence that it is cell-permeant in this context? Is it blocking MCU? Since the authors can show mitochondrial calcium upon FMLP, they should also demonstrate that Ru360 is indeed working and inhibiting mitochondrial calcium uptake. The sentence "The MCU inhibitor Ru360did not cause further reduction of chemotaxis in MFN2 knockdown dHL-60 cells (Supplementary Fig. 6c, d and Supplementary Movie. 12), indicating that MCU and MFN2 lies in the same pathway in terms of regulating chemotaxis in dHL-60 cells" is speculative. In general, there is no solid demonstration that the effect is calcium-mediated. As for Rac, it is surprising to see that Rac inhibition has no effect on cell migration. Rac is known to promotes migration in fibroblasts and other cell types and Rac deficiency inhibits migration (see for example Steffen et al, JCS 2013). Two sets of experiments are absolutely required: 1) verify this in fibroblasts since it has been elegantly shown that Rac is essential in these cells for migration; 2) analyse the effect of Rac inhibitors in pPak kinetics.

Significance

As presented here, the manuscript has a modest significance. The audience would be specialised: cell migration, cell signalling. My expertise is immunology, cell activation, cell migration, cell signalling.

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Reply to the reviewers

We thank the reviewers and the editor for the insightful and thorough assessment of our manuscript. In this response to review letter, we have listed the original review (black text) and responded to each critique after it.

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

Yang et al. submitted a manuscript describing the detection of pseudogenes ("retrocopies") of APOBEC3 (A3) genes in primates. The evolutionary history and relationship to specific A3s was analyzed and speculated that the maintained A3 retrocopies had a functional role at least early in the evolution and some may have still now. Functional data on some of the expressed retrocopies are presented on L1 and HIV.

The authors claim that "retrocopying expands the functional repertoire of A3 antiviral proteins in primates". While almost of the genetic findings were published recently (Ito et al. 2020), the authors should more clearly describe how their data differ or confirm the data of Ito et. al.

We thank the reviewer for their helpful comments which have guided revisions to our manuscript. We have taken steps to clarify the dramatic differences between our work and the recent publication from Ito, Gifford, and Sato.

Foremost, we respectfully disagree with the reviewer that the genetic findings in our work were contained within the Ito, et al manuscript. Using a computational screen of assembled mammalian genome, the Sato group cataloged the gain and loss of APOBEC3 genes during the evolution of mammals. They found a fascinating correlation between the dynamics of A3s and ERVs that formed the precis of the paper. From their genome-wide search for A3s, Ito et al describe several retrocopies of A3s in two New World Monkey species, one of which retains a full-length open reading frame, leading to the statement that this gene may be functional.

We note that the retrocopies found in the Ito et al paper span only two of the more than 20 species in which we identify A3 retrocopies. Further, as a result of the breadth of our search for A3s, we find additional retrocopies in the same two New World Monkey species that were examined in the Ito et al paper. Finally, our study also examined functional capabilities of these additional A3s. These differences are highlighted by reviewer 3 who writes that relative to Ito et al, our manuscript studies the phenomenon of A3 retrocopies “more deeply both by in silico analyses and cell culture experiments.” Reviewer 3 also summarizes the most important difference in our studies – our work presents a “conceptual advance that the antiviral gene expansion has achieved not only via tandem gene duplication but also via gene retrocopying”.

Lastly, we want to point out that the findings of our manuscript and Ito et al. 2020 were made concurrently. Indeed, throughout the preparation process of this manuscript, we were both aware of each other’s findings and shared preprints with each other. Most of the participating journals in Review Commons have “scoop protection” mechanisms that typically extend 6 months after the publication of the first article (Ito et al was published Jan 2020), and our article was first submitted to Reviewer Commons on February 14, 2020. Therefore, we feel confident that the ‘no scoop’ policy applies to the minimal overlap between our paper and that of Ito et al.

Nevertheless, we have modified the text to more clearly acknowledge the parallel finding of some New World monkey retrogenes in the Ito, et al. paper.

The functional data (Fig. 6) are interesting, but in the current form not complete. The authors have to show protein expression in the transfected cells (A3, L1, HIV) and level of encapsidation into viral particles. In addition, please analyze if the retrocopies express cytidine deaminase active enzymes.

We thank the reviewer for this comment, and we have added a Western blot of the six long-ORF-containing retrocopies as Figure S5. In this blot (from early in the project), we detected protein production in 293T cells for 3/6 retrocopies. In later optimizations of subsets of this blot, we were able to detect expression of the marmoset A3G and the other two marmoset retrocopies (marmoset-2 and marmoset-4). Despite optimization attempts, we were unable to detect protein for one of the retrocopies that restricts HIV-1ΔVif (capuchin-C1). Unfortunately, at this time the included blot is the only one we have in which all 6 constructs are included on a single blot. Optimally, all 6 constructs would be side-by-side in a single blot with optimized conditions, and we are happy to complete this experiment as soon as we are able to return to our lab after the SARS-2 quarantine is lifted. However, we think the added blot shows that some of the retrocopies produce protein and the absence of detectable protein from capuchin-C1 could suggest that this retrocopy is especially potent in its restriction function or an idiosyncratic problem with detecting this protein using Western blot analyses.

We have not previously tested our lentiviral particles for levels of encapsidation of protein from each retrocopy. The value we see in this experiment is in explaining why some of the retrocopies that are expressed in producer cells may not restrict in target cells. While we note that precedent in the literature suggests that A3 proteins which restrict HIV-1ΔVif are invariably encapsidated, we would be happy to carry out this experiment when our lab reopens.

In response to the reviewer’s request to test deaminase activity for each retrocopy, we note that Figure 4 shows the intactness of the deaminase motif in each retrocopy. However, we feel that a description of the mechanisms of restriction of these retrocopies is not a major point of this paper and is beyond the scope of the current investigation.

Reviewer #1 (Significance (Required)):

Minor advance compared to Ito et al. 2020.

We respectfully and rigorously disagree with this assessment. Please refer back to the reviewer’s first comment. We defer, again, to Reviewer 3’s assessment that our work presents a “conceptual advance that the antiviral gene expansion has achieved not only via tandem gene duplication but also via gene retrocopying”. Moreover, we must point out that the Ito et al 2020 paper was entirely computational; indeed, several retrogenes that could computationally be predicted to be ‘dead’ were confirmed by us as having antiviral activity.

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

**Summary:**

Yang et al. study the expansion of APOBEC3 (A3) cytidine deaminases genes in primates. Authors find A3 retrocopies in several lineages in primates using Blast searches. Some are old and some are species specific. Some have disablements and some have intact ORFs. Authors study their mode of evolution, expression and functionality. Authors have performed detailed analyses including functional analyses. Some A3 retrocopies are broadly expressed and some have retained ability to restrain retroelements. I agree with the authors that their data supports that retrocopying has contributed the turnover in the repertoire of host retroelement restriction factors. Authors show that some retrocopies have remained active for long periods of time and they still show that they can restrict retroelements/retrovirus. This work provides an interesting example of immune system diversification. This study of the A3 family of proteins that are part of the vertebrate innate immune system and the data supporting turnover of these kind of immune system genes is strong. The work underscores that this is a way immunity genes evolve and it has parallels in the evolution of the TRIM gene family of immune genes. I just have a few comments. I think the work can gain from analyzing some aspects of the data in more detail and presenting the big picture in a summary table, even if it is just supplementary.

**Major comments:**

A3I is in many species. Does this mean it was preserved (i.e., functional for a while)? For how long have disabling mutations been accumulating? Can we get a sense of that? Even for other retrocopies, do we have a sense of how recent has the pseudogenization been? If it is very recent that means that the gene was active until not long ago.

Our analyses suggest that A3I was born in the common ancestor of simian primates and pseudogenized before the Catarrhini/Platyrrhini split. It is possible that A3I was functional within this extended period (~12-15 million years), but the presence of a shared truncating stop codon amongst all simian A3Is suggests the gene was no longer full-length at the time of diversification of the simians. Instead, the simian LCA likely encoded an A3I with a predicted ORF of 261 codons; if this truncated ORF were functional, it was then further truncated/pseudogenized with additional frame-breaking mutations which follow the phylogeny of primates.

We estimated the timeline of pseudogenization of each retrocopy using the species distribution of each syntenic retrocopy. We also note that we find full-length ORFs in three retrocopies which have been retained for a period of time at least as long as the age of the last common ancestor of the four New World monkeys. These old but intact retrocopies motivated our simulations of ORF retention rates (Figure 5).

In the PAML analyses test could be performed to test if the rate of evolution that are higher or lower than 1 for particular genes are actually significantly higher or lower than 1 for the particular gene comparing the likelihoods of the modes with the given rate with the one with the rate fixed to 1. Is there enough power to do this?

We thank the reviewer for pointing out this omission in our analysis. We did perform these tests and find a significant p-value for two of the nodes p=0.058 and p=0.025 respectively). We have updated the legend for figure S4 to incorporate these p-values

Page 9. It seems to me that the synteny data Figure S2 reveals they are derived from independent retroposition events and not duplications of segments because those would include flanking genes. Is this correct? Authors could comment on that.

Yes, we think that each retrocopy we show in Figure S2 is likely created via an independent retrotransposition event. We have clarified in the text that Figure S2 shows the genes used to establish synteny to support orthology of the retrocopies shared amongst multiple species and that each of these ortholog groups presumably originated via distinct retrotransposition events.

In figure S4, I am not sure why orthologous genes are not grouped together in the phylogeny and why p is smaller than 0.05. How should that figure and the probability be interpreted?

We thank the reviewer for their comments on this figure. First, the reviewer identified an error in the tree in which the branch labels for ‘night monkey-C2’ and ‘night monkey-SS1’ were inadvertently switched. The corrected tree now follows the pattern expected by the reviewer. Second, we employed RELAX to “determine whether selective strength was relaxed or intensified in one of these subsets relative to the other” (Wertheim, et al. MBE 2014). In this case, the p-value corresponds to the finding that the retrocopies (test branches) show intensification of selection relative to the intron-containing A3Gs (reference branches).

We have modified Figure S4 and the associated text to more clearly explain the specific hypothesis test we report.

It would be good to have a summary table that summarizes what genes have support for past or current functionality (preservation for long time or recent pseudogenization, expression, purifying or positive selection, ability to restrict retroelements) and in what lineages.

We agree with this reviewer suggestion. We have added the additional information including the number of frame disrupting mutations as a measure of age, intactness, and ability to restrict retroelements to Table S1. Thanks to this suggestion, Table S1 now serves as the master table to summarize the analyses of each retrocopy.

**Minor comments:**

1. Page 3. Authors say "...the exons and UTRs..." but UTRs are part of exons. Authors could talk about exons only that include protein-coding regions and UTRs.

Changed the text to "exons".

Page 7. I would say disabled instead of "... becoming degraded by mutation."

Fixed according to the reviewer's suggestion.

I would say neutral evolution not neutral selection.

Fixed according to the reviewer's suggestion.

Reviewer #2 (Significance (Required)):

This work provides an interesting example of immune system diversification. Authors study the APOBEC3 family of proteins that is part of the vertebrate innate immune system and the data supporting turnover of these kind of immune system genes. The work underscores that this is a way immunity genes evolve and it has parallels in the evolution of the TRIM gene family of immune genes revealing patterns in the mode of evolution of immunity genes. The audience of this work will be people interested in evolution of immunity, arms races and gene diversification and all evolutionary biologists interested in adaptation. I work in the field of comparative genomics and molecular evolution.

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

**Summary:**

This manuscript by Yang et al. is an well-written, intriguing paper highlighting the evolutionary significance of the gene creation via "retrocopying". The authors investigated the expansion of antiviral A3 genes via retrocopy in Primates, and found that A3G-like retrocopies have been generated repeatedly during primate evolution. A part of A3 retrocopies found in New World monkeys retained full length open reading flames and anti-lentiviral capacities. Interestingly, the spectrum of anti-retroelement activity of A3 retrocopies was different from the original (i.e., intron-containing) A3G gene in these species, suggesting the occurrence of the functional differentiation followed by gene amplification. However, one of the main findings that many A3 retrocopies are present in New World monkey is in-line to a previous report (i.e., Ito et al., 2020, PNAS), and the experimental validations were based on the human (not New World monkey's) retroelements. Nevertheless, this study deeply investigated the possible importance of A3 retrocopies for the host defense system evolution both by in silico analyses and cell culture experiments. This study provides the findings that can potentially expand our knowledge on the evolutionary arms races between retroelements and the hosts.

**Major:**

To strengthen the impact of this work, it would be better to increase the numbers of retroviruses in which the anti-retroviral capacities are investigated. I understand that it is difficult to examine retroviruses or L1s that are colonized naturally with New World monkeys, but I suppose it is not so difficult to investigate a variety of representative retroviruses such as murine leukemia virus (MLV) or the reconstructed human endogenous retrovirus K (HERV-Kcon). This additional experiment would be helpful to highlight that the spectrum of anti-retroviral activity of A3 retrocopies is divergent from the original A3G gene in these species and strengthen the concept to be proposed by this study.

The reviewer raises a fascinating question about whether retrocopies might have different restriction abilities relative to the other A3s in a given species. First, we feel that showing activity against one pathogen is sufficient for our claim that some of the A3 retrocopies have antiviral potential. Second, we discuss in the paper the idea that HIV-1 is not the actual target of these (or any) innate immune genes in New world primates. We argue that any other targets we might test would also be surrogates for the ‘true’ target of these genes.

**Specific:**

1, Since the authors found the expansion of "functional" repertoire of A3 retrocopies specifically in New World monkey, it would be better to rephrase the title as "Retrocopying expands the functional repertoire of APOBEC3 antiviral proteins in New World monkeys".

We thank the reviewer for this comment but point out that a large portion of our manuscript presents our work on primates outside the New World monkeys. The reviewer is correct to note that our finding of restriction activity is limited to New World monkey retrocopies, but we feel that the current title will attract a broader audience and reflects the broader relevance of this work.

2, It might be better to add a figure summarizing which A3 retrocopies in which species retain nearly full length ORFs. For example, how about making a figure like Fig. 2A for all the four representative New World monkey species?

We agree. We have added the length of the longest ORF for each retrocopy to Table S1.

3, Fig. 3

It would be helpful to clarify that which cell of the heatmap corresponds to the intact A3 retrocopies.

We have added labels to indicate the intact A3 retrocopies and adjusted the legend accordingly.

4, Page 4, line 5

It would be better to replace the word "protected" with "escaped" because this retrocopy subset should include the ones that are intact but not functional.

Changed as suggested.

5, Page 4, line 25

It would be better to rephrase "the common ancestor of mammals" as "the common ancestor of placental mammals" because A3 gene is absent in Marsupial.

Changed as suggested.

6, Page 5, line 5

Please rephrase "ongoing" as "recently-occurred".

Changed as suggested.

7, Page 6, line 19

I checked the multiple sequence alignment in File S1 and suspect that the codon (alignment) position of the shared premature stop codon is 261 (not 264).

We thank the reviewer for pointing out this discrepancy. We have revised the text to reflect the correct position of the shared stop.

8, Page 6, line 23

I could not understand the meaning of the sentence "Intriguingly, one lineage-specific mutation...".

Please specify the position of mutation which the authors mentioned (in File S1 or Fig. 1B).

This portion of the text refers to a reversion of a stop codon in the orangutan A3I; specifically, the stop codon shared in all simians acquired a second mutation that created a longer ORF in only this species. We have removed this sentence from the text for the sake of clarity.

9, Page 12, line 8

Please refer Fig. S4 here.

Changed as suggested.

10, Page 12, line 8

Please say "Significant relaxed selection was not detected" rather than "Our analysis detected no relaxation...".

Changed as suggested.

11, Page 12, line 8

Fig. S4 indicates "p=0.015", but the authors regard it as "not significant"?

We thank the reviewer for pointing out this confusing wording. We employ RELAX to “determine whether selective strength was relaxed or intensified in one of these subsets relative to the other” (Wertheim, et al. MBE 2014). In this case, the p-value corresponds to the finding that the retrocopies show intensification of selection.

We have modified Figure S4 to more clearly explain the specific hypothesis test for this p-value. We have also modified the text to clarify this point.

12, Page 12, line 9

Please here refer the data showing the claim "Instead, these A3G retrocopies have evolved more rapidly than...".

Changed as suggested; see previous point.

13, Page 12, line 11

Did the authors perform the statistical test on the dN/dS ratio analysis? If so, please mention the result of the test.

Yes we did. Please refer to Reviewer 2’s ‘Major Point 3’.

14, Page 12, line 15

It would be better to modify the phrase "show evidence of recurrent selection for functional innovation"

Changed as suggested.

Reviewer #3 (Significance (Required)):

This study provides a conceptual advance that the antiviral gene expansion has achieved not only via tandem gene duplication but also via gene retrocopying.

Compare to existing published knowledge.

Although one of the main findings that many A3 retrocopies are present in New World monkey is in-line to a previous report (i.e., Ito et al., 2020, PNAS), this study investigated the above finding more deeply both by in silico analyses and cell culture experiments.

Audience.

Evolutionary biologists and researchers in the field of viruses (particularly retroviruses including HIV-1) and transposable elements would be interested in this work.

Your expertise.

Bioinformatics, genome biology, viruses, and transposable elements

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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---

Referee #3

Evidence, reproducibility and clarity

Summary:

This manuscript by Yang et al. is an well-written, intriguing paper highlighting the evolutionary significance of the gene creation via "retrocopying". The authors investigated the expansion of antiviral A3 genes via retrocopy in Primates, and found that A3G-like retrocopies have been generated repeatedly during primate evolution. A part of A3 retrocopies found in New World monkeys retained full length open reading flames and anti-lentiviral capacities. Interestingly, the spectrum of anti-retroelement activity of A3 retrocopies was different from the original (i.e., intron-containing) A3G gene in these species, suggesting the occurrence of the functional differentiation followed by gene amplification. However, one of the main findings that many A3 retrocopies are present in New World monkey is in-line to a previous report (i.e., Ito et al., 2020, PNAS), and the experimental validations were based on the human (not New World monkey's) retroelements. Nevertheless, this study deeply investigated the possible importance of A3 retrocopies for the host defense system evolution both by in silico analyses and cell culture experiments. This study provides the findings that can potentially expand our knowledge on the evolutionary arms races between retroelements and the hosts.

Major:

To strengthen the impact of this work, it would be better to increase the numbers of retroviruses in which the anti-retroviral capacities are investigated. I understand that it is difficult to examine retroviruses or L1s that are colonized naturally with New World monkeys, but I suppose it is not so difficult to investigate a variety of representative retroviruses such as murine leukemia virus (MLV) or the reconstructed human endogenous retrovirus K (HERV-Kcon). This additional experiment would be helpful to highlight that the spectrum of anti-retroviral activity of A3 retrocopies is divergent from the original A3G gene in these species and strengthen the concept to be proposed by this study.

Specific:

1, Since the authors found the expansion of "functional" repertoire of A3 retrocopies specifically in New World monkey, it would be better to rephrase the title as "Retrocopying expands the functional repertoire of APOBEC3 antiviral proteins in New World monkeys".

2, It might be better to add a figure summarizing which A3 retrocopies in which species retain nearly full length ORFs. For example, how about making a figure like Fig. 2A for all the four representative New World monkey species?

3, Fig. 3 It would be helpful to clarify that which cell of the heatmap corresponds to the intact A3 retrocopies.

4, Page 4, line 5 It would be better to replace the word "protected" with "escaped" because this retrocopy subset should include the ones that are intact but not functional.

5, Page 4, line 25 It would be better to rephrase "the common ancestor of mammals" as "the common ancestor of placental mammals" because A3 gene is absent in Marsupial.

6, Page 5, line 5 Please rephrase "ongoing" as "recently-occurred".

7, Page 6, line 19 I checked the multiple sequence alignment in File S1 and suspect that the codon (alignment) position of the shared premature stop codon is 261 (not 264).

8, Page 6, line 23 I could not understand the meaning of the sentence "Intriguingly, one lineage-specific mutation...". Please specify the position of mutation which the authors mentioned (in File S1 or Fig. 1B).

9, Page 12, line 8 Please refer Fig. S4 here.

10, Page 12, line 8 Please say "Significant relaxed selection was not detected" rather than "Our analysis detected no relaxation...".

11, Page 12, line 8 Fig. S4 indicates "p=0.015", but the authors regard it as "not significant"?

12, Page 12, line 9 Please here refer the data showing the claim "Instead, these A3G retrocopies have evolved more rapidly than...".

13, Page 12, line 11 Did the authors perform the statistical test on the dN/dS ratio analysis? If so, please mention the result of the test.

14, Page 12, line 15 It would be better to modify the phrase "show evidence of recurrent selection for functional innovation"

Significance

This study provides a conceptual advance that the antiviral gene expansion has achieved not only via tandem gene duplication but also via gene retrocopying.

Compare to existing published knowledge.

Although one of the main findings that many A3 retrocopies are present in New World monkey is in-line to a previous report (i.e., Ito et al., 2020, PNAS), this study investigated the above finding more deeply both by in silico analyses and cell culture experiments.

Audience.

Evolutionary biologists and researchers in the field of viruses (particularly retroviruses including HIV-1) and transposable elements would be interested in this work.

Your expertise.

Bioinformatics, genome biology, viruses, and transposable elements

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 #2

Evidence, reproducibility and clarity

Summary: Yang et al. study the expansion of APOBEC3 (A3) cytidine deaminases genes in primates. Authors find A3 retrocopies in several lineages in primates using Blast searches. Some are old and some are species specific. Some have disablements and some have intact ORFs. Authors study their mode of evolution, expression and functionality. Authors have performed detailed analyses including functional analyses. Some A3 retrocopies are broadly expressed and some have retained ability to restrain retroelements. I agree with the authors that their data supports that retrocopying has contributed the turnover in the repertoire of host retroelement restriction factors. Authors show that some retrocopies have remained active for long periods of time and they still show that they can restrict retroelements/retrovirus. This work provides an interesting example of immune system diversification. This study of the A3 family of proteins that are part of the vertebrate innate immune system and the data supporting turnover of these kind of immune system genes is strong. The work underscores that this is a way immunity genes evolve and it has parallels in the evolution of the TRIM gene family of immune genes. I just have a few comments. I think the work can gain from analyzing some aspects of the data in more detail and presenting the big picture in a summary table, even if it is just supplementary.

Major comments:

1. A3I is in many species. Does this mean it was preserved (i.e., functional for a while)? For how long have disabling mutations been accumulating? Can we get a sense of that? Even for other retrocopies, do we have a sense of how recent has the pseudogenization been? If it is very recent that means that the gene was active until not long ago.
2. In the PAML analyses test could be performed to test if the rate of evolution that are higher or lower than 1 for particular genes are actually significantly higher or lower than 1 for the particular gene comparing the likelihoods of the modes with the given rate with the one with the rate fixed to 1. Is there enough power to do this?
3. Page 9. It seems to me that the synteny data Figure S2 reveals they are derived from independent retroposition events and not duplications of segments because those would include flanking genes. Is this correct? Authors could comment on that.
4. In figure S4, I am not sure why orthologous genes are not grouped together in the phylogeny and why p is smaller than 0.05. How should that figure and the probability be interpreted?
5. It would be good to have a summary table that summarizes what genes have support for past or current functionality (preservation for long time or recent pseudogenization, expression, purifying or positive selection, ability to restrict retroelements) and in what lineages.

Minor comments:

1. Page 3. Authors say "...the exons and UTRs..." but UTRs are part of exons. Authors could talk about exons only that include protein-coding regions and UTRs.
2. Page 7. I would say disabled instead of "... becoming degraded by mutation."
3. I would say neutral evolution not neutral selection.

Significance

This work provides an interesting example of immune system diversification. Authors study the APOBEC3 family of proteins that is part of the vertebrate innate immune system and the data supporting turnover of these kind of immune system genes. The work underscores that this is a way immunity genes evolve and it has parallels in the evolution of the TRIM gene family of immune genes revealing patterns in the mode of evolution of immunity genes. The audience of this work will be people interested in evolution of immunity, arms races and gene diversification and all evolutionary biologists interested in adaptation. I work in the field of comparative genomics and molecular evolution.

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 #1

Evidence, reproducibility and clarity

Yang et al. submitted a manuscript describing the detection of pseudogenes ("retrocopies") of APOBEC3 (A3) genes in primates. The evolutionary history and relationship to specific A3s was analyzed and speculated that the maintained A3 retrocopies had a functional role at least early in the evolution and some may have still now. Functional data on some of the expressed retrocopies are presented on L1 and HIV.

The authors claim that "retrocopying expands the functional repertoire of A3 antiviral proteins in primates". While almost of the genetic findings were published recently (Ito et al. 2020), the authors should more clearly describe how their data differ or confirm the data of Ito et. al.

The functional data (Fig. 6) are interesting, but in the current form not complete. The authors have to show protein expression in the transfected cells (A3, L1, HIV) and level of encapsidation into viral particles. In addition, please analyze if the retrocopies express cytidine deaminase active enzymes.

Significance

Minor advance compared to Ito et al. 2020.

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Reply to the reviewers

We thank the reviewers for their close reading and constructive comments on our manuscript. We believe that their insight has substantially strengthened our manuscript. Please find our response/revision plan for each comment below (in bold).

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

This is a very interesting study that addresses an important topic. In brief, the authors build on their previous data showing that LSD1 seems to be neuroprotective. Here they follow the hypothesis that Tau-dependent sequestration of LSD1 to the cytoplasm leads to loss of nuclear LSD1 function. Crossing Tau mutant mice (PS19) to heterozyous LSD1 knock out mice exacerbates phenotypes in PS19 mice, while viral overexpression of LSD1 rescues part of these phenotypes.

As said the data is interesting but lack mechanistic explanation that would allow in my view publication in a very high profile journal. Moreover, there are some data such as the RNA-seq that would not be acceptable in the present for by any journal. However, all of these issues could be addressed by the authors in case reviewers would refer to them.

The sequestration of LSD1 in the cytoplasm by tau, along with the co-localization of LSD1 with tau in human cases (in our previous Nature Communications paper-Christopher et al. 2017) provide a mechanistic explanation (sequestration) for why we are able to exacerbate and rescue tau-mediated neurodegeneration by modulating LSD1. As the reviewer pointed out, we believe that we can address all of the critiques brought up (see responses below). By addressing these critiques we believe that we can further substantiate the mechanism underlying our ability to functionally modulate tau-mediated neurodegeneration in vivo.

**Here are some specific issues.**

1 . Especially the proposed link of Tau-mediated sequestration of LSD1 to the cytoplasm is not fully supported by the data. A key finding shows that LSD1 is seen more in cytoplasm in PS19 mice. However, the biological relevance of this observation cannot be fully appreciated at present, since the magnitude of this phenotype is unclear. Approaches to perform a quantitative analysis in addition to the representative IHC images would be helpful.

The change in localization of LSD1 from nuclear to cytoplasmic that we observe in Tau PS19 mice is dramatic. We tried to convey this magnitude of sequestration in different brain regions by showing a range of representative images. Consistent with this, Reviewer 2 commented that “These data are very strong, the effect is impressive.” Nevertheless, we can attempt to further quantify the change in localization. To accomplish this, we can try two different methods. (1) We can add a nuclear marker and attempt to quantify the level of nuclear versus cytoplasmic LSD1 from the immunofluorescence images. (2) We can also attempt to generate nuclear versus cytoplasmic fractions and quantify LSD1 levels by western blot.

2 . Point 1 might be of specific importance since the subsequent experiments built on the idea that mice with already recued LSD1 levels should have a more severe phenotype in case of Tau pathology. However, they do not really address the role of Tau-mediated sequestration of LSD1 anymore. The authors employ mice that constitutively lack one allele of LSD1 which generally leads to a more severe phenotype in PS19 mice. This is very interesting, but I wonder if reduced LSD1 levels might generally put the network in a more vulnerable state and that other detrimental stimuli that do not cause intracellular protein aggregation might have a similar effect. The authors realize this and address this question by comparing via RNA-seq the gene-expression changes observed in Lsd1+/+, Lsd1Δ/+, PS19 Tau, and PS19;Lsd1Δ/+ littermates. Comparatively few changes are observed. However, the major issue with this experiment is that an n=2/group is simply no acceptable anymore to be published in any serious journal. Thus, this data is not interpretable as it stands.

To further address the role of tau-mediated sequestration of LSD1, we can attempt to quantify (see above) nuclear versus cytoplasmic LSD1 in PS19 Tau mice with heterozygous Lsd1, and compare it to the level of sequestration observed in PS19 Tau mice alone.

To strengthen the RNAseq data, we can perform two additional replicates.__However, because (1) the RNAseq results were only used for genome-wide comparisons, (2) the replicates were very tight, and (3) the results were clear, it is very unlikely that additional replicates are going to alter the result. Thus, alternatively we might be able to alter the language of the manuscript to qualify the result somewhat. In this regard, it should be noted that reviewer 2 commented that “The data are very convincing, and provide a strong molecular base showing a tight overlap in the effected molecular pathways associated with both pathological tau and Lsd1 heterozygosity.” Reviewer 3 also commented that the transcriptomic dataset “__strengthen some of the conclusions.”

Reviewer #1 (Significance (Required)):

The data will be interesting to the field and help to further understand the role of LSD1 in neuroegenerative dieases linked to tauopathy.

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

The manuscript "The inhibition of LSD1 via sequestration contributes to tau-mediated neurodegeneration" by Amanda K. Engstrom, Alicia C. Walker, Rohitha A. Moudgal, Dexter A. Myrick, Stephanie M. Kyle and David J. Katz, is an excellent study that beautifully describes the implication of the epigenetic enzyme LSD1, as downstream mediator of tau pathology in neurodegenerative disease.

The same authors in a previous paper showed that i) loss of LSD1 in the adult mouse brain, leads to significant neuronal cell death and ii) loss of LSD1 induces genome-wide expression changes that significantly overlap with those observed in the brains of postmortem human AD. In this work, are presented initial evidences that in AD brain, LSD1 nuclear function could be disrupted by mislocalization to pathological tau.

In the present work, using the PS19 mouse model, the authors provide the first cytological evidence that pathological tau can prevent LSD1 from properly localizing to the nucleus in hippocampal and cortical neurons. These data are very strong, the effect is impressive. Crossing the PS19 mouse model of taupathology with a mouse model of LSD1 brain heterozygosity LSD1Δ /+, the authors provide functional data that the inhibition of LSD1 function contributes to tau induced neurodegeneration. Indeed, several pathological parameters measured in the PS19 mouse model, are exacerbated in a reduced genetic LSD1 background. Survival rate, motor activity measured with a rotarod test. The behavioral analysis is nicely paralleled by the analysis of spinal cord motor neurons, showing abnormal morphology in the double mutant mice, compared to the PS19. Overall morphology of the hippocampus shows decreased brain size and brain weight. The analysis is accompanied by MRI analysis, showing again very impressive results, with the double mutant being the most affected and the LSD1Δ /+ very similar to WT.

The second part of the work is aimed at demonstrating specificity of the functional interaction between tau pathology and LSD1. The authors provide a very well planned transcriptional profiling of the different mouse models, choosing the most relevant time point (prior the onset of neuronal cell death), very clearly justifying the rational of their choice. The data are very convincing, and provide a strong molecular base showing a tight overlap in the effected molecular pathways associated with both pathological tau and Lsd1 heterozygosity. As final approach, the authors rescue neurodegeneration in the hippocampus of PS19 Tau mice overexpressing LSD1 using a neuron-specific virus. Overall, these data establish LSD1 as a major downstream effector of tau-mediated neurodegeneration indicating that the LSD1 pathway is a potential late stage target for intervention in tauopathies, such as AD.

**Minor point:**

In material and Methods is missing a section dedicated to a detailed description of statistical analysis.

We have added a section to the materials and methods dedicated to a detailed description of statistical analysis (lines 509-518).

Reviewer #2 (Significance (Required)):

I believe that this work will be of great interest for the neurodegenerative together with the neuro-epigenetic field (my personal area of expertise). The identification of a clear new pathway implicated in AD and neurodegeneration together with the suggestion of a possible new therapeutic target (disruption of tau-LSD1 interaction) is of high potential impact for future studies.

We really appreciate this very positive review, which acknowledges the thoroughness of our results, the mechanistic insight that we provided and the “high potential impact” of our work.

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

The work on LSD1 in this manuscript is based on earlier studies that deleting the histone demethylase Lsd1 in adult mice leads to neuronal cell death and that the neurofibrillary tangles in Alzheimer's disease brains can be stained for LSD1.

The manuscript first shows that LSD1 is sequestered in PS19 tau transgenic mice, that a reduction of Lsd1 exacerbates the pathology and that overexpression rescues, complemented by a separate transcriptomic dataset used to strengthen some of the conclusions. As it currently stands, in my view, this work is very preliminary, and I am not sharing all conclusions made by the authors.

**My specific points are following the headers of the Results section:**

(1) Tau pathology depletes LSD1 from the nucleus in the PS19 Tau mouse model: What is clear from the images is that in the PS19 Tau tg mice LSD1 is depleted from the nucleus. What is not correct is that in WT it is only localized to the nucleus. What should be done, is to quantify the relative localization to the two compartments. In addition, a subcellular fractionation could be performed (see point further below).

LSD1 is strictly a nuclear protein with a well-defined nuclear localization signal that interacts with the importin __a__complex (Jin et al, J. Biochem 2014). Reference to this has been added to the text in the introduction (lines 53-54) and in the results (line 87-89). Nevertheless, we can also attempt subcellular fractionation and localization of LSD1 in the nuclear versus cytoplasmic fraction (see response to reviewer 1 above).

(2) Reduction of LSD1 increases the mouse tauopathy phenotype: 2.1. The PS19 Tau Tg mice have been crossed with an Lsd1 heterozygous mutant (LSD1 delta/-). I tried to find the reference as to how this mutant has actually been made (refs 32-34). Ref 32 describes a conditional KO (The position of the gene trap insertion (STOP), downstream of exon 3, truncates the LSD1 open reading frame within the SWIRM domain prior to the amine oxidase domain, which is essential for the catalytic activity of LSD1), which leaves a 210 amino acid truncated protein which is an obvious confound which should be mentioned and discussed. Besides from that, it is not clear to me how the Lsd1 gene was deleted for the current study, i.e. which promoter has been used.

The Lsd1 allele used in this study was generated in the Rosenfeld lab (Wang et al., Nature 2007). This was stated in the acknowledgements, but has now been added to both the main text (lines 105-109) and the methods (lines 386-391) for clarity. ThisLsd1 allele is a null allele that has also been used previously by both our group, as well as by additional groups (For example: Christopher et al., Nature Communications 2017 and Lyons et al., Cell 2013). In this current study, Lsd1 was deleted with the Vasa**-Cre transgenic line. Once the deletion allele passes through the germline, Lsd1 is heterozygous throughout the mouse. We deeply regret this oversight.

2.2. Fig S2 shows that LSD1 is reduced in the heterozygote, but increased in PS19 by 20% and then again decreased in the PS19 x LSD1 delta/-. Clearly, a subcellular fractionation or histological quantification is needed to understand what the levels are in the cytoplasm as compared to the nucleus.

The data referred to in Figure S2 is from bulk brain homogenate showing that there is a reduction of LSD1 in mice carry the deletion allele both in a wild-type and PS19 Tau background. Nevertheless, we can attempt subcellular fractionation and quantification of LSD1 localization in the nucleus versus cytoplasm (see response to reviewer 1 above) to further clarify this result.

2.3. The rescue in Fig 2A is really modest. Certainly, with tau in PS19 potentially trapping LSD1 in the cytoplasm there should be less of LSD1 in the nucleus when there is only one functioning allele. What is needed is a quantification of nuclear and cytoplasmic LSD1 in the genotypes.

We can attempt subcellular fractionation and quantification of LSD1 localization in the nucleus versus cytoplasm (see response to reviewer 1 above) to further clarify this result.

2.4. I don't agree with the statement: 'started only slightly earlier than PS19 Tau mice, but after the appearance of pathological tau in neurons (p. 6)' as tau pathology develops gradually and is present before the age of 6 months in this strain.

This statement refers specifically to the AT8 positive pathology that was quantified in this manuscript (Figure S6). This quantification shows that AT8 positive pathology is present in the hippocampus and cortex, when PS19 Tau mice with reduced LSD1 begin to decline. The text has been amended to clarify this (line 124-125).

(3) Tau pathology is not affected by change in LSD1 levels: This is to be expected as Tau is upstream of LSD1 in a pathocascade.

The quantification of tau pathology was included as a negative control. As we expected, tau pathology is not affected by the change in LSD1 levels. As the reviewer correctly points out, this data is consistent with our model, that tau pathology is upstream of LSD1.

(4) The functional interaction between tau pathology and LSD1 inhibition is specific: The specificity of the interaction needs to be tested by co-immunoprecipitations or proximity ligation assays and by mapping which domains of LSD1 and Tau have a role in trapping, using the appropriate positive and negative controls, as is being routinely done for these kinds of studies.

We too are very interested in whether LSD1 interacts directly or indirectly with tau pathology, and what domains of LSD1 are required for LSD1 to co-localize with tau pathology. However, to address these questions, we will need to perform multiple biochemical experiments (such as the ones suggested by the reviewer) on mice of different ages, as well as human cases. We believe that this is significantly beyond the scope of the current study, which is focused on the functional interaction between tau pathology and LSD1 in mice.

(5) Overexpression of LSD1 rescues neurodegeneration in the hippocampus of PS19 Tau mice: The data in Figure 5 are not convincing.

It is not clear why the reviewer is not convinced by the rescue data in Figure 5. Reviewer 1 acknowledged that “viral overexpression of LSD1 rescues part of these phenotypes” and reviewer 2 agreed that “the authors rescue neurodegeneration in the hippocampus of PS19 Tau mice overexpressing LSD1 using a neuron-specific virus.”

**Minor points:**

Abstract: The statement 'However, the mechanism through which tau contributes to neurodegeneration remains unknown.' is not correct and should be removed. There is a wealth of information on tau-based pathomechanisms available and several studies have identified proteins which become, as seems to be the case for LSD1, trapped by tau in the cytosol.

This statement in the abstract has been modified (lines 13-15).

Reviewer #3 (Significance (Required)):

This form asks me about my expertise. I am working on tau pathomechanisms since more than two decades and the revision experiments I am asking for is what we are doing in our own studies. I find the data on LSD1 interesting, but definitely more work needs to be done to substantiate the claims.

We thank the reviewer for their careful reading of the manuscript and appreciate that they found that the data on LSD1 are interesting.

Overall, we feel that the reviews of our manuscript are very positive. We hope that our response/revision plan will be suitable for publication.

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Referee #3

Evidence, reproducibility and clarity

The work on LSD1 in this manuscript is based on earlier studies that deleting the histone demethylase Lsd1 in adult mice leads to neuronal cell death and that the neurofibrillary tangles in Alzheimer's disease brains can be stained for LSD1.

The manuscript first shows that LSD1 is sequestered in PS19 tau transgenic mice, that a reduction of Lsd1 exacerbates the pathology and that overexpression rescues, complemented by a separate transcriptomic dataset used to strengthen some of the conclusions. As it currently stands, in my view, this work is very preliminary, and I am not sharing all conclusions made by the authors.

My specific points are following the headers of the Results section:

(1) Tau pathology depletes LSD1 from the nucleus in the PS19 Tau mouse model: What is clear from the images is that in the PS19 Tau tg mice LSD1 is depleted from the nucleus. What is not correct is that in WT it is only localized to the nucleus. What should be done, is to quantify the relative localization to the two compartments. In addition, a subcellular fractionation could be performed (see point further below).

(2) Reduction of LSD1 increases the mouse tauopathy phenotype: 2.1. The PS19 Tau Tg mice have been crossed with an Lsd1 heterozygous mutant (LSD1 delta/-). I tried to find the reference as to how this mutant has actually been made (refs 32-34). Ref 32 describes a conditional KO (The position of the gene trap insertion (STOP), downstream of exon 3, truncates the LSD1 open reading frame within the SWIRM domain prior to the amine oxidase domain, which is essential for the catalytic activity of LSD1), which leaves a 210 amino acid truncated protein which is an obvious confound which should be mentioned and discussed. Besides from that, it is not clear to me how the Lsd1 gene was deleted for the current study, i.e. which promoter has been used.

2.2. Fig S2 shows that LSD1 is reduced in the heterozygote, but increased in PS19 by 20% and then again decreased in the PS19 x LSD1 delta/-. Clearly, a subcellular fractionation or histological quantification is needed to understand what the levels are in the cytoplasm as compared to the nucleus.

2.3. The rescue in Fig 2A is really modest. Certainly, with tau in PS19 potentially trapping LSD1 in the cytoplasm there should be less of LSD1 in the nucleus when there is only one functioning allele. What is needed is a quantification of nuclear and cytoplasmic LSD1 in the genotypes.

2.4. I don't agree with the statement: 'started only slightly earlier than PS19 Tau mice, but after the appearance of pathological tau in neurons (p. 6)' as tau pathology develops gradually and is present before the age of 6 months in this strain.

(3) Tau pathology is not affected by change in LSD1 levels: This is to be expected as Tau is upstream of LSD1 in a pathocascade.

(4) The functional interaction between tau pathology and LSD1 inhibition is specific: The specificity of the interaction needs to be tested by co-immunoprecipitations or proximity ligation assays and by mapping which domains of LSD1 and Tau have a role in trapping, using the appropriate positive and negative controls, as is being routinely done for these kinds of studies.

(5) Overexpression of LSD1 rescues neurodegeneration in the hippocampus of PS19 Tau mice: The data in Figure 5 are not convincing.

Minor points:

Abstract: The statement 'However, the mechanism through which tau contributes to neurodegeneration remains unknown.' is not correct and should be removed. There is a wealth of information on tau-based pathomechanisms available and several studies have identified proteins which become, as seems to be the case for LSD1, trapped by tau in the cytosol.

Significance

This form asks me about my expertise. I am working on tau pathomechanisms since more than two decades and the revision experiments I am asking for is what we are doing in our own studies. I find the data on LSD1 interesting, but definitely more work needs to be done to substantiate the claims.

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Referee #2

Evidence, reproducibility and clarity

The manuscript "The inhibition of LSD1 via sequestration contributes to tau-mediated neurodegeneration" by Amanda K. Engstrom, Alicia C. Walker, Rohitha A. Moudgal, Dexter A. Myrick, Stephanie M. Kyle and David J. Katz, is an excellent study that beautifully describes the implication of the epigenetic enzyme LSD1, as downstream mediator of tau pathology in neurodegenerative disease.

The same authors in a previous paper showed that i) loss of LSD1 in the adult mouse brain, leads to significant neuronal cell death and ii) loss of LSD1 induces genome-wide expression changes that significantly overlap with those observed in the brains of postmortem human AD. In this work, are presented initial evidences that in AD brain, LSD1 nuclear function could be disrupted by mislocalization to pathological tau.

In the present work, using the PS19 mouse model, the authors provide the first cytological evidence that pathological tau can prevent LSD1 from properly localizing to the nucleus in hippocampal and cortical neurons. These data are very strong, the effect is impressive. Crossing the PS19 mouse model of taupathology with a mouse model of LSD1 brain heterozygosity LSD1Δ /+, the authors provide functional data that the inhibition of LSD1 function contributes to tau induced neurodegeneration. Indeed, several pathological parameters measured in the PS19 mouse model, are exacerbated in a reduced genetic LSD1 background. Survival rate, motor activity measured with a rotarod test. The behavioral analysis is nicely paralleled by the analysis of spinal cord motor neurons, showing abnormal morphology in the double mutant mice, compared to the PS19. Overall morphology of the hippocampus shows decreased brain size and brain weight. The analysis is accompanied by MRI analysis, showing again very impressive results, with the double mutant being the most affected and the LSD1Δ /+ very similar to WT.

The second part of the work is aimed at demonstrating specificity of the functional interaction between tau pathology and LSD1. The authors provide a very well planned transcriptional profiling of the different mouse models, choosing the most relevant time point (prior the onset of neuronal cell death), very clearly justifying the rational of their choice. The data are very convincing, and provide a strong molecular base showing a tight overlap in the effected molecular pathways associated with both pathological tau and Lsd1 heterozygosity. As final approach, the authors rescue neurodegeneration in the hippocampus of PS19 Tau mice overexpressing LSD1 using a neuron-specific virus. Overall, these data establish LSD1 as a major downstream effector of tau-mediated neurodegeneration indicating that the LSD1 pathway is a potential late stage target for intervention in tauopathies, such as AD.

Minor point:

In material and Methods is missing a section dedicated to a detailed description of statistical analysis.

Significance

I believe that this work will be of great interest for the neurodegenerative together with the neuro-epigenetic field (my personal area of expertise). The identification of a clear new pathway implicated in AD and neurodegeneration together with the suggestion of a possible new therapeutic target (disruption of tau-LSD1 interaction) is of high potential impact for future studies.

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Referee #1

Evidence, reproducibility and clarity

This is a very interesting study that addresses an important topic. In brief, the authors build on their previous data showing that LSD1 seems to be neuroprotective. Here they follow the hypothesis that Tau-dependent sequestration of LSD1 to the cytoplasm leads to loss of nuclear LSD1 function. Crossing Tau mutant mice (PS19) to heterozyous LSD1 knock out mice exacerbates phenotypes in PS19 mice, while viral overexpression of LSD1 rescues part of these phenotypes.

As said the data is interesting but lack mechanistic explanation that would allow in my view publication in a very high profile journal. Moreover, there are some data such as the RNA-seq that would not be acceptable in the present for by any journal. However, all of these issues could be addressed by the authors in case reviewers would refer to them.

Here are some specific issues.

1 . Especially the proposed link of Tau-mediated sequestration of LSD1 to the cytoplasm is not fully supported by the data. A key finding shows that LSD1 is seen more in cytoplasm in PS19 mice. However, the biological relevance of this observation cannot be fully appreciated at present, since the magnitude of this phenotype is unclear. Approaches to perform a quantitative analysis in addition to the representative IHC images would be helpful.

2 . Point 1 might be of specific importance since the subsequent experiments built on the idea that mice with already recued LSD1 levels should have a more severe phenotype in case of Tau pathology. However, they do not really address the role of Tau-mediated sequestration of LSD1 anymore. The authors employ mice that constitutively lack one allele of LSD1 which generally leads to a more severe phenotype in PS19 mice. This is very interesting, but I wonder if reduced LSD1 levels might generally put the network in a more vulnerable state and that other detrimental stimuli that do not cause intracellular protein aggregation might have a similar effect. The authors realize this and address this question by comparing via RNA-seq the gene-expression changes observed in Lsd1+/+, Lsd1Δ/+, PS19 Tau, and PS19;Lsd1Δ/+ littermates. Comparatively few changes are observed. However, the major issue with this experiment is that an n=2/group is simply no acceptable anymore to be published in any serious journal. Thus, this data is not interpretable as it stands.

Significance

The data will be interesting to the field and help to further understand the role of LSD1 in neuroegenerative dieases linked to tauopathy.

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Reply to the reviewers

We thank the Reviewers for the positive assessment of our work and their insightful remarks. Please find below a point-by-point response to each comment.

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

*The authors present a well written article describing distinct transcriptomic profiles generated by RNA sequencing analysis of hippocampus, a distinct anatomical area, at well spaced and defined time points of clinical progression following prion inoculation in an established mouse model. The authors contribute significantly in the detailed transcriptomic definition of changes during disease progression, especially during the early and almost asymptomatic stages.

The brain region chosen to perform their analysis is logical as the hippocampus shows clear signs of neuronal degeneration in prion disease progression and furthermore provides a well defined area for analysis that is easily accessible experimentally; Although, more information would be needed to strengthen this choice in relation to the hippocampus playing a key role in the initiation stages of the disease. It remains an anatomical subset of the whole brain and the study would benefit if extended to include other affected areas. *

The hippocampus is one of the most affected and therefore most studied regions during prion disease (Moreno et al., 2012, Nature). We have clarified this in the text (page 3). While the analysis of transcriptional changes in additional brain regions would be of interest, the main conclusions derived by the present analyses on the hippocampus already opens new perspectives to our understanding of this complex disease (e.g. premature pathological changes at 8 weeks occur long before the development of neuropathological and clinical signs). In light of the new findings observed from the first cohort of experimental animals, we designed the rest of the study to prioritize more analyses (e-g- splicing and RNA editing) and validations (e.g. second cohort, aging cohort, plasma administration cohort etc.) in order to provide comprehensive and robust dataset and corroborate our findings. We are currently working on a follow-up study thoroughly describing transcriptional changes during prion disease development in other brain regions. We believe that the inclusion of these data would not be instrumental to support the main conclusions of the present study and may unduly add complexity to the current manuscript.

The article presents comprehensive bioinformatics analysis of the gene expression profiles, during disease progression and continues focusing on two early stages whose profiles clearly cluster together. The authors elegantly query the transcriptomic data extrapolating clusters representative of different cell types and conclude that at preclinical stages microglial-related DEGs are enriched. Importantly, data trends are replicated in an independent animal cohort supporting the experimental design, reproducibility and bioinformatics analysis. Enriched microglial populations from challenged animals compared to controls, would have added more value to the approach.

We agree with the reviewer that certain cell types, including microglia should be investigated in more detail. We are currently working on a study investigating prion-induced changes in a cell-type specific manner using ribosomal profiling. While space reasons prevent us from adding these studies to the present manuscript, we are planning to publish a comprehensive searchable database that will include both the transcriptomics and translatomics data.

The authors proceed to conclude that these transcriptomic enrichment of microglial related DEGs are suggestive of driver events in the initiation of prion disease. Although the statement is gaining a lot of interest in the current literature, it is yet immature to conclude from only RNA sequencing data that microglial neuroinflammation is the causative driver event and not the result of the infection and subsequent neurodegeneration. Taking also into consideration the route of infection (ic) which is expected to initiate an acute immune response in the brain.

Towards that comment, the immunohistochemistry data should show increased immune reaction from the early time points pi.

While the simultaneous occurrence of microglia-related changes and motor decline suggests that microglia may be the ultimate drivers of prion disease progression, we agree that correlation does not prove causation, and have toned down our conclusions to this respect.

Clearly, microglia activation does not play a major role during the early stages of prion disease: we do not see any increased immune reaction at the early time points as the reviewer pointed out, nor do we see any RNA expression changes in microglia-enriched genes at the early time points.

We also don’t believe that the infection is the source of microglia activation for the following reason: if the inoculation itself would induce microglia activation we would expect a strong microglia response directly after the injection that should progressively decrease. Instead, we see no expression change in microglia-enriched genes until 16 wpi. We have clarified the corresponding sections in the text.

To address the reviewer’s point that the route of infection may contribute to the observed changes we have added the following datasets as new Supplemental Fig. 4:

We have analyzed prion induced changes 8wpi and the terminal stage from intraperitoneally inoculated mice (new Supplemental Fig. 4). The prion induced changes between the different routes of administration correlate at the respective timepoint, indicating that the induced changes are independent of the route of prion inoculation. To strengthen the point that the 8 wpi changes are indeed prion-dependent (and thus require in vivo prion replication by incorporation of cellular prion protein PrP), we have additionally included 8 wpi samples from PrP knock-out mice. The knockout mice don’t show any prion-induced changes at 8 wpi (new Supplemental Fig. 4), suggesting that the 8 wpi changes are not the result of the infection and more importantly, are in fact prion-dependent.

Also, the paper would gain significantly, if there were random as well as targeted (eg microglial specific) molecular targets selected, for independent validation by Real-time QC PCR and immunohistochemistry. This would be especially interesting if it was combined with the targets that showed selective splicing like Ctsa, a microglial related gene.

We respectfully disagree with the reviewer on this issue. In the early days of RNAseq, most scientists would validate their results with qPCR of select genes. However, by now RNAseq is widely accepted as the state-of-the-art technique to profile whole transcriptomes and is considered to be more reliable, accurate and sensitive compared to orthogonal methods such as RT-qPCR. Also, RNAseq and RT-qPCR data are highly correlated (typically ~85% and well above 90% when genes with a low expression are neglected; PMID: 28484260). The inclusion of an orthogonal technology is thus only needed when a) no biological replicates are available (potentially detrimental intra-group variability); b)definitive conclusions depend on genes with extremely low expression levels (potentially detrimental high dispersion); c) the main findings of an experiment revolve around one or a handful of genes (potentially detrimental false positives). None of the above applies to this study. Moreover, in terms of overall validation, we already include data from a second, independent cohort of mice with the same experimental settings (Supplementary Fig. 3), as well as from aged mice and from mice with plasma/saline treatment. We therefore maintain that qPCR verification is unnecessary in this instance and may potentially even produce confounders.

*RNA binding deaminase proteins show a similar pattern to a recent report, strengthening the finding that protein levels do not change and/or compensate with other RNA binding and editing enzymes, even though edited targets and editing frequency shift significantly.

The authors continue with RNA editing analysis concluding that they did not find any (apart from two targets being edited) differential RNA editing sites contradictory to a recent study. We believe that this contradiction is a premature conclusion since, the analysis was based on an older protocol that was published by the same group based on GTAK version 3.4.0 from 2011. The predicted RNA edited sites were only based in previously catalogued samples from hippocampus of young mice by Stilling et al 2014. They did not take into account C-U editing in all genomic locations in the whole brain regardless of aging or region. Also, the depth of sequencing was not taken into account which would increase the novel identification of editing sites instead of being limited to previously identified non-validated RNA editing. The study would significantly benefit from Sanger sequencing validation of random and non random edited targets. How do the identified targets validate? *

As suggested by the Reviewer, we have reanalyzed RNA editing using the same editing pipeline as Kanata et al. (PMID: 31492812), neither restricting the analysis to a pre-existing list of candidate sites, nor limiting the analysis to A-to-I editing events. Following this approach, a number of editing sites comparable to those reported by Kanata et al. were identified. However, we did not observe a statistical difference between control and PrD samples at the locus level.

As discussed in the manuscript Kanata et al, analyzed a different brain region using a different infection model. Furthermore, the fact that we assessed triplicates, and the application of strict filters in the selection of putative editing sites might have contributed to us not detecting differentially edited sites. While we used the same parameters linked to quality and depth of coverage, we only considered the intersection of both REDItools and VarScan2, and required that at least 2 out of 3 samples were edited. Regarding the validation of the editing events through Sanger sequencing, we believe it is outside of the scope of the present study because our main goal is not that of exactly pinpointing specific editing sites and hypothesizing on their potential effects. We rather view the editing analysis as an auxiliary layer to the main conclusions of the manuscript, and through the updated analysis and results we believe we have reached such a goal.

Finally, the study concludes with the administration of young plasma at 8 weeks (early stage of the disease) and the authors support that this intervention improves the phenotype of the affected animals without lifespan changes. In our view, this part of the study should either be omitted, or full transcriptomic and clinicopathological improvement should be demonstrated with clear emphasis on microglial-related molecular targets.

While plasma administration does not prolong lifespan and terminal prion-induced changes are very similar in plasma vs saline-treated animals (Fig. 6d-e), we did in fact observe a full transcriptomic improvement upon plasma administration at 8 wpi (Fig. 6b). We currently don’t know if prion-induced 8 wpi changes and the plasma-induced improved health span are linked to microglia-related changes (see also response above). We have therefore not put any additional emphasis on microglia-related targets. We therefore feel that the plasma experiments do add to the present paper, but we would be prepared to discuss with the editors whether it may be appropriate to omit this part and publish it separately. \*Minor comment:**

Other behavioral tests such as T-maze, Morris water-maze, novel object recognition, wouldn't it be better suited for memory assessment? *

Although we agree with the reviewer that these tests are better suited for memory assessment, the purpose of the rotarod evaluation (together with histological and biochemical tests) was to obtain an objective monitoring of clinical disease development. Rotarod assessment has been instrumental to objectivate the genetic or pharmacological modulation of prion disease development (e.g PMID: 29176838; PMID: 26246168; PMID:25502554). A more sophisticated behavioral assessment would go beyond the scope of this study and would require access to specific infrastructures which are not available in our veterinary bio contained research facility allowing the handling of prion-infected mice.

*Reviewer #1 (Significance (Required)):

The authors present a very detailed and informative transcriptomic profiling of a well structured in vivo experiment with a satisfactory number of time points that has provided significant transcriptomic and splicing information at the preclinical stage of the disease. The field would definitely benefit from such a profile oriented approach however the above should be sufficiently addressed. *

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

In the present paper by Sorce et al. the authors studied mRNA changes, splicing and editing alterations during the progression of prion disease in an experimental animal model (inocculated mice). The main findings are that changes in RNA processing and abundance occured very early at around 8 wpi. Interestingly, changes in microglia-enriched genes appeared early and coincided with the onset of clinical symptoms while neuronal genes were unchanged and played a more prominent role at later stages of the disease suggesting that glial cells might be the driving force and pivotal for the early stages and disease progression. Young plasma restored mRNA alterations and was beneficial for delaying neurological symptoms. This conclusion seems to be supported by the data and overall the study was well performed. The findings are clearly presented, the discussion is insightful and balanced and the figures are in general of high quality but there are some concerns that need to be clarified.

The authors could tackle the following comments with a straightforward revision:

1) On Page 3 it is mentioned that RNA sequencing was performed in n=3 samples per time point and for the 20 wpi time point in controls only n=2 samples have been used. Overall this is a very low sample size that needs to be increased. More samples need to be analyzed in order to provide biological relevance. *

We agree with the reviewer that, like in any other biological study entailing a certain degree of experimental variability, increased sample sizes always increase the statistical power and may allow for the detection of changes that might otherwise go undetected. However, there are opportunity costs that go along with enlarging the study. Also, the Swiss Animal Protection Law requires us to adhere to the 3R principles (Replacement, Reduction and Refinement of animal experimentations). We have aimed at using the minimum number of animals allowing us to identify a subset statistically significant and robust changes. Animal welfare considerations and an attempt to prevent an escalation of cost resulted in the majority of experiments being performed with 3 samples per time point.

It is accepted in the field that three replicates are sufficient to identify the vast majority of biologically relevant changes in mRNA abundance. Unless major claims are made about individual genes at the lower end of the expression’s dynamic range (which is not the case in this study), three replicates ensure that about 85 % of the relevant changes are accurately captured (PMID: 29767357; PMID: 26813401). This is particularly true when the variability across replicates is low and appropriate analysis tools, such as edgeR, are employed (PMID: 30726870).

We used age and gender matched inbred C57BL/6J mice in a microbiologically tightly-controlled environment (see Methods) to minimize interindividual variability. This allowed us to identify thousands of statistically significant prion-dependent changes, despite the low sample number.In few exceptions we sequenced two instead of three replicates (eg because a sample got lost, the RNA was degraded, or the sample did not pass quality control after sequencing). In these instances, we ensured that both replicates showed a high correlation and could thus still yield reliable results. Furthermore, we have validated the RNA expression changes in an independent cohort of mice with the same experimental settings (Supplementary Fig. 3), as well as from aged mice and from mice with plasma/saline treatment, indicating that the observed changes are robust.

2) On page 4 it is written that 'While clusters 2 and 3 consist predominantly of microglia and neuronal genes, cluster 1 and 4 contain genes enriched in multiple cell types'. A few sentences later, the authors write, that 'Neuronal genes almost exclusively belonged to clusters 3 and 4................, whereas microglia genes were essentially contained in clusters 1 and 2. These two statements are contradictory. Please explain and clarify.

Compared to clusters 2 and 3, the enriched genes in clusters 1 and 4 don’t predominantly fall into one category (eg cluster 4 contains ~30% neuronal-enriched genes, ~25% oligodendrocyte enriched genes, 20% endothelial-enriched genes – see Fig. 1d). However, of 203 neuronal-enriched DEGs, 143 are cluster 3 genes (~70%), 50 are cluster 4 genes (~25%), while only 10 are cluster 1 genes (~5%) and 0 are cluster 2 genes. To better illustrate this point, we have included these numbers as an additional Table in Supplementary Fig. 2.

3) On page 5 the authors claim that 'astro- and microgliosis became evident at 16 wpi...........' This statement is based solely on histological images and needs to be confirmed by quantification. However in Supplementary figure 5c astrocytes and microglia (GFAP and Iba1 staining) are almost not visible and the overview images too superficial. I recommend high resolution images and additional inserts and a solid quantification.

We have added high resolution pictures, additional inserts and a quantification of the stainings (new Supplementary Fig. 6).

4) On Page 6 the authors write 'We observed progressive decline in motor performance starting 18 wpi'. However, the graph in figure 3a clearly shows only a significant difference at 19 wpi'. This needs to be corrected.

• The decline in motor performance shows a visible trend at 18 wpi but only becomes statistically significant at 19 wpi. We have clarified this in the text.*

5) Figure 6c: it would make sense to combine both graphs (saline and plasma) for a direct comparison of prion infected mice that received saline or plasma so that potential differences would be easier to recognize ......although they seem to be pretty modest.

We have combined both graphs from Fig. 6c into one but believe that it becomes very difficult to extract any information from the figure. We shall defer to the reviewer’s judgment but we would prefer to keep the original figure.

Reviewer #2 (Significance (Required)):

The findings are of interest to a wide readership and the paper thus seems suited to be published, but there are some concerns that need to be clarified (see specific comments above).

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Referee #2

Evidence, reproducibility and clarity

In the present paper by Sorce et al. the authors studied mRNA changes, splicing and editing alterations during the progression of prion disease in an experimental animal model (inocculated mice). The main findings are that changes in RNA processing and abundance occured very early at around 8 wpi. Interestingly, changes in microglia-enriched genes appeared early and coincided with the onset of clinical symptoms while neuronal genes were unchanged and played a more prominent role at later stages of the disease suggesting that glial cells might be the driving force and pivotal for the early stages and disease progression. Young plasma restored mRNA alterations and was beneficial for delaying neurological symptoms. This conclusion seems to be supported by the data and overall the study was well performed. The findings are clearly presented, the discussion is insightful and balanced and the figures are in general of high quality but there are some concerns that need to be clarified.

The authors could tackle the following comments with a straightforward revision:

1) On Page 3 it is mentioned that RNA sequencing was performed in n=3 samples per time point and for the 20 wpi time point in controls only n=2 samples have been used. Overall this is a very low sample size that needs to be increased. More samples need to be analyzed in order to provide biological relevance.

2) On page 4 it is written that 'While clusters 2 and 3 consist predominantly of microglia and neuronal genes, cluster 1 and 4 contain genes enriched in multiple cell types'. A few sentences later, the authors write, that 'Neuronal genes almost exclusively belonged to clusters 3 and 4................, whereas microglia genes were essentially contained in clusters 1 and 2. These two statements are contradictory. Please explain and clarify.

3) On page 5 the authors claim that 'astro- and microgliosis became evident at 16 wpi...........' This statement is based solely on histological images and needs to be confirmed by quantification. However in Supplementary figure 5c astrocytes and microglia (GFAP and Iba1 staining) are almost not visible and the overview images too superficial. I recommend high resolution images and additional inserts and a solid quantification.

4) On Page 6 the authors write 'We observed progressive decline in motor performance starting 18 wpi'. However, the graph in figure 3a clearly shows only a significant difference at 19 wpi'. This needs to be corrected.

5) Figure 6c: it would make sense to combine both graphs (saline and plasma) for a direct comparison of prion infected mice that received saline or plasma so that potential differences would be easier to recognize ......although they seem to be pretty modest.

Significance

The findings are of interest to a wide readership and the paper thus seems suited to be published, but there are some concerns that need to be clarified (see specific comments above).

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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---

Referee #1

Evidence, reproducibility and clarity

The authors present a well written article describing distinct transcriptomic profiles generated by RNA sequencing analysis of hippocampus, a distinct anatomical area, at well spaced and defined time points of clinical progression following prion inoculation in an established mouse model. The authors contribute significantly in the detailed transcriptomic definition of changes during disease progression, especially during the early and almost asymptomatic stages.

The brain region chosen to perform their analysis is logical as the hippocampus shows clear signs of neuronal degeneration in prion disease progression and furthermore provides a well defined area for analysis that is easily accessible experimentally; Although, more information would be needed to strengthen this choice in relation to the hippocampus playing a key role in the initiation stages of the disease. It remains an anatomical subset of the whole brain and the study would benefit if extended to include other affected areas.

The article presents comprehensive bioinformatics analysis of the gene expression profiles, during disease progression and continues focusing on two early stages whose profiles clearly cluster together. The authors elegantly query the transcriptomic data extrapolating clusters representative of different cell types and conclude that at preclinical stages microglial-related DEGs are enriched. Importantly, data trends are replicated in an independent animal cohort supporting the experimental design, reproducibility and bioinformatics analysis. Enriched microglial populations from challenged animals compared to controls, would have added more value to the approach.

The authors proceed to conclude that these transcriptomic enrichment of microglial related DEGs are suggestive of driver events in the initiation of prion disease. Although the statement is gaining a lot of interest in the current literature, it is yet immature to conclude from only RNA sequencing data that microglial neuroinflammation is the causative driver event and not the result of the infection and subsequent neurodegeneration. Taking also into consideration the route of infection (ic) which is expected to initiate an acute immune response in the brain.

Towards that comment, the immunohistochemistry data should show increased immune reaction from the early time points pi. Also, the paper would gain significantly, if there were random as well as targeted (eg microglial specific) molecular targets selected, for independent validation by Real-time QC PCR and immunohistochemistry. This would be especially interesting if it was combined with the targets that showed selective splicing like Ctsa, a microglial related gene.

RNA binding deaminase proteins show a similar pattern to a recent report, strengthening the finding that protein levels do not change and/or compensate with other RNA binding and editing enzymes, even though edited targets and editing frequency shift significantly.

The authors continue with RNA editing analysis concluding that they did not find any (apart from two targets being edited) differential RNA editing sites contradictory to a recent study. We believe that this contradiction is a premature conclusion since, the analysis was based on an older protocol that was published by the same group based on GTAK version 3.4.0 from 2011. The predicted RNA edited sites were only based in previously catalogued samples from hippocampus of young mice by Stilling et al 2014. They did not take into account C-U editing in all genomic locations in the whole brain regardless of aging or region. Also, the depth of sequencing was not taken into account which would increase the novel identification of editing sites instead of being limited to previously identified non-validated RNA editing. The study would significantly benefit from Sanger sequencing validation of random and non random edited targets. How do the identified targets validate?

Finally, the study concludes with the administration of young plasma at 8 weeks (early stage of the disease) and the authors support that this intervention improves the phenotype of the affected animals without lifespan changes. In our view, this part of the study should either be omitted, or full transcriptomic and clinicopathological improvement should be demonstrated with clear emphasis on microglial-related molecular targets.

Minor comment:

Other behavioral tests such as T-maze, Morris water-maze, novel object recognition, wouldn't it be better suited for memory assessment?

Significance

The authors present a very detailed and informative transcriptomic profiling of a well structured in vivo experiment with a satisfactory number of time points that has provided significant transcriptomic and splicing information at the preclinical stage of the disease. The field would definitely benefit from such a profile oriented approach however the above should be sufficiently addressed.

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Reply to the reviewers

Reviewer #1:

**Summary**

Jang et al., address the important question of spatially localized or compartmentalized metabolic enzymes with a focus on the glycolytic enzyme PFK1. Using a good strategy of inserting a fluorescent tag at the endogenous PFK1 locus with tissue-specific inducible expression in C. elegans, combined with strong quantitative longitudinal imaging and innovative bioengineered microfluidic-hydrogels to control oxygen availability as well as optogenetic approaches, they show PFK1 condensates, which are not stress granules and not seen in normoxia, assemble with hypoxia. PFK1 condensates are dynamic, reversible, localized at the synapse in neurons, and recruit aldolase, another glycolytic enzyme. Although glycolytic proteins were previously shown to compartmentalize near the plasma membrane, and PFK1 was previously shown to assemble into filaments in vitro and be punctate at the plasma membrane in mammalian cells, evidence for cellular localized PFK1 condensates in animals is highly significant. The work includes strong biophysical characterization of PFK1 phase-separated condensates, but no clear indication of the composition of condensates. More significantly, the findings lack functional significance related to PFK1 activity or glycolytic flux with hypoxia vs normoxia. Despite previous work by this group showing that disrupting subcellular localization of glycolytic enzymes impairs neuronal activity in response with hypoxia, the reader is left with questions on the importance of localized and PFK1 condensates and their make-up .

**Major comments:**

Key conclusions are convincing, and most experimental approaches, biophysical characterization including thermodynamic principles, and data analysis are exemplary and well described. However, as indicated above, the work is limited to a descriptive analysis of cellular localization of PFK1 condensates and their biophysical properties without insights on functional significance relative to enzyme activity - or at least glycolytic flux or metabolic reprogramming with hypoxia. At best, only correlations can be drawn from hypoxia-induced localized PFK1 condensates and the authors' previous report (Jang et al., 2016) on hypoxia-regulated neuronal activity. Some insight or at least prediction in the discussion on the differences in spatially localized PFK1 in muscle vs neurons with regard to metabolic or energy distinctions should be included.

We have added additional discussions on the differences of the spatially localized PFK-1.1 in muscles versus neurons, explaining that in both tissues the cellular enrichment appears to be at sites predicted to have high ATP consumption (lines 128-133; 482-484).

Despite the strong biophysical analysis of condensates, several important features are not determined. First is at best a rudimentary analysis of the composition of condensates and also how PFK1 is assembled into these structures. For the former, is the core of the condensate predominantly PFK1 with perhaps aldolase only recruited to the periphery or is aldolase an integral component of the structure. Hence, is it a PFK1 condensate or a glycolytic condensate? For the latter question, is there a particular orientation for PFK1 in condensates, i.e a collection of filaments as previously reported, which might provide insight on assembly? Finally, and less critical but also important is the criterion for spherical, which is not well defined, and at least some idea or speculation on determinants for a spherical morphology - compared with filaments that have been reported for other non-glycolytic metabolic enzymes.

We have now co-expressed PFK-1.1 and ALDO-1 and examined their dynamic formation during hypoxic conditions. We observe PFK-1.1 and ALDO-1 form condensates simultaneously, with gradual enrichment of both molecules. We now include this new data in Figure 7E and Video 8; lines 422-441, 964-989). We also include genetic data demonstrating the ALDO-1 requires pfk-1.1 to form condensates, and that PFK-1.1 requires aldo-1 as well. Therefore, the enzymes are interdependent on each other to form condensates (Figures 7G, 7H, S7B, and S7C).

The spheroid geometry reflects liquid-like properties, which arises from surface tension of molecules loosely held together via multi-valent interactions. Filamentous arrangements reflect crystalline-like structures resulting from more stable interactions between molecules into solid-like states. While we did not perform high resolution studies, like Cryo-EM, to resolve this question, the spheroid geometry of PFK-1.1 condensates, along with its fluid-like properties, suggest the condensates are liquid-like compartment distinct to filamentous structures. We now add this discussion in lines 467-470.

The work is an important advance in our understanding on the self-assembly of metabolic enzymes by showing hypoxia-induced PFK1 condensates in vivo, their spatially-restricted subcellular localization in muscle cells and neurons, and their biophysical properties, the latter being distinct from those of stress granules. Taken together, these findings are more extensive than many previous reports on the assembly of metabolic enzymes into filaments or condensates, but fall short for new insights on functional significance.

We focus this study on the biophysical characterization of the condensates, and how that results in compartmentalized enrichment of glycolytic proteins. Examination of the functional significance of the phase separation to the enzymatic reactions in vivo is not currently possible because we lack probes we can use in vivo to measure the metabolites resulting from the reaction. We have now added discussion acknowledging this and framing its significance in the context of what has been published in the field (lines 484-492). For example, a recent manuscript in ChemRxiv demonstrated, in vitro, that the enzymatic activity of glycolytic proteins, hexokinase and glucose-6 phosphate dehydrogenase, promote these enzymes condensing into liquid droplets. The authors further found that the condensation accelerated the glycolytic reactions (Ura et al., 2020). This raises the question whether glycolytic proteins compartmentalize, and form condensates, in vivo, which we address in this manuscript. We capture this point in (lines 444-464) where we explain that, while it has long been hypothesized that glycolytic proteins like PFK-1 could be compartmentalized, this remained controversial due to lack of dynamic in vivo imaging. In our study, and through a systematic examination of endogenous PFK-1.1 via the use of a hybrid microfluidic-hydrogel device, we conclusively determine that PFK-1.1 indeed displays distinct patterns of subcellular localization in specific tissues in vivo.

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

This paper reports on the condensation of the glycolytic enzyme PFK-1 in response to hypoxic conditions in neurons of C. elegans. The authors employ a microfluidic-hydrogel device to dynamically monitor the relocalisation of PFK-1 from a mostly diffuse state to clusters in response to hypoxia and show that PFK-1 can undergo multiple rounds of PFK-1 clustering and dissolution. The authors work through the key features of a liquid-like compartment (sphericity, fusion, fast internal rearrangements) and give evidence that PFK-1 may have all three. Finally, the authors tag PFK-1 with the light-inducible multimerization domain Cry2 and find that even without light PFK-1 will constitutively form clusters that sequestrate endogenous PFK-1 as well as other glycolytic proteins. The strength of this work is that it is characterizing what appears to likely be phase separation in the context of a whole animal experiencing a stress that it could encounter in the natural world. A limitation of the work is that it is unclear what the functional implications are of condensates of PFK-1 at the molecular or cell scale.

**Major comments:**

-All experiments were performed using fluorescently tagged PFK-1 expressed from endogenous promoter or from the native genetic locus which is important for excluding overexpression artifacts. However, there is still risk that the GFP tag is driving the assembly process. In order to exclude tag-specific effects that may cause aggregation of the tetrameric PFK-1, ideally a control would be done in which PFK-1 is visualized through immunofluorescence experiments of WT cells. Alternatively, a short tag (e.g HA, His) as epitope for is an alternative .

We used fluorescent tags to observe the dynamic relocalization in vivo. While in the study we have not performed immunofluorescence, we established the validity of the labeling method by: 1) using monomeric versions of GFP; 2) using different fluorophores to show the same condensation phenomenon; 3) performing CRISPR for single copy insertions; 4) Demonstrating that different glycolytic proteins form condensates; 5) demonstrating the GFP-tagged versions of the protein are capable of rescuing the loss-of-function alleles and 6) Now adding new data demonstrating the observed localization specifically depend on the presence of other glycolytic proteins. This last result supports that GFP tag is not driving the assembly process of glycolytic condensate and that the glycolytic condensate formation requires the presence of specific molecules in the pathway. I add that we routinely use fluorophore markers to over a dozen distinct proteins that label subcellular compartments, and we have never observed the dynamic relocalization reported here, with the exception of other glycolytic proteins that interact with PFK, suggesting this is a property specific to glycolytic proteins, and, based on the genetic studies, dependent on the glycolytic reaction. We add and discuss these findings in Figures 7G, 7H, S7B, and S7C; lines 422-441, 964-989.

-For the Cry2-section, the complementation of the pfk-1 mutant supports functionality of the synaptic clustering phenotype. Are there other features of function that can be evaluated or could you look at how Cry-2 vs wt worms recover from different durations of stress or frequencies. Could you see if the Cry-2-fusion will rescue function to a partial-loss-of-function allele or a tetramerization deficient allele? A detailed analysis of the effects of constitutive presence of PFK-1-Cry2 clusters would be necessary to bolster claims that this is fully functional construct. Can enzyme activity be somehow monitored?

We did not observe any difference between wild-type worms and CRY2-expressing worms with regards to their development, survival, locomotive behavior or synaptic phenotype. While we can not discard the possibility that this is not a full rescue, with available tools, we can not distinguish the recue with PFK-1-Cry2 from that of just PFK-1.

-The analysis of the sphericity of clusters (4A) is limited due to the diffraction limit of light which limits an analysis of a compartment of this size. While this is a limitation of the live organism, this should be more clearly acknowledged.

We have included in the Methods section our criteria for quantifying condensates and avoiding diffraction limit artifacts. Briefly, “Considering the resolution limit of a spinning disc confocal (approximately 300nm), any structure with a diameter less than 500nm and an area smaller than 0.2 µm2 was excluded from the analyses”. To better clarify this point, we also now add a description of the criteria used in the main text (lines 242-243).

In addition, we observed that PFK-1.1 condensates are not perfect spheres, but constrained spheroids (which can not be explained by diffraction-limited point spread functions). We can explain the observed spheroid shapes based on liquid-like properties of the condensates, and the constrains of the diameter of the neurite. To better highlight this finding, we have now moved Figure S4E into the main figure (Figure 4B’).

-Fusion experiments (4C) do not fully exclude that clusters overlap instead of merging. It would be beneficial to show the foci for several subsequent frames. One would expect that upon fusion, the condensate size would increase, but video 3 suggests the opposite. It would be useful to quantify condensate size before and after fusion for several separate fusion events. -an alternative possible experiment would be the tagging of PFK-1 with a photoconvertible fluorophore (e.g. Dendra2) and subsequent analysis of fusion events

To better show the fusion events in Figure 4C, we now include all xy, yz, and zx plane views of before and after fusion events of Figure 4C (Figure S5B). We also added a quantification of four independent fusion events in which we compare the sum of the areas of the two puncta before fusion and the size of the area of the single punctum after fusion (Figure S5C). These data support that we are observing fusions events.

-4D). It is unclear if foci are indeed undergoing fission or if two clusters next to each other are moving apart.

For Figure 4D, in all the frames we had recorded, a single structure maintains a continuous signal until fission occurs and splits into two structures. To better present this event, we now include an unabridged version of figure of 4D in the supplement that shows all the frames captured (Figure S5D).

-The analysis of side-by-side growth and dissolution kinetics are interesting and a novel view into the non-equilibrium aspects of phase separation in cells.

-Purification of PFK-1 and in vitro reconstitution of condensates would be supportive of liquid-like characteristics although I don't think it is necessary however it would add a lot to the relevance to show enzyme activity is different +/- condensate state but I am not sure if an easy enzymatic assay exists in vitro.

We agree. But the significance of this particular paper, specifically in the context of the in vitro enzymatic work on glycolytic proteins, is to examine the dynamic in vivo localization and the biophysical characteristics of the condensates. To better underscore this in the context of the field, we add discussion of a recent in vitro manuscript demonstrating that liquid droplet formation of glycolytic proteins affect their enzymatic activity (Ura et al., 2020) (lines 444-464; 484-492). While we see the value of future studies reconstituting the glycolytic particles, we believe that is beyond the scope of this particular in vivo study.

**Minor comments:**

-Stress granules in other organisms (yeast paper) have different composition depending on stress type. To make the claim that the PFK-1 compartments are independent of SGs one would ideally test multiple different SG markers.

We selected the stress granule protein TIAR-1 because it is one of the most studied stress granule markers in C. elegans and it is reportedly one of the core proteins and universal components of stress granules irrespective of a stress type (Buchan et al., 2011; Gilks et al., 2004; Huelgas-Morales et al., 2016; Kedersha et al., 1999). Although we did not include images in the manuscript, we had tested a total of three stress granule markers: TIAR-1, TDP-43, and G3BP1 with similar results. We now added that as data not shown (lines 193-194).

-it should be stated in the main text that the microfluidic-hydrogel device was fabricated following previously published protocols

We have added the reference in the main text (line 170) to supplement what we had written in the Methods section: “A reusable microfluidic PDMS device was fabricated to deliver gases through a channel adjacent to immobilized animals, following protocols as previously described (Lagoy and Albrecht, 2015)”.

-Figure 4b: Y-axis should be changed from probability to fraction of occurrence

We have corrected this in both the figure and the figure legends (Figure 4B).

-The discussion should be less speculative concerning any effects seen in PFK1-Cry2 expressing C. elegans

We have modified the discussion as suggested.

-it is perplexing that a protein known to tetramerize with no disordered or RNA-binding domains forms condensates like this. Is there anything known from other systems of additional interacting proteins that may have features that promote liquidity and serve to fluidize these assemblies?

Condensates can form via multivalent interactions, which include, but is not exclusive, to disordered or RNA-binding domains. Because glycolytic proteins have dihedral symmetries that can facilitate multivalent interactions, we believe these structural properties, in combination with regulated conformational changes, promote multivalent interactions leading to their condensation. We had a statement in the discussion (lines 494-519) now add this more clearly in the results (lines 395-398).

Reviewer #2 (Significance (Required)):

Stimulus-induced phase separation has been observed for dozens of metabolic enzymes from various different pathways (reviewed in Prouteau, 2018). Several studies have published the formation of condensates through PFK-1 in diverse organisms (C. elegans, Yeast, human cancer cells) in response to hypoxia or in some cancer lines also without hypoxia (Jin, 2017, Jang, 2016, Kohnhorst 2017, etc.). A yeast study showed that PFK-1 condensates contain various other glycolytic enzymes and that condensate formation enhances glycolytic rates (Jin, 2017).

This study gives the advance of analyzing the dynamics of PFK-1 condensate formation in vivo in the context of a live animal using a microfluidic-hydrogel device and showing that PFK-1 relocalizes to reversible condensates within minutes of hypoxia. If further appropriate experiments (as mentioned above) are performed, this study would strongly suggest that the underlying process of PFK-1 condensate formation is liquid-liquid phase separation. Ideally, if at all feasible, it would be strengthened if there was some insight into the functional consequences of the condensed assemblies formed in hypoxia. These findings may be interesting to researchers working on glycolysis and metabolism in different cells but particularly in neurons.

Field of expertise

-Phase separation, microscopy, in vitro reconstitution

-no experience with C. elegans biology and do not have a practical handle on ease or difficulties of genetic manipulation of C. elegans or metabolic assays for PFK-1

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

**Summary:**

In this manuscript, the authors focus on the subcellular localization of the key glycolytic enzyme PFK-1.1 in C. elegans, initially in whole animals through GFP tagging of the endogenous locus and subsequently in single cells/tissues using a clever genome editing strategy that permitted tissue-specific expression of GFP-tagged PFK-1.1 from its endogenous locus. They observe that PFK-1.1 localization differs from cell-type to cell-type and can be dynamically reorganized in response to exogenous cues. Focusing on hypoxia, they observe that PFK-1.1 forms foci near synapses in neurons under this stress condition. These foci are not stress granules and they are dissolved upon re-oxygenation. These condensates have properties of liquid droplets and can mature (harden) over time. PFK-1.1 fused to the CRY domain can trigger condensate formation under normoxic conditions, which can co-recruit WT PFK-1.1 as well as aldolase.

**Major comments:**

The conclusions are convincing but the impact could be increased if the authors were able to demonstrate the physiological role that the observed phase separation plays in this stress response. Would it be possible to assess glycolytic flux under hypoxia vs normoxia?

It is currently not possible to assess glycolytic flux in vivo in our system, as we lack metabolic sensors (an active area of work we are trying to address, but will take several years to perform correctly). We have added discussion of new in vitro studies examining the consequences of metabolic flux due to glycolytic compartmentation into liquid droplets (Ura et al., 2020), and the significance of those findings in the context of our in vivo studies (lines 444-464; 484-492).

The authors should comment on viability during the hypoxia time course.

C. elegans can survive anoxic condition for a day (Powell-Coffman, 2010). Our hypoxic conditions last minutes, and we can rescue live C. elegans upon completion of the assays. We now include a description of this in the Methods (lines 1216-1218).

The co-clustering of ALDO-1 and PFK-1.1::mCh::CRY2 in Figure 7 should be properly quantified/statistically analyzed

We quantified the fraction of animals that displays ALDO-1 clustering in PFK-1.1::mCh::CRY2 co-expressing animals, as suggested (Figure S7C).

A control of mCh::CRY2 + ALDO-1::EGFP is missing from the experiments shown in Figure 7. Is the presence of mCh::CRY2 sufficient to drive ALDO-1::EGFP clustering?

As a control for the CRY2 tag promoting the formation of glycolytic condensates, we had co-expressed mCh::CRY2 with PFK-1.1::EGFP, which is insufficient to cause the formation of the condensate (Figure 7C). We have now added a new data where we show that in pfk-1.1 deletion mutants, ALDO-1 condensate formation is suppressed, which further demonstrates the dependency between PFK-1.1 and ALDO-1 (Figures 7H and S7C).

Does hypoxia trigger co-clustering of ALDO-1 and PFK-1.1?

To answer this question, we examined the dynamic formation of ALDO-1 and PFK-1.1 condensates by co-expressing the two proteins together and observed that hypoxia triggers their co-clustering. We now include this in Figure 7E and Video 8.

The authors speculate that hypoxia acts via diminished energy (altered ATP AMP ratios). Can this be measured? To support this hypothesis, the authors may wish to test if similar phase separation is triggered by mitochondrial poisons.

We currently lack sensors that can reliably measure, in vivo, the subcellular changes in energy or metabolic flux in C. elegans neurons. However, we previously did test mitochondrial mutants and observed that in those mutants we observe glycolytic condensates (Jang et al., 2016), supporting the idea that defects in energy production promotes the formation of glycolytic condensates.

**Minor comments:** Is 21% O2 not hyperoxic for worms?

While C. elegans are known to prefer lower percentage of oxygen than those in air, in the lab animals are reared in normal air. We therefore used 21% oxygen present in air as our normoxic conditions.

Can the authors speculate more on how do these condensates exhibit "memory" (how they're able to cluster in the same place repeatedly)? Is there any role for the cytoskeleton in mediating nucleation and/or condensation of PFK and glycolytic enzymes?

When we were testing the reversibility of PFK-1.1 condensates, we were not expecting the reappearance of PFK-1.1 condensates in the same place repeatedly. Our current speculation is that, because many glycolytic enzymes, such as PFK-1.1, are allosterically regulated by nucleotides, AMP/ATP ratio may play a role on where glycolytic condensates appear. In other words, the specific synaptic areas, where PFK-1.1 condensate repeatedly reappeared, may have different AMP/ATP ratio that may trigger the condensation of the glycolytic proteins in those locationsupon conformational changes in PFK-1. We can’t exclude, currently, the presence of nucleating factors at synapses that facilitate PFK-1 clustering, but we have not yet identified them. We now include a discussion of this (lines 494-519).

Do the authors think that these clusters are effectively G-bodies from yeast?

G-bodies from yeast also shows glycolytic proteins changing from its diffuse localization to punctate localization in response to hypoxia (Jin et al., 2017). G-bodies, like C. elegans glycolytic condensates, are forms of subcellular glycolytic organization within eukaryotic cells. Yet, G-bodies take 24 hours to form, while we observe the glycolytic clusters in C. elegans within minutes of hypoxic conditions. We will need to understand the composition and function of both to determine if these forms of glycolytic subcellular organization represent the same structure. We note that glycolytic clusters have also been observed in some human cancer cell lines (Kohnhorst et al., 2017). Observation of glycolytic compartments in multiple different species and cell types suggest that, although the regulation, composition and formation kinetics of the glycolytic condensates may differ, compartmentalization of glycolytic enzymes may be a conserved feature. We now add a sentence discussing this (line 535-537).

Reviewer #3 (Significance (Required)):

It is much appreciated that this study tackles the cell biology of signaling and metabolism, which is a fascinating but difficult to study aspect of molecular biology. This work conclusively documents the dynamic reorganization of metabolic enzymes in vivo in response to physiological stimuli. Such reorganization had been proposed previously but was controversial and difficult to study in a controlled way. This work not only confirms previous observations but further demonstrates that the dynamic reorganization is mediated by a liquid-liquid phase separation. What is lacking is a demonstration that this phase separation is physiologically important. Such observations would generate interest from a much broader audience; the present audience presently targeting people specifically interested in non-membrane organelles per se. The reviewer has expertise in cell signalling and its regulation by phase separation.

As we explain for Reviewer 1, we focus this study on the biophysical characterization of the condensates, and how that results in compartmentalized enrichment of glycolytic proteins. Examination of the functional significance of the phase separation to the enzymatic reactions in vivo is not currently possible because we lack probes we can use in vivo to measure the metabolites resulting from the reaction. We have now added discussion acknowledging this and framing its significance in the context of what has been published in the field (lines 484-492). For example, a recent manuscript in ChemRxiv demonstrated, in vitro, that the enzymatic activity of glycolytic proteins, hexokinase and glucose-6 phosphate dehydrogenase, promote these enzymes condensing into liquid droplets. The authors further found that the condensation accelerated the glycolytic reactions (Ura et al., 2020). This raises the question whether glycolytic proteins compartmentalize, and form condensates, in vivo, which we address in this manuscript. We capture this point in (lines 444-464) where we explain that, while it has long been hypothesized that glycolytic proteins like PFK-1 could be compartmentalized, this remained controversial due to lack of dynamic in vivo imaging. In our study, and through a systematic examination of endogenous PFK-1.1 via the use of a hybrid microfluidic-hydrogel device, we conclusively determine that PFK-1.1 indeed displays distinct patterns of subcellular localization in specific tissues in vivo.

**REFEREES CROSS-COMMENTING** Globally it seems that all reviewers feel that impact would be increased if the physiological consequence of PFK-1.1 condensates was examined. Other, specific comments seem fair.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors focus on the subcellular localization of the key glycolytic enzyme PFK-1.1 in C. elegans, initially in whole animals through GFP tagging of the endogenous locus and subsequently in single cells/tissues using a clever genome editing strategy that permitted tissue-specific expression of GFP-tagged PFK-1.1 from its endogenous locus. They observe that PFK-1.1 localization differs from cell-type to cell-type and can be dynamically reorganized in response to exogenous cues. Focusing on hypoxia, they observe that PFK-1.1 forms foci near synapses in neurons under this stress condition. These foci are not stress granules and they are dissolved upon re-oxygenation. These condensates have properties of liquid droplets and can mature (harden) over time. PFK-1.1 fused to the CRY domain can trigger condensate formation under normoxic conditions, which can co-recruit WT PFK-1.1 as well as aldolase.

Major comments:

The conclusions are convincing but the impact could be increased if the authors were able to demonstrate the physiological role that the observed phase separation plays in this stress response. Would it be possible to assess glycolytic flux under hypoxia vs normoxia?

The authors should comment on viability during the hypoxia time course.

The co-clustering of ALDO-1 and PFK-1.1::mCh::CRY2 in Figure 7 should be properly quantified/statistically analyzed

A control of mCh::CRY2 + ALDO-1::EGFP is missing from the experiments shown in Figure 7. Is the presence of mCh::CRY2 sufficient to drive ALDO-1::EGFP clustering?

Does hypoxia trigger co-clustering of ALDO-1 and PFK-1.1?

The authors speculate that hypoxia acts via diminished energy (altered ATP AMP ratios). Can this be measured? To support this hypothesis, the authors may wish to test if similar phase separation is triggered by mitochondrial poisons.

Minor comments: Is 21% O2 not hyperoxic for worms? Can the authors speculate more on how do these condensates exhibit "memory" (how they're able to cluster in the same place repeatedly)? Is there any role for the cytoskeleton in mediating nucleation and/or condensation of PFK and glycolytic enzymes? Do the authors think that these clusters are effectively G-bodies from yeast?

Significance

It is much appreciated that this study tackles the cell biology of signalling and metabolism, which is a fascinating but difficult to study aspect of molecular biology. This work conclusively documents the dynamic reorganization of metabolic enzymes in vivo in response to physiological stimuli. Such reorganization had been proposed previously but was controversial and difficult to study in a controlled way. This work not only confirms previous observations but further demonstrates that the dynamic reorganization is mediated by a liquid-liquid phase separation. What is lacking is a demonstration that this phase separation is physiologically important. Such observations would generate interest from a much broader audience; the present audience presently targeting people specifically interested in non-membrane organelles per se. The reviewer has expertise in cell signalling and its regulation by phase separation.

REFEREES CROSS-COMMENTING Globally it seems that all reviewers feel that impact would be increased if the physiological consequence of PFK-1.1 condensates was examined. Other, specific comments seem fair.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

This paper reports on the condensation of the glycolytic enzyme PFK-1 in response to hypoxic conditions in neurons of C. elegans. The authors employ a microfluidic-hydrogel device to dynamically monitor the relocalisation of PFK-1 from a mostly diffuse state to clusters in response to hypoxia and show that PFK-1 can undergo multiple rounds of PFK-1 clustering and dissolution. The authors work through the key features of a liquid-like compartment (sphericity, fusion, fast internal rearrangements) and give evidence that PFK-1 may have all three. Finally, the authors tag PFK-1 with the light-inducible multimerization domain Cry2 and find that even without light PFK-1 will constitutively form clusters that sequestrate endogenous PFK-1 as well as other glycolytic proteins. The strength of this work is that it is characterizing what appears to likely be phase separation in the context of a whole animal experiencing a stress that it could encounter in the natural world. A limitation of the work is that it is unclear what the functional implications are of condensates of PFK-1 at the molecular or cell scale.

Major comments:

-All experiments were performed using fluorescently tagged PFK-1 expressed from endogenous promoter or from the native genetic locus which is important for excluding overexpression artifacts. However, there is still risk that the GFP tag is driving the assembly process. In order to exclude tag-specific effects that may cause aggregation of the tetrameric PFK-1, ideally a control would be done in which PFK-1 is visualized through immunofluorescence experiments of WT cells. Alternatively, a short tag (e.g HA, His) as epitope for is an alternative .

-For the Cry2-section, the complementation of the pfk-1 mutant supports functionality of the synaptic clustering phenotype. Are there other features of function that can be evaluated or could you look at how Cry-2 vs wt worms recover from different durations of stress or frequencies. Could you see if the Cry-2-fusion will rescue function to a partial-loss-of-function allele or a tetramerization deficient allele? A detailed analysis of the effects of constitutive presence of PFK-1-Cry2 clusters would be necessary to bolster claims that this is fully functional construct. Can enzyme activity be somehow monitored?

-The analysis of the sphericity of clusters (4A) is limited due to the diffraction limit of light which limits an analysis of a compartment of this size. While this is a limitation of the live organism, this should be more clearly acknowledged.

-Fusion experiments (4C) do not fully exclude that clusters overlap instead of merging. It would be beneficial to show the foci for several subsequent frames. One would expect that upon fusion, the condensate size would increase, but video 3 suggests the opposite. It would be useful to quantify condensate size before and after fusion for several separate fusion events.

-an alternative possible experiment would be the tagging of PFK-1 with a photoconvertible fluorophore (e.g. Dendra2) and subsequent analysis of fusion events

-4D). It is unclear if foci are indeed undergoing fission or if two clusters next to each other are moving apart.

-The analysis of side-by-side growth and dissolution kinetics are interesting and a novel view into the non-equilibrium aspects of phase separation in cells.

-Purification of PFK-1 and in vitro reconstitution of condensates would be supportive of liquid-like characteristics although I don't think it is necessary however it would add a lot to the relevance to show enzyme activity is different +/- condensate state but I am not sure if an easy enzymatic assay exists in vitro.

Minor comments:

-Stress granules in other organisms (yeast paper) have different composition depending on stress type. To make the claim that the FPK-1 compartments are independent of SGs one would ideally test multiple different SG markers.

-it should be stated in the main text that the microfluidic-hydrogel device was fabricated following previously published protocols

-Figure 4b: Y-axis should be changed from probability to fraction of occurrence

-The discussion should be less speculative concerning any effects seen in PFK1-Cry2 expressing C. elegans

-it is perplexing that a protein known to tetramerize with no disordered or RNA-binding domains foms condensates like this. Is there anything known from other systems of additional interacting proteins that may have features that promote liquidity and serve to fluidize these assemblies?

Significance

Stimulus-induced phase separation has been observed for dozens of metabolic enzymes from various different pathways (reviewed in Prouteau, 2018). Several studies have published the formation of condensates through PFK-1 in diverse organisms (C. elegans, Yeast, human cancer cells) in response to hypoxia or in some cancer lines also without hypoxia (Jin, 2017, Jang, 2016, Kohnhorst 2017, etc.). A yeast study showed that PFK-1 condensates contain various other glycolytic enzymes and that condensate formation enhances glycolytic rates (Jin, 2017).

This study gives the advance of analyzing the dynamics of PFK-1 condensate formation in vivo in the context of a live animal using a microfluidic-hydrogel device and showing that PFK-1 relocalizes to reversible condensates within minutes of hypoxia. If further appropriate experiments (as mentioned above) are performed, this study would strongly suggest that the underlying process of PFK-1 condensate formation is liquid-liquid phase separation. Ideally, if at all feasible, it would be strengthened if there was some insight into the functional consequences of the condensed assemblies formed in hypoxia. These findings may be interesting to researchers working on glycolysis and metabolism in different cells but particularly in neurons.

Field of expertise

-Phase separation, microscopy, in vitro reconstitution

-no experience with C. elegans biology and do not have a practical handle on ease or difficulties of genetic manipulation of C. elegans or metabolic assays for PFK-1

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Referee #1

Evidence, reproducibility and clarity

Summary

Jang et al., address the important question of spatially localized or compartmentalized metabolic enzymes with a focus on the glycolytic enzyme PFK1. Using a good strategy of inserting a fluorescent tag at the endogenous PFK1 locus with tissue-specific inducible expression in C. elegans, combined with strong quantitative longitudinal imaging and innovative bioengineered microfluidic-hydrogels to control oxygen availability as well as optogenetic approaches, they show PFK1 condensates, which are not stress granules and not seen in normoxia, assemble with hypoxia. PFK1 condensates are dynamic, reversible, localized at the synapse in neurons, and recruit aldolase, another glycolytic enzyme. Although glycolytic proteins were previously shown to compartmentalize near the plasma membrane, and PFK1 was previously shown to assemble into filaments in vitro and be punctate at the plasma membrane in mammalian cells, evidence for cellular localized PFK1 condensates in animals is highly significant. The work includes strong biophysical characterization of PFK1 phase-separated condensates, but no clear indication of the composition of condensates. More significantly, the findings lack functional significance related to PFK1 activity or glycolytic flux with hypoxia vs normoxia. Despite previous work by this group showing that disrupting subcellular localization of glycolytic enzymes impairs neuronal activity in response with hypoxia, the reader is left with questions on the importance of localized and PFK1 condensates and their make-up .

Major comments:

Key conclusions are convincing, and most experimental approaches, biophysical characterization including thermodynamic principles, and data analysis are exemplary and well described. However, as indicated above, the work is limited to a descriptive analysis of cellular localization of PFK1 condensates and their biophysical properties without insights on functional significance relative to enzyme activity - or at least glycolytic flux or metabolic reprogramming with hypoxia. At best, only correlations can be drawn from hypoxia-induced localized PFK1 condensates and the authors' previous report (Jang et al., 2016) on hypoxia-regulated neuronal activity. Some insight or at least prediction in the discussion on the differences in spatially localized PFK1 in muscle vs neurons with regard to metabolic or energy distinctions should be included.

Despite the strong biophysical analysis of condensates, several important features are not determined. First is at best a rudimentary analysis of the composition of condensates and also how PFK1 is assembled into these structures. For the former, is the core of the condensate predominantly PFK1 with perhaps aldolase only recruited to the periphery or is aldolase an integral component of the structure. Hence, is it a PFK1 condensate or a glycolytic condensate? For the latter question, is there a particular orientation for PFK1 in condensates, i.e a collection of filaments as previously reported, which might provide insight on assembly? Finally, and less critical but also important is the criterion for spherical, which is not well defined, and at least some idea or speculation on determinants for a spherical morphology - compared with filaments that have been reported for other non-glycolytic metabolic enzymes.

Significance

The work is an important advance in our understanding on the self-assembly of metabolic enzymes by showing hypoxia-induced PFK1 condensates in vivo, their spatially-restricted subcellular localization in muscle cells and neurons, and their biophysical properties, the latter being distinct from those of stress granules. Taken together, these findings are more extensive than many previous reports on the assembly of metabolic enzymes into filaments or condensates, but fall short for new insights on functional significance.

Expertise is published on topic

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

In this work the authors investigate the dynamics of cell morphogenesis in a convenient in vivo system. They use the terminal cells of the embryonic tracheal system and comprehensively address how cell shape change (elongation in this case) takes place and how membrane is remodeled during the process. By combining different high-resolution techniques, i.e. in vivo imaging of terminal cells expressing different membrane markers and serial-section electron tomography, they describe the organelle organization (ER, Golgi, different types of vesicles) in the terminal cells during their elongation. They identify the presence of membrane structures/vesicles particularly abundant at the tip of the cell ahead of the growing tube. When they block endocytosis they find increased tube membrane and lack of basal membrane growth. In addition, in tomograms, they observe clear membrane defects, like invaginations that could even connect the tube and basal membranes. This correlates with the absence of vesicles at the tip observed in normal conditions.

The analysis of the nature of the vesicles observed indicated the accumulation of late endosomes and MVB, particularly at the tip of the growing terminal cells. Interfering with the formation of these MVB led to defects in the growth of both the tube and the basal membrane.

Altogether the authors propose a model in which the newly formed membrane (and transmembrane proteins) passes through the ER and Golgi and reaches the apical membrane. The incorporated membrane is then rapidly endocytosed and follows a maturation pathway through MVB, from where different cargoes and membrane would be sorted and recycled back to the apical (tube) membrane, or to the basal membrane through a transcytosis mechanism

Major comments:

Are the key conclusions convincing?

Most of the conclusions presented are convincing and supported by the results observed.

However, to my understanding, one of the key conclusions of this work (that membrane is transcytosed from the apical to the basal domain) is not fully convincing. A critical result to support the author's conclusion of apical-basal transcytosis is to find clear evidence of basal accumulation of a transcytosed marker.

1. The authors show accumulation of FGFR-GFP and Myospheroid as evidence. However, I find the results presented not very convincing. The accumulation of FGFR-GFP at the basal membrane in the control is not very clear in the images and movie presented. In addition, in the shibire mutant, some basal accumulation of FGFR-GFP seems to be detected (particularly in the movie). In the figures the authors show an increase of FGFR-GFP intensity when endocytosis is blocked, but this is not explained in the text.

If we understand the referee correctly, there are two parts to her/his concern:

1. Are the proteins we use in fact present basally in normal tracheal cells, i.e. are they good candidates for transcytosed cargo?

2.Do they change their localisation when endocytosis is disrupted? And this point can be divided into two aspects: a. do they change at the basal membrane? b. do they change at the apical membrane (this latter point is not questioned the referee)?

1. The FGFR and beta-integrin are the only known basal markers in tracheal cells. A major reason for being confident of their presence in the basal membrane, even though they are difficult to visualize, is that the biological function of both is at the basal membrane, with the FGFR receiving growth or chemotactic signals from the surrounding tissue, and integrins anchoring the branches on the underlying tissue. However, it is indeed the case that their expression levels are very low, and it is difficult to visualize them, whether by expression of GFP-labelled constructs or by immunofluorescence. We have pushed to the limit a number of methods to improve the detection, but we seem to be constrained by the biology of these molecules.

In addition to the low but detectable signal at the outer boundary cell, some signal is always visible within the cell, which we had in the past always interpreted as an artefact or background, but for which our findings here might provide alternative interpretations.

1. a. We agree with the referee’s assessment that FGFR::GFP is still detectable in the basal membrane after blocking endocytosis. This is, in our view, no contradiction to our model. The most parsimonious interpretation is that this is the FGFR that had already been delivered basally before we interfered with endocytosis, and which remains there after endocytosis is blocked.

b. In addition to this basal pool of FGFR, cells with blocked endocytosis accumulate abnormally high levels of FGFR at the apical membrane, in fact at much higher intensities than at the basal membrane. This is the more dramatic aspect of the phenotype, and our conclusions therefore rely not so much on a possible reduction of basal signal after blocking endocytosis (which would not be possible to demonstrate reliably), but rather on the abnormally enriched presence in the apical membrane.

A technical point: The increase in FGFR::GFP on endocytosis blockage that we show in Figure S4 corresponds to the cytoplasmic + apical pool of the FGFR. We used the Dof signal in the cell to create a mask of the total cell volume to 3D-segment the FGFR signal. Therefore, this analysis does not take into account the FGFR that is present in the basal membrane. We had explained this only in the methods section but will now describe it more explicitly in the Results section.

On the other hand, and very importantly, how does FGFR localization relates to its activity? The authors show that when endocytosis is prevented dpERK (i.e. a reporter of FGFR activation) is not decreased, indicating that FGFR is normally active. Wouldn't this suggest that FGFR is still localized basally to receive Bnl signal?

Indeed, and this is also what we see. This is not in conflict with any of the results or known functions of the receptor. If endocytosis is blocked, FGFR cannot be internalized and removed from the basal membrane, where it is needed to receive the FGF from the surrounding cells.

Our concern, and the reason for doing the experiment, had been that endocytosis might be required for FGF signaling, and that this might account for the failure of the cell to grow. But it turns out that our results show that this is not the case, at least not for the terminal cell in this time frame.

Actually, as the authors indicate, in larval tracheal cells the intracellular accumulation of the FGFR leads to a reduced FGF signal transduction (Chanut-Delalande et al., 2010), suggesting that reduced FGF signaling activity in these cells is due to less FGFR reaching the basal membrane.

That is true, and again, not inconsistent with our own results. In the cited study, trafficking was blocked at a step downstream of endocytosis. In this experimental situation, the internalisation of the FGFR would therefore occur as normal in these cells, but due to the impaired function of the ESCRT complex, intracellular processing, and therefore potential re-delivery to the basal membrane would be impaired. Furthermore, if (as we now propose) FGFR is also delivered via late endosomes in this context, blocking the ESCRT pathway should also impair initial FGFR delivery. In either way, initial delivery or re-delivery of the receptor being blocked, it is reasonable to assume that reduced signal transduction is the result of reduced basal FGFR.

Thus in our study we see no reduction in basal FGFR and no reduction (and even an increase) in signaling, while Chanut et al see reduced basal FGFR and reduced signaling, and the reason for this is that they interfere with a different step of the membrane trafficking pathway.

1. The results with Myospheroid are not very convincing either, as the authors just show a single confocal section of control and shibire mutants. In summary, I consider that this very important point needs to be better documented before concluding that apical membrane material containing basal cargoes is transcytosed to the basal membrane.

We observed this phenotype in several instances. We will quantify it for the resubmission.

1. Another conclusion that, to my opinion, should be better explained and documented, is the coordination of tube and basal membrane growth. Following the movements of the vesicles the authors conclude that there is a net displacement of these vesicles to the tip of the cell. This correlates with the presence of mature endosomes there. So the results postulate that transcytosis occurs at the tip, and therefore the growth of the basal membrane would occur preferentially at the tip. It has been demonstrated, as the authors indicate, that the tube membrane grows all along the length of the tube. How is then coordinated the tube and basal membrane growth? If, as the authors propose in their model, the tube membrane also grows after a process of endocytosis and recycling, wouldn't it be expected to have preferential tip growth? How do the authors reconcile all these observations with previously published results (Gervais and Casanova, 2010)?

The elegant study by Gervais and Casanova (2010) used a very clever method, which was however entirely non- quantitative (and did not make any claims to the contrary, either). The conclusion that material is delivered to the tube throughout its length was based on looking at the displacement relative to the base and the tip of the cell of short and transient secondary branches (seen for example in Fig. 1H in our paper). If the tube would primarily receive material at the tip, these secondary branches would not change their position with respect to the base of the cell. Instead, these branches tend to be displaced towards the tip, which shows that material is also added between the branch and the base of the cell. These branches are seen only in a fraction of wild type terminal cells and quantification is therefore difficult. Thus, the experiment shows convincingly that material is also delivered behind the transient branch and excludes a model by which all growth occurs only at the tip. But it does not discuss what proportion of the total is delivered along the length vs the tip of the branch.

Our model also does not contest the idea of ubiquitous membrane delivery over the length of the tube, either in the initial delivery step, or during redistribution. On the contrary, the presence along the length of the cell of vesicles carrying FYVE::GFP and Rab7, and of smaller MVB-like bodies in the EM sections, suggest that the pathway can also be deployed at a distance from the tip.

1. The serial-section electron tomography analysis is very interesting and identifies different sorts of vesicles. However, it is very unclear what the different vesicles referred in the models correspond to (in the in vivo imaging for instance). For instance, the small granular or the dense-core vesicles correspond to endocytic vesicles at different stages of maturation?

Based on distribution alone, it is almost impossible to determine which of these vesicles correspond to endosomes or to secretory vesicles, even for the extremely experienced EM experts in the team. We would require high resolution CLEM, and a wide range of fluorescent markers to be able to determine which population of vesicles found on EM correspond to each marker. Due to the broad distribution of these vesicles within the cell and their small sizes right now it would be extremely technically challenging to pursue this question (even though we too would love to know)

1. If there is a constant endocytosis from the apical membrane to generate the basal and build the definitive apical membrane, wouldn't it be expected to find many more vesicles around the tube? Wouldn't it be expected to find coated vesicles around or budding from the tube, as the coated vesicles observed budding from the basal membrane in Fig 2D? Or is the endocytosis observed mediated by non-clathrin coated vesicles?

We agree, and we had observed this to be the case, but had not quantified this. We have now analysed the distribution of coated pits and their density in the apical or the basal membrane of the cell. Overall, we found a higher density of endocytic events in the apical membrane than in the basal membrane. As the reviewer noticed, we also found that the majority of endocytic events in the basal membrane occur towards the tip of the cell. We will add these data to Figure S3.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The results of Serp accumulation upon MVB interference can lead to confusion (from line 404). The authors seem to suggest that Serp protein is exclusively produced in the FB and transported by transcytosis to reach the tracheal lumen. However, Serp is also produced in the tracheal cells themselves. In fact, serp expression in the FB seems to be detected by late embryogenesis, while expression in tracheal cells is detected much earlier (Dong et al. 2014; Luschnig et al. 2006; Wang et al. 2006). It was also shown that Serp undergoes a recycling mechanism from the lumen to the lumen, through the endosomes-TGN retrograde trafficking, that may also require Shrub (Dong et al, 2014; Dong et al 2013). Thus, it is unclear (and even unlikely) that the Serp found in the vesicles in Shrub-GFP mutants is derived exclusively from the transcytosed component from the FB. I suggest to better explore this issue or to remove this part.

We agree that the notion that the Serp we see is exclusively derived from the fat body is not correct. We observed Serp accumulation around Shrb::GFP sites in embryos at early and late stages, so it is likely that what we see is the result both of apical-to-apical redelivery of Serp (as reported in Dong et al., 2014a), and transcytosis of Serp from the basal membrane to the apical. We will therefore rewrite this. But regardless of whether we are looking at transcytosis, or apical-to-apical cycling, this experiment still reinforces the idea of our work that late endosomes serve as stations that collect material from and re-deliver it towards various compartments in the cell.

Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

As indicated before, more conclusive results for transcytosis should be provided.

I suggest that the authors determine the presence of apical and basal cargoes (FGFR) in the late endosomes found when Shrub activity is impaired. According to his model, both types of markers should accumulate there. A high accumulation of these markers in those endosomes would reinforce the hypothesis proposed.

We agree with the reviewer that this would be a great experiment. Since we only have an FGFR tagged with GFP we cannot do this experiment using the Shrb::GFP line, so instead we would have to use shrb mutants. This in itself is not a problem (see also below – we will be adding some data), but the experiment would require multi-generation crosses which could only be started once the current Covid-19 restrictions are lifted and labs opened again. Instead, we propose to cite the following supporting evidence. Chanut-Delalande et al., (2010) showed that mutants for components of the ESCRT pathway hrs and stam show intracellular accumulation of overexpressed FGFR::GFP in tracheal cells of the air sac primordium, and Dong et al., (2014a) show that shrb mutants accumulate Crb in late endocytic compartments in tracheal cells of the dorsal trunks. We would suggest it is very likely that terminal cells therefore also accumulate Crb and FGFR in late endosomes in the absence of Shrb. An experiment would be nicer, but we fear this is the best we can do at the moment.

While it has been reported that shrub-GFP act as a dominant negative in different contexts (Dong et al, 2014; Sweeney et al 2006) it is unclear why. So it would be desirable to confirm the results with a loss of function condition (either mutant or RNAi line).

We will now add data on shrb mutants where we find a phenotype that is similar as in Shrb::GFP overexpression.

Are the data and the methods presented in such a way that they can be reproduced?

The materials and methods section would benefit from more detailed explanations.

We agree.

Are the experiments adequately replicated and statistical analysis adequate?

Many of the experiments presented in this work are technically very challenging, like the in vivo analyses and particularly the serial-section electron tomography. This prevents having high numbers of replicates on occasions.

Minor comments:

1. In the abstract the authors state: "We show that apical endocytosis and late endosome-mediated trafficking determine the membrane allocation to the apical and basal membrane domains". I think that the authors show that "that apical endocytosis and late endosome-mediated trafficking is required for correct membrane growth", but I am not that sure that they show that it determines the membrane allocation

We agree and will change this.

1. References for the PH-GFP localization in cells should be provided. Which is the evidence that it only localizes to plasma membrane?

Apical localization of PH-GFP is preferential rather than exclusive. The construct has nevertheless been widely used as an ‘apical’ marker, based on the fact that this PH domain binds to PIP2, which is enriched in the apical domain of epithelial cells (e.g. Pilot, et al., 2006; Román-Fernández, et al., 2018).

1. It would be more adequate to always use the same terms to facilitate the reading. For instance, in several figures the membranes are referred as basal plasma membrane and tube membrane, but in others outer and inner membrane

We will go through the entire manuscript and use a consistent nomenclature.

1. Figure 3C,D and corresponding text are difficult to understand. The increase of fluorescence of the inner membrane seems to be very high, even higher than the corresponding to the outer membrane. Can the authors explain better this point and also describe better the method applied in the materials and methods section?

Rather than absolute amounts the graphs show fold increase over the amount of membrane at the beginning of the recording. We had used this representation for two reasons. First, the overall signal intensity can vary from one imaging set to the next, so comparing and representing absolute amounts from different datasets is not easily possible. However, we understand now why the representation in our plots was confusing. We will now show in Figure 3D how the total amount of plasma membrane in shibirets cells increases in the same way as in controls. For Figure 3C, we have found a better way of representing what percentage of the total membrane is present in each compartment as the cell grows. We will rewrite this part to make it clearer, and we will describe more thoroughly in the methods section how the analysis was done.

Significance:

This work represents an important advance for the field for several reasons. First of all it represents a technical advance because the authors are able to combine the traditional genetic analysis with two powerful techniques (in vivo imaging and serial-section electron tomography) to analyze single cell behavior at high resolution (temporal and spatial). In addition it represents a conceptual advance as it proposes a mechanism through which membrane growth is coordinated to regulate cell morphogenesis. The mechanism presented (endocytosis and transcytosis) is not new but they find evidence in an in vivo system.

It was previously known that tracheal terminal cells undergo a process of intracellular tube formation and cell elongation at the same time, but the mechanisms coordinating these two cell events were not known. The proposed mechanism may not only be relevant for the morphogenesis of tracheal terminal cells, but could represent a general mechanism of cell morphogenesis. Therefore, the paper should be relevant for research in the morphogenesis area but also in the cell biology field, as it shows how regulated membrane trafficking can control tissue morphogenesis

REFEREES CROSS-COMMENTING:

I agree with reviewer #4 on her/his comments and suggestions about analyzing the involvement of the recycling endosome in the process.

Reviewer #2 (Evidence, reproducibility and clarity):

In their manuscript, Mathew et al present a model in which transcytosis is utilized to deliver endocytosed apical membranes to supply basal membrane growth. The authors examined the developing terminal cell of the fruitfly tracheal system, which is a well established tubulogenesis model, as these cells form subcellular tubes by apical plasma membrane invagination. The authors show that basal membrane growth stops when endocytosis or endosomal transport is blocked, while the apical membrane grows excessively or membrane material accumulates in the cytosol, respectively.

Significance:

The authors used high-end microscopy (including CLEM and electron tomography) to support their model and in my opinion, the quality and the quantity of the presented data are indeed adequate for this. The text is well written, the figures are of superb quality, and several cartoons help to understand the presented data/experiments. Therefore I highly recommend submitting the manuscript to a cell biology journal in its present form.

Reviewer #3 (Evidence, reproducibility and clarity):

This paper investigates the role of membrane trafficking in growth of the polarized tracheal cell that forms a cellular projection containing a subcellular tube. The authors show that apical endocytosis and late endosome-mediated trafficking determine the membrane allocation to the apical and basal membrane domains. Basal plasma membrane growth stops if endocytosis is blocked, whereas the apical membrane grows excessively. Plasma membrane is initially delivered apically, and then appears to be continuously endocytosed, together with apical and basal cargo. The sorting and recycling of apical and basolateral membrane appears to occur in a novel organelle carrying markers of late endosomes and multivesicular bodies (MVBs). Inhibiting endocytosis eliminates this compartment.

The work in this paper is generally of high quality, and with one exception, quite comprehensive. The writing and figures are clear.

Major concerns:

1. A central focus of the paper is that balance between apical and basal membrane and the role of transcytosis in moving membrane from the apical compartment to the basolateral compartment. The current view is that transcytosis in mammalian cells usually goes through the recycling endosomes which are marked by rab11, although there is evidence for some trafficking through MVBs as well. In Drosophila, Rab11 positive recycling endosomes are frequently examined as part of endocytic system analyses. However, rab11 is not used as a marker in this paper and indeed there is no mention at all of recycling endosomes, even though recycling is at the core of the work. Since the authors do not examine rab11 or other possible markers of recycling endosomes, it is unclear whether the organelle they identify as carrying markers of late endosome and MVBs is some MVB/recycling endosome hybrid, or whether the organelle is completely distinct from the recycling endosome. Consequently, it is not possible to assess whether the observed trafficking either uses or does not use involve the recycling endosome. This ambiguity make is difficult to relate the observed trafficking to other systems. Minimally the authors should stain for rab-11 in WT and in some of the conditions where trafficking is perturbed and determine if the MVB-like compartment they are observing is rab11 positive, and whether the recycling endosome are affected by the perturbations. Further experiments may be needed to resolve whether any trafficking is going via the recycling endosome or this new MVB-type structure, but without even preliminary data on the relationship between the MVB compartment and the recycling endosome, its hard to say what might be appropriate or exactly how long addressing this will take. But just staining for rab11 in WT and a few mutant conditions to get a handle on what is up with the recycling endosomes in these cells should take less than a month.

We had done a number of experiments on Rab11 but did not include them because we felt they did not add any crucial insights on the mechanism we describe here. However, as the reviewer rightfully points out, Rab11 is a classical marker for recycling and transcytosis and we agree that the reader should know our results. We found that overexpressed Rab11::GFP as well as endogenously tagged Rab11:YFP are both highly enriched around the tube. Unlike Rab5 which is seen in widely spaced discrete vesicles, Rab11 forms a cloud of small puncta. We found very low overlap of Rab11 with CD4::mIFP-positive vesicles at the tip of the cell. This suggests that Rab11 is unlikely to be directly involved in the transcytosis pathway we describe here.

Loss of Rab11 was harder to analyse; Rab11-RNAi did not show any obvious phenotype, which could be due to high maternal contribution or to low knockdown efficiency, none of which we analysed in detail. Expression of a dominant negative Rab11 resulted in very early defects (reported by Le Droguen et al., 2015) which prevented us from analysing the role of Rab11 in tube formation. But because Rab11 does not localize to the compartment at the tip of the cell, we believe that this structure does not rely on Rab11 to transfer material from the apical to the basal membrane of the cell.

We will add the data on Rab11 distribution in Figure S6.

1. In addition to the above, I would recommend more discussion of how the authors' results relate to membrane trafficking and transcytosis in other systems. The recycling endosome should be considered, and it may be appropriate to draw comparisons to membrane trafficking in neurons that goes through MVBs (e.g. reviewed in VON BARTHELD and ALTICK Prog Neurobiol. 2011 Mar; 93(3): 313-340.). Although neurons are not hollow, they have definite morphological resemblance to tracheal terminal branches.

We thank the reviewer for the observation and will expand the discussion on MVB-mediated transcytosis in other systems.

1. line 204-208 "To test whether raised levels of Crb were responsible for the excessive apical membrane, as reported in other contexts (Pellikka et al., 2002; Schottenfeld-Roames et al., 2014; Wodarz et al., 1995), we knocked down Crb (Fig. S4G-H)." According to the legend for Fig s4, the authors express an RNAi construct against crbs. However, there does not appear to be any quantification of the amount knockdown of crb that was achieved. This is a concern for two reasons: 1) RNAi in the embryonic trachea works poorly for most genes (for unknown reasons) 2) This does not appear to be a clonal experiment but rather a pan tracheal driving of Crb RNAi. Loss of crbs would be expected to have very negative effects on tracheal morphogenesis (although this hasn't been rigorously tested to this reviewer's knowledge), but there doesn't appear to be any adverse effects of pan-tracheal crbs RNAi, suggesting that little if any knockdown of crb was actually achieved.

The authors either need to document the reduction of crbs or remove this paragraph. Preferably, they would be able to document the reduction of crb because they are trying to address an important point and if they can show the apical expansion is crb-independent, that would be an nice result.

Loss of Crumbs definitely is detrimental to embryonic epithelia, to different degrees (Tepass and Knust, 1990). We therefore could not use mutants but expressed an RNAi, which does not abolish Crb completely. We have now determined the degree of crb knockdown in our experiments. We stained Crb in embryos that expressed crb-IR in the entire tracheal system (but leaving the epidermal expression intact) and quantified the amount of Crb in the tracheal dorsal trunks, normalized to the signal in the epidermis. We found that in crb-IR embryos, Crb levels were reduced by around 50% compared to control siblings. We will add these results to Fig. S4 in the new version of the manuscript.

Minor concerns:

1. Lines 250. "By this interpretation, unscissioned membrane invaginations protruding from the subcellular tube would occasionally have touched the basal plasma membrane or its protrusions and fused with it, as transcytosing vesicles would have done in the normal situation."

I am not convinced by the argument that the bridging invaginations are fusing analogously to transcytosing vesicles because a protrusion/nascent vesicle coming from the apical surface should have rabs and V-SNAREs that should dock the protrusion/nascent vesicle with an endosomal compartment, not the basolateral surface. A transcytotic vesicle would have the rabs and V-SNAREs for the basolateral membrane. So it would seem that a fusion of the apical surface directly to the basolateral surface would have to be an ectopic event outside of the normal situation.

We agree that the fusion events could also be unrelated to the normal physiology of the animal. In other contexts (e.g. the embryonic epidermis, the synaptic bouton) blocking dynamin results in long membrane invaginations as a result of failure in membrane scission. In terminal cells, the apical and basal plasma membranes are very close to each other, and we believe this increases the chance of membrane invaginations meeting and fusion to take place. In addition, the long membrane invaginations we see seem to have been stripped of their clathrin coat suggesting that at least some aspects of ‘vesicle’ maturation proceed even though scission had failed. We also find evidence of small vesicles that resemble the contents of MVBs being deposited within the aberrant membrane invaginations. This suggests that MVBs are able to fuse with these unscissioned tubes and sheets, again indicating that the appropriate molecular markers are present, and the machinery in charge of generating these vesicles is active at the invaginated pits directly. In either case, we will rephrase our interpretations of these data and present it as speculation in the discussion section.

1. Significant figures. This is not a big deal, but the authors are over reporting their significant figures. E.g. "a 7.05-fold increase (+/-2.98 SD)" . With an SD that is 50% the value of the measurement, reporting to hundreds is definitely beyond the accuracy of measurement. Rounding to tenths would be more appropriate.

We agree with the reviewer. We will rephrase this section and use more appropriate metrics. As prompted by Reviewer #1, we modified the analysis that corresponds to this sentence, which also modified the way data is normalized also reducing the spread. This happened because in the new analysis we compare apical and basal signal for each timepoint, which allows better comparison between different cells.

Significance:

As there have not been that many studies on the dynamics of membrane trafficking during morphogenesis, the results should be of broad interest to those studying the endocytic system and the role of membrane trafficking in morphogenesis. However, the paper would be greatly strengthened if the authors considered the recycling endosome in their analysis and write up. As a well-known compartment for trafficking cargo and membrane to both the apical and basolateral surface, it is hard to know how to interpret the observed trafficking without knowing the involvement, or lack thereof, of recycling endosomes in this system.

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Referee #3

Evidence, reproducibility and clarity

This paper investigates the role of membrane trafficking in growth of the polarized tracheal cell that forms a cellular projection containing a subcellular tube. The authors show that apical endocytosis and late endosome-mediated trafficking determine the membrane allocation to the apical and basal membrane domains. Basal plasma membrane growth stops if endocytosis is blocked, whereas the apical membrane grows excessively. Plasma membrane is initially delivered apically, and then appears to be continuously endocytosed, together with apical and basal cargo. The sorting and recycling of apical and basolateral membrane appears to occur in a novel organelle carrying markers of late endosomes and multivesicular bodies (MVBs). Inhibiting endocytosis eliminates this compartment. The work in this paper is generally of high quality, and with one exception, quite comprehensive. The writing and figures are clear.

Major concerns:

-A central focus of the paper is that balance between apical and basal membrane and the role of transcytosis in moving membrane from the apical compartment to the basolateral compartment. The current view is that transcytosis in mammalian cells usually goes through the recycling endosomes which are marked by rab11, although there is evidence for some trafficking through MVBs as well. In Drosophila, Rab11 positive recycling endosomes are frequently examined as part of endocytic system analyses. However, rab11 is not used as a marker in this paper and indeed there is no mention at all of recycling endosomes, even though recycling is at the core of the work. Since the authors do not examine rab11 or other possible markers of recycling endosomes, it is unclear whether the organelle they identify as carrying markers of late endosome and MVBs is some MVB/recycling endosome hybrid, or whether the organelle is completely distinct from the recycling endosome. Consequently, it is not possible to assess whether the observed trafficking either uses or does not use involve the recycling endosome. This ambiguity make is difficult to relate the observed trafficking to other systems. Minimally the authors should stain for rab-11 in WT and in some of the conditions where trafficking is perturbed and determine if the MVB-like compartment they are observing is rab11 positive, and whether the recycling endosome are affected by the perturbations. Further experiments may be needed to resolve whether any trafficking is going via the recycling endosome or this new MVB-type structure, but without even preliminary data on the relationship between the MVB compartment and the recycling endosome, its hard to say what might be appropriate or exactly how long addressing this will take. But just staining for rab11 in WT and a few mutant conditions to get a handle on what is up with the recycling endosomes in these cells should take less than a month.

-In addition to the above, I would recommend more discussion of how the authors' results relate to membrane trafficking and transcytosis in other systems. The recycling endosome should be considered, and it may be appropriate to draw comparisons to membrane trafficking in neurons that goes through MVBs (e.g. reviewed in VON BARTHELD and ALTICK Prog Neurobiol. 2011 Mar; 93(3): 313-340.). Although neurons are not hollow, they have definite morphological resemblance to tracheal terminal branches.

-line 204-208 "To test whether raised levels of Crb were responsible for the excessive apical membrane, as reported in other contexts (Pellikka et al., 2002; Schottenfeld-Roames et al., 2014; Wodarz et al., 1995), we knocked down Crb (Fig. S4G-H)." According to the legend for Fig s4, the authors express an RNAi construct against crbs. However, there does not appear to be any quantification of the amount knockdown of crb that was achieved. This is a concern for two reasons: 1) RNAi in the embryonic trachea works poorly for most genes (for unknown reasons) 2) This does not appear to be a clonal experiment but rather a pan tracheal driving of Crb RNAi. Loss of crbs would be expected to have very negative effects on tracheal morphogenesis (although this hasn't been rigorously tested to this reviewer's knowledge), but there doesn't appear to be any adverse effects of pan-tracheal crbs RNAi, suggesting that little if any knockdown of crb was actually achieved. The authors either need to document the reduction of crbs or remove this paragraph. Preferably, they would be able to document the reduction of crb because they are trying to address an important point and if they can show the apical expansion is crb-independent, that would be an nice result.

Minor concerns:

-Lines 250. "By this interpretation, unscissioned membrane invaginations protruding from the subcellular tube would occasionally have touched the basal plasma membrane or its protrusions and fused with it, as transcytosing vesicles would have done in the normal situation." I am not convinced by the argument that the bridging invaginations are fusing analogously to transcytosing vesicles because a protrusion/nascent vesicle coming from the apical surface should have rabs and V-SNAREs that should dock the protrusion/nascent vesicle with an endosomal compartment, not the basolateral surface. A transcytotic vesicle would have the rabs and V-SNAREs for the basolateral membrane. So it would seem that a fusion of the apical surface directly to the basolateral surface would have to be an ectopic event outside of the normal situation.

-Significant figures. This is not a big deal, but the authors are over reporting their significant figures. E.g. "a 7.05-fold increase (+/-2.98 SD)" . With an SD that is 50% the value of the measurement, reporting to hundreds is definitely beyond the accuracy of measurement. Rounding to tenths would be more appropriate.

Significance

As there have not been that many studies on the dynamics of membrane trafficking during morphogenesis, the results should be of broad interest to those studying the endocytic system and the role of membrane trafficking in morphogenesis. However, the paper would be greatly strengthened if the authors considered the recycling endosome in their analysis and write up. As a well-known compartment for trafficking cargo and membrane to both the apical and basolateral surface, it is hard to know how to interpret the observed trafficking without knowing the involvement, or lack thereof, of recycling endosomes in this system.

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 #2

Evidence, reproducibility and clarity

In their manuscript, Mathew et al present a model in which transcytosis is utilized to deliver endocytosed apical membranes to supply basal membrane growth. The authors examined the developing terminal cell of the fruitfly tracheal system, which is a well established tubulogenesis model, as these cells form subcellular tubes by apical plasma membrane invagination. The authors show that basal membrane growth stops when endocytosis or endosomal transport is blocked, while the apical membrane grows excessively or membrane material accumulates in the cytosol, respectively.

Significance

The authors used high-end microscopy (including CLEM and electron tomography) to support their model and in my opinion, the quality and the quantity of the presented data are indeed adequate for this. The text is well written, the figures are of superb quality, and several cartoons help to understand the presented data/experiments. Therefore I highly recommend submitting the manuscript to a cell biology journal in its present form.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this work the authors investigate the dynamics of cell morphogenesis in a convenient in vivo system. They use the terminal cells of the embryonic tracheal system and comprehensively address how cell shape change (elongation in this case) takes place and how membrane is remodeled during the process. By combining different high-resolution techniques, i.e. in vivo imaging of terminal cells expressing different membrane markers and serial-section electron tomography, they describe the organelle organization (ER, Golgi, different types of vesicles) in the terminal cells during their elongation. They identify the presence of membrane structures/vesicles particularly abundant at the tip of the cell ahead of the growing tube. When they block endocytosis they find increased tube membrane and lack of basal membrane growth. In addition, in tomograms, they observe clear membrane defects, like invaginations that could even connect the tube and basal membranes. This correlates with the absence of vesicles at the tip observed in normal conditions. The analysis of the nature of the vesicles observed indicated the accumulation of late endosomes and MVB, particularly at the tip of the growing terminal cells. Interfering with the formation of these MVB led to defects in the growth of both the tube and the basal membrane. Altogether the authors propose a model in which the newly formed membrane (and transmembrane proteins) passes through the ER and Golgi and reaches the apical membrane. The incorporated membrane is then rapidly endocytosed and follows a maturation pathway through MVB, from where different cargoes and membrane would be sorted and recycled back to the apical (tube) membrane, or to the basal membrane through a transcytosis mechanism

Major comments:

- Are the key conclusions convincing?

Most of the conclusions presented are convincing and supported by the results observed. However, to my understanding, one of the key conclusions of this work (that membrane is transcytosed from the apical to the basal domain) is not fully convincing. A critical result to support the author's conclusion of apical-basal transcytosis is to find clear evidence of basal accumulation of a transcytosed marker. The authors show accumulation of FGFR-GFP and Myospheroid as evidence. However, I find the results presented not very convincing. The accumulation of FGFR-GFP at the basal membrane in the control is not very clear in the images and movie presented. In addition, in the shibire mutant, some basal accumulation of FGFR-GFP seems to be detected (particularly in the movie). In the figures the authors show an increase of FGFR-GFP intensity when endocytosis is blocked, but this is not explained in the text. On the other hand, and very importantly, how does FGFR localization relates to its activity? The authors show that when endocytosis is prevented dpERK (i.e. a reporter of FGFR activation) is not decreased, indicating that FGFR is normally active. Wouldn't this suggest that FGFR is still localized basally to receive Bnl signal? Actually, as the authors indicate, in larval tracheal cells the intracellular accumulation of the FGFR leads to a reduced FGF signal transduction (Chanut-Delalande et al., 2010), suggesting that reduced FGF signaling activity in these cells is due to less FGFR reaching the basal membrane. The results with Myospheroid are not very convincing either, as the authors just show a single confocal section of control and shibire mutants. In summary, I consider that this very important point needs to be better documented before concluding that apical membrane material containing basal cargoes is transcytosed to the basal membrane.

Another conclusion that, to my opinion, should be better explained and documented, is the coordination of tube and basal membrane growth. Following the movements of the vesicles the authors conclude that there is a net displacement of these vesicles to the tip of the cell. This correlates with the presence of mature endosomes there. So the results postulate that transcytosis occurs at the tip, and therefore the growth of the basal membrane would occur preferentially at the tip. It has been demonstrated, as the authors indicate, that the tube membrane grows all along the length of the tube. How is then coordinated the tube and basal membrane growth? If, as the authors propose in their model, the tube membrane also grows after a process of endocytosis and recycling, wouldn't it be expected to have preferential tip growth? How do the authors reconcile all these observations with previously published results (Gervais and Casanova, 2010)?

The serial-section electron tomography analysis is very interesting and identifies different sorts of vesicles. However, it is very unclear what the different vesicles referred in the models correspond to (in the in vivo imaging for instance). For instance, the small granular or the dense-core vesicles correspond to endocytic vesicles at different stages of maturation?. If there is a constant endocytosis from the apical membrane to generate the basal and build the definitive apical membrane, wouldn't it be expected to find many more vesicles around the tube? Wouldn't it be expected to find coated vesicles around or budding from the tube, as the coated vesicles observed budding from the basal membrane in Fig 2D? Or is the endocytosis observed mediated by non-clathrin coated vesicles?.

- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The results of Serp accumulation upon MVB interference can lead to confusion (from line 404). The authors seem to suggest that Serp protein is exclusively produced in the FB and transported by transcytosis to reach the tracheal lumen. However, Serp is also produced in the tracheal cells themselves. In fact, serp expression in the FB seems to be detected by late embryogenesis, while expression in tracheal cells is detected much earlier (Dong et al. 2014; Luschnig et al. 2006; Wang et al. 2006). It was also shown that Serp undergoes a recycling mechanism from the lumen to the lumen, through the endosomes-TGN retrograde trafficking, that may also require Shrub (Dong et al, 2014; Dong et al 2013). Thus, it is unclear (and even unlikely) that the Serp found in the vesicles in Shrub-GFP mutants is derived exclusively from the transcytosed component from the FB. I suggest to better explore this issue or to remove this part.

- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

As indicated before, more conclusive results for transcytosis should be provided.

I suggest that the authors determine the presence of apical and basal cargoes (FGFR) in the late endosomes found when Shrub activity is impaired. According to his model, both types of markers should accumulate there. A high accumulation of these markers in those endosomes would reinforce the hypothesis proposed.

While it has been reported that shrub-GFP act as a dominant negative in different contexts (Dong et al, 2014; Sweeney et al 2006) it is unclear why. So it would be desirable to confirm the results with a loss of function condition (either mutant or RNAi line)

- Are the data and the methods presented in such a way that they can be reproduced?

The materials and methods section would benefit from more detailed explanations.

- Are the experiments adequately replicated and statistical analysis adequate?

Many of the experiments presented in this work are technically very challenging, like the in vivo analyses and particularly the serial-section electron tomography. This prevents having high numbers of replicates on occasions.

Minor comments:

-In the abstract the authors state: "We show that apical endocytosis and late endosome-mediated trafficking determine the membrane allocation to the apical and basal membrane domains". I think that the authors show that "that apical endocytosis and late endosome-mediated trafficking is required for correct membrane growth", but I am not that sure that they show that it determines the membrane allocation

-References for the PH-GFP localization in cells should be provided. Which is the evidence that it only localizes to plasma membrane?

-It would be more adequate to always use the same terms to facilitate the reading. For instance, in several figures the membranes are referred as basal plasma membrane and tube membrane, but in others outer and inner membrane

-Figure 3C,D and corresponding text are difficult to understand. The increase of fluorescence of the inner membrane seems to be very high, even higher than the corresponding to the outer membrane. Can the authors explain better this point and also describe better the method applied in the materials and methods section?

Significance

This work represents an important advance for the field for several reasons. First of all it represents a technical advance because the authors are able to combine the traditional genetic analysis with two powerful techniques (in vivo imaging and serial-section electron tomography) to analyze single cell behavior at high resolution (temporal and spatial). In addition it represents a conceptual advance as it proposes a mechanism through which membrane growth is coordinated to regulate cell morphogenesis. The mechanism presented (endocytosis and transcytosis) is not new but they find evidence in an in vivo system. It was previously known that tracheal terminal cells undergo a process of intracellular tube formation and cell elongation at the same time, but the mechanisms coordinating these two cell events were not known. The proposed mechanism may not only be relevant for the morphogenesis of tracheal terminal cells, but could represent a general mechanism of cell morphogenesis. Therefore, the paper should be relevant for research in the morphogenesis area but also in the cell biology field, as it shows how regulated membrane trafficking can control tissue morphogenesis

REFEREES CROSS-COMMENTING: I agree with reviewer #4 on her/his comments and suggestions about analyzing the involvement of the recycling endosome in the process.

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Reply to the reviewers

Review comments Rebuttal

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary ** This manuscript describes the X-ray structure determination of two SMAD-DNA complexes confirm that SMAD family proteins bind at least two DNA sequences in a similar fashion, and explores dimer versus monomer formation of the non-DNA bounds forms of the proteins which could influence whether the proteins bind as monomers and dimers. This includes identifying a loop which appears to make a major contribution to this process. There is a lot of experimental work and analysis included. **Major comments: ** The overall conclusions of the manuscript are convincing, but some of the detailed analysis is not clear. The structures look good, the experiments look to be generally well controlled, although some details could be provided in the main text to be clear about what methodology is being used or how analysis was carried out and stepwise conclusions obtained. In particular the analysis of SAXS data is not clear. I'd like to see initial data analysis presented as per the guidelines of Trewhella et al 2017 (PMID: 28876235). There is some mention of data in the SASREF database, but it should be in the supplemental data.

We have prepared a table following this recommendation.

I can't see any evidence for the conclusions about open versus closed monomer state (how good were the fits obtained) - just a graph and a statement. If this can't be better justified please remove the conclusions about these states (they don't really add to the overall conclusions about monomer/dimer which are much less specific), but even the simple analysis supports mostly monomer and small amounts of dimer or higher aggregates. I would also like to see a clear explanation provided about why the MS data supports dimer over other oligomers

We have revised and simplified the SAXS section to clarify the main points. We have re-analyzed the conformations in solution, and the values are presented in new Table S4 and new Figure 3D. We have also included new panels (Figure 3E) and explanations with respect to the IM-MS data (pages 8-9).

State what thermal unfolding experiments are were carried out in the text (and why is the data biphasic?)

The biphasic graphics were interpreted as the presence of dimers and monomers in equilibrium. As suggested by the other reviewer, we have removed these sections as they do not contribute to clarify the main points of our work.

The concept of long versus short loops re domain swapping have been studied in the past but there isn't much reference to this.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2373619/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6349918/

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0039305

https://www.ncbi.nlm.nih.gov/pubmed/22411444

We now mention some related examples in page 8.

**Minor comments: **

The last couple of paragraphs of the introduction are a fairly comprehensive summary of the study overall and the conclusions of the paper. While presaging the key findings and conclusions is fairly common in an introduction this seems to be way too much detail. Unless it is a requirement of the journal, reduce these sections to a couple of sentences and use any other word count to explain your analysis better.

Thank you for this recommendation. We have rephrased and reduced this part of the introduction.

Figures are quite small and hard to see detail at 1X magnification (in both the main and Supplemental figures).

We have removed some panels that were not necessary and increased the size of the figures and labels.

NB.The difference in Tm of SMAD 5 over 8 doesn't seem particularly high as it’s only a couple of degrees (especially when SMAD4 is quite different). The explanation for the Ile>Cys mutation might be about competition of zinc ligation (except that it doesn't seem to cause issues for many zinc finger proteins) but more likely that you've replaced a reasonably bulky hydrophobic sidechain and therefore have lost a bunch of hydrophobic contacts.

We have removed this section entirely.

With respect to the Ile>Cys difference, the residue is located in a loop, and it does not participate in hydrophobic contacts. We still believe that its negative role in the stability of the domains arises from the competition for Zn coordination but we agree with the reviewer that quantifying its specific role is not obvious.

Reviewer #1 (Significance (Required)):

-This paper clarifies concepts about the state of isolated SMAD proteins (thought be largely monomeric in the absence of DNA) and DNA-binding preferences of these proteins. -I don't have specific expertise in the structure/function of SMAD proteins, but the study appears to include sufficient background to place the study in context.

-Audience will mostly be those interested in structure/function of SMAD proteins, with some protein engineers interested in the manipulation of monomeric versus dimer.

-I am a protein chemist and structural biologist with an interest in protein dimerization/oligomerization. I am familiar with most techniques presented, but don't have first-hand experience with IM-MS.

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

**Summary**

The manuscript by Ruiz et al examines how the receptor-activated SMAD (R-SMAD) transcription factors bind DNA, and specifically how the MH1 (DNA-binding domain) of the different classes contributes determining whether they bind as monomers or dimers.

In the context of the full length SMAD proteins, it is thought that hetero-trimers with one SMAD4 plus two R-SMADs are the functional unit. In general, the SMAD1/5/8 R-SMADs respond to BMPs whereas SMAD2/3 respond to TGF beta. However, what is less clear is how the specificity of the gene responses is determined, since all SMADS are able to bind to each of the two sequence classes of response element (GTCT and GC-rich, or 5GC).

Previous structural studies suggest that the major contacts between SMAD MH1 and DNA are very similar, irrespective of the particular SMAD or of whether they bind a TGTC or 5GC element. On DNA, MH1 domains have been observed as dimers, but there has been some concern as to whether this (at least in part) is a crystal artefact, or is perhaps forced by the specific DNA sequences use in these studies. For the BMP R-SMADS this may be less likely, since the amino-terminal helix 1 of one dimer is seen to be dislodged from its own intramolecular interactions allowing it to make contacts with the second MH1 domain in the dimer.

Here the authors test this question of MH1 dimerization and address differences between the BMP responsive and other SMADs. They first show by crystallography that SMAD5 and SMAD8 MH1 domains adopt similar dimeric conformations with the displaced helix 1, and bind to a single 5GC element via one of the MH1 domains. To get at whether these MH1 domains form dimers in solution, they use small angle X-ray scattering, NMR and mass spectrometry, to suggest that the SMAD5 and SMAD8 MH1 domains in solution do not fit with a single conformation, but are better modeled by a mixture of dimer and open monomer. Ion mobility MS also suggested a mix of dimer and open monomer for the BMP SMADs, whereas SMAD3 appeared to be primarily monomer. To test if the MH1 domains themselves encode this potential difference between SMAD5 and SMAD3, they swap loop 1 (6 versus 4 amino acids, between helices 1 and 2) from SMAD3 to SMAD5, and now in solution this chimera appears monomeric, and forms monomers when crystalized with DNA.

Major comments

1. Adding the SMAD3 loop to SMAD5 prevents the open dimer - does the reverse also work? Can you make SMAD3 form SMAD5-like open-dimers by adding the loop 1 sequence from SMAD5?

We have prepared new Smad3 chimeric constructs and we are currently screening crystallization conditions in order to obtain diffracting crystals (if possible). Unfortunately, due to the COVID-19 pandemic, access to our laboratory is highly restricted, while access to synchrotron and mass spectrometry facilities is not available), therefore this work has been postponed until the end of April/May. For this reason, the revised version of the manuscript does not refer to this question. We hope that we will be able to address it in the future.

1. Can the authors include similar schematic models for how the site spacing would be for SMAD2/3-SMAD4 complexes - adding the SMAD2/3/4 model to Figure 5C?

We have incorporated new panels to Figure 5 (Figure 5E,F).

1. The authors comment on the possibility that the dimer conformation dictates the spacing of the sites that will be bound in vivo. In this context, they refer to a previous paper (PMID: 29234012) to suggest differences in site clustering between BMP SMAD and TGF beta SMAD regions of the genome (from ChIP-seq) that fit with the spacing they imply here. However, the major difference shown in this work seems to be between the clustering of GC sites and GTCT sites irrespective of the pathway. Can the authors analyze existing ChIP-seq data to more specifically test the question they raise - ie that SMAD4 bound regions of the genome have different site clustering/spacing depending on whether they are BMP or TGF beta responsive?

Thank you very much for this recommendation. We agree with the reviewer that this information is very valuable and can help support our hypothesis on the different binding preferences of monomers and dimers of MH1 domains. We have performed this analysis and is now included as two new sections. The results are displayed as new Figure 5A,B.

1. I think Figure 2C,D is not really well described in terms of the importance to this work. As it is this data does not really seem to add very much, but perhaps I am missing the importance.

We have entirely removed this section in the new version of the manuscript.

5.Can the authors comment about the compressed GC element or BRE? This seems to be an unfavorable conformation. How might it be bound in vivo, is it an unusual element, or is it relatively widely found? Is it possible that in vitro it binds two MH1 domains, but in vivo might simply act as a normal 5GC, with an additional site nearby?

The BRE domain is less abundant than 5GCs and SBE sites, and in fact, this sequence is not enriched in the ChIP-Seq datasets that we have analyzed. We have included a sentence refering to these findings in page 11.

We have also revised the section comparing the 5GC and BRE-GC site and illustrate this interaction as well as the comparison to our 5GC complex by including two panel in Figure2 that before were displayed as supplementary information. The panels have been edited to clarify the similarities and differences between both complexes. Indeed, the protein-DNA complex made of one BRE motif bound by two MH1 domains as found in the PDB:5X6H crystal structure suffers from several issues, including compactness of the two overlapping Smad-binding motifs that led to distortion of DNA geometry, clashes at protein-protein interface and local cancelation of protein-DNA interactions.

In this new section (page 8) we include the sentence that “we believe that the most probable binding mode in vivo should be that observed in the 5GC and SBE complexes. It seems very unlikely that two MH1 domains would interact with a reduced BRE motif —using half of their protein binding site and causing a high distortion to the DNA structure— if there is the possibility to interact with neighboring sites (Figure 2B,C) using the full protein binding interface and a perfect accommodation to the DNA”.

**Minor comments: ** 1.In Figure 1B is one the two DNAs assumed? In the structure was it two MH1 to one DNA or two of each?

One DNA was hidden for clarity. The crystallographic structure is now shown in full. The crystal structure was solved for a complex made of two MH1 domains bound to a dsDNA molecule that included two Smad-binding sites.

2.Figure 2C and page 9: the stabilization of SMADs in the text and figure do not agree. Maybe just state the exact numbers from the figure in the text.

We have entirely removed this section in the new version of the manuscript.

3.In Figure S1C, can the authors label the retarded complexes on the gels?

Done.

4.Figure 4A - explain the asterisk (presumably the SMAD2 insert).

Yes, it corresponds to the Gly rich region present in loop1. It is indicated now.

5.In Figure 4B, C (and maybe D) can they color helix 1, loop 1, and helix 2 three separate colors, it might really emphasize the effect of the loop if it was more immediately visible.

We have improved these figures but we did not change the colors because the figure was getting even more complicated.

6.The legend to Figure 4 is missing F.

Thank you. This has now been corrected.

Reviewer #2 (Significance (Required)):

The authors conclude that the length of the loop between helices 1 and 2 determines the dimer versus monomer state - a shorter loop as in the BMP SMADS hinders the intramolecular interactions needed for the closed monomeric form, whereas the longer loop in the other SMADS allows the flexibility for these interactions so favors a more closed monomeric form. Showing that the dimers are not forced by crystallography or by binding to fixed DNA elements clearly adds to our understanding of the mechanisms of SMAD function, and it is of interest that the BMP and TGF beta SMADS are different in this respect.

They speculate that this may contribute to the specificity of the responses activated by BMP versus TGF beta signaling based on the requirements for different site spacing depending on whether an open (BMP) or closed (TGF beta) dimer of R-SMADS is present. This idea is likely to be of interest to anyone who studies the responses to the TGF beta superfamily of signaling molecules, and should spur additional experimentation to test it.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript by Ruiz et al examines how the receptor-activated SMAD (R-SMAD) transcription factors bind DNA, and specifically how the MH1 (DNA-binding domain) of the different classes contributes determining whether they bind as monomers or dimers.

In the context of the full length SMAD proteins, it is thought that hetero-trimers with one SMAD4 plus two R-SMADs are the functional unit. In general, the SMAD1/5/8 R-SMADs respond to BMPs whereas SMAD2/3 respond to TGF beta. However, what is less clear is how the specificity of the gene responses is determined, since all SMADS are able to bind to each of the two sequence classes of response element (GTCT and GC-rich, or 5GC).

Previous structural studies suggest that the major contacts between SMAD MH1 and DNA are very similar, irrespective of the particular SMAD or of whether they bind a TGTC or 5GC element. On DNA, MH1 domains have been observed as dimers, but there has been some concern as to whether this (at least in part) is a crystal artefact, or is perhaps forced by the specific DNA sequences use in these studies. For the BMP R-SMADS this may be less likely, since the amino-terminal helix 1 of one dimer is seen to be dislodged from its own intramolecular interactions allowing it to make contacts with the second MH1 domain in the dimer.

Here the authors test this question of MH1 dimerization and address differences between the BMP responsive and other SMADs. They first show by crystallography that SMAD5 and SMAD8 MH1 domains adopt similar dimeric conformations with the displaced helix 1, and bind to a single 5GC element via one of the MH1 domains. To get at whether these MH1 domains form dimers in solution, they use small angle X-ray scattering, NMR and mass spectrometry, to suggest that the SMAD5 and SMAD8 MH1 domains in solution do not fit with a single conformation, but are better modeled by a mixture of dimer and open monomer. Ion mobility MS also suggested a mix of dimer and open monomer for the BMP SMADs, whereas SMAD3 appeared to be primarily monomer. To test if the MH1 domains themselves encode this potential difference between SMAD5 and SMAD3, they swap loop 1 (6 versus 4 amino acids, between helices 1 and 2) from SMAD3 to SMAD5, and now in solution this chimera appears monomeric, and forms monomers when crystalized with or without DNA.

Major comments:

1.Adding the SMAD3 loop to SMAD5 prevents the open dimer - does the reverse also work? Can you make SMAD3 form SMAD5-like open dimers by adding the loop 1 sequence from SMAD5?

2.Can the authors include similar schematic models for how the site spacing would be for SMAD2/3-SMAD4 complexes - adding the SMAD2/3/4 model to Figure 5C?

3.The authors comment on the possibility that the dimer conformation dictates the spacing of the sites that will be bound in vivo. In this context they refer to a previous paper (PMID: 29234012) to suggest differences in site clustering between BMP SMAD and TGF beta SMAD regions of the genome (from ChIP-seq) that fit with the spacing they imply here. However, the major difference shown in this work seems to be between the clustering of GC sites and GTCT sites irrespective of the pathway. Can the authors analyze existing ChIP-seq data to more specifically test the question they raise - ie that SMAD4 bound regions of the genome have different site clustering/spacing depending on whether they are BMP or TGF beta responsive?

4.I think Figure 2C,D is not really well described in terms of the importance to this work. As it is this data does not really seem to add very much, but perhaps I am missing the importance.

5.Can the authors comment about the compressed GC element or BRE? This seems to be an unfavorable conformation. How might it be bound in vivo, is it an unusual element, or is it relatively widely found? Is it possible that in vitro it binds two MH1 domains, but in vivo might simply act as a normal 5GC, with an additional site nearby?

Minor comments:

1.In Figure 1B is one the two DNAs assumed? In the structure was it two MH1 to one DNA or two of each?

2.Figure 2C and page 9: the stabilization of SMADs in the text and figure do not agree. Maybe just state the exact numbers from the figure in the text.

3.In Figure S1C, can the authors label the retarded complexes on the gels?

4.Figure 4A - explain the asterisk (presumably the SMAD2 insert).

5.In Figure 4B, C (and maybe D) can they color helix 1, loop 1, and helix 2 three separate colors, it might really emphasize the effect of the loop if it was more immediately visible.

6.The legend to Figure 4 is missing F.

Significance

The authors conclude that the length of the loop between helices 1 and 2 determines the dimer versus monomer state - a shorter loop as in the BMP SMADS hinders the intramolecular interactions needed for the closed monomeric form, whereas the longer loop in the other SMADS allows the flexibility for these interactions so favors a more closed monomeric form. Showing that the dimers are not forced by crystallography or by binding to fixed DNA elements clearly adds to our understanding of the mechanisms of SMAD function, and it is of interest that the BMP and TGF beta SMADS are different in this respect.

They speculate that this may contribute to the specificity of the responses activated by BMP versus TGF beta signaling based on the requirements for different site spacing depending on whether an open (BMP) or closed (TGF beta) dimer of R-SMADS is present. This idea is likely to be of interest to anyone who studies the responses to the TGF beta superfamily of signaling molecules, and should spur additional experimentation to test it.

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---

Referee #2

Evidence, reproducibility and clarity

Summary:

This manuscript describes the X-ray structure determination of two SMAD-DNA complexes confirm that SMAD family proteins bind at least two DNA sequences in a similar fashion, and explores dimer versus monomer formation of the non-DNA bounds forms of the proteins which could influence whether the proteins bind as monomers and dimers. This includes identifying a loop which appears to make a major contribution to this process. There is a lot of experimental work and analysis included.

Major comments: The overall conclusions of the manuscript are convincing, but some of the detailed analysis is not clear. The structures look good, the experiments look to be generally well controlled, although some details could be provided in the main text to be clear about what methodology is being used or how analysis was carried out and stepwise conclusions obtained.

In particular the analysis of SAXS data is not clear. I'd like to see initial data analysis presented as per the guidelines of Trewhella et al 2017 (PMID: 28876235). There is some mention of data in the SASREF database, but it should be in the supplemental data. I can't see any evidence for the conclusions about open versus closed monomer state (how good were the fits obtained) - just a graph and a statement. If this can't be better justified please remove the conclusions about these states (they don't really add to the overall conclusions about monomer/dimer which are much less specific), but even the simple analysis supports mostly monomer and small amounts of dimer or higher aggregates. I would also like to see a clear explanation provided about why the MS data supports dimer over other oligomers State what thermal unfolding experiments are were carried out in the text (and why is the data biphasic?) The concept of long versus short loops re domain swapping have been studied in the past but there isn't much reference to this.

Minor comments: The last couple of paragraphs of the introduction are a fairly comprehensive summary of the study overall and the conclusions of the paper. While presaging the key findings and conclusions is fairly common in an introduction this seems to be way too much detail. Unless it is a requirement of the journal reduce these sections to a couple of sentences and use any other word count to explain your analysis better. Figures are quite small and hard to see detail at 1X magnification (in both the main and Supplemental figures). NB The difference in Tm of SMAD 5 over 8 doesn't seem particularly high as its only a couple of degrees (especially when SMAD4 is quite different). The explanation for the Ile>Cys mutation might be about competition of zinc ligation (except that it doesn't seem to cause issues for many zinc finger proteins) but more likely that you've replaced a reasonably bulky hydrophobic sidechain and therefore have lost a bunch of hydrophobic contacts.

Significance

-This paper clarifies concepts about the state of isolated SMAD proteins (thought be largely monomeric in the absence of DNA) and DNA-binding preferences of these proteins.

-I don't have specific expertise in the structure/function of SMAD proteins, but the study appears to include sufficient background to place the study in context.

-Audience will mostly be those interested in structure/function of SMAD proteins, with some protein engineers interested in the manipulation of monomeric versus dimer.

-I am a protein chemist and structural biologist with an interest in protein dimerization/oligomerisation. I am familiar with most techniques presented, but don't have first hand experience with IM-MS.

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Referee #1

This reviewer did not leave any comments

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Reply to the reviewers

We thank all the Reviewers for taking the time to evaluate our manuscript and providing us with constructive feedback. We are pleased to hear that all Reviewers appreciate the importance and significance of our study, commenting that our conclusions are ‘convincing, are supported by the presented experimental results’ and that our study ‘will yield novel insights into the regulation and function of PALB2 in DNA repair’.

Please refer to our point-by-point response to the specific points raised, in which we highlight a couple of key experiments to be conducted to refine our study in bold. We are grateful for all the reviewers’ remarks and suggestions, which will certainly lead to a substantial improvement in our manuscript.

Point-by-point response:

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Fournier et al. detect acetylation within the chromatin association motif (ChAM) of PALB2 and demonstrate that KAT2 can acetylate these 7 lysine residues within this region. They then generate K to R mutations (7R) or K to Q mutations (7Q) at these sites and perform assays of fluorescence recovery after photobleaching (FRAP) to measure mobility as a measure of chromatin association, RAD51 foci, PALB2 recruitment at sites of laser-induced DNA damage, and sensitivity to olaparib. They find increased mobility of the 7Q mutant of PALB2 but not 7R in the absence of exogenous DNA damage, as well as defects in DNA damage-induced RAD51 foci and resistance to olaparib. On this basis, the authors conclude that acetylation is required for the association of PALB2 with undamaged chromatin and that deacetylation permits mobilization and association with BRCA1 to enable proper DNA repair. While the manuscript is generally well-written, many of the systems are rather elegant, and this study may yield novel insights into the regulation and function of PALB2 in DNA repair, there are some missing experiments to be added and important contradictions that should be resolved in order to fully establish the new model the authors propose.

**Major comments:** 1.There are some concerns about the interpretation of experiments with the 7R and 7Q mutants of PALB2. For example, in the description of results in Fig. S2C, the authors state "K to R substitutions maintain the charge yet are unable to accept acetylation and hence mimic constitutively non-acetyl lysine". However, in Fig. 4B the association of the 7R mutant with chromatin is similar to WT and in Fig. 7D,E the relative immobility of the 7R mutant is very similar to WT PALB2. Thus, the conclusion that acetylation is required for PALB2 association with damaged undamaged chromatin and for release of PALB2 upon DNA damage does not appear justified. Perhaps the authors need to better consider whether the 7R mutant mimics acetylation because of its charge. Even so, the mutant then maintains the charge normally associated with acetylated PALB2, calling into question whether deacetylation indeed "releases PALB2 from undamaged chromatin".

We agree with the Reviewer’s point that there is no or little difference between WT and the 7R mutant in regard to their enrichment on non-damaged chromatin, as detected by fractionation (Fig 4B), or their mobility, as detected by FRAP (Fig 4D and E). Note that Fig. 7D is our model and Fig. 7E does not exist. As the Reviewer suggests, it is possible that, in contrast to the 7Q mutant, which is defective in both nucleosome and DNA binding (Fig. 2E and F), the 7R mutant may maintain its electrostatic interaction with DNA, while lacking its acetylation-mediated nucleosome interaction, masking the impact of substitutions. This assumption is in line with our model in which ChAM DNA binding assists HR repair, which is supported by the 7R mutant but not by the 7Q mutant. To better dissect the question raised by the Reviewer, we will conduct biochemical analyses of the ChAM 7R mutant, testing its direct interaction with nucleosomes and DNA; the results will be included in the revised manuscript (Experiment 1).

It is also worth noting that full-length PALB2 is enriched at a fraction of H3K36me3-marked exons (which comprise only 1-1.5 % of the whole genome), as shown in our previous genome-wide ChIP-seq analysis (Bleuyard et al., 2017, PNAS). Hence, it is also possible that bulk fractionation or FRAP analyses might not be sensitive enough to highlight the impact of the 7R mutation. Conversely, we foresee that the ChIP-qPCR method, detecting PALB2 association at defined genic regions as shown in Fig S5, will be more appropriate. Thus, in the revised manuscript, we will expand our ChIP-qPCR analyses to further validate our proposed model (Experiment 2).

2.Related to questions of interpreting results utilizing the 7R and 7Q mutants of PALB2, in Fig. 7B,C the 7R mutant but not 7Q supports RAD51 foci and resistance to olaparib similar to WT PALB2. The authors then state in the Discussion that "our work also suggests that caution should be exercised in the use of K to Q substitutions for functional studies of lysine acetylation". Thus, which mutant is giving the correct and reliable results?

We apologise for the miscommunication if this point was unclear. Using biochemical approaches, we established that ChAM acetylation, but not K to Q substitution, facilitates its association with nucleosomes (please compare Fig 2E and Fig 3B). This observation clearly demonstrates that K to Q substitution does not mimic acetylation at these residues, but instead renders PALB2 ChAM functionally null. The PALB2 7Q phenotypes therefore demonstrate the importance of the 7K patch for ChAM function in HR repair, rather than its acetylation status.

Perhaps even more importantly, if results with the 7Q mutant are suspect, the conclusion that deacetylation is required for HR (or DNA repair) is suspect because that is the only case where the authors see a defect in RAD51 foci and resistance to olaparib. Similarly, if the 7R mutant "mimics non-acetyl-lysine" then the fact that it has normal RAD51 foci and resistance to olaparib contradicts the conclusion that deacetylation is required for DNA repair.

Unfortunately, it is currently technically not possible to ‘lock’ the PALB2 7K patch in its acetylated status in vivo (i.e. preventing PALB2 dissociation from active genes). We thus agree with the Reviewer that it is difficult to draw definitive conclusions on the impact of constitutive PALB2 acetylation in HR, although the importance of the 7K-patch for the functionality of PALB2 is evidenced by the 7Q mutant phenotypes. Similarly, strictly speaking, our results using the 7R mutant support the notion that the ‘non-acetylated’ status of the 7K mutant, but not necessarily the dynamics of ‘de-acetylation’ events, can promote HR repair. In the revised manuscript, we will rephrase and clarify these points.

3.There are multiple concerns about Figs. 5 and S5. In Fig. 5A-C, difference in cell cycle progression after synchronization are relatively small and no rationale/interpretation is given for how this may be related to PALB2 function is given. In Fig. 5D,E differences in the levels of gamma-H2AX as a marker of DNA damage between different forms of PALB2 do not become readily apparent until about 6 or more days after addition of doxycycline. As such, it seems that these could be indirect effects and it is unclear how strongly this supports the importance of PALB2 acetylation in the DNA damage response.

We apologise for the miscommunication on these points. We have previously established that steady-state PALB2 chromatin association, jointly mediated by the ChAM and MRG15 interaction, protects a subset of active genes from DNA damage that may otherwise arise from replication-transcription conflicts (Bleuyard et al., PNAS 2017). The results presented in Fig 5 and S5 led us to propose that PALB2 chromatin association is, at least in part, mediated by the ChAM 7K patch, and its acetylation (hindered by 7Q and 7R substitutions, respectively) prevents DNA damage via a similar mechanism, i.e., protecting PALB2-bound genes during replication. This model nicely supports our observations that both 7Q/7R mutants exhibit slow S-phase progression and accumulation of gamma-H2AX over time. These points will be better articulated in the revised manuscript.

In Fig. S5, it is interesting that there are differences in the association of different forms of PALB2 with 3 distinct active loci, but no error bars or measures of statistical significance are given. Further at 2 of the 3 loci, the association of the 7Q mutant is closer to WT than the 7R mutant. Taken together, neither Fig. 5 nor Fig. S5 strongly support the key conclusion that acetylation regulates the association of PALB2 with actively transcribed genes to protect them.

We appreciate this constructive comment. The analysis was conducted once, albeit with three technical replicates, which explains why the results are presented without error bars. Nonetheless, we observe a consistent trend at three different loci, that both 7R and 7Q have chromatin association similar to the empty vector, which is background level (FLAG/IgG ChIP) and does not reflect real binding. The revised manuscript will include the results from three biological replicates with statistical evaluation (Experiment 2).

4.Figs. 6D-G and S6A-D conclude that "DNA damage triggers ChAM deacetylation and induces PALB2 mobilization" based upon FRAP experiments utilizing WT PALB2. But there is no control to demonstrate that this is a specific effect driven by the state of PALB2 acetylation. For example, DNA damage might cause global acetylation changes resulting in relaxed chromatin in which proteins that are not subject to acetylation-deacetylation also show increased mobility.

We thank the Reviewer for this valuable comment. It is true that we cannot formally exclude the possibility that changes in PALB2 mobility are indirect consequence of damage-induced chromatin reorganisation/increased chromatin mobility. However, our analyses clearly demonstrate that ChAM acetylation increases its association with nucleosomes (Fig. 3B), while non-nucleosome binding ChAM-null (7Q or deletion) increases PALB2 mobility (Fig. 2E, Fig. 4E and Fig. S4C). Further, WT PALB2 mobility increases after KAT2 depletion (i.e. reduction of chromatin acetylation of KAT2 targets, hence chromatin compaction) (Fig. 3F), but reduces upon KDAC inhibition (i.e. global increase in acetylation, hence chromatin relaxation) (Fig. 3G). Considering all these observations collectively, the increase in PALB2 mobility detectable upon DNA damage is unlikely to reflect global chromatin relaxation, and that PALB2 acetylation influences its mobility in both challenged and unchallenged cells. This point will be emphasised in the revised manuscript.

5.Fig. 7B shows that the 7Q mutant has diminished RAD51 foci while Fig. S7C,D suggests based upon a different methodology (laser-induced damage) that the 7Q mutant does not affect PALB2 recruitment. Since the issue of recruitment is key to the mechanism proposed, the authors should examine PALB2 foci instead as this may be a more sensitive assay of PALB2 recruitment.

We appreciate the Reviewer’s point. We would like to highlight, however, the well-documented role of BRCA1 in PALB2 recruitment to sites of DNA damage. This supports our notion that the 7Q mutant is recruited to sites of DNA damage, likely mediated via its interaction with BRCA1. As depicted in Fig. 7D, we propose that the 7K patch-mediated PALB2 engagement with damaged DNA, which is disrupted by the K to Q substitutions, is essential for proper RAD51 loading onto DNA, hence RAD51 foci formation and HR repair. This is in line with our observation that PALB2 ChAM deletion, similarly to the 7Q mutant, perturbs damage-induced RAD51 foci formation (Bleuyard et al., EMBO Rep. 2012). We believe that the laser-induced experiments provide high sensitivity and resolution for PALB2 recruitment kinetics, as the data were obtained with real-time live-cell imaging.

6.The authors state in the last sentence of the Results section that "lysine residues within the ChAM 7K-patch are indispensable for PALB2 function in HR" but never test the mutants for HR using reporter assays. The manuscript would be strengthened by performing such assays.

RAD51 foci formation and sensitivity to PARP inhibition are well-accepted readouts for HR repair. Conversely, we have been cautious about existing HR reporter systems, which evaluate gene-conversion or targeting events triggered by a ‘clean’ enzyme-induced DSB, but not an authentic repair of ‘dirty’ DSB induced by IR or olaparib.

7.The model for the role of ChAM acetylation in regulating PALB2 function presented in Fig. 7D is not fully supported by the data presented. Critically, while association with RAD51 and BRCA2 is tested in Fig. S7B, the authors hypothesize that deacetylation is required to release PALB2 to enable association with BRCA1 but this is not tested utilizing the mutants.

We appreciate the Reviewer’s point. It has been demonstrated that PALB2 interaction with BRCA1 is triggered by damage-induced PALB2 phosphorylation (Ahlskog et al., EMBO Reports, 2016), as well as removal of KEAP1-mediated ubiquitylation in S and G2 (Orthwein et al., Nature 2015). Our preliminary analyses further suggest that BRCA1-PALB2 interaction is highly dynamic, and we propose that damage-induced PALB2 modification and its mobilisation jointly facilitate this interaction.

Also, there are some specific points that should be considered in the context of the model. This includes how DNA damage may trigger deacetylation, and whether it is the deacetylated state or the process of deacetylation of ChAM that is critical. Also, if acetylation is important for protecting active genes in the absence of DNA damage, is deacetylation necessary to release PALB2 local or global. This is important, because if it is local there needs to be a specific mechanism for local deacetylation, while if deacetylation is global that could result in transcriptionally active genes becoming unprotected.

We thank this Reviewer for this valuable comment. We agree that, while this study establishes that ChAM is deacetylated upon DNA damage, it remains unclear whether the dynamic ‘de-acetylation’ of PALB2, rather than the ‘non-acetylated status’ of PALB2, is important for HR repair, and whether or not this is a local event. However, we would like to highlight that PALB2-bound genes are mostly periodic, e.g. those required for cell cycle progression (Bleuyard et al., 2017, PNAS). It would therefore be reasonable to speculate that DNA damage triggers the suppression of periodic gene expression as a part of DNA damage checkpoint signalling, possibly in a KDAC-dependent manner, which then allows release of PALB2 without risking DNA damage that could otherwise be caused by replication-transcription conflict. Mobilised PALB2 might then be recruited to sites of DNA damage for HR repair. Further study will be required to fully evaluate this model, for example by identifying the specific KDAC involved in ChAM deacetylation and tracking individual PALB2 molecules, which we consider to be beyond the scope of the present study. In the revised manuscript, we will better describe our model, and further detail the arising questions to be addressed in future studies.

**Minor Comments:** a.Some parts of the Materials and Methods are overly long (such as the subsection on "Protein purification" and "immunofluorescence microscopy") and could be shortened by consolidating experimental details that are largely the same for related processes.

We propose to move these experimental sections to the supplementary information.

b.In the description of Fig. 1D, the statement "7K-patch, which is common to PALB2 orthologs" is misleading since there is not complete conservation of each lysine residue across each ortholog.

We agree with this Reviewer’s comment and will amend the description in the revised manuscript accordingly.

c.Figs. 3E,F and S3B,C perform FRAP in cells with knockdown of KAT2A/B as a surrogate for chromatin association. The authors note that this global reduction in acetylation increases PALB2 diffusion, but there is concern that this experiment is not very informative because the increased mobility may have nothing to do acetylation of PALB2.

Please refer to our answer in response to the Reviewer’s point 4.

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

This manuscript reports the control of PALB2 - chromatin interaction by the acetylation of a particular lysine-rich domain of the protein called ChAM. This acetylation is shown to be mediated by the acetyltransferases KAT2A/B. Following these investigations, the authors made an effort to place their findings in the context of DNA replication and DNA repair. The proposed model is that the acetylation-dependent interaction of PALB2 with chromatin could ensure the protection of the genome during DNA replication and control DNA repair. **Specific remarks** 1 - Based on different experiments, essentially the one shown in Fig. 3B, the authors conclude that the acetylation of the ChAM domain enhances its association with nucleosomes. However, taking into account the experimental setting, this conclusion should be largely tuned down. Indeed, this enhanced acetylation-dependent nucleosome binding was observed when the experiment was carried out in the presence of excess of free naked DNA. Under these conditions, the non-acetylated ChAM fragments became mostly trapped by DNA (clearly shown in Fig. 3C/D), and hence would not be available for nucleosome binding, while the acetylated ChAM fragments would remain available for nucleosome association because of their reduced DNA-binding ability. Consequently, the acetylation of the ChAM domain would only play a role on the availability of PALB2 for chromatin/nucleosome binding and not directly stimulate nucleosome binding. Therefore, the nucleosome-binding capacity of ChAM by itself should not be dependent on ChAM domain acetylation.

If true, this hypothesis could also be relevant in vivo since the poly-K in the ChAM domain could also non-specifically interact with nuclear RNAs and hence its acetylation, by releasing it from nuclear RNAs, would make it available for chromatin-binding. The importance of RNAs in the regulation of PALB2 nucleosome-binding could be tested in the experiments shown in Fig. 2C and 2E by adding RNase to the pull-down medium (WT +/-RNase or addition of increasing exogenous RNAs).

We are grateful for the Reviewer’s detailed comments and find the potential involvement of RNA very intriguing. Indeed, transcriptionally active loci, which are bound by PALB2, are enriched in nascent RNA, and such local RNA may play an important role in promoting the association of acetylated PALB2 with nucleosomes. However, we believe that investigating the role of RNAs in PALB2 nucleosome binding is beyond the scope of this study. As discussed extensively in response to this Reviewer’s point 2 below, we believe the mode of interaction of ChAM with nucleosomes to be highly complex, being jointly mediated by the N-terminal conserved region and the C-terminal lysine cluster. We will discuss these issues more extensively in the revised manuscript.

2 - The real question is as follows. While acetylation makes the protein available for nucleosome binding, which part of the ChAM domain is actually mediating nucleosome binding and whether lysine acetylation could be directly involved in this binding. Another question would be to identify the elements in the nucleosome mediating this interaction, histones (core domain, tails, post-translational modifications, specific histone types), histone-DNA, etc...

We entirely agree with the Reviewer’s question – despite the increasing recognition of the physiological importance of the PALB2 ChAM and our efforts in understanding the mode of association of ChAM with nucleosomes (including the potential involvement of histone tail modifications), this specific question remains enigmatic.

Explicitly, our previous work demonstrated that substitutions of residues within the evolutionarily highly conserved N-terminal part of the ChAM perturb its association with nucleosomes (Bleuyard et al., 2017, PNAS; Bleuyard et al., 2017, Wellcome Open Research). A recent study by the laboratory of Prof Jackson proposed that basic residues across the ChAM are part of a binding interface with an acidic patch of histone H2A in its nucleosomal context (Belotserkovskaya et al., Nat Comm. 2020). Our results presented in this study introduced an additional complexity, showing that the C-terminal 7K basic patch is essential for ChAM-nucleosome interaction. Intriguingly, our study also suggests that the regions flanking ChAM, which are phosphorylated at multiple residues, play roles in regulating ChAM binding to nucleosomes (Fig 2B and C; please refer to our answer to the Reviewer’s minor point 6 too).

We are currently working towards solving the structure of ChAM in complex with a nucleosome, which may help to clarify this very important question. At this point, we think that the question about complete elements for the ChAM interaction with nucleosome is out of the scope of this manuscript, and should be addressed in future work. To make this point clear, we will provide an updated overview of the ChAM elements affecting nucleosome interaction in the revised manuscript.

3 - Taking into account the authors conclusions on the role of ChAM domain acetylation and its impact on PALB2 mobility, in Figure 4D/E, one should expect a difference of t1/2 when wild-type and 7R mutant are assayed by FRAP. At least the measures of t1/2 in the wild-type should have been more heterogeneous compared to the 7R mutant due to the acetylation of the wild-type PALB2 by the endogenous HATs (the impact of endogenous HATs on the wild-type sequence is shown in Fig. 3F). Could the authors comment on this?

We appreciate this Reviewer’s point. As mentioned in our responses to Reviewer 1’s points 1 and 3, we are unable to exclude the possibility that the 7R mutant still maintains its DNA-binding capacity, masking detectable change in its chromatin enrichment and mobility. Also, PALB2 in vivo chromatin association is limited to a small fraction of periodic genes, hence FRAP assay may not be sensitive enough to detect minute but critical differences. We will conduct biochemical assessment of the ChAM 7R mutant and ChIP-qPCR analyses to assess PALB2 binding to specific genes, results of which will be included in the revised manuscript (Experiments 1 and 2).

4 - It would be better to remove the data presented in Fig. 5 since, as currently presented, these investigations remain shallow and do not bring much information on what is happening. The presented data are rather confusing since, in the absence of further investigations, it is not clear which one(s) of the mechanisms involved in the control of DNA replication is controlled by PALB2 and many explanations, including artefacts, remain possible.

The manuscript would gain in interest if the authors would devote the functional studies only to the repair part (Fig.6 and 7).

We feel it is important to show Fig 5, as although the results may appear confusing, they highlight the importance of the acetylation of the 7K patch at the cellular level. Namely, the non-acetylatable 7R mutant fails to support normal cellular growth, likely due to its impaired association with active genes (Fig S5), which might provide in vivo evidence that non-acetylation of the 7K patch promotes PALB2 release from chromatin (please refer to our response to Reviewer 1’s point 3). We are confident that we will be able to clarify this point with our additional ChIP-qPCR analyses (Experiment 2).

**Minor points** 5 - High background of non-enzymatic acetylation of PALB2 fragments makes the identification of KAT2A/B specific acetylation not very convincing. The immunoblot detection of acetylation fragments shown in Figure S1 is much more convincing. Therefore, the authors may consider to present Fig S1 as a main Figure and Fig.1B as a supplementary one.

We will swap or add Fig S1B with or to Fig 1B in the revised manuscript.

6 - It would be interesting if the authors would comment on why the presence of regions flanking the ChAM domain (Fig. 1A, construct #5) significantly reduces chromatin (Fig. 1B) and nucleosome binding (Fig. 1C).

We are grateful for this Reviewer’s comment. Indeed, we noticed that the inclusion of the ChAM C-terminal flanking region perturbs its chromatin association. This region is highly enriched with serine and threonine residues which could be targeted for phosphorylation by cell cycle regulators (CDKs and PLK1) and DNA damage-responsive kinases (ATM and ATR). It is therefore tempting to speculate that, when phosphorylated, this flanking region could mask the basic patch of the ChAM, hence facilitating the release of PALB2 from undamaged chromatin region and its recruitment to sites of DNA damage. In the revised manuscript, we will provide the complete list of PTMs and discuss this point.

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

KAT2-mediated acetylation switches the mode of PALB2 chromatin association to safeguard genome integrity

The authors describe a series of experiments examining the consequence of acetylation, within a defined motif (Chromatin Association Motif; ChAM), on the cellular roles of the protein PALB2 (Partner and Localizer of BRCA2).

The key conclusions drawn by the authors are generally convincing and are supported by the presented experimental results, which indicate that acetylation of PALB2 by KAT2A/KAT2B modulates its cellular behaviour and response to DNA damage. However please see specific comments below:

**Major Comments**

Expression of full-length PALB2 in the heterologous host E. coli is highly problematic, as the WD40 domain is generally not correctly folded. The authors use the ArticExpress strain to try and solve/alleviate this problem - but it is clear from the materials and methods section that an ATP-wash step has had to be introduced in order to release the recombinant protein from the chaperone system encoded by the ArticExpress system; i.e. indicating poor / mis-folding. Whilst this does not strictly have an effect on the results presented in Figure 1 (detection of in vitro acetylation sites), they have implications for the wider scientific community, as this may lead to the erroneous assumption that is possible to produce functional / folded full-length PALB2 in this way.

We apologise if the manuscript conveyed the message that we are able to produce functionally active, full-length PALB2 in bacteria, which was clearly not our intention. Our aim was to test whether KAT2A was able to acetylate PALB2 in vitro. We agree that the folding and the biochemical properties (e.g. WD40-mediated BRCA2 binding) of the bacterially produced full length PALB2 were not fully assessed. We believe that this does not affect the overall conclusions of this study. In the revised manuscript, we will correct this error to make this point clear.

In vitro modification assays are prone to producing post-translational modifications that are not fully reflective of those observed in vivo, and therefore need to be treated with some caution. This is highlighted by the relatively low modification of K438 in vitro by KAT2A; esp. as this is an acetylation site that has been previously mapped in vivo (by the authors). It would have been useful to include / see the effects on PALB2 function in vivo by modification / alteration of this single site.

We appreciate the Reviewer’s constructive comment. Redundancy of acetylation acceptor residues within a lysine cluster is common, as is also the case for many ubiquitination events, hence we analysed the 7K patch mutant for phenotypic studies. For the same reason, we trust that the outcome of the characterisation of a K438 mutant would not significantly change our conclusions.

Figure 3C and Figure 3D do not fully support or reflect the conclusions drawn by the authors - any peptide containing a cluster of positive charged residues are likely to interact with DNA through charge neutralisation of the phosphodiester backbone, concomitantly any alteration to this region of charge (i.e. via acetylation) will perturb this interaction.

We totally agree with the Reviewer’s view and state, in the main text referring to the results shown in Fig. 3C and D, that “As anticipated, lysine acetylation, which neutralises the positive charge on the lysine side chain, conferred reduced affinity for negatively charged DNA”. In the revised manuscript, we will make this point clearer.

Furthermore, experiments performed with the synthetic acetylated peptides do not agree with those carried out with the GST-ChAM constructs - GST-ChAM interacts with the nicked and linear forms of the pBS plasmid (Figure 2F) but does not interact with the supercoiled form. The WT synthetic ChAM peptide, in contrast, interacts with all three plasmid states at high concentrations. It is suggested that these two figures are removed.

It is true that we cannot exclude the potential difference between GST-ChAM and synthetic ChAM peptide: for example, 26 kDa of GST, which can form a dimer, might mask the full biochemical properties of ChAM in DNA binding. However, we believe that the difference is more likely caused by the concentration of ChAM used. While we used the synthetic ChAM peptides at concentrations of 2.97, 5.94, 29.3 µM for Fig. 3C, we used 5.94 µM of GST-ChAM for Fig. 2F, for which we apologise for the omission of the experimental conditions used. This notion is supported by the side-by-side experiment, which was not shown in the original manuscript. In the revised manuscript, we will make these points clear.

p. 18 : the authors used a PALB2 variant, where the lysines in the 7K patch are mutated to arginine - but don't fully characterise the effects of introducing these particular mutations on the ability of the ChAM fragment to bind to DNA, or indeed to nucleosomes; this is an important control.

We appreciate the Reviewer’s comment. Biochemical analyses of the 7R mutant were not conducted, as ChAM produced in bacteria is not expected to be acetylated. Nonetheless, as also linked with the concerns of Reviewers 1 and 2, we recognise the importance of 7R biochemical characterisation for accurate interpretation of in vivo phenotypes. We will assess the DNA and nucleosome binding of the ChAM 7R mutant, which will be included in the revised manuscript (Experiment 1).

Figure 6 : it would be good to show a second supporting example for deacetylation of PALB2 in response to DNA damage - perhaps treatment with MMC?

We appreciate the Reviewer’s comment. Indeed, we have conducted the analysis upon MMC and Olaparib exposure. Curiously, however, no clear change of ChAM acetylation was detectable. Note that, for this experiment, we assessed the acetylation level of a GFP-fusion of ChAM, exogenously expressed in HEK293, along with endogenous gamma-H2AX as a readout of DNA damage signalling. Unlike ionising radiation, which triggered strong induction of gamma-H2AX (Fig. 6), no clear increase of gamma-H2AX was detectable upon MMC/Olaparib exposure. Hence, we propose that the reduction of ChAM acetylation reflects the cellular response to DNA damage. We will make these points clear in the revised manuscript.

**Minor Comments**

p. 16 : 'Our MS analysis of the chromatin-associated GFP-ChAM fragment identified actelyation of all seven lysines within the 7K-patch (Fig. 3A, marked with arrows).

This part of the manuscript is potentially a little confusing, as Fig. 3A references a series of synthetic peptides rather than the GFP-ChAM fragments themselves.

We appreciate the Reviewer’s point. Indeed, Fig. 3A shows 1) MS of the chromatin-associated fraction of GFP-ChAM (the top part with arrows) and 2) a schematic diagram of synthetic peptides that we used for biochemical analyses (the bottom part). In the revised manuscript, we will clarify this point and indicate the MS result and the schematic of synthetic peptides in two separate panels and refer each of them appropriately.

p. 20 : Furthermore, using the FRAP approach, we observed clear differences in diffusion rates of FE-PALB2 following damage by IR, MMC, or olaparib treatment... FE-PALB2 = FL-PALB2?

We apologise for the confusion. In our study, FE-PALB2 refers to Flag-EGFP tagged PALB2 (full-length). This is defined in the text “To this end, a tandem FLAG- and EGFP-tagged full-length wild-type (WT) PALB2 (FE-PALB2)” (p. 17).

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Referee #3

Evidence, reproducibility and clarity

KAT2-mediated acetylation switches the mode of PALB2 chromatin association to safeguard genome integrity

The authors describe a series of experiments examining the consequence of acetylation, within a defined motif (Chromatin Association Motif; ChAM), on the cellular roles of the protein PALB2 (Partner and Localizer of BRCA2).

The key conclusions drawn by the authors are generally convincing and are supported by the presented experimental results, which indicate that acetylation of PALB2 by KAT2A/KAT2B modulates its cellular behaviour and response to DNA damage. However please see specific comments below:

Major Comments

Expression of full-length PALB2 in the heterologous host E. coli is highly problematic, as the WD40 domain is generally not correctly folded. The authors use the ArticExpress strain to try and solve/alleviate this problem - but it is clear from the materials and methods section that an ATP-wash step has had to be introduced in order to release the recombinant protein from the chaperone system encoded by the ArticExpress system; i.e. indicating poor / mis-folding. Whilst this does not strictly have an effect on the results presented in Figure 1 (detection of in vitro acetylation sites), they have implications for the wider scientific community, as this may lead to the erroneous assumption that is possible to produce functional / folded full-length PALB2 in this way.

In vitro modification assays are prone to producing post-translational modifications that are not fully reflective of those observed in vivo, and therefore need to be treated with some caution. This is highlighted by the relatively low modification of K438 in vitro by KAT2A; esp. as this is an acetylation site that has been previously mapped in vivo (by the authors). It would have been useful to include / see the effects on PALB2 function in vivo by modification / alteration of this single site.

Figure 3C and Figure 3D do not fully support or reflect the conclusions drawn by the authors - any peptide containing a cluster of positive charged residues are likely to interact with DNA through charge neutralisation of the phosphodiester backbone, concomitantly any alteration to this region of charge (i.e. via acetylation) will perturb this interaction.<br> Furthermore, experiments performed with the synthetic acetylated peptides do not agree with those carried out with the GST-ChAM constructs - GST-ChAM interacts with the nicked and linear forms of the pBS plasmid (Figure 2F) but does not interact with the supercoiled form. The WT synthetic ChAM peptide, in contrast, interacts with all three plasmid states at high concentrations. It is suggested that these two figures are removed.

p. 18 : the authors used a PALB2 variant, where the lysines in the 7K patch are mutated to arginine - but don't fully characterise the effects of introducing these particular mutations on the ability of the ChAM fragment to bind to DNA, or indeed to nucleosomes; this is an important control.

Figure 6 : it would be good to show a second supporting example for deacetylation of PALB2 in response to DNA damage - perhaps treatment with MMC?

Minor Comments

p. 16 : 'Our MS analysis of the chromatin-associated GFP-ChAM fragment identified actelyation of all seven lysines within the 7K-patch (Fig. 3A, marked with arrows).

This part of the manuscript is potentially a little confusing, as Fig. 3A references a series of synthetic peptides rather than the GFP-ChAM fragments themselves.

p. 20 : Furthermore, using the FRAP approach, we observed clear differences in diffusion rates of FE-PALB2 following damage by IR, MMC, or olaparib treatment...

FE-PALB2 = FL-PALB2?

Significance

The paper is generally incremental in nature - offering additional information about the effects of acetylation within the ChAM region of PALB2 on its interaction with chromatin, and on the cellular response to DNA damage.

The reported findings would be of interest to scientists working in the area of DNA damage repair and DNA damage signalling, esp. those with a keen interest in the regulation and control of homologous recombination.

Field of expertise: Biochemistry / Biophysics / DNA damage response / Structural Biology

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Referee #2

Evidence, reproducibility and clarity

This manuscript reports the control of PALB2 - chromatin interaction by the acetylation of a particular lysine-rich domain of the protein called ChAM. This acetylation is shown to be mediated by the acetyltransferases KAT2A/B. Following these investigations, the authors made an effort to place their findings in the context of DNA replication and DNA repair. The proposed model is that the acetylation-dependent interaction of PALB2 with chromatin could ensure the protection of the genome during DNA replication and control DNA repair.

Specific remarks

1 - Based on different experiments, essentially the one shown in Fig. 3B, the authors conclude that the acetylation of the ChAM domain enhances its association with nucleosomes. However, taking into account the experimental setting, this conclusion should be largely tuned down. Indeed, this enhanced acetylation-dependent nucleosome binding was observed when the experiment was carried out in the presence of excess of free naked DNA. Under these conditions, the non-acetylated ChAM fragments became mostly trapped by DNA (clearly shown in Fig. 3C/D), and hence would not be available for nucleosome binding, while the acetylated ChAM fragments would remain available for nucleosome association because of their reduced DNA-binding ability.

Consequently, the acetylation of the ChAM domain would only play a role on the availability of PALB2 for chromatin/nucleosome binding and not directly stimulate nucleosome binding. Therefore, the nucleosome-binding capacity of ChAM by itself should not be dependent on ChAM domain acetylation.

If true, this hypothesis could also be relevant in vivo since the poly-K in the ChAM domain could also non-specifically interact with nuclear RNAs and hence its acetylation, by releasing it from nuclear RNAs, would make it available for chromatin-binding. The importance of RNAs in the regulation of PALB2 nucleosome-binding could be tested in the experiments shown in Fig. 2C and 2E by adding RNase to the pull-down medium (WT +/-RNase or addition of increasing exogenous RNAs).

2 - The real question is as follows. While acetylation makes the protein available for nucleosome binding, which part of the ChAM domain is actually mediating nucleosome binding and whether lysine acetylation could be directly involved in this binding. Another question would be to identify the elements in the nucleosome mediating this interaction, histones (core domain, tails, post-translational modifications, specific histone types), histone-DNA, etc...

3 - Taking into account the authors conclusions on the role of ChAM domain acetylation and its impact on PALB2 mobility, in Figure 4D/E, one should expect a difference of t1/2 when wild-type and 7R mutant are assayed by FRAP. At least the measures of t1/2 in the wild-type should have been more heterogeneous compared to the 7R mutant due to the acetylation of the wild-type PALB2 by the endogenous HATs (the impact of endogenous HATs on the wild-type sequence is shown in Fig. 3F). Could the authors comment on this?

4 - It would be better to remove the data presented in Fig. 5 since, as currently presented, these investigations remain shallow and do not bring much information on what is happening. The presented data are rather confusing since, in the absence of further investigations, it is not clear which one(s) of the mechanisms involved in the control of DNA replication is controlled by PALB2 and many explanations, including artefacts, remain possible.

The manuscript would gain in interest if the authors would devote the functional studies only to the repair part (Fig.6 and 7).

Minor points

5 - High background of non-enzymatic acetylation of PALB2 fragments makes the identification of KAT2A/B specific acetylation not very convincing. The immunoblot detection of acetylation fragments shown in Figure S1 is much more convincing. Therefore, the authors may consider to present Fig S1 as a main Figure and Fig.1B as a supplementary one.

6 - It would be interesting if the authors would comment on why the presence of regions flanking the ChAM domain (Fig. 1A, construct #5) significantly reduces chromatin (Fig. 1B) and nucleosome binding (Fig. 1C).

Significance

This manuscript brings interesting information on the control of the activity of PALB2 by acetylation which depends on specific cellular HATs. By doing so, it also opens the door to envision a role for a metabolic reprogramming of protein acetylation in the control of PALB2 activity, as discussed by the authors.

This is an interesting report but the functional part is rather weak, decreasing the interest of the manuscript as a whole. This report would gain in interest if the authors would develop the first part (Figs 1- 4), while reducing the second part (Figs 5 -7) to the presentation and discussion of the strongest functional data (see my comments).

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Referee #1

Evidence, reproducibility and clarity

Fournier et al. detect acetylation within the chromatin association motif (ChAM) of PALB2 and demonstrate that KAT2 can acetylate these 7 lysine residues within this region. They then generate K to R mutations (7R) or K to Q mutations (7Q) at these sites and perform assays of fluorescence recovery after photobleaching (FRAP) to measure mobility as a measure of chromatin association, RAD51 foci, PALB2 recruitment at sites of laser-induced DNA damage, and sensitivity to olaparib. They find increased mobility of the 7Q mutant of PALB2 but not 7R in the absence of exogenous DNA damage, as well as defects in DNA damage-induced RAD51 foci and resistance to olaparib. On this basis, the authors conclude that acetylation is required for the association of PALB2 with undamaged chromatin and that deacetylation permits mobilization and association with BRCA1 to enable proper DNA repair. While the manuscript is generally well-written, many of the systems are rather elegant, and this study may yield novel insights into the regulation and function of PALB2 in DNA repair, there are some missing experiments to be added and important contradictions that should be resolved in order to fully establish the new model the authors propose.

Major comments:

1.There are some concerns about the interpretation of experiments with the 7R and 7Q mutants of PALB2. For example, in the description of results in Fig. S2C, the authors state "K to R substitutions maintain the charge yet are unable to accept acetylation and hence mimic constitutively non-acetyl lysine". However, in Fig. 4B the association of the 7R mutant with chromatin is similar to WT and in Fig. 7D,E the relative immobility of the 7R mutant is very similar to WT PALB2. Thus, the conclusion that acetylation is required for PALB2 association with damaged undamaged chromatin and for release of PALB2 upon DNA damage does not appear justified. Perhaps the authors need to better consider whether the 7R mutant mimics acetylation because of its charge. Even so, the mutant then maintains the charge normally associated with acetylated PALB2, calling into question whether deacetylation indeed "releases PALB2 from undamaged chromatin".

2.Related to questions of interpreting results utilizing the 7R and 7Q mutants of PALB2, in Fig. 7B,C the 7R mutant but not 7Q supports RAD51 foci and resistance to olaparib similar to WT PALB2. The authors then state in the Discussion that "our work also suggests that caution should be exercised in the use of K to Q substitutions for functional studies of lysine acetylation". Thus, which mutant is giving the correct and reliable results? Perhaps even more importantly, if results with the 7Q mutant are suspect, the conclusion that deacetylation is required for HR (or DNA repair) is suspect because that is the only case where the authors see a defect in RAD51 foci and resistance to olaparib. Similarly, if the 7R mutant "mimics non-acetyl-lysine" then the fact that it has normal RAD51 foci and resistance to olaparib contradicts the conclusion that deacetylation is required for DNA repair.

3.There are multiple concerns about Figs. 5 and S5. In Fig. 5A-C, difference in cell cycle progression after synchronization are relatively small and no rationale/interpretation is given for how this may be related to PALB2 function is given. In Fig. 5D,E differences in the levels of gamma-H2AX as a marker of DNA damage between different forms of PALB2 do not become readily apparent until about 6 or more days after addition of doxycycline. As such, it seems that these could be indirect effects and it is unclear how strongly this supports the importance of PALB2 acetylation in the DNA damage response. In Fig. S5, it is interesting that there are difference in the association of different forms of PALB2 with 3 distinct active loci, but no error bars or measures of statistical significance are given. Further at 2 of the 3 loci, the association of the 7Q mutant is closer to WT than the 7R mutant. Taken together, neither Fig. 5 nor Fig. S5 strongly support the key conclusion that acetylation regulates the association of PALB2 with actively transcribed genes to protect them.

4.Figs. 6D-G and S6A-D conclude that "DNA damage triggers ChAM deacetylation and induces PALB2 mobilization" based upon FRAP experiments utilizing WT PALB2. But there is no control to demonstrate that this is a specific effect driven by the state of PALB2 acetylation. For example, DNA damage mmight cause global acetylation changes resulting in relaxed chromatin in which proteins that are not subject to acetylation-deacetylation also show increased mobility.

5.Fig. 7B shows that the 7Q mutant has diminished RAD51 foci while Fig. S7C,D suggests based upon a different methodology (laser-induced damage) that the 7Q mutant does not affect PALB2 recruitment. Since the issue of recruitment is key to the mechanism proposed, the authors should examine PALB2 foci instead as this may be a more sensitive assay of PALB2 recruitment.

6.The authors state in the last sentence of the Results section that "lysine residues within the ChAM 7K-patch are indispensable for PALB2 function in HR" but never test the mutants for HR using reporter assays. The manuscript would be strengthened by performing such assays.

7.The model for the role of ChAM acetylation in regulating PALB2 function presented in Fig. 7D is not fully supported by the data presented. Critically, while association with RAD51 and BRCA2 is tested in Fig. S7B, the authors hypothesize that deacetylation is required to release PALB2 to enable association with BRCA1 but this is not tested utilizing the mutants. Also, there are some specific points that should be considered in the context of the model. This include how DNA damage may trigger deacetylation, and whether it is the deacetylated state or the process of deacetylation of ChAM that is critical. Also, if acetylation is important for protecting active genes in the absence of DNA damage, is deacetylation necessary to release PALB2 local or global. This is important, because if it is local there needs to be a specific mechanism for local deacetylation, while if deacetylation is global that could result in transcriptionally active genes becoming unprotected.

Minor Comments:

a.Some parts of the Materials and Methods are overly long (such as the subsection on "Protein purification" and "immunofluorescence microscopy") and could be shortened by consolidating experimental details that are largely the same for related processes.

b.In the description of Fig. 1D, the statement "7K-patch, which is common to PALB2 orthologs" is misleading since there is not complete conservation of each lysine residue across each ortholog.

c.Figs. 3E,F and S3B,C perform FRAP in cells with knockdown of KAT2A/B as a surrogate for chromatin association. The authors note that this global reduction in acetylation increases PALB2 diffusion, but there is concern that this experiment is not very informative because the increased mobility may have nothing to do acetylation of PALB2.

Significance

The role of acetylation of PALB2 in the regulation and function of PALB2 was previously unknown. In this context, this is potentially an important manuscript in the DNA damage response and DNA repair field. Notably, these authors previously identified the ChAM motif which promotes chromatin association of PALB2 (JY Bleuyard et al. PMID:22193777) and demonstrated that PALB2 may associate with actively replicating genes aside of its general role in DNA repair (JY Bleuyard et al. PMID:28673974). In this context, the finding that acetylation of the ChAM motif appears to be associated with undamaged chromatin leads to the conclusion that acetylation may specifically regulate chromatin association of PALB2 and represent a switch for association with undamaged chromatin and damaged chromatin.

This work should be of specific interest to those working in the fields of DNA repair and DNA damage responses, as well as those with interests in chromatin biology. This study should be of general interest to individuals interested in cell biology, transcription, and cancer biology and therapeutics.

This reviewer's expertise is in the area of DNA damage responses and DNA repair.

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Reply to the reviewers

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

**General comments**

The manuscript 'Second messenger control of mRNA translation by dynamic ribosome modification' is a very interesting follow up on the research performed by the authors published in 2016. Here, the authors continue their study by determining the impact of the intricate RimABK pathway in Pseudomonas fluorescens on translational reprogramming by controlled modification of ribosomal protein S6 in response to environmental signals. The manuscript is interesting and well written, and the results are sound. However, in my opinion the general conclusion is not supported by experimental data and leaves several potential explanations open. Thus, I suggest to either perform in vitro translation experiments using ribosomes equipped with glutamated S6 to validate translational selectivity, or to soften the language on the working model shown in Figure 12.

The authors would like to thank reviewer 1 for their detailed review of our manuscript. We agree with the reviewer that alternative explanations are possible for the translational changes linked specifically to glutamation, as opposed to rimBK deletion. Our intention when writing the discussion was to clearly distinguish glutamation-specific effects from the large number of indirect translational changes associated with Hfq disruption and other uncharacterised consequences of rimBK deletion. With hindsight, we acknowledge that the discussion and the model in figure 12 should more clearly outline the possible alternative causes for the observed glutamation-specific translational regulation. We have modified the discussion and figure 12 (now figure 10) accordingly.

The reviewer further suggests that we perform in vitro translation experiments using ribosomes equipped with glutamated S6, to prove that glutamation controls translation directly. This is an excellent suggestion that would clarify this important point, and we will certainly attempt it as part of our future analysis of the Rim system. However, at this stage we feel these experiments are beyond the intended scope of this paper, which is to describe the signal inputs and mechanism of the RimABK system and to show evidence for both specific and secondary translational effects of ribosome modification.

**Specific comments**

Figure 1 and S1: The RT-PCR analysis shown here does not allow excluding transcription initiation at alternative promoters downstream of the one determined by 5'-RACE. However, an alternative promoter might contribute to relative ratios between the rimA, rimB, and rimK mRNAs. A Northern blot and/or primer extension analysis could clarify this assumption and would give more detailed insights into the specific regulation.

The reviewer is correct that alternative rim promoters could exist downstream of the amplified 5'-RACE region. To test this hypothesis, we conducted additional RT-PCR experiments to measure expression of rimA (the third gene of the polycistronic rimABK operon) in the same set of conditions as tested for rimK. Relative levels of rimA mRNA do not substantially differ from those seen for rimK, strongly suggesting that the promoter upstream of rimK controls expression of all three rim genes. We have added this dataset to figure S1 and have modified the relevant sections of the text.

Figure 2B: I'm confused by the results shown here! I do only see a reduction of RpsF in the presence of RimA, RimK and cdG. What indicates the modification? Please, explain the interpretation of the result in more detail. Shouldn't the modified RpsF shift due to the addition of glutamate residues?

The uncontrolled activity of RimK acting in the absence of RimB (e.g. the experiment represented in Fig 2B) typically results in a reduction of the unmodified RpsF fraction in the reaction, replaced with RpsF proteins with widely varying numbers of glutamate residues attached to their C-termini. The resulting modified RpsF fraction can appear as a smear of protein density throughout the gel. We have clarified the text surrounding figure 2 to make this more explicit.

Figure 2C: Why does the RpsF modification lead to a supershift? How many glutamate residues are added? Is the smear visible in lane 4 (RpsF+RimK) representing already the slightly modified RpsF protein, which upon addition of RimA results in a supershift? For all SDS-Page analyses shown in the manuscript the validation of the glutamation using the antibodies specific against poly-glutamate would be a great asset to facilitate their interpretation.

Pseudomonas fluorescens RimK appears to have unregulated ligase activity, with many hundreds of glutamates being added to each RpsF protein in the absence of RimB cleavage. In our 2016 paper (Little et al., PLoS Genetics) we use radiolabelled glutamate incorporation and mass spectrometry to show that the supershifted protein smear is composed entirely of RpsF units with C-terminal glutamate tails of varying length. (It is interesting to note that E. coli RimK, which does not have an accompanying RimB protease, can only add 4-15 glutamates to each RpsF protein). We have modified the text slightly to make this clearer.

The reviewer’s suggestion to stain the supershifted RpsF with the poly-E antibody is interesting but would likely only reiterate our published results with radiolabelled glutamate (Little et al. 2016).

Lines 236-238: '...strongly suggesting that the proteomic changes we observe are an active response to modification of ribosomally-associated RpsF proteins.' This is an important suggestion as it allows a flexible and very fast integration of the external signals into a specialized protein synthesis. Thus, it definitely deserves further analysis! Considering that the purified RimA and RimK proteins are available, in vitro modification of RpsF in the context of the purified ribosome would be an important experiment and would greatly increase the quality of the paper. Up to now the selective or specialized translation is pure hypothesis and might also be explained by indirect effects via e.g. increased interaction between the ribosome and HFQ that might mediate interaction with certain mRNAs and thus stimulate their translation.

We agree with the reviewer that direct measurement of translational changes in vitro would tell us a great deal about the mechanism of RimK regulation. This would enable us to confirm whether the glutamation-specific effect is direct, or if it functions through an as-yet uncharacterised indirect mechanism (such as interaction with another translational regulator). As stated above we feel these major experiments are beyond the scope of the current manuscript, although we are keen to do them (as part of a planned structural biology investigation of modified ribosomes). As stated elsewhere in our response, we have extensively revised the discussion text and figure 12 to clarify the limits of our current understanding and highlight the different potential regulatory routes for RpsF glutamation.

Lines 322: '...into a single output: the proportion of all ribosomally-associated RpsF proteins that have C-terminal poly-glutamate tails.' Considering the identification of a group of genes whose translation is altered by rimBK deletion, but not by RpsF glutamation (Class 1, Fig 11B), I would suggest softening this statement. If I interpret the data correctly, they pinpoint to a moonlighting function of the rim-pathway that does not target RpsF!

The genes whose translation is affected by rimBK deletion, but not by RpsF glutamation specifically, include all those genes whose translation is indirectly affected by downstream translational regulators, or through interaction with another affected gene target. As expected, there is substantial overlap between the rimBK and hfq translatomes (Grenga et al. 2017): this analysis can be included in the manuscript as a supplementary table if requested. Importantly, there is very little overlap between the Hfq translatome and those genes that are affected specifically by RpsF glutamation. One possibility is that Hfq interacts with RimK at the ribosome, and the loss of the RimK protein is a major factor in destabilising Hfq function in the ∆**rimK mutant. We have modified figure 12 (now figure 10) and expanded the discussion to include this hypothesis.

While we cannot exclude the possibility that RimK has other cellular targets in SBW25, we think this is unlikely to be a major cause for the results we see here. We have carefully examined the C-terminal peptides of proteins detected in our various proteomic assays and are confident that RpsF is the sole target of RimK in SBW25 under the conditions we tested. We also directly tested RimK interaction with purified Hfq and confirmed that Hfq is not a direct target of RimK modification.

Lines 377-76: '...distinguishing features in the primary or predicted secondary structures of the Rim-mRNAs...' As mentioned already above several indirect options are still open that could confer selectivity to the ribosome.

As stated above, the discussion has been rewritten to more completely reference the possible mechanisms by which RpsF glutamation may lead to translational regulation.

Reviewer #1 (Significance (Required)):

The key concept of the manuscript namely the impact of the intricate RimABK pathway in Pseudomonas fluorescens on translational reprogramming by controlled modification of ribosomal protein S6 in response to environmental signals is novel and will significantly impact the field.

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

**Summary**

The main question addressed by this research is how bacteria adapt to rhizospheric niche through the RimK ATPase glutaminase. This enzyme post-transcriptionally modifies the ribosomal protein RpsF in a process of complex regulation. Regulation is mediated by c-di-GMP that is degraded by the phosphodiesterase RimA and the protease RimB exerts a role opposite of RimK. Novel findings include the finding of RimK acting as a four-state ATPase, depending on the binding of RimA, c-di-GMP or both. Another important finding is the opposite roles of RimK and RimB on the glutamation/deglutamation of RpsF and the tendency to a steady state of four glutamate residues in the RpsF protein. The authors also use proteomics to determine the effect of glutamation, specially at low temperature and under nutrient limitation.

We thank the reviewer for their positive review of the manuscript and address their comments below.

**Major comment**

In my opinion, the results obtained with the Hfq regulation by RimK blur the message. I firmly think that the Ms is very solid with the results obtained in relation with the RimABK/RpsF regulation in P. fluorescens shown as a model in the Figure 12. Moreover, in this final model presented by the authors (fig. 12) they not included the results related with Hfq. These results could be part of another paper.

We agree with the reviewer that the Hfq independent effects of RpsF are an exciting finding and should be a major focus of the paper. That said, we feel that the additional work we have done showing how Hfq is affected by RimK should also be retained in the manuscript in some form. Our data (e.g. figure 8) indicate that Hfq is responsible for a large (indirect) fraction of the ∆rimK phenotype, so understanding how it is affected is important to understand how RimK functions. Based on comments from reviewers 2 and 3 we have reviewed the manuscript text (including data on Hfq) to make the narrative as focussed and clear as possible. We have also redesigned figure 12 (now Fig 10) to consider comments from all three reviewers and have changed the text in the discussion to match this.

**Minor comments**

In figure 4A, what is lane 5?

Lane 5 contains RimB without ADP. The figure legend has been modified accordingly, and we thank the reviewer for highlighting this error.

Line 159 change "suppression of RimK band-shifting" by "suppression of RpsF band shifting"

This has been fixed.

Reviewer #2 (Significance (Required)):

The Ms. is very interesting and deeply describes the relation between environmental conditions, c-di GMP second messenger and the RpsF ribosomal protein posttranscriptional modification in order to respond to low temperatures and changes in nutrient availability. The research developed in this manuscript is original and novel in the field and includes new advances in the signal transduction pathways implicated in the regulation of bacteria adaption to the environment. Besides, the research design and technical methodology is original and includes multidisciplinary approaches of interest to the research community in general.

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

**Summary** Post-transcriptional control of protein abundance is an important yet poorly examined regulatory process by which bacteria respond to signals found in the environments they grow in. The authors' team have previously identified, described and published details around a novel regulatory pathway involving the ribosomal modification protein RimK, regulator proteins RimA and RimB, and the widespread bacterial second messenger cyclic-di-GMP (cdG). In the current manuscript builds considerably on those previous findings and goes several steps further, through various approaches including protein biochemistry, computational modelling, quantitative proteomics and ribosomal profiling, the authors described how the RimABK pathway as a novel translator system that controls bacterial adaption to the rhizosphere in the bacterium Pseudomonas fluorescens. They show that the system achieves this through specific controlled modification of the ribosomal protein RpsF. I read the article with excitement and overall the manuscript describes an extensive data set that will be of considerable interest to many readers in several fields. However, I have made a few points below that the authors need to take on board and address. If these issues are addressed, I believe it will make the presented data much clearer to the reader, tidy up a few ambiguities and make the article a little more accessible to many non-specialist readers.

We thank the reviewer for their thorough and positive assessment of the manuscript. We address their specific points below.

**Major Comments**

1) The major finding described in the manuscript and the one that will be of significant interest to reader is that a novel post-translational ribosomal modification regulatory mechanism involving Rim system controls bacterial adaption. The second messenger cdG only plays a small part in this complicated process. Therefore, I believe the title needs to be revised to capture the scope and key findings of the manuscript.

We are happy to change the title along the lines suggested by the reviewer. We propose: Control of mRNA translation by dynamic ribosome modification as a new title.

2) The authors present a lot of interesting data; however, I found the manuscript a bit of a dense read. I find the key findings are diluted within the text. I would ask that the authors to make it a little more focused. For example, on the regulatory role of RimK and its influence on Hfq and RpsF has been detailed previously so could be placed in supporting information and briefly mention when required. Also, the experiments on the pvdIJ pathway could be removed or placed in supporting information as they are not the main focus of the manuscript. Fig 5 and 6 could be combined as one figure as well.

We have modified the manuscript throughout to make it clearer, more concise, and to focus as much as possible on new findings rather than reiterating what we showed in our last manuscript. In line with the reviewers’ recommendation, we have moved the pvdIJ data into the supplementary material (Fig S3) and merged figures 5 and 6 into one. In addition, to support our data on the importance of RpsF glutamation for ribosomal regulation we used Western blotting to confirm that RpsF4/10glu variants incorporate normally into SBW25 ribosomes in vivo (added as supplemental data Fig S5).

As stated elsewhere, we feel that key data on the relationship between Hfq and RimK should remain in the main manuscript, although we have reviewed the text thoroughly to try to ensure it is as focussed as possible and have moved some results to supplementary material as suggested.

3) The authors propose a four-state kinetic model for RimK ATPase activity with RimA and cdG (described in Fig2 and Table S1). However, later in the manuscript the authors demonstrate that RimB also stimulates RimK ATPase activity, but this seems to have smaller impact than RimA and cdG (Fig 2E, Fig 3A). Why RimB was not included in the ATPase kinetic model of RimK? Does including the RimB data suggest there might be more conformational states for RimK?

Thank you for raising this point. The reviewer is correct in that this data does indeed suggest another level of ATPase activity of RimK. We have added text to the manuscript to reflect this. We have also extended the supplemental Table S1 to include these equations.

4) The authors claim that the suppressive effect of cdG on RimK was depended on the enzyme activity (PDE domain) of RimA. This was tested using an enzymatically inactive RimA variant (RimA-E47A). However, in Fig 3E the amount of RimA-E47A used in the assay seems to be significantly less than wildtype RimA. Additionally, in Fig 2B, the authors show that addition of cdG also stimulates RpsF modification with or without RimA (lane 4-6). I would ask the authors to clarify these points.

It is difficult to directly compare protein variants due to differences in solubility post-purification. Due to difficulties in purifying this (less soluble) form of RimA, co-purifying contaminants have also probably influenced the determination of RimA-E47A concentration to some extent. This restricts us to making largely qualitative statements about protein function, as we do here. Despite its poor solubility and low concentration, RimA-E47A is still able to stimulate RimK. Furthermore, the relatively low concentration of RimA-E47A in our assays would render it at least as susceptible to any effects of cdG addition as WT RimA, meaning we can be confident that cdG has no effect on RimK stimulation by this variant.

Our model incorporates direct stimulation of RimK by cdG alongside its effect on RimA. We show evidence for this in this manuscript and in our 2016 paper.

5) The authors claim that high levels of cdG increase the ratio of RimB protease activity to RimK glutamate ligase activity. However, there is no experiment to provide direct evidence to support this. Please tone down the language used or provide evidence. On the same point Fig 6 was not explained in the main text to support this conclusion. Please include an explanation.

The hypothesis that high cdG levels favour RimB activity over RimK stems from the observation that cdG suppresses RimK activity (by abolishing RimA stimulation) but does not affect RimB. We have data showing that increasing cdG levels suppresses RpsF band shifting in vitro in an assay containing all three Rim proteins (Fig 4). However, we agree the hypothesis that cdG controls the ratio of RimB to RimK activity by controlling the activity of RimK currently lacks explicit, direct evidence and we have modified the text to tone down the language.

An explanation for Figure 6 (now 5b) has been added to the manuscript as requested.

6) In some of the figures/images, for example, Fig2B and Fig 3E, RimA is shown as a major band. However, in other figures/images, for example, Fig 2D, Fig 3D, RimA seems to be two bands. The authors should explain the reason for this.

Based on extensive experimentation, we are confident that the second band present in some of our assays is a cleavage product of RimA. This is an experimental artefact that is linked to concentration and protein stability in vitro. We must stress that the presence of an inactive fraction of RimA in our assays does not affect the conclusions we are able to draw from these experiments. A note has been added to the relevant section of the text.

**Minor comments**

• Line 151, should be RpsF band-shifting instead of RimK.

• Fig 4A there is no legend for lane 5, which made it very difficult to understand the data presented.

Please see above. These two minor errors will be fixed.

• The layout of some figures could be improved.

We have revised the layout of several figures, in line with the reviewer’s suggestion.

• If it is possible to have Fig 11 as a Venn diagram or some intuitive diagram, it will help the readers gain access to the data and understand the results.

We respectfully disagree with the reviewer here. We have tried several different presentation styles for these data, but ultimately considered scatter charts to be the most effective, in line with our previous study of Hfq regulation in Pseudomonas (Grenga et al. Frontiers in Microbiology 2017).

Fig 12 is very neatly laid out. However, I don't feel it captures the dynamic nature of the system. I am just wondering if the authors could break it down so that it describes the changes relating to environmental conditions and/or different cdG levels?

Figure 12 (now Figure 10) has been modified to reflect to comments of all three reviewers.

Reviewer #3 (Significance (Required)):

The manuscript provides detailed evidence to demonstrate a dynamic, post-translational ribosomal modification mechanism which is an important feature of prokaryotic (potentially archaeal and eukaryotic) environmental adaptation. This is an exciting manuscript and one many will wish to read. The data provided will be of interest to scientists working in many fields including microbiology, biochemistry and plant pathology.

I have several areas of expertise including genomics, molecular microbiology, small molecule signalling and regulation, micro-host interaction, adaptation,

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Referee #3

Evidence, reproducibility and clarity

Summary

Post-transcriptional control of protein abundance is an important yet poorly examined regulatory process by which bacteria respond to signals found in the environments they grow in. The authors' team have previously identified, described and published details around a novel regulatory pathway involving the ribosomal modification protein RimK, regulator proteins RimA and RimB, and the widespread bacterial second messenger cyclic-di-GMP (cdG). In the current manuscript builds considerably on those previous findings and goes several steps further, through various approaches including protein biochemistry, computational modelling, quantitative proteomics and ribosomal profiling, the authors described how the RimABK pathway as a novel translator system that controls bacterial adaption to the rhizosphere in the bacterium Pseudomonas fluorescens. They show that the system achieves this through specific controlled modification of the ribosomal protein RpsF. I read the article with excitement and overall the manuscript describes an extensive data set that will be of considerable interest to many readers in several fields. However, I have made a few points below that the authors need to take on board and address. If these issues are addressed, I believe it will make the presented data much clearer to the reader, tidy up a few ambiguities and make the article a little more accessible to many non-specialist readers.

Major Comments:

1) The major finding described in the manuscript and the one that will be of significant interest to reader is that a novel post-translational ribosomal modification regulatory mechanism involving Rim system controls bacterial adaption. The second messenger cdG only plays a small part in this complicated process. Therefore, I believe the title needs to be revised to capture the scope and key findings of the manuscript.

2) The authors present a lot of interesting data, however, I found the manuscript a bit of a dense read. I find the key findings are diluted within the text. I would ask that the authors to make it a little more focused. For example, on the regulatory role of RimK and its influence on Hfq and rpsF has been detailed previously so could be placed in supporting information and briefly mention when required. Also, the experiments on the pvdIJ pathway could be removed or placed in supporting information as they are not the main focus of the manuscript. Fig 5 and 6 could be combined as one figure as well.

3) The authors propose a four-state kinetic model for RimK ATPase activity with RimA and cdG (described inFig2 and Table S1). However, later in the manuscript the authors demonstrate that RimB also stimulates RimK ATPase activity but this seems to have smaller impact than RimA and cdG (Fig 2E, Fig 3A). Why RimB was not included in the ATpase kinetic model of RimK? Does including the RimB data suggest there might be more conformational states for RimK?

4) The authors claim that the suppressive effect of cdG on RimK was depended on the enzyme activity (PDE domain) of RimA. This was tested using an enzymatically inactive RimA variant (RimA-E47A). However, in Fig 3E the amount of RimA-E47A used in the assay seems to be significantly less than wildtype RimA. Additionally, in Fig 2B, the authors show that addition of cdG also stimulates RpsF modification with or without RimA (lane 4-6). I would ask the authors to clarify these points.

5) The authors claim that high levels of cdG increase the ratio of RimB protease activity to RimK glutamate ligase activity. However, there is no experiment to provide direct evidence to support this. Please tone down the language used or provide evidence. On the same point

Fig 6 was not explained in the main text to support this conclusion. Please include an explanation.

6) In some of the figures/images, for example, Fig2B and Fig 3E, RimA is shown as a major band. However, in other figures/images, for example, Fig 2D, Fig 3D, RimA seems to be two bands. The authors should explain the reason for this.

Minor comments

1) Line 151, should be RpsF band-shifting instead of RimK.

2) Fig 4A there is no legend for lane 5, which made it very difficult to understand the data presented.

3) The layout of some figures could be improved.

4) If it is possible to have Fig 11 as a Venn diagram or some intuitive diagram, it will help the readers gain access to the data and understand the results.

5) Fig 12 is very neatly laid out. However, I don't feel it captures the dynamic nature of the system. I am just wondering if the authors could break it down so that it describes the changes relating to environmental conditions and/or different cdG levels?

Significance

The manuscript provides detailed evidence to demonstrate a dynamic, post-translational ribosomal modification mechanism which is an important feature of prokaryotic (potentially archaeal and eukaryotic) environmental adaptation. This is an exciting manuscript and one many will wish to read. The data provided will be of interest to scientists working in many fields including microbiology, biochemistry and plant pathology.

I have several areas of expertise including genomics, molecular microbiology, small molecule signalling and regulation, micro-host interaction, adaptation, virulence and pathogenies.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The main question addressed by this research is how bacteria adapt to rhizospheric niche through the RimK ATPase glutaminase. This enzyme post-transcriptionally modifies the ribosomal protein RspF in a process of complex regulation. Regulation is mediated by c-diGMP that is degraded by the phosphodiesterase RimA and the protease RimB exerts a role opposite of RimK. Novel findings include the finding of RimK acting as a four-state ATPase, depending on the binding of RimA, c-diGMP or both. Another important finding is the opposite roles of RimK and RimB on the glutamation/deglutamation of RpsF and the tendency to a steady state of four glutamate residues in the RspF protein. The authors also use proteomics to determine the effect of glutamation, specially at low temperature and under nutrient limitation.

Major comment:

In my opinion, the results obtained with the Hfq regulation by RimK blur the message. I firmly think that the Ms is very solid with the results obtained in relation with the RimABK/RpsF regulation in P. fluorescens shown as a model in the Figure 12. Moreover, in this final model presented by the authors (fig. 12) they not included the results related with HfQ. These results could be part of another paper.

Minor comments:

In figure 4A, what is lane 5? Line 159 change "suppression of Rimk band-shifting" by "suppression of RpsF band shifting"

Significance

The Ms. is very interesting and deeply describes the relation between environmental conditions, c-di GMP second messenger and the RfsF ribosomal protein posttranscriptional modification in order to respond to low temperatures and changes in nutrient availability. The research developed in this manuscript is original and novel in the field and includes new advances in the signal transduction pathways implicated in the regulation of bacteria adaption to the environment. Besides, the research design and technical methodology is original and includes multidisciplinary approaches of interest to the research community in general.

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Referee #1

Evidence, reproducibility and clarity

General comments

The manuscript 'Second messenger control of mRNA translation by dynamic ribosome modification' is a very interesting follow up on the research performed by the authors published in 2016. Here, the authors continue their study by determining the impact of the intricate RimABK pathway in Pseudomonas fluorescens on translational reprogramming by controlled modification of ribosomal protein S6 in response to environmental signals. The manuscript is interesting and well written, and the results are sound. However, in my opinion the general conclusion is not supported by experimental data and leaves several potential explanations open. Thus, I suggest to either perform in vitro translation experiments using ribosomes equipped with glutamated S6 to validate translational selectivity, or to soften the language on the working model shown in Figure 12.

Specific comments

Figure 1 and S1: The RT-PCR analysis shown here does not allow excluding transcription initiation at alternative promoters downstream of the one determined by 5'-RACE. However, an alternative promoter might contribute to relative ratios between the rimA, rimB, and rimK mRNAs. A Northern blot and/or primer extension analysis could clarify this assumption and would give more detailed insights into the specific regulation.

Figure 2B: I'm confused by the results shown here! I do only see a reduction of RpsF in the presence of RimA, RimK and cdG. What indicates the modification? Please, explain the interpretation of the result in more detail. Shouldn't the modified RpsF shift due to the addition of glutamate residues?

Figure 2C: Why does the RpsF modification lead to a supershift? How many glutamate residues are added? Is the smear visible in lane 4 (RpsF+RimK) representing already the slightly modified RpsF protein, which upon addition of RimA results in a supershift? For all SDS-Page analyses shown in the manuscript the validation of the glutamation using the antibodies specific against poly-glutamate would be a great asset to facilitate their interpretation.

Lines 236-238: '...strongly suggesting that th e proteomic changes we observe are an active response to modification of ribosomally-associated RpsF proteins.' This is an important suggestion as it allows a flexible and very fast integration of the external signals into a specialized protein synthesis. Thus, it definitely deserves further analysis! Considering that the purified RimA and RimK proteins are available, in vitro modification of RpsF in the context of the purified ribosome would be an important experiment and would greatly increase the quality of the paper. Up to now the selective or specialized translation is pure hypothesis and might also be explained by indirect effects via e.g. increased interaction between the ribosome and HFQ that might mediate interaction with certain mRNAs and thus stimulate their translation.

Lines 322: '...into a single output: the proportion of all ribosomally-associated RpsF proteins that have C-terminal poly-glutamate tails.' Considering the identification of a group of genes whose translation is altered by rimBK deletion, but not by RpsF glutamation (Class 1, Fig 11B), I would suggest to soften this statement. If I interpret the data correctly, they pinpoint to a moonlighting function of the rim-pathway that does not target RpsF!

Lines 377-76: '...distinguishing features in the primary or predicted secondary structures of the Rim-mRNAs,...' As mentioned already above several indirect options are still open that could confer selectivity to the ribosome.

Significance

The key concept of the manuscript namely the impact of the intricate RimABK pathway in Pseudomonas fluorescens on translational reprogramming by controlled modification of ribosomal protein S6 in response to environmental signals is novel and will significantly impact the field.

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Reply to the reviewers

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

In this study by Wegwitz et al, the authors examine the tumour promoting properties of RNF40 (and the H2B monoubiquitinylation catalysed by it) in Her2 driven breast cancer.

They report, using publicly available data, that increased RNF40 expression is associated with reduced overall and disease-free survival.

Using a mouse model, where they crossed the Erbb2 (mouse Her2) under the control of the MMTV promoter with conditional Rnf40 deletion constructs, the authors found that deletion of Rnf40 simultaneous to Her2 overexpression resulted in a prolonged tumour-free survival, somewhat reduced tumour growth kinetics and tumour incidence.

siRNA silencing of Rnf40 in two Her2 positive breast cancer cell lines resulted in reduced proliferation, clonogenicity and tumour sphere formation and cellular motility.

Transcriptome analysis revealed pathways that could explain the phenotype, like increased apoptosis and actin cytoskeleton regulation. The authors then took further some candidates in the later pathway to investigate the mechanism. They find that Rnf40 loss impacts on actin cytoskeletal dynamics. They also investigate the impact on focal adhesion signalling integrity.

Finally, they investigate the relationship between the transcriptome and H3K4me3 and H2Bub1 landscape in the presence or absence of Rnf40.

The manuscript is convincing regarding the tumour promoting roles of Rnf40, but the key claim that H2B monoubiquitinylation is essential for activation of the Rho/Rock/Limk pathway, where genes are down regulated upon Rnf40 loss resulting in decreased tumourigenicity of cells, is so far not convincing.

"Together, these findings support the hypothesis that the actin regulatory gene network is dependent on direct epigenetic regulation by RNF40 through modulation of H2Bub1 and a trans-histone cross-talk with H3K4me3 levels in HER2-positive BC cells."

Although the correlation is apparent, at this point it's unclear if the phenotype is dependent on the catalytic activity of Rnf40 or it's a non-catalytic effect. Generating a catalytic mutant RNF40 and test it at least in the cell lines studied would be desirable.

We thank the reviewer for this comment and agree that the addition of data with a catalytic mutant RNF40 could strengthen our findings and further clarify mechanisms involved. Thus, in a resubmission we will directly address this point by performing knockdown/rescue experiments with either wildtype or a RING finger mutant RNF40. This will be done by transfecting cells with expression constructs for either wildtype or mutant RNF40 proteins followed by knockdown of endogenous RNF40 using siRNAs targeting the 3’ UTR. Experiments central to our take-home message will be performed (e.g., cell migration, target gene expression, Western blot for H2Bub1, F-actin formation). Together, we hope these experiments will help significantly solidify the message of this paper and further clarify the individual role of RNF40 within the RNF20/40 heterodimer.

**Other comments that need a response:**

1."we investigated RNF40 expression and H2Bub1 levels by immunohistochemical staining of 176 primary BC tumors and 78 brain metastases."

In Fig 1 I can only count 41 primary BC tumours and 73 brain metastases. Numbers don't add up. Also, how is "low" defined as opposed to negative? What is used as controls?

We apologize for this mistake. We corrected the numbers of primary and metastatic HER2-positive specimens used in this study.

2."Moreover, HER2-positive metastatic BC samples showed a particularly high expression of RNF40 compared to primary tumors"

Figure 1 or Fig S1A does not contain data on HER2-positive metastatic BC

We think there might have been a confusion regarding this point. The manuscript does provide information about RNF40 and H2Bub1-staining in primary HER2-positive breast cancer lesions as well as HER2-positive brain cancer metastasis specimens in Fig.1A-C as well in Fig.S1A (representative brain metastases are shown in IHC pictures). This is stated both in the main text as well as in the respective figure legends. However, if for some reason this remains unclear, we would certainly be open to suggestions as to how we can modify the respective sections to improve their clarity.

3."tumors did not display a loss of either RNF40 or H2Bub1 (Fig. 1H) when compared to the adjacent normal mammary epithelium (Fig. S1F)."

I don't understand what I see in Fig S1F, where is the tumour, what is adjacent?

We agree with the reviewer that splitting tumor staining in the main Fig 1 and normal tissues in Fig S1 makes a comparison difficult. We will therefore edit the Fig S1F and provide there an overview of tumor and surrounding normal tissues together with magnifications of the respective areas. This should significantly ease a comparison of both RNF40 and H2Bub1 in tumor and adjacent normal tissues.

4."homozygous loss of Rnf40 (Rnf40fl/fl) resulted in dramatically increased tumor-free survival of MMTV-Erbb2 animals (Fig.1E)." This is overinterpretation of the data, I would not call it dramatic, just significant.

The MMTV-Erbb2 mouse model is a very reliable mouse model for the induction of HER2-positive lesions. In our hands, the tumor incidence in these animals was 100% with a median tumor free survival of 166 days. In comparison, approx. 20% of the Rnf40fl/fl animals (3 out of 14) never developed the disease during the 18 month observation. The animals that still developed lesions had a median tumor free survival of 241 days, which represents a delay of 75 days (45% delay). In light of this, it seems to us that the effect of RNF40 loss on HER2–positive lesions is, indeed, remarkably strong. However, we do not wish to give an impression of over-interpreting or misrepresenting the data. For that reason we modified the wording in the main manuscript according to the reviewer’s suggestion (line 140: “dramatically” was replaced with “pronounced”).

5."loss of Rnf40 led to strongly reduced tumor growth kinetics (Fig.1G)." Is this result significant, I did not see an evaluation of statistical significance in this data.

As suggested by the reviewer, we have performed additional analyses to examine the statistical significance. We have now included the results of these tests in the respective figure.

6."Rnf40fl/fl lesions displayed a heterogeneous pattern of RNF40 expression (Fig.1H), suggesting that the few tumors that did develop in this model were largely caused by an incomplete loss of the Rnf40 allele." If this conclusion is suggested, the authors should check if the "escaper" cells have failed to flox the Rnf40 allele on the genetic/protein level. Otherwise it's not conclusive.

The reviewer brings up an interesting and important point about the heterogeneous loss of RNF40 in “escaper” tumors. Very important to note is that these “escaper” tumors developed significantly later and three animals never developed tumors. Thus, the “escaper” phenotype is rare (at the cellular level) and is likely similar to the selective process that occurs in human tumorigenesis and tumor progression. It is well established through a number of publications that deletion of genes essential for tumorigenesis via Cre-based systems frequently results in a specific selection for the rare instance that the Cre-mediated excision is ineffective. These “escaper” cells can then grow out and proliferate because they do not suffer from deletion of the floxed allele. This effect has also been established when combining MMTV-HER2 and MMTV-Cre. For example, analogous findings were recently published by Costa, et al., in Nature Communications (doi: 10.1038/s41467-019-11510-4) where the MMTV-Cre-mediated deletion of Pak4 resulted in impaired MMTV/HER2 or MMTV-PyMT-driven tumorigenesis, but occasional tumors did appear, which all retained some degree of PAK4 expression. This effect, which we have also seen in our system, was also reported by Miao, et al. in Cancer Research (doi: 10.1158/0008-5472.CAN-11-1015) in 2011. In their work the authors observed that deletion of the Myb gene also impaired MMTV-HER2-driven tumorigenesis and those tumors that developed in Myb flox/flox mice displayed a late onset and invariably retained MYB expression. Similar findings have been reported in a number of other tumor types and with various Cre drivers. Thus, we posit that these findings provide further support for the essential role of RNF40 in HER2-driven tumorigenesis to the extent that rare, RNF40/H2Bub1-expressing “escaper” cells are positively selected for during tumorigenesis and tumor progression.

In order to definitively establish this, we propose performing dual immunofluorescence staining of Rnf40 flox/flox tumors to verify that H2Bub1 is exclusively and universally lost together with RNF40 and that each case of a complete loss of RNF40 also results in a complete loss of detectable H2Bub1 staining. Additionally, we will assess the efficiency of the cre mediated deletion of Rnf40 exons 3 and 4 in Rnf40fl/fl animals by detecting their presence using a conventional PCR approach.

1. Fig S4D - is this clonogenic assay? How many replicates were done, biological technical?

We apologize for the imprecise description of this figure. We edited the manuscript accordingly and included details about the number of replicates.

8."Additionally, treatment with either CYM-5441 (Fig.4J) or …"

Fig 4J is missing! It makes this section rather hard to follow. Fig S4F-G, how many replicates were done, biological technical?

We thank the reviewer for noticing this error. The figures were indeed inappropriately referenced in the text. This error has been corrected.

9."Consistent with our analyses based on changes in H3K4me3 occupancy, genes downregulated upon RNF40 silencing displayed the most prominent decrease in H3K4me3 in the gene body (the 3' end of the peak)"

The impact of these mods changes is hard to judge because they are rather small (I would not use the wording prominent).

As implied by the reviewer, we will replace the word “prominent” with “noticeable”.

Also, are there many other "peak narrowing" genes but they are not downregulated?

The point mentioned here is very interesting. The bioinformatic analyses performed in this study solely focused on the relationship of significantly regulated genes and the H3K4me3 peak narrowing at their TSSs. However, we did not analyze the global regulation of genes showing H3K4me3 peak narrowing near the tSS. As this information might be of high relevance for this study, we propose to investigate this interesting aspect in the revised version of the manuscript.

In fact, our analyses have revealed that a large fraction of genes with H3K4me3 narrowing peaks do not show an appreciable decrease in expression. To better understand the epigenetic features determining the sensitivity of genes to H3K4me3 peak narrowing, we studied the occupancy of several histone marks at differently behaving genes. We discovered that sensitive genes globally present lower occupancy of histone modifications which are known to positively influencing gene transcription. We therefore propose that the epigenetic context (i.e., presence of additional histone modifications) strongly determines whether the loss of H2Bub1, and ensuing narrowing of H3K4me3 near the TSS, results in decreased transcription.

Statistical analysis missing: for example in Fig 2C, Fig 2E, Fig 3G what is n=?, how many technical, biological replicates were analysed?

This information has been added to the revised manuscript.

Fig 4E seems to be a partial duplication of Fig 3D!

The samples in Fig. 3D and 4E originate from independent experiments. In Figure 4D, we indeed provide again PARP and Casp3 signal for siRNF40 samples in order to allow a direct comparison of the effect magnitude between RNF40 depletion and ROCKi treatment.

**Minor:**

Figure referencing: it can be quite confusing to see a different ordering of figures compared to the referencing in the manuscript, for example Fih 1H is referenced in the text before Fig 1F, G. The authors should change the order in the main figures....

We thank the reviewer for pointing this out. We have updated the figure order in the revised manuscript accordingly.

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

In this study, Wegwitz et al propose that the E3 ubiquitin ligase RNF40 is highly expressed in HER2+ breast cancer tumours and correlates with poorer survival, using their own and TCGA data. Contrary to observations suggesting a tumour-suppressive role in other cancers, authors show using RNF40-knockout breast cancer mouse models and in vitro data shat RNF40 promotes tumour growth. RNF40 depletion impairs proliferation, survival and sphere formation by inducing apoptosis. In addition, RNF40 promotes cell migration by upregulating expression of cytoskeletal proteins (ROCK1, VAV3, LIMK2) and their effectors such as phosphorylated cofilin. Authors show elegant partial rescue experiments of the effect of RNF40 depletion on apoptosis and survival.

Given that RNF40 function seems to be context-dependent, findings from this study could have broad significance for other cancers with high RNF40. It also provides some mechanistic data (that should be improved as suggested below) linking this ubiquin ligase to the cytoskeletal machinery and, therefore, control of migration and also proliferation and survival.

Data are well presented and most conclusions are supported by the data. However, there are some gaps at the mechanistic level. Since migration is controlled by RNF40 in vitro, evaluation of metastatic ability in vivo (local invasion for example as suggested below) should be evaluated and would strengthen this part too.

**Major comments**

1. Fig.1A-B, S1A. Specificity of RNF40 antibody should be shown, which could be done quite easily in the tumours from the knockouts. From the datasheets, antibodies recognize human protein only.

We thank the reviewer for this suggestion and apologize for this mistake. The antibody utilized in the IHC studies is actually from Abcam (ab191309) and, in fact, recognizes both species. Table S5 has been corrected accordingly.

It is unclear when the murine tumours were analysed, at endpoint? This should be stated.

We thank the reviewer for this comment. Indeed, all IHC analyses were performed after dissection (endpoint). This information will be added to the manuscript. Kinetic analyses of tumor growth (i.e., Fig. 1G) were performed on the same mouse cohort.

Could authors establish cell lines from the mouse tumours (knockout, partial knockout escapers..)? These could be very useful tools to evaluate key in vitro findings from the study.

The reviewer makes an interesting suggestion. Unfortunately, we were not able to establish cell lines from this model and have since stopped breeding this mouse line (due to the relocation of the principle investigator). However, we did try to generate RNF40-deficient breast cancer cell lines using the CRISPR/Cas9 technology. Interestingly, all attempts failed, supporting the fact that the loss or RNF40 is lethal for the cancer cells. However, to further establish this, for the revision we propose to transfect HCC1954 cells with CRISPR/Cas9 constructs targeting exons 3 and 4, similar to our mouse model. We will then assess the evolution of RNF40-negative cells population over time (i.e., via immunofluorescence staining for H2Bub1). This assay should inform about the expected growth “disadvantage” following RNF40 loss.

Fig.1F-G: since RNF40 controls the cytoskeletal machinery and therefore, migration (Fig. 2G) in the RNF40 knockout tumours, was metastasis (if observed) affected? Or if there was no growth in distant organs detected in the time frame of these experiments, was invasion (and/or pattern of invasion or mode of invasion (morphology of invading cells)) into adjacent tissues affected upon RNF40 depletion? This would add in vivo relevance to the in vitro mechanistic findings, especially since the authors later showed that p-cofilin was also decreased in the RNF40-depleted mouse tumours (Fig.4D).

We agree that the metastasis data from our mouse genetic tumor model would significantly help solidify our findings. Unfortunately, the MMTV-Erbb2 mouse model (overexpressing wildtype Erbb2 gene) only rarely develops distal metastases. In our analyses, we only ever observed two macroscopically visible metastases (one wt/wt and one in an Rnf40flox/flox mouse). However, we feel that the reviewer’s suggestion is a one and will follow this suggestion and attempt to examine possible changes in local invasion of primary tumors into adjacent tissues.

Fig.3: most results using HCC1954 cell line. Key findings should be validated in other cell lines.

We agree with the reviewer about the importance of cross validation of findings using different cell lines. For this purpose, we have now generated data with an additional HER2-positive cell line. These data using the SKBR3 cell line were performed for several of the key experiments. Key findings about phenotypic changes (growth kinetics and colony formation), Ki67 protein levels differences and mRNA regulation of identified regulators of actin cytoskeleton (VAV3, ROCK1, LIMK2 and PFN2) will be included in the revision for both cell lines. Furthermore, as seen in the HCC1954 cell line, an increase of the apoptosis marker cleaved PARP as well as a loss of VAV3 and ROCK1 protein levels was also observed upon RNF40 knockdown in SKBR3 cells. These data will be included in the revised manuscript.

Fig.3A: authors state "both pathways remained intact following RNF40 depletion". However, from those blots, siRNF40 clearly increases pERK and slightly pAKT, which would be unexpected according to previous data in Fig.2. Authors could show quantifications of different blots, or show a more representative blot if increase in pERK was not consistently observed. Was this also seen in SKBR3 cell line?

We thank the reviewer for this comment. Initially, we had anticipated that oncogenic signaling may be decreased in the Rnf40 conditional knockout model. However, much to our surprise, the activity of the downstream signaling actually appears to be increased. In fact, the increase in AKT and ERK1/2 phosphorylation following RNF40 silencing was consistent across different experiments and replicates. While this finding is also consistent with our previous results in an ER-positive system (e.g., see Prenzel, et al., 2011), we do not understand the mechanistic underpinnings of this finding. Importantly though, while consistent, we do not feel that this increase explains the observed phenotype. Nevertheless, to more precisely show the overall change of p-ERK/ERK and p-AKT/AKT, in the revision we will provide a densitometry quantification for both cell lines. We will also modify the sentence to more precisely describe this finding and make the point that since these pathways are not reduced/impaired, they are unlikely to be responsible for the increased apoptosis observed upon RNF40-KD. Western blots assessing p-ERK/ERK and p-AKT/AKT levels in SKBR3 upon RNF40 knock-down will also be added into the supplementary data of the revised manuscript (Fig.S3).

For Fig.3G and Fig.S3A, authors selected genes from this set, how was this done (fold change?). Was expression of the other family members (ROCK2, LIMK1, etc) or of Rho GTPases regulated too?

This information was indeed missing in the manuscript. We have modified the figure legend and the main text accordingly in order to provide the information about the cutoff used in the Enrichr analysis. Regarding the expression of other family members of the actin regulatory network, in the past we performed a more, in depth and focused analysis of our RNA-seq data, broadening our view to other members of the RHO/RAC/CDC42 pathways. While we did identify a few further potentially regulated target genes (e.g. ROS1 or PAK6), these genes were either only weakly expressed or weakly regulated. For this reason, we presumed that these factors could only play a marginal role in the observed phenotype and have focused our attention on the robust part of the signature.

Fig.4B: this may not help, decrease of p-cofilin by Vav3 knockdown is way less dramatic compared to RNF40 depletion or ROCK inh treatment. See comment below regarding other effectors such as Myosin.

Indeed, the consequence of VAV3 loss on p-cofilin levels is less pronounced than the effect observed upon RNF40 knockdown or ROCK1i treatment. Given the fact that RNF40 loss not only affects VAV3 expression, but also has additional direct effects on the expression of other pathway members, this may be expected. We do, however, feel that the VAV3 regulation is likely one component of the effects of RNF40 loss. In addition, it has also been shown that VAV3 is not the only GEF regulating the activity of RHO kinases upstream of ROCK1. Therefore, we would also expect that VAV3 loss only partially reduces ROCK1 activity and therefore only partially phenocopies the effects observed. We will expand the description of these findings in the revised manuscript to reflect these views.

Fig.4C: does ROCK inh reduce RNF40 levels? It may from the immunofluorescence picture.

We thank the reviewer for this comment. In fact, we have examined this possibility. However, no significant changes in RNF40 protein levels were observed upon RKI-1447. If helpful, we can provide Western blot data demonstrating this in the supplemental figures.

Fig.4H-I: the sphingosine 1-phosphate receptor-3 agonist (CYM-5441) partially rescued the effects of RNF40. Since S1P signalling involves Rho GTPase activation -presumably downstream of VAV3 -which is a GEF for Rho, Rac and Cdc42- and upstream of ROCK, LIMK, was activity of these Rho GTPases affected upon RNF40 depletion? This would strengthen the mechanism.

The reviewer points at an interesting aspect of the actin regulation. Indeed we expect that the reduction of VAV3 levels upon RNF40 loss would significantly influence the activity of the downstream client GTPases. However, the measurement of RHO-GTPase activity is tricky and expensive. Furthermore, as mentioned in the previous comment (#7, part 1) VAV3 is only one component of the four major genes encoding critical actin cytoskeleton regulatory proteins regulated upon RNF40 loss, and the only factor upstream of RHO-GTPases. The reduction of downstream ROCK1, LIMK2 and PFN2 levels also influence the activity of this pathway downstream of RHO-GTPase activity. We therefore focused our efforts on assessing F-actin and p-cofilin levels as these may provide more sensitive readouts about the consequence of RNF40 loss on this signaling cascade. However, if the reviewer considers this information as indispensable, we would attempt to investigate changes in Rho-GTPase activity by commercially available Active Rho Detection Kits, although this will significantly delay the resubmission of a revised manuscript.

Related to this, was Myosin II activity (phosphorylated MLC2) affected -since its upstream regulators, especially ROCK are controlled by RNF40?

We thank the reviewer for this insightful suggestion. To address this possibility, we will test this hypothesis for the revised manuscript as suggested by performing Western blot analysis for phosphorylated MLC2.

Fig. S5E:

Authors should consider presenting data of decreased histone methylation of cytoskeleton regulators in main Fig. 5, since this is an important conclusion of this part.

As suggested we will shift the information currently presented in figure S5E to the main figure 5.

Statistics should be revised throughout the manuscript. Comparisons of more than 2 groups should be performed with ANOVA or similar multiple comparison test (instead of t-test).

We thank reviewer for this comment. We replaced statistical tests with the appropriate ANOVA in the respective graphs and updated the legends accordingly.

**Minor comments**

Statement of significance mentions "Anti-HER2-therapy resistance", but this is a misleading since the paper does not deal with therapy resistance. Or are the cell lines used in the study resistant to anti-HER2?

We thank the reviewer for this suggestion. While resistance to anti-HER2 therapy remains one of the major clinical challenges in the treatment of HER2 positive BC lesions, we agree that our data do not strictly address this point. Thus, we have modified the sentence accordingly.

In line with this, authors could add some lines of thought on how RNF40 could be targeted in the clinical/pre-clinical context, which could inform further translational studies.

This is a great suggestion. In the revised manuscript we will include additional text to specifically address this point.

Line 117 "Moreover, HER2-positive metastatic BC samples showed a particularly high expression of RNF40 compared to primary tumors".

Perhaps rephrase, was it that the expression level (intensity) was higher or that the % of positive cells/tumours was higher in the brain mets?

This is a critical point that we will consider in the revised manuscript. We have modified the sentence accordingly to read, “Moreover, the incidence of RNF40-high specimens was higher in HER2-positive brain metastases compared to primary tumors (Fig.1A-B)”.

Fig.1D and S1C. while S1C shows TCGA data, it is unclear which set of patients is Fig.1D (since text says publicly available data, line 118-119), are these their own set of patients (used in Fig.1A-B)? This should be specified in the text, legend.

The origin of the data shown in Fig.1D for relapse free survival of RNF40high and RNF40low patients (KM plotter) is mentioned in the figure legend (kmplot.com) and in the material and method section. However, since this was not apparent, to increase the readability, we have now added a statement about the publically available database of origin for every output graph in the main text as well in the legend and supplementary material.

Line 122. Authors should be careful with this conclusion so far, a correlation between expression and cancer stage/survival does not necessarily mean a tumor suppressive/supportive role.

We thank the review for this comment. We agree with this statement. Therefore, we have carefully rephrased this sentence as following, “In summary, these data demonstrate that RNF40 expression is maintained in HER2-driven primary metastatic BC and that its expression correlates with poor prognosis in these patients.”

Fig. S4E: there are missing labels in graph (control, siRNF40).

The labels have been added.

Panels in some figures are discussed in text randomly and not following same order. For example, Fig.1 (after panel D, then panel H, then back to E, F, G), S3, 4A-C,

We reorganized the order of the different panels of Fig1 to increase the readability. We further screened the main text for similar problems and modified the respective figures accordingly.

Fig.1E: I would suggest changing the line colours, so Rnf40wt-wt line is red and the fl-fl is black, therefore it is similar to panel D (high Rnf40 red, low in black).

We thank the reviewer for this suggestion. Accordingly, we have now indicated low RNF40 expression in red (Figure 1D, 1E and S1B) in the same way that we have indicated RNF40 expression throughout the rest of the study.

Supp Videos: for reviewers and readers, it would help that video has a label while it plays, otherwise after downloading it, video name does not tell whether it is control or RNAi.

As suggested we have renamed the video files of for each condition and added a label informing about the identity of the sample while the video plays.

Reviewer #2 (Significance (Required)):

Given that RNF40 function seems to be context-dependent, findings from this study could have broad significance for other cancers with high RNF40, or even in other pathological contexts -if any- that cursed with high RNF40.

It also provides some mechanistic data (that should be improved as suggested in comments) linking this ubiquitin ligase to the cytoskeletal machinery and, therefore, control of migration and also proliferation and survival. This will also advance the field.

Area of expertise

Actin-myosin cytoskeleton, Rho GTPases-ROCK, cancer, metastasis, cell signalling

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

**Summary:**

Wegwitz and colleagues present extensive and detailed data focussed on the role of the E3 ubiquitin ligase and ring finger protein RNF40 in HER-2 associated breast cancer. It is clear that the role of RNF40 and its major substrate histone H2B (monoubiquitination of histone H2B at lysine 120; H2Bub1) as part of a complex with RNF20, is not a simple one in the context of malignancy. This group, and others, have previously reported on the intriguing role of RNF40 that can in certain circumstances function to suppress tumorigenesis, and in other circumstances function to support tumorigenesis. While H2Bub1 has been shown to be lost in many different malignancies, these investigators show that in HER-2 associated breast cancer, this is not the case. In fact, the results presented in this study show that RNF40-mediated H2Bub1 is important for the expression of genes involved in the actin cytoskeleton and the downstream FAK signalling cascade. Supporting this, mining of a public database showed that RNF40 mRNA was high in HER-2 associated breast cancers and was correlated with a worse prognosis (overall and RFS, relapse free survival). The investigators also used a mouse model (MMTV-Erbb2) generating a tri-transgenic (MMTV-Erbb2; MMTV-Cre; Rnf40flox) that allowed breast tissue specific overexpression of HER2 at the same time as KO of Rnf40, so mimicking the human disease. In fact, mouse tumours recapitulated the human results, including in disease free survival (lower with higher Rnf40), and with less tumours seen when there was less Rnf40 (the Rnf40 floxed tumours appeared heterogenous in staining patterns for Rnf40 and H2Bub1, supporting the concept of "escaper" cells, positive for both Rnf40 and H2Bub1 that would be positively selected during tumorigenesis).

The authors also took a cell biology approach to studying HER2 positive breast cancer and RNF40 using two HER2 positive cell lines (HCC1954 and SKBR3). RNF40 was down-regulated using siRNA and numerous functional studies showed that targeting RNF40 suppressed behaviours consistent with tumorigenesis (proliferation, migration, clonogenic survival, spheroid formation, growth kinetics). Furthermore, down-regulation of RNF40 in the HCC1954 cell line followed by GSEA identified gene signatures associated with apopotosis and the actin cytoskeleton regulatory pathway (e.g. ROCK1, VAV3, LIMK2, PFN2). They further showed that phospo - cofilin (that occurs downstream of ROCK1) was reduced in RNF40 down-regulated cells, also implicated in regulation of the actin cytoskeleton. Phalloidin staining for F-actin showed disruption of the cytoskeleton in RNF40 down-regulated cells. Additionally, the ROCK1 inhibitor, RKI-1447, showed similar effects to depletion of RNF40.

The authors then sought to determine whether the RNF40 associated gene expression in HER2 positive cells was in fact happening through H2Bub1 and the active histone mark H3K4me3 it has been reported to cross-talk with. RNF40 regulated genes (up or down-regulated) showed lower levels of H2Bub1 occupancy compared to non-regulated genes. H3K4me3 was lost in most genes influenced by RNF40 down-regulation, including genes associated with the actin regulatory pathway. The overall conclusion is that RNF40 is a major epigenetic regulator of the actin regulatory gene network in HER 2 positive breast cancer and could be a therapeutic target.

**Major comments: major issues affecting the conclusions.**

(1) What is happening at the gene level for both H2Bub1 and H3K4me3 in the context of RNF20 down-regulation is complex and would benefit from inclusion of a schematic, or a series of schematics describing different scenarios, as the text is quite difficult to follow.

This is a very constructive proposition. We will attempt to follow this suggestion in order to simplify the message of the respective section by providing to schematic illustrations depicting the cascade of events occurring upon RNF40 loss in the cancer cells.

It is not entirely clear that the changes seen in H3K4me3 are a direct result of cross-talk with H2Bub1 (some literature reports that there is no cross-talk between these histone marks for instance). It is also not entirely clear how the other histone marks investigated support the main discoveries of the paper. The authors need to consider this in the way that they present the data and their interpretation of it.

The reviewer addresses an important point about the mechanistic aspect of the RNF40-dependent epigenetic regulation. We and others have shown that RNF40-mediated H2B monoubiquitination is a central step for activation of the COMPASS complex and the TSS-proximal broadening of H3K4me3 (PMID:31733991, 19410543, 22505722, 28209164). However, the situation certainly is not as straight forward as it is in yeast, where the vast majority of H3K4 trimethylation is H2Bub1-dependent. To what degree global H3K4me3 levels are dependent upon the H2B ubiquitin ligases RNF20 and RNF40 appears to vary, depending upon the investigated system (probably the variation in the literature referred to by the reviewer). However, in our work, we reproducibly see widespread H3K4me3 peak narrowing specifically on RNF40-dependent genes, in a context-dependent manner (i.e., genes displaying these effects are different according to the system investigated). To support and consolidate the central function of the H2Bub1-H4K4me3 crosstalk in our system, we propose to perform rescue experiments: siRNAs targeting the 3’UTR of RNF40 will be co-transfected with an expression construct encoding for either a wild type or a ΔRING (catalytic inactive) form of RNF40 lacking the endogenous 3’UTR. The ability of ectopically expressed wild-type, but not catalytic inactive RNF40, to rescue the expression of the identified actin cytoskeleton genes and downstream signaling should provide a solid argument to support the hypothesis of our study. We will also include additional discussion about the potential different H3K4 methyltransferases that may potentially be involved.

(2) RNF40 is known to work in a complex with RNF20 to monoubiquitinate histone H2B at lysine 120 (H2Bub1). In experiments where RNF40 has been down-regulated, did the authors also note down-regulation of RNF20 (as has been previously reported).

This is an interesting question from the reviewer. We indeed observed a consistent reduction of RNF20 protein levels upon RNF40 knockdown (and vice versa) in different cell systems, including the HER2-positive cell lines HCC1954 and SKBR3.

Is the data presented likely to be the result of abrogation of the complex rather than RNF40 specifically?

Although particularly difficult to answer, the use of a catalytic mutant in key experiments should at least partially shed light on this aspect (as proposed in the answer to Reviewer #3’s question 1). In that case, the complex integrity can be maintained while specifically abrogating RNF40 ubiquitin ligase activity.

While I am not asking for experiments to be repeated with down-regulation of RNF20, some consideration of this needs to be included in the Discussion. Is RNF20 also highly expressed in HER2 positive breast cancer (TCGA, KM Plotter data).

We absolutely agree with the reviewer’s point of view. As an obligate binding partner of RNF40, RNF20 indisputably plays an important function in the phenotype caused through RNF40 loss. We will therefore carefully further discuss this aspect in the revised manuscript. Preliminary analyses based on the TCGA dataset point at a high expression of RNF20 in HER2-positive lesions. Furthermore, survival analysis of HER2+ BC patients based on the same dataset showed that patients with high RNF20 expression harbor an unfavorable prognosis, similar to what we have seen with RNF40. We may therefore implement these expression and survival data in the revised manuscript.

**Minor comments: important issues that can confidently be addressed.**

(3) It would appear that immunohistochemistry for RNF40 and H2Bub1 on human samples is only reported as "low" or "high". This is perhaps not dealing with the full spectrum of IHC scores, such as completely absent, although the methods do note a "null" value (no detectable staining). Were there no "null" results? Please define the criteria for "low" or "high".

Indeed, specimens lacking H2Bub1 or RNF40 staining were attributed the “null” scoring. However, while we have observed null staining in other BC subtypes (e.g., see Bedi, et al., 2015), none of the HER2 positive BC samples were found to be negative for either RNF40 or for H2Bub1. However, for the revision, we will provide representative examples of null-stained tumor specimens (from other BC subtypes) for RNF40 and H2Bub1 from the same tissue microarray.

(4) I think there might be some confusion in labelling of Fig 1A and B as the legend states that all breast cancers are on the left and the HER-2 positive on the right, for each of primary tumours and brain mets, but I think one is under the other? Labelling should be checked in this figure.

We apologize for this mistake. This has been corrected in the figure legend accordingly.

(5) What this IHC data doesn't show is whether RNF40 and H2Bub1 levels are always correlated in individual tumours (i.e. RNF40:H2Bub1, high:high OR low:low OR null:null). Can the authors please include and comment on this data.

The reviewer has made a very interesting point here. We will comment on this point in the revision.

(6) Please include overall survival data (KM Plotter) as a panel in figure 1, alongside RFS for RNF40 expression levels (currently in Supplementary).

We added the OS as well the RFS data from the same database next to each other in the main figure.

(7) Spheroid formation looks to only be shown in a single cell line (HCC1954). Was the other cell line not suitable for spheroid studies? Some comment should be made and care taken not to "overclaim" as text notes two cell lines.

SKBR3 are unfortunately not suitable for tumor sphere formation assay. We may provide instead a soft agar assay with SKBR3 cells upon. If needed, we may replace the SKBR3 cell line with BT474 for this specific experiment

(8) It would have been interesting to see results of a GSEA in the mouse mammary tumours as a complement to human. Is there a reason why this wasn't undertaken?

Rnf40fl/fl tumors present a large fraction of “escaper” cancer cells retaining RNF40 expression. For this reason, bulk sequencing of such tumors would likely only provide a “diluted” molecular signature consequent to RNF40 loss. For this reason this experiment has not been done.

(9) Conclusions are made about RNF40 in HER2 positive cells only in the context of H2Bub1 and H3K4me3. Without having conducted similar experiments in HER2 negative breast cancer cell lines / models, it is difficult to draw the conclusion that this is HER2 positive specific. Can the authors either soften some of their conclusions along this line, or consider repeating some of their data in HER2 negative models.

The scope of the study has deliberately been set on HER2-positive malignancies, because former studies already extensively studied the impact of H2Bub1 loss in TNBC and Luminal BC (PMID 28157208, 18832071). We will therefore modify the manuscript text accordingly and soften the appropriate sections as suggested by the reviewer.

(10) RNF40 likely has substrates other than histone H2B. There is a report describing interactions with RNF40 (STARING) and syntaxin for e.g., (Chin et al., 2002 J Biol Chem 277:35071-9). Can the authors please comment on other potential substrates of RNF40 in light of their data that focuses only on its epigenetic role as a regulator of the actin cytoskeleton.

Our study was mainly focused only on the gene expression program driven by RNF40 in HER2+ BC. We therefore do not know nor have we focused on other novel non-histone substrates. We will, however, allude to this possibility in a revised manuscript.

Reviewer #3 (Significance (Required)):

Nature and Significance of the Advance:

Clinically, this work provides a significant advance in that it is zeroing in on HER2 positive breast cancer and generating fundamental data that could underpin development of a new therapy for this malignancy. Conceptually, it is expanding knowledge of how the E3 ubiquitin ligase RNF40 is functioning as an epigenetic modifier of a specific type of malignancy by being important for the actin cytoskeleton.

Work in Context of Existing Literature:

As acknowledged by the authors, this work builds on a previous publication of theirs (Xie et. al., 2017 "RNF40 regulates gene expression in an epigenetic context-dependent manner." Genome Biol). They have other recent papers on RNF40 (Schneider et al., 2019 "The E3 ubiquitin ligase RNF40 suppresses apoptosis in colorectal cancer cells", Clin Epigenetics; Kosinsky et al., 2019 "Loss of RNF40 decreases NF-kappaB activity in colorectal cancer cells and reduces colitis burden in mice", J Crohns Colitis). H2Bub1 is one of the least well studied histone modifications and as such, this study of one of its key histone writers, RNF40, is significant in elucidating the significance of this histone mark.

Audience:

This paper will suit a discovery-based science audience interested in epigenomic regulation of malignancy. Further, it will suit those looking for new drug development strategies for malignancy.

My Field of Expertise:

Basic scientist with expertise in epigenetic/epigenomic regulation in malignancy; cell and molecular biology. I felt capable of reviewing all aspects of this paper.

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 #3

Evidence, reproducibility and clarity

Summary:

Wegwitz and colleagues present extensive and detailed data focussed on the role of the E3 ubiquitin ligase and ring finger protein RNF40 in HER-2 associated breast cancer. It is clear that the role of RNF40 and its major substrate histone H2B (monoubiquitination of histone H2B at lysine 120; H2Bub1) as part of a complex with RNF20, is not a simple one in the context of malignancy. This group, and others, have previously reported on the intriguing role of RNF40 that can in certain circumstances function to suppress tumorigenesis, and in other circumstances function to support tumorigenesis. While H2Bub1 has been shown to be lost in many different malignancies, these investigators show that in HER-2 associated breast cancer, this is not the case. In fact, the results presented in this study show that RNF40-mediated H2Bub1 is important for the expression of genes involved in the actin cytoskeleton and the downstream FAK signalling cascade. Supporting this, mining of a public database showed that RNF40 mRNA was high in HER-2 associated breast cancers and was correlated with a worse prognosis (overall and RFS, relapse free survival). The investigators also used a mouse model (MMTV-Erbb2) generating a tri-transgenic (MMTV-Erbb2; MMTV-Cre; Rnf40flox) that allowed breast tissue specific overexpression of HER2 at the same time as KO of Rnf40, so mimicking the human disease. In fact, mouse tumours recapitulated the human results, including in disease free survival (lower with higher Rnf40), and with less tumours seen when there was less Rnf40 (the Rnf40 floxed tumours appeared heterogenous in staining patterns for Rnf40 and H2Bub1, supporting the concept of "escaper" cells, positive for both Rnf40 and H2Bub1 that would be positively selected during tumorigenesis). The authors also took a cell biology approach to studying HER2 positive breast cancer and RNF40 using two HER2 positive cell lines (HCC1954 and SKBR3). RNF40 was down-regulated using siRNA and numerous functional studies showed that targeting RNF40 suppressed behaviours consistent with tumorigenesis (proliferation, migration, clonogenic survival, spheroid formation, growth kinetics). Furthermore, down-regulation of RNF40 in the HCC1954 cell line followed by GSEA identified gene signatures associated with apopotosis and the actin cytoskeleton regulatory pathway (e.g. ROCK1, VAV3, LIMK2, PFN2). They further showed that phospo - cofilin (that occurs downstream of ROCK1) was reduced in RNF40 down-regulated cells, also implicated in regulation of the actin cytoskeleton. Phalloidin staining for F-actin showed disruption of the cytoskeleton in RNF40 down-regulated cells. Additionally, the ROCK1 inhibitor, RKI-1447, showed similar effects to depletion of RNF40. The authors then sought to determine whether the RNF40 associated gene expression in HER2 positive cells was in fact happening through H2Bub1 and the active histone mark H3K4me3 it has been reported to cross-talk with. RNF40 regulated genes (up or down-regulated) showed lower levels of H2Bub1 occupancy compared to non-regulated genes. H3K4me3 was lost in most genes influenced by RNF40 down-regulation, including genes associated with the actin regulatory pathway. The overall conclusion is that RNF40 is a major epigenetic regulator of the actin regulatory gene network in HER 2 positive breast cancer and could be a therapeutic target.

Major comments: major issues affecting the conclusions.

(1) What is happening at the gene level for both H2Bub1 and H3K4me3 in the context of RNF20 down-regulation is complex and would benefit from inclusion of a schematic, or a series of schematics describing different scenarios, as the text is quite difficult to follow. It is not entirely clear that the changes seen in H3K4me3 are a direct result of cross-talk with H2Bub1 (some literature reports that there is no cross-talk between these histone marks for instance). It is also not entirely clear how the other histone marks investigated support the main discoveries of the paper. The authors need to consider this in the way that they present the data and their interpretation of it.

(2) RNF40 is known to work in a complex with RNF20 to monoubiquitinate histone H2B at lysine 120 (H2Bub1). In experiments where RNF40 has been down-regulated, did the authors also note down-regulation of RNF20 (as has been previously reported). Is the data presented likely to be the result of abrogation of the complex rather than RNF40 specifically? While I am not asking for experiments to be repeated with down-regulation of RNF20, some consideration of this needs to be included in the Discussion. Is RNF20 also highly expressed in HER2 positive breast cancer (TCGA, KM Plotter data).

Minor comments: important issues that can confidently be addressed.

(3) It would appear that immunohistochemistry for RNF40 and H2Bub1 on human samples is only reported as "low" or "high". This is perhaps not dealing with the full spectrum of IHC scores, such as completely absent, although the methods do note a "null" value (no detectable staining). Were there no "null" results? Please define the criteria for "low" or "high".

(4) I think there might be some confusion in labelling of Fig 1A and B as the legend states that all breast cancers are on the left and the HER-2 positive on the right, for each of primary tumours and brain mets, but I think one is under the other? Labelling should be checked in this figure.

(5) What this IHC data doesn't show is whether RNF40 and H2Bub1 levels are always correlated in individual tumours (i.e. RNF40:H2Bub1, high:high OR low:low OR null:null). Can the authors please include and comment on this data.

(6) Please include overall survival data (KM Plotter) as a panel in figure 1, alongside RFS for RNF40 expression levels (currently in Supplementary).

(7) Spheroid formation looks to only be shown in a single cell line (HCC1954). Was the other cell line not suitable for spheroid studies? Some comment should be made and care taken not to "overclaim" as text notes two cell lines.

(8) It would have been interesting to see results of a GSEA in the mouse mammary tumours as a complement to human. Is there a reason why this wasn't undertaken?

(9) Conclusions are made about RNF40 in HER2 positive cells only in the context of H2Bub1 and H3K4me3. Without having conducted similar experiments in HER2 negative breast cancer cell lines / models, it is difficult to draw the conclusion that this is HER2 positive specific. Can the authors either soften some of their conclusions along this line, or consider repeating some of their data in HER2 negative models.

(10) RNF40 likely has substrates other than histone H2B. There is a report describing interactions with RNF40 (STARING) and syntaxin for e.g., (Chin et al., 2002 J Biol Chem 277:35071-9). Can the authors please comment on other potential substrates of RNF40 in light of their data that focuses only on its epigenetic role as a regulator of the actin cytoskeleton.

Significance

Nature and Significance of the Advance:

Clinically, this work provides a significant advance in that it is zeroing in on HER2 positive breast cancer and generating fundamental data that could underpin development of a new therapy for this malignancy. Conceptually, it is expanding knowledge of how the E3 ubiquitin ligase RNF40 is functioning as an epigenetic modifier of a specific type of malignancy by being important for the actin cytoskeleton.

Work in Context of Existing Literature:

As acknowledged by the authors, this work builds on a previous publication of theirs (Xie et. al., 2017 "RNF40 regulates gene expression in an epigenetic context-dependent manner." Genome Biol). They have other recent papers on RNF40 (Schneider et al., 2019 "The E3 ubiquitin ligase RNF40 suppresses apoptosis in colorectal cancer cells", Clin Epigenetics; Kosinsky et al., 2019 "Loss of RNF40 decreases NF-kappaB activity in colorectal cancer cells and reduces colitis burden in mice", J Crohns Colitis). H2Bub1 is one of the least well studied histone modifications and as such, this study of one of its key histone writers, RNF40, is significant in elucidating the significance of this histone mark.

Audience:

This paper will suit a discovery-based science audience interested in epigenomic regulation of malignancy. Further, it will suit those looking for new drug development strategies for malignancy.

My Field of Expertise:

Basic scientist with expertise in epigenetic/epigenomic regulation in malignancy; cell and molecular biology. I felt capable of reviewing all aspects of this paper.

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 #2

Evidence, reproducibility and clarity

In this study, Wegwitz et al propose that the E3 ubiquitin ligase RNF40 is highly expressed in HER2+ breast cancer tumours and correlates with poorer survival, using their own and TCGA data. Contrary to observations suggesting a tumour-suppressive role in other cancers, authors show using RNF40-knockout breast cancer mouse models and in vitro data shat RNF40 promotes tumour growth. RNF40 depletion impairs proliferation, survival and sphere formation by inducing apoptosis. In addition, RNF40 promotes cell migration by upregulating expression of cytoskeletal proteins (ROCK1, VAV3, LIMK2) and their effectors such as phosphorylated cofilin. Authors show elegant partial rescue experiments of the effect of RNF40 depletion on apoptosis and survival.

Given that RNF40 function seems to be context-dependent, findings from this study could have broad significance for other cancers with high RNF40. It also provides some mechanistic data (that should be improved as suggested below) linking this ubiquin ligase to the cytoskeletal machinery and, therefore, control of migration and also proliferation and survival.

Data are well presented and most conclusions are supported by the data. However, there are some gaps at the mechanistic level. Since migration is controlled by RNF40 in vitro, evaluation of metastatic ability in vivo (local invasion for example as suggested below) should be evaluated and would strengthen this part too.

Major comments

1.Fig.1A-B, S1A. Specificity of RNF40 antibody should be shown, which could be done quite easily in the tumours from the knockouts. From the datasheets, antibodies recognize human protein only.

2.It is unclear when the murine tumours were analysed, at endpoint? This should be stated. Could authors establish cell lines from the mouse tumours (knockout, partial knockout escapers..)? These could be very useful tools to evaluate key in vitro findings from the study.

3.Fig.1F-G: since RNF40 controls the cytoskeletal machinery and therefore, migration (Fig. 2G) in the RNF40 knockout tumours, was metastasis (if observed) affected? Or if there was no growth in distant organs detected in the time frame of these experiments, was invasion (and/or pattern of invasion or mode of invasion (morphology of invading cells)) into adjacent tissues affected upon RNF40 depletion? This would add in vivo relevance to the in vitro mechanistic findings, especially since the authors later showed that p-cofilin was also decreased in the RNF40-depleted mouse tumours (Fig.4D).

4.Fig.3: most results using HCC1954 cell line. Key findings should be validated in other cell lines.

5.Fig.3A: authors state "both pathways remained intact following RNF40 depletion". However, from those blots, siRNF40 clearly increases pERK and slightly pAKT, which would be unexpected according to previous data in Fig.2. Authors could show quantifications of different blots, or show a more representative blot if increase in pERK was not consistently observed. Was this also seen in SKBR3 cell line?

6.For Fig.3G and Fig.S3A, authors selected genes from this set, how was this done (fold change?). Was expression of the other family members (ROCK2, LIMK1, etc) or of Rho GTPases regulated too?

7.Fig.4B: this may not help, decrease of p-cofilin by Vav3 knockdown is way less dramatic compared to RNF40 depletion or ROCK inh treatment. See comment below regarding other effectors such as Myosin. Fig.4C: does ROCK inh reduce RNF40 levels? It may from the immunofluorescence picture.

8.Fig.4H-I: the sphingosine 1-phosphate receptor-3 agonist (CYM-5441) partially rescued the effects of RNF40. Since S1P signalling involves Rho GTPase activation -presumably downstream of VAV3 -which is a GEF for Rho, Rac and Cdc42- and upstream of ROCK, LIMK, was activity of these Rho GTPases affected upon RNF40 depletion? This would strengthen the mechanism.

Related to this, was Myosin II activity (phosphorylated MLC2) affected -since its upstream regulators, especially ROCK are controlled by RNF40?

9.Fig. S5E: Authors should consider presenting data of decreased histone methylation of cytoskeleton regulators in main Fig. 5, since this is an important conclusion of this part.

10.Statistics should be revised throughout the manuscript. Comparisons of more than 2 groups should be performed with ANOVA or similar multiple comparison test (instead of t-test).

Minor comments

1.Statement of significance mentions "Anti-HER2-therapy resistance", but this is a misleading since the paper does not deal with therapy resistance. Or are the cell lines used in the study resistant to anti-HER2? In line with this, authors could add some lines of thought on how RNF40 could be targeted in the clinical/pre-clinical context, which could inform further translational studies.

2.Line 117 "Moreover, HER2-positive metastatic BC samples showed a particularly high expression of RNF40 compared to primary tumors". Perhaps rephrase, was it that the expression level (intensity) was higher or that the % of positive cells/tumours was higher in the brain mets?

3.Fig.1D and S1C. while S1C shows TCGA data, it is unclear which set of patients is Fig.1D (since text says publicly available data, line 118-119), are these their own set of patients (used in Fig.1A-B)? This should be specified in the text, legend.

4.Line 122. Authors should be careful with this conclusion so far, a correlation between expression and cancer stage/survival does not necessarily mean a tumor suppressive/supportive role.

5.Fig. S4E: there are missing labels in graph (control, siRNF40).

6.Panels in some figures are discussed in text randomly and not following same order. For example, Fig.1 (after panel D, then panel H, then back to E, F, G), S3, 4A-C,

7.Fig.1E: I would suggest changing the line colours, so Rnf40wt-wt line is red and the fl-fl is black, therefore it is similar to panel D (high Rnf40 red, low in black).

8.Supp Videos: for reviewers and readers, it would help that video has a label while it plays, otherwise after downloading it, video name does not tell whether it is control or RNAi.

Significance

Given that RNF40 function seems to be context-dependent, findings from this study could have broad significance for other cancers with high RNF40, or even in other pathological contexts -if any- that cursed with high RNF40. It also provides some mechanistic data (that should be improved as suggested in comments) linking this ubiquitin ligase to the cytoskeletal machinery and, therefore, control of migration and also proliferation and survival. This will also advance the field.

Area of expertise Actin-myosin cytoskeleton, Rho GTPases-ROCK, cancer, metastasis, cell signalling

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

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Referee #1

Evidence, reproducibility and clarity

In this study by Wegwitz et al, the authors examine the tumour promoting properties of RNF40 (and the H2B monoubiquitinylation catalysed by it) in Her2 driven breast cancer. They report, using publicly available data, that increased RNF40 expression is associated with reduced overall and disease-free survival. Using a mouse model, where they crossed the Erbb2 (mouse Her2) under the control of the MMTV promoter with conditional Rnf40 deletion constructs, the authors found that deletion of Rnf40 simultaneous to Her2 overexpression resulted in a prolonged tumour-free survival, somewhat reduced tumour growth kinetics and tumour incidence. siRNA silencing of Rnf40 in two Her2 positive breast cancer cell lines resulted in reduced proliferation, clonogenicity and tumour sphere formation and cellular motility. Transcriptome analysis revealed pathways that could explain the phenotype, like increased apoptosis and actin cytoskeleton regulation. The authors then took further some candidates in the later pathway to investigate the mechanism. They find that Rnf40 loss impacts on actin cytoskeletal dynamics. They also investigate the impact on focal adhesion signalling integrity. Finally, they investigate the relationship between the transcriptome and H3K4me3 and H2Bub1 landscape in the presence or absence of Rnf40.

The manuscript is convincing regarding the tumour promoting roles of Rnf40, but the key claim that H2B monoubiquitinylation is essential for activation of the Rho/Rock/Limk pathway, where genes are down regulated upon Rnf40 loss resulting in decreased tumourigenicity of cells, is so far not convincing. "Together, these findings support the hypothesis that the actin regulatory gene network is dependent on direct 271 epigenetic regulation by RNF40 through modulation of H2Bub1 and a trans-histone cross-talk with H3K4me3 272 levels in HER2-positive BC cells." Although the correlation is apparent, at this point it's unclear if the phenotype is dependent on the catalytic activity of Rnf40 or it's a non-catalytic effect. Generating a catalytic mutant RNF40 and test it at least in the cell lines studied would be desirable.

Other comments that need a response:

1."we investigated RNF40 expression and 114 H2Bub1 levels by immunohistochemical staining of 176 primary BC tumors and 78 brain metastases." In Fig 1 I can only count 41 primary BC tumours and 73 brain metastases. Numbers don't add up. Also, how is "low" defined as opposed to negative? What is used as controls?

2."Moreover, HER2-positive metastatic BC samples showed a117 particularly high expression of RNF40 compared to primary tumors" Figure 1 or Fig S1A does not contain data on HER2-positive metastatic BC

3."tumors did not display a loss of either 132 RNF40 or H2Bub1 (Fig. 1H) when compared to the adjacent normal mammary epithelium (Fig. S1F)." I don't understand what I see in Fig S1F, where is the tumour, what is adjacent?

4."homozygous loss of Rnf40 (Rnf40fl/fl134 ) resulted in 135 dramatically increased tumor-free survival of MMTV-Erbb2 animals (Fig.1E)." This is overinterpretation of the data, I would not call it dramatic, just significant.

5."loss of Rnf40 led to 139 strongly reduced tumor growth kinetics (Fig.1G)." Is this result significant, I did not see an evaluation of statistical significance in this data.

6."Rnf40fl/fl 142 lesions displayed a 143 heterogeneous pattern of RNF40 expression (Fig.1H), suggesting that the few tumors that did develop in this 144 model were largely caused by an incomplete loss of the Rnf40 allele." If this conclusion is suggested, the authors should check if the "escaper" cells have failed to flox the Rnf40 allele on the genetic/protein level. Otherwise it's not conclusive.

7.Fig S4D - is this clonogenic assay? How many replicates were done, biological technical?

8."Additionally, treatment with either CYM-5441 (Fig.4J) or 225" Fig 4J is missing! It makes this section rather hard to follow. Fig S4F-G, how many replicates were done, biological technical?

9."Consistent with our analyses based on changes in H3K4me3 occupancy, genes downregulated upon RNF40 256 silencing displayed the most prominent decrease in H3K4me3 in the gene body (the 3' end of the peak)" The impact of these mods changes is hard to judge because they are rather small (I would not use the wording prominent). Also, are there many other "peak narrowing" genes but they are not downregulated?

10.Statistical analysis missing: for example in Fig 2C, Fig 2E, Fig 3G what is n=?, how many technical, biological replicates were analysed?

11.Fig 4E seems to be a partial duplication of Fig 3D!

Minor:

Figure referencing: it can be quite confusing to see a different ordering of figures compared to the referencing in the manuscript, for example Fih 1H is referenced in the text before Fig 1F, G. The authors should change the order in the main figures....

Significance

It's an interesting study that associates epigenetic regulation of actin cytoskeletal dynamics in Her2 driven breast cancer.

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Reply to the reviewers

Rebuttal_ Preprint RC-2020-00156

We thank the editor for handling our manuscript and both reviewers for their constructive critiques. We provide below a detailed list of results already available and experiments we propose to perform to address the reviewers’ comments and improve the quality of our manuscript.

Reviewer #1

In this manuscript, Obacz et al. investigated the role of IRE1 signaling in regulating the recruitment of myeloid cells in glioblastoma multiforme (GBM) microenvironment. They show that inhibition of IRE1 signaling decreased polynuclear neutrophil (PN) infiltration to GBM tumors in an animal model; conversely, IRE1 activation correlated with higher expression of myeloid cells-attracting chemokines in GBM. They also show that IRE1-XBP1s pathway promotes proinflammatory chemokines in GBM tumor cells through upregulation of UBE2D3, which leads to degradation of the NFκB inhibitor IκB and activation of NFκB downstream signaling. Their finding of a novel IRE1/XBP1s/UBE2D3/NFκB axis is important for understanding the basis of pro-tumoral inflammation in GBM, potentially in other 'immune hot' cancers. The manuscript is well written and the conclusion is well supported by the experiments. However, there are a few critical points that need to be addressed to strengthen their study**.

We thank this reviewer for his/her positive comments on our work and for the suggestions made to improve its relevance

Review#1 point 1: In this study, the authors used the GBM primary cell line RADH87 with stable overexpression of wild-type (WT) IRE1 or a truncated IRE1 variant. The expression of wild-type IRE1 was confirmed by Western analysis (Figure S1D). However, the expression of truncated IRE1 variant was not shown.

Response 1.1. The expression on truncated IRE1 variant (designated as Q780* - 80 KDa) is shown in Fig.S1D, following the expression on wild-type (WT) IRE1 (110 KDa). This point will be indicated in the revised version of the supplemental figure legends.

In addition, without tunicamycin treatment, there was no visible difference in XBP1s expression between the cells expressing WT or the mutant IRE1.

Response 1.2. Under basal condition, XBP1 splicing is indeed limited and therefore, there is no detectable difference in XBP1s expression level between IRE1 WT and Q780*. In contrast, under tunicamycin treatment (acute stress), reduced XBP1 mRNA splicing is observed (Fig.S1D) thus confirming the functionality of the Q780* truncated form. Of note, RNAseq was performed on these cell lines and basal splicing was quantified showing that even though it is an event that occurs at low frequency, it is decreased in cells expressing the Q780* mutant (this information will be added in the revised manuscript, data are available and analyses ongoing).

In the Boyden chamber assay (Figure 1C, D), conditioned medium from these cells were used; it was not described whether the cells were treated (e.g. with tunicamycin) to activate the IRE1 pathway. ** Response 1.3. Cells were not treated with Tunicamycin to excluded the impact of other UPR arms in the induction of cytokines expression/myeloid cells attraction. As a consequence, it is the basal secretome (found in conditioned media) that was used in those experiments to evaluate cell migration. We have now strong evidences that blunting IRE1 signaling (either genetically or pharmacologically) has a strong impact on GBM cells proteome and in particular on their secretome even if under basal conditions (manuscript in preparation). This information together with the fact that basal XBP1 mRNA splicing is reduced in IRE1 signaling deficient (Q780* expressing) cells, indicate that in GBM cells, constitutive IRE1 activity contributes to modulate the composition of their secretome towards chemoattraction of myeloid cells. This point will be further detailed in the results and discussion sections of the revised manuscript.

Review#1 point 2: The evidence that the mRNA expression of UBE2D3 positively correlates with IRE1/XBP1s pathway is weak. First, In Figure 3D, the correlation between the mRNA expression of UBE2D3 and XBP1 does not seem strong. In addition, as XBP1 mRNA level does not reflect IRE1 activation (as opposed to that of XBP1s), the level of XBP1s instead of total XBP1 should be assessed. Furthermore, such correlation should be validated in additional GBM cohorts/datasets.

Response 2. We agree that the correlation between UBE2D3 and XBP1 mRNA levels in TCGA GBM cohort might not be strong. However data presented in Fig3D were significant. Values indicated in green were Pearson’s correlation values (r). This point will be included in the revised figure legends. Moreover, in the revised version of the manuscript we propose to directly correlate the levels of XBP1s mRNA with the expression levels of SYVN1, UBE2D3 and UBE2J1 mRNAs. These data are available from the RNAseq data obtained from the TCGA cohort and already used previously by us (Lhomond et al. Embo Mol Med 2018). In addition, following this observation we have carried out a number of experimental validations using both established and primary GBM cell lines with genetic modifications of XBP1/XBP1s expression as well as ER stress-dependent induction of XBP1s and we clearly demonstrated that XBP1s mRNA levels correlate with UBE2D3 mRNA expression levels (Fig.3G-H, Fig.S2D-E). In addition, in Fig3E using our IRE1 activity signature we have shown a strong correlation between UBE2D3 and XBP1s, which is even more robust than simply correlating the mRNA levels. Data are already available and analyses are ongoing.

Review#1 point 3: The results in Figure 3 indicated that XBP1s acts as a transcriptional regulator of UBE2D3 expression. However, it is not clear whether this effect in GBM cells is direct or indirect. Further experiments such as chromatin immunoprecipitation and reporter assays are required to clarify this point.

Response 3. We agree with this reviewer’s point. Although we have scrutinized the publicly available ChIPseq databases and found UB2D3 among potential XBP1-regulated genes, we did not validate this observation in our model. To address this point we propose to perform ChIP experiments in cells overexpressing a tagged form of XBP1s and validate the presence of UBE2D3 promoter fragments in our experimental system. Moreover, these experiments will also be carried out with endogenous XBP1s (in-house XBP1s antibodies Pluquet et al. Cancer Res. 2013) in our primary GBM lines under basal and ER stress conditions. At last, to further document this, luciferase reporter assays using the UBE2D3 promoter (whose length would be defined based on ChIP experiments and the presence of XBP1s binding sites) upstream the luciferase ORF could be performed. Both ChIP and reporter assays have to be performed.

Review#1 point 4: In addition to UBE2D3, the two other ubiquitin-protein ligases, SYVN1 and UBE2J1, may also be implicated in the degradation of IκB. Did the authors assess their potential role on IκB degradation in their model system?

Response 4. We thank this reviewer for this suggestion. We have previously tested the impact of SYVN1 on IkB degradation with results showing a lot of variation. Indeed even though the trend of our results indicated that SYVN1 silencing appeared to lead to a slight increase in IkB expression, they never reached statistical significance. Variability in the results might be due to the efficacy of SYVN1 silencing and as such we propose to repeat further these experiments with SYVN1 siRNA smart pools to improve silencing efficacy. Moreover, SYVN1 has been shown to also contribute to the ubiquitylation and degradation of IRE1 (Gao et al. Embo Rep 2008; Sun et al. Nat Cell Biol 2015) and has its expression regulated by IRE1 activity (Dibdiakova et al. Neurol Res 2019), it might represent as well a very interesting target to study. Regarding UBE2J1, the situation is less documented. However, it was shown that this E2 works together with SYVN1 in conserved manner to contribute to ERAD (Chen et al. Nat Plants 2016). As such it might also be interesting to test whether the silencing of UBE2J1 impacts on IkB expression. To sum up, we propose to test experimentally whether the silencing of UBE2J1 or SYVN1 or both together impacts on IkB expression (we need to perform the experiments).

Review#1 point 5: The authors only used ectopic expression of relevant proteins to test their hypothesis in U87 and RADH87 cells. It is necessary to validate these findings using siRNAs/inhibitors for IRE1 and UBE2D3 in a GBM cell line that expresses high levels of endogenous IRE1 and UBE2D3.

Response 5. We propose to test the effect of SYVN1 and UBE2J1 silencing on IkB expression in U87 and RADH87 cells in the revised version of the manuscript (see above). In addition to address this reviewer’s comment, we propose to use U87 and RADH87 cells overexpressing IRE1 (Lhomond et al. 2018) and treat them with MKC886, or with siUBE2D3 or with both and evaluate whether in those conditions the NFkB pathway is affected. These experiments should be carried out relatively easily provided that all the recombinant cell lines, drugs and siRNA are already available.

Review#1 point 6: In Figure 3I: The protein expression of UBE2D3 should be shown.

Response 6: We had included control experiments with UBE2B3 expression in FigS3B in the initial version of the manuscript. We will include UBE2D3 expression for Fig3I in the revised version of the manuscript (these data are already available).

Review#1 point 7: In the right panel of Figure 3I: What do the labels #1, 2, 5 mean? Clear descriptions should be provided in the figure legend.

Response 7. Those labels correspond to different RADH87 cell lines stably overexpressing UBE2D3 protein. The validation of UBE2D3 expression using Western blotting will be included in FigS3B of the revised version of the manuscript (data are already available).

Review#1 point 8: In Figure S1D: The expression levels of the truncated IRE1 variant should be shown.

Response 8. The expression on truncated IRE1 variant (designated as Q780*) is shown in Fig.S1D, following the expression on wild-type (WT) IRE1. This point will be indicated in the revised version of the supplemental figure legends.

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Reviewer #2

In the current study, the authors generate evidence supporting a novel pathway downstream of IRE1α/XBP1s in GBM cells involving the activation of an E2-ubiquitin ligase, UBE2D3. In order to do this, they use a combination of patient derived and established cell lines engineered to overexpress IRE1 mutants, XBP1s or UBE2D3. They claim that UBE2D3 is upregulated downstream of XBP1s in GBM cells, and functions to activate NF-kB through the degradation of IkB, thus promoting CXCL2/IL-6/IL-8 production and the subsequent recruitment of monocytes and polymorphonuclear (PN) cells to the tumor microenvironment. However, the article has major shortcomings that need to be addressed before considering its publication

We thank this reviewer for his/her constructive comments on our work.

Review#2 point 1: Fig. 1: Classification of immune cells infiltrating GBM. The characterization of immune infiltrate in GBM is too simplistic. Monocytes, monocyte-derived macrophages and microglia are treated as equivalents along the text (IBA1+), making the story hard to follow. At least in mice, these populations can be easily distinguished based on CD45/CD11b/Ly6C expression (see for example Zhihong Chen et al., Cancer Research, 2017). Can the authors further analyze which of those population are actually affected under IRE1 deficiency and/or UBE2D3 overexpression? On the other hand, it is rather questionable that all CD11b negative cells are exclusively T cells, as suggested in Fig 1B. Can the authors provide evidence and/or references to support their gating strategies?

Response 1: We thank the reviewer for this comment. Our objective was to test the impact of IRE1 modulation on the infiltration of myeloid cells in the tumor, and we did not plan to describe this effect on the complete and detailed infiltrating myeloid populations in GBM which could represent a full study on its own. However, to address this reviewer’s critique we propose to complete the characterization of the myeloid population in our mouse model using IHC by adding Ly6C staining for macrophages and granulocytes. We did not select flow cytometry approach to explore this point as suggested by the reviewer (Cheng, Cancer Res, 2017), but instead IHC was preferred as we thought that the localization of the infiltrated immune cells was important to evaluate (periphery vs. core of the tumor). The information about the localization of immune cells is already available and will be added to the revised manuscript. Concerning the second point raised by this reviewer, the strategy to characterize the immune population in human GBM specimen was to combine CD45 and CD11b markers as previously described by Hussain et al. Neuro-Oncol 2006 and Parney et al. J Neurosurg 2009. Moreover, the analysis of additional markers allowed us to confirm that CD45+ CD11b+ cells were mainly monocytic cells (that also co-expressed CD14, CD168, CD64 and HLA-DR); CD45 low CD11b high cells were granulocytes (CD66B, CD15 and CD16); and CD45 high CD11b low cells were mainly CD3+ T cells. These data are already available and will be added to the revised manuscript.

Review#2 point 2: Fig. 1: RADH IRE1 Q780\ model - Can the authors further validate the IRE1 deficiency of their model cell line RADH87 IRE1Q780*? It appears to have severely reduced IRE1 levels when compared to the RAD87-IRE1WT cell line (figS1D). Furthermore, the WT and not the truncated form seems to be predominantly expressed. Intriguingly, XBP1 is still being spliced after tunicamycin treatment in this mutant line. All these results differ significantly from the U87-Q780* cell line originally published by Lhomond et al., 2018. Can the authors comment on these differences? Was there a mixture in cell lines? *

Response 2: We agree with the reviewer that the level of IRE1Q780* expression on RADH87 cells is lower than the IRE1WT expression (Fig.S1D). As observed by this reviewer, XBP1 was still spliced in Q780* cells but XBP1s expression was reduced as shown in Figure S1D. This is mostly due to the ratio between the expression endogenous IRE1 and that of Q780*, which as previously shown (Lhomond et al; 2018) acts as a dominant negative and preempts endogenous IRE1 signaling. The differences observed are also probably due to the methods used, indeed we measured XBP1 and XBP1s mRNA expression in U87 cells (Lhomond et al. 2018), whereas XBP1s protein expression was used with RADH87 cells (introducing the RNA translation parameter that was not monitored in U87 cells). Differences could be also linked to the cell lines as we used the U87 immortalized and RADH87 primary cell lines.

Review#2 point 3: Fig. 1: Impact of IRE1 inhibition on recruitment of myeloid cells to the TME. The experiment in figure 1E-F, which is the only in vivo evidence supporting a role of IRE1 signaling on myeloid cell recruitment, is very hard to interpret. The authors show no evidence that IRE1 is being inhibited under the treatment and if so, up to which extent. Furthermore, what are the cells targeted by MKC in this setting? The differences in the infiltration of PN cells seem very slight, nothing is mentioned regarding the number of mice per group, or the statistical analysis performed. I would suggest performing a simpler experiment to demonstrate an intrinsic effect of IRE1 signaling in GBM cells, comparing the recruitment of myeloid cells in tumors generated by GL261 cells expressing WT vs deficient forms of IRE1.

Response 3: The mouse model used in the paper is fully described in (Le Reste BioRxiv 2020 - doi: https://doi.org/10.1101/841296) and all the details about the procedures can be found in this manuscript. This model was developed to recapitulate in mice the standard of care for GBM patient including surgical resection. In addition, drug delivery in brain tumors is often an issue due to the blood-brain barrier. Therefore, the IRE1 inhibitor was delivered locally after resection of the tumor, exposing both tumor and stromal cells. To quantify the myeloid cell recruitment in Fig1E-F, at least thirty random fields from tumor tissue and at least thirty random fields from tumor periphery were quantified for control (PLUG) and MKC-treated group (2 mice/group). The number of positive cells in tumor tissue and tumor periphery were then pulled together for statistical analyses. The significance of the differences in myeloid cells recruitment between control (PLUG) and MKC-treated group was estimated using unpaired student t-test. At least 8 tumors of each group were analyzed comprising 2 to 3 sections of each and 10 fields per section. In addition, we have also performed the experiments using GL261 cells knockout for IRE1, the data are already available and could be possibly added to the revised manuscript.

Review#2 point 4: Fig. 2: Correlation between IRE1 signature and cytokine/chemokine signature. In the IRE1 signature as determined in the EMBO Mol Med paper (and to which the authors continuously refer) 6 out of 38 (15%) of the genes correspond to cytokines and/or chemokines (Il6, Il1b, Cxcl2, Cxcl5 and Ccl20) (Lhomond et al., 2018). Besides the fact that it is very unclear how this signature was obtained in the first place, it is rather surprising that in the current paper the authors correlate this "IRE1 activity" signature with the same or other cytokines/chemokines mRNA levels and come to the conclusion that there is a high correlation (fig 2A). Isn't this to be expected? Can the authors clearly explain how the IRE1 signature was determined and prove that their "IRE1 signature" is, in fact, representing IRE1 activity? For instance, it is important to cross validate their results by using an independent signature of IRE1 activity (e.g. ChipSeq XBP1s targets, Chen et al., 2014)?

Response 4: We thank this reviewer for asking for precisions about the procedure. The IRE1 signature was fully described in Lhomond et al. 2018 and was obtained from transcriptome datasets obtained from U87 modified for IRE1 activity (Pluquet et al., 2013). IRE1 was validated on GBM patients and appeared as an important tool to evaluate IRE1 activity in tumor specimen not only in GBM but also in other tumor types (Rubio-Patiño C, Cell Metab 2018). Furthermore, IRE1 activity was also directly linked to the pro-inflammatory tumor cell secretome in various studies such as Logue et al. 2018. As indicated by this, some cytokines/chemokines studied in this work were indeed part of the IRE1 signature and correlation between this signature and their expression was indeed expected. However the other main cytokines/chemokines studied here were not present in the IRE1 signature indicating that IRE1 could have been involved in the regulation of their expression. As proposed by reviewer#2, we will include in the revised version of the manuscript the analysis of cytokines/chemokines from the dataset ChipSeq XBP1s targets (Chen et al. 2014), although this study was performed on breast tumors.

Review#2 point 5: Fig 2: XBP1s controlling cytokines/chemokines expression in GBM cells - As suggested by the data on fig1C-D and fig2E, IRE1 appears to be constitutively active in GBM cells, as IRE1 deficiency is sufficient to cause a defect in chemokine production. However, as shown in fig S1D, XBP1s protein was not detected under basal conditions, suggesting that the deficiency in chemokine production in IRE1-deficient cell lines is XBP1s-independent. Can the authors further discuss these results?

Response 5: We thank the reviewer for commenting this point. We think that indeed IRE1 is constitutively active in GBM cells. As we have tested XBP1s protein expression in untreated and tunicamycin-treated RAD87 cells (FigS1D), and we will also provide real time qPCR data to show the presence of basal XBP1s mRNA levels (data already available). We agree that the way we presented the results are misleading and undermine the basal expression of XBP1s. This will be fixed in the revised manuscript.

Review#2 point 6: Fig 3: IRE1/XBP1s/UBE2D3/NF-kB axis - Authors must show the activation status of NF-kB in parental U87 cells (Fig3A), as this is a critical evidence to support that IRE1a-deficient U87-DN cells are defective in chemokine production due to an impairment in NF-kB signaling. In addition, even when tunicamycin treatment induce XBP1s and UBE2D3 (figS2D) it does not induce IkB degradation nor NF-kB phosphorylation in parental U87 and RADH87 cells (figS3C) as one should expect if IRE1/XBP1s/UBE2D3/NF-kB pathway is operating in these cells. How can this be explained? Only after XBP1s or UBE2D3 overexpression, NF-kB signaling appears to be affected.

Response 6: As shown in Fig3A, U87 cells deficient for IRE1 signaling (DN) exhibit decreased NFkB signaling as exemplified by decreased phospho-NFkB and phospho-IkB compared to control U87 cells proficient for IRE1 signaling. In our manuscript, we mainly focused on the activation of the IRE1/XBP1s/UBE2D3/NFkB signaling axis under basal condition. One could speculate that tunicamycin treatment leads to a strong stress response that others mechanisms are activated that overwhelm the IRE1/XBP1s/UBE2D3 pathway we are describing herein. For instance, it has been demonstrated that the IRE1/JNK signaling was linked to NFkB activation upon acute ER stress (Tam et al. PLoS One. 2012;7(10):e45078; Schmitz et al. Biomedicines. 2018 Jun; 6(2): 58.) and furthermore PERK activation upon thapsigargin or tunicamycin treatment was also found to promote NFkB activation (Deng et al. DOI: 10.1128/MCB.24.23.10161-10168.2004; Fan et al. Cell Death Discov. 2018 Feb 12;4:15). We believe that the pathway we describe here might be linked to constitutive activation of IRE1 signaling (proper to tumor cells) rather than acute activation of this pathway and be compatible with sustained proliferation. To further document this point, we have already generated data about the phosphorylation status of NFKB in GL261 cells KO for IRE1 compared to the parental cells (data will be provided in the revised version of the manuscript). In addition we are currently investigating the correlation between IRE1 activity signature and that of NFkB as defined previously (Jin et al. Cancer Res. 2014 May 15; 74(10): 2763–2772.), results should be available shortly and will be added in the revised manuscript.

Review#2 point 7: Fig 4: UBE2D3 and MIB1 – The authors should discuss better what is the possible interaction between UBE2D3 and MIB1. As shown in fig4G, silencing of MIB1 cause a severe increase in UBE2D3 protein levels but this is not commented in the text.

Response 7: We thank the reviewer for this comment. We believe that MIB1 might also controls the expression of UBE2D3. The data are already available and will be included in the revised version of the manuscript.

Review#2 point 8: Fig 6: Chemokines driving recruitment of myeloid cells to UBE2D3 overexpressing tumors. A formal demonstration that GL261-UBE2D3 tumors recruit higher numbers of MM and PNs through an enhanced production of CXCL2, IL-6 and/or IL-8 is lacking. For instance, they could compare the infiltration of myeloid cells in GL261-UBE2D3 vs GL261-UBE2D3-CXCL2KO tumors.

Response 8: To address this point, we propose to test the expression of these cytokines/chemokines in the GL261 tumors after resection using ELISA. These experiments could be carried out in IRE1 KO tumors, in UBE2D3 overexpressing tumors and performed for instance using perfusion of CXCL2, IL6 or IL8 neutralizing antibodies or cells KO for these chemokines. These experiments could be performed but might lead to inconclusive results (not statistically significant) if there is redundancy between the roles of those chemokines. As such, we think that we could provide in vitro information about the respective roles of these chemokines in recruiting MM and PNs but that at present stage the in vivo demonstration is to premature.

Review#2 point 9: Authors must provide replicates of the blots to sustain their claims: FigS1D, Fig3A, Fig3I, Fig4G.

Response 9: Replicates and quantifications are already available and will be provided in the revised version of the manuscript.

Review#2 point 10: The authors should include a better description of the methods regarding bioinformatic analysis. For instance, which genes where used for MM/PN/T cell signatures in fig1A/S1A?.

Response 10: We thank the reviewer#2. This information is available and a complete description will be included in the revised version of the manuscript.

Review#2 point 11: Missing statistical significance on fig 2C and fig 6A to support their claims.

Response 11: Statistical values will be included in the revised manuscript.

Review#2 point 12: Fig2F is presented in the text as mRNA levels but in the figure as protein levels.

Response 12: This point will be fixed in the revised version of the manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary: your understanding of the study and its conclusions.

In the current study, the authors generate evidence supporting a novel pathway downstream of IRE1α/XBP1s in GBM cells involving the activation of an E2-ubiquitin ligase, UBE2D3. In order to do this, they use a combination of patient derived and established cell lines engineered to overexpress IRE1 mutants, XBP1s or UBE2D3. They claim that UBE2D3 is upregulated downstream of XBP1s in GBM cells, and functions to activate NF-kB through the degradation of IkB, thus promoting CXCL2/IL-6/IL-8 production and the subsequent recruitment of monocytes and polymorphonuclear (PN) cells to the tumor microenvironment. However, the article has major shortcomings that need to be addressed before considering its publication

Major comments: major issues affecting the conclusions.

-Fig. 1: Classification of immune cells infiltrating GBM The characterization of immune infiltrate in GBM is too simplistic. Monocytes, monocyte-derived macrophages and microglia are treated as equivalents along the text (IBA1+), making the story hard to follow. At least in mice, these populations can be easily distinguished based on CD45/CD11b/Ly6C expression (see for example Zhihong Chen et al., Cancer Research, 2017). Can the authors further analyze which of those population are actually affected under IRE1 deficiency and/or UBE2D3 overexpression? On the other hand, it is rather questionable that all CD11b negative cells are exclusively T cells, as suggested in Fig 1B. Can the authors provide evidence and/or references to support their gating strategies?

-Fig. 1: RADH IRE1 Q780 model Can the authors further validate the IRE1 deficiency of their model cell line RADH87 IRE1Q780? It appears to have severely reduced IRE1 levels when compared to the RAD87-IRE1WT cell line (figS1D). Furthermore, the WT and not the truncated form seems to be predominantly expressed. Intriguingly, XBP1 is still being spliced after tunicamycin treatment in this mutant line. All these results differ significantly from the U87-Q780* cell line originally published by Lhomond et al., 2018. Can the authors comment on these differences? Was there a mixture in cell lines?

-Fig. 1: Impact of IRE1 inhibition on recruitment of myeloid cells to the TME. The experiment in figure 1E-F, which is the only in vivo evidence supporting a role of IRE1 signaling on myeloid cell recruitment, is very hard to interpret. The authors show no evidence that IRE1 is being inhibited under the treatment and if so, up to which extent. Furthermore, what are the cells targeted by MKC in this setting? The differences in the infiltration of PN cells seem very slight, nothing is mentioned regarding the number of mice per group, or the statistical analysis performed. I would suggest performing a simpler experiment to demonstrate an intrinsic effect of IRE1 signaling in GBM cells, comparing the recruitment of myeloid cells in tumors generated by GL261 cells expressing WT vs deficient forms of IRE1.

-Fig. 2: Correlation between IRE1 signature and cytokine/chemokine signature In the IRE1 signature as determined in the EMBO Mol Med paper (and to which the authors continuously refer) 6 out of 38 (15%) of the genes correspond to cytokines and/or chemokines(Il6, Il1b, Cxcl2, Cxcl5 and Ccl20) (Lhomond et al., 2018). Besides the fact that it is very unclear how this signature was obtained in the first place, it is rather surprising that in the current paper the authors correlate this "IRE1 activity" signature with the same or other cytokines/chemokines mRNA levels and come to the conclusion that there is a high correlation(fig 2A). Isn't this to be expected? Can the authors clearly explain how the IRE1 signature was determined and prove that their "IRE1 signature" is, in fact, representing IRE1 activity? For instance, it is important to cross validate their results by using an independent signature of IRE1 activity (e.g. ChipSeq XBP1s targets, Chen et al., 2014)?

-Fig 2: XBP1s controlling cytokines/chemokines expression in GBM cells As suggested by the data on fig1C-D and fig2E, IRE1 appears to be constitutively active in GBM cells, as IRE1 deficiency is sufficient to cause a defect in chemokine production. However, as shown in fig S1D, XBP1s protein was not detected under basal conditions, suggesting that the deficiency in chemokine production in IRE1-deficient cell lines is XBP1s-independent. Can the authors further discuss these results?

-Fig 3: IRE1/XBP1s/UBE2D3/NF-kB axis Authors must show the activation status of NF-kB in parental U87 cells (Fig3A), as this is a critical evidence to support that IRE1a-deficient U87-DN cells are defective in chemokine production due to an impairment in NF-kB signaling. In addition, even when tunicamycin treatment induce XBP1s and UBE2D3 (figS2D) it does not induce IkB degradation nor NF-kB phosphorylation in parental U87 and RADH87 cells (figS3C) as one should expect if IRE1/XBP1s/UBE2D3/NF-kB pathway is operating in these cells. How can this be explained? Only after XBP1s or UBE2D3 overexpression, NF-kB signaling appears to be affected.

-Fig 4: UBE2D3 and MIB1 The authors should discuss better what is the possible interaction between UBE2D3 and MIB1. As shown in fig4G, silencing of MIB1 cause a severe increase in UBE2D3 protein levels but this is not commented in the text.

-Fig 6: Chemokines driving recruitment of myeloid cells to UBE2D3 overexpressing tumors.<br> A formal demonstration that GL261-UBE2D3 tumors recruit higher numbers of MM and PNs through an enhanced production of CXCL2, IL-6 and/or IL-8 is lacking. For instance, they could compare the infiltration of myeloid cells in GL261-UBE2D3 vs GL261-UBE2D3-CXCL2KO tumors.

Minor comments: important issues that can confidently be addressed.

Authors must provide replicates of the blots to sustain their claims: FigS1D, Fig3A, Fig3I, Fig4G. The authors should include a better description of the methods regarding bioinformatic analysis. For instance, which genes where used for MM/PN/T cell signatures in fig1A/S1A? Missing statistical significance on fig 2C and fig 6A to support their claims. Fig2F is presented in the text as mRNA levels but in the figure as protein levels.

Significance

Significance

In general, there is a clear interest both from academia and pharma companies to understand the role of the UPR in tumor biology and how the UPR shapes the immune compartment. This is highly relevant as the UPR is a novel drug target in cancer therapy, but unfortunately many inconsistent data are around. However, as the paper is now, it will not contribute to clarify these inconsistencies.

Compare to existing published knowledge.

Unfortunately, there are many studies around with inconclusive results and strong claims based on poorly validated tools.

Audience. Tumor immunologists, UPR field

Your expertise. Role of the UPR in immune cells and anti-tumor biology.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript, Obacz et al. investigated the role of IRE1 singaling in regulating the recruitment of myeloid cells in glioblastoma multiforme (GBM) microenvironment. They show that inhibition of IRE1 signaling decreased polynuclear neutrophil (PN) infiltration to GBM tumors in ananimal model; conversely, IRE1 activation correlated with higher expression of myeloid cells-attracting chemokines in GBM. They also show that IRE1-XBP1s pathway promotes proinflammatory chemokines in GBM tumor cells through upregulation of UBE2D3, which leads to degradation of the NFκB inhibitor IκB and activation of NFκB downstream signaling. Their finding of a novel IRE1/XBP1s/UBE2D3/NFκB axis is important for understanding the basis of pro-tumoral inflammation in GBM, potentially in other 'immune hot' cancers. The manuscript is well written and the conclusion is well supported by the experiments. However, there are a few critical points that need to be addressed to strengthen their study.

Major comments:

1.In this study, the authors used the GBM primary cell line RADH87 with stable overexpression of wild-type (WT) IRE1 or a truncated IRE1 variant. The expression of wild-type IRE1 was confirmed by Western analysis (Figure S1D). However, the expression of truncated IRE1 variant was not shown. In addition, without tunicamycin treatment, there was no visible difference in XBP1s expression between the cells expressing WT or the mutant IRE1. In the Boyden chamber assay (Figure 1C, D), conditioned medium from these cells were used; it was not described whether the cells were treated (e.g. with tunicamycin) to activate the IRE1 pathway.

2.The evidence that the mRNA expression of UBE2D3 positively correlates with IRE1/XBP1s pathway is weak. First, In Figure 3D, the correlation between the mRNA expression of UBE2D3 and XBP1 does not seem strong. In addition, as XBP1 mRNA level does not reflect IRE1 activation (as opposed to that of XBP1s), the level of XBP1s instead of total XBP1 should be assessed. Furthermore, such correlation should be validated in additional GBM cohorts/datasets.

3.The results in Figure 3 indicated that XBP1s acts as a transcriptional regulator of UBE2D3 expression. However, it is not clear whether this effect in GBM cells is direct or indirect. Further experiments such as chromatin immunoprecipitation and reporter assays are required to clarify this point.

4.In addition to UBE2D3, the two other ubiquitin-protein ligases, SYVN1 and UBE2J1, may also be implicated in the degradation of IκB. Did the authors assess their potential role on IκB degradation in their model system?

5.The authors only used ectopic expression of relevant proteins to test their hypothesis in U87 and RADH87 cells. It is necessary to validate these findings using siRNAs/inhibitors for IRE1 and UBE2D3 in a GBM cell line that expresses high levels of endogenous IRE1 and UBE2D3.

Minor comments:

1.In Figure 3I: The protein expression of UBE2D3 should be shown.

2.In the right panel of Figure 3I: What do the labels #1, 2, 5 mean? Clear descriptions should be provided in the figure legend.

3.In Figure S1D: The expression levels of the truncated IRE1 variant should be shown.

Significance

In this manuscript, the authors report some of the molecular mechanisms by which IRE1-XBP1s signaling controls GBM immune infiltration. They show that a novel IRE1/UBE2D3 signaling axis, mediated by XBP1s, regulates NF-κB activation, which subsequently promotes pro-inflammatory responses and the recruitment of immune/inflammatory cells to the tumor site. This study provides significant new information on the role of IRE1 in GBM. The findings also establish a basis for potential new approaches to improve the efficacy of current immunotherapies, also in other cancer types, which needs to be further explored.

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Reply to the reviewers

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

In this report, van Schaik et al., modified an established CUT and RUN method and combined it with previously used DamID to identify Lamin Associated Domains (LADs) with better temporal resolution. Previous DamID experiments labeled locations where lamin proteins were present within a 5-25 hour window while the new technique, pA-DamID, labels DNA within a 30 minute window providing better temporal resolution. The authors used this technique to identify LADs at multiple stages of the cell cycle and applied this protocol to different cell types. The authors FIND differences when comparing data sets between cell cycle time points and cell lines.

We thank the reviewer for the helpful comments.

**Major points:**

1) The data sets generated and displayed in this manuscript seem incomplete. In Figure 1G, the authors compare lamin B2 vs. lamin B1 generated LADs in HAP-1 cells and lamin A/C vs lamin B2 LADs in hTERT-RPE cells. In figure S4, panel C compares lamin B1 and lamin B2 in K562 cells and lamin B2 and lamin A/C in hTERT-RPE cells. It would have been informative to have a complete dataset for lamin B1, lamin B2, and lamin A/C identified LADs in all cell lines analyzed. The information provided from these datasets would be useful to the scientific community.

We did not think it was necessary to generate every lamin pA-DamID data set in every cell line, given that previous DamID studies indicated that lamins A, B1 and B2 give the same genome-wide pattern {Meuleman, 2013, 23124521; Kind, 2014; 24717229}. However, we agree with the reviewer that the missing data sets lead to a sense of incompleteness and might distract the reader from the main message of the manuscript. We suggest to generate Lamin B1 pA-DamID in hTERT-RPE cells– provided that the current Corona virus shutdown will not prevent us from doing this experiment. Doing so, we 1) have a complete lamin data set in hTERT-RPE cells, which we study in most detail in this manuscript 2) can compare all lamins within the same cell type 3) can compare all Lamin B1 DamID data to the corresponding Lamin B1 pA-DamID data.

2) The authors discovered that LADs reposition during progression through the cell cycle. It would have been interesting to know whether these changes have transcriptional consequences? One could perform RNA-SEQ experiments to discover if LAD occupancy results in transcriptional changes and choose a few genes to confirm the findings with RT-PCR. Is this the same for lamin B1, lamin B2, and lamin A/C occupied LADs? Analyze if there are any genomic features such as CTCF or transcription factor binding sites that correlate with the loss of LADs.

In the first part of this point, the reviewer suggests to look at transcriptional consequences of changes in NL interactions. To address this point, we require some measure of nascenttranscription during the cell cycle, which is not available in any of the studied cell lines. A potential experiment would be to map polymerase occupancy with pA-DamID / CUT&RUN or run-on transcription with any other method at the synchronized time points. However, this experiment is not trivial and we feel that this goes beyond the scope of this manuscript, which focuses on the development of pA-DamID and the m6A-Tracer with a proof-of-principle example of NL binding dynamics during the cell cycle.

In the second part of this point, the reviewer asks whether changes in NL binding correlate with genomic features such as CTCF binding sites or transcription factor binding sites. In the manuscript, we already include correlations with various active features (active gene density / replication timing) (Fig. 3E-G, 4C-E), that generally correlate well with transcription factor binding. We have added CTCF peaks as comparison (Fig. S7F).

3) The authors state that using H3K27me3/H3K9me3 in pa-DamID showed no enrichment. This is surprising considering that both modifications are enriched in heterochromatin and at the nuclear periphery. It appears that the peripheral enrichment is masked by the larger overall internal pool. The authors should discuss this observation and comment on the sensitivity of the method to detect local enrichment versus the global levels of a protein or modification in pa-DamID.

We believe that H3K27me3 and H3K9me3 histone modifications show the expected pattern in their distribution in the nucleus. However, due to the peripheral mask slightly extending beyond the cell boundaries, the calculated peripheral enrichment is underestimated. This has been better described in the figure legend.There is a small enrichment at the nuclear periphery compared to diffuse Dam and untargeted pA-Dam (Fig. 1B/1C/1F). To further support the pA-DamID data quality of these histone modifications, we have added a comparison with ENCODE ChIP-seq data tracks in K562 cells (Fig. S3C).

**Minor points:**

Figure 1: Change colors for Figure 1F and Figure 2D. The colors are hard to discern.

Figure 2B: Please mark which antibody was used for this analysis.

Figure 2C: Please also overlay data from pA-DamID lamin A/C experiments.

Figure 4: Please mention which antibody was used for the pA-DamID experiments used to generate this dataset.

Figure 5: Please mention which antibody was used for the pA-DamID experiments used to generate this dataset.

Figure S5 C and D: Please mention which antibody was used for the pA-DamID experiments.

We have made edits to address the minor comments above. However, we do not have Lamin A/C data in HAP-1 and K562 cells to add to Fig. 2C.

Reviewer #1 (Significance (Required)):

The major contribution of this manuscript is the description of an improved method to map LADs. This is a valuable contribution. By using this new method, the findings of this paper provide some new insight in LAD dynamics throughout the cell cycle although the experiments are largely phenomenological. This is a technically sound study.

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

The paper describes a new method for detecting Lamin associated DNA domains, which allows better time resolution than classical DamId. It is a good idea and its functionality is demonstrated in tissue culture cells. There are minor insights but it is important that we advance the field with new and better technologies, thus this version amply suffices to give evidence of that.

We thank the reviewer for the positive feedback.

Reviewer #2 (Significance (Required)):

The audience is all persons working on chromatin organization in the nucleus, which is a large audience. The data are clear as they basically are proof of principle for a new technique. There is nothing major to request as revision. They might cite papers on damID in worms and tissue specific applications of this in living organisms, as this is likely to be the situation that is most interesting in the long run. The resolution (in bp) would be interesting to know and validate.

We have extended the discussion on new applications of pA-DamID.

We now compare data quality and resolution between DamID and pA-DamID, focusing on the mapping of NL interactions (Fig. S4D-E).These plots indicate similar data quality and resolution between the two methods.

I have no other major revisions to request.

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

In the manuscript by Schaik et al (Van Steensel laboratory) the authors describe a very clever approach to identifying Lamina Associated Domains (LADs) using the principles of the 'cut-n-run' strategy. Specifically, they engineer the Dam methyltransferase used in canonical DamID in frame with a protein A moiety capable of interacting with an antibody (in this case lamins--B1, B2 and A/C). After permeabilization, cells are incubated with antibodies, then pA-Dam purified protein--for a brief time window--is added to mark associated DNA with GmATC. This technique is a valuable contribution to the field, particularly since, as the authors point out,an advantage of pA-DamID is that the labeled DNA can also be visualized in situ using the m6A-Tracer, before this DNA is sequenced. This allows for validation of findings and is highly amenable to cell sorting technologies. In addition, this technology allows for a time-resolved measure of LADs not currently available by standard DamID. The authors apply this technology to four different cell types. They noted that the 'maps' generated by the is technology differed from canonical DamID at very specific regions (small LADs in very localized regions) . They then embark on a series of experiments to show that these differences arise from cell cycle -related differences that are differentially picked up by the methods--with the pA-DamID allowing for dissection of more discrete cell cycle stages/configurations. In general they find an initial preference for sub-telomeric LADs to associate with the nuclear lamina fist, then more centromeric. There is some data suggesting loss/gain of LADs in specific regions/with specific features. The manuscript is well written and the data well presented. However, there are some points that need to be addressed . Overall, there is some oversimplification or omission of previous data in the field, a lack of clarity in how some of the data was interpreted, and some areas where clarification and/or additional analyses would be helpful. I sincerely hope the authors find the following critiques to be useful. Thank you for the opportunity to review your very nice work.

We thank the reviewer for the constructive and very detailed comments, these have been extremely helpful in improving the manuscript.

**Introduction:**

**Microscopy studies found that telomeres are enriched near the NL in early G1 phase, leading to the hypothesis that telomeres may assist in NL reassembly onto chromatin [13].**

● There have been numerous studies identifying the timing and disposition of INM proteins and Lamins at m the end of mitosis (during NE reformation). Why are you citing just this one? (e.g. ​Thomas Dechat et al., 2004; T. Dechat et al., 2000; Ellenberg et al., 1997; Haraguchi et al., 2001​)

We have expanded the introduction to better cover previous work on the reforming NL (paragraph 2) and initial genomic interactions with the NL (paragraph 3).

**Furthermore, during S-phase B-type lamins have been found to transiently overlap with replication foci in the nuclear interior, at least in some cell types [20]**

● While, technically, this has indeed been reported, this study is from 1994 and has not been repeated. The cells used in this study (3T3 fibroblasts) are widely used and others have not noted this phenomenon. Soften this.

**Other studies have indicated that lamins are important for DNA replication [reviewed in 21].**

● Likewise, direct roles for lamins in replication are controversial (acknowledged in the small section of the cited review on the role of lamins in replication).

● Perhaps combine the two sentences above to soften the implication that this is a "known" role of B-type lamins. e.g. "A handful of studies have implicated a role for B-type lamins in replication, but the direct role of the lamina in this process remains unclear. Nonetheless, ......"

This is a very good suggestion by the reviewer. We agree that literature has been controversial and should be approached with care. We have followed the advice and changed this.

**Results:**

**So far, the cell cycle dynamics of genome - NL interactions have primarily been studied by microscopy. While these studies have been highly informative, they were often limited to a ​few selected loci.​**

● Please cite your own study (Kind et al.) and other recent papers (Luperchio et al.-​https://www.biorxiv.org/content/10.1101/481598v1​;; Zhang et al., Nature-​https://www.nature.com/articles/s41586-019-1778-y​;) in which they were either 1) not limited to a few selected loci and/or 2) not microscopy-directed studies? There is an argument to be made here for the resolution (time and b.p.) you have achieved through your studies that these studies did not.

To our knowledge, there have been no high-throughput microscopy studies of many individual loci performed studying this. Microscopy has been performed of collective sequences (i.e. all LADs (Kind, 2013 and indeed Luperchio, 2018)), which provide additional insights but lack sequence information in the images. We have expanded the introduction to better acknowledge these microscopy studies that are not limited to single loci.We feel that observations on LAD domain clustering (Luperchio) and B compartment formation (Zhang) are better suited for the Discussion, given that these observations are not directly related to genome – NL contact dynamics. We already discussed B compartment formation in the discussion, but now also include the observed LAD domain clustering. Also, we have discussed data resolution in more detail in the results (see reviewer #2).

How does this data correlate with TSA-seq, another antibody-based method developed by the Belmont lab, but collaboratively developed for use in identifying LADs (ie Dam alternative) with the Van Steensel group?​ I can imagine there are numerous advantages to this approach (radius of "labeling" being one).

TSA-seq provides a different perspective on genome – NL interactions, given its distance dependence rather than contact. We have added a comparison with TSA-seq to the Discussion.

**When Dam-Lamin B1 is expressed in vivo for 5-25 hours during interphase, LADs that interact with the NL become progressively labeled, eventually resulting in a layer of labeled chromatin of up to ~1 μm thick [8]. This is because LADs are in dynamic contact with the NL. We expected that in pA-DamID this layer would be thinner, because the NL-tethered Dam is only activated for 30 minutes. In addition, permeabilization depletes small molecules including ATP and thus prevents active DNA remodeling in the nucleus [26]. Indeed, pA-DamID yields a m6A layer that is ~2.5 fold thinner than the layer in cells that express Dam-Lamin B1 in vivo (Fig. S2A-C). This is not an artifact due to collapse of chromatin onto the NL caused by the permeabilization, because permeabilization of cells expressing Dam-Lamin B1 in vivo did not significantly reduce the thickness of the m6A layer compared to directly fixed cells (Fig. S2C). The thin layer of labeled DNA obtained by pA-DamID points to an improved temporal resolution of pA-DamID compared to conventional DamID.**

● I think this requires a bit more care. Your previous work clearly demonstrates LADs are dynamic. Others in the field have shown that these domains are also constrained within the larger sub-chromosomal compartment (self-interaction) of LADs (e.g. Luperchio 2018) within a chromosome. So, this is truly a temporal "snapshot" that may miss some regions of LADs that are less directly (or more dynamically) associated with the lamina, but still compartmentalized into the larger LAD sub-chromosomal compartment. It is unclear if the treatment used for this study perturbs these LAD-lamina​ dynamic​ interactions--one can imagine that the LADs are much less mobile generally under the protocol described in your supplemental information. In other words, ​LADs don't collapse, nor do they behave in the same way they would after permeabilization​. The technique has compromised some of that --which is actually fine for most of the purposes in this manuscript, but this needs to be discussed.

As the reviewer points out, there are fundamental differences between DamID and pA-DamID in their m6A deposition that should be clear from the text. We elaborated on this in the comparison between pA-DamID and DamID.

● In addtion, imaging data showing dam-LaminB1/2 plus m6A-tracer is missing (figure S2). This should be included. Is the intensity of the "tracer" similar between conditions? If so, were the exposures kept constant in all images? This is important since the decay rate is highly related to intensity of signal.

We are afraid that this figure has been misinterpreted. We have changed the figure labels and legend to explain it better. The HT1080 Dam-Lamin B1 clonal cells (new clone kindly supplied by Jop Kind) still showed significant variation in m6A-Tracer intensity per cell, suggesting different expression levels of Dam-Lamin B1. To create optimal images for halfway decay estimation, laser settings were changed between images. This has now been mentioned more clearly in the methods.

**In some cell types, especially in​ HCT116 and ​hTERT-RPE​ cells, we noted local discrepancies between the two methods (Fig. 2A,bottom panel). These differences involve mostly regions with low signals in DamID that have higher signals in pA-DamID. However, such differences are not obvious in HAP-1 and K562 cells.**

● Only HCT116 data is shown in the indicated figure. hTERT-RPE cells are shown in the accompanying supplemental figure and use a different antibody (lamin B2) as the target for the pA-Dam.

We have changed the pointer to include the supplementary figure.

(See reviewer #1 for a similar comment.)We agree that the comparison between Lamin B1 DamID and Lamin B2 pA-DamID in hTERT-RPE cells leads to sense of incompleteness and confusion. We suggest to generate Lamin B1 pA-DamID data in hTERT-RPE cells to solve this – provided that the current Corona virus shutdown will not prevent us from doing this experiment.

This brings up another point: the data (log2 ratio schema) shown in figure 2 is for HCT116 lamin B1 pA-Dam. Yet, the subsequent studies for transient/building interactions during G1 and into S (Figure 3) are done in hTERT-RPE cells using lamin B2. To be consistent, data from lamin B2 should be used in both figures (it seems lamin B2 data is available for all cell types). The comparison of Dam-Lamin B1 can be addressed in the Venn overlays (as they are now) and in the supplements. The hTERT-RPE data should be in Figure 2 since it is followed up on in the subsequent figure (ie it fails to meet the definition of being relegated to 'supplemental' data).

As written in the response above, we suggest to generate Lamin B1 pA-DamID data in hTERT-RPE data.This will allow us to make a more consistent story and address these comments.

**suggesting that the separation of LADs and inter-LADs becomes progressively more pronounced after mitosis. Nevertheless....**

● This is overstated, especially given the previously mentioned work (Luperchio, Zhang). More accurate to say LADs ​association with the nuclear lamina becomes more pronounced​. LADs (predominantly B-compartment) and inter-LADs (predominantly A-compartment) show much earlier separation from each other. This may be distinct from association with the lamina. This is an important distinction as it may lead to different hypotheses regarding mechanisms of LAD targeting/association with the lamina.

We agree that this is an overinterpretation of our data. We have changed the phrasing to make it more accurate.

**Progression from prometaphase to late telophase in HeLa cells takes about 1 hour [33], suggesting that this timepoint captures the initial interactions with the reforming NL. Remarkably, the majority of these interactions is shared with later time points, indicating that most LADs can interact with the NL throughout interphase and are defined (and positioned at the NL) very soon after mitosis.**

● There is wide variability in this number, some cells rapidly exit, others take significantly longer. This number is an average (and, for what it's worth, based on a very compromised cancer cell line). The "interactions' mapped are likely reflecting the ensembe measurements of the many cells that have transited into G1. Also, this statement seemingly directly contradicts the premise of many of your following data/interpretations of a sort of step-wise wave or prefered interactions from telomere proximal toward centromeric regions. This also disagrees with your previous work (Kind et al) and more recent work regarding positioning to the NL very soon after mitosis. Again, this is BULK (many cells of a continuum of configurations) versus single cell observations. This is overstated.

We felt that there was a need to explain why we interpret the 1h time point as the initial interactions with the NL and included this reference, but the reviewer is correct that this number can vary greatly between cell types and conditions. We have removed the reference and now include FACS and imaging data supporting this claim directly.

We have changed the phrasing of these results to make our interpretation clearer.

**We next looked into characteristics of the dynamic LADs. At early time points, LADs with decreasing interactions do not have lower pA-DamID scores than stable LADs, suggesting that their ​detachment from the NL is not simply due to weak initial ​binding**

● The methods used here are dynamic proximity measures. Words like "binding" and "attachment" should be avoided (use interacting, associated, etc )

Good point. We have replaced all occurrences of these words.

**LAD dynamics are linked to telomere distance and LAD size in multiple cell types**

● Perhaps I am missing something, but I find relatively little data showing centromere-proximal LADs across cell cycle stages (referring here to Log2 ratio plots similar to what is shown for telomere-proximal LADs, Supplemental figure 6 is the only place where this is obvious.).

To better illustrate the inverse dynamics of telomeres and centromeres in hTERT-RPE cells, we have changed Fig. 3B to a full chromosome overview.

● In addtion, it seems to me that you are arguing in this and the preceding section for the following parameters: intensity of the LAD region. ie small, telomere-proximal, more euchromatic, AND less "intensely" associated.

● What is a "small" LAD? 100 kb or less? In Figure 2 (HCT1016, log 2 ratios), the original observation that leads into a discovery of changing NL associations through the cell cycle, the LAD that changes appears to be at least average size. Perhaps a "small" LAD adjacent to an "average" LAD. Nor do the signals appear to be all that low. There are regions within this sub-chromosomal plot that do appear to be "small" "low intensity" LADs. I am uncertain what parameters are defining these attributes. Are the cut-offs the same between cell types (ie is there a rule here?).

We do not set any cut-offs for any features that we compare with. We took the strategy to define stable and dynamics LADs (Fig. 3C) and ask whether there are differences in feature distributions, including LAD size, replication timing and other features. As you can see in Fig. 3E, LADs with decreasing NL interaction are smaller than stable or increasing LADs. This strategy is consistent between cell lines. To assist the reader in following our reasoning, we have added LAD domains and their differential status to Fig. 3B.

● The rules outlined above seem to break down across the different cell types. In particular, the number of active genes per Mb seems to have very little correlation overall with LADs that change. In addition, it is very unclear if "LAD size" is really a readout of both size AND intensity of interactions (understanding that this is not necessarily a direct quantitative measure of interactions).

This comment reflects our reasoning why we added a comparison between cell types in Fig. 4. Indeed, we find no general trend that active gene density correlates with LADs with decreasing NL interactions in every cell type. In contrast, LADs with decreasing NL interactions are consistently close to telomeres and smaller in size than stable or increasing LADs. We made it clearer that LAD size solely reflects the genomic size in basepairs.

**Correlation of pA- determined LADs that change into G1/S with B-compartment sub-types**

● There is certainly Hi-C data on most (all?) of the cell types analyzed in this manuscript. It would be very useful for the authors to parse out how the gain/loss LADs correlate with the B1, B2. A1, A2 (etc) compartment classifications. This may help to address the point above.

We have now included a comparison with Hi-C sub-compartments (Fig. 4F).

**Nucleosomal pattern of pA-DamID digestion/amplification (figure S3)**

● Onset of apoptosis needs to be ruled out. The nucleosomal (laddering) pattern could be due to DNA getting cleaved through programmed cell death pathways after permeabilization. These fragments could easily be amplified by the subsequent DamID protocol.

Amplification of apoptotic fragments, if present, is visible in DamID assays using the negative controls. Every library preparation, we include one or more negative controls in which we omit DpnI. If apoptotic fragments are present in this negative control, these can ligate to the DamID adapter and result in amplification, which we consistently do notsee. We have added a supplementary figure that shows this (Fig. S3A).

**Definition of 'bulk' assays**

● All of the assays were done in bulk. Some were synchronized, some were not. This is important since the implication is that anything not 'bulk' is single-cell. Throughout the manuscript and in the figures, please refer to the conditions as 'synchronized' versus 'unsynchronized'

The reviewer is correct that our terminology is wrong. We changed all occurrences of “bulk” to “unsynchronized”.

**Much of supplemental Figure 6 should be in a main figure**

● It is puzzling why the first (and most easily seen/interpreted) description of LAD organization relative to telomeres/centromeres after exit from mitosis is relegated to supplemental figures. It is a foundational experiment(s) for the paper.

We have changed the zoomed-in Fig. 3B with a chromosome overview that better captures this main observation. We see the remainder of Fig. S6 as technical controls and details of the experiment that are useful to include but not necessary as main figure.

**pA-Dam is possibly influenced by cell-cycle related chromatin accessibility (particularly at mitotic exit)**

● During the transition from mitosis to early G1, there are dynamic changes to chromatin state that are directly coupled to the cell cycle. A recent report, for instance, highlights that interactions of antibodies (or other proteins) with H3K9me2/3 modifications is likely influenced by phosphorylation of histone tails. The dynamics of histone modification/chromatin state possibly occluding or interfering with the interpretation of the results must be discussed.

Similar to DamID, pA-DamID utilizes a Dam-control to measure DNA accessibility and control for this. We show that a change in pA-DamID score is due to changes in NL reads, while the Dam reads do not change (Fig. S6F). In other words, we find no evidence that a change in chromatin state impacts the accessibility as measured by our Dam-control and thereby influences the results. We now repeat this observation in the discussion.

Reviewer #3 (Significance (Required)):

N/A

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Referee #3

Evidence, reproducibility and clarity

In the manuscript by Schaik et al (Van Steensel laboratory) the authors describe a very clever approach to identifying Lamina Associated Domains (LADs) using the principles of the 'cut-n-run' strategy. Specifically, they engineer the Dam methyltransferase used in canonical DamID in frame with a protein A moiety capable of interacting with an antibody (in this case lamins--B1, B2 and A/C). After permeabilization, cells are incubated with antibodies, then pA-Dam purified protein--for a brief time window--is added to mark associated DNA with GmATC. This technique is a valuable contribution to the field, particularly since, as the authors point out,an advantage of pA-DamID is that the labeled DNA can also be visualized in situ using the m6A-Tracer, before this DNA is sequenced. This allows for validation of findings and is highly amenable to cell sorting technologies. In addition, this technology allows for a time-resolved measure of LADs not currently available by standard DamID. The authors apply this technology to four different cell types. They noted that the 'maps' generated by the is technology differed from canonical DamID at very specific regions (small LADs in very localized regions) . They then embark on a series of experiments to show that these differences arise from cell cycle -related differences that are differentially picked up by the methods--with the pA-DamID allowing for dissection of more discrete cell cycle stages/configurations. In general they find an initial preference for sub-telomeric LADs to associate with the nuclear lamina fist, then more centromeric. There is some data suggesting loss/gain of LADs in specific regions/with specific features. The manuscript is well written and the data well presented. However, there are some points that need to be addressed . Overall, there is some oversimplification or omission of previous data in the field, a lack of clarity in how some of the data was interpreted, and some areas where clarification and/or additional analyses would be helpful. I sincerely hope the authors find the following critiques to be useful. Thank you for the opportunity to review your very nice work.

Introduction:

Microscopy studies found that telomeres are enriched near the NL in early G1 phase, leading to the hypothesis that telomeres may assist in NL reassembly onto chromatin [13].

● There have been numerous studies identifying the timing and disposition of INM proteins and Lamins at m the end of mitosis (during NE reformation). Why are you citing just this one? (e.g. ​Thomas Dechat et al., 2004; T. Dechat et al., 2000; Ellenberg et al., 1997; Haraguchi et al., 2001​)

Furthermore, during S-phase B-type lamins have been found to transiently overlap with replication foci in the nuclear interior, at least in some cell types [20]

● While, technically, this has indeed been reported, this study is from 1994 and has not been repeated. The cells used in this study (3T3 fibroblasts) are widely used and others have not noted this phenomenon. Soften this.

Other studies have indicated that lamins are important for DNA replication [reviewed in 21].

● Likewise, direct roles for lamins in replication are controversial (acknowledged in the small section of the cited review on the role of lamins in replication).

● Perhaps combine the two sentences above to soften the implication that this is a "known" role of B-type lamins. e.g. "A handful of studies have implicated a role for B-type lamins in replication, but the direct role of the lamina in this process remains unclear. Nonetheless, ......"

Results:

So far, the cell cycle dynamics of genome - NL interactions have primarily been studied by microscopy. While these studies have been highly informative, they were often limited to a ​few selected loci.​

● Please cite your own study (Kind et al.) and other recent papers (Luperchio et al.-​https://www.biorxiv.org/content/10.1101/481598v1&#x200B;; Zhang et al., Nature-​https://www.nature.com/articles/s41586-019-1778-y&#x200B;) in which they were either 1) not limited to a few selected loci and/or 2) not microscopy-directed studies? There is an argument to be made here for the resolution (time and b.p.) you have achieved through your studies that these studies did not.

● How does this data correlate with TSA-seq, another antibody-based method developed by the Belmont lab, but collaboratively developed for use in identifying LADs (ie Dam alternative) with the Van Steensel group?​ I can imagine there are numerous advantages to this approach (radius of "labeling" being one).

When Dam-Lamin B1 is expressed in vivo for 5-25 hours during interphase, LADs that interact with the NL become progressively labeled, eventually resulting in a layer of labeled chromatin of up to ~1 μm thick [8]. This is because LADs are in dynamic contact with the NL. We expected that in pA-DamID this layer would be thinner, because the NL-tethered Dam is only activated for 30 minutes. In addition, permeabilization depletes small molecules including ATP and thus prevents active DNA remodeling in the nucleus [26]. Indeed, pA-DamID yields a m6A layer that is ~2.5 fold thinner than the layer in cells that express Dam-Lamin B1 in vivo (Fig. S2A-C). This is not an artifact due to collapse of chromatin onto the NL caused by the permeabilization, because permeabilization of cells expressing Dam-Lamin B1 in vivo did not significantly reduce the thickness of the m6A layer compared to directly fixed cells (Fig. S2C). The thin layer of labeled DNA obtained by pA-DamID points to an improved temporal resolution of pA-DamID compared to conventional DamID.

● I think this requires a bit more care. Your previous work clearly demonstrates LADs are dynamic. Others in the field have shown that these domains are also constrained within the larger sub-chromosomal compartment (self-interaction) of LADs (e.g. Luperchio 2018) within a chromosome. So, this is truly a temporal "snapshot" that may miss some regions of LADs that are less directly (or more dynamically) associated with the lamina, but still compartmentalized into the larger LAD sub-chromosomal compartment. It is unclear if the treatment used for this study perturbs these LAD-lamina​ dynamic​ interactions--one can imagine that the LADs are much less mobile generally under the protocol described in your supplemental information. In other words, ​LADs don't collapse, nor do they behave in the same way they would after permeabilization​. The technique has compromised some of that --which is actually fine for most of the purposes in this manuscript, but this needs to be discussed.

● In addtion, imaging data showing dam-LaminB1/2 plus m6A-tracer is missing (figure S2). This should be included. Is the intensity of the "tracer" similar between conditions? If so, were the exposures kept constant in all images? This is important since the decay rate is highly related to intensity of signal.

In some cell types, especially in​ HCT116 and ​hTERT-RPE​ cells, we noted local discrepancies between the two methods (Fig. 2A,bottom panel). These differences involve mostly regions with low signals in DamID that have higher signals in pA-DamID. However, such differences are not obvious in HAP-1 and K562 cells.

● Only HCT116 data is shown in the indicated figure. hTERT-RPE cells are shown in the accompanying supplemental figure and use a different antibody (lamin B2) as the target for the pA-Dam.

● This brings up another point: the data (log2 ratio schema) shown in figure 2 is for HCT116 lamin B1 pA-Dam. Yet, the subsequent studies for transient/building interactions during G1 and into S (Figure 3) are done in hTERT-RPE cells using lamin B2. To be consistent, data from lamin B2 should be used in both figures (it seems lamin B2 data is available for all cell types). The comparison of Dam-Lamin B1 can be addressed in the Venn overlays (as they are now) and in the supplements. The hTERT-RPE data should be in Figure 2 since it is followed up on in the subsequent figure (ie it fails to meet the definition of being relegated to 'supplemental' data).

suggesting that the separation of LADs and inter-LADs becomes progressively more pronounced after mitosis. Nevertheless....

● This is overstated, especially given the previously mentioned work (Luperchio, Zhang). More accurate to say LADs ​association with the nuclear lamina becomes more pronounced​. LADs (predominantly B-compartment) and inter-LADs (predominantly A-compartment) show much earlier separation from each other. This may be distinct from association with the lamina. This is an important distinction as it may lead to different hypotheses regarding mechanisms of LAD targeting/association with the lamina.

Progression from prometaphase to late telophase in HeLa cells takes about 1 hour [33], suggesting that this timepoint captures the initial interactions with the reforming NL. Remarkably, the majority of these interactions is shared with later time points, indicating that most LADs can interact with the NL throughout interphase and are defined (and positioned at the NL) very soon after mitosis.

● There is wide variability in this number, some cells rapidly exit, others take significantly longer. This number is an average (and, for what it's worth, based on a very compromised cancer cell line). The "interactions' mapped are likely reflecting the ensembe measurements of the many cells that have transited into G1. Also, this statement seemingly directly contradicts the premise of many of your following data/interpretations of a sort of step-wise wave or prefered interactions from telomere proximal toward centromeric regions. This also disagrees with your previous work (Kind et al) and more recent work regarding positioning to the NL very soon after mitosis. Again, this is BULK (many cells of a continuum of configurations) versus single cell observations. This is overstated.

We next looked into characteristics of the dynamic LADs. At early time points, LADs with decreasing interactions do not have lower pA-DamID scores than stable LADs, suggesting that their ​detachment from the NL is not simply due to weak initial ​binding

● The methods used here are dynamic proximity measures. Words like "binding" and "attachment" should be avoided (use interacting, associated, etc )

LAD dynamics are linked to telomere distance and LAD size in multiple cell types

● Perhaps I am missing something, but I find relatively little data showing centromere-proximal LADs across cell cycle stages (referring here to Log2 ratio plots similar to what is shown for telomere-proximal LADs, Supplemental figure 6 is the only place where this is obvious.).

● In addtion, it seems to me that you are arguing in this and the preceding section for the following parameters: intensity of the LAD region. ie small, telomere-proximal, more euchromatic, AND less "intensely" associated.

● What is a "small" LAD? 100 kb or less? In Figure 2 (HCT1016, log 2 ratios), the original observation that leads into a discovery of changing NL associations through the cell cycle, the LAD that changes appears to be at least average size. Perhaps a "small" LAD adjacent to an "average" LAD. Nor do the signals appear to be all that low. There are regions within this sub-chromosomal plot that do appear to be "small" "low intensity" LADs. I am uncertain what parameters are defining these attributes. Are the cut-offs the same between cell types (ie is there a rule here?).

● The rules outlined above seem to break down across the different cell types. In particular, the number of active genes per Mb seems to have very little correlation overall with LADs that change. In addition, it is very unclear if "LAD size" is really a readout of both size AND intensity of interactions (understanding that this is not necessarily a direct quantitative measure of interactions).

Correlation of pA- determined LADs that change into G1/S with B-compartment sub-types

● There is certainly Hi-C data on most (all?) of the cell types analyzed in this manuscript. It would be very useful for the authors to parse out how the gain/loss LADs correlate with the B1, B2. A1, A2 (etc) compartment classifications. This may help to address the point above.

Nucleosomal pattern of pA-DamID digestion/amplification (figure S3)

● Onset of apoptosis needs to be ruled out. The nucleosomal (laddering) pattern could be due to DNA getting cleaved through programmed cell death pathways after permeabilization. These fragments could easily be amplified by the subsequent DamID protocol.

Definition of 'bulk' assays

● All of the assays were done in bulk. Some were synchronized, some were not. This is important since the implication is that anything not 'bulk' is single-cell. Throughout the manuscript and in the figures, please refer to the conditions as 'synchronized' versus 'unsynchronized'

Much of supplemental Figure 6 should be in a main figure

● It is puzzling why the first (and most easily seen/interpreted) description of LAD organization relative to telomeres/centromeres after exit from mitosis is relegated to supplemental figures. It is a foundational experiment(s) for the paper.

pA-Dam is possibly influenced by cell-cycle related chromatin accessibility (particularly at mitotic exit)

● During the transition from mitosis to early G1, there are dynamic changes to chromatin state that are directly coupled to the cell cycle. A recent report, for instance, highlights that interactions of antibodies (or other proteins) with H3K9me2/3 modifications is likely influenced by phosphorylation of histone tails. The dynamics of histone modification/chromatin state possibly occluding or interfering with the interpretation of the results must be discussed.

Significance

N/A

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Referee #2

Evidence, reproducibility and clarity

The paper describes a new method for detecting Lamin associated DNA domains, which allows better time resolution than classical DamId. It is a good idea and its functionality is demonstrated in tissue culture cells. There are minor insights but it is important that we advance the field with new and better technologies, thus this version amply suffices to give evidence of that.

Significance

The audience is all persons working on chromatin organization in the nucleus, which is a large audience. The data are clear as they basically are proof of principle for a new technique. There is nothing major to request as revision. They might cite papers on damID in worms and tissue specific applications of this in living organisms, as this is likely to be the situation that is most interesting in the long run. The resolution (in bp) would be interesting to know and validate.

I have no other major revisions to request.

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Referee #1

Evidence, reproducibility and clarity

In this report, van Schaik et al., modified an established CUT and RUN method and combined it with previously used DamID to identify Lamin Associated Domains (LADs) with better temporal resolution. Previous DamID experiments labeled locations where lamin proteins were present within a 5-25 hour window while the new technique, pA-DamID, labels DNA within a 30 minute window providing better temporal resolution. The authors used this technique to identify LADs at multiple stages of the cell cycle and applied this protocol to different cell types. The authors FIND differences when comparing data sets between cell cycle time points and cell lines.

Major points:

1) The data sets generated and displayed in this manuscript seem incomplete. In Figure 1G, the authors compare lamin B2 vs. lamin B1 generated LADs in HAP-1 cells and lamin A/C vs lamin B2 LADs in hTERT-RPE cells. In figure S4, panel C compares lamin B1 and lamin B2 in K562 cells and lamin B2 and lamin A/C in hTERT-RPE cells. It would have been informative to have a complete dataset for lamin B1, lamin B2, and lamin A/C identified LADs in all cell lines analyzed. The information provided from these datasets would be useful to the scientific community.

2) The authors discovered that LADs reposition during progression through the cell cycle. It would have been interesting to know whether these changes have transcriptional consequences? One could perform RNA-SEQ experiments to discover if LAD occupancy results in transcriptional changes and choose a few genes to confirm the findings with RT-PCR. Is this the same for lamin B1, lamin B2, and lamin A/C occupied LADs? Analyze if there are any genomic features such as CTCF or transcription factor binding sites that correlate with the loss of LADs.

3) The authors state that using H3K27me3/H3K9me3 in pa-DamID showed no enrichment. This is surprising considering that both modifications are enriched in heterochromatin and at the nuclear periphery. It appears that the peripheral enrichment is masked by the larger overall internal pool. The authors should discuss this observation and comment on the sensitivity of the method to detect local enrichment versus the global levels of a protein or modification in pa-DamID.

Minor points:

Figure 1: Change colors for Figure 1F and Figure 2D. The colors are hard to discern.

Figure 2B: Please mark which antibody was used for this analysis.

Figure 2C: Please also overlay data from pA-DamID lamin A/C experiments.

Figure 4: Please mention which antibody was used for the pA-DamID experiments used to generate this dataset.

Figure 5: Please mention which antibody was used for the pA-DamID experiments used to generate this dataset.

Figure S5 C and D: Please mention which antibody was used for the pA-DamID experiments.

Significance

The major contribution of this manuscript is the description of an improved method to map LADs. This is a valuable contribution. By using this new method, the findings of this paper provide some new insight in LAD dynamics throughout the cell cycle although the experiments are largely phenomenological. This is a technically sound study.

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Reply to the reviewers

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Reply to the Reviewers:

We would like to thank the reviewers for taking the time to provide us with insightful and constructive comments, which helped us in improving the manuscript. We have performed additional experiments and data analysis while also improving the presentation of our analysis. In addition, we have noted one experiment (bulk RNA-sequencing of pax2a-Low and pax2a-High thyrocyte population) for further revision to address the concerns raised by Reviewer #1.

Please find below our point-by-point response. Please note that the figure references in the response refer to the ‘Revised Manuscript’, ‘Revised Figures’ and ‘Revised Supplementary Data’ file.

Reviewer #1:

Gillotay et al. performed an unbiased profiling of the zebra fish thyroid gland and captured different cell populations by single-cell gene expression analysis. Using bioinformatic tools, they identified seven clusters corresponding to expected, but also poorly characterized, sub-populations, such as non-follicular epithelial cells. They also found two transcriptionally distinct types of thyrocytes and validated this heterogeneity using a new transgenic Pax2a reporter line. Using this tool, they identified and located Pax2a-low and -high thyrocytes within thyroid follicles. Finally, they highlighted a dense intercellular signaling network based on ligands expressed by the diverse sub-populations present in the thyroid and receptors expressed by the thyrocytes. This is a descriptive work calling for more in-depth analyses.

The authors thank the reviewer #1 for acknowledging the strengths of the manuscript and for stating that it could be of interest to a larger audience. We appreciate the reviewer’s advice on supportive experiments and for improving the clarity of the analysis and presentation. We have tried to experimentally address the concerns of the reviewer and hope this provides in-depth substantiation of our observations.

**Major comments on main conclusions :**

Conclusion 1 : Identification of 7 clusters

• This reviewer was particularly surprised by the relative abundance of the different sub-populations identified. For example, 267 thyrocytes out of 6249 cells from the thyroid gland is less than 5% of the total thyroid cell number. In comparison, authors identified three times more immune cells or non-follicular epithelial cells. Authors should comment on these numbers, on the dissection (contaminants?) and dissociation procedure.

Do these relative abundance reflect the proportion of thyrocytes, immune, stromal cells they normally observe in adult thyroid sections ? It would be interesting to have a better resolution for figure 6A in order to evaluate the number of nuclei stained only with DAPI as compared to nuclei stained with DAPI and surrounded by E-cadherin staining. Based on the image, this reviewer seriously doubts that follicular cells represent less than 5% of the total cell number in this organ, considering that the colloid is a cell-free zone.

We fully agree with the reviewer statement. We have clarified the nature of the dissociated tissue in Results, Methods and Fig. 1 C- G. In this, we have performed 3D confocal imaging of the region utilized for single-cell RNA-Seq. (Fig. 1C). Further, we quantified percentage of thyrocytes in transverse sections across the dissociated region (Fig. 1 D – E). Our results demonstrate presence of 5.9 ± 1.9 % thyrocytes in the region. Lastly, we provide the FACS plots of the cells utilized for single-cell RNA-Seq. In this, we obtained around 4% thyrocytes among the live cells. Taken together, our quantifications of the thyrocyte proportion in the tissue matches well with the percentage of thyrocytes obtained in the single-cell atlas, suggesting lack of thyrocyte loss during the procedure.

We agree with the reviewer’s observation that the follicle lumen represents a cell-free zone. However, it is worth noting that the cells surrounding the follicles, in particular gills and stroma, have a higher cell density. Additionally, the zebrafish thyroid follicles sit loosely on the ventral aorta, thus making it difficult to manually separate the follicles from the surrounding tissue without destroying the organ. We avoided injuring the organ to minimize cell-death associated with manual dissection.

• When using the webtool developed on the thyrocyte population, one can notice that only a small fraction of the thyrocyte population expresses common thyroid-specific genes such as Tpo or duox. Was this expected ? It would be interesting to comment on this observation and confirm using standard localization technique to demonstrate that this is real and not due to the sequencing. Is it specific to the zebrafish ? On the other hand, Tg is expressed in most thyrocytes, but surprisingly also in all the clusters at a fairly good level. This should be commented... Is it normal ? due to the sequencing quality ? or clustering ?

We believe these are technical issues related to single-cell sequencing and thank the reviewer for their insight in seeing this.

The non-uniform expression of thyroid-specific marker genes (Tpo and duox) likely represents dropout effects, possibly due to the low expression levels of these genes. To address this issue, we propose to perform bulk RNA-Seq. of thyrocytes (segregated by pax2a expression levels). Bulk RNA-Seq. is more sensitive than single-cell RNA-Seq. and should provide the expression levels of these genes in the thyrocytes.

The expression of Tg in non-thyrocyte population likely represents cross-contamination of free RNAs released from ruptured cells. Since Tg mRNA is highly expressed in the thyroid follicular cells, release of the mRNA from a few injured cells would contaminate the cell suspension, leading to its detection in non-thyroid cells. However, the expression represents background noise signal. To test this, we utilized DecontX, a recently developed approach for background correction (S. Yang et al. 2020). In this, the expression of a gene is modelled as a mixture of expression in the expected population plus background expression. With this, we could robustly reduce Tg mRNA expression in non-thyrocytes (Supp. Fig. 3). This supports our hypothesis that the Tg expression in non-thyrocytes likely represents cross-contamination of mRNA from ruptured cells.

• In order to validate and locate the different populations identified in the thyroid, this reviewer suggests to perform in situ hybridization or immunostaining, based on the specific marker genes identified in each cluster. This experiment could lead to the precise identification of the different sub-populations and their respective localization. These experiments would also help in the interpretation of the cellular interaction network.

We have characterized the different cell types surrounding the thyroid follicles using various reporter lines. The data is presented in Fig. 4.

Conclusion 2 : two distinct types of thyrocytes

• This is an interesting observation. However, from a non-expert it is difficult to understand why the authors propose two populations. Based on the points distribution (Figure 4A), this reviewer would rather identify 3 or 4 clusters but not the two shown in red and blue.... Did the authors impose two populations for the clustering ? Did they perform a permutation test to confirm the pax2a significant fold change seen between clusters is not a false positive generated by the clustering ? Could they show, as a supplementary file, the same graph with points colored based on Pax2a expression ?

We concur with the reviewer that the number of potential clusters in thyrocyte population might be more than two. In-fact, there is no upper limit to the diversity present in the thyrocyte population. However, our message in the manuscript is that the population is not homogenous, and there are at-least two populations based on pax2a expression level. In this regard, we do believe that we have only scratched the tip of the iceberg and further investigation is needed to completely answer this issue. Nonetheless, we are the first to demonstrate genetic heterogeneity among the population.

The clustering of thyrocytes followed the guidelines suggested by Seurat package. For thyrocytes, we utilized the principal components displaying significant deviation from uniform distribution. For cluster identification, we utilized a resolution of 0.3, which was same as the one utilized for clustering the entire organ (further details on this provided as a response to Minor Concern #3 by Reviewer #1). A plot of pax2a expression, along with tg expression, is provided as Supp. Fig. 8.

Finally, to strengthen our observation, we conducted independent analysis of transcriptional diversity in the population using a recently developed method called ROGUE (Ratio of Global Unshifted Entropy) (Liu et al. 2019). The method provides genes that display transcriptional heterogeneity within the cell population. Assessment is based on expression entropy, a measure of the degree of uncertainty, or promiscuity, in the expression of a gene (Teschendorff and Enver 2017). For this, we utilized raw counts of thyrocytes so as to provide an alternative analysis of our data. The analysis, presented as Fig. 6D, demonstrates significant entropy, or transcriptional heterogeneity, for pax2a and cathepsin B (ctsba). The full list of genes displaying transcriptional heterogeneity in thyrocytes is provided as Supp. Table 4.

• This reviewer was also surprised by the relatively "heavy" approach (generation of the Pax2a reporter line) used to demonstrate the existence of two types of thyrocytes. Knowing that the reporter line was validated with a very good Pax2a antibody... The use of the reporter line is a bit short. The authors could for example validate the two populations of Figure 4A using the Pax2a FACS-sorted cells and RT-qPCR.

We completely agree with the suggestion of the author and plan to perform bulk RNA-sequencing using the pax2a reporter line to corroborate our results. This is the advantage provided by the generation of knock-in line. In addition, we have performed antibody staining against endogenous pax2a protein (Fig. 8 E – F), which validates the transcriptional heterogeneity observed in our single-cell RNA-Seq. data.

• In addition, data available in the sequencing dataset could be used to prove that the two populations are really active thyrocytes. This reviewer would suggest to present a table with the expression level of common and thyroid-specific genes such as TshR, Nis, Tpo, Duox, Tg, Pax2, TTF1 and other known transcription factors in the two populations to demonstrate that these two types of cells are indeed thyrocytes. Finally, image quality (Figure 6) could be improved and high-magnification images with several thyrocyte marker could be shown to convince the readers.

We strongly agree with the reviewer that this is a very important concern to address. To address this, we have taken three steps:

We have included the expression level of tg in the two populations in Fig. 6C and Supp. Fig. 8. We performed antibody staining against pax2a on thin section obtained from Tg(tg:nls-EGFP) animals (Fig. 8 E-F). In this, we observed pax2a-Low cells with tg reporter expression, suggesting that they are indeed differentiated thyrocytes. We plan to perform bulk RNA-sequencing of cells from pax2a-Low and pax2a-High population. This will allow us to validate the transcriptional differences observed by single-cell RNA-Seq., and allow us to demonstrate expression of thyroid-specific markers genes that are missing from our dataset (for instance, duox).

Conclusion 3 : cellular interaction network

• Most of the interactions revealed by the analysis seem to belong to the extracellular matrix and not to classical ligands such a Wnt, TGFb, FGF, PDGF,..... could the authors comment on this ? Considering that both endothelial cells and epithelial cells assemble their own basement membrane, the analysis will obviously reveal interactions between endothelial cells and epithelial cells....

We appreciate that the reviewer pointed this out. The enrichment of ligands related to extracellular matrix, and not growth factors, likely represents the homeostatic nature of the organ. Growth at 2 and 8 mpf is low (if not absent). Correspondingly, gene expression related to development and cell-cycle might be reduced. As stated in response to the next concern, extending the atlas to juvenile stage (1 mpf) would be beneficial to understand the regulators of cell-cycle.

However, to improve the cellular interaction network, we have incorporated physical information from the characterization of cell-populations surrounding the thyroid follicles (Fig. 4). Our experiments suggested that stromal, gills and NFE do not physically contact the thyrocytes. Thus, interactions based on ligands incorporated into the cell membrane were removed for these cell-populations.

**Minor comments to improve the Ms :**

• Could you explain how from 2 x 12 000 FACS-sorted live-cells (from six animals at each stage; 2 mpf and 8 mpf) you obtain 6249 cells (pooled of 2 mpf and 8 mpf), and why the two stages were first sorted separately and then pooled (?), as no differential analysis is carried out for the two stages.

The number of cells obtained for analysis represents cells that were successfully incorporated into droplets during library preparation and generated high-quality data that passed quality control (Supp. Fig. 2). FACS sorted cells were utilized for droplet generation using 10X Chromium that encapsulates cells with single-Poisson distribution (Zheng et al. 2017). This leads to approximately 50% cell capture rate, which is the ratio of the number of cells detected by sequencing and the number of cells loaded. Thus, we obtained 13,106 sequenced cells from 24,000 input cells (54.6 % cell capture rate). Further, the quality control criteria removed 6,857 low-quality cells (52.3 % dropout rate). We chose a stringent cut-off for quality control so as to remove technical artefacts from the analysis. We have added these detail to the Result section.

We pooled the two samples as the stages represent the range of homeostasis in zebrafish. We decided not to include differential expression between the two stages as the number of cells in multiple clusters were too low for individual stages (less than 100), and thus not trustworthy. It future, it would be of interest to extend the analysis for rapidly-growing juvenile (less than 1 mpf) and old-age (greater than 1.5 ypf animals) animals and to perform single-cell or bulk RNA-Seq. with high cell numbers. We have mentioned this drawback in the discussion section.

• Which method was used for the graph-based clustering ? KNN ? Louvain ? Random walk ?

The details have been added to the Method section. Specifically, the clustering was performed using graph-based method, shared nearest neighbour (SNN), which is default for Seurat 2.3 package.

• How did you define the numbers of clusters ?

The number of clusters were defined by using the first five principal components as they displayed significant deviation from uniform distribution as accessed by JackStraw analysis. Further, a resolution of 0.3 was used in Seurat as the clusters generated by this parameter could be annotated using a cell-type specific marker from literature.

• Figure 4B, the color-code for the expression level would help the reader.

The color code has been added (Fig. 6B in revision).

• Figure 4C, violin plot for Pax2a: why do we find cells that do not express this gene in the two populations ? The same is true for tbx2a and ahnak ... is the clustering optimal ?

The detected expression of pax2a depends on its biological expression and technical dropout rate. Thus, the pax2a-High cluster also contain cells with no detectable expression of pax2a. Similar detection dynamics can be expected for other genes.

We have experimentally validated the variability in pax2a expression using antibody staining for endogenous pax2a protein in tg:nls-EGFP transgenic line (Fig. 8 E-F). With this, we can validate the presence of pax2a heterogeneity within thyrocytes.

• Figure 4C, blue violin plot for ptp4a3 does not seem to fit with the distribution of the points.

Due to the high number of cells that do not express ptp4a3, the cells collapse on each other at the bottom of the graph, thus making the violin plot seem different from the distribution. However, the plots were made with Seurat without changing any parameters.

• what is the function of tbx2a, ahnak, ptp4a3 and dusp5, which are not mentioned in the text.

The genes have been implicated in regulation of cell proliferation. However, we acknowledge that the evidence based on a handful of genes needs to be strengthened. For this, we have removed the figure panels from the revision, and will instead identify genetic markers based on bulk RNA-sequencing analysis of pax2a-High and pax2a-Low population.

• Line 195: "pax2amKO2 reporter expression perfectly overlapped with PAX2A antibody staining". This reviewer would be more cautious as the images (Fig. 5 C, D and F) do not show a perfect colocalization: one can observe only blue or only red staining.

We have edited the text to “The pax2apax2a-T2A-mKO2 (abbreviated as pax2amKO2) reporter expression overlapped with PAX2A antibody staining in a majority of regions at 9.5 hours post-fertilization.” The regions with single colors in Fig. 5C (Fig. 7C in revision) are due to differences in staining intensity between different regions.

• Line 246: it is proposed to "study the functional and replicative differences among the two sub-populations of thyrocytes". This reviewer completely agrees and the suggestion made (vide supra) to use the datasets to assemble a table with the expression level of common and thyroid-specific genes such as TshR, Nis, Tpo, Duox, Tg, Pax2, TTF1 and other known transcription factors in the two populations could already give some indications on the functionality of these two types of cells. Expression of genes involved in cell-cycle control and/or apoptosis would be another possibility to better characterize the two populations. Lastly, the authors could perform the comparative analysis of ligand-receptor pairs between these two sub-populations to examine if they differentially interact with their environment.

We agree with the reviewer for the need to characterize the two populations in detail. Using the current dataset, we are limited to the 265 genes differentially expressed between the two thyrocyte states. Thus, we propose to perform bulk RNA-sequencing of the two populations to obtain a better picture of the cellular identity. In this, we will perform sequencing of each population in triplicates. With this, we will avoid the dropout effects suffered by the single-cell analysis. The experiment would demonstrate the differentiation status of each cell population, and provide insights into other pathways active within each population. Further, we will identify ligands and receptors that are enriched in a particular population.

Text improvement:

Intro: thyroglobulin (TG) appears twice (line 46 and 49)

Results: Fig. 5 (not 8) (line 203 and 205)

Figure 3: stromal? (not skeletal)

Figure 4: fold change scale is missing

Figure 5: Thyroid gland (Gland)

Figure sup 2: Fluorescence-activated cell sorting (FACS) of zebrafish thyroid gland

Figure sup 3: number of unique molecular identifier

Figure sup 4: "are present in the zebrafish"

We thank the reviewer for pointing these errors. We have edited them.

Reviewer #1 (Significance (Required)):

The work performed by Gillotay et al. is clearly novel but descriptive. It provides a useful database to propose hypotheses to further study the thyroid gland. The single-cell RNA-seq analysis brings a molecular appreciation of the various thyroid cell populations, thyrocyte heterogeneity and intercellular signaling network. Although focused on the thyroid, results will be of interest to a larger audience than the thyroid community, especially the demonstration (if further and better validated) of thyrocyte heterogeneity and the intercellular communication possibilities.

In response to comments by Reviewer 1, we plan to perform bulk RNA-Sequencing of pax2a-Low and pax2a-High thyrocytes. We believe that this will help address all the remaining concerns of the reviewer.

Reviewer #2:__ __

**Summary**

In this manuscript the authors present a single-cell transcriptome atlas of the zebrafish thyroid gland (possibly also including some adjacent tissues depending on how the dissection was performed, see comments below). The atlas includes cell clusters that are expected to be found in the thyroid of any higher vertebrate species (thyrocytes, blood vessels, lymphatic vessels, immune cells and fibroblasts), but also musculature/gills and a less well-defined population of non-follicular epithelium. The data will be made publicly available as a resource, by what seems to be a user-friendly web-interface (more accessible to a broader audience than customary raw sequencing data deposition, that I suppose will also be provided). The results are used to describe putative autocrine or paracrine signaling networks. The authors are able to further subdivide the thyrocyte cell cluster into two sub-populations with different transcriptomic features. Interestingly, these populations differ in their expression of for instance the key transcription factor pax2a, which is further demonstrated by the use of a novel zebrafish reporter strain.

In general, this is a clearly interesting, novel, nicely structured and well written manuscript and the data presented seems to be of high quality.

We would like to thank reviewer 2 for the constructive comments. We are glad that the reviewer finds our work interesting, novel and of high quality. We appreciate the reviewer’s advice on additional experiments, analysis and on improving the clarity of the text. We have addressed all the concerns raised, and hope that our revised manuscript satisfies the reviewer.

**Major comments** Key conclusions are convincing and performed with scientific rigor. As will be further discussed below the seemingly superficial description of the extent of tissue that was subjected to transcriptome analysis makes it a bit difficult to assess reproducibility outside the authors' lab.

We acknowledge the lack of clarity in the description of the tissue utilized for single-cell analysis. We have corrected this providing a detailed step-by-step protocol for dissecting the organ in Methods Section, titled ‘Dissection of the zebrafish thyroid gland’. Additionally, using immunofluorescence based imaging of the region and FACS, we estimate the proportion of thyroid follicular cells within the region. The results are presented in Fig. 1 C – G.

As far as I can see very little methodological detail or information is provided about how the dissection of the thyroid region was performed, more than that "the thyroid gland was collected" or that "we dissected out the entire thyroid gland". This is essential to understand the significance of the cell populations that are described based on the transcriptomic data. The section "Tissue collection" describes dissection of the thyroid for whole-mount imaging. From Fig. 6A it seems that substantial amounts of non-thyroid tissue are included in this dissection. Is it the same kind of dissection that was performed for transcriptomics? Was the string of thyroid follicles shelled out from their surroundings or was some kind of en bloc dissection, including other neighboring tissues, performed (as suggested from the transcriptome cell populations data including e.g. gill transcripts)? In the latter case it would be good if the authors discuss in more detail what other tissues or structures that are expected to also be included in the dissected tissue and transcriptomic data.

In response to this concern of the reviewer, we have improved the clarity of the text in Results and Methods section. We have added the following text to the Result section, “The zebrafish thyroid gland is composed of follicles scattered in the soft tissue surrounding the ventral aorta (Fig. 1 A, B). Ventral aorta extends from the outflow tract of the zebrafish heart and carries blood from the ventricle to the gills. Dissection of the ventral aorta associated region (detailed in Methods section) provided us with tissue that included the thyroid follicles and parts of zebrafish gills (Fig. 1C). Using Tg(tg:nls-EGFP) transgenic line, which labels thyrocytes with nuclear green fluorescence (Fig. 1D), we estimated presence of 5.9 ± 1.9 % thyrocytes within the dissociated region (Fig. 1E).”

Further, the Methods section now defines a step-by-step protocol for dissociation (‘Dissection of the zebrafish thyroid gland’).

In addition, we have improved the characterization of the dissected region, as stated in response to the next comment.

It would facilitate understanding if the thyroid is outlined in Fig. 1A as well as the region that was dissected for downstream single cell sequencing.

A whole-mount 3D reconstruction of the region is presented in Fig. 1C. A transverse section from the region is presented as Fig. 1D, while quantification of the percentage of thyrocytes in the transverse sections is provided in Fig. 1E.

The clusters seem logical given what cell types that could be expected in the region (but depending on how dissection was performed). The only exception is cluster 7 (non-follicular epithelium; NFE). I do understand that relative sizes of the clusters do not necessarily reflect the endogenous relative abundance of different cell types, as I guess they may be more or less prone to enzymatic dissociation, survival etc. Nevertheless, the number of cells in the NFE cluster (831 cells) seems sizeable relative to the number of thyrocytes (267 cells). In my opinion, a major weakness of the current manuscript is that little detail is provided about this cell population and that no attempt to at least spatially localize these cells is presented.

Detailed characterization of the cell-populations surrounding the thyroid follicles is now presented in Fig. 4. In addition, we have quantified the percentage of thyrocytes in the region (Fig. 1 E), which demonstrates that thyrocytes represent 5.9 ± 1.9 % of the cell-population. Additionally, we have presented FACS analysis of the dissociated region as Fig. 1 F - G, which corroborates the imaging based quantification. Both quantifications are in the same range as the proportion of thyrocytes identified in the single-cell analysis (4.27 % - 267 out of 6249 cells). Thus, we do not believe that cell-loss had a big impact on the relative abundance of thyrocytes in the single-cell atlas.

A detailed characterization of NFE cells is provided in response to the next three comments, which includes visualization of the population using TP63 / p63 antibody staining in Fig. 4D. Particularly, Fig. 4D contains 72 thyrocytes and 302 TP63+ nuclei, thereby pointing to the higher relative abundance of NFE in the region.

The NFE cells are characterized by TP63 expression and the authors speculate that they might show homology to main cells of solid cell nests. From previous zebrafish literature it seems like what is supposedly ultimobranchial bodies (or ultimobranchial glands more similar to those in avian species) are located rather distant from the thyroid follicles (Alt et al 2006). Is it possible that these structures are included in the dissection that has been performed? As solid cell nests are supposed to be ultimobranchial body remnants (with calcitonin positive and calcitonin negative epithelial cells) it would be good if the authors discuss in more detail what is known about the ultimobranchial bodies in zebrafish, if they are located inside the zebrafish thyroid, in an anatomical region that is included in the dissected tissue of this study or in a region that is likely not included.

As stated by the reviewer, the ultimobranchial bodies lie distant to the thyroid gland. They lie as a pair of follicles on top of the sinus venous (Alt et al. 2006), which is a blood vessel that delivers blood to the atrium. In contrast the thyroid follicles sit loosely on top of ventral aorta that connects to the ventricle via the outflow tract (Fig. 1B). Thus, the collection of the ultimobranchial bodies is not expected. This is also corroborated by the absence of calcitonin (calca) expression in the NFE (Supp. Table 1). This has been added to Discussion section.

In higher vertebrates, P63 expression is typically seen in basal cells of stratified epithelia (as for instance in the esophagus), in myoepithelial cells, in the urothelium and in the thymus. Is it possible that the TP63 expressing NFE population corresponds to cells of the zebrafish thymus (that might perhaps explain the seemingly large immune cell population in cluster 4)? Could TP63 expressing NFE cells represent the esophagus (if included in the dissection)? As so little detail is provided about the dissection procedure this opens up for speculation and it would be good if the authors discuss these possibilities, as some of them might perhaps be unlikely or even impossible given regional anatomy of the zebrafish and how the dissection was performed.

The dissection region is now characterised in detail in Fig. 1 C – E. The presence of immune cells (macrophages) in the proximity of thyroid follicles is validated in Fig. 4B. The presence of NFE is presented as Fig. 4D, and explained in detail in response to the next comment.

To gain better understanding of the sizeable TP63 expressing NFE population the authors briefly mention the possibility of future studies utilizing a TP63 reporter. If a reporter line is not available, immunofluorescent detection of P63 (as presented for PAX2A in Fig. 5 and E-cadherin in Fig. 6) would be desirable to provide more insight into the location of the NFE population. Given the proficiency the authors demonstrate in this manuscript when it comes to zebrafish imaging, at least whole-mount immunostaining of P63 in the region that was dissected for transcriptome analysis seems clearly feasible, both with respect to resources and time needed (perhaps in the range of 1-3 months).

To clarify the presence of NFE cells, we have followed the suggestion of the reviewer and performed immunostaining against TP63. The result is presented as Fig. 4D. The staining was performed on thin (8 µm) sections, allowing us to ensure uniform antibody penetration. As depicted in the image, TP63+ cells are part of the gills. This population possibly represents a progenitor population, similar to the TP63+ basal layer in the zebrafish (Guzman et al. 2013) and mammalian (A. Yang et al. 1999) epithelium. Additionally, a sub-set of TP63+ cells were observed in the region between the thyroid follicles and gills. Our data provides an exciting opportunity for an in-depth study of these cells in future, particularly using tp63-regulatory region driven transgenic reporter and Cre lines.

**Minor comments** It is a little bit confusing that different color coding for the various cell populations is used in Fig. 3B as compared to Figs. 1 and 2.

The color coding for Fig. 3B (Fig. 5B in revised manuscript) has been modified in accordance to the once used in Fig. 1 and 2.

In the discussion of the intercellular interaction network (Fig. 3B) the authors clearly point out that anatomical barriers are not modelled. Nevertheless, it would be more informative if this description was able to sort out ligands that are secreted, from ligands that are not secreted and would require physical interaction between thyrocytes and a different cell population for signaling to occur.

We have now improved the intercellular network to resemble the putative physical interactions. By characterizing the different cell-populations present in the atlas (Fig. 4), we recognized that gills, stromal and NFE are not in physical proximity of the thyrocytes. Thus, these three cell-populations would not be able to communicate via ligands attached to the cell membrane. Hence, we have pruned the network to remove cell-membrane attached ligands for these three cell-populations. Only secreted ligands were considered for the mentioned cell-populations. In accordance, the figure and Supplementary Table 2 has been updated.

The authors describe thyroid solid cell nests as "... lumen containing irregular structures located between the thyroid lobes in mammals...". Solid cell nests of the thyroid in higher vertebrates (e.g. humans and dogs) are located within the thyroid lobes and not between the lobes (i.e. the right and left thyroid lobes). Moreover, the authors write that "Recently, epithelial cells have been reported in a structure called the Solid Cell Nests (SCN) of the thyroid..." and give reference to a paper from 1988. If that is recent or not might be a matter of opinion, but to the best of my knowledge, solid cell nests were describe already by Getzowa in 1907 and I suppose that the epithelial identity (or at least epithelioid morphology) has been appreciated for long.

We thank the reviewer for pointing this out. We have added the reference to the original study by Dr. Sophia Getzowa identifying SCN (Getzowa 1907). As the original study is in German, we have also added a reference to a recent article attributing the discovery of SCN to Dr. Getzowa and performing immunohistochemistry analysis of the tissue (Ríos Moreno et al. 2011). Notably, the authors note the presence of TP63 staining, along with the absence of TG and Calcitonin staining, in SCN main cells – similar to the expression profile of NFE in our atlas. Finally, we have edited the text to accurately describe their location in the mammalian thyroid gland.

Reviewer #2 (Significance (Required)): The authors provide a single-cell transcriptomic atlas of the zebrafish thyroid gland. To the best of my knowledge this is certainly a unique resource. In that sense it provides novel and significant information that will surely facilitate our further understanding of thyroid biology. It will surely be of great interest and value to the thyroid community, but probably also to a wider audience interested in e.g. zebrafish biology, endodermal biology and the biology of endocrine glands. Their finding and direct demonstration of transcriptional heterogeneity within the thyrocyte population is very interesting, also in different contexts of thyroid disease. However, I leave it to other referees to comment on the conceptual uniqueness of the current manuscript (i.e. a single-cell transcriptomic atlas of a zebrafish organ). Does it provide conceptually unique information, or does it add to an expanding collection of single-cell transcriptomic atlases of zebrafish organs?

References:

Alt, Burkhard, Saskia Reibe, Natalia M. Feitosa, Osama A. Elsalini, Thomas Wendl, and Klaus B. Rohr. 2006. “Analysis of Origin and Growth of the Thyroid Gland in Zebrafish.” Developmental Dynamics 235 (7): 1872–83. https://doi.org/10.1002/dvdy.20831.

Getzowa, Sophia. 1907. “Über Die Glandula Parathyreodeaa, Intrathyreoideale Zellhaufen Derselben Und Reste Des Postbranchialen Körpers.” Virchows Archiv Für Pathologische Anatomie Und Physiologie Und Für Klinische Medizin 188 (2): 181–235. https://doi.org/10.1007/BF01945893.

Guzman, A., J. L. Ramos-Balderas, S. Carrillo-Rosas, and E. Maldonado. 2013. “A Stem Cell Proliferation Burst Forms New Layers of P63 Expressing Suprabasal Cells during Zebrafish Postembryonic Epidermal Development.” Biology Open 2 (11): 1179–86. https://doi.org/10.1242/bio.20136023.

Liu, Baolin, Chenwei Li, Ziyi Li, Xianwen Ren, and Zemin Zhang. 2019. “ROGUE: An Entropy-Based Universal Metric for Assessing the Purity of Single Cell Population.” BioRxiv, January, 819581. https://doi.org/10.1101/819581.

Ríos Moreno, María José, Hugo Galera-Ruiz, Manuel De Miguel, María Inés Carmona López, Matilde Illanes, and Hugo Galera-Davidson. 2011. “Inmunohistochemical Profile of Solid Cell Nest of Thyroid Gland.” Endocrine Pathology 22 (1): 35–39. https://doi.org/10.1007/s12022-010-9145-4.

Teschendorff, Andrew E., and Tariq Enver. 2017. “Single-Cell Entropy for Accurate Estimation of Differentiation Potency from a Cell’s Transcriptome.” Nature Communications 8 (1): 15599. https://doi.org/10.1038/ncomms15599.

Yang, A, R Schweitzer, D Sun, M Kaghad, N Walker, R T Bronson, C Tabin, et al. 1999. “P63 Is Essential for Regenerative Proliferation in Limb, Craniofacial and Epithelial Development.” Nature 398 (6729): 714–18. https://doi.org/10.1038/19539.

Yang, Shiyi, Sean E. Corbett, Yusuke Koga, Zhe Wang, W Evan Johnson, Masanao Yajima, and Joshua D. Campbell. 2020. “Decontamination of Ambient RNA in Single-Cell RNA-Seq with DecontX.” Genome Biology 21 (1): 57. https://doi.org/10.1186/s13059-020-1950-6.

Zheng, Grace X. Y., Jessica M. Terry, Phillip Belgrader, Paul Ryvkin, Zachary W. Bent, Ryan Wilson, Solongo B. Ziraldo, et al. 2017. “Massively Parallel Digital Transcriptional Profiling of Single Cells.” Nature Communications 8 (1): 14049. https://doi.org/10.1038/ncomms14049.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this manuscript the authors present a single-cell transcriptome atlas of the zebrafish thyroid gland (possibly also including some adjacent tissues depending on how the dissection was performed, see comments below). The atlas includes cell clusters that are expected to be found in the thyroid of any higher vertebrate species (thyrocytes, blood vessels, lymphatic vessels, immune cells and fibroblasts), but also musculature/gills and a less well-defined population of non-follicular epithelium. The data will be made publicly available as a resource, by what seems to be a user-friendly web-interface (more accessible to a broader audience than customary raw sequencing data deposition, that I suppose will also be provided). The results are used to describe putative autocrine or paracrine signaling networks. The authors are able to further subdivide the thyrocyte cell cluster into two sub-populations with different transcriptomic features. Interestingly, these populations differ in their expression of for instance the key transcription factor pax2a, which is further demonstrated by the use of a novel zebrafish reporter strain. In general, this is a clearly interesting, novel, nicely structured and well written manuscript and the data presented seems to be of high quality.

Major comments

Key conclusions are convincing and performed with scientific rigor. As will be further discussed below the seemingly superficial description of the extent of tissue that was subjected to transcriptome analysis makes it a bit difficult to assess reproducibility outside the authors' lab.

As far as I can see very little methodological detail or information is provided about how the dissection of the thyroid region was performed, more than that "the thyroid gland was collected" or that "we dissected out the entire thyroid gland". This is essential to understand the significance of the cell populations that are described based on the transcriptomic data. The section "Tissue collection" describes dissection of the thyroid for whole-mount imaging. From Fig. 6A it seems that substantial amounts of non-thyroid tissue are included in this dissection. Is it the same kind of dissection that was performed for transcriptomics? Was the string of thyroid follicles shelled out from their surroundings or was some kind of en bloc dissection, including other neighboring tissues, performed (as suggested from the transcriptome cell populations data including e.g. gill transcripts)? In the latter case it would be good if the authors discuss in more detail what other tissues or structures that are expected to also be included in the dissected tissue and transcriptomic data.

It would facilitate understanding if the thyroid is outlined in Fig. 1A as well as the region that was dissected for downstream single cell sequencing.

The clusters seem logical given what cell types that could be expected in the region (but depending on how dissection was performed). The only exception is cluster 7 (non-follicular epithelium; NFE). I do understand that relative sizes of the clusters do not necessarily reflect the endogenous relative abundance of different cell types, as I guess they may be more or less prone to enzymatic dissociation, survival etc. Nevertheless, the number of cells in the NFE cluster (831 cells) seems sizeable relative to the number of thyrocytes (267 cells). In my opinion, a major weakness of the current manuscript is that little detail is provided about this cell population and that no attempt to at least spatially localize these cells is presented.

The NFE cells are characterized by TP63 expression and the authors speculate that they might show homology to main cells of solid cell nests. From previous zebrafish literature it seems like what is supposedly ultimobranchial bodies (or ultimobranchial glands more similar to those in avian species) are located rather distant from the thyroid follicles (Alt et al 2006). Is it possible that these structures are included in the dissection that has been performed? As solid cell nests are supposed to be ultimobranchial body remnants (with calcitonin positive and calcitonin negative epithelial cells) it would be good if the authors discuss in more detail what is known about the ultimobranchial bodies in zebrafish, if they are located inside the zebrafish thyroid, in an anatomical region that is included in the dissected tissue of this study or in a region that is likely not included.

In higher vertebrates, P63 expression is typically seen in basal cells of stratified epithelia (as for instance in the esophagus), in myoepithelial cells, in the urothelium and in the thymus. Is it possible that the TP63 expressing NFE population corresponds to cells of the zebrafish thymus (that might perhaps explain the seemingly large immune cell population in cluster 4)? Could TP63 expressing NFE cells represent the esophagus (if included in the dissection)? As so little detail is provided about the dissection procedure this opens up for speculation and it would be good if the authors discuss these possibilities, as some of them might perhaps be unlikely or even impossible given regional anatomy of the zebrafish and how the dissection was performed.

To gain better understanding of the sizeable TP63 expressing NFE population the authors briefly mention the possibility of future studies utilizing a TP63 reporter. If a reporter line is not available, immunofluorescent detection of P63 (as presented for PAX2A in Fig. 5 and E-cadherin in Fig. 6) would be desirable to provide more insight into the location of the NFE population. Given the proficiency the authors demonstrate in this manuscript when it comes to zebrafish imaging, at least whole-mount immunostaining of P63 in the region that was dissected for transcriptome analysis seems clearly feasible, both with respect to resources and time needed (perhaps in the range of 1-3 months).

Minor comments

It is a little bit confusing that different color coding for the various cell populations is used in Fig. 3B as compared to Figs. 1 and 2.

In the discussion of the intercellular interaction network (Fig. 3B) the authors clearly point out that anatomical barriers are not modelled. Nevertheless, it would be more informative if this description was able to sort out ligands that are secreted, from ligands that are not secreted and would require physical interaction between thyrocytes and a different cell population for signaling to occur.

The authors describe thyroid solid cell nests as "... lumen containing irregular structures located between the thyroid lobes in mammals...". Solid cell nests of the thyroid in higher vertebrates (e.g. humans and dogs) are located within the thyroid lobes and not between the lobes (i.e. the right and left thyroid lobes). Moreover, the authors write that "Recently, epithelial cells have been reported in a structure called the Solid Cell Nests (SCN) of the thyroid..." and give reference to a paper from 1988. If that is recent or not might be a matter of opinion, but to the best of my knowledge, solid cell nests were describe already by Getzowa in 1907 and I suppose that the epithelial identity (or at least epithelioid morphology) has been appreciated for long.

Significance

The authors provide a single-cell transcriptomic atlas of the zebrafish thyroid gland. To the best of my knowledge this is certainly a unique resource. In that sense it provides novel and significant information that will surely facilitate our further understanding of thyroid biology. It will surely be of great interest and value to the thyroid community, but probably also to a wider audience interested in e.g. zebrafish biology, endodermal biology and the biology of endocrine glands. Their finding and direct demonstration of transcriptional heterogeneity within the thyrocyte population is very interesting, also in different contexts of thyroid disease. However, I leave it to other referees to comment on the conceptual uniqueness of the current manuscript (i.e. a single-cell transcriptomic atlas of a zebrafish organ). Does it provide conceptually unique information, or does it add to an expanding collection of single-cell transcriptomic atlases of zebrafish organs?

Own field of expertise: thyroid gland biology, endoderm biology

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Referee #1

Evidence, reproducibility and clarity

Gillotay et al. performed an unbiased profiling of the zebra fish thyroid gland and captured different cell populations by single-cell gene expression analysis. Using bioinformatic tools, they identified seven clusters corresponding to expected, but also poorly characterized, sub-populations, such as non-follicular epithelial cells. They also found two transcriptionally distinct types of thyrocytes and validated this heterogeneity using a new transgenic Pax2a reporter line. Using this tool, they identified and located Pax2a-low and -high thyrocytes within thyroid follicles. Finally, they highlighted a dense intercellular signaling network based on ligands expressed by the diverse sub-populations present in the thyroid and receptors expressed by the thyrocytes. This is a descriptive work calling for more in-depth analyses.

Major comments on main conclusions :

Conclusion 1 : Identification of 7 clusters

• This reviewer was particularly surprised by the relative abundance of the different sub-populations identified. For example, 267 thyrocytes out of 6249 cells from the thyroid gland is less than 5% of the total thyroid cell number. In comparison, authors identified three times more immune cells or non-follicular epithelial cells. Authors should comment on these numbers, on the dissection (contaminants?) and dissociation procedure. Do these relative abundance reflect the proportion of thyrocytes, immune, stromal cells they normally observe in adult thyroid sections ? It would be interesting to have a better resolution for figure 6A in order to evaluate the number of nuclei stained only with DAPI as compared to nuclei stained with DAPI and surrounded by E-cadherin staining. Based on the image, this reviewer seriously doubts that follicular cells represent less than 5% of the total cell number in this organ, considering that the colloid is a cell-free zone.
• When using the webtool developed on the thyrocyte population, one can notice that only a small fraction of the thyrocyte population expresses common thyroid-specific genes such as Tpo or duox. Was this expected ? It would be interesting to comment on this observation and confirm using standard localization technique to demonstrate that this is real and not due to the sequencing. Is it specific to the zebrafish ? On the other hand, Tg is expressed in most thyrocytes, but surprisingly also in all the clusters at a fairly good level. This should be commented... Is it normal ? due to the sequencing quality ? or clustering ?
• In order to validate and locate the different populations identified in the thyroid, this reviewer suggests to perform in situ hybridization or immunostaining, based on the specific marker genes identified in each cluster. This experiment could lead to the precise identification of the different sub-populations and their respective localization. These experiments would also help in the interpretation of the cellular interaction network.

Conclusion 2 : two distinct types of thyrocytes

• This is an interesting observation. However, from a non-expert it is difficult to understand why the authors propose two populations. Based on the points distribution (Figure 4A), this reviewer would rather identify 3 or 4 clusters but not the two shown in red and blue.... Did the authors impose two populations for the clustering ? Did they perform a permutation test to confirm the pax2a significant fold change seen between clusters is not a false positive generated by the clustering ? Could they show, as a supplementary file, the same graph with points colored based on Pax2a expression ?
• This reviewer was also surprised by the relatively "heavy" approach (generation of the Pax2a reporter line) used to demonstrate the existence of two types of thyrocytes. Knowing that the reporter line was validated with a very good Pax2a antibody... The use of the reporter line is a bit short. The authors could for example validate the two populations of Figure 4A using the Pax2a FACS-sorted cells and RT-qPCR.
• In addition, data available in the sequencing dataset could be used to prove that the two populations are really active thyrocytes. This reviewer would suggest to present a table with the expression level of common and thyroid-specific genes such as TshR, Nis, Tpo, Duox, Tg, Pax2, TTF1 and other known transcription factors in the two populations to demonstrate that these two types of cells are indeed thyrocytes. Finally, image quality (Figure 6) could be improved and high-magnification images with several thyrocyte marker could be shown to convince the readers.

Conclusion 3 : cellular interaction network

• Most of the interactions revealed by the analysis seem to belong to the extracellular matrix and not to classical ligands such a Wnt, TGFb, FGF, PDGF,..... could the authors comment on this ? Considering that both endothelial cells and epithelial cells assemble their own basement membrane, the analysis will obviously reveal interactions between endothelial cells and epithelial cells....

Minor comments to improve the Ms :

• Could you explain how from 2 x 12 000 FACS-sorted live-cells (from six animals at each stage; 2 mpf and 8 mpf) you obtain 6249 cells (pooled of 2 mpf and 8 mpf), and why the two stages were first sorted separately and then pooled (?), as no differential analysis is carried out for the two stages.
• Which method was used for the graph-based clustering ? KNN ? Louvain ? Random walk ?
• How did you define the numbers of clusters ?
• Figure 4B, the color-code for the expression level would help the reader.
• Figure 4C, violin plot for Pax2a: why do we find cells that do not express this gene in the two populations ? The same is true for tbx2a and ahnak ... is the clustering optimal ?
• Figure 4C, blue violin plot for ptp4a3 does not seem to fit with the distribution of the points.
• what is the function of tbx2a, ahnak, ptp4a3 and dusp5, which are not mentioned in the text.
• Line 195: "pax2amKO2 reporter expression perfectly overlapped with PAX2A antibody staining". This reviewer would be more cautious as the images (Fig. 5 C, D and F) do not show a perfect colocalization: one can observe only blue or only red staining.
• Line 246: it is proposed to "study the functional and replicative differences among the two sub-populations of thyrocytes". This reviewer completely agrees and the suggestion made (vide supra) to use the datasets to assemble a table with the expression level of common and thyroid-specific genes such as TshR, Nis, Tpo, Duox, Tg, Pax2, TTF1 and other known transcription factors in the two populations could already give some indications on the functionality of these two types of cells. Expression of genes involved in cell-cycle control and/or apoptosis would be another possibility to better characterize the two populations. Lastly, the authors could perform the comparative analysis of ligand-receptor pairs between these two sub-populations to examine if they differentially interact with their environment.

Text improvement: Intro: thyroglobulin (TG) appears twice (line 46 and 49) Results: Fig. 5 (not 8) (line 203 and 205) Figure 3: stromal? (not skeletal) Figure 4: fold change scale is missing Figure 5: Thyroid gland (Gland) Figure sup 2: Fluorescence-activated cell sorting (FACS) of zebrafish thyroid gland Figure sup 3: number of unique molecular identifier Figure sup 4: "are present in the zebrafish"

Significance

The work performed by Gillotay et al. is clearly novel but descriptive. It provides a useful database to propose hypotheses to further study the thyroid gland. The single-cell RNA-seq analysis brings a molecular appreciation of the various thyroid cell populations, thyrocyte heterogeneity and intercellular signaling network. Although focused on the thyroid, results will be of interest to a larger audience than the thyroid community, especially the demonstration (if further and better validated) of thyrocyte heterogeneity and the intercellular communication possibilities.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** The work reports finding a molecular genetic basis for individual differences in behavior in different strains of outbred mice, even including individual behavioral differences between mice of the same inbred genetically isogenic strain. The authors were able to measure copy numbers for the tandemly repeated intronic snoRNA clusters SNORD115 and SNORD116 and found correlation with measures of anxiety in open-field test and elevated plus maze. Expression data for previously proposed targets of these snoRNAs are also provided.

We note that this description represents only part of the achievements in our paper. The key of our paper is that we have not only used "different strains of outbred mice", but in addition one very different species of mouse (Apodemus) and a Guinea pig species. We believe that the test in very different species with very different genetic backgrounds is the crucial proof for the specificity of the effect.

**Major comments:** 1.The techniques to measure copy numbers are challenging and the authors' conclusion that ddPCR was their method of choice is convincing. They were able to obtain limited optical mapping (Bionano zephyr) data, only for SNORD116 and only in mouse, but these data are useful to corroborate those obtained with ddPCR. 2.Figure 3 reports single copy numbers for individuals that are presumably heterozygous. Do we have to assume that the numbers reported represent the larger alleles since the ddPCR method does not allow to distinguish two different size alleles, as was shown for optical mapping?

The numbers are derived from the whole genome DNA, i.e. represent the cumulative copy number of both alleles. We have updated the text to make this clear.

3.The analyses reported do not take into account the specific parental origin of the alleles used in the regression analyses. Since PWSCR-specific SNORDs are only expressed from the paternal chromosomes, this generates some uncertainty about the whole dataset.

We had explained why it is not possible to distinguish the two alleles with the current technology. Hence, it is evidently also not possible to determine which allele comes from the paternal side. In fact, given that we showed that copy numbers can change every generation, even the knowledge of which chromosome is the paternal one would not predict its copy number. Accordingly, it lies in the nature of the whole phenomenon that this uncertainty is given. It is therefore just the more surprising that we still can observe correlations that are much stronger than has been shown for natural alleles of any genetic locus implicated in behavioral traits so far.

4.Lines 353-365: The ankrd11 exon-specific RNAseq data are confusing and too preliminary. More work needs to be done to resolve the splice variants in this region and their relationship to SNORD116 copy numbers. Alternatively lines 356-361 should be deleted.

We have included the data to show that the mechanism must be different from the one that is seen for Htr2c. This difference is clearly documented and we should therefore like to retain it. What is missing is to show the actual mechanism by which SNORD116 causes the alternative splicing. This will require more biochemical approaches that go beyond the current study.

5.In all tested rodents, higher SNORD copy number was correlated with higher relative anxiety score. In the human samples, however, higher anxiety scores were associated with lower copy numbers. These apparently contradictory results are not mentioned in the abstract, and are not satisfactory explained in the text.

We have decided to leave the human data out from the current manuscript. First, the behavioral tests for the rodents are indeed not directly comparable with the questionnaire scores in humans. Second, in human genetics one usually asks the results to be confirmed in an independent study. hence, we plan to extend the human data, but to present them eventually in a follow-up paper.

6.Extension to other species would be desirable but was limited by availability of genomic data: Results are presented for wood mouse only for SNORD115 and for the guinea pig for SNORD116.

Given that we show a strong correlation between SNORD115 and SNORD116 copy numbers for those species where the information is available for both loci, we do not think that this is a major limitation of our study.

**Minor comments:** 1.The authors present skull shape data related to SNORD116 copy numbers, but fail to consider how these data are relevant to the craniofacial abnormalities reported in an ankrd11 mutation. Barbaric et al (2008) reported a dominant ENU- induced mutation caused shortened snouts, wider skull, deformed nasal bones, reduced BMD, reduced osteoblast activity and reduced leptin levels. This phenotype was traced to a heterozygous missense mutation (conserved glutamate to lysine change) in an HDAC binding site. They postulated that the mutation fails to recruit HDACs to a transcription complex and to inhibit hormone-receptor activated gene transcription. What is the postulated link between this mechanism and the here reported skull shape data correlated with SNORD copy number variation?

The described missense mutation is located in the differentially spliced exon, i.e. a direct functional link is given. We have added this information to the text and compared the direct phenotypic effects from their study and our study.

2.The observed co-variation of copy numbers between the two SNORD clusters could indicate a duplication involving the entire region, This could be tested by determining the dosage of IPW, UBE3a and Snrpn genes.

While this is a theoretical possibility, it was not described in the literature before. Also, in our systematic survey of copy number variation in mouse populations (Pezer et al. 2015) we did not find a deviation of these genes from expected diploid copy number in any of the populations analysed.

3.Line 129 "the RNA coding region" and Line 148 "snoRNA coding parts" (and elsewhere) does seems correct, as by definition, this is non-coding RNA. The region they are referring to could be called the "processed C/D box snoRNA". The mechanism that generates these C/D box snoRNAs is well established: the "genes" are located in introns of host genes - and after transcription - the spliced out introns are exonucleolytically trimmed to the functional sizes. Both SNORD115 and 116 clusters are within a large transcript that originates from the SNRPN promoter of the paternal allele.

We adjusted the wording to make clear that we refer to the mature RNAs.

4.Figure 2 does not show data on skull shape as claimed in the legend.

We apologize - this was a carry-over from an older version of this figure. The skull shape analysis had been moved to a new figure in the current version of the manuscript.

5.S1 Figure: Snprn should be Snrpn

Thank you for spotting the error - we have corrected this

Reviewer #1 (Significance (Required)): This provocative work proposes the regulation of behavioral variance by dosage changes of a regulatory RNA. The dosage changes are apparently caused by dynamic and frequent alteration in copy number. This is a novel concept and worthy of publicizing. Extensive data documentation is provided for others to analyze and possibly replicate. The data potentially throw light on the function of the tandemly repeated imprinted snoRNA clusters in the PWS critical region. Novel aspects of this work include the discovery of copy number variation of these snoRNAs; and validation of a target of SNORD116: Ankrd11 is one of many potential targets of SNORD116 that was previously computationally predicted, this paper reports experimental evidence for this interaction. The work would be of interest to researchers in behavioral evolution, non-coding RNA function, epigenetics and overall genome evolution. Define your field of expertise with a few keyword: Molecular genetic disorders, Prader-Willi syndrome, mouse models

Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary** Maryam Keshavarz et al. aimed at seeking the molecular basis underlying individual behavioral variance within populations. Focusing on the Prader-Willi Syndrome (PWS) gene complex, which has been well recognized being associated with neurodevelopmental disorders, anxiety and metabolic issues, the authors found that the levels of PWS region's small nucleolar RNAs SNORD 115/116 of individual animals correlated with these individuals' behavioral test scores. The variations in transcript processing of some anxiety-associated target genes also revealed correlation with SNORD 115/116 copy numbers. Authors implicated that the copy numbers of SNORD 115/116 within PWS plausibly influenced behavioral variances among individuals. • Authors first validated that the droplet digital PCR (ddPCR) was suitable for quantifying variations in copy numbers of genomic clusters. Their ddPCR data showed confident correspondence with reads calculation of whole-genome-seq dataset. Also, ddPCR showed good replicability and congruent tissue-to-tissue similarity. • Authors found the ddPCR-measured SNORD copy numbers from several mice populations showed significant regression with SNORD RNA levels, respectively. Also, the anxiety profiling using Open Field Test and Elevated Plus Maze test indicated a significant regression between SNORD copy numbers and anxiety profiling scores, namely individual mouse with higher copy numbers received higher relative anxiety scores. Some other representative genes outside PWS, such as Sfi1 and Cwc22, failed to show such copy number-anxiety score regression. • Authors applied RNA-seq of individual mice with different SNORD 115/116 copy numbers and analyzed potential target gene regions. They found the level of alternative splice-resulted exon Vb of gene Htr2c, a serotonin receptor, was positively correlated with SNORD 115 copy number. Also, an alternative splicing product of gene Ankrd11, a chromatin receptor regulating GABA receptor, was found to positively correlated with SNORD 116 copy number. Positive correlation to SNORD copy numbers also occurred to some Htr2c and Ankrd downstream genes. • Authors used a landmark-based analysis to score mice craniofacial features and found the scores were in relationship with SNORD 116 copy numbers. • Authors also found significant regression between SNORD copy numbers and behavioral evaluations in other rodents. In humans, the Tridimensional Personality Questionnaire (TPQ) based evaluation also showed correlation with SNORD 115 and 116 copy numbers. **Major comments** The study mainly revealed important correlations between copy numbers of 2 small nucleolar RNAs and cognitive behavioral variance of different individual animal. Although very useful and important findings, the study did not provide any evidence about the causality between SNORD 115/116 and the observed behavioral phenotypes. For instance, • #1: the behavioral observations (i.e. anxiety) may not be merely regulated by the PWS gene complex.

It is already well understood that the respective behavioral observations have a polygenic basis. But our data show that the SNORD copy numbers act as major modulators of the behavior.

• #2: the paper did not show if manipulations on mouse SNORD 115/116 could affect target genes as well as the consequential behavioral phenotypes.

A direct interaction between SNORD115 and its target gene HTr2c has previously been shown in cell culture experiments. Further, we show that the commonly used inbred mouse strain C57Bl6 carries already different copy number alleles that would not be different from artificial manipulation of the copy number. There is a long tradition in mouse genetics to accept also spontaneous alleles as genetic proof, not only the alleles that were created by artificial intervention.

Further, as also pointed out in response to reviewer 1, in the absence of the possibility to do a direct genetic manipulation in a given genetic background, we use the comparative analysis between different genetic backgrounds to prove causality.

Reviewer #2 (Significance (Required)): Authors provided a potential molecular basis regulating the PWS region, which is a genomic imprinted gene complex and related to many neurodevelopmental diseases in mammals, including humans. Considerably cost-saving than whole-genome deep-seq, the application of droplet digital PCR on copy number (esp. in stretching regions) measurement can overcome some technical difficulties, for example, qPCR has limit in resolution when differentiating subtle variance in copy numbers; the Nanopore seq and current mapping algorithm show difficulties when placing the internal repeats also. Authors proposed SNORD copy number as a potential explanation to the individual-to-individual variance within the same species or even the same population.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

Summary

Maryam Keshavarz et al. aimed at seeking the molecular basis underlying individual behavioral variance within populations. Focusing on the Prader-Willi Syndrome (PWS) gene complex, which has been well recognized being associated with neurodevelopmental disorders, anxiety and metabolic issues, the authors found that the levels of PWS region's small nucleolar RNAs SNORD 115/116 of individual animals correlated with these individuals' behavioral test scores. The variations in transcript processing of some anxiety-associated target genes also revealed correlation with SNORD 115/116 copy numbers. Authors implicated that the copy numbers of SNORD 115/116 within PWS plausibly influenced behavioral variances among individuals.

• Authors first validated that the droplet digital PCR (ddPCR) was suitable for quantifying variations in copy numbers of genomic clusters. Their ddPCR data showed confident correspondence with reads calculation of whole-genome-seq dataset. Also, ddPCR showed good replicability and congruent tissue-to-tissue similarity.

• Authors found the ddPCR-measured SNORD copy numbers from several mice populations showed significant regression with SNORD RNA levels, respectively. Also, the anxiety profiling using Open Field Test and Elevated Plus Maze test indicated a significant regression between SNORD copy numbers and anxiety profiling scores, namely individual mouse with higher copy numbers received higher relative anxiety scores. Some other representative genes outside PWS, such as Sfi1 and Cwc22, failed to show such copy number-anxiety score regression.

• Authors applied RNA-seq of individual mice with different SNORD 115/116 copy numbers and analyzed potential target gene regions. They found the level of alternative splice-resulted exon Vb of gene Htr2c, a serotonin receptor, was positively correlated with SNORD 115 copy number. Also, an alternative splicing product of gene Ankrd11, a chromatin receptor regulating GABA receptor, was found to positively correlated with SNORD 116 copy number. Positive correlation to SNORD copy numbers also occurred to some Htr2c and Ankrd downstream genes.

• Authors used a landmark-based analysis to score mice craniofacial features and found the scores were in relationship with SNORD 116 copy numbers.

• Authors also found significant regression between SNORD copy numbers and behavioral evaluations in other rodents. In humans, the Tridimensional Personality Questionnaire (TPQ) based evaluation also showed correlation with SNORD 115 and 116 copy numbers.

Major comments

The study mainly revealed important correlations between copy numbers of 2 small nucleolar RNAs and cognitive behavioral variance of different individual animal. Although very useful and important findings, the study did not provide any evidence about the causality between SNORD 115/116 and the observed behavioral phenotypes. For instance,

• #1: the behavioral observations (i.e. anxiety) may not be merely regulated by the PWS gene complex.

• #2: the paper did not show if manipulations on mouse SNORD 115/116 could affect target genes as well as the consequential behavioral phenotypes.

Significance

Authors provided a potential molecular basis regulating the PWS region, which is a genomic imprinted gene complex and related to many neurodevelopmental diseases in mammals, including humans.

Considerably cost-saving than whole-genome deep-seq, the application of droplet digital PCR on copy number (esp. in stretching regions) measurement can overcome some technical difficulties, for example, qPCR has limit in resolution when differentiating subtle variance in copy numbers; the Nanopore seq and current mapping algorithm show difficulties when placing the internal repeats also.

Authors proposed SNORD copy number as a potential explanation to the individual-to-individual variance within the same species or even the same population.

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

Learn more at Review Commons

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Reply to the reviewers

Reviewer #1:

(Evidence, reproducibility and clarity (Required)):

This study aims to develop tools for yeast researchers to automatically segment and classify yeast colonies. The machine learning method enables rapid classification compared to manual counting.

\*MAJOR CONCERNS:***

Please include additional details about the types of images that must be captured for segmentation and categorization. It is important to provide details of what level of magnification might be needed during image capture. We anticipate that providing clear protocols for altering thresholds to classify colonies might be one way to overcome this challenge

That’s correct. Details on image acquisition, such as the level of magnification, are important to obtain accurate results.

To address this, we provide a detailed protocol in our companion article on ProtocolExchange: https://protocolexchange.researchsquare.com/article/nprot-7305/v1

We have updated the manuscript to include this link.

While the program crops colonies and segments them accurately, there is no spatial information of where these colonies are located in the image. This loss of spatial information limits the ability to use this platform to identify colonies of interest following experiments such as a genetic screen.

In principle it would be possible to retain the location of each cropped colony in the form of (x,y) coordinates in pixels. This could be included in a future release. However, we doubt the utility of such information for a genetic screen, unless identification of a positive hit could be linked to robotic picking of the identified colony (which would certainly go beyond the scope of this work). In reality, researchers will pick positive hits manually anyways, making our pipeline superfluous for such an application. We emphasize that we have developed our pipeline for large-scale quantification of red/white color assays. Here the pipeline makes a huge difference, as compared to manual counting.

The inability to accurately recognize sectored colonies as sectored (rather than red) is a significant limitation to the usage of this program for quantitative assays. While differentiating between red and white colonies is useful, the conclusion by the authors about its value for quantitative assays is limited unless variegation can be accurately defined. The authors should either soften this conclusion or qualify what quantitative measurements might mean given the limitations of their classification program. This somewhat diminished our overall enthusiasm.

The reviewer correctly points out that our algorithm shows lower accuracy when differentiating between red and variegating colonies than when differentiating between white and non-white colonies (including red, variegating and pink). Given this observation, we initially focused on predicting white vs. non-white colonies with our tool. However, the output of our pipeline also includes more granular predictions of numbers of white, red, pink and variegating colonies. We therefore leave it up to the user to decide which level of granularity is more appropriate, taking into account the tradeoff between granularity and accuracy. In particular, we note that for the colonies we tested, splitting the non-white category of predictions into red, variegating and pink resulted in a decrease of sensitivity from 0.98 for the non-white category to 0.86-0.88 for the individual categories, while the corresponding specificity showed a smaller reduction from 1.0 to 0.97-0.98. Considering that a lack of any predictions for the red, pink, and variegating categories effectively prohibits the researcher from detecting them at all, even a reduced sensitivity may be better than nothing and therefore acceptable in this case. In order to make this clearer in the text, we provide a more detailed comparison of performance metrics between levels of prediction, which may help to guide the user’s decision.

This program must be benchmarked with other colony classifiers. Cell Profiler is an example of a popular yeast colony segmentation program. How does this machine learning based tool compare with other colony segmentation and categorization programs. One possibility is to include an additional figure that compares their program with clear benchmarks. The outcome of effort based on benchmarking is not as important since we believe it is useful to have many alternatives for yeast segmentation and categorization. We think this revision would be essential to the manuscript and would add significant value.

We have used other approaches and were not satisfied with the outcomes. Hence, we developed our own pipeline, specifically designed to accurately distinguish red from white colonies and quantify such assays at a large scale.

When using CellProfiler we could not reliably distinguish variegating, pink, and red colonies. White colonies show up in the Red, Blue and Green channel, Red colonies mainly in the Red Channel. Therefore, variegating, pink and red colonies can be distinguished from white by reduced Blue and Green values, which is indirect and caused several issues. One of the problems was reflection of the flash during image acquisition, giving two reflective white patches on each colony that differed in pixel size depending on the magnification and colony size. We tried to prevent reflection with a ‘tent’, which reduced but could not eliminate the reflection. Therefore, the MaxIntensity of the Green/Blue channel was always the same of each colony, impeding classification. Furthermore, most red/pink colonies had a slim white rim, which was sometimes bigger/smaller and the relative area of rim to colony depends on the colony size, which made it impossible to tell a bit variegating from red by the output values from CellProfiler.

If deemed useful by the editors, we will be happy to mention this in the manuscript. A systematic comparison with other classifiers seems to be a bit of an overkill though. As stated by this reviewer, the outcome of such comparison would not matter much. It is important that the community has several approaches to choose from, so that the best solution can be found for each specific application.

\*MINOR CONCERN***

The program currently saved cropped images of each segmented colony. This takes up a lot of storage space. It might be useful to provide an option to save or not save these cropped images. This flexibility will be valuable for users but does not detract from the major conclusions of the manuscript.

While we appreciate that the need to save individual images of cropped colonies may be a drawback for some users, in the current implementation it is not possible to avoid this step. One could imagine a scenario in which all cropped images were stored in RAM prior to classification rather than written to a computer’s disk; however, we believe that most users would have more limitations on the availability of RAM than on disk storage, therefore making this option also not feasible.

The authors have provided excellent examples of colonies they believe are red, white or sectored. More accurately defining a pink colony would be valuable for users of this program. How much of red is classified as pink by this program?

As the reviewer points out, it is difficult to give an objective definition of a pink colony. In this case, we relied exclusively on subjective expert annotations to define which colonies were pink (as well as for all other categories).

We acknowledge that this may introduce some error into the model, as there may be some overlap between red and pink colonies or between pink and variegating colonies; however, this problem also exists in the case of manual annotation. As shown in Figure 1d, for the colonies we tested, 4 out of a total of 55 colonies annotated as red by an expert were predicted as pink by our algorithm. We would like to emphasize that our pipeline alleviates biases between different researchers who would be annotating colony color manually, therefore improving reproducibility. Such biases could be subjective or objective, such as different monitors used to inspect the images.

Providing an example data set with the protocol would be helpful for users with limited Python experience. In combination with their protocol on Protocol exchange, this would serve as a valuable resource for novices in programming.

We agree with the reviewer’s suggestion and will be happy to provide an example dataset used in the manuscript. We will defer to the journal’s guidelines as to the best way to share these raw images.

One technical issue of the program is that the program tries to open all files in the specified folder even if they aren't jpg. This causes problems if there are additional or hidden files in the folder and the program cannot process the additional files.

We appreciate the reviewer pointing out this issue and have fixed it in a new version of the code.

Reviewer #1 (Significance (Required)):

This manuscript describes a machine learning approach to segment and categorize yeast colonies based on a red/white selection assay. The approach has been implemented using Python which makes this widely accessible to many researchers. Their detailed protocol on Protocol Exchange is a valuable resource which made it possible for us to evaluate its performance. The program meets its goals of reducing user time via manual counting. It is also reasonably accurate in discriminating between red and white colonies based on our initial tests. However, there are several important concerns that the authors will need to address before this manuscript can become a valuable resource for the yeast community. It is important to note that our framework is one where we have a great interest in quantitative yeast genetics but cannot evaluate the strengths and weakness of the computational approach. So much of the review is focussed on what would be needed to make this tool more user appropriate.

Reviewer #2

(Evidence, reproducibility and clarity (Required)):

\*Summary:***

Carl et al present an application of a deep learning-based image analysis able to segment and classify individual yeast colonies by their phenotype in a special plate. They evaluated the method and show that it provides the accuracy similar to the one achieved by experts' manual classification.

\*Major comments:***

The key conclusions are convincing. The evaluation is performed on 3 datasets showing different properties (strong presence of phenotype, almost lack of the phenotype, gradual change of the phenotype).

The claims are carefully formulated. The deep learning methodology (training, validation, using modern technologies such as transfer learning, Unet, augmentation) is carefully designed and carried out. The evaluation is sound. The limitations are discussed.

For a short paper as it's formulated currently, no additional experiments are necessary.

The methods are implemented and are available on GitHub.

However, I'd strongly recommend to share also the data used in the paper, to make possible the reproduction of the results as well as to be used as examples for future users.

As stated above, we agree with the reviewer’s suggestion and will be happy to provide an example dataset used in the manuscript. We will defer to the journal’s guidelines as to the best way to share these raw images.

No replicates are provided unfortunately. The manuscript would benefit from showing results from replicates, especially because they should be easily obtainable.

It is not clear to us to which experiment the reviewer is referring. All of the results presented in Figure 2 did include replicates, as detailed in the figure legend.

\*Minor comments:***

I'm not familiar with the state of the art to judge on whether prior studies are referenced.

The text and fitures are very clear and well formulated.

Reviewer #2 (Significance (Required)):

Despite the conceptual innovation is average, the method is well-developed and seems to be very useful for yeast analysis.

I'm not an expert in the application area to judge the state of the art. The carried out deep learning methodology is top notch.

The manuscript can be interesting and useful for experts using the described assay for yeast.

My expertise is in omics, image analysis, and machine learning.

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 #2

Evidence, reproducibility and clarity

Summary:

Carl et al present an application of a deep learning-based image analysis able to segment and classify individual yeast colonies by their phenotype in a special plate. They evaluated the method and show that it provides the accuracy similar to the one achieved by experts' manual classification.

Major comments:

The key conclusions are convincing. The evaluation is performed on 3 datasets showing different properties (strong presence of phenotype, almost lack of the phenotype, gradual change of the phenotype).

The claims are carefully formulated. The deep learning methodology (training, validation, using modern technologies such as transfer learning, Unet, augmentation) is carefully designed and carried out. The evaluation is sound. The limitations are discussed.

For a short paper as it's formulated currently, no additional experiments are necessary.

The methods are implemented and are available on GitHub.

However, I'd strongly recommend to share also the data used in the paper, to make possible the reproduction of the results as well as to be used as examples for future users.

No replicates are provided unfortunately. The manuscript would benefit from showing results from replicates, especially because they should be easily obtainable.

Minor comments:

I'm not familiar with the state of the art to judge on whether prior studies are referenced.

The text and fitures are very clear and well formulated.

Significance

Despite the conceptual innovation is average, the method is well-developed and seems to be very useful for yeast analysis.

I'm not an expert in the application area to judge the state of the art. The carried out deep learning methodology is top notch.

The manuscript can be interesting and useful for experts using the described assay for yeast.

My expertise is in omics, image analysis, and machine learning.

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 #1

Evidence, reproducibility and clarity

This study aims to develop tools for yeast researchers to automatically segment and classify yeast colonies. The machine learning method enables rapid classification compared to manual counting.

MAJOR CONCERNS:

Please include additional details about the types of images that must be captured for segmentation and categorization. It is important to provide details of what level of magnification might be needed during image capture. We anticipate that providing clear protocols for altering thresholds to classify colonies might be one way to overcome this challenge

While the program crops colonies and segments them accurately, there is no spatial information of where these colonies are located in the image. This loss of spatial information limits the ability to use this platform to identify colonies of interest following experiments such as a genetic screens.T

The inability to accurately recognize sectored colonies as sectored (rather than red) is a significant limitation to the usage of this program for quantitative assays. While differentiating between red and white colonies is useful, the conclusion by the authors about its value for quantitative assays is limited unless variegation can be accurately defined. The authors should either soften this conclusion or qualify what quantitative measurements might mean given the limitations of their classification program. This somewhat diminished our overall enthusiasm.

This program must be benchmarked with other colony classifiers. Cell Profiler is an example of a popular yeast colony segmentation program. How does this machine learning based tool compare with other colony segmentation and categorization programs. One possibility is to include an additional figure that compares their program with clear benchmarks. The outcome of effort based on benchmarking is not as important since we believe it is useful to have many alternatives for yeast segmentation and categorization. We think this revision would be essential to the manuscript and would add significant value.

MINOR CONCERN

The program currently saved cropped images of each segmented colony. This takes up a lot of storage space. It might be useful to provide an option to save or not save these cropped images. This flexibility will be valuable for users but does not detract from the major conclusions of the manuscript.

The authors have provided excellent examples of colonies they believe are red, white or sectored. More accurately defining a pink colony would be valuable for users of this program. How much of red is classified as pink by this program?

Providing an example data set with the protocol would be helpful for users with limited Python experience. In combination with their protocol on Protocol exchange, this would serve as a valuable resource for novices in programming.

One technical issue of the program is that the program tries to open all files in the specified folder even if they aren't jpg. This causes problems if there are additional or hidden files in the folder and the program cannot process the additional files.

Significance

This manuscript describes a machine learning approach to segment and categorize yeast colonies based on a red/white selection assay. The approach has been implemented using Python which makes this widely accessible to many researchers. Their detailed protocol on Protocol Exchange is a valuable resource which made it possible for us to evaluate its performance. The program meets its goals of reducing user time via manual counting. It is also reasonably accurate in discriminating between red and white colonies based on our initial tests. However, there are several important concerns that the authors will need to address before this manuscript can become a valuable resource for the yeast community. It is important to note that our framework is one where we have a great interest in quantitative yeast genetics but cannot evaluate the strengths and weakness of the computational approach. So much of the review is focussed on what would be needed to make this tool more user appropriate.