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Reply to the reviewers

Response to Reviewers Comments

We would like to thank all reviewers for carefully considering our manuscript and providing useful suggestions/ideas. The general consensus was that our study provides an important conceptual advance that reveals a new way of thinking about kinetochore phosphatases. However, in light of our surprising findings, it was suggested that additional experiments would be required to fully validate our conclusions. In particular, it was seen as important to test whether PLK1 can activate MPS1 from the BUB complex and to confirm that PP1 and PP2A are effectively inhibited in situations where MELT dephosphorylation can occur normally (Figure 3).

In general, we agree with these and the other points raised by the reviewers, therefore we plan to address all comments as outlined in detail below.

The major new additions to the final paper will be the following:

1) Experiments to test how BUB-bound PLK1 affects MPS1 activity.

2) Experiments to determine the efficiency of phosphatase inhibition in figure 3.

3) Experiments to test whether maintaining PLK1 at the BUB complex causes SAC silencing defects

4) Evolutionary analysis demonstrating that the PLK1 and PP2A-binding modules have co-evolved in the kinetochore BUB complex. This analysis, which has been performed already, strengthens our manuscript because it provides additional independent evidence for a functional relationship between PLK1 and PP2A on the BUB complex.

Reviewer #1

Minor comments:

1) The authors propose that PP1-KNL1 and BUBR1-bound PP2A-B56 continuously antagonise PLK1 association with the BUB complex by dephosphorylating the CDK1 phosphorylation sites on BUBR1 (pT620) and BUB1 (pT609). It is therefore expected that converting these residues to aspartate would increase PLK1 recruitment. It would be interesting to verify if this hypothesis fits with the proposed model.

Response: The general idea to maintain PLK1 at the BUB complex is a good one, but unfortunately polo-box domains do not bind to acidic negatively charged residues. Instead we will attempt to maintain PLK1 at the BUB complex using alternatively approaches (as suggested by reviewer 2).

2) In Figure 1E, are the mean values for BubR1WT+BubWT and BubR1WT+Bub1T609 both normalized to 1? If so, this fails to reveal the contribution of Bub1 T609 for the recruitment of PLK1 when PP2A-B56 is allowed to localize at kinetochores.

Response: The values will be updated and normalised to the BubR1WT+BUB1WT control. We have also performed additional experiments already and overall the results reveal a small reduction in kinetochore PLK1 following BUB1-T609A mutation and a larger reduction upon combined BUBR1-T620A mutation.

3) What underlies the increase in Bub1 levels at unattached kinetochores of siBubR1 cells (Figure S1C?) Is this caused by an increase in Bub1 T609 phosphorylation and consequently unopposed PLK1 recruitment, which consequently increases MELT phosphorylation?

Response: We suspect that PLK1 is not the cause of the increased BUB1 levels because PLK1 kinetochore levels are actually decreased in this situation (Figure S1A).

4) Although the immunoblotting from Figure S1D indicates that BubR1T620A and Bub1T609A are expressed at similar levels as their respective WT counterparts, some degree of single-cell variability is expected to occur. As a complement to Figure 1B,C and Figure S1E,F could the authors plot the kinetochore intensity of BubR1 pT620 and Bub1T609 relative to the YFP-BubR1 and YFP-Bub1 signal, respectively?

Response: There is indeed variability in the level of re-expression of BUBR1/BUB1 on a single cell level, which can at least partially explain the variation on BUBR1-pT620 and BUB1-pT609 observed within in each condition. We can upload these scatter plots at resubmission and include in the supplementary, if required.

5) The authors nicely show that excessive PLK1 levels at the BUB complex are able to maintain MELT phosphorylation and the SAC (independently of MPS1) when KNL1-localised phosphatases are removed (Figures 2A,B). However, it should be noted that PLK1 is able to promote MPS1 activation at kinetochores and so, whether AZ-3146 at 2.5 uM efficiently inhibits MPS1 under conditions of excessive PLK1 recruitment should be confirmed. Can the authors provide a read-out for MPS1 activation status or activity (other than p-MELTs) to exclude a potential contribution of residual MPS1 activity in maintaining the p-MELTs and SAC?

Response: This is a good point because although PLK1 can phosphorylate the MELTs it can also activate MPS1, although it is unknown whether it can do this from the BUB complex. We had left a dotted line in Figure 4B to include this possibility, but we will now test this directly with additional experiments.

6) To examine whether PLK1 removal is the major role of PP1-KNL1 and PP2A-B56 in the SAC or whether they are additionally needed to dephosphorylate the MELTs, the authors monitored MELT dephosphorylation when MPS1 was inhibited immediately after 30-minute of BI2356. This revealed similar dephosphorylation kinetics, irrespective of compromised PP1-KNL1 or PP2A-B56 activity, thus suggesting that these pools of phosphatases are not required to dephosphorylate MELTs. To confirm this and exclude phosphatase redundancy, the authors simultaneously depleted all PP1 and B56 isoforms or treated cells with Calyculin A to inhibit all PP1 and PP2A phosphatases. In both of these situations, the kinetics of MELT dephosphorylation was indistinguishable from wild type cells if MPS1 and PLK1 were inhibited together. These observations led to the conclusion that neither PP1 or PP2A are required to dephosphorylate the MELT motifs. Instead they are needed to remove PLK1 from the BUB complex. This set of experiments is well-designed and the results support the conclusion. However, it would be of value if the authors provide evidence for the efficiency of PP1 and B56 isoforms depletion and for the efficiency of phosphatase inhibition by Calyculin A. An alternative read-out for the activity of PP1 and PP2A-B56 (other than p-MELT dephosphorylation) clearly confirming that both phosphatases are compromised when MPS1 and PLK1 are inhibited together could make a stronger case in excluding the contribution of residual PP1 or PP2A to the observed dephosphorylation of MELT motifs.

Response: This is also a good point. We had attempted many different combinations in Figure 3 to inhibit PP1/PP2A activity as efficiently as possible. This is especially important considering the “negative” results on pMELT are very surprising. However, we will now test how efficiently we have inhibited PP1 and PP2A phosphatase function in these experiments.

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

Major comments:

1) In its current state I am not convinced that the key conclusions are fully supported by the experiments and alternative conclusions/interpretations can be drawn. For example the level of MELT phosphorylation will be determined by the balance of kinase and phosphatase activity and if they do not achieve 100% inhibition of Mps1 in their assays then they are not strictly monitoring dephosphorylation kinetics in their assays. If the combination of Mps1 and Plk1 inhibition then more strongly inhibits Mps1 then dephosphorylation kinetics becomes faster. Thus subtle differences in Mps1 activity under their different conditions could lead to misleading conclusions but in its present state a careful analysis of Mps1 activity is not provided. This lack of complete inhibition also applies to the phosphatases and the experiments in Figure 3E indicates that their Calyculin preparation is not really active as at steady state MELT phosphorylation levels are much less affected than in for instance BubR1 del PP2A (Figure 2A as an example). Thus they likely still have phosphatase activity in the experiment in figure 3E making it difficult to draw the conclusions they do. A more careful analysis of kinase and phosphatase activities in their different perturbations would be recommendable and should be possible within a reasonable time frame.

Response: These are good points and we will now more carefully assess MPS1 and PP1/PP2A activities.

2) A more stringent test of their model would also be needed. What happens if Plk1 is artificially maintained in the Bub complex? The prediction would be that SAC silencing should be severely delayed even when Mps1 is inhibited. This is a straightforward experiment to do that should not take too long. If the polobox can bind phosphoSer then one could also make BubR1 T620S to slow down dephosphorylation of this site (PPPs work slowly on Ser while Cdk1 have almost same activity for Ser and Thr).

Response: These are good suggestions and we will try to see if maintaining PLK1 at the BUB complex produces effects on the SAC.

3) Another issue is the relevance of Plk1 removal under normal conditions. As their quantification shows in figure 1D-E (I think there is something wrong with figure 1E - should likely be Bub1) the contribution of BubR1 T620 and Bub1 T609 to Plk1 kinetochore localisation seems minimal. Thus upon SAC satisfaction there is not really a need to remove Plk1 through dephosphorylation as it is already at wild type levels. It is only in their BubR1 and KNL1 mutants that there is this effect so one has to question the impact in a normal setting. This is consistent with the data in Figure S1D showing no phosphorylation of these sites under unperturbed conditions.

Response: The major finding of this study is that kinetochore phosphatases are primarily needed to supress PLK1 activity on the BUB complex and thereby prevent excessive MELT phosphorylation. The relevance of this continued PLK1 removal under normal conditions is clear, because when it cannot occur (i.e. if the phosphatases are removed) then the SAC cannot be silenced unless PLK1 is inhibited. Therefore, whilst it is true that PLK1 localisation to the BUB complex is low under normal conditions, that is because the phosphatases are working to keep it that way. The relevance of that continual removal is an interesting, but in our opinion, separate question that will require a new body of work to resolve. One possibility is that PLK1 recruitment is a continual dynamic process, that is perhaps coupled to a particular stage in MCC assembly. For example, PLK1 could bind the BUB complex to recruit PP2A to BUBR1, before being immediately removed by PP2A. In this sense, PLK1 binding could still be functionally important even if it is only occurs transiently and steady state PLK1 levels are low. We will add a line to the discussion to highlight that it would be interesting to test PLK1 dynamics on the BUB complex in future.

4) They write that in the absence of phosphatase activity Plk1 becomes capable of supporting SAC independently (of Mps1 is implied). They do not show this - only that MELT phosphorylation is maintained. As Mps1 has other targets required for SAC activity I would rephrase this.

Response: Good point, this will be rephrased.

Reviewer #2 (Significance (Required)):

The advance is clearly conceptual and provides a new way of thinking about the kinetochore localized phosphatases. These phosphatases and the SAC have been immensely studied but this work brings in a new angle. The discussion would benefit from some evolutionary perspectives as the PP1 and PP2A-B56 binding sites are very conserved but the Plk1 docking sites on Bubs less so. This will be of interest to people in the field of cell division and researchers interested in phospho-mediated signaling.

Response: Since the paper was submitted, we performed evolutionary analysis to examine this point. We discovered that the PLK1 docking sites are surprisingly well conserved and, in fact, they appear to have co-evolved within the same region of MAD/BUB along with the PP2A-B56 binding motif. We believe this new data strengthens our manuscript because it argues strongly for an important functional relationship between PLK1 and PP2A. A new figure containing this evolutionary analysis will be included in the final version.

Reviewer #3

Major comments:

1. An important limitation of this study is that KNL1 dephosphorylation at MELT repeats is monitored only by indirect immunofluorescence using phospho-specific antibodies. Thus, reduction of phospho-KNL1 kinetochore signals could be due to protein turnover at kinetochores, rather than to dephosphorylation. This is a serious issue that could be addressed by checking KNL1 dephosphorylation during time course experiments by western blot using phospho-specific antibodies, as previously done (Espert et al., 2014).

Response: This is an important point that we feel is best addressed by examining total KNL1 levels at kinetochores (instead of simply total cellular levels by western blots). The reason is that KNL1 could potentially still be lost from kinetochores even if the total protein is not degraded. In all experiments involving YFP-KNL1 we observe no change in kinetochore KNL1 levels and this data will be included in the final version. We will also perform new experiments to examine total KNL1 levels in the BUBR1-WT/DPP2A situation to test whether KNL1 kinetochore levels are similarly maintained in these cells following MPS1 inhibition.

1. For obvious technical reasons, the shortest time point at which authors compare KNL1 dephosphorylation upon MPS1-PLK1 inhibition is 5 minutes. Based on immunofluorescence data, authors conclude that kinetics of KNL1 dephosphorylation are similar when kinases are inhibited, independent of whether or not kinetochore-bound phosphatases are active. However, in most experiments (e.g. Fig. 3B, 3C, 3E) lower levels of MELT phosphorylation are detected after 5 minutes of kinase inhibition when phosphatases are present than when they are absent, suggesting that phosphatases likely do contribute to KNL1 dephosphorylation. I suspect that differences between the presence and absence of phosphatases might even be more obvious if authors were to look at shorter time points, when phosphatases conceivably accomplish their function. I would therefore suggest that the authors tone down their conclusions, as their data complement but do not disprove the previous model.

Response: We appreciate that small differences can be seen in figure 3B and 3E at the 5-minute timepoint (between the WT and phosphatase inhibited situations). This may reflect a role for the phosphatases in dephosphorylation or in the ability of drugs such as BI-2536 (3B) or Calyculin A (3E) to fully inhibit their targets in the short timeframe. We will perform additional experiments to examine MPS1 and phosphatase activity under these conditions, in response to comments by reviewers 1 and 2. In the final version we will carefully interpret the new and existing data and, if required, modify the conclusions appropriately.

1. In all experiments cells are kept mitotically arrested through nocodazole treatment, which is not quite a physiological condition to study SAC silencing. This could potentially mask the real contribution of phosphatases in MELT dephosphorylation. Indeed, it is possible that higher amounts of phosphatases are recruited to kinetochores during SAC silencing than during SAC signalling (e.g. during SAC signalling Aurora B phosphorylates the RVSF motif of KNL1 to keep PP1 binding at low levels; Liu et al., 2010). What would happen in a nocodazole wash-out? Would phosphatases be dispensable in these conditions for normal kinetics of MELT dephosphorylation and anaphase onset if PLK1 is inhibited?

Response: All SAC silencing assays where performed in nocodazole for 2 main reasons: 1) PP2A-B56, PP1 or PLK1 can all regulate kinetochore-microtubule attachments, and thereby control the SAC indirectly. Therefore, performing our assays in the absence of microtubules allows us to make specific and direct conclusions about SAC regulation; 2) Previous work on pMELT regulation by PP1/PP2A in human cells was also performed following MPS1 inhibition in nocodazole (Espert et al 2014, Nijenhuis et al, 2014). Therefore, we are able to directly compare the contribution of PLK1 to the previously observed phenotypes, which allowed us to conclude that PLK1 has a major influence. Nevertheless, we appreciate the point that the influence of PLK1 could, in theory, be different during a normal mitosis when microtubule attachment can form. Therefore, we will attempt to address whether PLK1 inhibition can bypass a requirement for PP1/PP2A in SAC silencing during an unperturbed mitosis.

Other data are overinterpreted. For instance, the evidence that CDK1-dependent phosphorylation sites in Bub1 and BubR1 is enhanced when PP1 and PP2A-B56 are absent at kinetochores suggests but does not "demonstrate that PP1-KNL1 and BUBR1-bound PP2A-B56 antagonise PLK1 recruitment to the BUB complex by dephosphorylating key CDK1 phosphorylation sites on BUBR1 (pT620) and BUB1 (pT609)(Figure 1F)". Similarly, the claim "when kinetochore phosphatase recruitment is inhibited, PLK1 becomes capable of supporting the SAC independently" referred to Fig. 2C-D is an overstatement, as residual MPS1 kinase could be still active in the presence of the AZ-3146 inhibitor.

Response: These are good points and the indicated statements will be reworded.

Minor comments:

1. In many graphs (Fig. 1A-C, Fig. 2A,C) relative kinetochore intensities are quantified over "CENPC or YFP-KNL1". Authors should clarify when it is one versus the other.

Response: This will be clarified in the axis and in the methods.

1. The drawing in Fig. 1F depicts the action of PP1 and PP2A-B56 in antagonising PLK1 at kinetochores. Thus, the output should be SAC silencing, rather than activation.

Response: The SAC symbol will be removed from the schematic to avoid confusion and because it is not actually the focus of figure 1 anyway.

1. In the Discussion authors speculate that KNL1 dephosphorylation relies on a constitutive phosphatase with unregulated basal activity. Would a phosphatase be needed at all when MPS1 and PLK1 are inhibited? Could phosphorylated KNL1 be actively degraded?

Response: We will insert total KNL1 immunofluorescence quantification so show that KNL1 KT levels are not decreased in this situation. KNL1 remains anchored at kinetochore but the MELTs must be dephosphorylated to remove the BUB complex.

1. What happens to MPS1 when KNL1-bound PP1 and BUBR1-bound PP2A are absent? Do its kinetochore levels increase as observed for PLK1? And what about the kinetochore levels of Bub1 and BubR1?

Response: We have demonstrated previously that BUB1/BUBR1 increase in this situation in line with the pMELTs (Nijenhuis et al 2014;l Smith et al, 2019) – these papers will be referenced in relation to this. We will also address the effect of phosphatase removal on MPS1 activity, in response to comments by reviewers 1 and 2.

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Referee #3

Evidence, reproducibility and clarity

The Spindle Assembly Checkpoint (SAC) is a conserved surveillance device that responds to errors in kinetochore-microtubule attachments to ultimately prevent the onset of anaphase until all chromosomes are bipolarly attached. Current models of SAC posit that the Mps1 kinase initiates the SAC signalling cascade by phosphorylating the KNL1/Blinkin kinetochore scaffold at MELT repeats, in order to create phospho-docking sites for the hetero-tetrameric BUB complex made by BUB1-BUB3-BUB3-BUBR1. The BUB complex, in turn, promotes the assembly the Mitotic Checkpoint Complex (MCC), which prevents anaphase onset by inhibiting the E3 ubiquitin ligase Anaphase-Promoting Complex bound to its activator Cdc20 (APCCdc20). The polo-like kinase PLK1, which is recruited to kinetochores through its binding to BUBR1, contributes to the robustness of SAC signalling in human cells by cooperating with Mps1 in KNL1/Blinkin phosphorylation and by phosphorylating MPS1 itself, thereby enhancing its catalytic activity. While in human cells MPS1 is the predominant kinase in SAC signalling, aided by PLK1, in other organisms where MPS1 is absent, such as in nematodes, PLK1 functionally replaces MPS1 and is necessary for SAC activation. Once all chromosomes are bipolarly attached, SAC signalling is extinguished. Key to this process are the PP1 and PP2A-B56 phosphatases that antagonise KNL1 phosphorylation by MPS1 and PLK1 and also dephosphorylate the T-loop of MPS1 to lower its catalytic activity. Current models envision that PP1 and PP2A-B56 dephosphorylate the MELT repeats of KNL1 directly. Importantly, this has been formally tested for both PP2A-B56 in human cells (Espert et al., 2014) and PP1 in yeast (London et al., 2012).

In the present manuscript, the above model is challenged with the proposal that the main contribution of PP1 and PP2A-B56 to SAC silencing is to lower the levels of PLK1 at kinetochores, rather than to dephosphorylate KNL1. By interfering with the levels of these opposing kinases and phosphatases at kinetochores the authors describe an interesting interplay that confirms an overlapping function of PLK1 and MPS1 in KNL1 phosphorylation and highlights a role for the phosphatases in dampening PLK1 kinetochore levels. Consistently, inhibition of both Mps1 and PLK1 is sufficient to bring about KNL1 dephosphorylation upon inhibition of both phosphatases at kinetochores. The hypothesis is interesting and experiments are in general carefully designed and performed. It is clear from the presented data that PP1 and PP2A-B56 antagonize PLK1 kinetochore localisation and that the MELT repeats of KNL1 can be dephosphorylated even in the absence of phosphatases, provided that MPS1 and PLK1 are inhibited. However, in my opinion the results do not rule out that phosphatases actually have a primary and direct role in KNL1 dephosphorylation.

Major comments:

1. An important limitation of this study is that KNL1 dephosphorylation at MELT repeats is monitored only by indirect immunofluorescence using phospho-specific antibodies. Thus, reduction of phospho-KNL1 kinetochore signals could be due to protein turnover at kinetochores, rather than to dephosphorylation. This is a serious issue that could be addressed by checking KNL1 dephosphorylation during time course experiments by western blot using phospho-specific antibodies, as previously done (Espert et al., 2014).
2. For obvious technical reasons, the shortest time point at which authors compare KNL1 dephosphorylation upon MPS1-PLK1 inhibition is 5 minutes. Based on immunofluorescence data, authors conclude that kinetics of KNL1 dephosphorylation are similar when kinases are inhibited, independent of whether or not kinetochore-bound phosphatases are active. However, in most experiments (e.g. Fig. 3B, 3C, 3E) lower levels of MELT phosphorylation are detected after 5 minutes of kinase inhibition when phosphatases are present than when they are absent, suggesting that phosphatases likely do contribute to KNL1 dephosphorylation. I suspect that differences between the presence and absence of phosphatases might even be more obvious if authors were to look at shorter time points, when phosphatases conceivably accomplish their function. I would therefore suggest that the authors tone down their conclusions, as their data complement but do not disprove the previous model.
3. In all experiments cells are kept mitotically arrested through nocodazole treatment, which is not quite a physiological condition to study SAC silencing. This could potentially mask the real contribution of phosphatases in MELT dephosphorylation. Indeed, it is possible that higher amounts of phosphatases are recruited to kinetochores during SAC silencing than during SAC signalling (e.g. during SAC signalling Aurora B phosphorylates the RVSF motif of KNL1 to keep PP1 binding at low levels; Liu et al., 2010). What would happen in a nocodazole wash-out? Would phosphatases be dispensable in these conditions for normal kinetics of MELT dephosphorylation and anaphase onset if PLK1 is inhibited?
4. Other data are overinterpreted. For instance, the evidence that CDK1-dependent phosphorylation sites in Bub1 and BubR1 is enhanced when PP1 and PP2A-B56 are absent at kinetochores suggests but does not "demonstrate that PP1-KNL1 and BUBR1-bound PP2A-B56 antagonise PLK1 recruitment to the BUB complex by dephosphorylating key CDK1 phosphorylation sites on BUBR1 (pT620) and BUB1 (pT609)(Figure 1F)". Similarly, the claim "when kinetochore phosphatase recruitment is inhibited, PLK1 becomes capable of supporting the SAC independently" referred to Fig. 2C-D is an overstatement, as residual MPS1 kinase could be still active in the presence of the AZ-3146 inhibitor.

Minor comments:

1. In many graphs (Fig. 1A-C, Fig. 2A,C) relative kinetochore intensities are quantified over "CENPC or YFP-KNL1". Authors should clarify when it is one versus the other.
2. The drawing in Fig. 1F depicts the action of PP1 and PP2A-B56 in antagonising PLK1 at kinetochores. Thus, the output should be SAC silencing, rather than activation.
3. In the Discussion authors speculate that KNL1 dephosphorylation relies on a constitutive phosphatase with unregulated basal activity. Would a phosphatase be needed at all when MPS1 and PLK1 are inhibited? Could phosphorylated KNL1 be actively degraded?
4. What happens to MPS1 when KNL1-bound PP1 and BUBR1-bound PP2A are absent? Do its kinetochore levels increase as observed for PLK1? And what about the kinetochore levels of Bub1 and BubR1?

Significance

The nature of the advance is conceptual. This paper challenges (although I would rather say "integrates") the prevailing model of spindle checkpoint silencing.

The current model of SAC silencing envisions that PP1 and PP2A-B56 phosphatases oppose SAC kinases (Mps1 and Polo kinase) by directly dephosphorylating some of their targets (e.g. the kinetochore scaffold KNL1 and MPS1 itself). This work proposes instead that the main function of the above phosphatases is to keep low levels of the polo kinase PLK1 at kinetochores, which would otherwise boost KNL1 phosphorylation and assembly of SAC complexes.

People working in the fields of mitosis, chromosome segregation, aneuploidy, spindle checkpoint, kinases/phosphatases could be interested by these findings.

Reviewer's field of expertise: Cell cycle, mitosis, spindle assembly checkpoint

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Referee #2

Evidence, reproducibility and clarity

Summary:

The work focuses on the role of kinetochore localized protein phosphatases in the dephosphorylation of MELT motifs and SAC silencing. The focus is on PP1 bound to KNL1 and PP2A-B56 bound to BubR1 and uses largely RNAi rescue experiments in human cell lines combined with immunofluorescence analysis and time-lapse imaging. The authors show that kinetochore localized phosphatases antagonize the localization of the Plk1 mitotic kinase to kinetochores. This is due to the dephosphorylation of BubR1 T620 and Bub1 T609 that are binding sites for Plk1 on the kinetochore. The main conclusion is that if Plk1 kinetochore localisation is prevented then there is no longer a need for kinetochore phosphatases for SAC silencing and MELT dephosphorylation.

Major comments:

1) In its current state I am not convinced that the key conclusions are fully supported by the experiments and alternative conclusions/interpretations can be drawn. For example the level of MELT phosphorylation will be determined by the balance of kinase and phosphatase activity and if they do not achieve 100% inhibition of Mps1 in their assays then they are not strictly monitoring dephosphorylation kinetics in their assays. If the combination of Mps1 and Plk1 inhibition then more strongly inhibits Mps1 then dephosphorylation kinetics becomes faster. Thus subtle differences in Mps1 activity under their different conditions could lead to misleading conclusions but in its present state a careful analysis of Mps1 activity is not provided. This lack of complete inhibition also applies to the phosphatases and the experiments in Figure 3E indicates that their Calyculin preparation is not really active as at steady state MELT phosphorylation levels are much less affected than in for instance BubR1 del PP2A (Figure 2A as an example). Thus they likely still have phosphatase activity in the experiment in figure 3E making it difficult to draw the conclusions they do. A more careful analysis of kinase and phosphatase activities in their different perturbations would be recommendable and should be possible within a reasonable time frame.

2) A more stringent test of their model would also be needed. What happens if Plk1 is artificially maintained in the Bub complex? The prediction would be that SAC silencing should be severely delayed even when Mps1 is inhibited. This is a straightforward experiment to do that should not take too long. If the polobox can bind phosphoSer then one could also make BubR1 T620S to slow down dephosphorylation of this site (PPPs work slowly on Ser while Cdk1 have almost same activity for Ser and Thr).

3) Another issue is the relevance of Plk1 removal under normal conditions. As their quantification shows in figure 1D-E (I think there is something wrong with figure 1E - should likely be Bub1) the contribution of BubR1 T620 and Bub1 T609 to Plk1 kinetochore localisation seems minimal. Thus upon SAC satisfaction there is not really a need to remove Plk1 through dephosphorylation as it is already at wild type levels. It is only in their BubR1 and KNL1 mutants that there is this effect so one has to question the impact in a normal setting. This is consistent with the data in Figure S1D showing no phosphorylation of these sites under unperturbed conditions.

4) They write that in the absence of phosphatase activity Plk1 becomes capable of supporting SAC independently (of Mps1 is implied). They do not show this - only that MELT phosphorylation is maintained. As Mps1 has other targets required for SAC activity I would rephrase this.

5) The method section is extensive and contains sufficient information for reproducing data.

6) Data and statistical analysis is ok.

Significance

The advance is clearly conceptual and provides a new way of thinking about the kinetochore localized phosphatases. These phosphatases and the SAC have been immensely studied but this work brings in a new angle. The discussion would benefit from some evolutionary perspectives as the PP1 and PP2A-B56 binding sites are very conserved but the Plk1 docking sites on Bubs less so. This will be of interest to people in the field of cell division and researchers interested in phospho-mediated signaling.

Field of expertise: kinetochore/phosphatases/bub proteins Jakob Nilsson

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Cordeiro et al provides a series of compelling evidences to support a provocative conclusion: PP2A-B56 and PP1 are critical for SAC silencing mainly by restraining and extinguishing autonomous kinase activity at kinetochores. This finding challenges the prevailing view of PP2A-B56/PP1-mediated KNL1-MELT dephosphorylation as a major SAC silencing event. This represents a paradigm change in the field and opens an important goal for future research: determine the phosphatases that dephosphorylate the MELTs. In my view this paper delivers an important clarification on how PP1-KNL1 and PP2A-B56 actually drive SAC silencing. This is a nice study and will move the field forward. The manuscript is globally solid, very well written and the conclusions are generally supported by the experimental data. However, I do have some issues with the following points, which in my view, if unaddressed, may leave the conclusion a bit fragile:

Minor comments:

1) The authors propose that PP1-KNL1 and BUBR1-bound PP2A-B56 continuously antagonise PLK1 association with the BUB complex by dephosphorylating the CDK1 phosphorylation sites on BUBR1 (pT620) and BUB1 (pT609). It is therefore expected that converting these residues to aspartate would increase PLK1 recruitment. It would be interesting to verify if this hypothesis fits with the proposed model.

2) In Figure 1E, are the mean values for BubR1WT+BubWT and BubR1WT+Bub1T609 both normalized to 1? If so, this fails to reveal the contribution of Bub1 T609 for the recruitment of PLK1 when PP2A-B56 is allowed to localize at kinetochores.

3) What underlies the increase in Bub1 levels at unattached kinetochores of siBubR1 cells (Figure S1C?) Is this caused by an increase in Bub1 T609 phosphorylation and consequently unopposed PLK1 recruitment, which consequently increases MELT phosphorylation?

4) Although the immunoblotting from Figure S1D indicates that BubR1T620A and Bub1T609A are expressed at similar levels as their respective WT counterparts, some degree of single-cell variability is expected to occur. As a complement to Figure 1B,C and Figure S1E,F could the authors plot the kinetochore intensity of BubR1 pT620 and Bub1T609 relative to the YFP-BubR1 and YFP-Bub1 signal, respectively?

5) The authors nicely show that excessive PLK1 levels at the BUB complex are able to maintain MELT phosphorylation and the SAC (independently of MPS1) when KNL1-localised phosphatases are removed (Figures 2A,B). However, it should be noted that PLK1 is able to promote MPS1 activation at kinetochores and so, whether AZ-3146 at 2.5 uM efficiently inhibits MPS1 under conditions of excessive PLK1 recruitment should be confirmed. Can the authors provide a read-out for MPS1 activation status or activity (other than p-MELTs) to exclude a potential contribution of residual MPS1 activity in maintaining the p-MELTs and SAC?

6) To examine whether PLK1 removal is the major role of PP1-KNL1 and PP2A-B56 in the SAC or whether they are additionally needed to dephosphorylate the MELTs, the authors monitored MELT dephosphorylation when MPS1 was inhibited immediately after 30-minute of BI2356. This revealed similar dephosphorylation kinetics, irrespective of compromised PP1-KNL1 or PP2A-B56 activity, thus suggesting that these pools of phosphatases are not required to dephosphorylate MELTs. To confirm this and exclude phosphatase redundancy, the authors simultaneously depleted all PP1 and B56 isoforms or treated cells with Calyculin A to inhibit all PP1 and PP2A phosphatases. In both of these situations, the kinetics of MELT dephosphorylation was indistinguishable from wild type cells if MPS1 and PLK1 were inhibited together. These observations led to the conclusion that neither PP1 or PP2A are required to dephosphorylate the MELT motifs. Instead they are needed to remove PLK1 from the BUB complex. This set of experiments is well-designed and the results support the conclusion. However, it would be of value if the authors provide evidence for the efficiency of PP1 and B56 isoforms depletion and for the efficiency of phosphatase inhibition by Calyculin A. An alternative read-out for the activity of PP1 and PP2A-B56 (other than p-MELT dephosphorylation) clearly confirming that both phosphatases are compromised when MPS1 and PLK1 are inhibited together could make a stronger case in excluding the contribution of residual PP1 or PP2A to the observed dephosphorylation of MELT motifs.

To summarize, this is a very good paper and will definitely cause an important impact in the field of mitosis.

Significance

This manuscript provides an important conceptual advance for the field of mitosis, specifically to the topic of mitotic checkpoint regulation. It remains elusive how the spindle assembly checkpoint is silenced. While previous studies have shown that PP1-KNL1 and PP2A-B56 contribute to suppress SAC signaling, how they do so is unclear. This study provides important insight into this matter. Cordeiro and colleagues demonstrate that in contrast with previous expectations, PP1 and PP2A promote SAC silencing, not by directly dephosphorylating MELT motifs on KNL1, but instead by removing PLK1 from the Bub complex. The authors find that these phosphatases antagonise CDK1- phosphorylations on BubR1 and Bub1 to dampen PLK1 levels. This activity is crucial to prevent PLK1 from maintaining MELT phosphorylation in an autocatalytic manner, thus (probably) allowing prompt SAC silencing following stable kinetochore-microtubule attachments. The described mechanism extends our view of how the SAC is regulated and should be of interest to those in the field of mitosis. The findings described in this paper allow us to better understand how cells silence the SAC. This is a top priority in the field, as the inability to timely quench SAC signaling can result in chromosome segregation errors. Determining the phosphatases that actually dephosphorylate the MELT motifs will be an essential next step forward

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Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript "Unconventional kinetochore kinases KKT2 and KKT3 have a unique zinc finger that promotes their kinetochore localization" by Marciano et al. describes functional and structural work on two unique kinetochore-localized proteins in kinetoplastids, KKT2 and KKT3. While the kinetochores of most eukaryotes are built on top of a histone H3 variant known as CENP-A (or CenH3), kinetoplastids lack CENP-A. Kinetoplastids also lack homologs of most conserved kinetochore proteins and instead possess an unique complement of kinetochore proteins, as described in earlier work by the lead author, B. Akiyoshi.

The current manuscript follows up this earlier work and seeks to understand how two putative kinases, KKT2 and KKT3, localize to the kinetochores of kinetoplastids. They begin by mapping the regions of both proteins (in Trypanosoma brucei) that are required for kinetochore localization. In both cases, a conserved "central domain" is sufficient for kinetochore localization. They then purify and determine the structure of a KKT2 central domain from a related species (Bodo saltans), and show that it possess two zinc-binding domains, termed Znf1 and Znf2. A more diverged KKT2 from Perkinsela has Znf1, but not Znf2. The authors go on to show that the Znf1 region in particular is important for localization of both KKT2 and KKT3 to kinetochores, and for long-term cell survival, in Trypanosoma brucei.

Major Comments:

• The work is well done, well described, and described in such a way that it should be reproducible.

• No page numbers - this makes it difficult to refer to different parts of the text...

• Introduction (page 2), fourth-from bottom line: the authors refer here to "regional centromere" but have not defined this term (I assume, as opposed to point-centromeres of budding yeast?). I suggest rephrasing.

• Page 4, bottom: The discussion of KKT2 kinetochore localization brings up a lot or questions. First, can the authors use an assay like yeast two-hybrid to test for pairwise interactions between KKT2 domains and other kinetochore proteins? This could provide direct functional data on the role of these various domains in kinetochore localization. Second, if individual domains are being recruited to kinetochores by their non-constitutive binding partners, wouldn't this be evident if the authors looked at localization at different points in the cell cycle, and/or with dual localization tracking the putative binding partners? Could transient localization of some of the domains explain the intermediate localization phenotype observed for some domains in KKT2?

• Page 6: The authors note that KKT2 Znf2 bears strong similarity to DNA-binding canonical Zinc fingers, and even note the high conservation of some putative DNA-binding residues. Have the authors tested for DNA binding by this protein? Can the authors at least model DNA binding and see if that would result in a clash, given the packing of Znf2 against the larger Znf1?

Minor Comments:

• Page 5: I'm skeptical as to whether these zinc-binding domains, especially Znf1, should really be referred to as "fingers"

• Page 8: At the beginning of the section describing KKT3 cellular experiments, I think the authors need to make it much more explicit that T. brucei KKT3 shares both Znf1 and Znf2 with KKT2.

• Figure S1A: The gap between lanes in the middle of the major peak is really confusing (it's not even clear that this is two different SDS-PAGE gels next to one another). I initially thought that KKT2 was in both peaks, given the labeling of this figure. I suggest labeling the lanes specifically, or cropping the picture, to avoid confusion.

Significance

This work is interesting, well done, and described nicely. It highlights how unique and different the kinetochores of kinetoplastid species are, and brings up a number of questions about how these kinetochores are specified and how they function. The structural work is also interesting and well-done. Unfortunately, the work as a whole does not make any strong mechanistic conclusions, leading to a somewhat dissatisfying conclusion.

The work could be significantly strengthened if the authors were able to make a direct functional conclusion about the roles of the Znf regions of KKT2 and/or KKT3, for example detecting DNA binding in vitro, or detecting a specific pairwise interaction between this region and another kinetochore protein.

This work will most likely appeal to researchers in the cell division and kinetochore architecture fields, although since kinetoplastids are so unique the link between this work and most other kinetochore work is unclear. This is in a way exciting: we don't yet know much about how these kinetochores relate to other eukaryotes' kinetochores.

My field of expertise is structural biology and biochemistry, with experience in kinetochore architecture and structure.

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Referee #2

Evidence, reproducibility and clarity

Kinetoplastids have unconventional kinetochores that lack CENPA nucleosomes that normally dictates the position of the kinetochore in most other eukaryotes. Marciano and colleagues analyse KKT2 and KKT3, two consistutively localized kinetoplastid kinetochore proteins that may contribute to kinetochore positioning on centromeric DNA. They find that in both proteins the central, cysteine-rich domains are sufficient to support centromere localization but that in KKT2 also other domains can do so by themselves. They then obtain crystal structures of the KKT2 central domain from bodo saltans and show it consists of 2 Zinc-finger structures (Znf1 and Znf2) of which the first is conserved in Perkinsella. Mutations of Znf1 and Znf2 in KKT2 and homologous mutations in KKT3 show that Znf1 is crucial for centromere localization and viability, while Znf2 is dispensible for both.

The paper presents a pretty straighforward characerization of functional domains in KKT2 and KKT3 with respect to centromere localization. The authors nicely show a unique Zn-finger structure (Znf1) of KKT2 and show it is crucial for localization. The study does not end up delivering an answer to the questions posed in the manuscript, namely how centromeres and therefore kinetochores are specified in kinetoplastids. The paper could do with some attempts to get to this, based on the presented data. For example, does Znf1 bind centromeric DNA, does it bind nucleosomes, is it essential for recruiting the other KKTs, etc.

The experiments are in general well presented but some could be better controlled:

• localization of KKT2 and KKT3 mutants is never verified to be centromeres, we have to believe the dots in the DAPI region are centromeres.
• in some cases mutants are made in full-length (FL) background (viability, sometimes localization), but in other cases only in isolated domains. The former should be done for all assays. This is also important to show that central domain of KKT2 and KKT3 is necessary for localization.
• The data of F2 are interpreted to mean that PDB-like domain and middle region get to kinetochores by binding transient KT components, even though KKT2 itself is constitutive. That interpretation would really be strenghtened by showing the KKT2 fragments are now transient also.

Significance

The paper presents a pretty straighforward characerization of functional domains in KKT2 and KKT3 with respect to centromere localization. The authors nicely show a unique Zn-finger structure (Znf1) of KKT2 and show it is crucial for localization. The study does not end up delivering an answer to the questions posed in the manuscript, namely how centromeres and therefore kinetochores are specified in kinetoplastids. The paper could do with some attempts to get to this, based on the presented data. For example, does Znf1 bind centromeric DNA, does it bind nucleosomes, is it essential for recruiting the other KKTs, etc.

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Referee #1

Evidence, reproducibility and clarity

Although most studied eukaryotes display similarities in their overall kinetochore structures to mediate chromosome segregation, kinetoplastid species display highly divergent kinetochores with no clear relationships to canonical kinetochore components. Prior work from the Akiyoshi lab and others has identified kinetochore proteins in Trypanosomes and other kinetoplastids. The identification of these proteins has provided a toolkit to begin to reveal the features that guide the function and assembly of these structures during chromosome segregation. Despite differences in protein composition, all kinetochores must display key properties including their ability to bind to both microtubules and chromosomal DNA. This paper focuses on the mechanisms by which kinetoplastid kinetochore components are targeted to centromere regions, an exciting question due to the apparent DNA sequence-independent nature of these associations. In other eukaryotes, this sequence independent association is specified through the action of histone variants. In contrast, it is unclear how DNA interactions occur in kinetoplastids.

This paper begins by reasoning that the proteins responsible for DNA interactions and defining the location of the centromere would localize persistently to centromeres. Thus, they focus on two constitutively localized proteins with sequence similarity to each other, KKT2 and KKT3. The authors analyze these proteins using a combination of domain analysis to test the localization requirements for these proteins, mass spectrometry analysis of interacting proteins, mutational analysis to test specific residues for localization and function, and most importantly determination of the structure of a kinetochore targeting domain, which reveals a zinc finger structure. The structural work in particular is both interesting and reveals a feature of these proteins that was not obvious based on initial sequence analysis. Overall, this paper appears to be carefully executed, rigorous, and well controlled, but could benefit from additional experiments that would extend the impact of their findings.

1. From the information presented, it seems like there are only two possibilities to explain the role of the zinc finger domains in directing centromere targeting. First, this could mediate a protein-protein interaction. The authors attempt to assess this using their mass spec experiments, but this does not absolutely rule this out as this interaction may not persist through their purification procedure (low affinity or requires the presence of DNA, such as for a nucleosome). Second, this could reflect direct DNA binding by the zinc finger. Although the existing paper is solid and highlights a role for the zinc finger domains in the localization of these proteins, it would be even better if the authors were to at least assess DNA binding in vitro with their recombinant protein. Comparing its behavior to a well characterized DNA-binding zinc finger protein would be powerful for assessing whether direct DNA binding could be responsible for its centromere localization.
2. The code for KKT2 and KKT3 localization is complicated by the multiple regions that contribute to their targeting. This includes both the zinc finger domain that the authors identify here, as well as a second region that appears to act through associations with other constitutive centromere components. Due to this, it feels that there are several aspects of these proteins that are incompletely explored. First, the authors show that the Znf1 mutant in KKT2 localizes apparently normally to centromeres, but is unable to support KKT2 function in chromosome segregation. This suggests that this zinc finger domain could have a separable role in kinetochore function that is distinct from centromere targeting. Second, although the authors identify these minimal zinc finger regions as sufficient for centromere localization, they do not test whether this behavior depends on the presence of other KKT proteins. This seems like a very important experiment to test whether recruitment of the zinc finger occurs through other factors, or whether it could act directly through binding to DNA or histones.
3. Based on the description of kinetoplastid centromeres that the authors provide, it is actually unclear to whether these are indeed sequence independent. The authors state that "There is no specific DNA sequence that is common to all centromeres in each organism [Trypanosomes and Leishmania], suggesting that kinetoplastids also determine their kinetochore positions in a sequence-independent manner." However, it remains possible that there are features to this DNA that are responsible for defining the centromere. In principle, enriched clustering of a short motif that may elude sequence comparisons could be responsible for specifying these regions. It would be helpful to use caution with this statement, and I would also encourage the Aikyoshi lab to test this directly in future work, such as using strategies to remove a centromere or alter its position.
4. It would be helpful to provide a schematic of kinetoplastid kinetochore organization based on their studies to date (possibly in Figure 1) to provide a context for the relationships between the different KKT proteins tested in this paper.

Significance

This paper provides a nice advance in understanding the molecular architecture and functional organization of kinetoplastid kinetochores. As these remain understudied, this work is valuable for revealing the chromosome segregation behaviors in these medically-relevant parasites. In addition, due to the divergence in overall kinetochore function from other eukaryotes, this work will help provide insights into the logic by which kinetochores function and are organized. The existing paper represents a solid advance in understanding the structure and requirements for KKT2 and KKT3 kinetochore targeting through this novel zinc finger domain. However, conducting some of the additional experiments made above, such as testing DNA binding and the requirements for other KKT proteins for zinc finger localization, would allow the authors to make stronger statements and a more impactful advance.

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Referee #2

Points of Critique

I would urge them to reposition as a descriptive study rather than making too many grand statements about drug sensitivities.### Other Comments

Van Alphen et al. describe phosphotyrosine proteomic profiling of a panel of AML cell lines and two patient-derived AML samples. Subsequent analyses attempt to identify potentially targetable kinases and pathways that would be considered vulnerabilities for drug treatments. Some hypotheses from these analyses are tested by drug treatment. The patient-derived samples are analyzed as a proof of concept and compared to the cell line profiles.

I think that this study is a potentially valuable resource, but might be served better if positioned more as a catalog of pY signaling in AML and less as a drug-targeting effort. The analysis graphs and charts are quite handy, and perhaps they could be served on a more interactive website that could be expanded in the future as the authors continue similar studies. However, I find that many of the conclusions are overstated and that some of the internal logic is inconsistent. Further, while the bioinformatics analyses are carefully planned and well intentioned, I was confused by the inconsistent quantitative metrics used in different parts of the manuscript and curious why a more modern isobaric labeling technique wasn’t used to compare among this relatively small panel of cell lines. Below I offer several points that could be addressed to help to improve this manuscript.

1. The authors claim that sensitivity to drugs predicted by their inferred kinase activity metrics “validates” their predictions. However, all of the drugs tested have demonstrable polypharmacology. How can they be sure the targets being hit that cause loss in viability are the same ones that they have predicted? Also, it seems curious that they only tested quizartinib in predicted FLT3-GoF lines and ponatanib in inferred FGFR-GoF lines. How do we know that these drugs just don’t kill all lines? It would be more convincing to show some lines where these drugs did not cause loss in viability.

2. Along these same lines, phosphoproteomics seems like a long path to identify vulnerabilities in cancer cell lines. Screening drugs on cell lines is cheaper and easier. Indeed, the CTD2 project has a drug screening arm (as did CCLE), and new Cancer Dependency Map screening is enlarging these screens. These projects also have more comprehensive genetic characterization of the cell lines involved. If the logic is that the drug treatments “validate” the predictions of aberrant kinase activity, couldn’t the drug screening be used to make these predictions, to be later validated by phosphoproteomics? Perhaps screen all TKIs against the AML cell lines and see what common targets emerge?

3. I think that claiming that the patient results match the cell line results is a bit of selective interpretation. The first thing I was drawn to is the whopping amounts of MAPK14 phosphorylation identified by their analysis in these samples. MAPK14 - a.k.a. p38 MAPK alpha - also has drugs that target it. If you were making a therapeutic hypothesis, wouldn’t you start with a p38 inhibitor rather than a FLT3 inhibitor? You already knew that the patients had FLT3-ITD, so you’d probably be starting there anyway. While MAPK14 is found to be phosphorylated in the cell lines as well, the degree to which it rises to the top in the patient samples is striking. This also illustrates an issue with drawing inferences from cell lines to real patient samples.

4. On p.15, you state: “P15: “Kinase activity ranking analysis of the FLT3-ITD mutant cell lines MV4-11, MOLM-13, and Kasumi-6, and the V617F JAK2 cell line HEL showed a lower ranking of FLT3 and JAK phosphorylation than expected based on their mutation status, compared to other kinases (position 6-10). Interestingly, other high-ranking kinases in these cell lines were generally located downstream in the FLT3 and JAK2 cellular signaling hierarchy, thereby still implicating FLT3 and JAK2 as primary suspects of driver activity.”

Perhaps this demonstrates the limitations of the approach? Genetics says FLT3 and/or JAK2 are mutated, proxy measurements say its activated, but the way you are estimating direct activity is not so great? Would a targeted panel on activation loop sites be better?

1. Figure 6 is an interesting analysis but how it was generated is unclear. Again, polypharmacology of the drugs make it hard to interpret. Are the graphs for all potential targets? Just some? Weighted by in vitro IC50 concentrations and/or binding affinities?

Minor points:

IC50 is inappropriate nomenclature here, which describes the concentration at which 50% of an enzyme’s activity is inhibited. The authors should substitute EC50 throughout, as they are referring to the concentration at which viability is decreased by 50%.

Ibrutinib is described as a pan-KI - this is confusing and misleading. It is a pretty specific BTK inhibitor.

Please update sup table 5 to show the exact nature of the mutations. Also, why does Kasumi-6 have two different (presumably) allelic ratios for FLT3 (presumed ITD?)?

Methods - p7 “as described elsewhere” - reference needed?

The statistical rationale is not well explained.

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Referee #1

Other Comments

This manuscript describes phosphotyrosine-focused phosphoproteomics for 16 AML cell lines to obtain molecular profiles of pY towards personal therapy using proper molecular targeting drugs. This is the revised (re-submitted) version and the authors added new data analysis especially on the relationship between kinase-ranking parameters and drug IC50 profiles to kinases. These results indicate the current progress and limitation of phosphoproteomics combined with genomics data and the related computational tools. Overall, the precise descriptions of the experimental procedures as well as the high quality of the experimental datasets are quite useful for researchers in this field. This manuscript should be published after some revisions shown below:

(1) This research group just published a paper on kinase ranking using phosphoproteome datasets, named INKA. Mol Syst Biol. 2019 Apr 12;15(4):e8250. doi: 10.15252/msb.20188250 INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases.

INKA seems quite similar to the approaches in this manuscript. The authors should mention about INKA. Especially the parameters in Figure 6 should be described clearly whether these are the same in INKA or not.

(2) Figure 1 as well as Abstract and the first section of RESULTS: the numbers of phosphotyrosine sites and phosphotyrosine peptides should also be described in addition to the current description.

(3) Figure 2: the color for mutation is overlapped with colors for the heatmap. To avoid the misunderstanding, the authors should use the different colors.

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Review by Peer 4429 (Weight = 1.00)

Introduction: The manuscript evaluates the use of genomic prediction in rice to prevent the accumulation of arsenic in rice grains. This is a food safety issue. Genomic prediction could be an appealing strategy for breeding of rice varieties less prone to accumulate arsenic in grains. Genomic prediction could bridge between current strategies based on land management (genetic improvement is cumulative and permanent) and recently proposed genome editing (for which target causal mutations need to be identified first).

Merits: The study seems original in its proposal of genomic prediction for this particular problem. The authors contextualize in the Introduction the potential interest of genomic prediction against other strategies, including management and genome editing.

The manuscript is quite broad in scope, as it tackles (1) genetic variation of the traits, (2) genome-wide association study GWAS, and (3) genomic prediction.

Despite the low number of significant associations in the GWAS, some of the ones that are detected have annotation terms that could make them interesting candidates for further study.

References are appropriate for the study.

Critique: Because it covers so much ground, the manuscript is quite long and dense. I think it could be softened a little in some sections. Instead it feels a little bit rushed when it comes to genomic prediction, considering that several prediction methods and strategies are used.

While genomic prediction is contextualized against other strategies in the Introduction, some of the results are not discussed as compared with other strategies. For example, there could be a greater effort to discuss the results of GWAS in light of the identification of targets required for genome editing (building on L327-336). There should also be a greater effort in discussing the several methods used for genomic prediction and potentially how genetic architecture from GWAS may help explain the differences between methods; for instance, if genomic prediction is concluded to be the best strategy, which method of all tested is recommended?

I am not totally comfortable with the interpretation that the authors make of the comparison between phenotypic and genomic selection (L346-362). Phenotypic selection is producing 5 to 10% more genetic gain than the genomic (L344-345). This is a large difference that cannot be disregarded. The authors also claim that at equal cost of phenotyping and genotyping, genomic prediction would be preferred. While I agree with the logic that genomic data has the additional benefit that it can be applied to any trait, phenotyping of each of these potentials traits would also be needed with a certain routine to re-train the predictive equation. The authors acknowledge to some extent these points but, because overall phenotypic selection seems to be a better strategy for the specific case of arsenic tolerance and because the suitability of genomic prediction is established as dependent on genotyping costs, the title and conclusions seem a little bit misleading.

It is clear that the paper was written with the Materials and Methods after the Introduction and it was later moved to the end of the manuscript. As a consequence, abbreviations are not properly defined when first read.

Discussion: The manuscript offers a broad perspective on a topic of interest, affecting food safety, and proposes a sensible approach to mitigate it. The study is very detailed about the genetic variation of the traits and GWAS results and overall tackles all important points of discussion. However, it is slightly more vague on the genomic prediction section: several methods and strategies are tested but not described in the Methods section with enough detail and not thoroughly discussed. The authors conclude that genomic prediction would be a more suitable strategy to breed for arsenic-tolerant rice compared to other marker-assisted breeding strategies. However, it seems from the results that genomic prediction still underperforms compared to phenotypic selection and this should be put into context too. This manuscript contains some interesting research and it could be suitable for publication, but some revision is recommended as indicated.

Additional Comments for Authors

• L38: Be explicit. Mitigation of what?

• L59: Please define "Aus genetic group".

• L96: Be explicit. Which three traits?

• Also L96: The distributions in Fig 1 seem to depart from a normal distribution.

• Genomic prediction results: There is an n>p problem here, considering that 100 to 300 accessions but ~20,000 markers were used. Bayes A (one of the methods highlighted as most promising) fits all the markers in every iteration; Bayes B and C fit a pre-defined proportion of markers "pi" (could the authors specify to what value that parameter "pi" was set?); etc.

• Revise English. Several typos and minor grammar errors.

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Review by Peer 1755 (Weight = 1.00)

Introduction: This paper presents a Bayesian model of mating in a fish, that combines behavioural data on encounters and matings with genetic parentage data. It contrasts this model with classical analyses that use only particular facets of these data.

Merits: In my opinion, this paper's most important merits are:

That the model makes conceptual sense, and is presented in a way that is fairly easy to follow.

That the authors share the model code and data. This will make the model a lot more useful for other researchers.

That the paper is well written.

Critique: Despite this, I think there are things that could be clarified or improved:

1. There seems to be a considerable skew in the reproduction data. This is expected, but this comes with a risk violating the assumptions of common statistical models. Does the models used adequately capture this? In particular, the correlation coefficients (Figure 1) must be largely driven by single influential data points.

2. Given the above skew and structure of the data and that the model results extrapolates quite a bit from what was observed, it would be nice to see more through checks and discussion about the validitiy of the model. How well the model can reproduce features of the data? The posterior predictions in Figure 4 seem to indicate that the model fits data rather poorly? But I may be mistaken, and the manuscript does not interpret these results much.

3. I got the JAGS model to run with only minor editing (that is, moving the data generating code to its own file). However, I can't, using the data in the script, recover the scatterplots and Pearson correlations displayed in Figure 1. I assume my analysis (see attached Sweave pdf output) is wrong somehow, suggesting a need for better documentation so that readers such as myself can understand the data. It may help to clarify what variables are what, which samples have been omitted (from what analyses and for what reasons), and store the data in tabular format in addition to the JAGS input format. It would also be a nice addition to have the code used for running the model and summarising the results -- it would save a user quite a bit of effort without much work on behalf of the authors.

4. The sample sizes for data on releasing of gametes are particularly small. One wonders how much information they contribute? Similarly, both observations (line 248) and modelling (line 305-307) suggest that many encounters were not observed. How does this affect conclusions? This ability to deal with incomplete data is highlighted as a feature of the model. Is there arguments or data that show that it is successful?

5. In the Introdution and Abstract, one of the motivations for this approach is to capture effects of interactions of the phenotypes within a pair. But then, "Unfortunately our dataset is too small to properly infer the effect of interaction" (line 428-429). First, previous the focus on this unused feature of the model seems misplaced. Second, it is not clear when a dataset is too small and how you know that (presumably by trying a model not shown?).

6. I think this paper would benefit from more illustration. Figures 1 and 3 are hard to read with small differently shaped symbols, line patterns, and overplotting. I would suggesting making separate plots for males and females to alleviate some of the clutter. Figure 1 b is particularly unreadable. The plots of posteriors are fine, and probably should be in the paper, but I think they should be supplemented with some descriptive graphics that give a feel for the structure of the data and the behaviour of the fish. I would even love to see some visuals of fish mating, maybe stills from the video recordings (or even a supplementary video). Of course, this may be limited by space requirements of the target journal, or nor to the author's taste. But I think you underestimate how cool some of these things are, especially if you aim for a wide audience not well versed in fish mating research.

Discussion: This is likely beyond the scope of this paper, but I feel that a lot of the questions about the model -- does it work on small datasets; does it successfully account for unobserved encounters; how does its parameters relate to the "classical" measures of sexual selection -- could better be answered with simulated than with real data. I sympathise the use of real data: a good biological example is a lot more convincing to biologists than simulations. However, I feel that there are often too many uncertainties in comparing methods on real data. Results of different methods differ, like the "classical" and the new analyses in this study. But which are right?

Additional Comments for Authors

1. The paper would benefit from a two sentence explanation of opportunity for selection, what it measures, and the distinction between opportunity for selection and opportunity for sexual selection.

2. L8-10: The opening of the abstract sets up the paper to be rather technical, jumping directly into marginal sums of matrices. I think you may want to rethink that approach if the goal is too reach, as the author message said, "a wide audience of ecologists and evolutionary biologists".

3. For the same reason, I'd advice against the introduction of a 3-dimensional array on line 34. Even if that is mathematically correct, it is immediately going to be summed to the a parental table. Therefore, the 3-dimensional structure doesn't really contribute much, except act as an obstacle to mathematically less savvy readers.

4. L48-49: "strong link" could be made more precise.

5. Line 123-124: "The experimental setup is the one used in the "constant environment" treatment in Gauthey et al. (2016)." What is the relationship between this work and Guthey et al 2016? Can this be made clearer?

6. Lines 226: "po" is not defined in this section. I think the manuscript would benefit from being checked an extra time for mathematical symbols, when they are defined, how they are referred to, and if they can be spelled out in text to help the reader.

7. Line 270: "Model output" is not a very informative subtitle. I'd suggest dividing the Results into one subsection on the data set, one on the "classical" analyses of sexual selection, and one on the model.

8. Some of the chocies about model structure (specifically, use of informative priors) is discussed in comments in the model code, but not in the Methods. They should be in the Methods too.

Review by Peer 1765 (Weight = 0.88)

Introduction: This paper aims to solve a long-standing issue in sexual selection studies in natural populations: that genetic and behavioural data tell us different things about separate stages of sexuals selection and, therefore, often focus on different processes in sexual selection. While behavioural data tend to focus on mate sampling and mate choice, genetic data provide evidence on the resulting mating/reproductive success. This paper makes an important step in trying to combine both types of data in order to analyse the complete process of sexual selection. Such a tool could substantially advance the field of sexual selection in natural populations. I was very enthousiastic about this approach, until I arrived at Figure 4, which shows that the predictions from model the authors suggest does not correlate at all with the observed data from their case study, suggesting the model is possibly very well thought through, but does not represent the data well. Without empirical evidence, I do not see any reason to put the results of the model above those of the classical methods.

Merits: The paper describes the model used in a way that is mostly very clearly understandable for non-modelers, which is important for the general use of the proposed method. Moreover they include a case-study which very nicely links the theory to experimental data.

Critique: The suggested model provides different results from more classical methods of analysing the data. The authors then go on to defend the model as a better way to analyse the data, because they find different results. However, they do not provide evidence that the results from the model fit the data better than the results from the classical analyses. In fact, Figure 4 shows that the model is actually rather bad in predicting observed encounter rates, gamete releases and offspring numbers, because there seems to be no correlation whatsoever between observed and predicted data. For example, many females that did sire large numbers offspring were not predicted to have any offspring according to the model (Fig. 4c). This is not discussed in the paper. I do commend the authors for testing their model on a case study, and combine a theorethical appraoch with an experimental one, but the difference between predicted and observed data should be discussed. The authors could compare the model predictions to the predictions from the classical analyses and see which analyses fit best with the observed data.

Terminology: Encounter rate is a term that is generally reserved for random events depending on population density and sex ratio. However, the way it is used in the case study (which is certainly the most practical for field observations) includes a certain effect of attraction. In most species, males and females do not generally end up close to a spawning ground/ nest without being attracted by some aspect of the individual or this particular nest. The authors are likely aware of this, because they test for an effect of female size on encounter-rate. The fact that they do not find such an effect does not exclude that their may have been attraction to other characteristics of the female or the nest-site. Therefore, I would suggest to use another word for encounter (for example inspection or visit) to avoid confusion between an event where individuals have likely already been attracted to each other (as used in the case study) and a random "encounter". The latter is, however, impossible to quantify in the field, because it is generally impossible to spot whether two individuals have noticed each other and I see no reason to include it in the model.

Discussion: The paper addresses a very important issue in the study of sexual selection: how to combine behavioural and genetic data to study the strength of sexual selection. As the authors rightly argue, both types of data omit important processes in sexual selection and very few studies manage to get both types of data for all (or even most) mating events. The model they suggest would make use of incomplete behavioural and genetic data to explain the underlying processess. Such a model could provide an important tool for sexual selection studies. However, the case study the authors provide suggests that the model is not very good at predicting real case scenarios. Therefore, the autors should investigate how the model could be changed to reflect their experimental data. Doing so would provide an important paper that would be very valuable to the field.

Review by Peer 1758 (Weight = 0.85)

Introduction: This manuscript offers a statistical alternative to classical sexual selection gradient analysis by using Bayesian inference that allows accounting for male and female effects simultaneously. Furthermore, the authors highlight that mating success is generally underestimated because it is based on the genetic assignment of offspring. The authors use their own data on the mating behaviour and reproductive output of brown trout to compare the results from classical selection analysis with their Bayesian model and find differences between the two.

Merits: This manuscript is relevant because it highlights limitations of classical sexual selection gradient analysis, and offers a statistical alternative to empiricist with suitable data. I have the following suggestions, which I hope will be useful in revising the authors' original contribution. Also, I welcome that the authors made their research transparent by adding their data and code. However, I want to make clear that I could not review their code because of incompatibilities with JAGS and my software. ​

Critique: The authors statistical alternative is motivated by two shortcomings to (a) account for the interdependence of females and males in sexually reproducing species and (b) getting a grip on the copulatory behaviour instead of inferring it from offspring data. Whilst I agree that (b) is pressing, (a) depends on the mating systems, e.g. in strictly monogamous species, male and female identity overlap and fitting both would not be informative or appropriate for the analysis of sexually selected individual phenotypic traits. Hence, the applicability of the authors' model would profit from information on its suitability for different mating systems, i.e. expand on "a variety of biological systems", l24, in the discussion. Also, the authors approach also relies on empirical data. In other words, the best model does not change that if mating success lacks behavioural observations, and it usually does, we can only make incomplete inferences. In my view, the main contribution of this manuscript is thus to serve as an important reminder of the complexities at play and the importance of comprehensive data collection, rather than a new tool for measuring sexual selection. Also, the pitfalls and shortcomings, (e.g. bias in stochasticity, what is the null model, operational sex ratio) when measuring sexual selection have been comprehensively illustrated here (Klug, Heuschele, Jennions, & Kokko, 2010) and here (Jennions, Kokko, & Klug, 2012). So, I recommend a more inclusive portrait of the matter and attuning with published jargon (e.g. Table 1 in (Klug, Heuschele, Jennions, & Kokko, 2010).

• I advocate that the full results of the linear regression analyses as well as the alternative JAGS model are presented in table format in the main text. Results in the supporting information get missed easily, and plots cannot substitute full estimates.

• The authors could expand more on discussing their most interesting finding, which is the discrepancy between their results using classical regression analyses and Bayesian analysis.

Discussion: This manuscript is motivated by two shortcomings of the classical sexual selection gradient analysis. I agree with the relevance of one of them (i.e. measuring mating success) and yet argue that the relevance of accounting for the additive effects of the sexes for reproductive success is highly dependent on the species mating system, which the authors should address. I also think that the authors should make clearer that their analysis still depends on empiricists collecting data on mating success. I welcome the authors approach to use their own data to compare whether body size of male and female brown trout might be sexually selected. If the authors revise the current version, their manuscript will serve as an important reminder of what to look out for when analysing potentially sexually selected traits.

References Jennions, M. D., Kokko, H., & Klug, H. (2012). The opportunity to be misled in studies of sexual selection. Journal of Evolutionary Biology. http://doi.org/10.1111/j.1420-9101.2011.02451.x

Klug, H., Heuschele, J., Jennions, M. D., & Kokko, H. (2010). The mismeasurement of sexual selection. Journal of Evolutionary Biology. http://doi.org/10.1111/j.1420-9101.2009.01921.x

Schlicht, E., & Kempenaers, B. (2013). Effects of social and extra-pair mating on sexual selection in blue tits (Cyanistes caeruleus). Evolution, 67(5), 1420-1434. http://doi.org/10.1111/evo.12073

Additional Comments for Authors l14: be clearer on "costly" or delete because costs were not measured

l27: add or consider selection gradient, see Table 1 in Klug et al 2010

l44: ambiguous "to do so". Which of the indices exactly?

l52 infertile not unfertile

l53 reference "cost of reproduction"

l64 reference costs

l65 back up the claim of "are essential to understand..."

l68 better name the "fourth definition"

l88-89 reference

l93 define "a pair", e.g. socially monogamous? This could be an opportunity to introduce the mating system you want to target

l109-111 reference?

l113-115 reference?

l116 in brown trout? Please add citation

l120 "a" semi-natural...

l120-123 split into two sentences to improve readability, e.g. This period represents the trout...

l124: chemically communicated?

l129: highly female biased, which might be biological meaningful or a catching bias, please explain. Plus this skew in adult sex ratio will affect the variance in mating success, i.e. "chance variation in mating success is higher when there are fewer potential mates per individual of the focal sex" (Jennions et al 2012), this affects both your statistical approaches but it nowhere mentioned

l132 how did you sex? Molecularly?

l145: one or multiple observers? also "taken" not "took"

l148 any proof? repeatability tests? references for the claim?

l149 say how you dealt with the 30% for analyses

l150 rephrase "the zone", e.g. female nesting/egg release site, etc.

l156 consider "spawning" or gamete release instead of copulating

l159 "degree day" reads misplaced, only use estimate of time after spawning

l172 its

l186 consider making clearer that zero's were included

l247 depending on where you want to submit avoid fish jargon: "redd"

l249 give output of all linear regression analyses in table

l271 I suggest moving these to the main text

l278 why not report Credible Intervals instead of SDs? Also, SDs show high uncertainty in estimates, which should be addressed in the discussion

l333-4 reference

l336 rephrase "to account for..."

l335 give time unit, e.g. over the course of the experiment

l336 Comment: I disagree because sexual selection is commonly referred to as the opportunity for evolutionary change, which is the variance in relative fitness and should consider all reproductively mature adults, hence should be measured among individuals that do and do not interact/mate. Especially the latter is usually omitted, but ignoring unmated individuals in a population will automatically inflate the variance of the successful subset (see also (Schlicht & Kempenaers, 2013)).

l418-19 rephrase, unclear

Plots: General comment: It might be the pdfs but the quality of plots is low and generally offsetting the raw data a bit, e.g. jittering would help viewing individual data points

Review by Peer 1761 (Weight = 0.67)

Introduction: The authors point out how the study of mating systems only using behavioural observations or genetic data usually fails to explain accurately the breeding processes and reproductive outcomes, as well as their relationship with sexual selection features.

They propose a model that combines both behavioural and genetic data, and a phenotypic trait linked to sexual selection, using brown trout as model species.

Their model includes several breeding variables behavioural and genetic, and it very adaptable as is able to incorporate other environmental or biological variables if needed.

They show how genetic and behavioural results analyzed separately may differ. Also, how the results from their model and the classic regression analyses to analyse this data also differ, and so, they aim to explain why.

Merits: The model they have built seems flexible enough to be adapted to multiple taxa and systems.

Critique: There is no reference at all about ethics permissions to perform the described experiment. I am quite shocked about this since high numbers of individuals from a wild population were killed.

There is no mention on the conservation status of the species, the permits obtained to carry out the capture and experiment, the effect of the capture system on the ecosystem, or the explanation/justification for the use of lethal methods.

For example, I find electrofishing highly non-targeted and I wonder how was its impact on other non-target fish (and non-fish) species. I believe that assembling a team of fishermen to get the same number of adult specimens would be easy enough to arrange.

My point is not whether the methods were ethically acceptable or not (that is for the journals' ethics committees to decide) but to, at least, justify and explain their use.

Model testing: I understand that in ecology studies usually researchers don't get all behavioural or all genetic data, and that is what the models try to compensate for. However, when testing models in a biological system the ideal situation is to work in a system where almost all information can be collected (ussualy under lab conditions), build a model with all that information, and then subsample the data (as to simulate a real ecological study) to test the model performance.

In this study, however, the initial sampling for the data is quite small, specially for behavioural observations (30min/day). Then, the results from the model are quite different from the results obtained from more classic approaches. The authors offer some hypotheses to explain these differences, but they can't be really tested to see whether the authors' model results are better in explaining the system or not.

All that said, I have to admit that I lack the mathematical background to fully understand and evaluate the model design and performance, and a more qualified researcher should do that.

Discussion: Although the experimental approach to test the validity of the model predictions could have been better, their attempt to combine behavioural and genetic data in mating system studies and relate it to sexual selection is an important step forward in the behavioural field.

Hopefully, more efforts like this will be made to reconcile both aspects of the study of mating systems that rapidly changed from behavioural observations only to genetic analyses only.

Review by Peer 1773 (Weight = 0.51)

Introduction: In accordance with traditional approach to estimate the effect of sexual selection on phenotypic trait the number of mates should be regressed on a target phenotypic trait in a separate model for each sex. Such analysis ignores common investment of the sexes into mating success. The authors propose a new approach, which allow combining behavioral and genetic data, thereby enabling to gather information through the successive processes of encounter, gamete release and offspring production.

Merits: The new approach accounted for the three-dimensional structure of the data: males, females and mating occasions. This allowed a qualified definition of mating success and disentangling the joint effects of male and female phenotypes on the different components of reproductive success. Three important features that lack in the traditional approach characterize the authors' model:

1) conditioning of each process (encounter, gamete release and offspring production) on the preceding one,

2) simultaneous estimation of the effect of male and female phenotype,

3) random individual effects.

​The authors tested their model on a brown trout and obtained quite different results for the two approaches.

​The model can be used for a variety of biological systems where behavioral and genetic data are available.

Critique: The model should be tested on a larger sample.

The title of the manuscript is not very successful.

​There is a couple of misprints: p. 7 l. 139 and p. 8 l. 159.

Discussion: This is very important when new algorythms allow to obtain more information from the same set of data. Hopefully, it would be of great importance if the model can be developed to account for real behavior traits in species presenting complex courtship behavior like Drosophila for instance.

[Note: this preprint has been peer reviewed by eLife. The decision letter after peer review, based on three reviews, follows. The decision was sent on 21 May 2019.]

Summary

Masachis, Darfeuille et al. analyse a type I toxin - antitoxin (TA) module of the major human gastric pathogen Helicobacter pylori (Hp). Expression of toxins encoded by Type I modules is controlled by small, labile, cis-encoded antisense RNAs and often also by complicated mRNA metabolism that envolves conserved mRNA folding pathways and/or mRNA processing. Using a combination of elegant and robust in vitro and in vivo methods, the authors first show that that the aapA3/IsoA3 TA system of Hp is regulated in a way very similar to that of the homologous aapA1/IsoA1 system from the same organism (Figs 1 and 2). This initial part of the manuscript sets the stage for the next step, where the authors employ a powerful genetic screen combined with deep sequencing to identify single nucleotide changes that abolish production of the AapA3 toxin (Fig. 3). This principle, which was invented by the authors, is technically robust, intellectually attractive and very powerful, and may yield novel insights that at present cannot be reached by other approaches. In particular, the authors discover that single point mutations outside the toxin gene reading frame suppress toxin gene translation. Focusing on the translation initiation region, they discover two mRNA hairpin structures that, when stabilized by single base changes, reduce translation by preventing ribosome binding (Figs 4-6). They propose that these structures are metastable and form during transcription to keep the toxin translation-rate low, as explained in the model figure (Fig. 7).

Essential Revisions

All of the reviewers thought the quality of the experimental work in the manuscript is outstanding and the conclusions are justified. However, all thought it would be nice to have additional evidence of the proposed metastable structures in the nascent toxin mRNA. While the reviewers understood this might be technically difficult, they agreed that it is worth a try and had the following suggestions.

1) Phylogeny (i.e. nucleotide co-variation in sequence alignments) was previously used to deduce the existence of stem-loop structures not only in ribosomal RNAs but also in mRNAs (e.g., hok mRNAs). Did the Authors consider using this approach to support the existence of the proposed metastable structures in the nascent toxin transcript? This possibility depends on the actual homologous sequences available and is not possible in all cases. If phylogeny indeed supports the existence of the metastable structures, the Authors could look for coupled nucleotide covariations that would support a conserved mRNA folding pathway (that is, one mRNA sequence elements pairs with two or more other elements during the fife-time of the mRNA) . The Authors state in the Discussion that "these local hairpins were previously predicted to form during the co-transcriptional folding pathway of several AapA mRNAs (Arnion et al., 2017)." However, they authors did not explain how these hairpins were predicted. It is worth explaining this central point.

2) Although transient structures are by definition hard to detect, the authors could try in vivo structure probing (DMS) of truncated mRNAs 1-64 and 1-90 to demonstrate the existence of the first and the second metastable structures, respectively.

3) It is preferable to carry out 2D structure predictions on the naturally occurring transcript, not a sub-sequence. 2D structure prediction generated by algorithms such as RNAfold (or Mfold) that are guided by delta-G stability optimisation are sensitive to the sequence context, so the correct sequence needs to be used to be able to draw conclusions. Additionally, the findings presented in Figure 3D could be analyzed a bit further to produce significant, independent evidence for some structure features. Specifically,

Figure 2 caption:

• lines 184 - 186: "2D structure predictions were generated with the RNAfold Web Server (Gruber, Lorenz, Bernhart, Neuböck, & Hofacker, 2008) and VARNA (Darty, Denise, & Ponty, 2009) was used to draw the diagrams."
• Please state clearly whether any of the results of the experimental 2D structure probing were used as input to RNAfold (i.e. as additional constraints to the prediction algorithm).

Figure 3D:

• Please add coloring to the peaks depending on which codon position they overlap (1, 2 or 3) and carefully discuss the corresponding results, also in the context of the 2D structure elements.
• Given that you have a decent number of pair-mutations, analyze them to see whether any correspond to RNA structure base-pairs (and whether any of the pair mutations rescue the base-pair and thus affect the system differently). This would serve as additional, independent evidence of 2D structure probing and predictions.

[Note: this preprint has been peer reviewed by eLife. The decision letter after peer review, based on three reviews, follows. The decision was sent on 17 June 2019.]

Summary

Natural Killer (NK) and the ILC1 subset of innate lymphoid cells share related functions in host defense but have been argued to arise from distinct pathways. Park et al present new evidence challenging this concept. They show that murine Toxoplasma gondii infection promotes the differentiation of NK cells into an ILC1-like cell population which is stable and long-lasting, even after the infection has been cleared. These T. gondii induced cells, unlike Eomes+CD49a- NK cells, are Eomes-CD49a+T-bet+ and therefore resemble ILC1 cells. The authors additionally show that their differentiation involves Eomes down regulation and is STAT-4 dependent, However, in common with NK cells and distinct from ILC1 the T. gondii induced "ILC-like" population circulates to blood and lungs. Finally, the authors employ single cell RNAseq to examine the heterogeneity of the major T. gondii induced innate lymphocyte populations and their NK vs ILC relatedness as assessed by gene expression. Together, their observations establish a previously unappreciated developmental link between NK and ILC1cells in the context of infection.

The 3 reviewers and editor agree that this is an important contribution that sheds new light on the developmental relationship of NK and ILC1 cells, a scientific issue that has received considerable attention in the innate immunity field. Although extensive, most of the criticisms raised can be addressed by revisions to the manuscript. One additional experiment is requested to provide a missing control.

Essential Revisions

All reviewers had a major concern about how this new population of T. gondii induced innate cells should be referred to in the manuscript. Based on the single cell RNAseq data, these cells (cluster 10) are still closer to NK cells than to ILC1s (Figure 5f and Suppl Fig 4e) despite their loss in Eomes expression and acquisition of CD49a expression. Thus, one could easily think of them as "Eomes negative NK" or "ex-NK" cells rather than ILC1s, and to simply refer to them as Eomes-CD49a+ ILC1 cells may be misleading . For this reason, the authors should modify the title of the paper and change their designation throughout the manuscript. We suggest "ILC1-like" as a good descriptor. In addition, although it is clear that the "Eomes negative NK" cells that are generated during T. gondii infection are transcriptionally and epigenetically distinct from the NK cells in the steady state and NK cells after infection (Figure 7 and suppl Figure 6), these "Eomes negative NK" cells referred to as "T. gondii-induced ILC1s" were not directly compared with classical ILC1s. Based on the single cell RNAseq data, these cells may not express many of the ILC1-related signature genes. Therefore, again, the authors need to be cautious in referring to them as ILC1 cells.

A second concern was that the NK 1.1 depletion shown in Supplemental figure 1 was performed with a PBS rather than isotope matched immunoglobulin control which is considered unacceptable. The authors should repeat at least once with proper control Ig to make sure this is not issue. It is not necessary to repeat entire survival curve just experiments shown in A and B and initial survival to make sure there is no death in controls vs. antibody treated.

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• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>