Many schools and daycares are sadly closed at the moment because of COVID19 pandemic.

Rashida, as an SLA student, talked about the issue she met before, and explained why this process can help. Her letter is a great evidence to show that our action is effective.

Author Response:

Reviewer #1:

The authors of this study carried out two carefully designed field and a glasshouse experiment simulating effects of rapid warming on soil carbon loss. They did this by transplanting alpine turfs from their cold environment to lowland warm environment. They found that when lowland plants were inserted into alpine turfs under these lowland climatic conditions (referred to as warming treatment combined with warm-adapted plant introduction) they rapidly increased soil microbial decomposition of carbon stocks due to root exudates feeding the microbes.

The question is how well this experimental setup mimics what would happen if lowland plants would be inserted into alpine turfs in situ (which have already experienced considerable warming over the past decades), perhaps with an additional warming treatment there.

The Reviewer alludes to two pertinent points here. The Reviewer’s first point considers whether lowland plants would function similarly (and, by extension, have the same effect on the soil system) if moved from the warmer lowland site to the cooler alpine site. This is a fascinating question in its own right, in that it raises questions about how migrations of non-adapted genotypes far beyond range edges (e.g. via human activity) impact recipient ecosystems. However, although we agree that alpine ecosystems have warmed considerably in recent decades, we cannot be confident that the high elevation sites in our study are already within the climate niche of the lowland focal species. As such, to address our research questions in situ at the high sites would have required additional warming treatments, which come with their own set of disadvantages (see our second point to this comment, below). We also refer the Reviewer to specific questions about adaptation below (see R6), although we see that we were not careful enough about the rationale for our design in the previous version of the manuscript. We have therefore added a clarifying sentence to the Main Text as follows:

L101: “In short, the experiments used here examined how the arrival of warm-adapted lowland plants influences alpine ecosystems in a warmed climate matching lowland site conditions (i.e. turf transplantation to low elevation plus lowland plant addition) relative to warming-only (i.e. turf transplantation to low elevation) or control (i.e. turf transplantation within high elevation) scenarios.”

Second, the Reviewer implicitly raises a point about whether our chosen approach of simulating warming plus lowland plant arrival (i.e. transplantation plus addition of lowland plants) is the most appropriate, specifically by suggesting an alternative option of adding lowland plants to (possibly experimentally-warmed) alpine turfs at the high elevation origin site. Here, it was essential to create a climate scenario in which lowland plants would survive and operate within their climatic niche (i.e. relative to their home conditions) once planted into alpine turfs, rather than perform sub-optimally (e.g. be in a potentially inferior competitive position) or be unable to persist at all. The most parsimonious and reliable way to ensure this was to transplant alpine turfs to a site with a lowland temperature regime, with transplantations also being shown to outperform other methods when novel species interactions are involved (Yang et al. 2018). Most importantly, it was crucial to select a method that warmed the entire plant-soil system rather than only the air (e.g. open-top chambers, IR lamps; Marion et al. 1997; Aronson et al. 2009) or soil (e.g. heating cables; Hanson et al. 2017), and did so realistically throughout the year regardless of the weather (e.g. open-top chambers only work on sunny days in the summer; Marion et al. 1997) or a power supply (e.g. IR lamps, heating cables). Transplantation remains the only way to achieve this (Hannah 2022; Shaver et al. 2000). We now clarify our logic in the manuscript as follows:

L91: “Elevation-based transplant experiments are powerful tools for assessing climate warming effects on ecosystems because they expose plots to a real-world future temperature regime with natural diurnal and seasonal cycles while also warming both aboveground and belowground subsystems. This is especially true if they include rigorous disturbance controls (here, see Methods) and are performed in multiple locations where the common change from high to low elevation is temperature (here, warming of 2.8 ºC in the central Alps and 5.3 ºC in the western Alps). While factors other than temperature can co-vary with elevation, such factors either do not vary consistently with elevation among experiments (e.g. precipitation, wind), are not expected to strongly influence plant performance (e.g. UV radiation) or in any case form part of a realistic climate warming scenario (e.g. growing-season length, snow cover).”

A further question is if alpine plants inserted in turfs at alpine climatic conditions would have a similar effect as lowland plants inserted in turfs at lowland climatic conditions.

We interpret “turfs” to mean “lowland turfs” here, since we did insert lowland plants into alpine turfs under lowland climatic conditions (i.e. the WL treatment). We found that adding alpine plants to alpine turfs in alpine climatic conditions (i.e. planting disturbance control, see Methods) had no effect on alpine soil carbon content. By extension, we would expect that adding lowland plants to lowland turfs in lowland climatic conditions would have no effect on lowland soil carbon content. While not explicitly tested, including this treatment would not change our finding that adding lowland plants to alpine turfs causes a reduction in soil carbon content relative to adding alpine plants to alpine turfs. Given this, we have left the text as is, but are happy to revisit this issue based on further discussion with the Reviewer/Editor.

I suggest that the authors consider these questions when they draw conclusions about the results from their experiments. It would also be interesting to discuss the relevance of sudden strong warming effects relative to slower warming, potentially allowing ecosystems to adjust via changes in genetic composition of species (i.e. evolution) or species composition of communities (i.e. community assembly).

Thank you for this excellent suggestion. We absolutely agree that anything short of a decadal experiment is unable to detect the role of longer-term evolutionary or community processes on soil carbon dynamics. While this doesn’t eliminate the need for experiments that consider shorter timescales, it is important to explicitly state this limitation. As suggested, we have added a sentence discussing this possibility in the concluding paragraph:

L387: “While our findings demonstrate that lowland plants affect the rate of soil carbon release in the short term, short-term experiments, such as ours, cannot resolve whether lowland plants will also affect the total amount of soil carbon lost in the long term. This includes whether processes such as genetic adaptation (in both alpine and lowland plants) or community change will moderate soil carbon responses to gradual or sustained warming.”

We also agree that it is extremely challenging to undertake warming experiments that do not initially “shock” the system through a sudden change in temperature. Having said this, alpine ecosystems are adapted to rapid within- and between-season temperature changes, making such shocks less relevant here.

Reviewer #2:

The authors were trying to test whether the migration of lowland plants into alpine ecosystems affects the warming impact on soil carbon. To achieve this goal, the authors first did two field experiments (moving intact turf from high-elevation to low-elevation to simulate warming) in the Alps, and then did a greenhouse pot study to explore the potential mechanisms for the results observed in the field experiments.

The main strenghs of this work are the combination of a field experiment (conducted at two sites) and a greenhouse pot experiment (to explore the detailed mechanisms). Moreover, a number of techniques were used to measure plant traits, soil DOM and microbial properties (e.g. CUE, growth) which help to find the potential mechanisms.

We thank the Reviewer for this positive comment.

The main weaknesses of this work are below:

1) The two field experiments are very short-term (<1 year), but the results were that warming and/or warming+lowland plants led to very high amount of soil C loss (up to ~40%, Fig. 1). I was shocked to see these results as many field warming studies have shown undetectable change in SOC even after years or decades. The authors did not provide a good explanation for this rapid and large change in SOC.

We apologise for the confusion. We’re unsure where “up to ~40%” comes from here, so we have taken the Reviewer’s later suggestion of changing the annotation on Fig. 1 to contrast C versus WL treatments (Western Alps = 25.6 ± 7.2 mg g-1; Central Alps = 25.3 ± 8.6 mg g-1) rather than W versus WL treatments.

With regards to the magnitude of soil carbon loss observed, we express soil carbon content in mg g-1 (i.e. mass-based per-mil), not cg g-1 (i.e. mass-based percent). This is so that we could use percent changes in the text to highlight the numeric magnitude of differences between treatments without confusing them with mass-based percent soil carbon – although we appreciate that this also caused confusion. To clarify, converting the above C versus WL treatment contrasts from mg g-1 to mass-based percent yields 2.56% ± 0.72% for the Western Alps experiment and 2.53% ± 0.86% for the Central Alps experiment. While it is striking that the WL treatments lost ~2.5% (~25 mg g-1) soil carbon in one year, such a loss is not extraordinary. To avoid future confusion, we have clarified the units in the Fig. 1 caption as follows:

L77: “Mean ± SE soil carbon content (mg C g-1 dry mass; i.e. mass-based per-mil) in alpine turfs transplanted to low elevation (warming, W; light grey), transplanted plus planted with lowland plants (warming plus lowland plant arrival, WL; dark grey) or replanted at high elevation (control, C; white). Data are displayed for two experiments in the western (left) and central (right) Alps, with letters indicating treatment differences (LMEs; N = 58).”

2) The greenhouse experiment was used to explore the potential reasons for the amplified loss of soil C in the field experiment. However, a key result was based on incubation of disturbed soils (8 g) and a two-pool modeling of the respiration data from the short-term incubation. This may not provide a good estimate of the true turnover rate of SOC under different plant species (even in the greenhouse condition). If rhizosphere priming was the proposed mechanism (as hinted by the authors), a better approach (such as 13C labeling) is needed to measure microbial respiration from intact soils (with plant/root presence).

We agree with the Reviewer that using an approach such as 13C-labelling would have provided more direct evidence that lowland plants cause a rhizosphere priming effect. However, although some of our evidence comes from disturbed soils (i.e. microbial respiration), some (i.e. soil pore water) also comes from intact pots prior to harvest and we now also include another line of evidence from plant root biomass. In short, we draw on multiple lines of evidence suggesting that root exudates were involved, and note that Reviewer #3 thought our approach and interpretation on this aspect of the study was robust.

Having said this, we acknowledge that we were too confident in our interpretation here, so we have added caveats to the text as follows:

L207: “While not directly measured here, a nine-day decay period corresponds to the time expected for newly photosynthesised CO2 to be released through root exudation and respired by soil microbes, suggesting that this carbon pool was mostly root exudates.”

L215: “While further directed studies are required to resolve whether root exudates are truly involved, our findings collectively suggest that lowland plants have the capacity to increase total root exudation into alpine soil relative to resident alpine plants.”

3) Some details of the sampling or measurement are very crucial and affect the results/interpretations. For example, in the field experiment, the soil core was only 1-cm diameter. Considering the spatial heterogeneity of soil carbon in field plots, this small volume may not well represent the true soil condition. Moreover, in the field plots, did soil bulk density change after planting of lowland plants or warming? This will affect the measured SOC concentration (mg/g) even the SOC stock (g/m2) did not change.

We agree with the Reviewer that taking a single soil core of 1 cm diameter in each plot would not have been robust. We did not do this. While we used 1 cm diameter cores to minimise disturbance, we took three cores per plot to account for within-plot heterogeneity and combined them into a composite sample. This is stated in the Methods as follows:

L523: “In each plot, we created a composite sample from three cores (ø = 1 cm, approx. d = 7 cm) no closer than 7 cm from a planted individual and from the same quarter of the plot used for ecosystem respiration measurements (see below; Supplementary Fig. S1).”

We also agree that bulk density measurements were an important omission in the initial submission. We note that this point was fleshed out by Reviewer #3, below, so we refer the Reviewer to our response to that comment for further details.

Reviewer #3:

The authors investigated the effect of warming and herbaceous plant migration on soil carbon (C) content using an ecosystem monolith transplant experiment along an elevation gradient in the Swiss Alp mountains. They observed, approximately 1 year after the transplant, that warming alone had little effect on soil carbon content (monoliths transplanted to a lower elevation with higher temperature remained unchanged in C content) but that the presence of lowland (warm-adapted) herbaceous plants in combination with warming had a negative effect on soil C content. The authors then conducted a glasshouse experiment and used a series of field and laboratory measurements to explore potential mechanisms explaining the observed changes in soil C content in the field. They concluded that soil C losses under lowland plant migration were likely mediated via increased microbial activity and CO2 release from soil C decomposition.

The research questions are extremely relevant to our understanding of the feedback between soil C dynamics and climate warming and remain an unexplored part of this debate. Moreover, both field and laboratory experimental designs are robust, with all the relevant and necessary validation checks needed for transplant experiments; the laboratory techniques employed to measure the range of microbial and plant variables potentially explaining soil C dynamics are adequate and modern; and the statistical analyses are appropriate. These elements make the present data set very relevant and valuable. The manuscript is also very well and clearly written.

We thank the Reviewer, and are delighted that they think the study is extremely relevant, novel, experimentally robust, cutting-edge and valuable.

However, I have two major concerns, casting doubt respectively on the main field results and on the proposed explanatory mechanisms.

First, at no point is bulk density mentioned and it does not appear to have been measured. This is critical because changes in soil C concentration (which was measured and reported here, in mg C g-1 soil) does not necessarily indicate an actual change in the quantity of C present in the soil (C stock, in unit mass C per unit soil volume, or per unit surface area to a constant depth) if this is accompanied by a change in bulk density: if less C per unit mass of soil (lower C concentration) is concurrent with more mass of soil in a constant volume (higher bulk density), this could mean that no change in C stocks actually occurs (or that even an increase occurs). In the present study, it is possible that the presence of lowland plants increased bulk density as compared to only alpine plants, compensating the lower C concentration and resulting in no change in C stocks. This is perhaps not likely, but it is too critical an issue not to be quantified (or at the very least discussed).

This is an excellent point, and one also raised by Reviewer #2. To clarify, we initially decided against measuring bulk density because it is destructive and the experiments were still being used for other studies. Having said this, we agree with the Reviewer that more consideration of soil bulk density was needed, so we have rectified this in three ways. First, although the western Alps experiment has now been taken-down, to address this comment we took new soil cores to measure bulk density in the central Alps experiment in 2021 to indirectly confirm that no changes occurred in the presence versus absence of lowland plants. They did not, and we now include these data in the Methods as follows:

L539: “It was not possible to take widespread measurements of soil bulk density due to the destructive sampling required while other studies were underway (e.g. ref 28). Instead, we took additional soil cores (ø = 5 cm, d = 5 cm) from the central Alps experiment in 2021 once other studies were complete to indirectly explore whether lowland plant effects on soil carbon content in warmed alpine plots could have occurred due to changes in soil bulk density. We found that although transplantation to the warmer site increased alpine soil bulk density (LR = 7.18, P = 0.028, Tukey: P < 0.05), lowland plants had no effect (Tukey: P = 0.999). It is not possible to make direct inferences about the soil carbon stock using measurements made on different soil cores four years apart. Nevertheless, these results make it unlikely that lowland plant effects on soil carbon content in warmed alpine plots occurred simply due to a change in soil bulk density.”

Second, in the Main Text we now caution readers against translating soil carbon content changes to soil carbon stock in absence of coupled measurements of soil bulk density as follows:

L113: “We caution against equating changes to soil carbon content with changes to soil carbon stock in the absence of coupled measurements of soil bulk density (Methods). Nevertheless, these findings show that once warm-adapted lowland plants establish in warming alpine communities, they facilitate warming effects on soil carbon loss on a per gram basis.”

Finally, we have altered the language throughout the manuscript (including the title) to make it clearer that we focussed on soil carbon content/concentration – not stock.

Second, even assuming that no changes in bulk density occurred and that indeed soil C stocks decreased under warming combined with lowland plant migration, the interpretation of the results are, in my view, at least incomplete. Certainly, the results do not support the claim that soil C losses were mediated via increased microbial decomposition of soil C with the certainty suggested by the authors. Generally speaking, I see three issues with the interpretation:

• Very schematically, increased microbial respiration and soil C losses from decomposition is only one of two equally likely pathways potentially explaining soil C losses (the other being decreased C inputs to the soil from the plant community). The possibility that decreased soil C content was simply mediated by decreased inputs of C to the soil is hardly explored at all in the study (there is a quick mention of it (L155), but differences in plant biomass are interpreted only for their correlations with microbial activity (L160-166), not as a component of the C balance. Plant traits are measured and analysed but not in a way that can be used to test the hypothesis of changing C inputs. The presence of "more productive traits" (L141) for the lowland plants does not directly relate to differences in the quantity of C inputs to the soil, nor is it interpreted in relation to inputs. Even the interpretation of changes in ecosystem respiration seem to omit the possibility of changes in plant respiration (L208): "depressed microbial respiration per unit of soil was also evident at the ecosystem scale in that warming accelerated total ecosystem respiration but its effect was dampened in plots containing lowland plants". This statement was made despite no significant differences in microbial respiration per unit soil in the field data, and disregards the possibility that the dampened effect in plots with lowland plants could be due to lower plant respiration.

This is an excellent point. We have performed new analyses of the plant trait/biomass data from the field experiment, included additional measurements/analyses of NEE and GPP from the field experiments (originally omitted due to space, which was a mistake!) and have rewritten all relevant sections in the manuscript to change the focus to a shifting balance between soil carbon inputs and outputs. Importantly, our original interpretation remains robust – i.e. that lowland plants most likely operate by accelerating soil carbon outputs, not decelerating soil carbon inputs – but we are careful to present our conclusions with an appropriate level of caution.

• For the glasshouse experiment, I agree that the results indicate that (L115); "lowland plants accelerated microbial activity by increasing the quantity of root exudates", but not that (L112): "these findings together imply that lowland plants accelerate alpine soil C loss" because stimulating microbial activity is not per se an indicator of soil C loss. It is now well-known that the activity of microbes is not only a motor for soil C losses, but also a key mechanism leading to transformation of C inputs from plants that leads to the subsequent stabilisation of C in the soil. This is actually clearly stated further down in the manuscript when interpreting the field microbial data (L190. Furthermore, there is no direct evidence that the pots with lowland plants were losing more C than those without. Therefore, results from the glasshouse experiment could be interpreted differently: a larger fast cycling pool of soil C constituted of recently photosynthetically fixed exudates associated with higher microbial activity could well be interpreted as an early indicator of more C stabilisation, particularly since the absorbance index seems to indicate more microbially derived product in the DOC. It would have been great to measure microbial biomass C over time (as well as CUE, and mass specific growth and respiration), to see if higher respiratory activity was associated with higher biomass. The lack of differences in microbial biomass between the plant community treatments at the end of the 6 weeks does not show that the quantity of microbial biomass produced over the whole incubation period remained constant. In a word, more respiration of a larger fast cycling pool is not an indicator of future soil C loss (in the presence of plants).

We thank the Reviewer for raising this important point. On reflection, we agree that the previous version of the manuscript did not give sufficient consideration to the possibility for increased microbial activity (and, indeed, respiration) in the glasshouse experiment to signal soil carbon accumulation via increased microbial growth. Having said this, all pots began with the same soil and microbial biomass remained unchanged between alpine and lowland plant treatments at the end of the six-week experiment. By extension, no net microbial growth occurred during this timeframe, making it unlikely that the accelerated respiration observed under lowland plants was indicative of soil carbon accumulation. Sadly, while we can deduce that intrinsic rates of respiration were higher, we can only speculate that growth remained unchanged (no new measurements can be done since growth measurements require fresh soil). We have rewritten the respective section in the manuscript in light of this and the Reviewer’s other comments, which includes the following caveat:

L181: “These findings support the hypothesis that lowland plants have the capacity to increase soil carbon outputs relative to alpine plants by stimulating soil microbial respiration and associated CO2 release. While accelerated microbial respiration can alternatively be a signal of soil carbon accumulation via greater microbial growth, such a mechanism is unlikely to have been responsible here because it would have led to an increase in microbial biomass carbon under lowland plants, which we did not observe.”

• The interpretation of the microbial variables measured in the field line up better with current conceptualisations of the role of microbes in C cycling (but overall interpretation still lacks consideration for plant C inputs). However, interpreting those data measured once 1 year after the transplant to explain the changes that happened gradually over this whole year is a risky and difficult exercise. How do we know that CUE, Rmass, Gmass etc… measured then represent what they were a day, a week, a month before? There is an attempt to deal with this timing issue by comparison with the glasshouse experiment, but only Cmic and Rmass can really be compared and it only very partially fills in the gap in time. Besides, the interpretation of this comparison can be questioned: in the glasshouse, Rmass was higher for the lowland plant pots (as compared to alpine plant at constant temperature) but actually remained constant between the comparable treatments W and WL in the field (Fig 2m). The results from the field, therefore, do not "support observations from the glasshouse experiment" in this context (L197) and neither do they "confirm (…) that this persists for at least one season" (L199). Finally, the thinking around the pulsed nature of C losses seems misplaced because there are no evidence that soil C losses had stopped after a year in the field (no measurements of soil C content are presented after that year).

With regards to plant carbon inputs, we refer the Reviewer to their previous comment for corresponding revisions. With regards to specific comparisons between the glasshouse and field experiments, we have now deleted the sentences in question and have interpreted our results as follows:

L329: “Thus, despite lower rates of ecosystem respiration overall, alpine soil microbes still respired intrinsically faster in warmed plots containing lowland plants. Moreover, accelerated microbial respiration, but not growth, implies that alpine soils had a higher capacity to lose carbon under warming, but not to gain carbon via accumulation into microbial biomass, when lowland plants were present. These findings align with observations from the glasshouse experiment that lowland plants generally accelerated intrinsic rates of microbial respiration (Fig. 3), although in field conditions this effect occurred in tandem with warming.”

With regards to soil carbon loss being pulsed, while there is support for such a mechanism, we agree that this is one of several hypotheses and with only two timepoints we were too confident about it in the original submission. We have now reshaped this section of the manuscript entirely to be more cautious about the temporal dynamics involved. For instance, the section title now reads “Lowland plant-induced soil carbon loss is temporally dynamic”. Some other notable changes are:

L286: “Importantly, lowland plants had no significant bearing over net ecosystem exchange (Fig. 5a), implying that although lowland plants were associated with soil carbon loss from warmed alpine plots (Fig. 1), this must have occurred prior to carbon dioxide measurements being taken and was no longer actively occurring.”

L293: “By contrast, ecosystem respiration in warmed alpine plots was depressed in the presence versus absence of lowland plants (Fig. 5c). These findings generally support the hypothesis that lowland plants affect the alpine soil system by changing carbon outputs. However, they contrast with expectations that lowland plants perpetually increase carbon outputs from the ecosystem and thus raise questions about how soil carbon was lost from warmed plots containing lowland plants (Fig. 1).”

L320: “Carbon cycle processes are constrained by multiple feedbacks within the soil system, such as substrate availability and microbial acclimation, that over time can slow, or even arrest, soil carbon loss. We thus interrogated the state of the soil system in the field experiments in the western Alps experiment to explore whether such a feedback may be operating here, in particular to limit ecosystem respiration once soil carbon content had decreased in warmed alpine plots containing lowland plants.”

L354: “Taken together, one interpretation of our findings is that the establishment of lowland plants in warming alpine ecosystems accelerates intrinsic rates of microbial respiration (Fig. 3, Fig. 6a), leading to soil carbon release at baseline levels of microbial biomass (Fig. 1, Fig. 3c), a coupled decline in microbial biomass (Fig. 6c) and a cessation of further carbon loss from the ecosystem (Fig. 5a, Fig. 6d).”

L358: “Although such a mechanism has been reported in other ecosystems, applying it here is speculative without additional timepoints because field soil measurements came from a single sampling event after soil carbon had already been lost from the ecosystem. For instance, an alternative mechanism could be that soil microbes acclimate to the presence of lowland plants and this decelerated microbial processes over time.”

L368: “Beyond the mechanism for lowland plant effects on alpine soil carbon loss, it is conceivable that soil carbon loss is not isolated to a single season, but will reoccur in the future even without further warming or lowland plant arrival. This is especially true in the western Alps experiment where warming yielded a net output of carbon dioxide from the ecosystem (Fig. 5a). Moreover, in our field experiments we simulated a single event of lowland plant establishment and at relatively low abundance in the community (mean ± SE relative cover: 4.7% ± 0.7%), raising the possibility that increases in lowland plant cover or repeated establishment events in the future could facilitate further decreases in alpine soil carbon content under warming.”

Reviewer #4:

This manuscript took alpine grasslands as a model system and investigated whether lowland herbaceous plants contributed to the short-term dynamics of soil carbon under the context of climate warming. The authors find that warming individually does not render significant changes in alpine soil carbon, but corporately causes ~52% of carbon loss with lowland herbaceous plants in two short periods of field experiments. They further show that alpine soil carbon loss is likely mediated by lowland herbaceous plants through root exudation, soil microbial respiration, and CO2 release. This work adds in an interesting way to the ongoing debate on whether a positive climate feedback will be mediated by plant uphill range expansion in alpine grasslands, where climate warming may lead to a rapid loss of soil carbon.

The claims of this manuscript are well supported, but some aspects of background information in the studied alpine systems and field experiment design need to be clarified.

1) There is an extremely high level of carbon stored in the alpine soils (Figure 1). Climate warming will certainly lead to a great loss of soil carbon in the study systems that could contribute to the positive climate feedback. However, it is unclear for me how the effects of climate warming on soil carbon are relevant to the ongoing climate change in the studied alpine grasslands. It is therefore reasonable to provide more background information about ongoing climate change, and whether the simulated climate warming (i.e., 2.8 oC in central alps and 5.3 oC in western alps, Line 328-329) is realized as real-world climate change in the local systems. In addition, it seems that the manuscript aims to address a question that is of global concern, but my concern is about how the findings could be generalized to other regions.

We thank the Reviewer for pointing this out. With regards to the amount of soil carbon stored in the alpine soils, we refer the Reviewer to comments from Reviewer #2. With regards to the magnitude of warming expected in mountain regions, we agree with the Reviewer that the original submission lacked context. We have therefore added specific values as suggested:

L59: “They are experiencing both rapid temperature change (0.4 to 0.6 ºC per decade) and rapid species immigration…”

With regards to how findings could be generalised to other regions or ecosystems, this is an important point that requires further research – and which we raise in the concluding paragraph. However, we see that we could have been more explicit about validating our findings in other mountain regions, so we have amended the sentence in question as follows:

L400: “Future work should focus on testing the conditions under which this feedback could occur in different mountain regions, as well as other ecosystems, experiencing influxes of range expanding plant species, on quantifying how deeply it occurs in shallow alpine soils, and on estimating the magnitude of the climate feedback given both ongoing warming and variation in rates of species range shifts.”

2) I understand that the manuscript considers elevation as a natural gradient of climate change, which makes it possible to compare soil carbon dynamics in lowlands with alpine grasslands under climate warming. I also understand that the authors have done everything they can to control for the disturbances caused by transplanting that has been well justified by the supplementary data (e.g., Figure S6). However, it is unclear how the authors controlled for the influences of other factors given there are huge differences between lowlands and alpine grasslands, such as differences in wind, solar radiation, humidity, and the length of growing season.

This is an excellent point. We note that Reviewer #1 also raised this point, so we refer the Reviewer to our response to that comment for further details.

3) It is generally known that different species respond to climate warming differently. Some species may be sensitive to climate warming and have traits aiding to dispersion that could expand their living ranges to some degree, while others may adjust themselves to adapt to climate warming and may not migrate to alpine systems. It is therefore cautious to assume that all the lowland species have the same dispersal ability. In other words, it is unclear how lowland plant species are selected for the field transplanting experiment (Line 284-290). Do all the lowland plant species selected have the potential to migrate to alpine systems?

This is an excellent question. In short, the specific dispersal abilities of lowland species used are currently unknown and will certainly vary. However, all are widespread and we assume have the capacity to migrate to higher elevations, given that horizontal distances between high and low elevation sites were in both cases less than 2 km. We now clarify this in the manuscript as follows:

L433: “While exact dispersal distances for selected lowland species are unknown, all species are widespread and are expected to migrate uphill under warming and the horizontal distance between high and low sites in the field experiments was always less than 2 km.”

4) The authors acknowledge that "we did not perform a reverse transplantation (that is, from low to high elevation), so we cannot entirely rule out the possibility that transplantation of any community to any new environment could yield a loss of soil carbon" (Line 318-320). When I read the title "lowland plant migrations into alpine grasslands …", I thought lowland plant species that were transplanted from low to high elevation. In fact, it is just the opposite to my thoughts. Without performing a reverse transplantation experiment, I am not sure the conclusion will stand that "lowland plant migrations into alpine grasslands amplify soil carbon loss under climate warming". In addition, it is unclear whether lowland plant effects stand alone or depend on climate warming based on the results in Figure 1 that lowland plant treatment is missing, and it is impossible to test the interactions between lowland plant and climate warming.

We apologise for the confusion. This comment echoes other comments from Reviewer #1 asking us to be more explicit about the treatments used when interpreting findings, to caveat the step in logic from transplantation to warming and to acknowledge throughout the manuscript that lowland plant effects were dependent on transplantation in the field experiment. We therefore refer the Reviewer to our responses to those comments for details on how we resolved this. We have also modified the title and abstract to more accurately represent the experimental design, as follows:

Title: “Lowland plant arrival in alpine ecosystems facilitates soil carbon loss under experimental climate warming”

L30: “Here we used two whole-community transplant experiments and a follow-up glasshouse experiment to determine whether the establishment of herbaceous lowland plants in alpine ecosystems influences soil carbon content under warming. We found that warming (transplantation to low elevation) led to a negligible decrease in alpine soil carbon content, but its effects became significant and 52% ± 31% (mean ± 95% CIs) larger after lowland plants were introduced at low density into the ecosystem.”

With regards to testing the interaction between warming and lowland plants, while we acknowledge that not performing a fully-factorial design limited our ability to explicitly separate lowland plant versus warming effects on alpine soil, both are occurring simultaneously due to climate warming and we thus focussed effort on simulating such a scenario with greater experimental replication and at multiple locations. We note that Reviewers #1, #2 and #3 thought that this approach was robust. Importantly, the statistical analyses performed are valid for such an experimental design, and we have clarified and nuanced our interpretation throughout to avoid reaching beyond it.

Author Response:

Reviewer #1 (Public Review):

This paper uses a combination of confocal and electron microscopy to localize gap junctions in the outer retina. Electrical coupling between photoreceptors is an important aspect of retinal function, and past work provides (often indirect) evidence for rod-rod, rod-cone and cone-cone coupling. The work described here indicates that rod-cone coupling dominates. The combination of techniques is quite convincing and very elegant. My concerns are primarily about the appeal of the work to non-retina readers. Some of these concerns could be mitigated by a more accessible presentation of some of the results. Suggestions along these lines, and a few other minor issues, follow.

Introduction:

The introduction is a bit retina-centric. I think more needs to be done to explain how each type of coupling (rod-rod, rod-cone, cone-cone) could impact retinal processing, and why it is important to resolve which are present or dominant. One issue that could get emphasized is the difference between gap junctions between like cell types (presumably involved in lateral spread of signals, averaging, etc) and between unlike cells (potentially providing an alternate path for signal flow - as in the secondary rod pathway).

We have included new text in the introduction to address this issue. We have tried to provide background material of a general nature and we have included some introductory text about different types of gap junctions, as requested. We thank reviewer 1 for this helpful suggestion.

Cone-cone coupling:

It would be helpful to put the conclusions about rod-cone and cone-cone coupling together. The paragraph starting on line 585 is a bit confusing that way. It starts by summarizing evidence that blue cones are not coupled with red/green cones. But then (in mouse) all the cones are coupled to rods, so that specific exclusion of blue cones seems unlikely to hold. You come back to this a bit later in the discussion, and there indicate that there appears to be weak cone-cone coupling. Merging the text in those two locations might help. It might also help to make the (seemingly clear) prediction that blue and green cone signals in mouse will get mixed.

Thank you for pointing out that this section is not clear. It seems two different points are muddled: 1) Blue cones do not make gap junctions with other cones, perhaps to minimize spectral mixing: the evidence from primate and ground squirrel suggests that blue cones are not coupled to red/green cones or green cones. 2) In contrast, we find no evidence of color selectivity in rod/cone coupling: green cones and blue cones are both coupled to all nearby rods. Thus, rod signals can be injected into the downstream pathways of both blue and green cones.

We have rewritten the text and separated these points into separate paragraphs for clarity, as below.

Revised Text:

Blue cone pedicles are also coupled to rods.

In the cone networks of primate and ground squirrel retina, there is good evidence that blue cones are not coupled to neighboring red/green (primate) or green cones (ground squirrel) (Hornstein et al., 2004; Li and DeVries, 2004; O’Brien et al., 2012). In the primate retina, the telodendria of blue cones are few in number and too short to reach the neighboring red/green cones (O’Brien et al., 2012). Thus, blue cones appear to be electrically separated from other cones in these two species, perhaps to maintain spectral discrimination (Hsu et al., 2000). In the mouse retina, although the blue cones were identified by Behrens et al., (2016), we were unable to find any cone to cone gap junctions, regardless of color (see below).

In contrast to the selective connections between cones in some species, rods were coupled to both blue and green cones indiscriminately in the mouse retina (present work) and in primate retina (O’Brien et al., 2012). Blue cones, identified in confocal work by the presence of S-cone opsin, and in SBF-SEM by their connections with blue cone bipolar cells (Behrens et al., 2016; Nadal-Nicolás et al., 2020), and green cones both made telodendrial contacts at Cx36 clusters with all nearby rod spherules (Fig. 4). Thus, we find no evidence for color specificity in rod/cone coupling. In fact, a single rod spherule may be coupled to both blue and green cones (Fig. 5, supplement 5). Therefore, rod signals can pass via the secondary rod pathway into both blue and green cones and their downstream pathways. Considering blue cone circuits specifically, rod input to blue cone bipolar cells and downstream circuits is predicted via the secondary rod pathway, in addition to the previously reported primary rod pathway inputs from AII amacrine cells to blue cone bipolar cells (Field et al., 2009; Whitaker et al., 2021).

Relation to other circuits:

Are there implications of the present results for gap junctional coupling in other circuits that could be emphasized? Things like the open probability how strongly it can be modulated seem like points of general interest - but I don't have enough expertise to know if those are established facts on other systems. Some of that is touched on in the Discussion, but quite briefly.

In an effort to keep the discussion short, we have perhaps been too abrupt. We have added text to the discussion to include some general issues concerning gap junctions.

Location of Cx36:

Can you speculate on why Cx36 is generally located at the mouth of the synaptic opening in the rod spherule? This was a very clear result, but it was unclear (at least to me) if it was important.

This is an interesting topic and we have expanded the discussion to consider potential functions and mechanisms.

Added to discussion:

The position of rod/cone gap junctions, at the base of the rod spherule, close to the opening of the post-synaptic cavity, appears to be systematic in that the vast majority of rod/cone gap junctions occur at this site. We may speculate that gap junctions are localized with some of the same scaffolding proteins that occur at the rod synaptic terminal, but the functional significance of this repeated motif is unknown. In mutant mouse lines, where Cx36 has been deleted from either rods or cones, cone telodendria are still present and they still reach out to contact nearby rod spherules in the absence of rod/cone gap junctions. Therefore, the specificity of synaptic connections is not determined or maintained by the presence of Cx36 gap junctions.

Reviewer #2 (Public Review):

Previous studies demonstrate that modulation of gap junctional coupling in the outer plexiform layer of the mouse retina regulates the balance between sensitivity and resolution. The authors use optical and electron microscopy to structurally characterize this coupling. They find that gap junctional coupling in mouse OPL is produced by a dense meshwork of cone photoreceptor telodendrions that selectively innervate the rim surrounding the synaptic openings of rod photoreceptor spherules. The density of this coupling network is such that each cone is coupled to dozens of rods and each rod is coupled to multiple cones. Rod/rod and cone/cone gap junctions were not detected.

The combination of antibody labeling, reconstruction of the photoreceptor terminal network, and ultrastructural analysis provides a remarkably clear view of the gap junctional connectivity that constitutes the first stage of visual processing. A few results are only weakly supported due to sample size or technical limitations. However, the overall conclusions are well supported and the data is presented with unusual transparency. The map of the network organization of photoreceptor coupling generated here is an important contribution to visual science.

Optical imaging:

The quality of the confocal imaging is high and the images of the Cx36 distribution relative to rod spherules is convincing. There does seem to be a significant amount of processing in the images and a lack of background signal in antibody images. Whether this processing is due to the airy scan software or additional filtering and thresholding, it can be difficult to judge the distribution of signal in several images.

In general, there was no filtering or processing of any confocal images, except for adjusting brightness and contrast. However, we may have been over-zealous in reducing the background. Therefore, we have adjusted Figures 1 and 2 to include more background as requested, to enable the reader to better judge the specificity of the immunolabeling. In addition, we have prepared supplementary figures to show the individual channels with background, as well as the combined images, to be absolutely clear and transparent. Finally, for each confocal image, the confocal series from which it was derived has been archived and is publicly accessible.

Former Figure 1D, now Fig. 2D is an exception because it shows a 3D projection of the colocalization between a single EGFP labeled cone pedicle and Cx36. We have revised this figure, providing new 2D optical sections to show how the image was prepared, in addition to revising the final 3D projection, labeling it as a 3D projection with colocalized Cx36.

Electron microscopy:

The authors perform annotations on two previously acquired volume EM datasets. The first serial blockface EM dataset is relatively low resolution and lacks ultrastructural labeling but is used effectively to reconstruct the terminal morphology and points of contacts between photoreceptors. The second EM data set uses FIB SEM to obtain smaller voxel sizes from tissue stained in such a way that the darkened membranes of putative gap junctions are distinct from surrounding membrane. Most measures of gap junction number come from the ultrastructure free dataset. In isolation, counting of gap junctions in this type of image volume could be unreliable. However, comparing the putative gap junctions in this dataset to the morphology and distribution of Cx36 antibody clusters in the confocal imaging and the darkened plaques in the FIB SEM images greatly increases confidence that the network description of rod/cone gap junctional coupling is accurate.

Quantification:

Most quantification is presented with an unusually high degree of transparency, with scatterplots showing all data points, data source files showing the animals that data came from, and standard deviations being supplied in descriptive statistics. There are a few places where Ns are difficult to determine or the analysis is not quite clear. For several results, claims are made when the sample size is too small to be sufficiently confident. The reconstruction of 5 blue cones suggests that, overall, blue cones are not radically different from other cones in their terminal morphology or gap junctional coupling to rod spherules. Claims that the blue cones are identical to other cones in most measures or that their telodendrions are smaller, but not statistically smaller are not well supported by the sampling. Similarly, the fact that the 6 nearby cones closely analyzed for cone/cone gap junctions yield no junctions, strongly suggests that vast majority of gap junctions are cone/rod gap junctions. However, the sample is too small to argue that there could not be infrequent, atypical, or region-specific cone/cone gap junctions.

We have addressed the issues of blue cones and cone/cone coupling to soften our conclusions and explicitly point out the small numbers.

Estimate of open channels:

The authors estimate that 89% of gap junction channels are open during times of maximum rod/cone coupling and point out that this number is surprisingly high relative to previous estimates. However, this estimate appears to be subject to many significant potential errors. The estimate combines previous freeze fracture studies of the density of gap junctions from various species and various parts of the retina the measurements of the length and width of the gap junctions in the current study. Differences in tissue processing, density variation within and between systems, reconstruction error, and variation and error in the inputs to the model could all contribute to an underestimate of the total number of channels linking mouse rods and cones. Moreover, without an accounting of these issues, the real error bars on the range of possible open channels would seem to include both surprising and less surprising estimates of open gap junction fractions.

This is a major issue. In short, for the calculations of open probability, we have estimated the cumulative errors, added these numbers to the text and attached an appendix showing the statistical analysis. We have also added a section to the discussion to address the possible sources of error enumerated by reviewer 2.

Reviewer #3 (Public Review):

In the presented work, Ishibashi and colleagues combine immunohistochemistry, analysis of a publicly available large scale 3D EM dataset and smaller but more detailed newly acquired EM datasets to qualitatively and quantitatively study gap junctions of mouse rod and cone axon terminals. The existence of rod-to-cone gap junctions has been known before, but the use of larger 3D EM data allows to determine an average number of contacts as well as an estimate of the strength of gap junctions. This as well as the (very likely) exclusion of direct cone-to-cone coupling in the mouse as opposed to some other mammals are the main contributions of this paper and one more puzzle piece of the big picture of mouse retinal connectivity. However, while the findings are a valuable addition towards a complete picture of the connectivity in the mouse retina, the novelty of the findings is limited to the number of contacts per photoreceptor and gap junction sizes.

In my opinion, while the authors present a thorough analysis of their data, the manuscript in its current state has stylistic flaws on the motivational side. To me, abstract and introduction lack a motivation or stronger statement of relevance for this analysis. Similarly, while each individual analysis is discussed one by one, I'm missing a broader discussion of the implications of the findings for the field and possible directions for future research to highlight relevance for a broader readership.

Thank you for the positive comments. We have rewritten and added material to the Abstract, Introduction and Discussion in an attempt to explain the reasoning for this study and to explain the findings to a broader audience.

I believe these questions are so important to consider when creating any lesson! As someone who needs time adjusting to new tools or apps, I have experience many times that I have had to focus more on how to use the tool than the content we were using it for. I think when you consider these questions, it helps make sure the students will all benefit from the tool. It may take some extra time during planning, but it will be more beneficial in the long run.

Author Response:

First we would like to thank the reviewers for their very kind words regarding our manuscript and for their helpful suggestions for how to improve our paper. We believe their suggestions have helped to strength the paper as a whole. We will address below the specific weaknesses that the reviewers have brought up and describe how we have modified our manuscript in response to these suggestions.

Reviewer #1:

This is an interesting study of the relation between vividness of visual imagery and the pupillary light response that can result from it. The authors collected data in two experimental paradigms, which they ran in two independent samples. One of these samples was a larger group of psychology students; the other a self-reported group of people with aphantasia. In a first paradigm, the authors show that a lack of vivid imagery is associated with a smaller (or even absent) pupillary light response. Using a second paradigm, binocular rivalry, they show that the degree to which imagery primes binocular rivalry is correlated (to a degree that is quite striking) with the magnitude of the pupillary light response to imagined stimuli. These results were obtained both for low-scoring individuals in the large sample as well as for the aphantasics. The study provides objective evidence for the absence of imagery in individuals that self-report as aphantasic.

The paper is well written and all the necessary controls for potentially confounding variables are in place. For instance, age or visual persistence are discussed and excluded as alternative explanations based on convincing analyses. A particular strength of the manuscript is that the authors report positive results for pupillary responses in the group with aphantasia. That is, these individuals show regular pupillary responses to changes in physical stimulus brightness as well as to cognitive load. Another strength is that the group of aphantasics was invited separately and not determined post-hoc in the initial sample.

In summary, there is a lot to like about this paper. I have three comments / questions that I think should be addressed, however.

1. A point that I would like to see analyzed and discussed is the role of eye movements. The authors do not report any analyses of fixation behavior or the frequency of saccades in the two groups. These should be analyzed and reported. The only mention of fixation control is in lines 423-424, but the authors remain at a very superficial level, stating that footage from this scene camera of the pupil labs eye tracker was "assessed to ensure fixation on the computer monitor". Does this mean that participants could look anywhere provided they looked at the monitor?



We have now analysed the eye-movements of participants to assess whether or not they might be driving some of our findings, which we agree is a very important additional analysis to add to this paper to confirm our findings are not being driven by eye-movements. When analysing both eccentricity and the number of saccades participants made there was no differences between the two groups when imagining the triangles (see supplementary figures s7 and s11). There was also no correlation between eccentricity data and either the imagery pupillary light reflex or binocular rivalry priming. Taken together it seems unlikely that the observed pupillary light response during imagery is being driven by eye-movements.

1. In Figure 1D (also lines 120-124), the authors show a correlation between vividness ratings and the pupillary light response. I assume that participants differ substantially in their distributions of responses. So these correlations could be a consequence of individual differences or they could provide evidence for trial-by-trial variation. There might be ways to find out. For instance, is there evidence for these correlations at the level of individuals? Does the correlation persist if individual vividness-response distributions are normalized to span the same range for each observer?

We would like to clarify the analysis we ran. Figure 1D is the results of 2 x 4 linear mixed-effects analysis, not correlations. This model included subject identity as a random effect (see Methods section of our paper) and therefore the effects reported were computed at the subject level. We report in the text, effects that are significant at the level of the sample. This does not exclude the possibility of inter-individual differences, but we are not sure how interpretable a single-subject analysis is in the current study.

1. In lines 314-315, the authors state that the pupillary light response to imagined stimuli may serve as an objective indicator of aphantasia. I think this is taking the interpretation of the data too far, mainly for two reasons. First, the authors haven't shown that low pupillary light response predicts aphantasia in a group of people that does not self-report as aphantasics before the test. Second, the absence of a pupillary light response (in a new sample with no additional controls) could also indicate a lack of motivation to engage in imagery. The authors should thus clarify that such tests would always have to be combined with positive tests that show the commitment of participants to the task instructions.

We agree that it is very important to include positive controls in not only pupillary light response imagery tasks, but any task that measures imagery or any other internal experience. We have now expanded on this point in our discussion as well as reporting on the mock binocular rivalry trials that were included in the priming imagery task as a control for potential response biases.

Reviewer #2:

Kay et al. investigated visual mental imagery in the general population and the lack thereof in individuals with aphantasia by measuring the pupillary light response to imagined light and dark shapes. Their findings are twofold. First, they show a link between pupil size change and perceived vividness of imagery and corroborate this finding using another established objective measure of vividness. Secondly, they found a lack of such a pupillary light response in a group of individuals who maintain no visual experience of imagery. This demonstrates the usefulness of using the pupillary light response as a measure of subjective vividness of imagery and potentially demonstrates the first physiological finding in aphantasia.

Strengths

The experiment incorporates several different dimensions into a single clean design that is useful for isolating and tracking multiple relevant measures. First, by having the brightness of the perceived and imagined shapes vary across trials, the authors could show that changes in the pupillary light response correspond to changes in imagined brightness. The authors also added in an independent number-of-objects dimension since pupil size also varies with cognitive effort. This provided evidence that aphantasic subjects were attempting to imagine, since the pupil size did change with set size, even when it didn't change with brightness. Finally, by having subjects report the perceived vividness of each imagined image, the authors could link subjective experience of imagery to the pupillary light response.

The authors also strengthen their findings by comparing changes in pupil size to an objective measure of imagery vividness. By leveraging the fact that imagery mimics vision's ability to bias a perception during binocular rivalry, the authors avoid the severe limitations present in measures that rely on introspection only.

Weaknesses

Due to the inherently private nature of mental imagery, ruling out fabrication or demand characteristics is extremely difficult. This is especially true in aphantasia research, as we are often looking for the absence of an effect rather than an enhancement. Readers should keep in mind that, while the authors made some effort to confirm that the aphantasic subjects were attempting to imagine, the potential for this and other biases were not ruled out. Without the use of probes to test subjects on the remembered/imagined objects and reporting the outcomes of catch trials, it is difficult to tell whether subjects were fully engaging with the stimuli.

Readers should also take the pupillary light response as a tool to add to the battery of assessments for aphantasia, not as one that a diagnosis can be based on alone. While the authors do show a group level difference in pupil size in response to imagined shapes and claim it as a "new low-cost objective measure for aphantasia", it should be remembered that this manuscript does not demonstrate the tool's efficacy in identifying individual subjects with aphantasia. The absence/presence of an imagery pupillary light response does not confirm/rule out aphantasia.

Overall, the manuscript helps characterize an intriguing condition that until relatively recently received little empirical attention. These findings support the internal experiences described by aphantasic individuals, experiences that are often met with skepticism. Importantly, the authors have also offered the field a new objective physiological approximation of imagery vividness which can be incorporated into a number of study designs examining changes in imagery. The majority of previous measures relied on self-report alone and often suffered from the limitations of language (e.g., what it means for something to be "like vision" can be very different for different people). This manuscript also adds to the growing body of evidence of the power of internally generated signals, which can apparently reach all the way down the visual hierarchy to the eyes themselves.

We are in full agreement that when we investigate the internal contents of the mind we need to be mindful of the many caveats that exist when relying on people’s ability to introspect. We agree that future studies should expand on our research by adding in further controls, such as having participants report what item they were asked to remember at the end of the trial. However researchers should also keep in mind that changing the demands of a task can alter how participants undertake a given task. For example by emphasising remembering the items, rather than creating detailed vivid images in mind, participants may revert to a non-visual imagery strategy to remember the items, such as labelling the items. This may be particularly easy to do in the current study as the items being imagined are simple geometric shapes. Indeed it was important to avoid this potential pitfall here with our aphantasic population as we have previously shown that aphantasic individuals can perform a wide array of visual working memory tasks despite their lack of visual imagery. We believe that the addition of a set-size/cognitive load condition, plus our added reporting on mock trails helps to answer some of these potential response bias issues, but future research can and should further investigate these potential biases in greater detail.

The second point Reviewer #2 brings up is a very good one, that no one singular measure in isolation, at this point in time, can be used to ‘diagnose’ aphantasia. The field is very young and we are still in the process of understanding exactly what aphantasia is. For example there may be many subtypes of aphantasia, with previous work from our group and others showing that aphantasic individuals are heterogenous in their reporting of how other imagery modalities are affected. We agree with Reviewer #2’s point that a battery of tests, potentially comprising questionnaires (e.g. VVIQ), psychophysical tasks (e.g. binocular rivalry paradigm) and physiological (e.g. skin conductance, pupillometry) should be aimed for where possible in testing aphantasic populations. The pupillary light response is a new tool that can be added to this arsenal.

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Reply to the reviewers

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

In this manuscript by Wang and colleagues, the authors analyse single-cell RNA-seq (scRNAseq) data by applying transition path theory to infer gene regulatory network (GRN) changes along the transition (reaction coordinate, trajectory) between free energy stable states (i.e. cell types). The work aims to understand how stable cell types, and their regulatory programs (combination of active and repressed genes) switches during differentiation/reprogramming/response (i.e. cell phenotypic transition/CPT). The premise of the work is to assess whether genes within GRNs undergo step-wise repression, state-change and activation (& vice-versa; analogous to SN1) or concurrently regulate gene expression (analogous to SN2). The GRNs are inferred based on highly variable genes and their expression dynamics from RNA velocity over CPT, across 3 scRNA-seq datasets.

The authors first analyse public scRNA-seq dataset of 3003 human A549 adenocarcinomic basal epithelial cells treated with TGF-b for 0hrs, 8hrs, 1 day and 3 days (4 timepoints). The authors select two stable states (Day0-untreated; Epithelial and Day 3-treatment; Mesenchymal) using local kernel densities and set transition paths using Dijkstra shortest path, dividing state space into Voronoi cells (i.e. reaction coordinate value), and constructed single-cell GRNs based on RNA velocity differences (n=500 genes) and a linear model (from Qiu et al). This GRN is based on expression and velocity estimates, and does not distinguish direct from indirect regulation. Calculating interaction frequency (edges) across two stable states over 4 louvain clusters, the authors find global increase in effective edges that correlates with increased active genes; but with variable trend within inter-cluster edges. To quantify the concerted GRN changes between clusters, the authors utilise a "frustration" score (from Tripathi et al 2020). The average frustration score increases and peaks at day 1 treatment, followed by a decline over terminal stable state (day 3-treatment); similar to interaction frequency trends. The author also separately measure network heterogeneity and repeat analysis using alternative transition matrix. The authors conclude that EMT proceeds through concerted regulation of multiple genes first with an increase in inter-cluster edges, frustration and heterogeneity followed by a decrease into final stable state. The authors apply the analysis to scRNA-seq data from (i) pancreatic endocrine differentiation where Ngn3-low progenitors give rise to Ngn3-high, then Fev-high and into glucagon producing a-endocrine cells; (ii) dendate gyrus; radial glial cell differentiation into nIPCs, neuroblast, immature granule and mature granule cells. In both cases, the authors observe concerted regulation with initial increase in inter-community edges, heterogeneity during differentiation followed by decrease towards final stable state. **

The study and ideas in the manuscript are interesting and the methods would be potentially be useful. However, there are a few specific and general comments stated below, which the authors should try to address.

1 • P4: "RC increases first and reaches a peak when cells were treated with TGF-β for about one day, then decreases (Fig. 1G)". It would be better to label the figure with the treatment information. *

Reply: Thanks for your advice. In the revised manuscript, we analyzed two additional datasets, and moved the EMT result in the supplemental Fig. EV8. In the new Fig. 1d, we marked the cell types along the reaction coordinate.

*2 • Fig. 1G and EV1D: Why are the trends different? *

Reply: In the original figures, ____Fig____.1g is the frustration score and EV1D shows the variation of pseudo-Hamiltonian along the reaction coordinate. The frustration score is the focus of this work. We also calculated the pseudo-Hamiltonian since it has been used in the literature. However, we realized that showing both of the results might lead to confusion, so we deleted all pseudo-Hamiltonian results in the revised manuscript.

* 3 • How is the appropriate community/cluster/Louvain resolution selected? This can have a major impact on number of cell states, types and transition path from initial to final state. *

Reply: The number of cell states, types and transition path from initial to final state____ are not determined from the community/cluster/Louvain analyses. For the EMT data, we assume most cells in the initial treatment time are epithelial cells, and those in the final time point are mesenchymal cells. For other datasets, we followed the original publications to assign cell types based on known marker expression.

The Louvain method was applied to coarse grain the gene regulation network, and it does not affect the number of cell states, types and transition path, which were determined separately. To address the reviewer’s question, we also use the Leiden method to adjust the resolution ____(1)____. The resolution does not affect the result. The results are added to Fig. EV12. We tried three different resolution values 0.8,1.0 and 1.2. The number of inter-community edges consistently shows the trend that it increases first then decreases.

Figure EV12 Cell-specific variation of the number of effective inter-community edges between communities calculated with different resolution parameter values for dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b), and bone marrow marrow hematopoiesis (c). Each dot represents a cell and the color represents the number of inter-community edges____.

• * What effect does the Louvain resolution have on e.g. frustration scores? * Reply: The resolution of community division algorithm doesn’t affect the frustration scores, since the frustration score is based on the gene-gene interactions instead of community assignment.

• * The authors match resolution to samples/timepoints/known prior cell types i.e. 3-4 communities. However it is unclear whether this is enough to describe entire differentiation/transition process. * Reply: This is a good question. In one above reply we have explained how the cell types were determined____. We also agree with the reviewer that these coarse-grained communities cannot reflect the overall heterogeneity and dynamics of the whole process. Notice in most of our analyses (e.g., reaction coordinate and transition paths), we treated the transition as continuous and the distribution of single cell data points in all datasets cover the whole space that involved in cell phenotype transition. The coarse-grained analyses are for further mechanistic insights on how gene regulatory networks are reorganized during the transition process.

• * Gene selection: The selection based on minimum 20 counts as highly expressed genes is arbitrary and dependent on sequencing depth. Perhaps the authors could show distribution of gene counts for the datasets and have a data-driven filtering criteria * Reply: Thanks for the advice. The number 20 is a default value suggested in the package (scVelo) we use, and in another package dynamo the default number is 30. Following the reviewer’s suggestion (together with the next question on the influence of all highly variable genes), we looked for a data-drive filtering criterion. The method has been described in different tools ____(2-4)____. We first grouped the genes into 20 bins by their mean expression values, and____ scaled their dispersions by subtracting the mean of dispersions and dividing standard deviation of dispersions____. Figure EV9 shows the distribution of the minimum shared counts. ____As one can see, most genes counts are larger than 10, and using a smaller value causes error in the following velocity analysis. Therefore we set the minimum shared counts as 10 in the new results.

Figure EV9 Shared counts distribution of the datasets. (a) Dentate gyrus neurogenesis; (b) Pancreatic endocrinogenesis; (c) Bone marrow hematopoiesis.

• * The choice of 500 variable genes (for human A549 cells) is also quite arbitrary. Perhaps, the authors could compare how additional genes (all highly variable genes) affects their analysis and interpretation. * Reply: ____Thanks. Following previous question on shared counts and ____data-driven filtering criteria____,____ we take all the highly variable genes into consideration. The details of gene selection and binarization are given in the Materialss and Methods (Materials and Methods 2) section.

• * How are other factors (sequencing depth, genes detected, #of cell types, multiple branches) affects the connectivity between communities at different phases of transition/development? * Reply: This is a good question. The A549 EMT dataset has a sequence depth of 40000-50000. The ____dentate gyrus neurogenesis dataset____ has a sequence depth of 56,700 reads. A saturation depth would be close to 1,000,000, but there is a compromise between cell number and depth. There are genes that are not detected even under the saturation reads setting. That is why the preprocessing is needed. On the other hand, the network we inferred include both direct and indirect interaction, so the influence of sequence depth and gene number detected can be reduced to a certain extent. We used a random subset of the selected gene and performed the same analyses. The results are consistent with what we obtained using all the genes (Fig. EV11b). With the new gene selection criteria (Materials and Method 2), our analyses are not related with the number of cell types.

We did analysis on another beta branch of pancreatic endocrinogenesis data. The other branches show the same results (Fig. EV4). There are two additional branches in the pancreatic endocrinogenesis dataset. It has been reported that the RNA velocity estimation for the epsilon branch is incorrect ____(3)____. There are too few cells in the delta branch for reliable analyses. Therefore we didn’t present results for these two branches.

Figure EV4 Analyses on the branch of glucagon producing β-cells in pancreatic endocrinogenesis.

(a) Transition graph based on RNA velocity.

(b) The RCs and corresponding Voronoi cells. The large colored dots represent the RC points (start from blue and ends in red). The small dots represent cells with color as cell type.

(c) Frustration score along the RCs.

(d) Cell-specific variation of effective intercommunity regulation. Each dot represents a cell. Color represents the number of effective intercommunity edges within each cell in the GRN.

• • Are the velocity graph, transition matrix and further shortest path estimation derived in a reduced latent space, and if so, how much (nPCs) and what impact does it have. Presumably, the density estimation is not performed in expression space. Reply: Yes. ____The calculation of transition matrix is based on neighbor information. The calculation of neighbors was in the reduced latent space in scVelo and Dynamo. We performed the same analysis by varying number of principal components. The results are similar because the first several components account for large proportion of variance. Figure R1 shows the results of dentate gyrus neurogenesis with the number of principal components being 10, 20 and 30, respectively. In the revised manuscript, we delete the step of using density estimation constrain to simplify the procedure. __Figure R1 Frustration scorer along RCs (left) and cell specific variation of number of effective intercommunity edges (Each dot represents a cell and color represents the number of effective intercommunity edges) in the GRN within each cell (right) when using different number of PCs in analyses (dentate gyrus neurogenesis): (a) number of PCs is 10.*__

(b) number of PCs is 20. (c) number of PCs is 30

* - The figure legends and labels were hard to read. These should be improved for better readability. *

Reply: Thanks. We modified the figure legends and labels.

* - A suggestion would be move the initial results section to methods and highlight the biological interpretation. *

Reply: Thanks for your advice. We moved large part of this section to the Materials and Methods.

*The authors could highly which GRN and representative genes/edge pairs are highest ranked within inter-community and to overall final stable states. *

Reply: Thanks. We list some representative gene pairs in the Table. EV 2&EV 3 &EV 4 for different datasets. And we performed gene enrichment analysis for each community.

* - How does the GRN inference compare to current state-of-the-art GRN inference scRNA-seq methods? *

Reply: we used the method GRISLI to perform the same analysis ____(5)____. The results are similar to what obtained with our current method (Figure EV6). We want to emphasize that the focus of this work is not on another GRN inference method, but discussing some general principles of GRN reorganization during a cell phenotypic transition process.

Figure EV6 Analyses of datasets of dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b), and hematopoiesis (c) based on GRN inferred with GRISLI.

(a) Frustration score along the RCs of dentate gyrus neurogenesis (left) and cell-specific variation of the number of inter-community edges (right). Each dot represents a cell and color represents the number of inter-community edges in GRN within each cell.

(b) Same as in panel (a), except for pancreatic endocrinogenesis.

(c) Same as in panel (a), except for hematopoiesis.

* - How do extremely noisy/stochastic genes vary in metrics between final stable states? How are the metrics affected by number of cells and stochasticity of expression within a given cluster/community. *

Reply: To address this question, we selected two genes, Id2 and Cdkn1c, with high variance and compare their distributions in the initial and final states. ____The gene distributions show significant shift between the Ngn3 low EP cells and Alpha cells (Fig. R2 a &b left).____ Then we randomly selected a subset (half) of cells and compared the distributions of these high-variance genes in the sub-population (Fig. R2 a&b right). The results are similar to the full-set results.

Fig. R2 Comparison of gene distribution in the initial and final states in pancreatic endocrinogenesis. (a) Comparison of the distribution of gene Id2 at the initial and final states (left), and in the randomly selected sub-population at the initial and final states (right). (b) Comparison of the distribution of Cdkn1c at the initial and final states (left), and in the randomly selected sub-population at the initial and final states (right).

* - Given that the author's approach includes both direct and indirect genes effects, the authors could further prune genes based on existing TF databases or protein-protein validated networks. *Reply: This is a good suggestion. We will work on this idea in future work. As we mentioned, due to constrains of data quality, only tens of transcription factors can be analyzed in these dataset. We list some regulations of transcription factors inferred with current method in Table EV1.

• *It is unclear which GRNs are already known and which ones are novel and biologically relevant * Reply: We compare some regulations inferred with the method and compare these interactions w____ith some references in Table. EV1____.

* - It would be good for authors to comment when there are multiple bifurcations instead of A-B transitions. Particularly in datasets with multiple discrete stable states. *Reply: This is a good question.____ In our analysis, we focus on the transition from one stable state to another stable state. For transition process with multiple bifurcations like____ the pancreatic endocrinogenesis, the results are similar across different branches. For the transition that goes through multiple discrete stable states, for example, a transition from state A____à____B____à____C, we expect to observe two peaks in the frustration score and the number of inter-community edges. We added some discussions in the Discussion section.

• *Another suggestion would be to highlight gene expression of selected markers based on f-regression and mi over the trajectory * Reply: As we modified the criteria of gene selection, we plotted trajectories of some high-variance genes versus the reaction coordinate obtained with different datasets in Fig. EV10 based on current criteria.

Figure EV10 ____Typical trajectories of high variance genes versus RCs of dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b) and bone marrow ____hematopoiesis ____(c).

* - If possible, a proof of principle could be re-analysis of a perturbation scRNA-seq dataset (e.g. where one path/transition path is stalled) *

Reply: Thanks. This is a really a good suggestion. We will perform more systematic studies in future work.

* Reviewer #1 (Significance (Required)): Nature and significance of advance: The study and ideas in the manuscript are interesting and the methods would be potentially be useful to community. Compare to existing published knowledge: *

*Audience: Predominantly computational audience *

*Your Expertise: PI with background in experimental, computational biology and expertise in single-cell genomic tools and developmental biology *

*

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

Understanding the cellular and molecular basis of cell type or cell state transitions occurring during development or reprogramming is a fundamental challenge. scRNA-seq has provided a window into gene expression programs across thousands of cells undergoing such transitions. Wang and colleagues leverage scRNA-seq and develop an approach to reverse engineer gene regulatory network underlying cells along a path from one cell type/state to another, and characterize community-level properties of this network associated with various stages of the cell phenotype transition. The study is innovative and rigorous, and their results point to how intercommunity interactions increase and then decrease, indicating a concerted regulatory rewiring that orchestrates transitions. Application of their approach to three different datasets also shows that this trend is consistent across three different transitions and maybe a general trend. However, there are some major and minor concerns that need to be addressed.

**Major comments and questions**

1. The analogy to SN1 and SN2 mechanisms of chemical bond formation is very nice.
2. What is the basis for the two statements made in paragraph 3 of Introduction (beginning with "A question arises ...") about transitions being sequential or concurrent? Please *Reply: Thanks. We added references in this paragraph.

* 2.1. Provide references to previous experimental and computational studies that have investigated developmental and reprogramming gene expression programs. *

Reply: Thanks. We added a paragraph in the Introduction.

*

2.2. Describe specific examples of findings that support the two possible transitions highlighted here. Why couldn't transitions happen through an entirely gradual process involving changes to overlapping subsets of genes. *

Reply: Thanks. In the review paper of Naomi Moris et. al., they proposed the hypothesis that cell phenotype transition is similar to a chemical reaction ____(6)____. Thus we extrapolate this hypothesis and test it in our study. For the example of SN1 mechanism, ____Kalkan et al. showed that mouse embryonic stem cells can exit from ____naïve pluripotency____ but remain uncommitted ____(7)____.

Just like the SN1 and SN2 mechanisms are two extremes in chemical reactions and there are cases lie in between, for cell phenotypic transitions we agree with the reviewer that such gradual process may exist. Actually the result in Fig. EV4d shows that the frustration score remains flat for the Fev+ ____à____ Beta transition, suggesting a possible gradual process. With the analyses provided in this work, such as the reaction coordinate, frustration score, heterogeneity, and inter-/intra- community edges, one may perform more systematic studies on a larger number of datasets and enumerate/classify possible patterns of transitions.

• Please make plots of the number of effective intra-community edges vs. number of active genes to support the statement that these two numbers are correlated. *

Reply: We plotted the corresponding intra-community active genes and calculated its correlation coefficient with the number of effective intra-community edges in dentate gyrus neurogenesis (Fig. EV1d). ____The correlation coefficients are 0.91,0.96, 0.99 and 0.96 for community 0, 1, 2 and 3 separately.

* A bunch of notations are not clear:

4.1. What is the "r" in "strongest intercommunity interactions at r = 10 (Fig. 1F)"? Is it the same as the "r" mentioned in the Methods section? *

Reply: r____ is the index number of the discretized reaction coordinate. We added it when we define the reaction coordinate. We modified the conflict usage of r in Materials and Method 4.

4.2. What is "s_i" in "cell-specific effective matrix, Fbar_ij = (2*s_i - 1)*F_ij"? Also, that description of F_ij, f_ij, and H should be moved to the Methods section, and a more high-level, intuitive description should instead be included in this Results paragraph. Reply: represent the binarized gene expression state. is 0 for when gene is in low expression level (silence) and is 1 when gene is in high express level (active). We modified this part following your advice.

* How were the h_f and h_m thresholds chosen? *

Reply: and are based on the distribution of each dataset. Following suggestions from another reviewer, we modified this part. All the highly variable genes were selected and the genes were binarized with the Silverman’s bandwidth method and ____K____means (Materials and Methods 2).

* What is the "density of each single cell" ("⍴_t")? The formulation of the penalty of the distance between cells i and j (the expression with -logP_ij...) is unclear. What is the intuition behind it? What is r? How were the values of r (0.5 and 0.8) chosen? *

Reply: The probability density of cells in the expression space is based on the kernel density estimation. Intuitively, a region in the expression space with more cells is more likely passed by more cell trajectories. The values are based on the distribution of kernel density estimation in different datasets.

In the modified manuscript, we used trajectory simulation and deleted this assumption for simplification.

* One of the reasons the authors state to justify the choice of PLSR is "In the scRNA dataset, the number of genes is often comparable to or larger than the number of cells." This is not true most of the time. In nearly all recent studies, the number of cells is way larger than the number of genes measured. *

Reply: The PLSR method definitely can be used for the data whose number of cells is larger than the number of genes. Also the PLSR method was applied on cells that are the k nearest neighbors of each reaction coordinate, which are a subset of the whole dataset (Materials and Methods 5). While we mainly presented results with the PLSR method, in this revised manuscript we also added results with another method of GRISLI (Materials and Methods 9). The results are similar with what we obtained with PLSR.

* There is a fleeting reference to a nice previous finding that supports their observations: "several lines of evidence support that EMT proceeds through a concerted mechanism. Indeed, both in vivo and in vitro studies have identified intermediate states of EMT that have co-expressed epithelial and mesenchymal genes (Pastushenko et al, 2018; Zhang et al, 2014)". The authors should thoroughly survey the literature related to EMT transition, development of pancreatic endocrine cells, and development of the granule cell lineage in dentate gyrus, to find more previously identified molecular/cellular features relevant to cell state/type transitions, compared and contrasted with findings from this study. *

Reply: Thanks. We added references on these cell phenotype transitions and modified the corresponding part. We do want to point out that the main focus of this work is that all these processes share a common feature of transient increase of intercommunity interactions.

* What is the "dynamo" package, which is supposed to contain a Python notebook? As of now, the code and data have not been made available. Both need to be released along with thorough documentation on how to run the code to reproduce the analyses described here. *Reply: Thanks. Dynamo is a python package accompanying our recent publication ____(8)____. We uploaded the code on Github and added the link of Dynamo.

* **Minor comments and questions**

1. Replace "confliction" throughout the manuscript with "conflict" or "conflicting" as appropriate. *

Reply: Thanks. We modified them.

* Paragraph two of the Introduction (beginning with "Another example of transitions ...") is missing multiple references, esp. for the last four sentences. *

Reply: Thanks. We added references.

* There are direct quotes from previous papers like "predicts the future state of individual cells on a timescale of hours". The authors are highly encouraged to check for usage of exact phrasing using available text software such as iThenticate. *

Reply____: ____Thanks a lot for pointing out this severe mistake. We re-edited the manuscript and checked with iThenticate. *

*

• "Each community contains both E and M genes": what does this mean? *

Reply: The E (M) genes are defined as those genes that are active or have high expression levels in epithelial (mesenchymal) state or sample. As we reorganized the manuscript, we add this explanation for all datasets in the caption of Fig.1i.*

*

• Reference to Qui 2021 is missing in the "Path analysis" subsection under Methods. *

Reply: We added it in the Methods.

* Fix: "transition between the cells that their sample time points are successive" in Methods. *

Reply: Thanks. ____We modified it.

* In Methods, under "Network inference", it is "partial least square regression" (not *least* s square). *

Reply: Thanks. We modified it.

* Figure 1: The cyan, magenta, and lime in 1C are very hard to see and, perhaps, the grey of the points can be made lighter. Also, change the red and green colors for the arrows in 1I to something else. These colors are not colorblind-friendly. *

Reply: Thanks. We re-plotted the figures and changed the colormap.*

*

• Periods and commas are missing at several places. Reply: Thanks. We modify these and re-edit the manuscript.

Reviewer #2 (Significance (Required)):

The study uses RNA-velocity calculated from scRNA-seq data in an inventive way to characterize paths that reflect cell phenotype transitions. Then, a sparse gene regulatory network is reverse engineered from the data and the community structure within this network is examined at various stages along the transition to make observations about inter- and intra-community regulation and network "frustration". However, the study lacks the context of existing literature in terms of previous work studying cell transitions both experimentally and computationally. Adding this context (as suggested in the comments) will considerably improve the utility and significance of the findings. Overall, this study will be of broad interest to researchers interested in development and reprogramming as well as computational scientists developing and applying methods for scRNA-seq data analysis, trajectory inference, and network reconstruction. All the comments and questions raised here are based on my background and expertise in omics data (including scRNA-seq) analysis and network biology.

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

The authors analyze three datasets of Single cell RNA velocity measured during phenotypic transition. They infer the gene regulatory network in each case and characterize the transition between the initial and final expression states (in which different sets of genes are expressed). Their motivating question was to find whether during such transitions first genes characterizing the initial state are no longer expressed and only then the genes associated with the final state start expressing or alternatively there is gradual transition through an intermediate state in which subsets of both initial and final state genes are transiently expressed.

They define a measure of regulatory frustration representing the mismatch between regulatory signals a gene receives and its current expression state. They conclude that phenotypic transitions involve transient interactions between otherwise non-interacting gene modules and a temporary increase of gene frustration, which is relaxed once the final expression state is reached.

The study uses of advanced inference and machine learning methods.

I find the question studied in this manuscript interesting, opening avenue to further questions and studies and relevant to different scientific communities. Personally I think that the focus of the paper should be the exposition of the methods used this manuscript would benefit from a longer format, but that depends of course on the journal they are aiming at. *

*

Statistical analysis is missing. Especially since the authors mention the potential of over-fitting due to large number of genes (on the order of the number of cells) - I think the authors should provide a sensitivity analysis testing how sensitive are the conclusions to the choice of cells or genes by applying the methods to subsets of the cells / genes. *

Reply: Thanks. For the subset of cells, we randomly selected cells from the dataset and performed the analyses (Fig. EV11a). For the subset of genes, we selected a subset of genes randomly and performed the analyses (Fig. EV 11b). We found the results are not affected. We also perform another statistical analysis by varying the value of resolution in community detection algorithm. And we found that the conclusion on variation of inter-community edges is not affected (Fig. EV12).

Figure EV11 Statistical analyses of dentate gyrus neurogenesis. Each dot represents a cell and color represents the number of inter-community edges.

(a) Frustration score along the RCs (left) and cell-specific variation of the number of inter-community edges (right) of a randomly selected sub-population of 2000 cells (from a total of 3184 cells);

(b) Frustration score along the RCs (left) and cell-specific variation of the number of inter-community edges) (right) of cells on the space of 400 randomly selected genes (from a total of 678 genes).

*What is the meaning of the distribution in the frustration plots? *

Reply: For each cell we calculated a frustration score. Therefore for cells in each Voronoi cell (which is a geometric cell, don’t be confused with the biological “cells”) along the reaction coordinate (Fig.1d, Fig. 2b &2g), we obtained a distribution of the frustration scores.*

In general, the conclusions are well-justified, but I think some statements in the discussion are inaccurate: "intercommunity interactions of a GRN are indeed minimized' - are they minimal or are they only lower at the stable states? There are two stable states - for which of them is intercommunity interaction lower? *

Reply: Thank. We agree with the reviewer and modified the writing. Comparing with the transition state, the number of intercommunity interactions is less for the stable states. ____The datasets' quality are not high enough for us to investigate whether ____"intercommunity interactions of a GRN are indeed minimized”.*

It is written in the discussion that 'for all three datasets frustration decreases with differentiation', but then Fig. 1g shows the opposite (final state is more frustrated than initial state). It is interesting to discuss the differences between the datasets analyzed in that respect and what could cause transition to a more frustrated state. I suggest that the authors also refer in the discussion to related questions and possible follow-up studies, such as: what determines the duration of the phenotypic transition? A relevant number is the switching time of a single gene. *

Reply: Good suggestion. Compared to other datasets, we found that the result of EMT shows larger variances. The relative difference of the frustration score is also affected by the GRN inference algorithm. For example, the difference between initial and final frustration scores of the pancreatic endocrinogenesis is more significant when using the GRISLI method (Figure EV6b). Given these, the trend that the frustration scores in the transition states transiently increase keep consistent.

Our conclusion is limited by the quality of the data. So we delete this part of discussion in the manuscript.

Qiu et al. have shown that splicing-based ____RNA velocities are relative, while metabolic-labeling-based RNA velocities are more quantitative and accurate____(8)____. We will re-analyze this problem if data with metabolic labeling becomes available.

* The authors mention at the end that the networks can often reach multiple final states from a common initial states. Do such transitions share some of their path (and in particular the intermediate frustrated state)? Given the intermediate connected state, it would be interesting to characterize the network stability to perturbations. *

Reply: This is a very important question. To reliably address these questions, we need higher quality data. We plan to characterize the network stability to perturbations in future studies, while in our recent paper using a full nonlinear modeling framework____(8)____, we performed in silico perturbations.

* While interesting, the manuscript itself is unfortunately hard to read and would benefit from major editing, including better exposition of the science and language editing. *

Reply: Thanks. We revised the manuscript extensively.*

Methods: Description of PCA and 'revised finite temperature string method' are missing in the Methods section. *

Reply:____ Thanks. PCA is used in RNA velocity analysis for dimension reduction. We added this in Materials and Methods 3. The revised string method is in Materials and Methods ____4.

*

Some examples:

Figure captions are very short and often non-informative. Some variables are not defined (or only defined later on) and the reader then needs to guess their meaning: it took me a while to understand what is 'r' in Fig. 1f and what 'r=10' (p. 4) means. *

Reply: Thanks. ____r____ represents the index number of reaction coordinates. We added this in the manuscript where we define reaction coordinates.*

p. 4: what are 'f' (as opposed to F) and 's_ij' and 's_j' (expression states?) Or is fs_ij one variable? What does a Hamiltonian of a cell mean (p. 4, bottom)? *

Reply: is the regulation of gene ____j on gene i, and is the expression state of gene i (0 for silence, and 1 for active expression). is the frustration value of regulation from gene j to gene i.

The pseudo Hamiltonian value is proposed in the literature as an analogy of ____the magnetic systems following the work of Boolean model in EMT ____(9)____. A high Hamiltonian value indicates that the cell is in an unstable state. In the original manuscript we included this quantity since it has been discussed in the literature. However we found it causes confusion and is not necessary for our discussions, so we removed the pseudo-Hamiltonian results in the revised manuscript. * P. 4: how are 'E and M genes' defined? *

Reply: The E (M) genes are defined as those genes that are active or have high expression levels at the epithelial (mesenchymal) state or sample. We explained our general strategy in the caption of Fig.1i . * What does 'network heterogeneity' (p. 5) mean? *

Reply: Network heterogeneity measures how homogenously the connections are distributed among the genes____(10)____. A high heterogeneity ____means that some genes have high degree of connectivity (the so-called hubs), while some have low degree of connectivity.

*

Fig. 1 is too tiny and hard to read and details are missing. *

Reply: Thanks. We modified this figure and caption.*

A glossary for all the acronyms used would be very helpful. *

Reply: Thanks. We added glossary in the manuscript.*

Language (some examples):

p. 5 bottom: Another system is on development... invitro -> in vitro

p. 6: 'measure on developmental potential' -> measure of... *

Reply: Thanks. We modified these and re-edited the whole manuscript.*

Reviewer #3 (Significance (Required)):

This study presents a methodological advance in demonstrating the application of data analysis methods to study developmental phenotypic transitions. High throughput measurements and computation power available today enable putting to test theoretical conjectures, as made by Waddington. I think this is a promising line of research, which could be used to further develop the computational methods as well as to further our understanding of developmental transitions and potentially develop associated mathematical modeling frameworks.

This study should be of interest to a diverse readership composed of developmental biologists as well as to quantitative biologists and CS researchers applying optimization techniques and data analysis methods to high-throughput biological data.

I am not an expert on the computational methods applied in this manuscript and hence cannot assess their correct use and statistical analysis.

*

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Reply to the reviewers

We are very grateful to the three referees for their constructive comments and suggestions which have helped improve the quality of our manuscript.

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

In the publication HAT-field: a very cheap, robust and quantitative point-of-care serological test for Covid-19 by Joly and Ribes the authors describe an adaption and an improved protocol to their previously published haemagglutination based test to detect antibodies to SARS-CoV-2 in patient blood (Towsend et al., 2021). In detail, they analyzed the effect of several adaptions including buffer optimization, plate coating, usage of patient whole blood instead of washed RBCs and plasma. Additionally they tested different temperatures and stability of the reagents, namely the nanobody-RBD construct IH4-RBD. For validation they compared their optimized HAT-field assay with Jurkat-S&R as a FACS-based assay.

Major comments:

Introduction: This section is rather short and could benefit from a broader overview of currently established methods and assays to detect appropriate immune responses against SARS-CoV-2. The author are advised to summarize the current literature in the field more comprehensively and not only focus on their own work.

Response: Hundreds of different tests to monitor immune responses against SARS-CoV-2 have been described to date, and the literature on these various tests is vast, with new articles coming out almost on a daily basis. We would not feel either that the introduction of our rather technical paper would benefit from being lengthened by such a review of the current literature, or even competent to carry out such a summary. Following the referee’s suggestion, we have, however, introduced a new sentence and given three references providing relatively recent overviews on the subject of immune-monitoring.

Cross-reactivity with IH4-RBD. In Figure 6, the authors highlight the samples in red and orange that showed cross-reactivity with IH4-RBD. In their discussion, however, the authors state that only 2 of 60 (3%) were cross-reactive. In making this statement, they ignore the proportion of cross-reactive samples that were also positive in the Jurkat S&R assay. Therefore, the authors should acknowledge in the discussion that the actual number of cross-reactive samples was higher.

Response*: The statement in the discussion about 2 cross reactive samples out of 60 concerns the results obtained after an incubation of one hour under normal gravity, and not the two red dots in each of the three graphs of figure 6, which correspond to the two negative samples which gave false-positive results in HAT plasma titrations after spinning (Figure 6C), for which we correctly state in the discussion that 12 samples showed cross-reactivity on IH4 alone. The data presented in Figure 6B corresponds to HAT-field after spinning, for which we correctly state in the discussion that 5 out of 60 showed cross-reactivity (4 orange dots + 1 red dot, the second red dot having a score of 0, in accordance with the fact that this sample showed no cross reaction on IH4 alone in HAT-field after spinning). *

*To try to prevent this possible confusion, we have now clarified what data we are referring to at the start of that paragraph in the discussion. *

Quantitative Assay. Since the HAT assay does not allow determination of the absolute number of antibodies reactive to SARS-CoV-2 in the blood samples, the authors should refrain from claiming that the HAT-field is a quantitative assay.

Response*: Since immune sera are inherently polyclonal, they contain a multitude of different types of antibodies of different affinities and avidities, and we are not aware of any technique that allows to determine the “absolute number” of antibodies directed against a given antigen in such samples. *

*For many serological tests, including ELISA and the initial protocol of HAT, serum or plasma titrations are used as a means to obtain what is widely considered as a quantitative evaluation of the amounts of antibodies in blood samples. Even FACS-based assays such as the Jurkat-S&R-flow test we have used, are commonly considered as quantitative but those only provide relative results and not absolute numbers. *

We perceive that the close correlations we find between the results of the HAT-field protocol and those of the Jurkat-S&R-flow test as well as with serum titrations using the standard HAT protocol warrants considering the results of HAT-field as being as quantitative as those obtained with all those other tests.

Morphological read out For field application, the morphological description of the observed deposits ("teardrop" vs. "button") could be problematic and might lead to bias depending on the user. Thus, the authors should provide a clearer description for phenotype classification.

Response: We have now introduced a specific paragraph detailing how to score HAT assays in the Methods section, as well as a new figure providing a graphic description of positive, partial and negative RBCs deposits.

Minor comments: Title: the authors should remove "very"

Response*: We have now removed the word ‘very’ from the title, and thank the referee for this helpful suggestion. *

By the way: What are the costs of IH4-RBD for a 96 well plate? Who will produce this reagent? Is the sequence of the IH4 fully disclosed?

Response*: As specified in our original paper (see Townsend et al. 2021), the plasmid coding for the IH4-RBD is available upon request from Alain Townsend (Oxford, UK). Furthermore, his laboratory funded the production of 1 gram of the IH4-RBD reagent by a commercial company, and professor Townsend has been graciously sending aliquots of 1 mg of this reagent, which suffice for several thousand tests, to all the laboratories that have requested it from him. *

*In its initial format, HAT only required 100 ng of IH4-RBD per well, corresponding to a cost of 0.0027 £ per well. For the HAT-field protocol, 5 times more reagent is needed, thus bringing the cost of the reagent to 1.5 cts per test, to which one would have to add a similar cost for the IH4-reagent alone. This would thus bring the cost of the two reagents to approximately 3 cts, which is still lower than the price of any of the cheap disposable plasticware necessary for the test (lancet, pipet, plastic tube and portion of a plate). *

The sequence of the IH4 nanobody is indeed fully disclosed (see figure 1 of Townsend et al. 2021), and has actually been protected by a patent ( US9879090B2 ). Whilst IH4 can be used freely for research purposes, licensing rights would have to be taken into consideration by any health authority wishing to use the technique broadly, or for any commercial distribution.

The usage of the CR3022 as positive control for neutralizing antibodies should be reconsidered since this antibody does not confer viral neutralization. Other well describe antibodies blocking the ACE2:RBD interface might be better suited.

Response*: CR3022 was the one that we had at our disposal, but other mAbs can certainly be used instead of as positive controls, and this is actually indicated in the detailed HAT-field protocol provided. Since the use of a positive control is only to ensure that the IH4-RBD has not been degraded and works as well as expected, and that any negative samples are not due to a very rare glycophorin mutation that could prevent IH4 from binding to it at the surface of RBCs, we are not sure why using a mAb with neutralizing activity would necessarily be better than the CR3022 mAb. *

Figure 2: Please state the concentration of IH4-RBD used. As stated in the figure legends for Figure 2 B, the authors should show the result all 4 replicates (incl. SD)

Response: The concentration of IH4-RBD was 1 m*g/ml, i.e. the normal concentration for standard HAT tests. This was already indicated in the Methods section, but has now been added to the legend of Figure 2. *

Whilst 4 experiments were indeed carried out, which all gave similar results, i.e. showed that using PBS-N3 or PBN did not hinder HAT performance, but could instead result in a slight increase in HAT sensitivity, those various experiments were not all exact replicates of the experiment shown on figure 2. Furthermore, performing of those various experiments was spread over a period of over a year, using different reagents, thus precluding numerical comparisons between the various results. We have clarified this issue by rewording the final statement to “Comparable results were obtained in four similar experiments.”

Figure 3: Although the authors showed stability of IH4-RBD at 2 µg/ml they do not provide data for the stabilities at higher dilutions. As the authors suggest to predistribute the IH4-RBD in plates they should at least discuss this issue.

We thank the referee for raising this valid point, which has now been discussed in the paragraph entitled “Practical considerations for performing HAT assays” in the Methods section: “One aspect that will have to be considered for the design and use of such individual strips of wells will be to ensure that, upon storage, the various dilutions of IH4-RBD are as stable in such strips as the working stocks of IH4-RBD (2 mg/ml) tested in Figure 3.”

Figure 6/Supplementary Figure 1 and 3 The presentation of the data is not accurate, as many of the points (samples) are obviously identically positioned in the graph. The authors should choose a different representation of their data. E.g. they could adjust the size of the points to the number of overlapping samples.

Response: We thank the referee for raising this issue, which was also pointed to by referee #2. This apparent inaccuracy is due to the fact that, on these plots, the scales for both x and Y axes used discrete values, which indeed results in multiple points overlapping on top of one another. This was resolved by adding numbers next to the positions where several dots overlapped

Wording / text length In the current manuscript the text is very long. Thus, the authors should shorten it to report the essential findings more appropriately. Additionally they should check for correct English wording.

Response*: We thank the referee for this remark, which helped us realize that the excessive length of the manuscript was mostly due to an extensive discussion of highly technical and practical points. The corresponding paragraphs were indeed out of place in the general discussion, and have not been deleted but have been moved to the Methods section since we feel that they contain very important information for people who would actually start to performing HAT assays. *

Reviewer #1 (Significance (Required)):

In summary, the authors describe the HAT-field test as a simple PoC test for the detection of SARS-CoV-2 antibodies in patients. Because of its ease of use and robustness, the test appears to be particularly well suited for use in countries with underdeveloped health care or limited testing facilities, as also reported previously. The value of this manuscript lies mainly in the detailed description of the protocol and its validation. In this context, the adaptations described are certainly useful and helpful from a practical point of view, but do not provide significant new scientific insights. In light of these considerations, we recommend that this work be submitted to an appropriate journal specializing in the publication of such methods

Expertise The reviewers have established and published different serological assays to monitor immune responses against SARS-CoV-2

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

In this paper, the authors developed a feasible protocol for an affordable point-of-care serological test for SARS-CoV-2. This method was adapted from the HAT plasma titration test that the authors previously published. Specifically, the test utilizes a 96-well plate pre-coated with the RBD of SARS-CoV-2 spike glycoprotein fusing to a red blood cell targeting nanobody (IH4). By adding microliters amount of the blood or plasma samples to the plate, it allows the detection of antibodies against RBD by measuring the level of hemagglutination. In the current upgraded protocol (so called HAT-field), the authors made major modifications including optimizations of buffer and experimental protocol and the use of pre-titrated IH4-RBD on the plate, which collectively helped to lower the sample consumptions, improved the stability and the sensitivity of detection, and made the test more user-friendly under non-clinical settings.

Major comments: My major concerns are related to the robustness and quantitative capability of this approach. Specifically: It seems that multiple variables may impact the results. These include volume of droplets, the presence/absence of serum IH4 or BSA cross-reactive antibodies, and the amount (%) of red blood cells which may vary substantially among samples. Could you find a way to normalize the results (e.g., the discrepancy shown in Figure 6) instead of only leaving them as false-positives or false-negative?

Response*: Regarding the volume of the droplets, in other words, the amount of blood collected and used in an assay, two sentences in the manuscript underline the fact that this is not a critical variable: *

In the Results section “the precise volume of blood collected is not critical; it may vary by as much as 30% with no detectable influence on the results.”

In the discussion: “On this subject, we have found that increasing the amount of whole blood per well (in other words using blood that is less dilute) has very little influence over the HAT-field results, and, if anything, adding more blood can sometimes reduce the sensitivity, albeit never by more than 1 dilution.”

Consequently the % of RBCs in samples seem unlikely to influence the HAT-field scores significantly. This is supported by the fact that, although men tend to have higher hematocrits than women, we have not noticed any detectable difference between men and women in the correlation of the HAT-field scores with those of the Jurkat-S&R-flow test.

We are not sure that we fully understand what discrepancy shown in Figure 6 the referee is pointing to, but if it is about the increase in the number of samples found to be cross reacting on IH4 alone when the sensitivity increases, in the discussion, we propose to perform tests using titrations of the IH4 nanobody alone simultaneously to using the IH4-RBD reagent, so as to minimize the number of samples that would be identified as false positives if only one concentration of IH4 alone was used as negative control. Comparing the titers obtained with IH4-RBD and IH4 alone will then provide some level of normalization for the samples cross reacting on IH4. As for the hypothetical presence of antibodies cross reacting on BSA alluded to by the referee, since such antibodies would not bind to RBCs, we do not think they would affect the HAT results.

Second, the score of the HAT-field ranges from 0 - 8. However, based on the current manuscript, it is not clear how the scoring and scaling works. How is the noise (non-specific antibody signal) defined here?

Response: We have now introduced a specific paragraph and a new figure detailing how to score HAT assays in the Methods section.

In addition, it is unclear how to translate the HAT-field score into a meaningful measure of protection by serum antibodies.

Response*: Documenting the correlation between HAT-field scores and levels of protection against SARS-CoV-2 infections and/or Covid-19 severity would indeed be extremely interesting. This would, however, require setting up a large scale clinical trial carried out over several months. This type of work could only be carried out by a large consortium including clinicians or even preferably a national health agency. This was, however, far beyond the reach of this initial project, which was based on the work of a single person on a shoestring budget. *

Can you provide more evidence to demonstrate that the test is quantitative? For example, performing additional orthogonal experiments to better validate the scoring and generate a correlation function?

Response*: Inasmuch as it would have been very interesting to perform additional serological tests from commercial sources on the samples of our cohort, such tests are all very expensive (e.g. ca. 500 € for one ELISA plate). This was in fact the main reason for developing the Jurkat-S&R-flow test in the first place, since it is much cheaper, more modular, and at least as sensitive as ELISA (see Maurel Ribes et al. 2021). The funds for this whole project came from a single 15 k€ grant obtained from the ANR, and we simply did not have access to the funds, or to the human resources to carry out such experiments based on commercial serological tests. *

Minor comments: Figure 6: are all results included? To me, it does not seem that all 60 samples data were included in the plot.

Response: We thank the referee for raising this issue, which was also pointed to by referee #1. This apparent inaccuracy is due to the fact that the scales for both x and Y axes used discrete values, which results in multiple points overlapping on top of one another. This was resolved by adding numbers next to the positions where several dots overlapped.

There are several redundant statements in the discussion and results section. Please make the text more concise.

Response: The discussion has now been shortened considerably, mostly by moving the paragraphs pertaining to technical considerations to the Methods section.

Reviewer #2 (Significance (Required)):

The current paper is built upon the improvement of previous published work. In addition, there are similar approaches that have been published. It was unclear if the current method is superior to other works.

Response: Whilst we have made no statement regarding whether the method we describe is superior to other methods, we are pretty confident that very few alternatives will be as frugal and simple as the HAT-field protocol described here. As alluded to in the final paragraph of the discussion, two recent reports have described that HAT could be performed on cards rather than in V-shaped wells, with semi-quantitative results being obtained in minutes. If such card-based approaches turn out to provide sensitivity and reliability comparable to those of the HAT-field protocol, they will certainly represent very interesting alternatives. As stated in our manuscript, we would be very interested if the comparative evaluation of the two approaches could be carried out by one or several independent third party.

My research involves the development of antiviral antibody therapeutics. This method may be used as a point-of-care tool for the measurement of serologic response to RBD in less developed countries. However, due to the high vaccination rate and large infected populations, the overall needs for such detection drastically decrease. The significance of the work and utilities of the test may expand with more experiments related to the variants.

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

This paper describes a low-cost robust and quantitative serological test based on haemgglutination, which could be used in resource limited settings for evaluating population-based and vaccine induced immunity. Neutralising antibodies to the receptor binding domain (RBD) on the SARS CoV-2 spike protein are an immunological correlate of protection. The HAT has a single reagent the RBD domain of SARS CoV-2 linked to a monomeric anti-erythrocyte single domain nanobody. When human polyclonal serum antibodies bind to the RBD they cross-link and agglutinate human red blood cells, resulting in haemagglutination which can be read visually.

This paper thoroughly evaluate the stability of the HAT reagents used to measure human and monoclonal antibodies examining the robustness of the HAT reagent. It provides a comprehensive protocol for conducting field based HAT with limited reagents. The test can evaluate is subjects have been infected using a simple finger prick to detect RBD specific antibodies. The field HAT can also be used to define people that can be susceptible to reinfection or in need of vaccination, With the use of RBDs from the variants of concern the test can be rapidly adapted to evaluate antibodies as new variants arise to evaluate surrogate correlates of protection to allow timely evaluation of vaccine effectiveness and predict the need for vaccine booster doses. The data are very comprehensively presented with good figures demonstrating the most appropriate buffer to store the IH4-RBD reagent and the robustness of the HAT over time at different temperatures. No additional experiments are needed and suitable numbers of replicates are included. All data, methods and reagents are comprehensively described.

Minor comments: The paper is well written but rather long in places and may have benefited from being more succinct.

Response: The excessive length of the manuscript was mostly due to an extensive discussion of highly technical and practical points. The discussion has now been shortened considerably, mostly by moving the paragraphs pertaining to technical considerations to the Methods section.

Panels in figures could be labelled as A, B, C etc to help in identifying the correct panel..

Response: We thank the referee for this helpful suggestion, which we have followed.

I would avoid the use of experiment and project and refer to next we confirmed... or in this paper or our results show Please make sure all abbreviation are defined upon first use. Perhaps include early in the paper that most of the work was conducted with the Wuhan RBD

Response: We thank the referee for these helpful suggestions, which we have followed to the best of our abilities. The abstract now contains a mention of the fact the work on optimizing the protocol was carried out with the IH4-RBD carrying the Wuhan version.

Figure 2: I would suggest placing either a solid line between the two halves of the plates to make it easier for the reader to differentiate between the two antibodies. It also would have been easier to read if the bottom PBS, PBS-N3 and PBN were at 45 degree angle. In B include the serum name (e.g. serum 197).

Response: We thank the referee for these helpful suggestions, which we have followed.

Legend to figure 4: please include the serum numbers after covid-19 patients. Perhaps include arrows to demonstrate the dilutions of serum and IH4-RBD in the figure.

Page 6 it might be easiest to use the same times as in figure 6 and use for example more than one year in the discussion

Response: We thank the referee for these helpful suggestions, which we have all followed.

Legend figure 6 perhaps replace dots with circles page 10 include the R values from figure 6 in the description of results.

Response: We are grateful to the referee for these helpful suggestions, but have not followed them since we do not feel that these changes would be real improvements.

Page 12 of note perhaps this can be moved to the methods ?

Response: This, and several other paragraphs of the Discussion, have now been moved to the Methods section.

Supplementary figure 2 A can be seen, is something missing here?

Response: An s was indeed missing : “A can be seen” corrected to “As can be seen “

*

Reviewer #3 (Significance (Required)):

This paper describes a simple rapid field test for evaluating antibodies to the receptor binding domain of the spike of SARS CoV-2 using the Wuhan and delta variant. Whilst high income countries can provide booster doses and extensive testing (either lateral flow or RT-PCR based) and contact racing to control the waves of the pandemic, low income countries have had limited access to Covid vaccine and the extent of previous waves of the pandemic in the populations are unknown.

This paper describes a robust and simple test for investigating human antibodies to SARS-CoV-2 which could be performed in resource limited settings providing a very useful tool for monitoring infection in the community and potentially for prioritising this scarce COVID-19 vaccines available.

This study builds upon the work conducted on the HAT and has extensively studied and optimised the test so that it could be used globally. This paper provides a comprehensive protocol and has simplified the test to ensure it could be used in LMICs.

This paper would be of great interest to a wide scientific audience who are interested in a rapid low-cost test to evaluate population based and vaccine induced immunity.

Reviewer: serological assays for use in virology and vaccinology. Suitable competence to review the whole paper *

Author Response:

Evaluation Summary

Challa and Ryu et al. systematically evaluated various combinations of ADP-ribose-binding modules to make sensors detecting poly(ADP-ribose). They developed and tested two indicator designs optimized for analyses in cell culture (dimerization-dependent GFP-based) or intact tissues (split Nano luciferase-based). Overall, with further experimental controls and quantification, this timely set of cell biology probes will be useful to study the biological functions of ADP-ribosylation in cultured cells and whole organisms.

We appreciate the positive and encouraging words from the reviewer. We also appreciate the helpful comments, criticisms, and suggestions, which we have endeavored to address fully.

Reviewer 1 (Public Review):

While these tools are more sensitive than existing tools, it is unclear whether a dynamic range of 6-fold (GFP) and 3-fold (luciferase) provide sufficient sensitivity for properly understanding the PAR dynamics (which was thought to increase as much as 100-fold in DNA damage settings). In addition, it is unclear whether the fold increases in both fluorescence and luminescence linearly correlate with the traditional measures by western blot.

We are pleased that the reviewer found our sensors to potentially useful. The reviewer provided a number of excellent comments and suggestions that have served as a useful guide for improving our paper. We have carefully considered all of the comments, insights, and suggestions from the reviewer and revised the manuscript accordingly. We think this has strengthened our conclusions and improved the paper considerably. We thank the reviewer for the careful and thorough review of our paper.

Figure 1F indicates on the western blot that there was a precipitous drop of PARylation after 5 min, but the GFP signal indicated a linear drop. It will be important to quantify the signals on western blots and test how correlate their data with the GFP/luciferase data in scatter plots for their various sets of data. Would this system under-estimate the changes and be not sensitive enough to subtle changes that may be 1-2 fold measured by traditional means

We agree with the reviewer that a comparison with existing PAR detection technologies will improve the manuscript. We now performed a comparative analysis of ELISA, Western blot, and immunofluorescence assays with live cell imaging using PAR-T GFP (Figures 6A, 6C, 6D). The results indicate that the detection range of PAR-T ddGFP is comparable to the established PAR detection assays. In addition, we also compared the live cell luciferase assays using PAR-T NanoLuc to Western blotting (Figure 6B) and found that these two assays are able to detect PAR changes at comparable levels. We would also like to emphasize that these sensors were developed to improve our ability to detect PAR changes in living cells and animals, which the existing techniques are not capable of doing.

Similarly, how is their quantitation in Figure 2 compared with traditional immunofluorescence?

We performed this comparison and observed that the changes in PAR levels as detected by live cell imaging using PAR-T ddGFP are comparable to the changes detected in immunofluorescence assays using the WWE-Fc reagent (Figure 6D and 6E).

Lastly, for the luciferase signal in Figure 3B and C, the corresponding signal in western blots are missing. Therefore, it is difficult to estimate the background signal. If Niraparib, as in other figures, eliminates PAR signals on western blot, these data would indicate half of the basal signal are background, which is rather high. Having said that, tool development is an evolution process. These tools will provide a good foundation for future development. Therefore, understanding these limitations (dynamic range, quantitative sensitivity correlation, and background) will provide a better assessment of the utility of these new tools for investigating PAR biology.

We appreciate the reviewer’s concern about the high background signal in Niraparibtreated samples. To answer this concern, we compared the dynamic range of PAR-T NanoLuc to Western blotting (Figure 6B) and found that the results from live cell luciferase assays using PAR-T NanoLuc are comparable to Western blotting using WWE-Fc. Of note, we were able to detect decreases in PAR levels with Niraparib using live cell luciferase assays using PAR-T NanoLuc, but not Western blotting. Based on these analyses, we can conclude that the changes in PAR levels at the basal level are very minimal, leading to only 50% decrease in PAR-T NanoLuc signal with Niraparib treatment (Figure 6B, Figure 5A-5C). Note that the decrease in PAR-T NanoLuc signal is greater when UV-treated cells were pre-treated with Niraparib, which is consistent with the results from Western blot analysis (Figure 5A).

Reviewer 2 (Public Review):

In this study, the authors attempted to extend their own work and that of others in the field in developing probes to detect the signaling molecule, poly-ADPribose (PAR) that can be used in the test tube, in cells and in tumor models. Major strengths include the development of a set of probes with data demonstrating utility and efficacy. Further, the authors show the assay to be useful in cell models and tumor models. Some weaknesses include what appears to be a high level of background in the assay. Further, regarding methods, the exact probes (sequences) being evaluated are not defined. This is one of several new PAR probes being developed over the last few years but may have widespread utility due to the quantitative nature of the bioluminescent assay.

We thank the reviewer for these thoughtful and encouraging comments, as well as the interesting, thought-provoking, and constructive criticisms that have prompted us to dig deeper and provide more evidence to support our claims

Reviewer 3 (Public review):

The major drawback is that, while the authors demonstrated some applications of these PAR trackers (PAR-T) in both culture cells and in animals, the data of PAR-T ddGFP on cancer cells and the data of PAR-T Nano luciferase may not be sufficient to support the authors' claim that the new tool can detect spatial and temporal dynamics of PAR in cells and in animals. That said, the new tools can potentially expand the capability of cell biologists to visualize and study the PAR production process in both normal and disease states with improved sensitivity and tissue compatibility.

We thank the reviewer for appreciating the potential utility of the PAR-T sensors, as well as the detailed and constructive criticisms that have prompted us to provide more evidence to support our claims. Addressing these comments has helped us to improve the paper.

One of the major issues of this manuscript is the lack of time-course data for PAR-T luminescent sensors to demonstrate temporal monitoring of PAR levels in animals. If the binding of two split Nano Luciferase parts is irreversible, the application might be limited. However, according to the literature (Scientific Reports volume 11, Article number: 12535 (2021)), the split Nanoluc technology should be able to detect dynamic changes. Either way, a set of time-course data would be necessary. The authors need to provide evidence to support their statement "The high sensitivity and low signal to noise ratios of the PAR-Trackers described here enable spatial and temporal monitoring of PAR levels in cells and in animals.

We agree with the reviewer’s comment that the original manuscript did not demonstrate that the PAR-T sensors can be used to detect spatio-temporal changes in PAR. To demonstrate that PAR-T NanoLuc can be used to detect time-dependent changes in PAR levels, we performed a time course of UV-mediated PARP-1 activation (Figure 5D). The results from this assay demonstrated that the dynamic changes in PAR in live cells, in response to DNA damage, can be recaptured using the PAR-T NanoLuc sensors. In addition, we also measured PARGi-mediated PAR accumulation in vivo in xenograft tumors (Figure 8 - figure supplement 1B-1D). We found that PAR can be detected readily in breast cancer cells when injected into mice. Upon treatment with PARGi, the luminescence from PAR-T NanoLuc increased significantly by 6 hours and then diminished by 24 hours. These data demonstrate that PAR-T NanoLuc can be used to track dynamic changes in PAR levels both in cells and in animals. While not in vivo, our work with spheroids also addresses this concern. See our response to the next comment below.

Figure 2- figure supplement 2. For the detection of spatial dynamics of PAR signals in cancer spheroids, the authors did not provide sufficient evidence as only static images of different spheroids in different conditions were provided. And 2 out of 3 fields of view only include one spheroid. In addition, there is no time-course image data showing the spatial patterns of PAR in cancer cells are dynamic.

We have now performed a quantitative analysis of multiple spheroids. As indicated in Figure 3B, we observed a significantly higher GFP fluorescence signal in spheroids derived Challa et al. (Kraus) – Rebuttal February 2, 2022 10 from PAR-T ddGFP expressing cells compared to those expressing ddGFP or those treated with Niraparib. To address the reviewer’s concern about using PAR-T ddGFP for spatio-temporal changes in cells, we included a video for live cell imaging of H2O2-mediated increase in PAR-T ddGFP (Figure 2 - figure supplement 2, video). We also developed an analysis approach that allows us to quantify the signals from the core of the spheroids separately from the periphery of the spheroids. We also performed a time course in 3D cancer spheroids to visualize the spatio-temporal changes in PAR levels (Figure 3C and 3D). The results from this experiment demonstrate that the PAR levels in cells at the core of the spheroids are relatively resistant to Niraparib treatment, as the PAR levels in cells at the core of the spheroid decrease at a lower rate when compared to PAR in the cells at the outer layer of the spheroid.

In the caption of Figure 2 -figure supplement 1 (B and C), it states "Immunofluorescence assay to track PAR formation in response to H2O2.", but there is no evidence showing any antibodies were used there.

We thank the reviewer for pointing out this error. It should have been written as live cell imaging, not immunofluorescence assay. We made this correction.

It seems that Figure 3 B and C does not support the statement "we observed specific detection of firefly luciferase with D-Luciferin and NanoLuc with furimazine with no cross-reactivity" And it is unclear why the authors refer Fig. 3B and C after that statement as those data seems not supporting this claim. Similarly, the statement "Moreover, the luminescence of PAR-T Luc is only 30-fold lower than intact firefly luciferase." Was not supported by Fig. 3B. In fact, the differences between PAR-T Luc and intact firefly luciferase were ~1000 fold in vivo, judging from Fig 5B. It is also unclear which data of the construct was used to plot Fig. 3C.

We thank the reviewer for this comment. We changed the scale bar to represent the true scale for the luminescence from Nano luciferase and Firefly luciferase. This indicates that the brightness of PAR-T NanoLuc is 30-fold lower than intact firefly luciferase. In Figure 3C, we plotted the ratio of PAR-T NanoLuc to firefly luciferase.

Fig. 4C, it seems that Firefly luciferase was consistently brighter with PARGi, and I wonder if such difference is statistically significant. The authors did not perform a twoway ANOVA test for the firefly luciferase dataset.

We included the statistics to indicate that these changes were not significant.

The statement "Moreover, none of these sensors can detect PAR accumulation in vivo." seems to lack support. Have the authors proved that with evidence? I would recommend using the following statement instead: "Moreover, none of these sensors has yet demonstrated detection of PAR accumulation in vivo

We made this change.

For the in vivo experiment, it is unclear about the benefits of normalizing the PAR-T radiance to the Firefly luciferase since the signals from Firefly luciferase did not overlap well with that from the PAR-T nano luciferase, which may cause bigger variations.

We thank the reviewer for raising this point. We normalize the luminescence from PAR-T NanoLuc to that from firefly luciferase to account for the variability in tumor size between the mice. We think this is an important control in the analysis. The luminescence from firefly luciferase represents the differences in tumor size between the mice. Hence, that signal is greater than the signal from PAR-T NanoLuc and is spread over a larger area.

Judging from the data of Fig 3 supplement 1E, the signal intensity from the split firefly luciferase-based PAR-T sensors was ~10000 fold less than intact firefly luciferase, not ~1000 fold. It makes more sense to give up the split firefly luciferase for ~10000 fold differences since the signal intensity from the split nano luciferase was ~1000 fold less than intact firefly luciferase (Fig 5B).

We noted the reviewers concern about the split firefly luciferase PAR-T. We agree with the reviewer that the split nano luciferase is brighter than the split firefly luciferase (Figure 4C and Figure 4 - figure supplement 1E). Although split nano luciferase is 1000-fold dimmer than the intact firefly luciferase in vivo (Figure 8B and Figure 8 - figure supplement 1A), this difference is only 30-fold in in vitro assays (Figure 4C). Hence, the comparison of sensors based on split firefly luciferase to split nano luciferase highlights our efforts to make a brighter sensor. Moreover, we included the split firefly luciferase data to compare the performance of WWE vs macrodomain in the development of the PAR-T NanoLuc sensor. Since firefly luciferase is frequently used for sensor development, we believe that it is important to include the results obtained from this sensor.

Therefore, developing tools to measure ADPR dynamics in cells and in vivo is critical for better understating the various biological processes mediated by ADPR". "understating" should be "understanding".

We corrected this error.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Estrach and colleagues seek to identify the ECM components that are key to regulating hair follicle stem cell (HFSC) activation using the highly-characterized mouse hair follicle as a model. They first use a targeted approach to examine key ECM components expressed by HFSC and find that Fibronectin (FN) is highly expressed. Further, wholemount analysis of the hair follicle reveals a meshwork of FN enveloping the hair follicle. They hypothesize that FN is a fundamental regulator of hair follicle (HF) cycling and then proceed to carry out longterm studies required to examine hair follicle cycling and knockout FN with two different HFSC Cre lines (Lrig1 and Krt19), as well as integrin coreceptor SLC3A2. They clearly show that absence of Fibronectin (FN) and SLC3A2 is detrimental to hair follicle stem cell activation and cycling (FN) and hair follicle identity (SLC3A2).

Overall comments:

The authors use the tail hair follicles as a model similarly to the highly-characterized, synchronous back skin hair follicles. However, the tail hair follicles are asynchronous (Braun et al. 2003, PMID: 12954714), thus reporting the age of the mouse from which the tail whole mounts came from is not sufficient to claim a HF cycle disorder - HF should be imaged in an unbiased manner and subsequently quantified for phase. The manuscript would greatly benefit from including more information in the figure legends, such as age of mice, number of mice and HF quantified, as well as what the error bars represent. Further, in samples where many HF were counted per mouse, these should be averaged and then the average per mouse displayed; super plots would be great to use here.

Major comments:

1. In Figure 1, the use of tail whole mount images indeed provides striking display of the fibronectin meshwork that envelops the hair follicle. However, addition of a marker of the regenerative phase (e.g. proliferation) and resting phase would provide more convincing evidence that this is the particular phase of the hair cycle that you have captured, especially given my overall comment regarding the asynchronous nature of the tail HF cycle.
2. The authors show that FN is expressed in early-mid anagen and conclude that FN is a regenerative signal. This claim should be substantiated with FN staining on more time points across the HF cycle to substantiate the argument that it is a regeneration-specific signal, found only in the telogen-anagen transition.
3. Lrig1-cre and K19-cre-mediated FN knockout result in HF that are thinner at D158 - this is not immediately apparent from histological sections. Can you use your thick sections to give better perspective?
4. The authors measure the width of the infundibulum from lightsheet microscope images. It is a bit difficult to position whole tissues using this technique, and the images that are shown are not from the same perspective, and thus measurement of the width is not accurate from these images. I suggest either removing this analysis or using more comparable images. Further, if this is a true phenotype, can you speculate on what the thickened infundibulum might mean?
5. The authors then show mislocalization of Lrig1+ cells to the infundibulum in absence of FN. Are other stem markers localized to the infundibulum or outside of the bulge? Further, what might the mislocalization of Lrig1+ cells might mean?
6. Please explain your conclusion after Figure 3i and at the end of the manuscript that states that FN is required for stem cell anchorage. I think that a very plausible explanation is that FN is required for stem cell function and identity, but anchorage of the SC lacks sufficient evidence. Further, your only evidence to support the anchoring theory only comes from expression of Lrig1 in FN knockout and no other markers. Are they also mislocalized? Please either tone down this conclusion on SC anchorage or provide stainings for more SC markers to show mislocalization in absence of FN.
7. In Figure 3l-o, you examine proliferation on the control vs the conditional deletion of FN in D30 and D158. However, in D30, these tissues are not at all directly comparable since one is obviously in anagen and the knockout in telogen. You must compare the anagen knockout sample, although this occurs a bit later than the control. Further, how was the infundibulum distinguished from the bulb in these control images?
8. In Figure 3P, you carry out RT-qPCR on whole skin to detect HFSC markers. This should have been carried out on sorted epithelial cells as isolation of whole back skin introduces bias to the system in that the number of stem cells may artificially look different in skin that is in anagen vs skin that is in telogen as the anagen skin has a different proportion of SC to progenitor cells to dermal cells. This concern is also similar to point 9 - the control and FN knockout at D30 are not comparable given that they are in different phases of the hair cycle.
9. Figure 4a these images need to be of the whole mouse - it is not possible to determine what we are looking at or where - there is not even a scale bar.
10. After Figure 4, you argue that because fibronectin expression resolves from healing dermis is the reason that hair follicles do not form, and site Dekonick and Blanpain (PMID: 30602767) - however this review makes no mention of the dynamics of fibronectin in wound healing. Further, evidence from Driskell et al (2013, PMID: 30602767) would suggest that it is the fibroblast population that responds to the wound that determines whether HF regenerate. And further, very large wounds do regenerate HF (Ito et al PMID: 30602767). In addition, this would all be fibroblast-derived FN, as opposed to the current study which examines keratinocyte-derived FN. Please reconsider this argument.
11. The authors knockout SLC3A2, an integrin coreceptor that is localized to the plasma membrane. They show a very similar, yet more severe phenotype to the Lrig1- and K19- mediated knockout of FN. Given the bidirectional communication that SLC3A2 is responsible for, can you reconcile whether the defects in the HF cycle and the HFSC are a result of outside-in or inside-out signaling? Further, is it possible that integrin function regulated by SLC3A2 is necessary for more than FN assembly? This could be especially relevant given that your targeted screen also identified Col17A1, which is well known to be required for HFSC function (Matsumura et al., PMID: 26912707)
12. It is intriguing that in the absence of HFSC-derived SLC3A2 that no FN network forms. Is FN expressed or is the assembly perturbed in the absence of properly functioning integrins? The authors conclude that the signaling cascade flows from fibronectin to integrin to SLC3A2, but do not test where the FN phenotype arises in the SLC3A2 knockout - is it due to aberrant assembly of the FN meshwork or a change in transcriptional or translational levels?
13. In the grafting assay in Supplemental Figure 3, keratinocytes undergo a de novo hair follicle morphogenesis - is Lrig1 expression maintained in order to carry out cre-mediated deletion? Further, the fibroblasts in this assay may adopt a wound-like phenotype, expressing FN, which you earlier claim to be required for hair follicle production in wounds. Yet in the absence of epithelial FN, no HF form. Can the authors reconcile this?

Minor comments:

1. In Figure 1a, the two populations are Lgr5+ and basal; please define what the basal population is in this experiment.
2. Significative is not a word.
3. In Figure 4 figure legend, there is reference to a grafting experiment but no experiment shown.
4. The authors delete FN in Lrig1+ or K19+ cells starting D19 and harvest at D30, and conclude that the hair follicles do not enter anagen after the second telogen, can you please include the data supporting the statement that mutant HF did not reenter the hair cycle after D65.

Significance

The authors show for the first time that fibronectin is expressed during cutaneous homeostasis and that it is required for normal function of the hair follicle stem cells. This is significant conceptual advance for the field of skin biology because fibronectin is thought to only be present in wounds: derived first from infiltrating serum and second from fibroblasts to act as provisional dermal ECM to support epithelialization during wound-response, which is ultimately resolved upon the conclusion of wound healing (reviewed in: Singer and Clark, PMID: 10471461). Further, FN has also been characterized as an EMT marker during cancerous progression (Lamouille et al, PMID: 24556840). Estrach and colleagues show that fibronectin is actually expressed by hair follicle stem cell keratinocytes and then is assembled into a meshwork that envelops the hair follicle and is in fact necessary for hair follicle stem cell homeostasis. This work would be broadly interesting to the field of stem cell biology as well as those working on extra cellular matrix signaling. My field is epithelial stem cells and more specifically hair follicle development and cycling.

Referee Cross-commenting

I have no disagreement with any of the points raised by the other reviewers. In fact, we seem to agree on the majority of the concerns. This includes the use of the tail wholemount model, the use of Lrig1-cre, selection of timepoint vs phase of the hair cycle, the appropriateness of the link between Fibronectin and SLC3A2, and further significant issues related to display of data and their reproducibility. Further, all of the major comments raised need to be addressed in order to properly evaluate the conclusions that the authors make. In my opinion, none of the comments raised here are unreasonable.

Author Response:

Reviewer #1:

The paper uses a microfluidic-based method of cell volume measurement to examine single cell volume dynamics during cell spreading and osmotic shocks. The paper successfully shows that the cell volume is largely maintained during cell spreading, but small volume changes depend on the rate of cell deformation during spreading, and cell ionic homeostasis. Specifically, the major conclusion that there is a mechano-osmotic coupling between cell shape and cell osmotic regulation, I think, is correct. Moreover, the observation that fast deforming cell has a larger volume change is informative.

The authors examined a large number of conditions and variables. It's a paper rich in data and general insights. The detailed mathematical model, and specific conclusions regarding the roles of ion channels and cytoskeleton, I believe, could be improved with further considerations.

We thank the referee for the nice comment on our work and for the detailed suggestions for improving it.

Major points of consideration are below.

1) It would be very helpful if there is a discussion or validation of the FXm method accuracy. During spreading, the cell volume change is at most 10%. Is the method sufficiently accurate to consider 5-10% change? Some discussion about this would be useful for the reader.

This is an important point and we are sorry if it was not made clear in our initial manuscript. We have now made it more clear in the text (p. 4 and Figure S1E and S1F).

The important point is that the absolute accuracy of the volume measure is indeed in the 5 to 10% range, but the relative precision (repeated measures on the same cell) is much higher, rather in the 1% range, as detailed below based on experimental measures.

1) Accuracy of absolute volume measurements. The accuracy of the absolute measure of the volume depends on several parameters which can vary from one experiment to the other: the exact height of the chamber, and the biological variability form one batch of cell to another (we found that the distribution of volumes in a population of cultured cells depends strongly on the details of the culture – seeding density, substrate, etc... - which we normalized as much as possible to reduce this variability, as described in previous articles, e.g. see2). To estimate this variability overall, the simplest is to compare the average volume of the cell population in different experiments, carried out in different chambers and on different days.

Graph showing the initial average volume of cells +/- STD for 7 spreading experiments and 27 osmotic shock experiments, expressed as a % deviation from the average volume over all the experiments.

The average deviation is of 10.9 +/- 8%

2) Precision of relative volume measurements. When the same cell is imaged several times in a time-lapse experiment, as it is spreading on a substrate, or as it is swelling or shrinking during an osmotic shock, most of the variability occurring from one experiment to another does not apply. To experimentally assess the precision of the measure, we performed high time resolution (one image every 30 ms) volume measurements of 44 spread cells during 9 s. During this period of time, the volume of the cell should not change significantly, thus giving the precision of the measure.

Graph showing the coefficient of variation of the volume (STD/mean) for each individual cell (n=44) across the almost 300 frames of the movie. This shows that on average the precision of volume measurements for the same cell is 0.97±0.21%. In addition, if more precision was needed, averaging several consecutive measures can further reduce the noise, a method which is very commonly used but that we did not have to apply to our dataset.

We have included these results in the revised manuscript, since they might help the reader to estimate what can be obtained from this method of volume measurement. We also point the reviewer to previous research articles using this method and showing both population averages and time-lapse data2–8 . Another validation of our volume measurement method comes from the relative volume changes in response to osmotic shock (Ponder’s relation) measured with FXm, which gave results very similar to the numbers of previously published studies. We actually performed these experiments to validate our method, since the results are not novel.

2) The role of cell active contraction (myosin dynamics) is completely neglected. The membrane tether tension results, LatA and Y-compound results all indicate that there is a large influence of myosin contraction during cell spreading. I think most would not be surprised by this. But the model has no contribution from cortical/cytoskeletal active stress. The authors are correct that the osmotic pressure is much larger than hydraulic pressure, which is related to active contraction. But near steady state volume, the osmotic pressure difference must be equal to hydraulic pressure difference, as demanded by thermodynamics. Therefore, near equilibrium they must be close to each other in magnitude. During cell spreading, water dynamics is near equilibrium (given the magnitude of volume change), and therefore is it conceptually correct to neglect myosin active contraction? BTW, 1 solute model does not imply equal osmolarity between cytoplasm and external media. 1 solute model with active contraction was considered before, e.g., ref. 17 and Tao, et al, Biophys. J. 2015, and the steady state solution gives hydraulic pressure difference equal to osmotic pressure difference.

This is an excellent point raised by the referee. We have two types of answers for this. First an answer from an experimental point of view, which shows that acto-myosin contractility does not seem to play a direct role in the control of the cell volume, at least in the cells we used here. Based on these results we then propose a theoretical reason why this is the case. It contrasts with the view proposed in the articles mentioned by the referee for a reason which is not coming from the physical principles, with which we fully agree, but from the actual numbers, available in the literature, of the amount of the various types of osmolytes inside the cell. We give these points in more details below and we hope they will convince the referee. We also now mention them explicitly in the main text of the article (p. 6-7, Figure S3F) and in the Supplementary file with the model.

A. Experimental results

To test the effect of acto-myosin contraction on cell volume, we performed two experiments:

1) We measured the volume of same cell before and after treatment with the Rho kinase ROCK inhibitor Y-27632, which decreases cortical contractility. The experiment was performed on cells plated on poly-L-Lysin (PLL), like osmotic shock experiments, a substrate on which cells adhere, allowing the change of solution, but do not spread and remain rounded. This allowed us to evaluate the effect of the drug. Cells were plated on PLL-coated glass. The change of medium itself (with control medium) induced a change of volume of less than 2%, similar to control osmotic shock experiments (maybe due to shear stress). When the cells were treated with Y-27, the change of volume was similar to the change with the control medium (now commented in the text p. 6-7, Figure S3F). To make the analysis more complete, we distinguished the cells that remained round throughout the experiment from the cells which slightly spread, since spreading could have an effect on volume. Indeed we observed that treatment with Y-27 induced more cells to spread (Figure S3F), probably because the cortex was less tensed, allowing the adhesive forces on PLL to induce more spreading9. Nevertheless, the spreading remained rather slow and the volume change of cells treated or not with Y-27 was not significantly different. This shows that, in the absence of fast spreading induced by Y-27, the reduction of contractility per se does not have any effect on the cell volume.

Graphs showing proportion of cells that spread during the experiments (left); average relative volume of round (middle) and spread (right) control (N=3, n=77) and Y-27 treated cells (N=4, N=297).

2) To evaluate the impact of a reduction of contractility in the total absence of adhesion, we measured the average volume of control cells versus cells which have been pretreated with Y-27, plated on a non-adhesive substrate (PLL-PEG treatment). This experiment showed that the volume of the cells evolved similarly in time for both conditions, proving that contractility per se has no effect on the cell volume or cell growth, in the absence of spreading.

Graphs showing average relative volume of control (N=5, n=354) and Y-27 (N=3, n=292) treated cells plated on PLL-PEG (left); distributions of initial volume for control (middle) and Y-27 treated cells (right) represented on the left graph.

Taken together these results show that inhibition of contractility per se does not significantly affect cell volume. It thus confirms our interpretation of our results on cell spreading that reduction of contractility has an effect on cell volume, specifically in the context of cell spreading, primarily because it affects the spreading speed.

B. Theoretical interpretation

In accordance with our experiments, in our model, the effect of contractility is implicitly included in the model because it modulates the spreading dynamics, which is an input to the model, i.e. through the parameters tau_a and A_0.

We do not include the effect of contractility directly in the water transport equation because our quantitative estimates support that the contribution of the hydrostatic pressure to the volume (or the volume change) is negligible in comparison to the osmotic pressure, and this even for small variation near the steady-state volume. The main important point is that the concentration of ions inside the cell is actually much lower than outside of the cell10,11. The difference is about 100 mM and corresponds mostly to nonionic small trapped osmolytes, such as metabolites12. The osmotic pressure corresponding to this is about 10^5 Pa. Taking the cortical tension to be of order of 1 mN/m and cell size to be about ten microns we get a hydrostatic pressure difference of about 100 Pa due to cortical tension. A significant change in cell volume, of the order observed during cell spreading (let’s consider a ten percent decrease) will increase the osmotic pressure of the trapped nonionic osmolytes by 10^4 Pa (their number in the cell remaining identical). For this osmotic pressure to be balanced by an increase in the hydrostatic pressure, the cortical tension would need to increase by a factor of 100, which we consider to be unrealistic. Therefore, we find it reasonable to ignore the contribution of the hydrostatic pressure difference in the water flux equation. It is also consistent with the novel experiments presented above which show that inhibition of cortical contractility changes the cells volume below what can be detected by our measures (thus likely at maximum in the 1% range). This is now explained in the main text and Supplementary file.

Regarding our minimal model required to define cell volume, the reason why we believe one solute model is not sufficient is fundamentally the same as above: the concentration of trapped osmolytes is comparable to the total osmolarity, which means that their contribution to the total osmotic pressure cannot be discarded. Secondly, within the simplest one solute model, the pump and leak dynamics fixes in inner osmolytes concentration but does not involve the actual cell size. The most natural term that depends on the size is the Laplace pressure (inversely proportional to the cell size in a spherical cell model). But as discussed above, this term may only permit osmotic pressure differences of the order of 100 Pa, corresponding to an osmolytes concentration difference of the order of 0.1 mM. That is only a tiny fraction of the external medium osmolarity, which is about 300 mM. Such a model could thus only work for extremely fine tuning of the pump and leak rates to values with less than about 1% variation. Furthermore, such a model could not explain finite volume changes upon osmotic shocks without involving huge (100-fold) cell surface tension variations, as discussed above. For these reasons, we believe that the one-solute model is not appropriate to describe our experiments, and we feel that a trapped population of nonionic osmolytes is needed to balance the osmolarity difference created by the solute pump and leak.

In the revised version of the manuscript, we have now added a section in Supplementary file and in the main text, explaining in more detail this approximation.

3) The authors considered the role of Na, K, and Cl in the model, and used pharmacological inhibitors of NHE exchanger. I think this part of the experiments and model are somewhat weak. I am not sure the conclusions drawn are robust. First there are many ion channels/pumps in regulating Na, K and Cl. The most important of which is NaK exchanger. NHE also involves H, and this is not in the model. The ion flux expressions in the model are also problematic. The authors correctly includes voltage and concentration dependences, but used a constant active term S_i in SM eq. 3 for active pumping. I am not sure this is correct. Ion pump fluxes have been studied and proposed expressions based on experimental data exist. A study of Na, K, Cl dynamics, and membrane voltage on cell volume dynamics was published in Yellen et al, Biophys. J. 2018. In that paper, they used different expressions based on previously proposed flux expressions. It might be correct that in small concentration differences, their expressions can be linearized or approximated to achieve similar expressions as here. But this point should be considered more carefully.

We thank the reviewer for this comment. Indeed, we have not well justified our use of the NHE inhibitor EIPA. Our aim was not to directly affect the major ion pumps involved in volume regulation (which would indeed rather be the Na+/K+ exchanger), because that would likely strongly impact the initial volume of the cell and not only the volume response to spreading, making the interpretation more difficult. We based our choice on previous publication, e.g.13, showing that EIPA inhibited the main fast volume changes previously reported for cultured cells: it was shown to inhibit volume loss in spreading cells, as well as mitotic cell swelling14,15. Using EIPA, we also found that, while the initial volume was only slightly affected, the volume loss was completely abolished even in fast spreading cells (Y-27 and EIPA combined treatment, Figure S5H). This clearly proves that the volume loss behavior can be abolished, without changing the speed of spreading, which was our main aim with this experiment.

The most direct effect of inhibiting NHE exchangers is to change the cell pH16,17, which, given the low number of H protons in the cell (negligible contribution to cells osmotic pressure), cannot affect the cell volume directly. A well-studied mechanism through which proton transport can have indirect effect on cell volume is through the effect of pH on ion transporters or due to the coupling between NHE and HCO3/Cl exchanger. The latter case is well studied in the literature18. In brief, the flux of proton out of the cell through the NHE due to Na gradient leads to an outflux of HC03 and an influx of Cl. The change in Cl concentration will have an effect on the osmolarity and cell volume.

We thus performed hyperosmotic shocks with this drug and we found that, as expected, it had no effect on the immediate volume change (the Ponder’s relation), but affected the rate of volume recovery (combined with cell growth). Overall, the cells treated with EIPA showed a faster volume increase, which is what is expected if active pumping rate is reduced. This is in contrast with the above mentioned mechanism of volume regulation which will to lead to a reduced volume recovery of EIPA treated cells. This leads us to conclude that there is potentially another effect of NHE perturbation. Changing the pH will have a large impact on the functioning of many other processes, in particular, it can have an effect on ion transport16. Overall, the cells treated with EIPA showed a faster volume increase, which is what is expected if active pumping rate is reduced.

On the model side, the referee correctly points out that there are many ion transporters that are known to play a role in volume regulation which are not included in Eq. 3. In the revised manuscript we now start with a more general ion transport equation. We show that the main equation (Eq.1 - or Supplementary file Eq.13) relating volume change to tension is not affected by this generalization. This is because we consider only the linear relation between the small changes in volume and tension. We note that the generic description of the PML (Supplementary file Eqs.1-6) can be seen as general and does not require the pump and channel rates to be constant; both \Lambda_i and S_i can be a function of potential and ion concentration along with membrane tension. It is only later in the analysis that we do make the assumption that these parameters only depend on tension. This point is now made clear in the Supplementary file.

There is a huge body of work both theoretical and experimental in which the effect of different ion transporters on cell volume is analyzed. The aim of this work is not to provide an analysis of cell volume and the effect of various co-transporters but is rather limited to understanding the coupling between cell spreading, surface tension and cell volume.

To analytically estimate the sign of the mechano-osmotic coupling parameter alpha we use a minimal model. For this we indeed take the pumps and channels to be constant. As it is again a perturbative expansion around the steady state concentration, electric potential, and volume, the expression of alpha can be easily computed for a model with more general ion transporters. This generalization will come at the cost of additional parameters in the alpha expression. We decided to keep the simpler transport model, the goal of this estimate is merely to show that the sign of alpha is not a given and depends on relative values of parameters. Even for the simple model we present, the sign of alpha could be changed by varying parameters within reasonable ranges.

Given these points, and the clarification of the reasons to use EIPA in our experiments, a full mechanistic explanation of the effect of this drug is beyond the scope of this work. Because of this we are not analyzing the effect of EIPA on the model parameter alpha in detail. We now clarified our interpretation of these results in the main text of the article.

Reviewer #2:

The work by Venkova et al. addresses the role of plasma membrane tension in cell volume regulation. The authors study how different processes that exert mechanical stress on cells affect cell volume regulation, including cell spreading, cell confinement and osmotic shock experiments. They use live cell imaging, FXm (cell volume) and AFM measurements and perform a comparative approach using different cell lines. As a key result the authors find that volume regulation is associated with cell spreading rate rather than absolute spreading area. Pharmacological assays further identified Arp2/3 and NHE1 as molecular regulators of volume loss during cell spreading. The authors present a modified mechano-osmotic pump and leak model (PLM) based on the assumption of a mechanosensitive regulation of ion flux that controls cell volume.

This work presents interesting data and theoretical modelling that contribute new insight into the mechanisms of cell volume regulation.

We thank the referee for the nice comments on our work. We really appreciate the effort (s)he made to help us improve our article, including the careful inspection of the figures. We think our work is much improved thanks to his/her input.

Reviewer #3:

The study by Venkova and co-workers studies the coupling between cell volume and the osmotic balance of the cell. Of course, a lot of work as already been done on this subject, but the main specific contribution of this work is to study the fast dynamics of volume changes after several types of perturbations (osmotic shocks, cell spreading, and cell compression). The combination of volume dynamics at very high time resolution, and the robust fits obtained from an adapted Pump and Leak Model (PLM) makes the article a step-forward in our understanding of how cell volume is regulated during cell deformations. The authors clearly show that:

-The rate at which cell deforms directly impacts the volume change

-Below a certain deformation rate (either by cell spreading or external compression), the cells adapt fast enough not to change their volume. The plot dV/dt vs dA/dt shows a clear proportionality relation.

-The theoretical description of volume change dynamics with the extended PLM makes the overall conclusions very solid.

Overall the paper is very well written, contains an impressive amount of quantitative data, comparing several cell types and physiological and artificial conditions.

We thank the referee for the positive comment on our work.

My main concern about this study is related to the role of membrane tension. In the PLM model, the coupling of cell osmosis to cell deformation is made through the membrane-tension dependent activity of ion channels. While the role of ion channels is extensively tested, it brings some surprising results. Moreover, the tension is measured only at fixed time points, and the comparison to theoretical predictions is not always as convincing as expected: when comparing fig 6I and 6J, I see that predictions shows that EIPA (+ or - Y27), CK-666 (+ or - Y27) and Y27 alone should have lower tension than in the control conditions, and this is clearly not the case in fig 6J. But I would not like to emphasize too much on those discrepancies, as the drugs in the real case must have broad effects that may not be directly comparable to the theory.

We apologize for the mislabeling of the Figure 6I (now Figure 5I). This plot shows the theoretical estimate for the difference in tension (in the units of homeostatic tension) between the case when the cell loses its volume upon spreading (as observed in experiments) compared to the hypothetical situation when the cell does not lose volume upon spreading (alpha = 0). The positive value of the tension difference predicts that the cell tension would have been higher if the cell were not losing volume upon spreading, which is the case for the treatments with EIPA and CK-666 (+ Y27) and corresponds to what we found experimentally.

It thus matches our experimental observations for drug treatments which reduce or abolish the volume loss during spreading and correspond to higher tether force only at short time.

We have corrected the figure and figure legend and explained it better in the text.

But I wonder if the authors would have a better time showing that the dynamics of tension are as predicted by theory in the first place, as comparing theoretical predictions with experiments using drugs with pleiotropic effects may be hazardous.

Actually, a recent publication (https://doi.org/10.1101/2021.01.22.427801) shows that tension follows volume changes during osmotic shocks, and overall find the same dynamics of volume changes than in this manuscript. I am thus wondering if the authors could use the same technique than describe in this paper (FLIM of flipper probe) in order to study the dynamics of tension in their system, or at least refer to this paper in order to support their claim that tension is the coupling factor between volume and deformation.

As was suggested by the referee, we tried to use the FLIPPER probe. We first tried to reproduce osmotic shock experiments adding to the HeLa cells 4% of PEG400 (+~200 mOsm) or 50% of H20 (-~170 mOsm) and measuring the average probe lifetime before and after the shock. We found significantly lower probe lifetime for hyperosmotic condition compared with control, and non-significant, but slightly higher lifetime for hypoosmotic shock. The magnitude of lifetime changes was comparable with the study cited by the reviewer, but the quality of our measures did not allow us to have a better resolution. Next we measured average lifetime for control and CK-666+Y-27 treated cells 30 min and 3 h after plating, because we have highest tether force values for CK-666+Y-27 at 30 min. We did not see a change in lifetime in control cells between 30 min and 3 h (which also did not see with the tether pulling). Cells treated with CK-666+Y-27 showed a slightly lower lifetime values than control cells, but both 30 min and 3 h after plating, which means that it did not correspond to the transient effect of fast spreading but probably rather to the effect of the drugs on the measure.

Graph showing FLIPPER lifetime before and after osmotic shock for HeLa cells plated on PLL- coated substrate. Left: control (N=3, n=119) and hyperosmotic shock (N=3, n=115); Right: control (N=3, n=101) and hypoosmotic shock (N=3, n=80). p-value are obtained by t-test.

Graph showing FLIPPER lifetime for control just after the plating on PLL-coated glass (the same data for control shown at the previous graph), 30 min (control: N=3, n=88; Y-27+CK-666: N=3, n=130) and 3 h (control: N=3, n=78; Y-27+CK-666: N=3, n=142) after plating on fibronectin-coated glass. p-value are obtained by t-test.

Because the cell to cell variability might mask the trend of single cell changes in lifetime during spreading, we also tried to follow the lifetime of individual cells every 5 min along the spreading. Most illuminated cells did not spread, while cells in non-illuminated fields of view spread well, suggesting that even with an image every 5 minutes and the lowest possible illumination, the imaging was too toxic to follow cell spreading in time. We could obtain measures for a few cells, which did not show any particular trend, but their spreading was not normal. So we cannot really conclude much from these experiments.

Graph showing FLIPPER lifetime changes for 3 individual cells plated on fibronectin-coated glass (shown in blue, magenta and green) and average lifetime of cells from non-illuminated field (cyan, n=7)

Our conclusions are the following:

1) We are able to visualize some change in the lifetime of the probe for osmotic shock experiments, similar to the published results, but with a rather large cell to cell variability.

2) The spreading experiments comparing 30 minutes and 3 hours, in control or drug treated cells did not reproduce the results we observed with tether pulling, with a global effect of the drugs on the measures at both 30 min and 3 hours.

3) Following single cells in time led to too much toxicity and prevented normal spreading.

We think that this technology, which is still in its early developments, especially in terms of the microscope setting that has to be used (and we do not have it in our Institute, so we had to go on a platform in another institute with limited time to experiment), cannot be implemented in the frame of the revision of this article to provide reliable results. We thus consider that these experiments are for further development of the work and are out of the scope of this study. It would be very interesting to study in details the comparison between the oldest and more established method of tether pulling and the novel method of the FLIPPER probe, during cell spreading and in other contexts. To our knowledge this has never been done so far, so it is not in the frame of this study that we can do it. It is not clear from the literature that the two methods would measure the same thing in all conditions even if they might match in some.

Authors Response:

Reviewer #2 (Public Review):

The authors use representational similarity analysis on a combination of behavioral similarity ratings and EEG responses to investigate the representation of actions. They specifically explore the role of visual, action-related, and social-affective features in explaining the similarity ratings and brain responses. They find that social-affective features best explain the similarity ratings, and that visual, action-related, and social-affective features each explain some of the variance in the EEG responses in a temporal progression (from visual to action-related to social-affective).

The stimulus set is nicely constructed, broadly sampled from a large set of naturalistic stimuli to minimize correlations between features of interest. I'd like to acknowledge and appreciate the work that went into this in particular.

The analyses of the behavioral similarity judgments are well executed and interesting. The subject exclusion criteria and catch trials for online workers are smart choices, and the authors have tested a good range of models drawn from different categories. I find the case that the authors make for social features as determinants of behavioral similarity ratings to be compelling.

I have a few questions and requests for additional detail about the EEG analyses. I appreciate that the authors have provided the code they used for all the analyses, and I'm sure that the answers to many if not all of my questions are there, but I don't have access to a Matlab license to run the code. Also, since the code requires familiarity with not just Matlab but with specific libraries to understand, I think that more description of the analysis in the paper would be appropriate.

Some more detail is needed in the description of the multivariate classifier analysis. The authors write (line 597-599): "The two pseudotrials were used to train and test the classifier separately at each timepoint, and multivariate noise normalization was performed using the covariance matrix of the training data (Guggenmos et al., 2018). "

I suspect I'm missing something here, because as written this sounds as if there was only one trial on which to train the classifier, which does not seem compatible with SVM classification. If only one trial was used to train the classifier, that sounds more like nearest-neighbor classification (or something else). Alternatively, if all different pseudo-trial averages - each incorporating a different subset of trials - were used for training, then that would seem to mean that some of the training pseudo-trials contained information from trials that were also averaged into the pseudo-trials used for testing. I don't know if this was done (probably not) but if it was it would constitute contamination of the test set. I think this part of the methods needs more detail so we can evaluate it. How many trials were used to train and to test for each iteration?

Thank you for raising this issue; we agree that our Methods section was unclear on this point. We used split-half cross-validation. There was one pseudotrial for training per condition (which was obtained by averaging trials). There was no contamination between the training and test sets, because the data was first divided into separate training and test sets, and only afterwards averaged into pseudotrials for classification. This procedure was repeated 10 times with different data splits to obtain more reliable estimates of the classification performance. We rewrote the corresponding section to make this clearer:

“Split-half cross-validation was used to classify each pair of videos in each participant’s data. To do this, the single-trial data was divided into two halves for training and testing, whilst ensuring that each condition was represented equally. To improve SNR, we combined multiple trials corresponding to the same video into pseudotrials via averaging. The creation of pseudotrials was performed separately within the training and test sets. As each video was shown 10 times, this resulted in a maximum of 5 trials being averaged to create a pseudotrial. Multivariate noise normalization was performed using the covariance matrix of the training data (Guggenmos et al., 2018). Classification between all pairs of videos was performed separately for each time-point. […] The entire procedure, from dataset splitting to classification, was repeated 10 times with different data splits.”

We also performed the decoding procedure with a higher number of cross-validation folds and found very similar results.

I think a bit more detail is also necessary to clarify the features used for the classification. My understanding is that each timepoint was classified as one action vs each other action on the basis of all the electrodes in the EEG for a given temporal window. Is this correct? (I'm guessing / inferring more than a little here.)

This is correct, and we agree that further clarification was needed in text. We have added this:

“Classification between all pairs of videos was performed separately for each time-point. Data were sampled at 500 Hz and so each time point corresponded to non-overlapping 2 ms of data. Voltage values from all EEG channels were entered as features to the classification model.

The entire procedure, from dataset splitting to classification, was repeated 10 times with different data splits. The average decoding accuracies between all pairs of videos were then used to generate a neural RDM at each time point for each participant. To generate the RDM, the dissimilarity between each pair of videos was determined by their decoding accuracy (increased accuracy representing increased dissimilarity at that time point).”

It would be useful to know how many features constituted each feature space. For example, was motion energy reduced to one summary feature (total optic flow for whole sequence?) For "pixel value", is that luminance? (I suspect so, since hue is quantified separately, but I don't think this was specified).

For motion energy, we used the magnitude of the optic flow, and calculated Euclidean distances between the vectorized magnitude maps rather than reducing it to summary features. We have included the dimensionality of each feature in Supplementary File 1b and we now refer to it in text:

“These features were vectorized prior to computing Euclidean distances between them (see Supplementary File 1b for the dimensionality of each feature).”

Pixel value was indeed the luminance, and we have clarified this in text.

More broadly, I would appreciate a bit more discussion of the role of time in these analyses. Each clip unfolds over half a second, so what should we make of the temporal progression of RDM correlations? Are the social and affective features correlated with later responses because they take more time to compute (neurally speaking), or because they depend on longer temporal integration of information? These two are not even exactly mutually exclusive, and I realize that it may be difficult to say with certainty based on this data, but I think some discussion of this issue would be appropriate.

This is a great point, although it is difficult to speculate based on this data. One way to get at this would be to examine how much social-affective processing relies on previously extracted features. Future work could look at the causality between early and later-stage EEG features (unfortunately our post-hoc attempts to address this via Granger-causal analysis were unsuccessful, likely due to insufficient SNR with our specific experimental design). Alternatively, this could be investigated in a follow-up experiment that varies how social information unfolds over time (e.g., images vs. videos or varying video duration). We now discuss this possibility in the manuscript:

“Given the short duration of our videos and the relatively long timescale of neural feature processing, it is possible that social-affective features are the result of ongoing processing relying on temporal integration of the previously extracted features. However, more research is needed to understand how these temporal dynamics change with continuous visual input (e.g. a natural movie), and whether social-affective features rely on previously extracted information.”

Reviewer #2 (Public Review):

The authors use representational similarity analysis on a combination of behavioral similarity ratings and EEG responses to investigate the representation of actions. They specifically explore the role of visual, action-related, and social-affective features in explaining the similarity ratings and brain responses. They find that social-affective features best explain the similarity ratings, and that visual, action-related, and social-affective features each explain some of the variance in the EEG responses in a temporal progression (from visual to action-related to social-affective).

The stimulus set is nicely constructed, broadly sampled from a large set of naturalistic stimuli to minimize correlations between features of interest. I'd like to acknowledge and appreciate the work that went into this in particular.

The analyses of the behavioral similarity judgments are well executed and interesting. The subject exclusion criteria and catch trials for online workers are smart choices, and the authors have tested a good range of models drawn from different categories. I find the case that the authors make for social features as determinants of behavioral similarity ratings to be compelling.

I have a few questions and requests for additional detail about the EEG analyses. I appreciate that the authors have provided the code they used for all the analyses, and I'm sure that the answers to many if not all of my questions are there, but I don't have access to a Matlab license to run the code. Also, since the code requires familiarity with not just Matlab but with specific libraries to understand, I think that more description of the analysis in the paper would be appropriate.

Some more detail is needed in the description of the multivariate classifier analysis. The authors write (line 597-599): "The two pseudotrials were used to train and test the classifier separately at each timepoint, and multivariate noise normalization was performed using the covariance matrix of the training data (Guggenmos et al., 2018). "

I suspect I'm missing something here, because as written this sounds as if there was only one trial on which to train the classifier, which does not seem compatible with SVM classification. If only one trial was used to train the classifier, that sounds more like nearest-neighbor classification (or something else). Alternatively, if all different pseudo-trial averages - each incorporating a different subset of trials - were used for training, then that would seem to mean that some of the training pseudo-trials contained information from trials that were also averaged into the pseudo-trials used for testing. I don't know if this was done (probably not) but if it was it would constitute contamination of the test set. I think this part of the methods needs more detail so we can evaluate it. How many trials were used to train and to test for each iteration?

I think a bit more detail is also necessary to clarify the features used for the classification. My understanding is that each timepoint was classified as one action vs each other action on the basis of all the electrodes in the EEG for a given temporal window. Is this correct? (I'm guessing / inferring more than a little here.)

It would be useful to know how many features constituted each feature space. For example, was motion energy reduced to one summary feature (total optic flow for whole sequence?) For "pixel value", is that luminance? (I suspect so, since hue is quantified separately, but I don't think this was specified).

More broadly, I would appreciate a bit more discussion of the role of time in these analyses. Each clip unfolds over half a second, so what should we make of the temporal progression of RDM correlations? Are the social and affective features correlated with later responses because they take more time to compute (neurally speaking), or because they depend on longer temporal integration of information? These two are not even exactly mutually exclusive, and I realize that it may be difficult to say with certainty based on this data, but I think some discussion of this issue would be appropriate.

What "creative solutions" did her doctors come up with?

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Reply to the reviewers

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We are grateful to the reviewers for their honest opinion regarding this work and plan to address the majority of the comments in a revised version either through new analysis or revision to the text, as we believe these will improve the manuscript by making some of the details clearer. There were few suggestions that will lead to substantiative changes to the findings. Here, we address the most salient critiques, the primary one being related to novelty.

We respectfully disagree, as our detailed analysis of the DNA methylome in Octopus bimaculoides represents a significant advance to understanding how the epigenome is patterned in non-model invertebrates in general, and cephalopods in particular. We acknowledge that the previous report that the octopus methylome resembles the few other invertebrates where low DNA methylation has been found, the finding was part of a multi-organism study last year (de Mendoza et al., 2021), which lacked any detailed investigation. Our study provides the first in depth analysis on methylation patterning, the relationship with transposons and gene expression, and reports the finding of other key epigenetic marks in O. bimaculoides, and in other cephalopods.

In short, we believe our study to be highly novel and that it represent the first analysis of this kind in cephalopods and one of the few existing in non-model invertebrate organisms. In addition, we identify the conservation of the histone code in cephalopods. While this may be expected, this is the first experimental evidence in this class and represents an important step forward to understand the epigenetic regulation of genes and transposons in invertebrates. Finally, we plan to provide an updated transcriptome annotation for O. bimaculoides that will be available for the scientific community as a new valuable resource. We believe these features will make this study highly cited.

We believe that findings like ours will complement several recent studies that extend the epigenetics field out of the current narrow focus on model organisms to understand how epigenetic mechanisms function in diverse animals. This provides new insights regarding the epigenetic mechanism of gene regulation in an emerging invertebrate model.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer 1 raised the following points that we are planning to address:

*- It is unclear why the authors did not use the original gene models of O. bimaculoides or tried to improve them. By only relying on adult tissue (but the relatively late hatchling stage), they would have omitted most developmentally expressed genes, that are incidentally also the ones that are subjected to extensive spatiotemporal gene regulation (which is also a problem to assess the role of methylation). I think more comparisons with existing gene models and how the newly generated stringtie models should be provided. *

We agree that using as many tissues and developmental stages as possible will expand the octopus transcriptome.

We plan to:

• Add RNA-seq data from stage 15 embryos to improve this.
• Compare the gene model used in the original version of the manuscript (Stringtie model to use in Trinotate for improving the annotation of the genes) to the existing annotation model and report on which has superior performance for annotating the * bimaculoides* transcriptome.
• Extend the annotation of the transcriptome which we undertook in a focused fashion in the first iteration of this manuscript. Reviewer 2 raised the following points that we are planning to address:

*- It is not exactly clear to me why the authors look for expression clusters in the first part of the manuscript? This information, while interesting, does not seem to be used in the methylation analysis. It is also somewhat contradictory because the authors first claim that, based on their GO-term enrichment analysis, that different expression clusters are associated with "complex regulatory mechanisms, potentially based in the epigenome". Yet at the end they conclude that, due to the global and tissue-overarching nature of methylation, this "argues against this epigenetic modification as a player in the dynamic regulation of gene expression". *

We thank the reviewer for pointing out this issue and we plan to clarify the point through changing the text and additional analysis. Since we found that the methylation pattern was stable across tissues, and that it corresponded to gene expression levels regardless of tissues, we concluded that the methylation pattern is not likely relevant for the tissue-specific gene expression pattern reported in Figure 1.

We plan to:

• Ask whether there is a correlation between the gene clusters generated in Figure 1 and the DNA methylation patterns identified in Figure 4. *- At least for the trees that are shown in the main figures it would be great to show support values. *

We thank the reviewer for this request.

We plan to:

• Add full Supplementary information regarding the support values in Supplemental Files for all the trees present in the main Figures. Reviewer 3 raised the following points that we are planning to address:

*- It would be great to see more data on cephalopod TET and MBD structure. For example, it would be interesting to know whether octopus TETs have a CxxC domain or whether MBD proteins harbor functional 5mC - binding domains. *

We agree that it would be of interest to examine the conservation of TET genes to expand upon the initial analysis by Planques et al 2021 showing that O. bimaculoides have one TET homolog, one MBD4 homolog and one MBD1/2/3 homolog. Detailed analysis of MBD4 protein has been already performed in de Mendoza et al. 2021 by using the protein sequence of O. vulgaris, as the MBD4 gene in the O. bimaculoides genome appears truncated.

We plan to:

• add the PFAM domain analysis for TET proteins This will be added as a new figure panel.
• Update the text to include the reference to the identification of MBD4/MECP2 as the invertebrate homologs of vertebrate MBD4. *- Even though RRBS provides limited insight into DNA methylation patterns, the authors could have done more to explore read-level 5mC information. For example, by studying single reads, the authors could deduce the numbers of fully methylated, unmethylated or partially methylated reads. Such analyses might provide valuable insight into potentially different modes of epigenetic inheritance in different tissues i.e are there tissues that favor fully methylated or unmethylated stretches of DNA vs tissues that favor partial methylation? *

We think this is a really interesting point. This has been partially addressed in a previous work (de Mendoza et al., 2021) which found limited to no partially methylated reads in whole-genome bisulfite sequencing from O. bimaculoides brain.

We plan to:

• Use Proportional Discordant Reads on the RRBS data we generated from 30 dpf hatchlings to assess the presence of discordant methylated reads across different tissues to assess possible different modes of epigenetic inheritance. We will use “WSHPackage” in R: https://academic.oup.com/nar/article/48/8/e46/5760751?login=true https://github.com/MPIIComputationalEpigenetics/WSHPackage/blob/master/vignettes/WSH.md#x1-50003.1 This will be added as a new figure panel.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer 1 raised the following points that we have already addressed:

We addressed all the comments raised by this Reviewer by revising the text, fixing references, typos and improving clarity.

Reviewer 2 raised the following points that we have already addressed:

We addressed all the minor Comments raised by this Reviewer regarding spelling errors and Supplementary Figures.

- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome.

We have now provided this data and new Supplemental Table 1 (refereed in the text as Table S1).

*- It is very exciting to see methylation of gene bodies and some correlation to their expression levels, but the authors may need to include a disclaimer that the methylation of TEs may go undetected due to the gapness of the genome. In fact, the authors may try to map their data onto a somewhat closely related Octopus sinensis genome sequenced with long reads available at NCBI to confirm overall pattern. It is likely though that due the evolutionary distance only gene bodies will have mapping. *

The thank the reviewer for this suggestion and we included a sentence in the Result session indicating that methylation of TEs may go undetected due to the poor annotation of the octopus genome.

*- The statistical reasoning (and methodology) behind how clusters in Figures 1 and 4 were defined is unclear. In particular, in Figure 4, it seems that the authors had asked the program to give four clusters in total - why was this number chosen? It seems that using the same generic clustering approach as in Figure 1 may benefit or confirm the results in Figure 4. *

We clarified the rationale in the Material and Methods session to describe the bioinformatic analysis. We will put the full code used in the manuscript in our GitHub page (https://github.com/SadlerEdepli-NYUAD/) to have a more comprehensive understanding of the Method used.

Reviewer 3 raised the following points that we have already addressed:

We addressed all the minor comments in the text and figures raised by this reviewer regarding typos and clarity.

*- There is little info on the generated 5mC data. To bolster its value as a resource, the manuscript should have a link to the table describing RRBS metrics. This should include: non-conversion rates, numbers of sequenced and mapped reads, read length and other info that the authors deem useful. *

We have now provided this data in a new Supplemental Table 1 (refereed in the text as Table S1).

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

Reviewer 1 raised the following points that we are not planning to address:

*- The newly sequence RNA-seq samples are using a ribodepletion protocol (RiboZero) while the other ones are using a polyA selection. This might be a slight problem to compare them quantitatively. Actually in the Figure 1, all 4 newly generated samples group together in the hierarchical clustering. *

We acknowledge the reviewer’s point here and agree that heterogeneity in library prep and batch is a common issue when comparing public available with newly generated datasets. This could account for the clustering of the Ribosomal RNA depleted (i.e. RiboZero) from polyA selected RNA libraries. While this could potentially introduce bias, we do not believe that it substantially alters any of the main findings or the interpretations of this data. Our purpose for carrying out the cluster analysis of transcriptomic data from multiple tissues was to identify distinct gene patterns that defined different tissue types. This was accomplished regardless of the potential confounding variable introduced by different library preparations. In addition, we used TMP which seems to help in the comparison across different samples when used for qualitative analysis such as PCA and cluster analysis (Zhao et al. 2020; DOI: http://www.rnajournal.org/cgi/doi/10.1261/rna.074922.120). Therefore, even if not ideal we think that this approach is still valuable.

*- I am not so sure about the way the authors used z-score normalized logTPMs and applied hierarchical clusters, this most likely would not fully alleviate the impact of expression level on the outcome compared to more advanced form of normalization and clustering. *

We agree with the reviewer that applying z-score or a logTPMs normalization would not fully resolve the technical variance in the direct comparison of libraries generataed with different RNA selection methods. We did not apply z-score on logTPMs but these 2 methods were applied separately: z-score on TPMs in Figure 1B to define the gene clusters and log2(TPM+1) in Figure 4E. We have clarified the text to reflect this.

*- I am not convinced that differences in western blot for histone modification could really provide a clear insight into their regulatory role. *

We agree with the reviewer that Western blotting for histone modifications does not provide deep insight into their regulatory role. However, this is the first description of these marks in any cephalopod, and we believe that reporting a finding from experimental evidence is important, even if the result is aligned with the existing paradigm. Moreover, the marked difference in levels of distinct histone marks across tissues supports the hypothesis that they play a regulatory role. We observed this in mice where difference abundance in western blot correspond to different abundance and enrichment also by ChIP-seq (Zhang et al., 2021 DOI: https://doi.org/10.1038/s41467-021-24466-1). Considering the limited tools available in this species, we still consider this an important finding.

Reviewer 2 raised the following points that we are not planning to address:

*- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome. I also wonder how much the gap-richness of the genome may affect the overall methylation estimate. If assembly permits, would it make sense to limit the sampled sites to areas where no flanking gaps are present (and sufficient scaffold length is available, maybe excluding very short scaffolds)? *

We added all the statistical values regarding the RRBS in a NEW Supplemental Table 1. We used a single base pair analysis approach (not tiling windows), so the data we extracted is not biased by the length of the scaffolds. This is confirmed by the fact that the DNA methylation value obtained in our RRBS data matches the findings observed in Whole Genome Bisulfite Sequencing (WGBS). Moreover, global DNA methylation values assessed by Slot blot analysis as a technique independent from genome assembly confirmed what observed with RRBS.

I think that the is possible and we could see it at more progressive schools. It is just like the banking industry, one bank got rid of overdraft fees and slowly we are seeing major banks do away with the fees as well. All it takes is for one brave institution and another to change the landscape of things.

• Fast Car is a song written and performed by American folk rock singer Tracy Chapman in 1986.
• As quoted by Tracy Chapman in an interview in 1986 with Canadian radio station CIDR, “I think that it was a song about my parents…..a new life together and my mother was anxious to leave home. [They]tried to make a life for themselves and it was very difficult going….my mother didn’t have a high school diploma and my father was a few years older….hard for him to create the ideal life that he wanted…in a sense I think they came together thinking that they would have a better chance of making it”
• The song became popular around the 1980s after Tracy Chapman performed it at the 70th birthday Nelson Mandela tribute.
• The song is narrative, and tells the story from the point of view of a woman who wants to get out of her hometown to leave her problems behind, such as taking care of her alcoholic father, after her mother left the family. She finds the addressee, a lover with the titular fast car, and sees it as an opportunity to get out of her hometown at last and start life anew. However, the reality is far from that, as the song continues, and reveals how even after moving to the city, she is still working a dead-end job and living in a homeless shelter, while her lover is unemployed. It parallels her own parents’ relationship, as her lover becomes a father and an alcoholic, and their relationship grows rocky. Eventually she decides that she does not need him anymore, and tells him to either clean up his act or leave.
• Hope and resilience are major themes in this song, as the persona continues to persevere despite the many challenges that are thrown at her in life, from her upbringing to her newfound problems.However, she remains optimistic and presses on, and we find hope in how she eventually achieves independence and supposedly leaves her problems behind.
• One can interpret the title ‘Fast Car’ as a symbol of escapism. However, as the song goes on, it is revealed that getting out of a situation is far from easy and even if you start anew, there will always be problems and challenges in life to overcome. Additionally, sometimes, you will find that people who are close to you make irresponsible and selfish decisions which affect you .In order to carry on a meaningful and purposeful life, you may have to give up on them. To prevent this from happening, it is essential to become independent and not place all your faith in someone else, just as the persona has done in Fast Car.

I think that this is so important. Being aware of the different privacy or security issues that may arise when using different technologies is something that everyone should be aware of in order to be safe. We have learned that not all tools are safe and there are many times where passwords and personal information can be stolen. Also, students may not actually fully research the privacy of a website so having a parent also research the tool/site can help ensure that all information will be safe.

It is so easy to click "accept" online which may be sharing your information and we often don't even think twice and should be paying closer attention to this.

I think this is a huge "however" when looking at how helpful an accessibility statement is. If there are huge gaps then the statement is not as helpful but also if it is quite extensive you have to remember that it is still the company writing it up, and they may be lacking in knowledge. I think one day regulations will be put in place for the VPAT but that is not where we are yet.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Formation of tubes in a developing organism may arise from the closure of a pre-existing polarized epithelium or from de novo polarization and cavity formation in group of dividing cells. The concept of apical membrane initiation site (AMIS) refers to the fact that polarity proteins as PAR3 accumulate at a point where the apical membrane will be created. This accumulation occurs as early as the two cell stage. Previous reports have demonstrated the importance of the division process in defining this AMIS, however, in the present work the authors in vitro 3D cultures of mESC to report a mitosis independent mechanism that creates an AMIS, induces the polarization of groups of two or more cells, and permits the formation of a central cavity. The report shows that the mechanism is fully dependent on the polarized accumulation of E-cadherin at the cell membrane in contact with the other cells. Moreover, the mechanism does not require mitosis or interaction with the extracellular matrix.

Major comments:

The main objective of the work is to demonstrate that AMIS creation and cavity formation can be mitosis independent and that it is dependent on the accumulation of E-cadherin at the midline between two cells in contact. To demonstrate these objectives, the authors perform 3D cultures of mESC. To rule out the requirement of mitosis the authors perform cultures that are treated with mitomycin C and the purify single cells that are cultured again. The authors show time-laps experiments demonstrating that individual cells that do not dived create an AMIS when they contact one to each other. With this cultures they demonstrate that the process does not require an interaction with ECM (provided by the matrigel) but requires E-cadherin, to demonstrate, that they use E-cadherin KO cells (the same line where E-cadherin has been deleted). The work is well written and the objectives very clear. The technology used and the experiments done are adequate and sufficient to accomplish the proposed objectives and the results obtained clearly support the conclusions reached. The methods are well explained and transparent to be reproduced elsewhere and the number of replicas and the statistical methods applied seem corrects to me, although I am just a biologist, not a mathematician. Although the objectives of the work, that are: to demonstrate that AMIS formation can be independent of mitosis and that AMIS requires E-cadherin, there are parts of the results that could be farther studied or at least discussed more thoroughly. Firstly, the authors show that in non-dividing cells an AMIS is formed at the first contact site between the two cells, they also show that in the absence of E-cadherin the cell maintains the polarization of centrioles and Golgi apparatus, in spite that no AMIS is formed, this indicates that the deposition of E-cadherin at the midline membrane is part of a more global polarization event that most likely is initiated by the a directional activity of the Golgi apparatus that may direct the delivery of mature E-cadherin in that particular direction, initiating or maintaining the basis for an AMIS, since recent work (already cited in the manuscript) has demonstrated the importance of cadherin maturation for polarity establishment and maintenance (Herrera et al, 2021), the actual results should be farther discussed in this context. Secondly, it was previously shown that in different epithelia, upon cell-cell contact, the aPKC complex (that includes Par3 and Par6) is recruited early to the contact site where with the participation of Cdc42, aPKC is activated generating an initial spot-like adherent junction (AJs) (Suzuki et al., 2002). In that case it is thought to be mediated by a direct interaction between the first PDZ domain of PAR-3 and the C-terminal PDZdomain-binding sequences of immunoglobulin-like cell adhesion molecules: JAM-1 and nectin-1/3 (Fig. 3) (Ebnet et al., 2001; Itoh et al., 2001; Takekuni et al., 2003). Thus it wold be interesting to know if AMIS formation in absence of cell division depends on JAM-1 or nectin and whether JAM-/Nectin signalling is sufficient to initiate the Golgi and centriole polarization and which is the mechanism governing it.

Minor comments:

As I mentioned before, the paper is well presented and very clear, yes it is simple, but simple is always better, no complicated graphics or letterings, thank you. Although in my opinion the work is very well written, I have to admit that I am not qualified to evaluate the literary style of the work since English is not my mother tongue, also I have not reviewed typographical errors since I think that is the work of the editorial, not of scientific reviewers. Please include the full reference of all the antibodies used, including the company and not just the catalog number

Quoted references:

Ebnet, K., Suzuki, A., Horikoshi, Y., Hirose, T., Meyer Zu Brickwedde, M. K., Ohno, S. and Vestweber, D. (2001). The cell polarity protein ASIP/PAR-3 directly associates with junctional adhesion molecule (JAM). EMBO J. 20, 3738-3748.

Itoh, M., Sasaki, H., Furuse, M., Ozaki, H., Kita, T. and Tsukita, S. (2001). Junctional adhesion molecule (JAM) binds to PAR-3: a possible mechanism for the recruitment of PAR-3 to tight junctions. J. Cell Biol. 154, 491-497.

Takekuni, K., Ikeda, W., Fujito, T., Morimoto, K., Takeuchi, M., Monden, M. and Takai, Y. (2003). Direct binding of cell polarity protein PAR-3 to cell-cell adhesion molecule nectin at neuroepithelial cells of developing mouse. J. Biol. Chem. 278, 5497-5500

Suzuki, A., Ishiyama, C., Hashiba, K., Shimizu, M., Ebnet, K. and Ohno, S. (2002). aPKC kinase activity is required for the asymmetric differentiation of the premature junctional complex during epithelial cell polarization. J. Cell Sci. 115, 3565-3573.

Significance

The paper describes for the first time that contrary to what was previously believed an AMIS can be generated without a cell division. This is very important because it opens the possibility that the mechanisms that originate the biologic cavities are in fact not really how we believed. The work is of interest of all cell biology scientists, specially working in developmental biology, cancer research.

My particular field of expertise is cell biology and signaling, always applied to particular events as nervous system development or cancer, in particular I am interested in Wnt/b-catenin and Sonic Hedgehog pathways.

The speaker foreshadows about how their relationship would undoubtedly end. The word ‘story’ symbolises their relationship, which both may have a start and an end. The different parts of a story can resemble the life in a relationship with someone you love. The ‘introduction’ resembles how they meet, the ‘rising action’ resemble how they have taken things further and started to date, the ‘climax’ can resemble the fights or arguments that start to take place inferred from the previous line. And it carries on until the ‘conclusion’, where they separate. From this, I can reflect on the theme of relationship as how it may only last for a brief moment, and shatter, which can hurt so much so it is hard to let go. I feel proud for the speaker even though he fails repetitively to let go but tries his best to do so

Reviewer #1 (Public Review):

In this manuscript, Yang et al. trained monkeys to play the classic video game Pac-Man and fit their behavior with a hierarchical decision making model. Adapting a complex behavior paradigm, like Pac-Man, in the testing of NHP is novel. The task was well-designed to help the monkeys understand the task elements step-by-step, which was confirmed by the monkeys' behavior. The authors reported that the monkeys adopted different strategies in different situations, and their decisions can be described by the model. The model predicted their behavior with over 90% accuracy for both monkeys. Hence, the conclusions are mostly supported by the data. As the authors claimed, the model can help quantify the complex behavior paradigm, providing a new approach to understanding advanced cognition in non-human primates. However, several aspects deserve clarification or modification.

1. The results showed that the monkeys adopted different strategies in different situations, which is also well described by the model. However, the authors haven't tested whether the strategy was optimal in a given situation.

Our approach to analyze monkeys’ behavior is not based on optimality. Instead, we centered around the strategies and showed that they described the monkeys’ behavior well. The model and its fitting process does not assume the monkeys were optimizing for something. Nevertheless, the fitting results suggested that the strategies that the monkeys chose were rational, which suggests validity of our model. As we have pointed out above, optimality is hard to define in such a complex game. In particular, most of the game is about collecting pellets, strategies that are only used in a small portion of the game can be ignored when searching for optimal solutions. We feel that further analyses on the issue of optimality would dilute the center message of the paper and choose not to include them here.

According to the results, the monkeys didn't always perform the task in an optimal way, as well. Most of the time, the monkeys didn't actively adopt strategies in a long-term view. They were "passively" foraging in the task: chasing benefit and avoiding harm when they were approached. This "benefit-tending, harm-avoiding" instinct belongs to most of the creatures in the world, even in single-cell organisms. When a Paramecium is placed in a complex environment with multiple attractants and repellents, it may also behave dynamically by adopting a linear combination of basic tending/avoiding strategies, although in a simpler way. In other words, the monkeys were responding to the change of environment but not actively optimizing their strategy to achieve larger benefits with fewer efforts. The only exception is the suicides. Monkeys were proactively taking short-term harms to achieve large benefits in the future.

One possible reason is that the monkeys didn't have enough pressure to optimize their choices since they will eventually get all the rewards no matter how many attempts they make. The only variable is the ghosts. Most of the time, the monkeys didn't really choose between different targets/ strategies. They were making choices between the chasing order of the options, but not the options themselves. It is similar to asking a monkey to choose either to eat a piece of grape or cucumber first, but not to choose one and give up the other one. A possible way to avoid this is to stop the game once the ghost catches the Pac-Man or limit each game's time.

The game is designed to force the players to make decisions quickly to clear the pellets, otherwise the ghosts would catch Pac-Man. Even in the monkey version of the game where the monkeys always get another chance, Pac-Man deaths lead to long delays with no rewards. They will not be able to complete the game if they do not actively plan their route, especially in the late stage when they must reach the scarcely placed dots while escaping from the ghosts. In addition, we provided additional rewards when a maze is cleared in fewer rounds (20 drops if in 1 to 3 rounds; 10 drops if in 4 to 5 rounds; and 5 drops if in more than 5 rounds), which added motivation for the monkeys to complete a game quickly.

The monkeys’ behavior also suggested that they did not just adopt a passive strategy. Our analyses of the planned attack and suicide behavior clearly demonstrated that the monkeys actively made plans to change the game into more desirable states. Such behavior cannot be explained with a passive foraging strategy.

2. It is well known that the value of an element is discounted by time and distance. However, in the model, the authors didn't consider it. A relevant problem will be the utility of the bonus elements, including the fruits and scared ghosts. Their utilities were affected not only by their value defined by the authors but also by effects, including their novelty and sense of achievement when they were captured, as the ghosts attracted relatively much more attention than the other elements (considering the number is 2 for them, see in figure 3E).

These are good points, and our strategies could be built with more complexity to account for other potential factors. However, we focused our investigation on how to account for monkeys’ behavior with a set of strategies. A set of simple strategies with a small number of parameters would make a strong argument.

Using a complex game such as Pac-Man allows us to investigate all of these interesting cognitive processes. We can certainly look at them in the future.

3. The strategies are not independent. They are somehow correlated to each other. It may result in, in some conditions, false alarming of more strategies than the real, as shown in figure 2A.

We have computed the Pearson correlations between the action sequences chosen with each basis strategy within each coarse-grained segment determined by the two-pass fitting procedure. As a control, we computed the correlation between each basis strategy and a random strategy, which generates action randomly, as a baseline. Most strategy pairs' correlations were lower than the random baseline. The results were now included in Supplementary (Appendix Figure 3).

Sometimes two strategies may give exactly the same action sequence in a game segment. To deal with this problem, now we include an extra step when we fit the model to the behavior, which was described in Methods:

“To ensure that the fitted weights are unique (Buja et al., 1989) in each time window, we combine utilities of any strategies that give exactly the same action sequence and reduce multiple strategy terms (e.g., local and energizer) to one hybrid strategy (e.g., local+energizer). After MLE fitting, we divide the fitted weight for this hybrid strategy equally among the strategies that give the same actions in the time segments.“

Moreover, as the reviewer correctly reasoned, correlations between the strategies would yield possibly more strategies. However, our finding is that the monkeys were using a single strategy most of the time. This possible false alarm would go against our claim. Our conclusions stand despite the strategy correlations.

It is hard to believe that a monkey can maintain several strategies simultaneously since it is out of our working memory/attention capacity.

Exactly, and we are among the first to quantitatively demonstrate that the monkeys’ mostly relied on single strategies to play the game.

Reviewer #2 (Public Review):

In this intriguing paper, Yang et al. examine the behaviors of two rhesus monkeys playing a modified version of the well-known Pac-Man video game. The game poses an interesting challenge, since it requires flexible, context-dependent decisions in an environment with adversaries that change in real time. Using a modeling framework in which simple "basic" strategies are ensembled in a time-dependent fashion, the authors show that the animals' choices follow some sensible rules, including some counterintuitive strategies (running into ghosts for a teleport when most remaining pellets are far away).

I like the motivation and findings of this study, which are likely to be interesting to many researchers in decision neuroscience and animal behavior. Many of the conclusions seem reasonable, and the results are detailed clearly. The key weakness of the paper is that it is primarily descriptive: it's hard to tell what new generalizable knowledge we take away from this model or these particular findings. In some ways, the paper reads as a promissory note for future studies (neural or behavioral or both) that might make use of this paradigm.

I have two broad concerns, one mostly technical, one conceptual:

First, the modeling framework, while adequate, is a bit ad hoc and seems to rely on many decisions that are specific to exactly this task. While I like the idea of modeling monkeys' choices using ensembling, the particular approach taken to segment time and the two-pass strategy for smoothing ensemble weights is only one of many possible approaches, and these decisions aren't particularly well-motivated. They appear to be reasonable and successful, but there is not much in the paper to connect them with better-known approaches in reinforcement learning (or, perhaps surprisingly, hierarchical reinforcement learning) that could link this work to other modeling approaches. In some ways, however, this is a question of taste, and nothing here is unreasonable.

Thanks for the suggestion. In the new revision, we include a linear approximate reinforcement learning model (LARL) (Sutton, 1988; Tsitsiklis & Van Roy, 1997). The LARL model shared the same structure with a standard Q-learning algorithm but used the monkeys’ actual joystick movements as the fitting target. The model, although computationally more complex than the hierarchical mode, achieves a worse fitting performance.

Second, there is an elision here of the distinction between how one models monkeys' behavior and what monkeys can be said to be "doing." That is, a model may be successful at making predictions while not being in any way a good description of the underlying cognitive or neuroscientific operations. More concretely: when we claim that a particular model of behavior is what agents "actually do," what we are usually saying is that (a) novel predictions from this model are born out by the data in ways that predictions from competing models are not (b) this model gives a better quantitative account of existing data than competitors. Since the present study is not designed as a test of the ensembling model (a), then it needs to demonstrate better quantitative predictions (b).

We concede to the point that our model, while fitting to the behavior well, does not directly prove that the monkeys actually solved the task in this way. The eye movement and pupil dilation analyses partly addressed this issue, as their results were consistent with what one would expect from the model. We also hope future recording experiments will provide neural evidence to support the model.

But the baselines used in this study are both limited and weak. A model crafted by the authors to use only a single, fixed ensemble strategy correctly predicts 80% of choices, while the model with time-varying ensembling predicts roughly 90%. This is a clear improvement and some evidence that *if* the animals are ensembling strategies, they are changing the ensemble weights in time. But there is little here in the way of non-ensemble competitors. What about a standard Q-learning model with an inferred reward function (that is, trained to replicate monkeys' data, not optimal performance). The perceptron baseline as detailed seems very poor as a control given how shallow it is. That is, I'm not convinced that the authors have successfully ruled out "flat" models as explanations of this behavior, only found that an ensembled model offers a reasonable explanation.

We hope the new LARL model provides a better baseline control as a flat model. It performs better than the perceptron, yet much worse than our hierarchical model. Yet, we must point out that any hierarchical models can be matched in performance with a flat model in theory (Ribas-Fernandes et al., 2011). The advantage of hierarchical models mainly lies in their smaller computational cost for efficient planning. Even in a much simpler task such as a four-room navigation task, a hierarchical model can plan much faster than a flat model, especially under conditions with limited working memory (M. Botvinick & Weinstein, 2014). Our Pac-Man task contains an extensive feature space while requiring real-time decision-making. The result is that a reasonably performing flat model would go beyond the limits of the cognitive resources available in the brain. Even for a complex flat model such as Deep Q-Network (it can be considered to be similar a flat model since it does not explicitly plan with temporal extended strategies (Mnih et al., 2015)), the game performance is much worse than a hierarchical model (Van Seijen et al., 2017). The performance of the monkeys was unlikely to be achieved with a flat model. In addition, we trained the monkeys by introducing the game concepts gradually, with each training stage focusing on certain game aspects. The training procedure may have encouraged the monkeys to generalize the skills acquired in the early stages and use them as the basis strategies in the later training stages when the monkeys faced the complete version of the Pac-Man task.

Reviewer #3 (Public Review):

Yang and colleagues present a tour de force paper demonstrating non-human primates playing a full on pac-man video game. The authors reason that using a highly complex, yet semi controlled video game allows for the analysis of heuristic strategies in an animal model. The authors perform a set of well motivated computational modeling approaches to demonstrate the utility of the experimental model.

First, I would like to congratulate the authors on training non-human primates to perform such a complex and demanding task and demonstrating that NHP perform this task well. From previous papers we know that even complex AI systems have difficulty with this task and extrapolating from my own failings in playing pac-man it is a difficult game to play.

Overall the analysis approach used in the paper is extremely well reasoned and executed but what I am missing (and I must add is not needed for the paper to be impactful on its own) is a more exhaustive model search. The deduction the authors follow is logically sound but builds very much on assumptions of the basic strategy stratification performed first. This means that part of the hierarchical aspect of the behavioral strategies used can be attributed to the heuristic stratification nature of the approach. I am not trying to imply that I do not think that the behavior is hierarchically organized but I am implying that there is a missed opportunity to characterize that hierchical'ness (maybe in a graph theoretical way, think Dasgupta scores) further.

All in all this paper is wonderful. Congratulations to the authors.

We thank the reviewer for the encouraging comments. We have included a new flat model in the new revision for comparison against our hierarchical model and discussed other experimental evidence to support our claim.

While multiple readings of a text in antiquity may have been rarer, due to the cheap proliferation of books, one can more easily "blaze a trail" through their reading to make it easier or quicker to rebuild context on subsequent readings.

Look at history of reading to see which books would have been more likely re-read, particularly outside of one's primary "area" of expertise.

Link to the trails mentioned by Vannevar Bush in As We May Think.

Author Response:

Reviewer #1:

In this work Warneford-Thomson et al. developed an approach for surveillance screening for SARS-CoV-2, which involves the isothermic amplification of a region of the SARS-CoV-2 nucleocapsid gene using RT-LAMP, followed by detection with deep sequencing. High-throughput and cost effectiveness is achieved by two sets of barcodes that allow up to about 37,000 samples to be combined into one deep sequencing run. Moreover, the authors demonstrate they can do the detection from saliva collected on paper, which should make sample collection easier.

The main strength of the work lies in the technical aspects, including setting up multiple controls such as a detection of a human gene, and multiplexing with detection of the influenza virus.

The main weakness is that there are multiple other papers either published or archived that use RT-LAMP for SARS-CoV-2 detection, deep sequencing for SARS-CoV-2 detection, or both. These are cited in the current work, which is very well written and presented. Whether this method is better than the others which have the same aim of developing cost-effective and high-throughput detection is not conclusively demonstrated as only 8 clinical saliva samples are examined.

We do not wish to claim that our method is better than the others. We think it has advantages and disadvantages and certainly it should be further optimized before scaling it up to population level. We have added these considerations to the text (lines 376–80).

Furthermore, the requirement for deep sequencing and batching many samples for cost-effectiveness will, in most situations, greatly increase turn-around time. This will make surveillance much less effective, since by the time results are fed back, the asymptomatically infected individual would have had more opportunity to transmit the infection to others.

We argue that time from sample to result is a mostly a function of logistics and not of the method. With proper set ups the time from sample collection to results could be < 16 hours, which would be compatible with population-level surveillance. We added these considerations to the text.

However, the deep sequencing step may be very useful for surveillance of circulating SARS-CoV-2 spike sequences to detect emerging variants within a population, provided this method can be modified to do it.

We agree and we mention this possibility in the discussion.

Reviewer #2:

In 'COV-ID: A LAMP sequencing approach for high-throughput co-detection of SARS-CoV-2 and influenza virus in human saliva', Warneford-Thomson et al. present a novel methodology to perform large numbers of COVID-19 tests in parallel. Their approach takes unprocessed saliva and requires only a small number of experimental steps before the results are sequenced overnight to generate many thousands of results. This straightforward experimental design should allow the protocol to be expanded to a number of settings where population-level monitoring is required in order to contain outbreaks and reduce transmission. In this paper, the authors demonstrate the efficacy of their approach and perform a large number of benchmarking experiments to quantify its sensitivity, specificity and limitations of detection. They are able to detect artificially created infections (spike-ins) with as low as 5 virions per µL and all clinically available samples agreed with the standard RT-qPCR test. This method can detect both SARS-CoV-2 and Influenza infection and can also be applied to saliva samples which have been collected on filter paper, a strategy which will further simplify the testing regime.

The authors have spent much time testing this approach but these have largely been limited to analysing artificially created infections. The only results which were obtained were from eight clinically derived samples which are presented in Figure 2E. Although all results from this approach agreed with the standard clinical test this is a small number of tests compared to the total number of tests which are reported in this paper. It is also only a small proof-of-principle experiment to justify a quick rollout of this technology.

We have now performed COV-ID on 120 additional patient samples (new Figure 2-figure supplement 2). These new results are described in the text.

The potential for this technology to perform rapid, high-throughput SARS-CoV-2 testing alongside the potential for very low sequencing costs (Figure 4G) is impressive. It is noted in the manuscript that this will require 96 unique barcodes but only 32 are tested here. All but three of these 32 work for the SARS-CoV-2 N2 primers and required STATH control but how will the remaining 67 primers be derived (i.e. is it realistic that this can be made to work to deliver the promise of this approach)?

The current COV-ID patient barcodes are 5 base pairs long. This allows for 4^5 = 1,024 combinations. Out of an abundance of caution, we excluded barcodes with homology to the reverse complement of the RT-LAMP primers used in any of the experiments (i.e. primers for SARS-CoV-2 N2, STATHERIN, ACTIN, and influenza virus) and then selected a set of 32 with Hamming distances of at least 2 from each other. This is now described more in detail in the methods.

Regarding the numbers, out of 1,024 5-bp barcodes, 404 were removed due to homology, leaving 620. Of these, we could find at least 163 with Hamming distance ≥ 2 from each other. Even with a substantial failure rate, this should allow for 96 working barcodes. If we had only considered clashes with N2 and STATHERIN primers, the number of available barcodes would be substantially higher.

Overall, this is an interesting paper which has very clear real-world application to helping to defeat the ongoing COVID-19 pandemic, but some extra validations are needed to fully demonstrate its performance in clinical and/or public health settings.

Author Response:

Reviewer #1:

The experiments are well designed, generally well controlled, and carefully conducted, and are thoughtfully and appropriately discussed. The authors make conclusions that are well supported by their results.

When describing the aptamer knockdown of the PPS, the authors explain that the western blot was too noisy for monitoring the knockdown, which is frustrating for the reader and must have been frustrating for the authors. The authors instead counter-intuitively use qRT-PCR to monitor the transcript abundance of the PPS transcript in the aptamer system - this aptamer system is thought to be a modifier of protein, not transcription or transcript abundance. The authors describe that this has been seen once before (using aptamer knockdown of PfFis1), and the authors of that study speculate that the TetR-DOZI aptamer might be degrading the target mRNA. This is a plausible explanation, but it isn't quite clear from the description how this experiment was performed. The authors explain that the knockdown parasites grew normally for three days, but the parasites may be becoming sicker over this period. It's therefore possible that the decrease in PPS mRNA abundance is a product, rather than a cause of the growth defect. Sick or dying parasites could plausibly impact the PPS differently to the two chosen controls, particularly since both control genes chosen have substantially longer half-lives than the PPS mRNA (according to the Shock and DeRisi datasets). I therefore I suggest that this experiment be performed in an IPP rescue scenario (where the parasites aren't dying) with biological replicates. There is no explanation of the replicates here, but the error bars in 6C are implausibly small for real biological replicates.

To address these concerns, we have added western blot data showing down-regulation of PPS expression in -aTc +IPP conditions, relative to a loading control. We have also repeated the growth assay and RT-qPCR experiment (in biological triplicate) under IPP-rescue conditions. Parasites samples harvested on day 3 of the IPP-rescue assay were analyzed by RT-qPCR and show reduced PPS mRNA abundance that is similar to (and slightly lower than) that observed without IPP supplementation. This similarity is not surprising to us, since the day 3 harvest in the original growth assay (without IPP) was 3 days before observing a parasite growth defect in -aTc conditions. With respect to the mechanism of transcript loss in the aptamer/TetR-DOZI system, the fate of transcripts in this system has not been investigated in depth. However, DOZI is believed to target bound mRNA to P-bodies, which are a known site of mRNA degradation in cells. We have unpublished data with multiple parasite proteins tagged with the aptamer/TetR-DOZI system. In all cases, we see strong reductions in mRNA abundance in -aTc conditions, suggesting that such decreases are a general property of this knockdown system.

Line 342 "These results directly suggest that apicoplast biogenesis specifically requires synthesis of linear polyprenols containing three or more prenyl groups." - I think that this might be overinterpreting those results - there could be a number of different reasons why polyprenols of different sizes do or don't rescue, including different solubility, diffusion, availability of transporters, predisposition to break down to useable subunits. Perhaps this needs a caveat.

We have modified the text here to remove “directly” and to acknowledge uncertainty in beta-carotene uptake: “Although it is possible that β-carotene is not taken up efficiently into the apicoplast, rescue by decaprenol, which is similar in size and hydrophobicity to β-carotene, suggests that apicoplast biogenesis specifically requires synthesis of linear polyprenols containing three or more prenyl groups.” We have also added the statement that “this hypothesis is further supported by additional results described in the next two sections”, referring to our identification of an apicoplast-targeted polyprenyl synthase.

Line 361 " the cytosolic enzyme, PF3D7_1128400" - I don't think we know the localisation of this protein based on the published data. The Gabriel et al study makes it clear the protein isn't apicoplast or mitochondrial, but it is punctate at stages in a pattern that doesn't look to me to be a straightforward cytosolic localisation (and the original authors don't describe it as cytosolic).

We agree that the localization of PF3D7_1128400 requires further investigation. The Gabriel study, which (surprisingly) is the only study we found that has examined localization of this protein by microscopy, observed diffuse signal in trophozoites consistent with cytoplasmic localization, in additional to focal, punctate signals in schizonts that were distinct from the apicoplast or mitochondrion. The authors described their results as, “Analysis by fluorescence microscopy of live parasites confirms expression along the intra-erythrocytic cycle and shows FPPS/GGPPS localization throughout the cytoplasm and also forming spots, which increase in number as parasites mature from trophozoite to schizont stages.” For simplicity we referred to FPPS/GGPPS localization as cytoplasmic but agree that available data suggest more a complex localization that requires further studies to understand. We have modified the text to indicate that available data suggests a complex cellular distribution that includes both the cytoplasm and additional sub-cellular foci outside the apicoplast and mitochondrion.

Line 423 "with strong prediction of an apicoplast-targeting transit peptide but uncertainty in the presence of a signal peptide". I don't think this describes well the bioinformatic analysis of the N-terminus. Although the experimental data are convincing that this is an apicoplast-targeted protein, bioinformatically this would not be predicted as an apicoplast protein. There is no obvious signal peptide, and "uncertainty" is too vague a descriptor. None of the versions of signalP, nor psort, predict this as possessing a signal peptide (which by definition means that PlasmoAP absolutely rejects it), and there is no obvious hydrophobic segment at the N-terminus that we would normally expect of a signal peptide. The toxoplasma hyperlopit doesn't suggest that the Toxoplasma orthologue is apicoplast, and the protein isn't found in the Boucher et al apicoplast proteome. This is somewhat of a mystery. It doesn't diminish the solid localisation data, with the excellent complementary data from IFA as well as the doxycycline+IPP experiment, but it should be pointed out clearly that this localisation isn't to be expected from the sequence analysis.

We thank the reviewer for this perspective and agree that SignalP is unable to identify a signal peptide at the N-terminus of PPS. We have modified the text to remove our description of “uncertainty” and explicitly state that SignalP is unable to identify a canonical signal peptide at the N-terminus of PPS.

We note that multiple proteins detected in the Boucher et al. apicoplast proteome also lack an identifiable signal peptide by SignalP yet are clearly imported into the apicoplast. These proteins include the key MEP pathway enzymes DXR (PF3D7_1467300) and IspD (PF3D7_0106900), holo ACP synthase (PF3D7_0420200), FabB/F (PF3D7_0626300), and the E1 subunit of pyruvate dehydrogenase (PF3D7_1446400). Thus, apicoplast import despite lack of identifiable signal peptide by SignalP is not unique to PPS but general to multiple (if not numerous) apicoplast-targeted proteins. These observations suggest to us that protein N-termini in Plasmodium can have sequence properties compatible with ER targeting that are broader and more heterogenous than other eukaryotic organisms that comprise the training sets upon which SignalP is currently based. It remains a future challenge to fully understand these properties.

With respect to the lack of PPS detection in the Boucher et al. apicoplast proteome, PPS appears to have a very low expression level and unusual solubility properties that require overnight extraction of parasite pellets in 2% SDS (or LDS) for detection. In our experience, the RIPA extraction conditions (which contained 0.1% SDS) used in the Boucher et al. study are insufficient to solubilize PPS, which may explain lack of PPS detection in their study.

To explicitly address these questions regarding PPS targeting to the apicoplast, we have added a new section to the Discussion to explore PPS targeting in the absence of a recognizable signal peptide, its unusual solubility properties and lack of detection in the Boucher et al. proteome, and planned future studies to further test, refine, and understand targeting determinants.

With respect to Toxoplasma, T. gondii appears to also express two polyprenyl synthase homologs, TGME49_224490 and TGME49_269430, that are ~30% identical (in homologous regions) to PF3D7_1128400 (FPPS/GGPPS) and PF3D7_0202700 (PPS), respectively. TGME49_224490 appears to be targeted to the mitochondrion in T. gondii (based on MitoProt and HyperLOPIT analysis), in contrast to its P. falciparum homolog, PF3D7_1128400, which localizes to the cytoplasm and other cellular foci outside the mitochondrion. TGME49_269430 does not appear to target the apicoplast in T. gondii (based on HyperLOPIT data), which contrasts with our determination of apicoplast targeting for the P. falciparum homolog, PF3D7_0202700. These differing localizations may suggest distinct cellular roles for these homologs in T. gondii compared to P. falciparum. We are also aware of a recent study (Pubmed 34896149) showing that loss of MEP pathway activity in T. gondii (due to loss of apicoplast ferredoxin) does not impact apicoplast biogenesis, in contrast to our observations in P. falciparum based on FOS treatment, DXS deletion, and PPS knockdown. These distinct phenotypes further suggest differences in isoprenoid utilization and metabolism between T. gondii and P. falciparum that remain to be understood. We have added a new section to the Discussion to address these considerations.

The section after line 344 "Iterative condensation of DMAPP with IP…", up until line 377 doesn't sit well within the section that has the heading "Apicoplast biogenesis requires polyprenyl isoprenoid synthesis". I suggest either creating a separate subheading for this material, or moving it into the start of the subsequent section "Localization of an annotated polyprenyl synthase to the apicoplast.".

We thank the reviewer for this suggestion, which we have followed. We have moved the referenced text to the beginning of the subsequent section to better align the text with that section heading.

Reviewer #2:

Minor comments:

The authors emphasize that this study reveals a previously unnoted interconnection between apicoplast maintenance and pathways that produce an output from the apicoplast to serve the cell. But is the prevailing view really that these two are separate? Isn't the interconnection already clear from many other studies and observations? E.g., the fatty acids produced inside the apicoplast provide membrane- and lipid- precursors for the rest of the cell as well as for the apicoplast itself (Botte et al., PNAS, 2013) (although not essential in Plasmodium blood stages). Other pathways that function inside the apicoplast such as the Fe-S cluster synthesis are critical to support enzymes that provide exported metabolites (e.g., IPP synthesis, IspG/H) and function in maintenance (e.g., MiaB) (Gisselberg et al., PLoSPath, 2013). Perhaps the authors could tone this conclusion down and acknowledge that maintenance and output are interconnected in other cases, which have been acknowledged in the literature.

We thank the reviewer for this perspective and agree that in Toxoplasma as well as in mosquito- and liver-stage Plasmodium there are multiple apicoplast outputs (i.e., metabolic products exported from the apicoplast) that contribute to parasite fitness, including IPP, fatty acids, and coproporphyrinogen III. To clarify, we are specifically referring to blood-stage Plasmodium in our manuscript, when heme and fatty acid synthesis are dispensable and where the prior literature has intensely focused on IPP as the key essential output of the blood-stage apicoplast and consistently stated that IPP is not required for organelle maintenance.

We agree that prior work has firmly established that apicoplast housekeeping functions (e.g., synthesis of proteins and Fe-S clusters) are required for organelle maintenance and to support IPP synthesis. However, our work is the first to demonstrate in blood-stage Plasmodium that the reverse is also true- that IPP as an essential apicoplast output is also required for organelle maintenance and that apicoplast maintenance and IPP synthesis are thus reciprocally dependent. We have modified the Discussion section to clarify these points and to explicitly acknowledge that apicoplast maintenance and other metabolic outputs may also be interdependent in Toxoplasma and other Plasmodium stages.

Could the authors elaborate more on the leader sequence predicting apicoplast localization for the PPS characterized here and discuss why it might have been missed in previous detailed study of apicoplast localised proteins (Boucher et al., PlosBiol, 2018)?

Please see our response above to Reviewer #1.

Could the authors discuss conservation of the PPS gene(s) in other Apicomplexa with (e.g., T. gondii) and without (e.g., Cryptosporidium spp.) an apicoplast? This could be relevant for other people in the field and could give further insights into the enzyme's role in apicoplast maintenance.

Please see our response above to Reviewer #1. Polyprenyl synthases are diverse enzymes that perform a variety of cellular functions, whose specific roles can differ between organisms. Although the two Plasmodium prenyl synthases show preferential homology with each of two different prenyl synthase homologs in Toxoplasma and Cryptosporidium (CPATCC_003578 and CPATCC_001801), the differing localizations of these homologs in each parasite suggest differing cellular roles. The differing dependence of apicoplast biogenesis on MEP pathway activity in T. gondii and P. falciparum and the absence of an apicoplast in Cryptosporidium further support differences in isoprenoid utilization and metabolism in these organisms. We have added a new section to the Discussion to address these considerations.

Reviewer #3:

The paper is very nicely written and was a true pleasure to read. The introduction is concise yet dense with all relevant background of our current understanding of functioning of the apicoplast in relation to IPP production and utilization. The rational of the experiments and the interpretation of the results are presented clearly and everything is discussed well in the context of the current understanding of the field. The main conclusion of the paper that isoprenoid is not solely essential for critical functions elsewhere in the cell, such as prenylation-dependent vesicular trafficking but also for apicoplast biogenesis via its processing by an essential polyprenyl synthase conserved with plants and bacteria is well substantiated and very exciting. The authors demonstrate an equally beautiful and clever use of available and newly generated genetic mutants in combination with complementary pharmacological interventions and metabolic supplementation. There are no true major weaknesses that could jeopardize the conclusions or change the interpretation of the results. However, the authors do consistently perform statistical analyses on data obtained from individual cells obtained in no more than two independent experiments, which in my humble opinion does not qualify for statistical analysis. That said, the results are so clear-cut that no statistics are required to convince me, or to quote Ernest Rutherford: '"If your experiment needs statistics, you ought to have done a better experiment."

We thank the reviewer for these positive comments and suggestions. For growth assays, we have performed a third biological replicate and updated those figures and the indicated statistical analyses. For microscopy experiments, we have removed p values.

Reviewer #1 (Public Review): 

The experiments are well designed, generally well controlled, and carefully conducted, and are thoughtfully and appropriately discussed. The authors make conclusions that are well supported by their results. 

When describing the aptamer knockdown of the PPS, the authors explain that the western blot was too noisy for monitoring the knockdown, which is frustrating for the reader and must have been frustrating for the authors. The authors instead counter-intuitively use qRT-PCR to monitor the transcript abundance of the PPS transcript in the aptamer system - this aptamer system is thought to be a modifier of protein, not transcription or transcript abundance. The authors describe that this has been seen once before (using aptamer knockdown of PfFis1), and the authors of that study speculate that the TetR-DOZI aptamer might be degrading the target mRNA. This is a plausible explanation, but it isn't quite clear from the description how this experiment was performed. The authors explain that the knockdown parasites grew normally for three days, but the parasites may be becoming sicker over this period. It's therefore possible that the decrease in PPS mRNA abundance is a product, rather than a cause of the growth defect. Sick or dying parasites could plausibly impact the PPS differently to the two chosen controls, particularly since both control genes chosen have substantially longer half-lives than the PPS mRNA (according to the Shock and DeRisi datasets). I therefore I suggest that this experiment be performed in an IPP rescue scenario (where the parasites aren't dying) with biological replicates. There is no explanation of the replicates here, but the error bars in 6C are implausibly small for real biological replicates. 

Line 342 "These results directly suggest that apicoplast biogenesis specifically requires synthesis of linear polyprenols containing three or more prenyl groups." - I think that this might be overinterpreting those results - there could be a number of different reasons why polyprenols of different sizes do or don't rescue, including different solubility, diffusion, availability of transporters, predisposition to break down to useable subunits. Perhaps this needs a caveat. 

Line 361 " the cytosolic enzyme, PF3D7_1128400" - I don't think we know the localisation of this protein based on the published data. The Gabriel et al study makes it clear the protein isn't apicoplast or mitochondrial, but it is punctate at stages in a pattern that doesn't look to me to be a straightforward cytosolic localisation (and the original authors don't describe it as cytosolic). 

Line 423 "with strong prediction of an apicoplast-targeting transit peptide but uncertainty in the presence of a signal peptide". I don't think this describes well the bioinformatic analysis of the N-terminus. Although the experimental data are convincing that this is an apicoplast-targeted protein, bioinformatically this would not be predicted as an apicoplast protein. There is no obvious signal peptide, and "uncertainty" is too vague a descriptor. None of the versions of signalP, nor psort, predict this as possessing a signal peptide (which by definition means that PlasmoAP absolutely rejects it), and there is no obvious hydrophobic segment at the N-terminus that we would normally expect of a signal peptide. The toxoplasma hyperlopit doesn't suggest that the Toxoplasma orthologue is apicoplast, and the protein isn't found in the Boucher et al apicoplast proteome. This is somewhat of a mystery. It doesn't diminish the solid localisation data, with the excellent complementary data from IFA as well as the doxycycline+IPP experiment, but it should be pointed out clearly that this localisation isn't to be expected from the sequence analysis. 

The section after line 344 "Iterative condensation of DMAPP with IP...", up until line 377 doesn't sit well within the section that has the heading "Apicoplast biogenesis requires polyprenyl isoprenoid synthesis". I suggest either creating a separate subheading for this material, or moving it into the start of the subsequent section "Localization of an annotated polyprenyl synthase to the apicoplast.".

Author Response:

Reviewer #1:

Significance: A central puzzle in evolutionary biology (and philosophy of biology) is the evolution of new (collective) entities that can evolve on their own right (e.g. the evolution of multicellular organisms from single cells). These evolutionary transitions are often conceptualized in terms of fitness decoupling (a fitness increase of the collective even as the fitness of the component particles decreases). Using a life-history model, the authors show that fitness decoupling is not possible when the conditions for fitness are the same. Thus, this paper has the potential to change how we think about the evolution of new collective entities.

Strengths: This paper is conceptually rich and the overall argument is clear. Re-analyzing previous data/models using their new framework highlights new patterns of fitness change in these transitions of individuality, and as such, it provides novel and exciting avenues of research.

Weaknesses: While the overall argument is clear, some of the details can be hard to follow (even as someone familiar with the literature). The initial description of their model is fairly clear, but given its conceptual novelty, the paper does not spend enough time developing the different concepts of fitness at the particle level.

Moreover, it is not entirely clear what is at stake: what is the role of fitness decoupling in our understanding of fitness transitions? And how does the proposed mechanistic ("trade-off breaking") model serve as a replacement? It seems to me like trade-off breaking is a characteristic of many evolutionary innovations, not only of major transitions. It seems even possible to envision groups that allow for an escape in a trade-off without leading to the evolution of a new "Darwinian" individual.

For example, one could conceive of a trade-off in zebras between time spent foraging and protection against predators. Coming together temporarily as a group is likely to allow for values outside this trade-off space (similar to those in Fig. 6). One could even imagine a new mutation that makes zebras switch activities (foraging/watching) depending on their position within the group. This mutation is only available to zebras that form groups (the phenotype does not exist in the absence of a group). But I would still want to argue that there is more to the evolution of new levels of individuality. Trade-off breaking seems (potentially) a necessary, but not sufficient step in these transitions.

And while the language of the authors is careful to not suggest sufficiency, it is not entirely clear how this approach helps us understand the particularity of these transitions.

Reviewer #1 asks first to clarify the stakes: what is the role of fitness decoupling in the explanation and how does tradeoff-breaking replace or supplement it? Second, they requested us to make a statement about the necessary or sufficient nature of tradeoff-breaking.

With respect to the second point, we argue that tradeoff breaking is not sufficient, but is probably necessary for an ETI to occur.

Let us now clarify the role of fitness decoupling and tradeoff breaking in the explanation of ETIs. It must be stressed that tradeoff breaking does not “replace” fitness decoupling; rather, tradeoff breaking is an event that cannot be understood readily in the framework of fitness decoupling. Thus, we claim that ETIs are better understood when seen through the lenses of traits and the evolutionary constraints that link them (i.e., tradeoffs) than via the export-of-fitness model (i.e., fitness decoupling). To illustrate this, we use the zebra herd example proposed by the reviewer. Coming together temporarily as a collective does not, in itself, constitute a tradeoff-breaking event, but rather simply a collective-formation event (similar to the first ace2 mutation in snowflake yeast or the first WS mutation in the Pseudomonas system). From this starting point, a number of mutations (i.e., change in traits values) can be fixed in the population that improve the performance of zebras within this environment. This is the “fast” part of the evolutionary trajectory that occurs on the ancestral tradeoff, which we called “low hanging fruit mutations” in the manuscript. As a consequence, “optimal herds within the ancestral tradeoff” evolve. As stated in the manuscript, if we assume that the tradeoff on traits is identical for lone zebra and zebra herd and also assume that the ancestral lone zebra exhibit trait values that are optimal (within these constraints) for lone zebras, it follows that the low-hanging fruit mutations that improve the zebra herd will probably reduce counterfactual fitness. This lowering of counterfactual fitness is not due to a “transfer” between real and counterfactual fitness (because there is nothing to transfer between real and counterfactual worlds), but is a consequence of the differential contribution of the traits involved in the tradeoff to the two fitness quantities. However, this specificity of the tradeoff might be significant because it could lead to stabilisation of the new collectives through ratchetting.

There is, indeed, “more to the evolution of new levels of individuality,” as pointed out by Reviewer #1. We claim that it involves rare mutations that would overcome the ancestral constraint and call them “tradeoff breaking mutations”. Tradeoff-breaking mutations are not bound by ancestral tradeoff; therefore, there is no a priori theoretical or biological reason to think they would have any positive or negative effect on counterfactual fitness. Here, we must stop using the zebra herd example because no tradeoff-breaking mutation occurred. However, the tradeoff-breaking lineages in the Pseudomonas example exhibit an improvement of both counterfactual and within-collective fitness. This observation does not fit within an export-of-fitness framework, but makes perfect sense in a traits-based view of ETIs—as a tradeoff-breaking mutation.

Reviewer #2:

This work reviews the influential "fitness decoupling" heuristic for understanding evolutionary transitions in individuality (ETIs), describes some of its limitations, and clarifies its interpretation. The review of the fitness decoupling account capably describes an interpretation of this framework that has frequently occurred in the literature, for example in Okasha 2006, Godfrey-Smith 2011, Hammerschmidt et al. 2014, Black et al. 2019, and Rose et al. 2020. However, it does not address the interpretation advanced by its authors, Richard Michod and colleagues, which they have clarified in several papers cited in the present work. Michod and colleagues have argued that the fitness decoupling account describes a changing relationship between the fitness of groups and the "counterfactual" fitness of their component cells, that is, the fitness the cells would have if they were removed from the group. This point is made explicitly in Shelton & Michod 2104 and Shelton & Michod 2020 and was present (though perhaps not as obvious) in Michod 2005 and later works, in contrast to the claim in the Glossary that this is a "relatively recent development of the fitness decoupling literature." The interpretation that Michod embraces is similar to what is here described as f2, the fitness of a "theoretical mono-particle collective", but that interpretation is not mentioned in the present work until Section 2.3. It is possible that an argument could be made that Michod and colleagues have not consistently interpreted fitness decoupling this way, or have made statements inconsistent with this interpretation, but no such argument is present in this work. Thus the impression conveyed is that Michod and colleagues consider decoupling of "commensurably computed fitnesses" possible, which is counter to their explicit statements on the topic.

The description of the limitations of the fitness decoupling heuristic (Section 2) is useful and goes a considerable distance toward clarifying the ways in which fitness decoupling can rigorously be interpreted. However, the final assessment (Section 2.3) does not make a compelling case for its central argument, the lack of utility of the fitness decoupling concept. Elsewhere in the work, the ratcheting model of Libby and colleagues is referenced in comparison to the tradeoff-breaking approach, but Section 2.3 does not acknowledge the relationship between Libby and colleagues' model and the counterfactual interpretation of the fitness decoupling heuristic. For example, the argument in Libby and Ratcliff 2014 that "If any of the yeast that evolved high rates of apoptosis within clusters were to leave the group and revert to a unicellular lifestyle, they would find themselves at a competitive disadvantage relative to other, low-apoptosis unicellular strains." and in Libby et al. 2016 that "…if G cells were to revert to unicellular I cells, they would be quickly outcompeted" are counterfactual fitness arguments essentially similar to that of Shelton and Michod 2020 that "the fitness a cell would have on its own declines as the transition progresses." Section 2 makes a convincing case that commensurable fitnesses cannot be decoupled, but by fixating on commensurability, which is not relevant to the counterfactual interpretation of fitness decoupling, Section 2.4 fails to make a convincing case that "fitness-decoupling observations do little to clarify the process of an ETI." That is, "because they are not commensurable" does little to explain why the counterfactual interpretation of fitness decoupling "does little on its own to clarify the process of an ETI," since commensurability is not a claim that the the counterfactual interpretation of fitness decoupling makes.

We agree with the reviewer on two essential points: (1) the decoupling of commensurably computed fitness is impossible when collectives have a finite size and (2) counterfactual fitness is not commensurable to particle or collective fitness.

While we recognise that Michod and collaborators did clarify that fitness decoupling referred to counterfactual fitness (although, to us, this becomes clear from 2015 onward), we argue that the fitness transfer (or export-of-fitness) metaphor implies (by its wording) a commensurability of fitnesses that undermine this welcome clarification.

Indeed, for a quantity to be transferred from one place—or component—to another, the source and destination must be commensurable. It is incorrect to talk about a transfer between counterfactual and actual quantities. A better choice of words to discuss the relative change of counterfactual and actual quantities would avoid the physical transfer metaphor and focus instead on the correlation of the two quantities. It must be noted that, despite the clarification of counterfactual fitness, the word “transfer” continues to be used in recent work (Davison & Michod, 2021).

This may seem like nitpicking; however, there is a real advantage in being careful about this. We do agree that, under some assumptions, counterfactual fitness would decrease while whole–life cycle particle fitness (or collective fitness) increases. From there, one might ask: what needs explaining? If one assumes an export-of-fitness framework, the transfer of fitness explains why it cannot be otherwise. If fitness decreases on one side, it must increase on the other. In other words, the existence of a tradeoff is taken for granted based on the improper physical metaphor. While there are strong reasons to think that such tradeoffs exist, they should be assessed in their own right and on a case-by-case basis rather than being assumed to hold. Otherwise, there is no way to make sense of the tradeoff-breaking scenario described in Section 4.

By the same token, the metaphor of “decoupling” often associated with the export-of-fitness model is misleading because it is used to describe a part of the evolutionary dynamics where counterfactual particle fitness and whole–life cycle particle fitness are strongly dependant on one another (even if their changes are anticorrelated), through the existence of the tradeoff.

Nevertheless, we welcome the reviewer’s urge to clarify our position and how this relates to Michod and colleagues’ counterfactual fitness proposal.

The model based on trade-offs and trade-off breaking is useful and likely to be of interest to theorists interested in ETIs. The observation that this model can reproduce the (counterfactual) fitness-decoupling observation is a useful in showing the how the two models relate. The result that counterfactual fitness decoupling is a consequence rather than a cause of the evolutionary dynamics is an important point (though perhaps obvious in retrospect, since counterfactuals, things to do not happen, can't be the causes of anything).

The caution in Section 3.3 that "the same [counterfactual fitness decoupling] observation will be made in any situation in which short-term costs are compensated by long-term benefits, not solely during ETIs" is a good point, and it sets up the argument that trade-off breaking is a "genuine marker for an ETI". However, no convincing case is made that the same criticism, that the observed phenomenon is not unique to ETIs, is not equally true of trade-off breaking. Some nice examples of trade-off breaking in the context of ETIs are given, but these do not amount to an argument that trade-off breaking is only observed during ETIs. The life history literature includes examples of trade-off breaking that are not related to ETIs, so it is not clear that trade-off breaking is either a reliable indicator of ETIs or superior in this respect to counterfactual fitness decoupling.

This point is in line with one of the points made by Reviewer #1. We have now clarified our position with respect to the generality of the tradeoff-breaking approach.

In the Discussion, the "inconveniences" associated with the fitness decoupling are cogent limitations of this heuristic. The "impossibility of decoupling between commensurable measures of fitness" is an important result, but it is not new and should thus probably not be presented as "[o]ur first main finding". Shelton and Michod 2014 includes a mathematical proof in the appendix that, given the model assumptions, "consideration of the births and deaths of colonies gives us exactly the same bottom line (fitness) as consideration of the births and deaths of lone cells." The second main finding, that "fitness decoupling observations cannot be reliably used as a marker for ETIs," is valid, but as described above, a convincing case is not made that trade-off breaking can be reliably used in this manner, either. Trade-off breaking may, however, be a useful way to think about ETIs in the other ways that are suggested, for example as key events and as stepping stones to new hypotheses.

We have now clarified our position.

I think here we are calling sensors/acuators/transducers as "applications". But this may be saying that the endpoint is where the data comes from, and not the hardware device. The question is, can application endpoints be at coordinator and router devices?

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Reply to the reviewers

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

The paper tackles an important problem regarding the effect of demographic dependent vaccination protocols on the reduction in the number of deaths with respect to the situation of no vaccination (say J). A compartmental SIRD model with reinfection Y is proposed, stratified in two (age dependent) groups, based on a binary reduction of a given contact map, and given infection fatality risk (IFR). Several countries are then analyzed.

As far as I understand we have a control variable v, parameters of the stratified model (i=1,2) tuned to match IFRi, and a control objective, i.e. minimization of J over one year.

The paper is well written. The final message and some theoretical passages are not completely clear, at least to me. I have the following observations that the authors may want to consider.

We thank the referee for the revision and are very glad that the overall evaluation is positive. Comments and suggestions have been thoroughly addressed, as we discuss in the following.

1) The study of stability of infection free and endemic equilibria should be better developed. The 5 equations can be reduced to 4 (neglecting D) and the characteristic of the reduced Jacobian used to characterize the local asymptotic stability of equilibria, instability, bifurcation points etc... Alternatively, one can use a co-positive Lyapunov function (LF). For instance, if we take the LF V=S+I+Y+R, we get $\dot V=-\mu_I I-\mu_Y Y \le 0$. If $\mu_I$ and $\mu_y$ are strictly positive all equilibria are characterized by (S*,0 0,R*) and D=1-S*-R*. So, I don't understand the phrase after (7,8), notice that Y cannot be zero in finite time. For $\mu_y=0$ then Y* can be nonzero. I guess that closed-form computation of S* and R* is possible as function of the parameters at least in the case v=0. The stability result should be cast in function of the current reproduction number (not explicitated) wrt to S and R.

The authors are invited to have a look at

1.1) Pagliara et al, "Bistability and Resurgent Epidemics in Reinfection Models", IEEE CSLetters, 2018,

for a theoretical analysis of stability on a similar (just a little bit simpler) model.

We appreciate the suggestions of the referee for improvement of this material. We have carried out an in-depth revision of the stability analysis and significantly extended it. The major addition has been, as suggested, a section relating the current reproductive number at equilibrium (we call it the asymptotic reproductive number in the text) to the fixed points of the dynamics for three different scenarios: general model, no vaccination, and zero mortality of reinfected individuals. As Pagliara et al. show in their paper, the connection between the fixed points and the reproductive number is not trivial, but it is possible to derive it through the next-generation matrix technique, as we now do. Additional references regarding this technique have been added. We have included a Table summarizing the stability analysis (page 2 in SI 3) at the end of this new section.

Other modifications include the reduction of 5 equations to 4 for the stability analysis and a clarification of possible equilibria (page 1 of SI 3), rephrasing and correcting our sentence after eqs. (7) and (8). We also attempted to obtain a closed-form computation of S* and R* but, to the best of our knowledge, concluded that it is not possible. We would be happy to pursue any insight in this respect the referee may have.

What said before should be also extended to the stratified model, where a "network" Rt could be defined, see for instance

1.2) L. Stella et al, "The Role of Asymptomatic Infections in the COVID-19 Epidemic via Complex Networks and Stability Analysis", SIAM J Cont. Opt., 2021, (arxiv.org/pdf/2009.03649.pdf)

We thank the referee for pointing out this reference. Following the analysis in Stella et al., we have carried out a stability analysis for the stratified model as well. The results are included in a new section (pages 7-10 in the SI 3).

2) It is not clear whether the free contagion parameters of the model have been fitted on real data (identification from infection and reinfection data). Notice that the interplay between vaccination strategies and NPI is important, see e.g.

*2.1) Giordano et al, Modeling vaccination rollouts, SARS-CoV-2 variants and the requirement for non-pharmaceutical interventions in Italy", Nature Medicine 2021, *

where progressive vaccination in reverse age order is considered together with different enforced NPI countermeasures.

In the first part of our study, parameters are intendedly left free because we aim at describing the generic behavior of the model. Still, we derive several inequalities and relationships between parameter ratios that seem to be sensible attending to what the different classes in the model stand for. This is as described in sections regarding model parameters when the two generic models (SIYRD and S2IYRD) are introduced. The aim is to represent both the generic dependence with some variables and a broad class of contagious diseases, so parameters are mostly free. In agreement with this approach, parameters can be also freely varied in the companion webpage.

In the second part of our study, the model is applied to COVID-19. In that case, we have used parameter values in agreement with observations, as (admittedly poorly) explained in pages 9-10 of the main text. Indeed, not enough information on parameter estimation was provided in the main text, and the SI 2 also needed some additional information. This has been amended. Let us explicitly mention that we have not fitted the dynamics of the model to any actual data set to fix specific values, as Giordano et al. do. In our case, we have first used different demographic data sets to evaluate contact rates and IFRs of the two population groups (these are parameters Mij and Ni in eqs. (7-10)). Secondly, recovery and death rates are estimated through the IFRi values for each age group i and the infectious period of COVID-19, that we fix at dI=13 days. Third, infection rate βSI=R0/dI has been estimated fixing R0=1, since the reproductive number of COVID-19 all over the world fluctuates around this value (Arroyo-Marioli et al. (2020) Tracking R of COVID-19: A new real-time estimation using the Kalman filter, PLoS ONE 16(1):e0244474). The reinfection rate is defined through its relationship with the infection rate, βRI= α1 βSI, where α1 was in the range 0-0.011 at early COVID-19 stages (Murchu et al. (2022), Quantifying the risk of SARS‐CoV‐2 reinfection over time, Rev Med Virol 32:e2260) and seems to be about 3-4 fold larger for the omicron variant (Pulliam et al., Increased risk of SARS-CoV-2 reinfection associated with emergence of the Omicron variant in South Africa, www.medrxiv.org/content/10.1101/2021.11.11.21266068v2). Given the relationships derived among parameters, our only free parameter was α2RY= α2 βRI, and we fixed it to α2=0.5 (i.e., reinfected individuals recover twice as fast as individuals infected for the first time).

Once more, it was not our goal to precisely recover specific trajectories of COVID-19 or to point at possible future scenarios, but to illustrate the dependence of major trends with model parameters. Also, the appearance of new variants requires the reevaluation of parameters. For example, omicron has different IFR (therefore different mortality and recovery rates), a different infectious period, and higher infection and reinfection rates. In this context, the interactive webpage (where we will update demographic profiles and IFR data as they become available) is a useful resource to simulate any situation different from current or past ones.

3) In the model the immunity waning is not explicitly considered (flux from R to S or better from a vaccinated compartment to S). It is clear that this complicates the model. Please discuss why the indirect way the waning is considered here is justified.

3.1) Batistela et al, "SIRSi compartmental model for COVID-19 pandemic with immunity loss", Chaos Soliton and fractals, 2021.

3.2) McMahon et al, "Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected,Recovered) Modeling Using Empirical Infection Data", JMIR Public health and surveillance, 2020.

Though the model does not consider an incoming flux of individuals to compartment S, the existence of a "backward" flux from R to Y yields a transient phenomenology analogous to models with increases in the S class. Indeed, it is these fluxes that cause persistent endemic states; otherwise, the S class is monotonously depleted until infection extinction.

In Batistela's et al. work, the possibility that individuals become reinfected is effectively implemented through a flux between the R and S classes, since only one class of infected individuals is considered and recovered individuals cannot be infected again. In our case, feeding back to S would mean that previous immunity is completely lost or that vaccines are not effective at all for some individuals. This is neither what McMahon et al. conclude when evaluating real data nor what more recent surveys indicate (see for instance the Science Brief published in October 2021 by the CDC, SARS-CoV-2 Infection-induced and Vaccine-induced Immunity, https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/vaccine-induced-immunity.html).

This nonetheless, complete immunity waning (feedback to the S class) and reinfections (feedback to a partly immune class experiencing overall lower severity of the disease) are equivalent to a large extent: the trend of COVID-19 seems to indicate that our Y class will be the "new S", and that fully naive individuals would arrive mostly due to demographic dynamics (birth and death processes, as also implemented by Batistela et al.). Summarizing, complete immunity waning is rare in the time scales considered in our simulations, while partial immunity that decreases the severity of the disease (after infection or vaccination) is the rule, in agreement with our choices.

4) Reduction of deaths wrt no vaccination is of course important, but also reduction of stress in hospitals. This is particularly important now with the advent in Europe of the omicron variant. Please discuss on the real message you want to convey to policy makers in the actual scenario of the pandemic.

The model in this work is deliberately simple. Our main goal was to explore the qualitative effects of demographic structure and disease parameters in protocols for vaccine administration. This was the reason to consider a mean-field model in a population structured into two groups. The main conclusion is that optimal vaccination protocols are demography- and disease-dependent. If this is so in our streamlined model, the more it will be in more realistic models, where one should include a finer stratification and, in all likelihood, heterogeneity in contagions. Our main message, therefore, is that there is no unique protocol for vaccine roll-out, valid for all populations and diseases. The abstract has been modified to highlight this conclusion.

Some qualitative considerations also allow us to draw preliminary conclusions on the reduction of stress in hospitals. Since the number of hospital admissions is proportional to the incidence of the disease, the number H of hospitalized individuals can be represented as H=a I + b Y, with a>>b due to the partial immunity of vaccinated or recovered individuals (which belong to class Y upon (secondary) contagion). Therefore, minimizing the burden on the healthcare system amounts to minimizing the number of individuals in the I class. Beyond non-pharmaceutical measures, I is minimized when individuals are transferred as fast as possible to the Y class, that is, maximizing vaccine supply and acceptance. In terms of our model parameters, this entails maximizing v and also θ (the maximum fraction of individuals eventually vaccinated), for instace through devoted awareness campaigns. These ideas have been included in the Discussion section.

Reviewer #1 (Significance (Required)):

The final message and some theoretical passages are not completely clear, at least to me.

Please discuss on the real message you want to convey to policy makers in the actual scenario of the pandemic.

As discussed above, we have modified the manuscript following the advice given by the Reviewer. We think that both the presentation and the theory are clearer now.

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

In this paper, a compartmental model of the propagation of an infection with vaccination and reinfection is studied. The impact that changes in the rates of these two processes have on disease progression and on the number of deaths is analyzed. In order to highlight the overall effect of the demographic structure of populations and the propagation of a given disease among different groups, the population is divided into two subpopulations and the model is extended to the two-dimensional case. In addition to the study of equilibria and their relative stability, the model is then applied in the case of COVID-19. Different vaccination strategies are studied using real demographic data and with a population split between under 80 and over 80 individuals. It is observed that for low vaccination rates, the advisable strategy is to vaccinate the most vulnerable group first, in contrast to the case of sufficiently high rates, where it is appropriate to vaccinate the most connected group first. The simulations show also that with a low fatality ratio, the strategy that yields the greatest reduction in deaths is vaccination of the group with the most contacts, while the situation is reversed for higher fatality ratio.

The model and simulations presented are interesting and valuable. The comparison of the behavior of the model in the 4 different countries is very interesting, as well as the webpage created by the authors.

We thank the referee for the very positive evaluation and are very glad that the study is found interesting and valuable.

As minor comment, I think that the introduction of the model needs a more extensive literature review. For example, there is no mention of the classic SIR model of Kermack and McKendrick (1927) and other works on the introduction to epidemic models, which form the basis of the model presented by the authors.

The referee is right. There is a long history of extensions and applications since Kermack & McKendrick introduced the SIR model that we obviated. This has been amended by adding an introductory paragraph with several new references at the beginning of the Models section, page 3 in the main text.

Reviewer #2 (Significance (Required)):

The model presented by the authors is quite original and simple enough to be suitable to different contexts and scenarios.

Compared to previous work, this paper makes a twofold contribution, as explained by the authors. First, the introduction of reinfections shows the existence of long transients (or quasi-endemic states) that may precede the transition to a truly endemic state predicted for COVID-19. Second, the simplicity of model allows the characterization of systematic effects due to, at least, group size, demographic composition, and IFRs.

I am involved in the study and analysis of epidemic models accompanied by network effects. I think this paper is a good contribution, although preliminary, in the analysis of the vaccination process and in the search for the optimal strategy.

We thank the Reviewer and are glad that our goal, offering a model as simple as possible to obtain meaningful conclusions, is appreciated.

Author Response:

Reviewer #1 (Public Review):

In this paper, Qin et al. investigated the molecular mechanism of phospholamban (PLN) linked dilated cardiomyopathy (DCM), using structural approaches combined with biophysical measurements. Structures of the catalytic domain of protein kinase A (PKAc) in complex with PLN peptides (both wild-type and the R9C and A11E DCM mutants) provide insights into the mechanism of substrate recruitment and how it is perturbed in the disease state. Qin et al. show convincingly that the mutant peptides all have lower affinity for PKA than the wild-type peptide, suggesting models in which heterozygous DCM mutations act via sequestering PKA and thereby preventing phosphorylation of the wild-type peptide may be incorrect.

The authors highlight significant differences between their structure of the WT-PLN:PKAc complex, which has a 1:1 stoichiometry, and a previous structure of the complex (PDB 3O7L), which has 1 PLN bound between two PKAc monomers (a 1:2 complex). The authors posit that the stoichiometry observed in 3O7L is an artifact of the crystal lattice, and does not occur in solution, supporting this with analysis of the elution volumes of the peptide complexes on size exclusion chromatography compared to PKAc alone. They further suggest that the AMP-PNP ligand included in the 3O7L structure is not bound, based on analysis of Fo-Fc maps calculated from the deposited coordinates. Inspecting 3O7L I am not convinced of this last point - it seems more likely that a technical error was made in assigning or refining the B-factor of the ligand in 3O7L, because there is clearly density present in SA-omit maps for the nucleotide.

Taking these results together, the authors suggest a mechanism for DCM, whereby mutations in PLN result in lower affinity for PKA, and consequently reduced phosphorylation. This seems plausible and well supported by the data, although in the ADP-Glo assay used here, the reductions in phosphorylation observed for some of the mutant peptides are rather modest. However, as the authors state, it is plausible that even relatively subtle changes in PLN phosphorylation could have substantial effects on Ca2+ homeostasis via increasing SERCA inhibition.

We thank the reviewer for the appreciation of our work.

Reviewer #2 (Public Review):

Strengths:

The authors presented new high-resolution 3D crystal structures of the PKA catalytic domain (PKAc) in complex with PLN WT or mutant peptides (residues 8-22) containing the DCM-associated PLN mutations (R9C or A11E). These are novel and important data given that the present structures are dramatically different from those reported previously. The authors made convincing argument that the 3D model reported previously may result from a crystallization artifact.

By characterizing the interactions between the PKAc domain and PLN WT or DCM-associated mutant peptides using surface plasmon resonance (SPR) analysis, the authors convincingly showed that the DCM-associated PLN mutations at positions 9, 14, and 18 alter the conformation of the PLN peptide and reduce the binding affinity of the PLN peptide with PKAc. These data provide an explanation how some DCM-associated PLN mutations at these positions reduce the level of PKA-dependent phosphorylation of PLN.

The authors also performed nuclear magnetic resonance (NMR) to determine the structural dynamics of PLN WT, R9C, P-Ser16, and P-Thr-17 peptides. These NMR structures combined with the SPR analysis also support their conclusion that PLN phosphorylation and DCM-associated PLN mutations have an impact on its conformation.

We thank the reviewer for the comments.

Weakness:

The present study used PLN-derived peptides (aa 8-22). Although technically challenging, it is important to consider if the full-length WT or mutant PLN will behave the same as those observed with the peptides. This is especially crucial in light of the prior work showing substantially different structures using a different segment of PLN.

We are fully aware of the potential risk to draw conclusion from an isolated peptide instead of the full-length PLN as a transmembrane protein. In the previous study, people showed that the PLN peptide could be used as a good model substrate that gets phosphorylated as efficiently as the full-length PLN protein (L. R. Masterson et al., Dynamics connect substrate recognition to catalysis in protein kinase A. Nat Chem Biol 6, 821-828 (2010); D. K. Ceholski, C. A. Trieber, C. F. Holmes, H. S. Young, Lethal, hereditary mutants of phospholamban elude phosphorylation by protein kinase A. The Journal of biological chemistry 287, 26596-26605 (2012)). These results together with our biochemistry results suggest the tail peptides are indeed active substrates of PKA. Due to the technical difficulty, we were not able to crystallize PKAc in complex with the full-length PLN. To explain the potential difference between the peptides and the full-length PLNs, we added more text in the discussion section “Additionally, the trend of the reduced phosphorylation by DCM mutations can be significantly affected by the oligomerization state of PLN. Ceholski et al. showed that R9C severely inhibits PKA phosphorylation in the context of full-length pentameric PLN, but has a much milder effect in the context of full-length monomeric PLN or an isolated tail peptide [41].”

Although it is convincing that DCM-associated PLN mutations likely reduce the interaction between PKAc and PLN (assuming that the peptides behave the same as the full-length PLN with respect to interaction with PKA) and, as a result, the PKA dependent phosphorylation of the mutant PLN, it is unclear how this impaired interaction between PKA and PLN mutant could explain the effects of the DCM-associated PLN mutations on SERCA function (either reduced or enhanced PLN-dependent inhibition of SERCA, as proposed previously). In this regard, can the authors predict if the DCM-associated PLN R9C mutation reduces or increases SERCA inhibition based on the results of their present study?

It is indeed controversial how PLN mutations cause DCM. Previous studies have shown that the DCM mutations in PLN might change this regulation in either a phosphorylation-dependent or phosphorylation-independent manner. Our results show that the mutations may act through both manners: 1) the mutations reduce the phosphorylation level of PLN, which has been shown to enhance the inhibition of SERCA and inhibit the uptake of Ca2+; 2) the mutations change the conformation of PLN before binding to PKA or SERCA, which could have additional consequences, such as altered assembly state of PLN, phosphorylation of PLN by CaMKII, or changes in interactions of PLN with the lipid membrane. This could impact in either directions, reducing or increasing SERCA inhibition, which is difficult to predict based on our data. We added the explanation in the discussion “While decreased PLN phosphorylation is likely an important contributor to the physiological dysfunction associated with familial DCM, disease-causing mutations in PLN may have additional consequences, such as altered assembly state of PLN, phosphorylation of PLN by CaMKII, or changes in interactions of PLN with the lipid membrane. The influence of such factors on SERCA inhibition are unclear. In principle, they might further increase inhibition of SERCA and act in conjunction with lower PKA-mediated phosphorylation to manifest the disease symptoms. Conversely, it is possible that these factors could decrease the inhibition of SERCA, partially compensating for the decreased phosphorylation level, and mitigating the symptoms.”

It is also unclear how reduced PKA phosphorylation of mutant PLN could lead to DCM. PLN is unlikely to be significantly phosphorylated by PKA at rest (in other words, PLN is likely to be phosphorylated by PKA during stress, i.e. during the adrenergic fight-or-flight response). Therefore, it is puzzling how such reduced PKA-dependent phosphorylation of PLN would significantly affect the PLN function during the absence of flight-or-flight response.

As explained above, we think that this regulation could be through both phosphorylation-dependent and phosphorylation-independent manner. Even only considering the phosphorylation-dependent manner, the DCM phenotype could be due to an accumulation of the Ca2+ imbalance in the cell over repeated cycles of cardiac muscle contraction upon chronic accumulation of the sporadic phosphorylation events. It is also possible that the mutations affect the CaMKII-dependent regulation of PLN, which leads to DCM.

Given that the DCM-associated PLN mutations have significant effects on the conformation of PLN itself, at least in the form of short-peptides, it is possible that these mutations could affect the folding, oligomerization, trafficking, degradation, etc., in addition to PKA-dependent phosphorylation. The relevance and contribution of reduced PKA-dependent PLN phosphorylation to DCM remain unresolved.

We agree with the reviewers that both phosphorylation-dependent and phosphorylation-independent manners could contribute to the DCM disease phenotype. It remains unresolved which factor is the major contributor. We have added a statement in the discussion (see point above).

Reviewer #3 (Public Review):

This manuscript describes an elegant study utilizing the crystal structures for the elucidation of the disease mechanism of familial dilated cardiomyopathy. It has been known for decades that the mutations in PLN are associated with DCM, but the underlying mechanism remains controversial. In my opinion, Prof Yuchi and co-authors did excellent job on revealing the high-resolution crystal structures of PKA-phospholamban complexes, representing both the native and diseased states. Combined with various of biophysical and biochemical methods, including SPR, ADP-glo, thermal melts, NMR, etc, the authors systematically investigated the correlations between the PLN conformation, the binding affinity, and the phosphorylation level. The mechanism of PKA phosphorylation on another related substrate, ALN, was also convincingly revealed. The results are very helpful for understanding the pathological mechanism of PLN-related DCM. More importantly, the atomic structures of PKA-phospholamban complexes lay a solid foundation for the structure-based rational design of therapeutic molecules that can reverse the effects of the DCM-causing mutations in the future, e.g. by stabilizing the interactions between PLN and PKA.

We thank the reviewer for the appreciation of our work.

Author response:

Reviewer #2 (Public Review):

This work by Castledine et al. addresses the important question of whether results from in vitro (laboratory-based) evolution studies may be useful for predicting evolution during phage therapy in a clinical setting. In order to explore this question, the authors cultured a set of bacterial isolates from a patient pre- and during phage therapy, as well as phages from several time points during therapy. They then experimentally evolved (in vitro) a mixture of the bacterial isolates from the patient in the absence of phage, or in the presence of phage using two different treatments (phage added once or added repeatedly). Overall, they observed similarities between the evolutionary outcomes (genomic and phenotypic) in vitro and in the patient. Resistance evolved rapidly in the patient and in vitro under phage selection, and similar genomic changes were observed in both environments. The approach of using bacterial isolates directly from the patient (as well as the phages used for therapy) in vitro is clever, and the observed similarities are compelling.

We thank the reviewer for appreciating the novelty in our results and methodology.

However, I think there are some limitations with the study that should be addressed in the text.

In particular, (1) While the similarities in vitro and in the patient are quite interesting, there are some differences that were dismissed as being minor without justification. Calling the results "highly parallel" is a bit subjective - in vitro in the repeated phage treatment (which is suggested to be most similar to the clinical context), there did appear to be phage coevolution that was not observed in vivo. The tradeoffs/relationships between traits (as shown in Fig. 3) also differed to some extent.

We agree this could have been more objectively phrased at the start of the discussion – this has been edited to reflect this. We have highlighted the differences between in vivo and in vitro treatments with respect to phage evolution. Moreover, we have also highlighted that the observed trade-offs had different underlying mechanisms which may not always result in parallel evolutionary changes between in vivo and in vitro environments.

Additionally, for the genomic results only a subset of variants were plotted (those in genes of known function), but there were far more significant variants in genes of unknown function that were not included. It is difficult to assess whether the genomic findings are truly similar across environments if only a fraction of those results were presented in the manuscript.

We chose to concentrate on genes of only known function so that we could better understand their potential significance, and also because the figures and analyses (Figures 4 and 5) would become extremely complex and large and uninterpretable with genes of unknown function included. This is especially true for Figure 5, which would have required us to show 284 rows if all genes would have been included. Ultimately, whichever way we do this exploratory analysis, it is going to be difficult to see if findings are truly similar across environments because we only have a single patient who had phage therapy.

However, we have redone the analysis with all of the significant genetic changes (SNPs and indels from both known and unknown genes) included.

Figure 5 has been recreated and is now included as "Figure 5 - Figure supplement 1". All of the statistical analysis on (a) the number of SNP/indels seen (b) genetic distance from ancestor and (c) alpha diversity give quantitatively similar results. That is, although all the estimates are generally much higher after including many more genetic variants, all of the significant results from both the overall model fit and post-hoc multiple comparisons remain the same. One interesting result that came out of looking at all the genetic changes was that for genetic variants occurring in a gene of known function, 56% (28 out of 50) were de novo mutations, whereas this value was only 42% (98 out of 234) for variants in genes of unknown function.

We then looked at the proportion of genetic variants (both in known and unknown genes) found in vitro that were also found in vivo. For genes of known function, 62% of genetic variants were found in vivo (31 of 50) and this was comparable to the 65% of genetic variants in genes of unknown function (153 of 234). Of the 26 genes of known function with differences identified in the in vitro analysis, 16 (61%) were also found to have genetic changes in vivo. The equivalent metric for genes of unknown function was 86% (85 of 99). Similar to in vitro, variants occurring in a gene of known function were more likely to be de novo mutations (77%) compared to variants occurring in a gene of unknown function (46%).

While these patterns and exploratory analyses are interesting, they have extremely limited statistical power and therefore do not alter the conclusions or results of the work presented. For these reasons, we have chosen not to include these results in the already long manuscript. We have added a line to say we have done it both way:

“We performed all downstream statistical analyses on (a) only genetic variants in genes of known function and (b) all genetic variants.”

And we also added a line at the beginning of the genomic analysis results section:

“Results were not affected whether we included only genetic variants occurring in genes of known function or all genetic variants (Figure 5-Figure supplement 1). As we were interested in attributing potential functions to the variants identified, we only present the results for genetic variants occurring in genes of known function.”

(2) Much of the text is framed around whether in vitro outcomes are predictive of those in vivo, but this study only included results from a single patient. Thus, it is impossible to know whether these findings are by chance or representative of a more general relationship between in vitro and in vivo evolution.

We agree that having a single patient for our in vivo comparison limits the generalisability of our results. We have highlighted this in the revised manuscript. However, that our replicated in vitro experiments agreed broadly with our in vivo results and that of other studies (finding resistance-virulence trade-offs) suggests that at least in some circumstance in vitro dynamics are predictive of in vivo dynamics. Further studies are clearly needed (and hopefully will arise as a consequence of this work) to determine the generalisability of this finding and the circumstances where this parallelism might break down.

(3) Although the evolutionary outcomes appear to be similar, the pathogen was successfully cleared from the patient but persisted throughout experimental evolution. Whether the pathogen is successfully eliminated or not is presumably the most important clinical outcome, and while this difference is not surprising, it is an important one to point out to the reader. Essentially, evolution was similar to some extent but the consequences of evolution for bacterial persistence in each environment were quite different.

We have now highlighted this difference to the reader in the revised manuscript.

Author Response:

Reviewer #1 (Public Review):

• Line 141: It would be beneficial to better understand how the sequenced sample of the population corresponds to the PCR confirmed sample of the population, in order to understand possible selection biases in the sequence data. Could you elaborate on how the composition of sequence PCR confirmed cases matches the composition of PCR confirmed cases, by the demographic characteristics listed in Table 1.

Early in the pandemic (March-April), we tried to sequence every SARS-CoV-2 positive case diagnosed in our KWTRP laboratory from Coastal Kenya. However, with the sharp increase in the number of identified cases from the month of May 2020 onwards, and a limited in-house sequencing capacity, we changed strategy to sequence only a sub-sample of the identified positives. The criteria for sub-sampling included having a cycle threshold of < 30.0, spatial representation (at county level) and temporal representation (at month level). The consequent number and proportion of samples sequenced across the study period months and across the counties is summarized in Fig. 2C-E with the sample flow provided in Figure 2-figure supplement 1.

In the revised manuscript we have provided a comparison of the demographic characteristics of the sequenced cases versus non-sequenced cases (shown as Table 2). The participants providing the sequenced and non-sequenced positive samples had a similar gender distribution and similar probabilities of being from either from Wave one or Wave two. However, the distribution of sequenced vs non-sequenced cases differed significantly in age distribution, nationality and travel history. Specifically in the sequenced sample, there were more participants in 30–39 years age bracket compared to the non-sequenced samples, a disproportionately representation of non-Kenyan nationals and persons with a recent international travel history in the sequenced sample.

• Line 283: I am particularly interested in the observed inter county flows, but it is hard to interpret the numbers. Considering population sizes in each county, what are the phylogenetically observed import rates per 100,000? What are the rate ratios? Based on the observed data, is there any evidence that imports into coastal Kenya occurred statistically significantly through Mombasa?

We thank the reviewer for these comments.

In the revised manuscript we have added two new tables (1 & 4) which detail the population size in each of the six Coastal Kenya counties, population density and estimated import/export rates (per 100,000) for the counties.

The alluvial plots are descriptive regarding genome flows. The underlying data on the pattern of virus movement is inferred using the ancestral state reconstruction which an established phylogenetic approach that has been applied elsewhere to infer SARS-CoV-2 local and global movement (Wilkinson et al, Science 2021, Tegally et al, Nature, 2021).

The results we obtained from ancestral state reconstruction of Mombasa being a major gateway for variants entering the coastal region of Kenya is consistent with (a) the county showing the highest number circulating of lineages (n=28) compared to the other five remaining counties of Coastal Kenya, (b) approximately half (n=21, 49%) of the detected lineages in coastal Kenya had their first case identified in Mombasa and (c) Mombasa had an early wave of infections compared to the other Coastal counties.

We are not aware of an approach to consider statistical significance on these plots. The graphical display is based on the observed number events, and we would argue this is more appropriate than presenting absolute rates which would be susceptible to sampling bias.

Is it possible to account for potential bias in sequence sampling in these calculations, perhaps as done in Bezemer et al AIDS 2021? It should be possible to adjust for the proportion of sequenced individuals in PCR confirmed individuals, and it might also be possible to back calculate infected cases from cumulative reported deaths and to adjust for the proportion of sequenced individuals in infected individuals?

The reviewer suggests helpful methods to examine sampling bias, but we found this beyond scope here. Our method was based on ancestral location state reconstruction of the dated phylogeny. The approach has been used elsewhere to answer similar questions (Wilkinson et al, Science 2021, Tegally et al, Nature, 2021). The Bezemer paper uses maximum parsimony ancestral state reconstruction algorithm implemented in phyloscanner, and the Bayesian method applied to impute incomplete sampling is applicable to chains of transmission which we have not tried to reconstruct in our analysis.

Considering my earlier recommendation to document sequence sampling representativeness in Table 1, if Mombasa is found to be oversampled relative to infections, then it might also be helpful to perform sensitivity analyses in which sequences from over-represented locations are down-sampled. Another option might be to consider the approaches considered in de Maio PLOS Comp Bio 2015, or Lemey Nat Comms 2020. Thank you for investigating potential caveats and substantiating your findings in more detail.

In the revised manuscript we have clarified that our sequenced sample was proportional the number of positive cases reported in the respective Coastal Kenya counties (see-Fig.2E and Table 1).

The De Maio method uses BASTA (BAyesian STructured coalescent Approximation) into BEAST for purposes of phylogeographic analysis to compare ability to discriminate a zoonotic reservoir vs the implausible alternative cryptic human transmission. Analyses developed from these methods would be valid and interesting to apply to our dataset but would be a major new analysis and beyond the scope of the present paper. We have therefore taken the approach of: a) more clearly acknowledging sampling bias (see below) and b) undertaking sensitivity analyses (Supplementary File 5, see below). Using the larger global background sequence sets selected in a different way (more geographically balanced relative to the first round that was random), we still find that most of the virus introductions into coastal Kenya occurred via Mombasa consistent with our previous analysis.

The results are consistent with the case numbers in that (i) Mombasa experienced an earlier peak during wave one relative to other counties and (ii) had in total more cases than all the other five counties, and (iii) was commonly the first county of detection for many of the identified lineages in the region. However relative to its population, the border county of Taita Taveta had a higher import rate (13.5. per 100,000 people) compared to that of Mombasa (11.6 per 100,000 people), Table 4

Observations from our sensitivity analyses (Supplementary File 5) are included in the revised manuscript. We found that the absolute number of estimated viral imports/exports and intercounty transmission events fluctuated depending on the number of Coastal Kenya sequences and size of global comparison dataset but with a clear pattern of (a) counted events increasing with sample size (b) with Mombasa County consistently leading in the number of events; imports or exports.

• Line 292: The results are of course subject to differences in sequencing rates in each of the countries listed, and differences in reporting of these data.

This is a valid concern; to mitigate the bias that arises with these differences, unlike in the previous comparison dataset where we randomly selected a specified number of samples per month for each continent, in the revised analysis we have done the selection at country level. We limited the comparison data to maximum of 30 genomes per country per month per year. In this way, countries with high sequencing rates do not become overrepresented in our comparison dataset.

Some of these biases could be elicited through comparison to international travel data. For example, are the US and England also the top two countries from which most travellers arrive into Kenya? If such additional analyses are out of scope, it seems warranted to either strongly point to the substantial limitations of this analysis, or remove it altogether.

We concur with the reviewer on the potential bias that could exist in conclusions that arise from inferring sources of importations based on genomic data alone, available from only a few countries. However, vital quality and curated international travel data into Kenya during the study period was not available to us at the time of this analysis. We have therefore agreed to remove the previous analysis on potential origins and destinations of observed Kenya lineages from the revised manuscript.

What is perhaps striking is that Tanzania is entirely missing from this list, given extensive spread there. Another analysis that could be useful is a comparison of country specific lineage compositions, which might bypass some of the difficulties associated with substantial differences in sequence sampling/reporting rates.

SARS-CoV-2 genomic data from Tanzania has not been publicly shared to date, and hence is not included. And as indicated above, we have removed the analysis that was trying to infer sources of SARS-CoV-2 importations into Kenya.

To hypothesize on the potential lineages circulating in Tanzania, we have added a sentence detailing that 5 Pango lineages were identified among the 34 Tanzanian nationals who provided samples that were sequenced: B.1 (n=10), B.1.1 (n=10), B.1.351 (n=8), A (n=5) and A.23.1 (n=1)

• Line 536: it seems problematic that the data used in the import/export analysis did not contain all available African sequences. Can these be included in the corresponding analysis please.

In the revised manuscript we have included all accessible, good quality and contemporaneous Africa genomes in the revised manuscript (n=21,150). However due to the huge computational processing power need to process the phylogenetics for such large sequence data sets, we split the analysis into two parts, each with approximately 10,000 genomes (see Figure 3-figure supplement 1).

Notably with the increased sample size (including the analysis of 390 more genomes from coastal Kenya), we detected far more imports of SARS-CoV-2 into Coastal Kenya compared to our previous analysis (n=280 vs n=69) but only a modest change in exports (n=95 vs n=105) and inter-county virus movement events (239 vs 190).

Reviewer #2 (Public Review):

Agoti et al. analyzed SARS-CoV-2 samples collected from infected patients in coastal Kenya, collected between March 2020 and February 2021. This period spans the first two waves of COVID-19 in Kenya, and the authors aimed to understand the lineages circulating throughout the region, in comparison to the virus circulating elsewhere in Kenya and in the world. The manuscript is clearly written, and the figures and results are thorough and well described throughout. These data add to our understanding of COVID-19 in Kenya and in East Africa, and the discussion of how different lineages spread in Kenya (single clusters versus dispersed over several regions) is both interesting and potentially useful for informing public health measures.

The analyses are well done and excellently presented, but this paper is significantly lacking in a discussion of how sampling bias may affect the stated conclusions. Additionally, the paper focuses almost exclusively on genomic data and fails to closely examine epidemiological factors that may better contextualize the results presented.

We thank the reviewer for bringing this to our attention, we have added the paragraph below to the revised manuscript.

“Sampling bias is a potential limitation of this study arising from the fact that (a) demographic characteristics (age distribution, travel history and nationality) of the sequenced versus non-sequenced sub-sample differed significantly, (b) <10% of confirmed SARS-CoV-2 infections in Coastal Kenya were sequenced, prioritizing samples with a Ct value of <30.0 (Table 1); (c) the Ministry of Health case identification protocols were repeatedly altered as the pandemic progressed (Githinji et al., 2021) and (d) sampling intensity across the six Coastal counties differed, probably in part due to varied accessibility of our testing center that is located in Kilifi County (Figure 1A and Table 1). This may have skewed the observed lineage and phylogenetic patterns. To better contextualize the genomic analysis results, close examination of the case metadata is important, but unfortunately there was a lot of the metadata was missing (e.g., travel history, nationality, Table 2) which made it hard to integrate genomic and epidemiological data in an analysis. Although all analyzed genomes had > 80% coverage, very few were complete or near complete (>97.5%, n=344) due to amplicon drop-off or low sample quality and this may have reduced the overall phylogenetic signal.”

Specifically:

1) The authors do not discuss the potential effects of sampling on their import/export analyses. For example, they find that the USA and England are in the top six country sources of SARS-CoV-2 importation into coastal Kenya, as well as in the top six country destinations of viral export from the region. These two countries have generated huge numbers of sequences compared to the rest of the world, which may clearly bias these findings. While the authors do evaluate the sensitivity of their analyses by repeating them with different global subsamples, it is unclear if these subsamples corrected for large discrepancies in available data from different parts of the world.

We concur and appreciate that sampling bias is indeed a common limitation in the type of analysis we have undertaken given the variation in data collection across geographies. Some of the approaches we took to correct for this have been highlighted in our responses to reviewer #1.

In the revised manuscript, we have undertaken a reanalysis with a larger and more representative dataset at all scales of observation (Figure 3-figure supplement 1). Specifically, for the global dataset, we have revised our sub-sampling script to pick up the comparison dataset uniformly across months and countries for non-African countries. All the available African genomes have been included in our analysis including 605 collected in Kenya outside the coastal regional.

Similarly, the authors find that new variant introductions were mainly through Mombasa city, but most of the Kenyan sequences were from this region, so it is perhaps unsurprising that more lineages were found there. The authors should repeat their analyses with a more representative global subsample, or at the very least discuss these caveats in the discussion and discuss what other evidence there may be to support their findings.

Our sequencing rate by county is approximately proportional to the total number of cases seen in the county (Table 1 and Figure 2E). For Coastal Kenya, the revised manuscript included 389 additional genomes from coastal Kenya that became available while the manuscript was under review.

Thus, in the revised manuscript, we have addressed the valid sampling bias concerns of the reviewers and editor by: (i) increasing the number of analyzed genomes in our dataset for previously under-represented periods and regions, (ii) including contemporaneous Kenyan genomes from outside the coastal counties in our import/export analysis, (iii) including all available Africa genomes into the analysis and selecting a balanced global sub-sample for inclusion into the analysis. In addition, were have also provided a paragraph in the discussion section highlighting sampling bias as a caveat to interpretation of the findings of the current study:

“The accuracy of the inferred patterns of virus importations to and exportations from coastal Kenya are in part dependent on both the representativeness of our sequenced samples for Coastal Kenya and the comprehensiveness of the comparison data from outside Coastal Kenya. Our sequenced sample was proportional the number of positive cases reported in the respective Coastal Kenya counties (Figure 2E and Table 1). Also, we carefully selected comparison data to optimize chances of observing introductions occurring into the coastal region (e.g. by using all Africa data). But still there remained some important gaps e.g. non-coastal Kenya genomic data was limited (n=605). Despite this, we think the results from ancestral state reconstruction indicating that Mombasa is a major gateway for variants entering coastal Kenya is consistent with (a) the county showing the highest number lineages circulating (n=28) during the study period compared to the other five remaining Coastal counties Kenya, (b) approximately half (n=21, 49%) of the detected lineages in coastal Kenya had their first case identified in Mombasa and (c) Mombasa had an early wave of infections compared to the other Coastal counties and (d) is the most well connected county in the region to the rest of the world (large international seaport and airport and major railway terminus and several bus terminus).”

2) Restriction measures enforced by the Kenyan government are briefly introduced at the very beginning of the manuscript and then mentioned at the very end as a possible explanation for observed transmission patterns. However, there is very limited discussion of the potential effect of restriction measures throughout, and no formal analyses are presented using this kind of epidemiological information. Adding formal analyses to back up the hypothesis that relaxation of interventions may have driven the second wave of infections would make this paper much stronger and potentially more interesting.

In the revised manuscript, we have detailed the restriction measures the government of Kenya put in place in the introduction, methods, and results sections and discussed where appropriate on how we think they impacted the observed transmission patterns. We have added Supplementary Table 1 that provides the dates the various measures took effect or were relaxed.

In a separate piece of work (Brand et al, 2021 published in Science journal, 10.1126/science.abk0414), we investigated the potential drivers of the first three waves of infection observed in Kenya and we have appropriately referenced this in the revised manuscript.

We feel that additional analyses on the impact of the restriction measures on SARS-CoV-2 epidemiology and the lineage patterns observed are beyond the scope of this work whose focus was primarily genomic epidemiology.

3) Generally, the text of the manuscript focused on waves of SARS-CoV-2 transmission, while the analyses presented data aggregated by month. A clearer connection between month and wave (particularly visually, on the figures themselves) would aid in interpretation of the data presented.

This is a valid concern and a good suggestion. In the revised manuscript, for all temporal plots, we have added a line to demarcate when we switched from wave one to wave two period. Similarly, for several analyses, we have provided aggregations by wave period rather than by month.

4) One of the strengths of this manuscript is the depth to which the authors discuss the detection of specific lineages in coastal Kenya. However, there is limited discussion of these results in the context of when various lineages appeared or disappeared globally, though these details are presented in a table. Discussing the appearance of the various lineages (was it surprising to see a particular lineage at a certain time or in a certain place?) would also improve this manuscript.

In the revised manuscript, we have compared the patterns of lineage detection locally compared to all Kenya and to all continents in the newly added Figure 3. We have also discussed this aspect for the most frequent 4 lineages in both Wave one and Wave two.

Author Response:

Reviewer #1:

Hauser et al, analyze two large datasets of GPCR-G protein interactions/couplings ("Inoue" and "Bouvier"), comparing and combining them with the widely-used literature-based Guide to Pharmacology (GtP) database. As the Inoue and Bouvier datasets were based on different experimental setups, this enables the identification of which couplings are supported by more than one method. The authors also establish a normalization protocol that enables to move from qualitative to quantitative comparisons and identify couplings that might be either below are above a rigid threshold. Overall, the paper describes a new resource and the methodologies used to build this resource. The resulting coupling map is available through the GPCRdb website, a widely used resource in the field.

The authors have thus improved the ability of researchers to assess prior results and compare them to their own new data. This resource clearly and significantly upgrades options currently available and will likely be of interest and prove quite useful to scientists both in academia and in industry.

We thank the reviewer for so nicely describing the study and its prospective application.

Weaknesses include:

• The data is described mostly by broad numbers, such as the number of receptors or coupling in a subset, or percentages. While this is helpful to understand the data, this reviewer found it hard to follow the mountain of numbers. A suggestion would be to add a section where the authors pick selected examples of particular experimental data and show how their combine database can resolve previously unanswered (or wrongly answered) questions of GPCR/G protein coupling.

We have removed numbers in several places throughout Results where we had included multiple measures e.g., absolute numbers and percentages. Furthermore, where an overall number has been broken down into distributions, e.g., across different G proteins of families thereof, we moved other numbers to parentheses.

The different sections of Results that answer questions of GPCR-G protein coupling have now been presented more clearly by updating their headings and grouping them all in a subsection of part of Results called “Research Advances – Insights on GPCR-G protein selectivity”. These sections are all based on our “combined database”/coupling map. In each such section, we start at the overall level – covering all GPCRs and/or G proteins – but then give selected examples thereof that are weaved into and exemplifies the text. This approach has also been used in the new Results section “Differential tissue expression gives G proteins in the same family large spatial selectivity”, which gives selected examples of G proteins with specific tissue expression profiles.

Given that the paper has already exceeded the maximum of 5,000 words by quite a bit, we think that this approach of weaving selected examples into each selectivity insight section is the most appropriate, and that it brings most clarity. Furthermore, we hope that readers will be inspired to use our coupling map to generate additional questions for future experiments.

• The paper does not reveal new biological findings. For example, while some emphasis is placed on new data on G15, it would be helpful to take the extra step and use this to suggest new biological insights.

eLife’s author guidelines (https://reviewer.elifesciences.org/author-guide/types) state that “Tools and Resources articles do not have to report major new biological insights or mechanisms, but it must be clear that they will enable such advances to take place, for example, through exploratory or proof-of-concept experiments.” In case this manuscript is published as a Tools and Resources paper, it may therefore be sufficient to provide the foundation for future studies to reveal new biological findings.

Nevertheless, the coupling map led to biological findings relating to patterns and mechanisms of GPCR-G protein selectivity that were not described in the original studies. I.e., while this study did not generate new data, it arrived at new insights based on published data. This seems to be in line with eLife’s publication format “Research Advances” (https://reviewer.elifesciences.org/author-guide/types), and the Analysis format of several other journals. Some insights described herein have not been presented before while others have been updated in scope and precision. Furthermore, we have added a new section of Results with insights on G protein expression profiles and co-expression.

We have clarified this by updating the headings of the sections that present these insights, and grouped them under a common subheading of Results termed “Research Advances – Insights on GPCR-G protein selectivity”. However, in case we have overlooked very recent studies describing some of the same biological insights, we would please like to ask for their references and would be more than willing to revise the manuscript again to incorporate them. Furthermore, if the Reviewer is missing a particular analysis that is critical to understand GPCR-G protein coupling, please let us know.

• The authors cautiously label couplings supported by only one dataset as "unsupported". It would seem more helpful to grade couplings by a reliability scale, providing users with a wider set of data. Perhaps only couplings that are directly conflicted by negative data should be labeled as unsupported?

We understand that the term “unsupported” has been used in a confusing way. We have now replaced this term with “unique” and explained all terms in Table 1 of the revised manuscript.

To address the need for a means to grade or filter couplings by reliability, we have added the following paragraph to the manuscript:

“To enable any researcher to use the coupling map, we have availed a “G protein couplings” browser (https://gproteindb.org/signprot/couplings) in GproteinDb (2). By default, this browser only shows “supported” couplings with evidence from two datasets, but there is an option (first blue button) to changes the level of support to only one (for most complete coverage of GPCRs) or to three (for the highest confidence) sources. We propose a standardized terminology to describe couplings based on their level of experimental support from independent groups (Table 1). The criterion of supporting independent data, and the terms “proposed” and “supported”, are already used by the Nomenclature Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR) for GPCR deorphanization. Furthermore, the online coupling browser allows any researcher to use only a subset of datasets, or to apply filters to the Log(Emax/EC50), Emax, and EC50 values. Finally, users can filter datapoints based on a statistical reliability score in the form of the number of SDs from basal response."

Furthermore, we have added references to the online G protein coupling browser in the:

(1) Introduction ending: “On this basis, we develop a unified map of GPCR-G protein couplings that can be filtered or intersected in GproteinDb …”, (2) Fig. 2 legend ending: “Note: Researchers wishing to use this coupling map, optionally after applying own reliability criteria or cut-offs, can do so for any set of couplings in GproteinDb (1).” (3) Fig. S2 ending: “Unique couplings are hidden by default in the online G protein couplings browser in GproteinDb, as they await the independent support by a second group.”

To many scientists the most reliable option is to involve NC-IUPHAR. Gloriam is a corresponding member of NC-IUPHAR, which has mentioned the possibility of involving its many worldwide pharmacological experts to update GtP on a case-by-case basis for receptors. For example, many of the “novel” couplings jointly supported by Bouvier and Inoue may be added. This option is advantageous as it involves experts in each receptor system (often with knowledge of other relevant studies) and is backed by the authoritative organization.

• Given that this manuscript includes authors from both the Inoue and Bouvier studies, I can understand why they are not directly assessing which of the two datasets (in relation to the GtP) might be more accurate. Nevertheless, I believe this assessment should be done and that the advantages and disadvantages of the two experimental systems discussed clearly.

We believe that the three-way intersection of couplings is the most informative and therefore preferred over individual comparison of each of the Inoue and Bouvier datasets to GtP. GtP is unfortunately not suitable as a stand-alone resource – neither to contradict nor support couplings (on the G protein subtype level). This is because GtP is incomplete (especially for G12/13) and does not provide any information on the level of G protein subtypes, only families. The three-way interactions will always use GtP but adds a second dataset on top of this when validating a third dataset. Our manuscript already included a three-way intersection of datasets, allowing readers to conclude which dataset might be more accurate (then Fig. 3 and Spreadsheet 3) on a per-G protein basis.

In the revised manuscript, we have rewritten this section, which now has the heading “Bouvier’s and Inoue’s biosensors appear more sensitive for G15 and, Gs and G12, respectively. We have also made a completely new figure, Fig. 7, which more clearly illustrates for which G proteins that Bouvier and Inoue may have overrepresented or underrepresented couplings. This section specifically investigates the question of whether differential sensitivity can explain “unique” couplings. However, such unique couplings can either be due to overrepresentation or instead be true positives that are missing in GtP because of incompleteness and in the other biosensor due to lower sensitivity. Unfortunately, we will not be able to distinguish these possibilities until the research community has gained additional datasets from independent biosensors with as high sensitivity.

Whereas our study compares datasets rather than experimental systems, we have added a paragraph in the Discussion describing which aspects should be considered when choosing a biosensor. There, we reference a review from last year dedicated to biosensors and describing their pros and cons (3), and the accompanying paper by Bouvier et al. (4), comparing several aspects of the experimental system used by Inoue et al. It is also important to note that the most advantageous biosensor may be one of the two for which data is analyzed in our paper. For many studies, researchers may instead be better off with another biosensor, for example those from Lambert/Mamyrbekov (5), Roth (2) (Gαβγ sensors first described in (6-11)) or Inoue (unpublished dissociation assays using wt G proteins fused with LgBit and HiBit). These are all referenced in the Discussion.

References:

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Reviewer #2:

This study is a meta-analysis of previously reported studies on G protein-coupled receptor (GPCR) coupling to G proteins. The data sets are from three distinct sources: a compendium compiled by the International Union of Basic & Clinical Pharmacology (IUPHAR), and two data sets compiled by two separate laboratories. Each of these data sets describes the coupling of members of the superfamily of non-sensory GPCRs (~200 genes) to the large family of G protein alpha subunits (~20 genes). The authors try to arrive at a consensus for receptor-G protein coupling from the three data sets, as well as identify and highlight differences or incongruencies. Compiling these vast data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies. A better understanding of receptor-G protein selectivity and/or promiscuity will ultimately help in identifying safer therapeutics.

We appreciate the summary and the explanation of the usefulness of our meta-analysis and its potential impact.

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Reply to the reviewers

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

This article focuses on one possible outcome of protein sequence evolution after duplication, in which the residue distribution at specific positions of a multiple sequence alignment becomes uncoupled from the distribution expected from the phylogeny of the protein family. The authors call these events "residue inversions" and interpret them as the result of functional pressures on family members with diverging cellular roles. Based on a theoretical model of residue evolution after duplication of the coding gene, the authors describe the criteria for categorizing a particular position in a protein as a "residue inversion" and develop an algorithm to identify such events in a multiple alignment. They then apply their approach to the family of Epidermal Growth Factor Receptors in Teleost fishes and identify 19 EGFR positions in a dataset of 88 fish genomes, which satisfy the criteria of "residues inversions". They provide support to the scoring scheme used in their approach through a simulated evolution run and conclude from a comparison of their positions to the ones predicted by SPEER to represent Specificity Determining Sites that the two are largely orthogonal and may therefore complement each other in sequence-based function prediction.

Major comments: 1. Throughout the paper, the functional involvement of positions subject to "residue inversions" is indirect, inferred from the literature, and in parts sparse and tenuous. It therefore remains unclear to what extent the interpretation that "residue inversions" represent functional adaptations is correct. The authors acknowledge this uncertainty in several places, including the Conclusions.

We agree with the reviewer that without experimental validation an uncertainty about the data interpretation remains, however testing protein function on a large scale and in non-model organisms is extremely challenging. Since we were aware of this obstacle, we validate our conclusions in different ways: 1. the theoretical model and the simulated MSA both show a lower chance of observing residue inversions than what we detected in the teleost fish EGFR example. 2. previous literature highlighted an identified inverted residue as the possible cause of sub-functionalization of teleost fish EGFR. 3 We generated the alpha fold models of teleost fish EGFR and performed molecular dynamic simulation of the two copies, in complex with the ligand. In our simulations, we see the same trend that we observe with the inter-paralog inversions at the functional level. The new results have been integrated in line 692-706.

"Residue inversion" is a very unintuitive term, which took me several readings to penetrate and made reading the article difficult. The authors may wish to reconsider this term. Naively, a residue inversion would be the swapping of residues between two positions, such that a residue expected in position A is found in position B, while the residue expected in B is found in A. That is what I suspect most readers will think.

We acknowledged that the terminology might be confusing. We therefore decided to define it as inter-paralog inversion of amino acids throughout all the text.

Is the phenomenon described here just a curiosity, or an important aspect of divergent evolution after duplication? The authors seem to be of two minds about it, calling the phenomenon "rare" in the Abstract, but an "important and understudied outcome of gene duplication" in the Introduction, then hedging again that it "might be rare" in the Conclusions. The benefits of recognizing such positions are also formulated with great caution, for example in lines 309-311: "In summary, the identification of residue inversion event has the potential to improve functional residue predictions".

We agree with the reviewer that we did not yet test the recurrence of this event on a large scale, however this does not exclude that this event is frequent. This work is focused on the observation, characterization, and implications of this event. Considering this comment and the one below we decided to perform a further analysis (see below for more details).

Additionally, the analysis of the frequency of this event at the whole-organism scale on multiple organisms, while interesting, would be out of the scope of this paper, if not just because it requires a totally different (large-scale) approach compared to the one used in here. This type of analysis is also limited by the absence of a database collecting intermediate knowledge that would speed up the initial part of ortholog classification at a broad range.

Finally, by rarity we mean the statistical chance of the event, not considering the effective chance of observing it from the real data. In fact, we rectified in the text using the reviewer’s observation.

OLD VERSION (ppXX):

Our work uncovers a rare event of protein divergence that has direct implications in protein functional annotation and sequence evolution as a whole.

NEW VERSION:

Our analysis shows a new way to investigate an important and understudied outcome of gene duplication.

It would probably strengthen the article substantially if the authors would (I) use their program to scan a large number of multiple alignments in order to establish more reliably how frequent this phenomenon actually is, and whether it is universal or a specifc aspect of eukaryotic, maybe even only vertebrate evolution; and then (II) mapped the positions identified on structural models for the proteins, obtained by homology modeling or AlfaFold prediction, in order to substantiate their potential origin as functional adaptations.

We thank the reviewer for the thoughtful suggestions. (I) we tested the inter-paralog inversion score at the proteome level using a reduced dataset (70) of reference teleost fish proteomes from Uniprot. We obtained 54 proteins that duplicated in the teleost specific whole genome duplication, then we run our pipeline on it. We found that the overall distribution of scores is more similar to the simulated evolution experiment rather than to the EGFR test case. We integrated the new results and discussion in a new paragraph and new figure in line 708-716.

(II) We considered also the analysis requested in the second point. Unfortunately, we could not extract any meaningful data from the AlphaFold models.

Reviewer #1 (Significance (Required)):

A method to improve the functional annotation of proteins in a paralogous family would be very useful, given the abundance of sequence data.

We thank the reviewer for acknowledging the importance of the question that we have addressed.

I am knowledgeable in varios aspects of molecular evolution and functional annotation. I am neither a mathematician, nor a developer of phylogenetic methods, so I cannot judge these aspects of the paper.

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

Review of Pascarelli and Laurino titled “Identification of residue inversions in large phylogenies of duplicated proteins”

I find the topic of the paper very exciting and long overdue. Indeed, I was under the impression that the question of parallel evolution in paralogous copies must have been addressed long ago: to my surprise, having looked in depth at the literature, that is only partially so. The manuscript, therefore, addresses a relatively novel and fundamental question of broad interest.

We thank the reviewer for his positive comment.

Having said this, I also found the manuscript to suffer from an identity problem, which in many places encroaches on the underlying quality of the science. I will structure my review into three concerns: the identity issues, the novelty issue and the emergent quality issues from the two.

Identity issues:

The manuscript is primarily dealing with an evolutionary issue – or I am biased to see it this way as an evolutionary researcher myself. Nevertheless, much of the language and terminology of the paper either misuses evolutionary terms or invents new ones in its place with a bias towards a protein chemistry perspective. Specifically, what the authors call “residue inversions” is called “parallel evolution” or “convergent evolution” in the literature. Also, "residues" are typically used for physical amino acids in a structure. If we are talking about sequence level “amon acid” would be a better term. The issue is further confounded by the meaning of “inversion” in genetics as a single mutation that inverts the position of nucleotides (i.e. an “AT” becomes “TA”).

I strongly recommend for the authors to become familiarized with the common usage of existing and widely used terms in evolutionary biology that describe the phylogenetic patterns they see: parallel evolution, convergent evolution, homoplasy, etc, and to use them consistently throughout the manuscript.

The same goes for "mutation", which the authors confuse on two levels: evolutionary and biochemical. Sometimes the authors refer to “mutation” of amino acids (which can be entertained at some level, but from a genetic perspective only nucleotides mutate – in the protein biochemistry field this term is frequently applied to amino acid residues, which is the basis of the identity issue). However, since the authors also use “mutation” to refer to a “substitution” (which is what we call a mutation that has become fixed in evolution) this creates another level of confusion. I urge the authors to change this aspect of the language of the manuscript to better reflect evolutionary concepts.

As part of the language issues I am not sure how meta-functionalization in the author’s view differs either from neofunctionalization or specialization of duplicated genes.

We thank the reviewer to point out the terminology issue, this will also help reaching a broader audience. We clarify the confusion surrounding the terms “mutation” and “residue inversion” by changing the former to “substitution”, while the latter to “inter-paralog inversions” (see also other reviewer comments).

We understand the importance of the usage of the correct term to talk about this event of protein sequences evolution. Therefore, we used convergent and parallel evolution accordingly when we discussed the nuances between Metafunctionalization and parallel evolution in the text, in lines 188 and 399.

Novelty issues:

As I mentioned, the issue of parallel evolution of gene duplications is an extremely interesting topic. I was sure that the people who studied parallel evolution, or those interested in gene duplications, must have published extensively on this. However, my search of the literature revealed only a modest pre-existing effort. Nevertheless, previous efforts are not entirely non-existent and should be cited and discussed in this paper too. The most pertinent example is

https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-020-01660-1

which has an identical setup from what I can tell (compare Figure 1 in each paper).

This paper was not hard to find using "parallel evolution", thus my focus on the language issues in the previous section.

We thank the reviewer for his suggestion, we included the relevant papers in the text in lines 520-523. Interestingly, the cited paper shows that a comprehensive analysis of the fate of duplicated genes at the sequence level was done. However, in this paper, the ‘fate’ of a paralog is determined by counting the number of sites that support one or the other fate, independently of the orthologous relationship. In our study, we start from the orthologous relationship to pre-determine the fate of the paralogous protein, then we identify the sites that break this assumption. Our type of analysis is deemed to work only where the orthologous relationship is unequivocal. That is the reason why we chose an example with relatively short branch lengths after duplication (the teleost specific duplication). Our rationale is that with a higher genome coverage across organisms, resolving the orthologous relationship will get easier in time. However, our study focuses on a distinct case (asymmetric divergence) where the diverging paralogs converge to the same phenotype. In such a case, neutral substitutions related to the ancestral relationship of a protein can be filtered out to better search for functional adaptations.

Content issues:

The lack of attention to evolutionary concepts, in my opinion, provided some missed opportunities for the authors to attack the problem in a more convincing fashion. Specifically, in the setup to distinguish between parallel evolution of paralogues versus orthologues ("inversion" versus "species-specific adaptation" in the author's text) one must be able to distinguish between the two copies and assign true evolutionary relationship. In practice, that is not always possible based on tree lengths or topologies alone because of confounding factors such as independent duplications or gene conversion events.

I would feel better about the results of this study if the following two things were integrated.

The use of synteny to better determine homologous relationships (declare copies to be true paralogues if they occupy the same syntenic region). To compare the frequency or parallel evolution of paralogues versus orthologues as a null model of the expected number of parallel events in paralogous copies.

We agree that a synteny analysis has to be included. We tested it for the EGFR proteins in fish and the results support the orthologous relationship of EGFRa and EGFRb in the two groups compared (Cypriniformes versus other teleosts). The results were included in the text and in the Supplementary figure in lines 303-305.

The second point targets the way the model derives the expectations: at the author's own admission the model makes a number of unrealistic assumptions, ") equal branch length between the two paralogs; 2) only zero to one mutation can occur in each of the six branches; 3) after a mutation, each residue is equiprobable; 4) no selective pressure; 5) the probability of a mutation on a branch solely depends on the branch length (mutation rate). The authors do not really test the resulting tree on deviation from these assumptions (I am sure that it does not conform) but essentially comparing the occurrence of parallel events in paralogues versus orthologues may solve the problem with a less restrictive set of assumptions (that one expects an equal number of parallel events in paralogues and orthologues unless there is some paralogue-specific selection pressure, which is what the authors are looking for.

We compared the occurrence of the two outcomes in both the simulation and in the real data. In all cases, the two score distributions have a very similar shape, with a 99th percentile score of respectively 0.062 and 0.113. Most sites in an alignment (>99%) are not expected to be inverted and will have scores very close to 0, making the identification of inversions a quest for outliers. Furthermore, in case of the real data, each distribution can be independently affected by different selective pressures that might bias the background distribution. While the inversion in paralogs is expectedly involving few, functional, residues, the inversion in orthologs is expected to have a broad effect. For example, a temperature adaptation might shift the number of polar residues on the protein surface (see for example: https://academic.oup.com/peds/article/13/3/179/1466666). Also, a different protein chosen for analysis might generate a different background distribution of the two events. In the larger dataset, the similarity of the two distributions is even more (99th percentile of 0.07 and 0.08). Because of the shown similarity of the two event distributions, and the possible issues with different selective pressures, we leave the analysis suggested by the reviewer as a post-processing possibly performed by the user. We report a summary of this result born from the reviewer’s observation in line 478.

In summary, I believe that the topic is very interesting, the authors potentially found a new aspect of evolution of a specific gene family. However, in my opinion a major revision is needed to unite this text with the terms in the field, the previous publication and to integrate the two additional analyses I suggested.

Minor Comments:

I started adding these specific comments before generalizing the broader deviation from the common evolutionary language. There are more further along in the manuscript, but in the interest of time I will not articulate them here hoping that the authors will first try a major revision targeting these issues.

Line 64: While neutral mutations help to determine the phylogenetic position of a protein, mutations of functional residues are a signal of functional shifts that might occur independently of the phylogeny. - this is quite misleading. All substitutions (neutral or beneficial) have a phylogenetic signal. In any case, this is discussed here in phylogenetic terms: https://pubmed.ncbi.nlm.nih.gov/10742039/

We corrected the sentence to refer to divergence time instead of phylogenetic signal.

OLD VERSION:

While neutral mutations help to determine the phylogenetic position of a protein, mutations of functional residues are a signal of functional shifts that might occur independently of the phylogeny.

NEW VERSION:

While neutral substitutions are directly proportional to the time of divergence, a change in functional residues could be a signal of a functional shift that might occur independently of the divergence time.

Line 107: "under high evolutionary pressure" - I do not know what evolutionary pressure is nor why it can be high or low.

We corrected the term to “selective pressure”.

OLD VERSION:

Lorin et al. showed that both copies of EGFR might have been retained because they are involved in the complex process of skin pigmentation (40), which is under high evolutionary pressure in most fish.

NEW VERSION:

Lorin et al. showed that both copies of EGFR might have been retained because they are involved in the complex process of skin pigmentation (40), a trait that is under selective pressure in most fish

Line 112 "linearly inherited across orthologs" - linear is a poor choice of a word here. The first thing that comes to my mind is quadratic inheritance as an alternative. Perhaps the authors are looking for "vertical" versus "horizontal" - these are established terms in phylogenetics (think "horizontal gene transfer").

We corrected the term to “vertically inherited”.

OLD VERSION

Therefore, the power to predict functional residues is limited by our ability to track protein function on the phylogenetic tree when it is not linearly inherited by orthologs.

NEW VERSION

Therefore, the power to predict functional residues is limited by our ability to track protein function on the phylogenetic tree when it is not vertically inherited by orthologs.

It is my invariant practice to reveal my identity to the authors,

Fyodor Kondrashov

Reviewer #2 (Significance (Required)):

Addressed in the above

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

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Referee #2

Evidence, reproducibility and clarity

Review of Pascarelli and Laurino titled "Identification of residue inversions in large phylogenies of duplicated proteins"

I find the topic of the paper very exciting and long overdue. Indeed, I was under the impression that the question of parallel evolution in paralogous copies must have been addressed long ago: to my surprise, having looked in depth at the literature, that is only partially so. The manuscript, therefore, addresses a relatively novel and fundamental question of broad interest.

Having said this, I also found the manuscript to suffer from an identity problem, which in many places encroaches on the underlying quality of the science. I will structure my review into three concerns: the identity issues, the novelty issue and the emergent quality issues from the two.

Identity issues:

The manuscript is primarily dealing with an evolutionary issue - or I am biased to see it this way as an evolutionary researcher myself. Nevertheless, much of the language and terminology of the paper either misuses evolutionary terms or invents new ones in its place with a bias towards a protein chemistry perspective. Specifically, what the authors call "residue inversions" is called "parallel evolution" or "convergent evolution" in the literature. Also, "residues" are typically used for physical amino acids in a structure. If we are talking about sequence level "amon acid" would be a better term. The issue is further confounded by the meaning of "inversion" in genetics as a single mutation that inverts the position of nucleotides (i.e. an "AT" becomes "TA").

I strongly recommend for the authors to become familiarized with the common usage of existing and widely used terms in evolutionary biology that describe the phylogenetic patterns they see: parallel evolution, convergent evolution, homoplasy, etc, and to use them consistently throughout the manuscript.

The same goes for "mutation", which the authors confuse on two levels: evolutionary and biochemical. Sometimes the authors refer to "mutation" of amino acids (which can be entertained at some level, but from a genetic perspective only nucleotides mutate - in the protein biochemistry field this term is frequently applied to amino acid residues, which is the basis of the identity issue). However, since the authors also use "mutation" to refer to a "substitution" (which is what we call a mutation that has become fixed in evolution) this creates another level of confusion. I urge the authors to change this aspect of the language of the manuscript to better reflect evolutionary concepts.

As part of the language issues I am not sure how meta-functionalization in the author's view differs either from neofunctionalization or specialization of duplicated genes.

Novelty issues:

As I mentioned, the issue of parallel evolution of gene duplications is an extremely interesting topic. I was sure that the people who studied parallel evolution, or those interested in gene duplications, must have published extensively on this. However, my search of the literature revealed only a modest pre-existing effort. Nevertheless, previous efforts are not entirely non-existent and should be cited and discussed in this paper too. The most pertinent example is

https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-020-01660-1

which has an identical setup from what I can tell (compare Figure 1 in each paper).

This paper was not hard to find using "parallel evolution", thus my focus on the language issues in the previous section.

Content issues:

The lack of attention to evolutionary concepts, in my opinion, provided some missed opportunities for the authors to attack the problem in a more convincing fashion. Specifically, in the setup to distinguish between parallel evolution of paralogues versus orthologues ("inversion" versus "species-specific adaptation" in the author's text) one must be able to distinguish between the two copies and assign true evolutionary relationship. In practice, that is not always possible based on tree lengths or topologies alone because of confounding factors such as independent duplications or gene conversion events.

I would feel better about the results of this study if the following two things were integrated.

The use of synteny to better determine homologous relationships (declare copies to be true paralogues if they occupy the same syntenic region). To compare the frequency or parallel evolution of paralogues versus orthologues as a null model of the expected number of parallel events in paralogous copies.

The second point targets the way the model derives the expectations: at the author's own admission the model makes a number of unrealistic assumptions, ") equal branch length between the two paralogs; 2) only zero to one mutation can occur in each of the six branches; 3) after a mutation, each residue is equiprobable; 4) no selective pressure; 5) the probability of a mutation on a branch solely depends on the branch length (mutation rate). The authors do not really test the resulting tree on deviation from these assumptions (I am sure that it does not conform) but essentially comparing the occurrence of parallel events in paralogues versus orthologues may solve the problem with a less restrictive set of assumptions (that one expects an equal number of parallel events in paralogues and orthologues unless there is some paralogue-specific selection pressure, which is what the authors are looking for.

In summary, I believe that the topic is very interesting, the authors potentially found a new aspect of evolution of a specific gene family. However, in my opinion a major revision is needed to unite this text with the terms in the field, the previous publication and to integrate the two additional analyses I suggested.

Minor Comments:

I started adding these specific comments before generalizing the broader deviation from the common evolutionary language. There are more further along in the manuscript, but in the interest of time I will not articulate them here hoping that the authors will first try a major revision targeting these issues.

Line 64: While neutral mutations help to determine the phylogenetic position of a protein, mutations of functional residues are a signal of functional shifts that might occur independently of the phylogeny. - this is quite misleading. All substitutions (neutral or beneficial) have a phylogenetic signal. In any case, this is discussed here in phylogenetic terms: https://pubmed.ncbi.nlm.nih.gov/10742039/

Line 107: "under high evolutionary pressure" - I do not know what evolutionary pressure is nor why it can be high or low.

Line 112 "linearly inherited across orthologs" - linear is a poor choice of a word here. The first thing that comes to my mind is quadratic inheritance as an alternative. Perhaps the authors are looking for "vertical" versus "horizontal" - these are established terms in phylogenetics (think "horizontal gene transfer").

It is my invariant practice to reveal my identity to the authors,

Fyodor Kondrashov

Significance

Addressed in the above

i guess in a SF world the thought of having a diverse appearance is important in order to combat prejudice but in the world we live in today i don't know if thats always the case. How many ads do you see today with people of color for companies who in the past haven't always been the most diverse (the film industry). Its like white people have found away to hide their red hands behind our colored faces for the perception of diversity. who is diversity really helping if white people are the ones cashing in. Hers is a list of companies that appear to promote diversity and inclusion but actually don't. Amazon, Apple, Bank of america, Cisco, Facebook, KFC, Lego, L'Oreal, Loius Vuitton. all of these companies have spent a lot of money to appear one way but when you look at who is really profiting on their board of directors, they are all white and mostly men. To be clear i think Delany's point is valid and i agree with it but a lot has happened since and we cant continued to be fooled at face value. Forget having a seat at the table, if it's only for perception.

Reviewer #1 (Public Review):

The premise of this paper is that a significant amount of microbial diversity might be maintained not purely through resource partitioning, as has been the thrust of multiple recent papers over the last few years, but perhaps also through "physical" differences between organisms---here manifested by the detachment rate of heterotrophic bacteria from resources in the form of particulate matter. I completely agree with that premise, and agree that this is an underexplored niche axis that is important to account for when seeking to understand coexistence and diversity.

As with any mathematical model, the assumptions made are critical to get right, and different assumptions about the details of resource uptake, dispersal, and competition may lead to different conclusions. So my comments primarily relate to some of these mathematical choices, as well as to their explanation in the text.

-- In framing the paper, I think the authors are right to focus on dispersal and detachment as under-explored mechanisms. But readers will benefit from reference to other work (even on particle-associated microbes) related to resource diversity, succession, and crossfeeding. That can only help put the current study in context with other mechanisms for the maintenance of microbial diversity.

-- There is a population growth process when a cell settles on a new particle. This is assumed to be logistic growth, though in the end, it seems likely that the precise dynamics of the growth process don't matter so much as the final abundance (carrying capacity). However, this seemed subtle to me for three reasons:

(i) Will detachment rate directly affect carrying capacity?

(ii) Is carrying capacity occurring when microbes fill out the surface of a particle, or when they have eaten the entire volume of a particle?

(iii) If the former, will particles continue to be shed from the particle as growth continues approximately linearly?

It's possible that none of this matters too much if all that's important is a final population size. However, it might help to clarify the process for readers if we have a conceptual picture of what this final population size represents (surface of particle being filled? or volume of particle entirely eaten up) and if there is a truer picture of the dynamics than logistic growth.

-- The relationship between the trade-off (between different detachment rates) derived in Eq 2 versus the optimal detachment rate (derived in the methods) is framed a little confusingly. If I understand correctly, the "trade-off" actually comes from the condition that a population will have net non-negative growth rate in the absence of other populations with different strategies. So it may be reasonable to frame this as a threshold---a necessary condition rather than a sufficient condition for a given population to persist. The reason I say this is that it is a bit confusing to have a trade-off that suggests a range of detachment rates can coexist so long as they differ in their carrying capacities, since it is then stated that the optimal detachment rate outcompetes all the others. Maybe I misunderstood something important being assumed about the carrying capacity for the optimal case, but a trade-off that also has an optimum is an odd outcome.

-- In the end, it seems critical that for multiple strategies to be maintained in the population that there is not only whole-particle mortality (which in effect is highly correlated catastrophic dynamics for an individual microbial population), but that the inflow of resources itself fluctuates. Did I interpret that correctly? Readers may appreciate a slightly clearer description of how this environmental stochasticity differs from the previous possibility of whole-cell mortality, and this also left me wondering how to quantity the kind of environmental stochasticity that will generally lead to multiple strategies coexisting.

-- In summary, I think this is a terrific idea and promising analysis that will bear fruit. But I also wanted to understand how robust is the outcome of coexistence to the various assumptions in the model.

This passage shows how it is acceptable and even accepted to have to change your research as you go along. This makes research feel more free and creative rather than strict and boring.

I can attest to this as many times before I have changed my original question or approach after uncovering some bits of research.

Author Response

Reviewer #1 (Public Review):

This is a very solid and exciting study.

We thank the reviewer for finding our study to be very solid and exciting.

I have several suggestions, comments and questions:

1. The authors focused on examining the role of C129 as a regulator of PTPN22 redox sensitivity based on a published crystal structure of the catalytic domain. It would be great if they could demonstrate the existence of the disulfide bond between C129 and C227 also experimentally (in T cells).

As we understand it, it is requested that the disulfide bond between C227 and C129, as previously suggested by Tsai et al. (2009) (1) with pure protein, should be documented to actually occur in the activated T cells. We fully agree that this would improve the study and we have therefore made several attempts to demonstrate this oxidation, or the oxidation state of the active site Cys residue in PTPN22 in situ. However, as we had also expected, it has proven to be technically very challenging. Nevertheless, as the functional consequence of the PTPN22 oxidation and the effect of the C129S mutation is clearly documented in the mouse, using in vivo experiments, we still think it is valid to conclude that the reversible oxidation state of PTPN22 as well as the involvement of the Cys129 residue regulates the function of PTPN22 in vivo, which is the main conclusion of our study.

1. To this end, there are other cysteine residues in the vicinity of C227 such as the C231 that might be involved in the redox regulation PTPN22. The authors should at least discuss the their possible involvement.

It is correct that Tsai et al. (2009) (1) found that mutating C231 to serine dramatically reduced phosphatase activity, thus suggesting its importance in catalysis. Reactivation assays showed higher reactivation rates for C231S mutants, and they suggested that C231 suppresses reactivation in a reducing environment by competing with C227 for reduction in the catalytic pocket. Therefore, C231 could also be a target for negative regulation of PTPN22. However, our project was from the start limited to the intention of studying whether PTPN22 could be shown to be redox regulated in vivo through modification of key cysteine residues, and the aim has not been to give the full picture of how the molecule is regulated. We have now extended this point in the discussion in the paper.

1. How is mutation of C227 affecting T cell function? Are the effects similar with those of C129S?

This would be interesting but to analyze if also the cysteine at 227 is regulating the T cell activation by creating another transgenic C227S mouse is outside the scope of the study. As said above and clearly described in the study, we have focused on the redox-mediated effects through C129 and hope that the reviewer can agree with us that this rather focused study is solid and fully sufficient for publication on its own merits.

1. Although the in vitro evaluation of the PTPN22 activity is of highest quality, it would be good to demonstrate that C227 redox status is modified under physiological conditions. 25-100 µM H2O2 is a high concentration that might not be reached within a cell and might be lethal for T cells.

See response to point 1.

1. C129 seems not to be mutated in patients with autoimmunity but is an excellent tool to test the importance of C227 redox regulation and the findings of this study suggest that its over-oxidation will support autoimmune responses. When considering the clinical relevance of the study, a drug that will protect the oxidation of the catalytic cysteine and/or stabilize the disulfide bond would have beneficial effects. The authors could test such pharmacological modulators in isolated T cells.

Indeed, such modulators would be very interesting to test; however, developing such drugs can hardly be demanded to be within the scope of this study. We have however included a statement on this topic in the Discussion of the manuscript.

1. The authors discuss that NOX2-derived ROS most likely originate from antigen presenting cells. I fully agree with this discussion. However, some studies have proposed that NOX2 plays an important role also in T cells, a finding which was not confirmed by other following studies. It would be great if the authors could address this controversial issue in regards to their findings.

The finding that the ROS that modify PTPN22 in fact come from the interacting APC rather than from the T cell itself we believe is very important. However, we have not made a major point of this as we have shown that aspect before in other studies, and we wanted in the current paper to focus on the take home message that PTPN22 could hereby be shown to be redox regulated in vivo. However, the last word about the source of ROS has not been said. The controversy whether the Ncf1 containing NOX2 complex is functionally expressed in T cells stems from the paper by Jackson et al. in Nat Immunol 2004 (2). We have not been able to reproduce those findings and in addition we have never detected a NOX2 dependent response in pure T cells, which has also been shown in several of our papers. There are certainly many pitfalls, contaminating NOX2 expressing cells, NOX2 containing exosomes and peroxides, and even NOX2 complexes picked up by interactions with antigen presenting cells. However, it is dangerous to completely exclude that Ncf1 could be expressed at minimal levels or to exclude that functional NOX2 complex can indeed be formed in T cells, and we all know that minute levels of any peroxide as produced by cells could have an impact on cellular functions. But, based on the present knowledge we conclude that T cells do not functionally express Ncf1-containing NOX2 complexes. We have now added two references to enlighten this point, (3, 4; refs. 38 & 39 in the manuscript).

1. Fig. 1: Is the addition of bicarbonate affecting the pH and thus the activity of PTPN22?

No, we believe that addition of bicarbonate is not acting by an altered pH but is instead required for formation of peroxymonocarbonate when reacting with H2O2, which is subsequently the molecular species that bypasses the cellular antioxidant systems in order to oxidize the active site Cys residues of target PTPs. This was shown by us in an earlier publication (Dagnell et al, ref. 11 in the manuscript) (5) and a sentence has now been added in the Discussion to further emphasize this point.

1. The H2O2 concentration dependence of PTPN22_C129S should also be shown as for WT (see Fig. 1B)

We agree with the reviewer that titration of the mutant with additional H2O2 concentrations could potentially have been done, but we thought that the comparison of WT and C129S enzyme side-by-side using either 0 µM, 25 µM or 50 µM as in Fig. 1D was a sufficient comparison in H2O2 sensitivity. Unfortunately, we do not have the possibility to analyze more purified C129S mutant protein at the moment and it would require a major effort to run those additional experiments. We thereby hope that the reviewer would agree with having the data presented as they currently are to be sufficient.

1. Quantification of the slope based on only 3 measuring points is not accurate (Fig. 1D).

Each data point in those curves represents the mean ± S.D. derived from duplicate samples ran three different times, with clearly very low standard deviations. Thus, we believe that the data are reliable and that the statistically significant difference when comparing the slopes between WT and the C129S mutant as shown in the figure, should be trustworthy.

1. The pinna thickness measurements shown in Fig. 3B and C suggest that in NCF1 mice C129S has no effect. However, the thickness in NCF1 mice is already much higher than in WT mice (compare B and C). Does this mean that NOX2-derived ROS are the only factor that affects C227 redox properties?

The effects of the decreased ROS due to the Ncf1 mutation is likely to have consequences for the functions of many proteins, in different pathways, and not only of PTPN22. The sum effect is that the Ncf1 mutated mice responds stronger than the wild type, which explains the difference. However, the main message here is that if there is no ROS from the NOX2 complex, the effect of the PTPN22 mutation is lost.

1. The results shown in Fig. 5D could be moved to a supplementary figure.

We prefer to keep it within Fig 5 as it is more logical in the context or the other parts of this figure. Of course, if there is a space layout problem, we can consider moving it.

1. The calcium measurements are not convincing and the differences are rather small. The y axis labels show 50K, 100K etc. Are this ratio values? If yes the imaging settings need to be optimized. Why is the mutant labeled as Pep? How is the C129S affecting calcium signaling? These observations need be examined in more detail or maybe calcium is not playing an important role.

We agree that the differences in calcium measurements are not very large but have nevertheless been repeated several times, and there is a significant difference as shown. The calculation is done on the slope of the curve, which is independent of the absolute values given on the y-axis. We agree that the figure was not properly labeled and have now changed this.

1. I would suggest a more extensive evaluation of the proteomic data presented in Fig. 6D. The results might be very exciting and can further increase the impact of this study.

We fully agree with this. We have chosen not to go into details of the results of the proteomic analysis. The data shown confirms our conclusion and we did not plan to identify the downstream targets of the PTPN22 oxidative regulation. Highlighting some of these targets will require biological confirmation, which can be done but must await future work. The full dataset has however been deposited in PRIDE for any reader interested to analyze the results further.

1. Is 24h BSO treatment not toxic for the T cells (ferroptosis)?

We have not seen any evidence for toxicity upon the BSO treatment of T cells in vitro, which however has been more thoroughly checked by others. Gringhuis et al (JI, 2000) (6) have shown immunofluorescence staining on T cells 72 hours post BSO treatment with intact cell membranes. Additionally, Carilho et al. (Chem. Cent. J., 2013, 7:150) (7) noted no changes in Jurkat T cell viability after 24 hours at a maximum dose of 100 µM BSO.

Reviewer #3 (Public Review):

The manuscript by James, Chen Hernandez et al. reveals a novel function for PTPN22 oxidation in T-Cell activation. The authors used a broad array of methods to demonstrate that PTPN22 is catalytically impaired in addition to being more sensitive to reversible oxidation in vitro. In the characterization process, the authors found that PTPN22 could be directly reduced by Thioredoxin Reductase and that oxidation of PTPN22 oxidation could be easily monitored by the appearance of a faster migrating band in non-reducing gels. Supporting the hypothesis that the catalytic Cysteine forms a disulfide with a backdoor Cysteine (Cys129), the authors found that this C129S mutant is prone to oxidation and cannot be reduced back to its active form by Thioredoxin Reductase. Using a new mouse model in which this key Cysteine of PTPN22 is mutated to a Serine residue (PTPN22C129S mutant) and can presumably not form a stabilizing redox intermediate between the catalytic Cys residue and this backdoor Cys (C227-C129), the authors study how the oxidation prone mutant affects T-Cell activation. The authors find that the C129S mutant mouse showed an increased T-Cell dependent inflammatory response that was dependent on activation of the reactive oxygen species-producing enzyme NOX2. This data adds an interesting redox twist to the function of PTPN22 in T-Cells that contributes to conversation on the protective effects of reactive oxygen species against inflammatory diseases in vivo.

Strengths:

The in vitro characterization of the WT and C129S mutant form of PTPN22 is very thorough. Determination of the Km and Kcat highlights the differences between the two enzymes that go beyond redox regulation of the phosphatase. The reduction studies are masterfully done and highlight a novel reduction mechanism that merits to be further studied in cells. Demonstrating that PTPN22C129S is prone to oxidation in vitro is a key and technically challenging result that may be applicable to other members of the PTP family that also form disulfides with a backdoor cysteine. Showing that PTPN22C129S mice (backcrossed to B6Q mice making them susceptible to autoimmune arthritis) displayed higher T cell activation in two models (DTH and GPI), in addition to studies in T cells stimulated with collagen, increased this reviewer's confidence that the PTPN22C129S mouse exhibited T-cell-dependent inflammatory response phenotype similar to the PTPN22 knockout phenotype. Validation of T-cell signaling events in PTPn22C129S T cells were in line with the in vitro characterization of the phosphatase.

We thank the reviewer very much for the detailed summary of our findings and the appreciative words.

Weaknesses: Although the paper has many strengths, some important weaknesses need to be addressed by the authors. In particular, the authors need to characterize better their mouse model and determine if PTPN22 is reversibly oxidized following TCR activation. If PTPN22 is oxidized, does it form an intramolecular disulfide between C227 and C129? The proposed model, that PTPN22C129S is more prone to oxidation, also has to be validated in vivo. Although this could be technically challenging in theory, the authors have shown that the migration pattern of the oxidized enzyme is different that of the reduced enzyme. Another major issue is that PTPN22 does not appear to be expressed in CD4+ T cells unless these cells are activated in vitro with anti-CD3/CD28 for 24 hours. This makes acute CD3-stimulation of CD4+ T cells studies - such as the measurement of acute calcium influx in Fig. 5E - very difficult to interpret. Perhaps the authors should explain why acute signal transduction studies in Figure 6 were performed in lymph node cells. If the reason is that PTPN22 (WT and C129S mutant) expression is higher, the authors should provide immunoblots for PTPN22 in these cells. Since the PTPN22C129S mouse model has not been sufficiently validated, the claims of the authors are unfortunately weakened and the underlying molecular mechanisms do not completely support their conclusions. However, given the clear in vitro work provided in figures 1 and 2, it is this Reviewer's opinion that the authors can address the issues related to the oxidation status of PTPN22 and of PTPN22C129S in vivo, support their claims, and make a significant contribution to the field.

We again thank the reviewer for the detailed summary of our findings and for the suggestions. With regards to the in vivo oxidation status of PTPN22, please see the discussion above.

1. Tsai SJ, Sen U, Zhao L, Greenleaf WB, Dasgupta J, Fiorillo E, et al. Crystal structure of the human lymphoid tyrosine phosphatase catalytic domain: insights into redox regulation. Biochemistry. 2009;48(22):4838-45.
2. Jackson SH, Devadas S, Kwon J, Pinto LA, Williams MS. T cells express a phagocyte-type NADPH oxidase that is activated after T cell receptor stimulation. Nat Immunol. 2004;5(8):818-27.
3. Gelderman KA, Hultqvist M, Holmberg J, Olofsson P, Holmdahl R. T cell surface redox levels determine T cell reactivity and arthritis susceptibility. Proc Natl Acad Sci U S A. 2006;103(34):12831-6.
4. Gelderman KA, Hultqvist M, Pizzolla A, Zhao M, Nandakumar KS, Mattsson R, et al. Macrophages suppress T cell responses and arthritis development in mice by producing reactive oxygen species. J Clin Invest. 2007;117(10):3020-8.
5. Dagnell M, Cheng Q, Rizvi SHM, Pace PE, Boivin B, Winterbourn CC, et al. Bicarbonate is essential for protein-tyrosine phosphatase 1B (PTP1B) oxidation and cellular signaling through EGF-triggered phosphorylation cascades. J Biol Chem. 2019;294(33):12330-8.
6. Gringhuis SI, Leow A, Papendrecht-Van Der Voort EA, Remans PH, Breedveld FC, Verweij CL. Displacement of linker for activation of T cells from the plasma membrane due to redox balance alterations results in hyporesponsiveness of synovial fluid T lymphocytes in rheumatoid arthritis. J Immunol. 2000;164(4):2170-9.
7. Carilho Torrao RB, Dias IH, Bennett SJ, Dunston CR, Griffiths HR. Healthy ageing and depletion of intracellular glutathione influences T cell membrane thioredoxin-1 levels and cytokine secretion. Chem Cent J. 2013;7(1):150.

Author Response

Reviewer #1 (Public Review):

Dias et al proposed a new method for genotype imputation and evaluated its performance using a variety of metrics. Their method consistently produces better imputation accuracies across different allele frequency spectrums and ancestries. Surprisingly, this is achieved with superior computational speed, which is very impressive since competing imputation softwares had decades of experience in optimizing software performance.

The main weakness in my opinion is the lack of software/pipeline descriptions, as detailed in my main points 36 below.

We have made the source code and detailed instructions available publicly at Github. The computational pipeline for autoencoder training and validation is available at https://github.com/TorkamaniLab/Imputation_Autoencoder/tree/master/autoencoder_tuning_pipeline.

1. In the neural network training workflow, I am worried it will be difficult to compute the n by n correlation matrix if n is large. If n=10^5, the matrix would be ~80GB in double precision, and if n=10^6, the matrix is ~2TB. I wonder what is n for HRC chromosome 1? Would this change for TOPMed (Taliun 2021 Nature) panel which has ~10x more variants? I hope the authors can either state that typical n is manageable even for dense sequencing data, or discuss a strategy for dealing with large n. Also, Figure 1 is a bit confusing, since steps E1-E2 supposedly precede A-D.

We included more details in the methods section to address this question. It is true that computing the entirety of this matrix is computationally intensive, thus, in order to avoid this complexity, we calculated the correlations in a sliding box of 500 x 500 common variants (minor allele frequency (MAF) >=0.5%). In other words, no matter how dense the genomic data is, the n x n size will always be fixed to 500 x 500. Larger datasets will not influence this as the additional variants fall below the MAF>=0.5% threshold. Thus, memory utilization will be the same regardless of chromosome length or database size. Please note that this correlation calculation process is not necessary for the end-user to perform imputation, since we already provide the information on what genomic coordinates belong to the local minima or “cutting points” of the genome. This computational burden remains on the developer side. The reviewer is right to point out that Figure 1 is misleading in its ordering, we have corrected this in the revision.

1. I have a number of questions/comments regarding equations 2-4. (a) There seems to be no discussion on how the main autoencoder weight parameters were optimized? Intuitively, I would think optimizing the autoencoder weights are conceptually much more important than tuning hyper-parameters, for which there are plenty of discussions.

These parameters are optimized through the training process described in “Hyperparameter Initialization and Grid Search / Hyperparameter Tuning” - where both the hyperparameters and edge weights are determined for each autoencoder for each genomic segment. There are 256 genomic segments in chromosome 22, and each segment has a different number of input variables, sparsity, and correlation structure. Thus, there is a unique autoencoder model that best fits each genomic tile (e.g.: each autoencoder has different weights, architecture, loss function, regularizes, and optimization algorithms). Therefore, while there are some commonalities across genomic tiles, there is not a single answer for the number of dimensions of the weight matrix, or for how the weights were optimized. Instructions on how to access the unique information on the parameters and hyperparameters of each one of the 256 autoencoders is now shared through our source code repository at https://github.com/TorkamaniLab/imputator_inference.

We included an additional explanation clarifying this point in the Hyperparameter Tuning subsection of the Methods.

(b) I suppose t must index over each allele in a segment, but this was not explicit.

That is correct, t represents the index of each allele in a genomic segment. We included this statement in the description of equation 2.

(c) Please use standard notations for L1 and L2 norms (e.g. ||Z||_1 for L1 norm of Z). I also wonder if the authors meant ||Z||_1 or ||vec(Z)||_1 (vectorized Z)?

We included a clarification in the description of equation 3. ‖𝑾‖𝟏 and ‖𝑾‖𝟐 are the standard L1 and L2 norms of the autoencoder weight matrix (W).

(d) It would be great if the authors can more explicitly describe the auto-encoder matrices (e.g. their dimensions, sparsity patterns if any...etc).

As we answered in comment 2.a, each one of the 256 autoencoders for each genomic segment is unique, so it would be unfeasible to describe the architecture, parameters, optimizers, loss function, regularizes, of each one of them. We realized it would be more suitable to share this information in a software repository and have now done so.

1. It is not obvious if the authors intend to provide a downloadable software package that is user-friendly and scalable to large data (e.g. HRC). For the present paper to be useful to others, I imagine either (a) the authors provide software or example scripts so users can train their own neural network, or (b) the authors provide pretrained networks that are downloaded and can be easily combined with target genotype data for imputation. From the discussion, it seems like (b) would be the ultimate goal, but is only part dream and part reality. It would be helpful if the authors can clarify how current users can benefit from their work.

We have now shared the pre-trained autoencoders (including model weights and inference source code) and instructions on how to use them for imputation. These resources are publicly available at https://github.com/TorkamaniLab/imputator_inference. We have added this information to the Data Availability subsection of the Methods.

1. Along the same lines, I also found the description of the software/pipeline to be lacking (unless these information are available on the online GitHub page, which is currently inaccessible). For instance, I would like to know which of the major data imputation formats (VCF/BGEN..etc) are supported? Which operating systems (window/linux/mac) are supported? I also would like to know if it is possible to train the network or run imputation given pre-trained networks, if I don't have a GPU?

We have now made the github repository publicly available. The description of the requirements and steps performed in the hyperparameter tuning pipeline is available at https://github.com/TorkamaniLab/Imputation_Autoencoder/tree/master/autoencoder_tuning_pipeline.

1. Typically, imputation software supplies a per-SNP imputation quality score for use in downstream analysis. This is important for interpretability as it helps users decide which variants are confidently imputed and which ones are not. For example, such a quality score can be estimated from the posterior distribution of an HMM process (e.g. Browning 2009 AJHG). Would the proposed method be able to supply something similar? Alternatively, how would the users know which imputed variants to trust?

We included further clarification in the data availability session of methods: Imputation data format. The imputation results are exported in variant calling format (VCF) containing the imputed genotypes and imputation quality scores in the form of class probabilities for each one of the three possible genotypes (homozygous reference, heterozygous, and homozygous alternate allele). The probabilities can be used for quality control of the imputation results.

We included this clarification in the manuscript and in the readme file of the inference software repository https://github.com/TorkamaniLab/imputator_inference.

1. I think the authors should clarify whether input genotypes must be prephased. That is, given a trained neural network and a genotype data that one wishes to impute, does the genotype data have to be phased? The discussion reads "our current encoding approach lacks phasing information..." which can be understood both ways. On a related note, I hope the authors can also clarify if the validation and testing data (page 7 lines 1423) were phased data, or if they were originally unphased but computationally phased via softwares like Eagle 2 or Beagle 5.

The input genotypes are not phased, nor pre-phased, and no pre-phasing was performed before imputation. We included further clarification on the method section, stating “All input genotypes from all datasets utilized in this work are unphased, and no pre-phasing was performed.”. We also included further clarification in the Discussion session.

1. It is unclear if the reported run times (Figure 6) includes model training time, or if they are simply imputing the missing genotypes given a pre-trained autoencoder? For the later, I think the comparison may still be fair if users never have to train models themselves. However, if users currently have to train their own network, I feel it is imperative to also report the model training time, even if in another figure/table.

The end-users do not have to train the models, the computational burden of training the models remains on the developer side, so the runtimes refer to the task of imputing the missing genotypes given a pre-trained autoencoder set. This allows for distribution without reference datasets. We included further clarification on the Performance Testing and Comparisons subsection of Methods.

Reviewer #2 (Public Review):

In this manuscript the authors introduce a segment based autoencoder (AE) to perform genotype imputation. The authors compare performance of their AE to more traditional HMM-based methods (e.g. IMPUTE) and show that there is a slight but significant improvement on these methods using the AE strategy.

In general the paper is clearly presently and the work in timely, but I have some concerns with respect to the framing of the advances presented here along with the performance comparisons.

Specific Points:

1. The authors aren't doing a good enough job presenting the work of others in using deep neural networks for imputation or using autoencoders for closely related tasks in population genetics. For instance, the authors say that the RNN method of Kojima et al 2020. is not applicable to real world scenarios, however they seem to have missed that in that paper the authors are imputing based on omni 2.5 at 97% masking, right in line with what is presented here. It strikes me that the RNNIMP method is a crucial comparison here, and the authors should expand their scholarship in the paper to cover work that has already been done on autoencoders for popgen.

This is an important comparison that we erroneously misrepresented. We have now separated out this particular application of the RNN-IMP in the introduction of the manuscript. The major difference is that RNN-IMP needs to be retrained on different input genetic variants, much like a standard HMM-based method. The computational burden of RNN-IMP remains on the end-user side. It appears that computational complexity is tremendous in this model, given that the only example the authors provided with their software consists of 100 genomes from 1000 Genomes Project to perform the imputation on Omni by de novo training of the data. Given their approach does not achieve the benefits of distributing a generalizable pre-trained neural network, and the computational burden associated with training these models on the 60K+ genomes we use in our manuscript, we have opted for stating the benefits and downsides of their approach in the introduction.

1. With respect to additional comparisons-Kenneth Lange's group recently released a new method for imputation which is not based on HMM but is extremely fast. The authors would be well served to extend their comparisons to include this method (MendelImpute)-it should be favorable for the authors as ModelImpute is less accurate than HMMs but much faster.

We appreciate the reviewer pointing out this additional method, however their parent manuscript clearly shows substantially inferior imputation performance relative to BEAGLE/Minimac etc. which we already compare against. There is not much to gain by performing this comparison. Our autoencoder-based approach is already generating results that are competitive with the best and most cited imputation tools, which are all HMM-based and outperforming MendelImpute. The outcome of this comparison is forecasted based upon the parent manuscript.

1. The description of HMM based methods in lines 19-21 isn't quite correct. Moreover-what is an "HMM parameter function?"

Thank you for catching this. We were referring to parameter *estimation and have corrected this in the manuscript.

1. Using tiled AEs across the genome makes sense given the limitations of AEs generally, but this means that tiling choices may affect downstream accuracy. In particular-how does the choice of the LD threshold determine accuracy of the method? e.g. if the snp correlation threshold were 0.3 rather than 0.45, how would performance be changed?

This choice is driven by the limitations of cutting-edge GPUs. 0.45 is the threshold that returns the minimum number of tiles spanning chromosome 22 with an average size per tile that fits into the video memory of GPUs. While developing the tiling algorithm, we tested lower thresholds, which made the tiles smaller and more abundant, and thus made the GPU memory workload less efficient (e.g. many tiles resulted in many autoencoders per GPU, which thus caused a CPU-GPU communication overhead). Due to the obstacles related to computational inefficiency, CPUGPU communication overhangs, and GPU memory limits, we did not proceed with model training on tiles generated with other correlation thresholds. We’ve added a paragraph explaining this choice in the manuscript.

1. How large is the set of trained AEs for chromosome 22? In particular, how much disk space does the complete description of all AEs (model + weights) take up? How does this compare to a reference panel for chr22? The authors claim that one advance is that this is a "reference-free" method - it's not - and that as such there are savings in that a reference panel doesn't have to be used along with the genome to be imputed. While the later claim is true, instead a reference panel is swapped out for a set of trained AEs, which might take up a lot of disk space themselves. This comparison should be given and perhaps extrapolated to the whole genome.

This is an interesting point. For comparison, the total combined uncompressed size of all pre-trained autoencoders together is 120GB, or 469MB per autoencoder. The size of the reference data, HRC chromosome 22 across ~27,000 samples is 1GB after compression – or nearly 10X the autoencoder size. Moreover, unlike in HMM-based imputation, the size of the pre-trained autoencoders does not increase as a function of the reference panel sample size. The size of the autoencoders remains fixed since the number of model weights and parameters remains the same regardless of sample size – though it will expand somewhat with the addition of new genetic variants. Another point to consider is that privacy concerns associated with distribution of reference data are mitigated with these pretrained autoencoders.

1. The results around runtime performance (Figure 6) are misleading. Specifically HMM training and decoding is being performed here, whereas for the AE only prediction (equivalent to decoding) is being done. To their credit, the authors do mention a bit of this in the discussion, however a real comparison should be done in Figure 6. There are two ways to proceed in my estimation - 1) separate training and decoding for the HMM methods (Beagle doesn't allow this, I'm not sure of the other software packages) 2) report the training times for the AE method. I would certainly like to see what the training times look like given that the results as present require 1) a separate AE for each genomic chunk, 2) a course grid search, 3) training XGBoost on the results from the course grid search, and 4) retraining of the individual AEs given the XGBoost predictions, and 5) finally prediction. This is a HUGE training effort. Showing prediction runtimes and comparing those to the HMMs is inappropriate.

We consider the prediction only during the runtime comparisons because only the prediction side is done by the enduser, whereas the computational burden remains on the developer side. For the HMMs, we included only the prediction time as well (excluded the time for data loading/writing, computing model parameters and HMM iterations). The pre-trained autoencoders, when distributed, can take as input any set of genetic variants to produce the output without any additional training or fine-tuning required.

1. One well known problem for DNN based methods including AEs is out-of-sample prediction. While Figure 5 (missing a label by the way) sort of gets to this, I would have the authors compare prediction in genotypes from populations which are absent from the training set and compare that performance to HMMs. Both methods should suffer, but I'm curious as to whether the AEs are more robust than the HMMs to this sort of pathology.

Our test datasets in Figures 4 and 5 are independent of the reference dataset. MESA, Wellderly, and HGDP are all independent datasets, never used for training, nor model selection. Only HRC was used as reference panel or for training, and ARIC was used for model selection during tuning. We included a statement in the methods clarifying this point.

Reviewer #3 (Public Review):

Over the last 15 years or so genotype imputation has been an important and widely-used tool in genetic studies, with methods based on Hidden Markov Models (HMMs) and reference panels emerging as the dominant approach. This paper suggests a new approach to genotype imputation based on denoising autoencoders (DAE), a type of neural network. This approach has two nice advantages over existing methods based on Hidden Markov Models (HMMs): i) once the DAE is trained on a reference panel the reference panel can be discarded, and users do not need access to the reference panel to use the DAE; ii) imputation using a DAE is very fast (training is slow, but this step is done upfront so users do not need to worry about it). The paper also presents data showing that the tuned DAE is competitive in accuracy with HMM methods.

I have two main concerns.

First, it is unclear to me whether the accuracy presented for the tuned DAE (eg Figure 3, Table 4) is a reliable reflection of expected future accuracy. This is because the tuning process was quite extensive and complex, and involved at least some of the datasets used in these assessments. While the paper correctly attempts to guard against overfitting and related issues by using separate Training, Validation and Testing data (p7), it seems that the Testing data were used in at least some of the development of the methods and tuning (eg p14, "A preliminary comparison of the best performing autoencoder..."; Figure 2 and Table 2, all involve the Testing data). Because of the complexity of the process by which the final DAE was arrived at it is unclear to me whether there is a genuine concern here, but it would seem safest and most convincing at this point to do an entirely independent test of the methods on genotype data sets that were not used at all up to this point.

MESA, Wellderly, and HGDP were not used for training, nor for tuning, they are completely independent. So all the results showing these datasets are completely independent. Only HRC and ARIC were used for training and validation/tuning, respectively. We included a statement in the methods session clarifying this point.

Moreover, HGDP in particular includes 828 samples from 54 different populations representing all continental populations and including remote populations like Siberia, Oceania, etc. This reference panel is described in more detail in the reference below and likely represents the most diverse human genome dataset available. Thus, we have externally validated generalizability on a dataset with much greater diversity than our training dataset:

Bergström A, et al. Insights into human genetic variation and population history from 929 diverse genomes. Science. 2020 Mar 20;367(6484):eaay5012.

Second, there is a potentially tricky issue of to what extent distributing a black box DAE trained on a reference sample is consistent with data sharing policies. Standards of data sharing have evolved over the last decade. Generally there currently seems to be little hesitation to publicly share "single-SNP summary data" such as allele frequency information from large reference panels, whereas sharing of individual-level genotype data is usually explicitly forbidden. It is not quite clear to me where sharing the fit of a DAE falls here, or how much information on individual genotypes the trained DAE contains. The current manuscript does not adequately address this issue.

Currently there are no official data sharing restrictions on deep learning data. We are aware that future policies may rise, and we have started a collaboration with Oak Ridge National Laboratory to explore differential privacy techniques and privacy concerns for these autoencoders. Another point to consider is that the autoencoders segment the genome, making reconstruction of an individual genome impossible even if reference data were somehow recoverable from the neural networks. Regardless, this is an interesting and important point that should be addressed in the manuscript and we have added a paragraph discussing this point.

Reviewer #4 (Public Review):

In this manuscript, Dias et al proposed a novel genotype imputation method using autoencoders (AE), which achieves comparable or superior accuracy relative to the state-of-the-art HMM-based imputation methods after tuning. The idea is innovative and provides an alternative solution to the important task of genotype imputation. The authors also conducted some experiments using three different datasets as targets to showcase the value of their approach. The overall framework of the method is clearly presented but more technical details are needed. The results presented showed slight advantage of AE imputation after tuning but more comprehensive evaluations are needed. In particular, the authors didn't consider post-imputation quality control. The reported overall performance (R2 in the range of 0.2-0.6) seems low and inconsistent with the imputation literature.

Overall, the method has potential but is not sufficiently compelling in its current form.

We show average accuracy of 0.2-0.6 in Table 4, but that is the average R2 per variant across all variants (no MAF filtering or binning applied). The reviewer points that the accuracy should be R2>0.8, but this R2>0.8 refers to common variants only (allele frequency >1%), and we have shown r2>0.8 for these variants (Figure 4). The aggregate accuracy displayed in Table 4 is lower because the vast majority of variants fall below 1% allele frequency threshold.

The references bellow demonstrate this issue and agree with our results:

References:

Rubinacci S, Delaneau O, Marchini J. Genotype imputation using the positional burrows wheeler transform. PLoS genetics. 2020 Nov 16;16(11):e1009049.

McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, Kang HM, Fuchsberger C, Danecek P, Sharp K, Luo Y. A reference panel of 64,976 haplotypes for genotype imputation. Nature genetics. 2016 Oct;48(10):1279.

Vergara C, Parker MM, Franco L, Cho MH, Valencia-Duarte AV, Beaty TH, Duggal P. Genotype imputation performance of three reference panels using African ancestry individuals. Human genetics. 2018 Apr;137(4):281-92.

Author Response

Reviewer #1 (Public Review):

The manuscript by Rekler and Kalcheim examines the role of neural tube-derived retinoic acid (RA) in neural crest development. They observe that the onset of expression of the RA-synthesizing enzyme RALDH2 in the dorsal neural tube coincides with the end of neural crest production. The authors propose that this local source of RA is essential to activate the transcription of Bambi other BMP inhibitors, leading to the disruption of BMP signaling. Loss of BMP activity at the dorsal neural tube would halt neural crest production, leading to the establishment of the definite roof plate. Thus, precise temporal regulation of RALDH2 in the dorsal neural tube would dictate the timing of neural crest production and the segregation of PNS and CNS progenitors.

Previous studies have already identified a role for RA in the control of the timing of neural crest production. MartinezMorales et al (JCB 2011) have shown that during early trunk development, mesoderm-derived RA works with FGF signaling to jumpstart the BMP/Wnt cascade that drives neural crest migration in the trunk. Rekler and Kalcheim choose to focused on a distinct function of RA at a later timepoint. The main contribution of the present study is the demonstration that - at later stages - RA produced by the neural tube has the opposite effect, acting to inhibit the BMP/Wnt cascade and halt neural crest production. Thus, RA would be a major regulator of the timing of neural crest production, acting to both trigger and repress neural crest migration.

The study's strengths lie in an experimental strategy that allows the authors to manipulate RA function in a stagespecific manner and therefore uncover a later role for the signaling system in neural crest production. The authors also show that RA inhibition results in an incomplete fate switch and results in the generation of cells that share regulatory features of neural crest and roof plate cells. A significant limitation of the study is that the molecular mechanisms that endow RA signaling with stage-specific functions remain unknown. This is of particularly important since the early vs. late RA seem to have opposing effects, acting to either promote or terminate neural crest production.

We thank this referee for her/his positive comments on our manuscript. We agree with the referee that a key question is understanding how RA signaling is differentially interpreted over time given its multistage activity in dorsal NT development.

This is based on the following findings: Years ago, we uncovered that the balance between activities of BMP/Wnt and noggin in the dorsal NT trigger the onset of NC EMT. Martinez-Morales et al. strengthened our findings by reporting that a balance between somitic RA and FGF works on the reported BMP/Wnt modules to initiate the process. This group found that at gastrulation stages, RA is required for NC specification, as revealed by analysis of VAD quail embryos. Next, during somite formation, somitic RA is necessary for the onset of emigration of specified NC progenitors but at advanced somite stages it is dispensable for the subsequent maintenance of cell emigration. Presently, we find that RP-derived RA ends NC production. Together, this highlights a dynamic behavior of RA at 4 sequential stages of NC ontogeny. Clearly enough, the two first effects are mediated by an influence of RA dorsoventral patterning of the early NT, as distribution of ventral NT markers was strongly affected. In our case, RA from the nascent RP has no such effects suggesting that RP-derived RA acts at a post-patterning phase to specifically affect the dorsal NT.

All things considered, we think that the problem is not simply a binary question of “opposing functions of RA signaling in starting or terminating NC production”. Instead, it is the understanding of a differential interpretation to the same morphogen by progenitor cells with changing states and at sequential stages.

To the referee’s request, we begun addressing the question of how does RA inhibit BMP signaling close to the RP stage. To this end, we decided first to examine the temporal regulation of Raldh2 expression that is restricted to the RP stage, and is therefore a prerequisite for the late activity of RA. Whereas repressing RA activity extends the NC phase including the continuous transcription of Foxd3, Sox9 and Snail2 (Fig.3), we now found that extending the activity of each of these transcription factors close to the RP stage represses the onset of Raldh2 transcription in the nascent RP (new Fig. 9). We interpret these results to mean that as long as NC genes are active in the dorsal NT (NC stage), local Raldh2 and consequent RA synthesis in the NT does not take place, so Raldh2 in RP is repressed by NC-specific traits. The significance of these data is twofold: first, they explain the late onset of Raldh2 production at the RP stage. Second, since we also report the reciprocal result, that RA represses NC genes (Fig.3), we conclude that a cross repressive interaction exists between NC and RP-specific genes downstream of RA, being an emerging temporal property of the network. These data further indicate that the changing roles of RA throughout development of the dorsal neural primordium, largely depend on a different interpretation of the signal mediated by changing and mutually repressive codes.

We have now presented these data in Fig.9. To clarify our thoughts further, we now provide a working model summarizing the effects of RA in NC to RP transition (Fig.10B).

Our article uncovers for the first time and thoroughly documents, a role of local RA activity on the end of NC production and ensuing RP architecture. We believe that a comprehensive elucidation of the molecular mechanism responsible for inhibition of BMP signaling by local RA is the next obligatory step. We show in this study the selective activation of BMP inhibitors by endogenous RA and previously found that one of them, Hes/hairy, indeed inhibits BMP signaling and NC EMT (Nitzan et al, 2016). Therefore we propose that upregulation of BMP inhibitors by RA is a possible mechanism. However, we also predict that this is not the only one, and a deeper understanding of this problem is beyond the scope of the present study.

Additional possibilities that fit with our data were now discussed: RA expression in somites vs. RP can be regulated by different enhancers and thus have distinct functions. For example, a specific enhancer driving expression of Raldh2 was found to be activated only at the definitive RP stage (Castillo et al., 2010). This enhancer contains Tcf binding sites and thus may be activated by Wnt signaling. In turn, as we show, RP-derived Raldh2 and resulting RA could negatively feed-back on Wnt signaling in the formed RP either directly or through BMP acting upstream of Wnt (now presented in Fig. 10B).

Another possible scenario is that RA represses BMP signaling by inactivating Smad proteins via ubiquitination, as shown to be the case in selected cell lines (Sheng et al., 2010). These possibilities were discussed and await to be systematically explored.

Comments:

Previous studies have demonstrated that early RA production (presumably from the mesoderm) is necessary for the expression of early dorsal neural tube / neural crest genes like Pax7, Msx, Wnt1, and even BMP ligands. This is in contrast to the local source of RA, which presumably would be silencing these genes. Thus, mesoderm-derived RA would have the opposite effect in these progenitors than the RA synthesized in the neural tube. The study does not provide a mechanism that explains these stage-specific effects of the morphogen.

As elaborated in our reply above to the general comment, we believe that RA whether emanating from somites or nascent RP, provides an initial signal that is later relayed upon target factors unique to each stage. It is possible that the precise source of factor plays a role; along this line we showed that somitic RA is dispensable for late events, reciprocally, there is no RA synthesis in the early NT that could affect NC cells. Having said that, there is RA activity in the NT at both stages and the output is still different. Hence, there should be more to this: In the revised version, we report that NC and RP-specific genes stand in a mutually repressive interaction downstream of RA, and this may contribute to the stage-specific effects of the morphogen.

The effects of RA manipulation are often examined with non-quantitative techniques, like in situ hybridization (Fig. 2, 3). The incorporation of quantitative approaches (e.g., qPCR) would allow for the precise characterization of phenotypes (and better estimation of penetrance, etc.). Furthermore, the study lacks molecular/biochemical strategies to define the regulatory linkages between genes and pathways. This is a considerable limitation of the study since it prevents the establishment of a regulatory axis that would directly connect RA signaling to the BMP pathway.

As the referee may notice, most genes examined are not restricted solely to the dorsal NT/RP domains. Since it is technically not accurate to isolate only the regions of interest for qPCR analysis, collecting entire NTs following unilateral or bilateral electroporations for qPCR would be highly inaccurate. In situ hybridization and immunohistochemistry provide a precise tool to assess the spatial localization of the transcripts/proteins of interest. To note is that in all cases examined, development of the color reactions was for the same length of time for control and experimental cases and photography was performed under identical conditions. Furthermore, in most cases, effects between treatments were dramatic, readily apparent at a qualitative level and easily quantifiable from ISH or fluorescent images.

As to regulatory linkages between genes and pathways, the referee is correct; we do not demonstrate direct molecular interactions between the different players at the biochemical level. The present study provides a wealth of novel data connecting morphogens such as RA with BMP and Wnt activities, and those with a variety of downstream genes specific for either NC or RP stages. The next step will be to ask about the precise nature of the linkages between specific molecules/pathways.

The function (and the regulation) of RALDH2 at the dorsal neural tube has been studied thoroughly, and RA is a known player in the dorsal-ventral patterning of the CNS. It is not clear to what extent the phenotypes observed by the authors are due to the disruption of a neural crest-intrinsic mechanism or if they are secondary to the overall changes in the cellular organization of the neural tube caused by loss of RA.

This is a good point as RA is known to have multiple effects on NT development whose nature changes with stage. Available data emanating from young caudal neural plate explants and from VAD embryos that lack RA, showed that early RA signaling from developing somites is required for ventral patterning of the neural tube (motoneurons and V1, V2 interneurons) and for neuronal differentiation (Diez del Corral et al, 2003, Sockanathan and Jessel, 1998, Liu et al, 2001, Maden et al, 1996). These effects were shown to depend, at least partly, on antagonistic activities of RA and FGF in mesoderm which affect ventral, but not dorsal NT patterning (Diez del Corral et al, 2003). Our study focuses on a later stage when D-V neural tube patterning is already established.

To address the referee’s comment, we now examined the effects of RA attenuation on expression of Pax7, a dorsal factor, and Hb9, a motoneuron-specific protein. We found that RARa403 does not affect the localization and/or extent of expression of Hb9, and causes only a mild 12% increase in the area of expression of Pax7. Consistent with these results, we also show in several figures that in the absence of RA signaling pSmad and Wnt activities, Foxd3, Snai2 and Sox9 expression patterns are prolonged in time but not in D-V extent.

These data corroborate that the effects documented are directed to the dorsal NT and do not result from overall changes in D-V patterning. The data were now added as Fig.7 Supplementary 1.

The authors rely solely upon overexpression constructs to manipulate the activity of the RA signaling pathway, which may be prone to artifacts. Furthermore, both overexpression constructs aim at inhibiting RA activity. This limits the impact of the work since there is no demonstration that RA is sufficient to activate BMP inhibitors and halt neural crest production.

The tools we used to repress RA signaling consist of RARa403 that acts as a pan-dominant negative construct to abrogate receptor activity, and Cyp26A1, an enzyme that degrades RA. To activate RA signaling in a ligand- independent manner, we now implemented VP16-RAR-alpha in the revised version of this manuscript. All these tools are extensively and routinely employed in the literature in a variety of animal species and were shown to act in vivo as expected both by others and further confirmed by us in the present study. Having said that, we are currently optimizing the CRISPR-Cas9 method for gene editing of RA-specific genes and hope to succeed in the near future.

We have now performed experiments to address the sufficiency of RA. Data were now added as Fig.5 Supplem 2 and 3 and Fig.6 Supp.2 .

As we expected, gain of RA function at NC stages is not sufficient to prematurely activate BMP inhibitors like BAMBI, to end prematurely BMP signaling (pSMAD) or NC EMT, to alter the dynamics of expression of NC-specific genes, or to cause an earlier appearance of RP-specific traits. This is fully consistent with RA being active at NC stages when BMP/Wnt signaling, NC EMT, etc are operational. The fact that RA is necessary but not sufficient for these processes further suggest that the key is how NC cells at various stages of their ontogeny and then RP cells, differentially interpret the signal given the profound changes in cellular and molecular landscapes apparent between these stages.

Reviewer #2 (Public Review):

The manuscript presents a novel role for RA signaling during development as the mediator of the switch that occurs in the dorsal neural tube after the neural crest cells have migrated and the roof plate forms. The finding is interesting and novel as the events that take place at the end of neural crest stage are poorly understood. The strengths of the manuscript are that the study is well planned and executed to show the interesting phenotype of delayed/disturbed roof plate formation accompanied with prolonged neural crest stage caused by inhibition of RA signaling in the dorsal neural tube. The results also show that RA signaling marks the RP territory and inhibits the DI1 interneurons from invading the region. The results bring novel information to the field. The original finding of the involvement of RA in the process was revealed in a RNAseq screen comparison between the neural crest and the roof plate (which was recently published by the same lab). However, the current study doesn't use any new technology such as high throughput screens or high resolution or live imaging etc., but rather relies mainly on "old fashioned" techniques: electroporation to induce transient inhibition of RA signaling in the dorsal neural tube followed by analysis of the phenotype by using chromogenic in situ hybridization. The chosen techniques are sufficient to convincingly show the point the authors want to make and the study serves as a reminder that fancy new techniques are not necessarily a requirement for creating a solid story. The manuscript is also well written and easy to follow.

We thank this referee for a very positive feedback on our study. Although we are always motivated by the implementation of new techniques, we agree that the primary goal is to answer a biologically meaningful question with suitable means.

Finally, the manuscript links the activation of RA signaling to the decline of BMP signaling and specifically the upregulation of BMP inhibitors in the dorsal neural tube at the end of the NC stage, but in its current form the proof of this proposed link remains weak.

Our article uncovers for the first time and thoroughly documents, a role of local RA activity on the end of NC production and ensuing RP architecture. We believe that a comprehensive elucidation of the molecular mechanism responsible for inhibition of BMP signaling by local RA is the next obligatory step. We show in this study the selective activation of BMP inhibitors by endogenous RA and previously found that one of them, Hes/hairy, indeed inhibits BMP signaling and NC EMT (Nitzan et al, 2016). Therefore we propose that upregulation of BMP inhibitors by RA is a possible mechanism. However, we also predict that this is not the only one, and a deeper understanding of this problem is beyond the scope of the present study.

Additional possibilities that fit with our data were now discussed: RA expression in somites vs. RP can be regulated by different enhancers and thus have distinct functions. For example, a specific enhancer driving expression of Raldh2 was found to be activated only at the definitive RP stage (Castillo et al., 2010). This enhancer contains Tcf binding sites and thus may be activated by Wnt signaling. In turn, as we show, RP-derived Raldh2 and resulting RA could negatively feed-back on Wnt signaling in the formed RP either directly or through BMP acting upstream of Wnt (this was now presented in a working model in Fig. 10B).

Another possible scenario is that RA represses BMP signaling by inactivating Smad proteins via ubiquitination, as shown to be the case in selected cell lines (Sheng et al., 2010). These possibilities were discussed and await to be explored systematically.

Similarly, the manuscript does not address the consequences of exposure of RA to the dorsal neural tube during NC stage and it thus remains unknown whether RA signaling is sufficient to end the NC stage and activate roof plate formation prematurely. Additional experiments of this kind would help clarify the role of RA in the dorsal neural tube and the reciprocal roles of the two signaling pathways (RA and BMP).

We have now performed experiments to address the sufficiency of RA. Data were now added as Fig.5 Supp.2 and Supp.3, and Fig.6 Supp.2, and discussed.

As we expected, gain of RA function at NC stages is not sufficient to prematurely activate BMP inhibitors, to end prematurely BMP signaling (pSMAD) or NC EMT, to alter the dynamics of expression of NC-specific genes, or to cause an earlier appearance of RP-specific traits.

This result is totally understandable in light of RA being anyway active (but not produced) in NT at NC stages (original Fig.1) when BMP/Wnt signaling, a NC-specific gene network, and NC EMT are operational.

The fact that RA is necessary but not sufficient for these processes further suggests that the key is in the following, perhaps complementary mechanisms: 1) a different interpretation of the same signal by NC progenitors at sequential stages of their ontogeny and then by RP cells, accounted for by the profound changes in cellular and molecular landscapes apparent between these stages. 2) the possibility that somite-derived versus RP-derived RA are differentially interpreted by the dorsal NT cells owing, for example, to a distinctive mode of ligand presentation (e.g; by CRABP1 expressed in RP but not NC, etc).

Author Response

Reviewer #1 (Public Review):

In Figure 1A, the authors should show TEM images of control mock treated samples to show the difference between infected and healthy tissue. Based on the data shown in Figure 1B-E that the overexpression of GFP-P in N. benthamiana leads to formation of liquid-like granules. Does this occur during virus infection? Since authors have infectious clones, can it be used to show that the virally encoded P protein in infected cells does indeed exist as liquid-like granules? If the fusion of GFP to P protein affects its function, the authors could fuse just the spGFP11 and co-infiltrate with p35S-spGFP1-10. These experiments will show that the P protein when delivered from virus does indeed form liquid-like granules in plants cells. Authors should include controls in Figure 1H to show that the interaction between P protein and ER is specific.

We agree with the reviewer and appreciate the helpful suggestion. As suggested, we added TEM images of control mock treated barley leaves. We also carried out immune-electron microscope to show the presence of BYSMV P protein in the viroplasms. Please see Figure 1–Figure supplement 1.

BYSMV is a negative-stranded RNA virus, and is strictly dependent on insect vector transmission for infecting barley plants. We have tried to fuse GFP to BYSMV P in the full-length infectious clones. Unfortunately, we could not rescue BYSMV-GFP-P into barley plants through insect transmission.

In Figure 1H, we used a PM localized membrane protein LRR84A as a negative control to show LRR84A-GS and BYSMV P could not form granules although they might associate at molecular distances. Therefore, the P granules were formed and tethered to the ER tubules. Please see Figure 1–Figure supplement 4

Data shown in Figure 2 do demonstrate that the purified P protein could undergo phase separation. Furthermore, it can recruit viral N protein and part of viral genomic RNA to P protein induced granules in vitro.

Because the full-length BYSMV RNA has 12,706 nt and is difficult to be transcribed in vitro, we cannot show whether the BYSMV genome is recruited into the droplets. We have softened the claim and state that the P-N droplets can recruit 5′ trailer of BYSMV genome as shown in Figure 3B. Please see line 22, 177 and 190.

Based on the data shown in Figure 4 using phospho-null and phospho-mimetic mutants of P protein, the authors conclude that phosphorylation inhibits P protein phase separation. It is unclear based on the experiments, why endogenous NbCK1 fails to phosphorylate GFP-P-WT and inhibit formation of liquid-like granules similar to that of GFP-P-S5D mutant? Is this due to overexpression of GFP-P-WT? To overcome this, the authors should perform these experiments as suggested above using infectious clones and these P protein mutants.

As we known, phosphorylation and dephosphorylation are reversible processes in eukaryotic cells. Therefore, as shown in Figure 5B and 6B, the GFP-PWT protein have two bands, corresponding to P74 and P72, which represent hyperphosphorylation and hypophosphorylated forms, respectively. Only overexpression of NbCK1 induced high ratio of P74 to P72 in vivo, and then abolished phase separation of BYSMV.

In Figure 5, the authors overexpress NbCK1 in N. benthamiana or use an in vitro co-purification scheme to show that NbCK1 inhibits phase separation properties of P protein. These results show that overexpression of both GFP-P and NbCK1 proteins is required to induce liquid-like granules. Does this occur during normal virus infection? During normal virus infection, P protein is produced in the plant cells and the endogenous NbCK1 will regulate the phosphorylation state of P protein. These are reasons for authors to perform some of the experiments using infectious clones. Furthermore, the authors have antibodies to P protein and this could be used to show the level of P protein that is produced during the normal infection process.

We detected the P protein existed as two phosphorylation forms in BYSMV-infected barley leaves, and λPPase treatment decreased the P44 phosphorylation form. Therefore, these results indicate that endogenous CK1 cannot phosphorylate BYSMV P completely.

Based on the data shown in Figure 6, the authors conclude that phase separated P protein state promotes replication but inhibits transcription by overexpressing P-S5A and P-S5D mutants. To directly show that the NbCK1 controlled phosphorylation state of P regulates this process, authors should knockdown/knockout NbCK1 and see if it increases P protein condensates and promote recruitment of viral proteins and genomic RNA to increase viral replication.

In our previous studies, BLAST searches showed that the N. benthamiana and barley genomes encode 14 CK1 orthologs, most of which can phosphorylated the SR region of BYSMV P. Therefore, it is difficult to make knockdown/knockout lines of all the CK1 orthologues. Accordingly, we generated a point mutant (K38R and D128N) in HvCK1.2, in which the kinase activity was abolished. Overexpression of HvCK1.2DN inhibit endogenous CK1-mediated phosphorylation of BYSMV P, indicating that HvCK1.2DN is a dominant-negative mutant.

It is important to note that both replication and transcription are required for efficient infection of negative-stranded RNA viruses. Therefore, our previous studies have revealed that both PS5A and PS5D are required for BYSMV infection. Therefore, expression of HvCK1.2DN in BYSMV vector inhibit virus infection by impairing the balance of endogenous CK1-mediated phosphorylation in BYSMV P.

Reviewer #2 (Public Review):

The manuscript by Fang et al. details the ability of the P protein from Barley yellow striate mosaic virus (BYSMV) to form phase-separated droplets both in vitro and in vivo. The authors demonstrate P droplet formation using recombinant proteins and confocal microscopy, FRAP to demonstrate fluidity, and observed droplet fusion. The authors also used an elaborate split-GFP system to demonstrate that P droplets associate with the tubulur ER network. Next, the authors demonstrate that the N protein and a short fragment of viral RNA can also partition into P droplets. Since Rhabdovirus P proteins have been shown to phase separate and form "virus factories" (see https://doi.org/10.1038/s41467-017-00102-9), the novelty from this work is the rigorous and conclusive demonstration that the P droplets only exist in the unphosphorylated form. The authors identify 5 critical serine residues in IDR2 of P protein that when hyper-phosphorylated /cannot form droplets. Next, the authors conclusively demonstrate that the host kinase CK1 is responsible for P phosphorylation using both transient assays in N. benthamiana and a co-expression assay in E. coli. These findings will likely lead to future studies identifying cellular kinases that affect phase separation of viral and cellular proteins and increases our understanding of regulation of condensate formation. Next, the authors investigated whether P droplets regulated virus replication and transcription using a minireplicon system. The minireplicon system needs to be better described as the results were seemingly conflicting. The authors also used a full-length GFP-reporter virus to test whether phase separation was critical for virus fitness in both barley and the insect vector. The authors used 1, 6-hexanediol which broadly suppresses liquid-liquid phase separation and concluded that phase separation is required for virus fitness (based on reduced virus accumulation with 1,6 HD). However, this conclusion is flawed since 1,6-hexanediol is known to cause cell toxicity and likely created a less favorable environment for virus replication, independent of P protein phase separation. These with other issues are detailed below:

1. In Figure 3B, the authors display three types of P-N droplets including uniform, N hollow, and P-N hollow droplets. The authors do not state the proportion of droplets observed or any potential significance of the three types. Finally, as "hollow" droplets are not typically observed, is there a possibility that a contaminating protein (not fluorescent) from E. coli is a resident client protein in these droplets? The protein purity was not >95% based on the SDS-PAGE gels presented in the supplementary figures. Do these abnormalities arise from the droplets being imaged in different focal planes? Unless some explanation is given for these observations, this reviewer does not see any significance in the findings pertaining to "hollow" droplets.

Thanks for your constructive suggestions. We removed the "hollow" droplets as suggested. We think that the hollow droplets might be an intermediate form of LLPS. Please see PAGE 7 and 8 of revised manuscript.

1. Pertaining to the sorting of "genomic" RNA into the P-N droplets, it is unlikely that RNA sorting is specific for BYSMV RNA. In other words, if you incubate a non-viral RNA with P-N droplets, is it sorted? The authors conclusion that genomic RNA is incorporated into droplets is misleading in a sense that a very small fragment of RNA was used. Cy5 can be incorporated into full-length genomic RNAs during in vitro transcription and would be a more suitable approach for the conclusions reached.

Thanks for your constructive suggestions. Unfortunately, we could not obtain the in vitro transcripts of the full-length genomic RNAs (12706 nucleotides). We have softened the claim and state that the P-N droplets can recruit the 5′ trailer of BYSMV genome as shown in Figure 3B. Please see line 22, 177 and 190.

According to previous studies (Ivanov, et al., 2011), the Rhabdovirus P protein can bind to nascent N moleculaes, forming a soluble N/P complex, to prevent from encapsidating cellular RNAs. Therefore, we suppose that the P-N droplets can incorporate viral genomic RNA specifically.

Reference: Ivanov I, Yabukarski F, Ruigrok RW, Jamin M. 2011. Structural insights into the rhabdovirus transcription/ replication complex. Virus Research 162:126–137. DOI: https://doi.org/10.1016/j.virusres.2011.09.025

1. In Figure 4C, it is unclear how the "views" were selected for granule counting. The methods should be better described as this reviewer would find it difficult to select fields of view in an unbiased manner. This is especially true as expression via agroinfiltration can vary between cells in agroinfiltrated regions. The methods described for granule counting and granule sizes are not suitable for publication. These should be expanded (i.e. what ImageJ tools were used?).

We agree with the reviewer that it is important to select fields of view in an unbiased manner. We selected the representative views and provided large views in the new Supplement Figures. In addition, we added new detail methods in revision. Please see Figure 4–Figure supplement 1, Figure 5–Figure supplement 1, and method (line 489-498).

1. In Figure 4F, the authors state that they expected P-S5A to only be present in the pellet fraction since it existed in the condensed state. However, WT P also forms condensates and was not found in the pellet, but rather exclusively in the supernatant. Therefore, the assumption of condensed droplets only being found in the pellet appears to be incorrect.

Many thanks for pointing this out. This method is based on a previous study (Hubstenberger et al., 2017). The centrifugation method might efficiently precipitate large granules more than small granules. As shown in Figure 4B, GFP-PS5A formed large granules, therefore GFP-PS5A mainly existed in the pellet. In contrast, GFP-PWT only existed in small granule and fusion state, thus most of GFP-PWT protein was existed in supernatant, and only little GFP-PWT protein in the pellet. These results also indicate the increased phase separation activity of GFP-PS5A compared with GFP-PWT. Please see the new Figure 4F.

Reference: Hubstenberger A, Courel M, Benard M, Souquere S, Ernoult-Lange M, Chouaib R, Yi Z, Morlot JB, Munier A, Fradet M, et al. 2017. P-Body Purification Reveals the Condensation of Repressed mRNA Regulons. Molecular Cell 68(1): 144-157 e145.

1. The authors conclude that P-S5A has enhanced phase separation based on confocal microscopy data (Fig S6A). The data presented is not convincing. Microscopy alone is difficult for comparing phase separation between two proteins. Quantitative data should be collected in the form of turbidity assays (a common assay for phase separation). If P-S5A has enhanced phase separation compared to WT, then S5A should have increased turbidity (OD600) under identical phase separation conditions. The microscopy data presented was not quantified in any way and the authors could have picked fields of view in a biased manner.

Thanks for your constructive suggestions. As suggested, turbidity assays were performed to show both GFP-PWT and GFP-PS5A had increased turbidity (OD600) compared with GFP. Please see Figure 4–Figure supplement 3.

1. The authors constructed minireplicons to determine whether mutant P proteins influence RNA replication using trans N and L proteins. However, this reviewer finds the minireplicon design confusing. How is DsRFP translated from the replicon? If a frameshift mutation was introduced into RsGFP, wouldn't this block DsRFP translation as well? Or is start/stop transcription used? Second, the use of the 2x35S promoter makes it difficult to differentiate between 35S-driven transcription and replication by L. How do you know the increased DsRFP observed with P5A is not due to increased transcription from the 35S promoter? The RT-qPCR data is also very confusing. It is not clear that panel D is only examining the transcription of RFP (I assume via start/stop transcription) whereas panel C is targeting the minireplicon.

Thank you for your questions and we are sorry for the lack of clarity regarding to the mini-replicon vectors. Here, we updated the Figure supplement 14 to show replication and transcription of BYSMV minireplicon, a negative-stranded RNA virus derivative. In addition, we insert an A after the start codon to abolish the translation of GFP mRNA, which allow us to observe phase separation of GFP-PWT, GFP-PS5A, and GFP-PS5D during virus replication. Use this system, we wanted to show the localization and phase separation of GFP-PWT, GFP-PS5A, and GFP-PS5D during replication and transcription of BYS-agMR. Please see Figure 6–Figure supplement 1.

1. Pertaining to the replication assay in Fig. 6, transcription of RFP mRNA was reduced by S5A and increased by S5D. However, the RFP translation (via Panel A microscopy) is reversed. How do you explain increased RFP mRNA transcription by S5D but very low RFP fluorescence? The data between Panels A, C, and D do not support one another.

Many thanks for pointing this out! We also noticed the interesting results that have been repeated independently. As shown the illustration of BYSMV-agMR system in Figure 6–Figure supplement 1, the relative transcriptional activities of different GFP-P mutants were calculated from the normalized RFP transcript levels relative to the gMR replicate template (RFP mRNA/gMR), because replicating minigenomes are templates for viral transcription.

Since GFP-PS5D supported decreased replication, the ratio of RFP mRNA/gMR increased although the RFP mRNA of GFP-PS5D is not increased. In addition, the foci number of GFP-PS5D is much less than GFP-PWT and GFP-PS5A, indicating mRNAs in GFP-PS5D samples may contain aberrant transcripts those cannot be translated the RFP protein. In contrast, mRNAs in GFP-PS5A samples are translated efficiently. These results were in consistent with our previous studies using the free PWT, PS5A, and PS5D.

Reference: Gao Q, et al. 2020. Casein kinase 1 regulates cytorhabdovirus replication and transcription by phosphorylating a phosphoprotein serine-rich motif. The Plant Cell 32(9): 2878-2897.

1. The authors relied on 1,6-hexanediol to suppress phase separation in both insect vectors and barley. However, the authors disregarded several publications demonstrating cellular toxicity by 1,6-hexanediol and a report that 1,6-HD impairs kinase and phosphatase activities (see below). doi: 10.1016/j.jbc.2021.100260,

We agree with the reviewer that 1, 6-hexanediol induced cellular toxicity. Therefore, we removed these results, which does not affect the main conclusion of our results.

1. The authors state that reduced accumulation of BYSMV-GFP in insects and barley under HEX treatment "indicate that phase separation is important for cross-kingdom infection of BYSMV in insect vectors and host plants." The above statement is confounded by many factors, the most obvious being that HEX treatment is most likely toxic to cells and as a result cannot support efficient virus accumulation. Also, since HEX treatment interferes with phosphorylation (see REF above) its use here should be avoided since P phase separation is regulated by phosphorylation.

We agree with the reviewer that 1, 6-hexanediol induced cellular toxicity and hereby affected infections of BYSMV and other viruses. In addition, 1, 6-hexanediol would inhibit LLPS of cellular membraneless organelles, such as P-bodies, stress granules, cajal bodies, and the nucleolus, which also affect different virus infections directly or indirectly. Therefore, we removed these results, which does not affect the main conclusion of our results.

Reviewer #3 (Public Review):

Membrane-less organelles formed through liquid-liquid phase separation (LLPS) provide spatiotemporal control of host immunity responses and other cellular processes. Viruses are obligate pathogens proliferating in host cells which lead their RNAs and proteins are more likely to be targeted by immune-related membrane-less organelles. To successfully infect and proliferate in host cells, virus need to efficiently suppressing the immune function of those immune-related membrane-less organelles. Moreover, viruses also generate exogenous membrane-less organelles/RNA granules to facilitate their proliferation. Accordingly, host cells also need to target and suppress the functions of exogenous membrane-less organelles/RNA granules generated by viruses, the underlying mechanisms of which are still mysterious.

In this study, Fang et al. investigated how plant kinase confers resistance against viruses via modulating the phosphorylation and phase separation of BYSMV P protein. They firstly characterized the phase separation feature of BYSMV P protein. They also discovered that droplets formed by P protein recruit viral RNA and other viral protein in vivo. The phase separation activity of P protein is inhibited by the phosphorylation on its intrinsically disordered region. Combined with their previous study, this study demonstrated that host casein kinase (CK1) decreases the phase separation of P protein via increasing the phosphorylation of P protein. Finally, the author claimed that the phase separation of P protein facilitates BYSMV replication but decreases its transcription. Taking together, this study uncovered the molecular mechanism of plant regulating viral proliferation via decreasing the formation of exogenous RNA granules/membraneless organelles. Overall, this paper tells an interesting story about the host immunity targeting viruses via modulating the dynamics of exogenous membraneless organelles, and uncovers the modulation of viral protein phase separation by host protein, which is a hotspot in plant immunity, and the writing is logical.

Thanks for your positive comment on our studies.

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

**Summary:**

The authors characterized a new lncRNA locus named FLAIL that controls flowering time in Arabidopsis thaliana. The functional validation of this locus is strongly supported by the use of several different tools (CRISPR-Cas9 deletions, T-DNA insertion, amiRNA gene silencing, and transgene complementation of KO lines). It is also suggested that FLAIL lncRNA works in trans but not in cis. There are strong observations supporting that FLAIL works in trans.

Moreover, it is suggested that FLAIL regulates gene expression by interacting with distant chromatin loci. This was assessed using RNA-Seq and ChIRP-Seq. Yet, the overlap between DEGs in the flail mutant and FLAIL binding sites at the chromatin is very small, with only 12 genes. From those, only 2 flowering genes' expression was rescued by FLAIL transgene complementation. The final conclusion that FLAIL lncRNA represses flowering by direct inhibition of the 2 flowering genes expression is correlative, and lacks genetic validation.

#1.1 We plan to support the conclusions in the manuscript genetically as the reviewer suggests. We started these experiments yet they will require the timeframe of the full revision.

In addition inspection of the supplementary file shows that the ChIRP analysis was done without filtering for the FDR so that some of the positive hits have an FDR of 0,232.

#1.2 We strengthened the manuscript by implementing and FDR filter of ChIRP-seq results. The distribution of FLAIL binding sites in Fig. S7B and Table S4, and overlapping numbers between DEGs and FLAIL-ChIRP in Fig. S8A were correspondingly updated.

In addition, many of the peaks land in intergenic regions with is not mentioned in the text a graph with the position of the peaks in respect to nearby genes would help.

#1.3 Thank you for the suggestion, we strengthened the manuscript with the requested analysis. We implemented the FDR filter, then we used "tssRegion" in ChIPseeker to set distance to the nearest TSS as (-1000, 1000), then most peaks were located in promoter regions (67.24%) and in intergenic regions with 16.38%. Since many papers present the position of the peaks by ChIPseeker (PMID: 32338596, PMID: 28221134, PMID: 31081251, PMID: 32012197, PMID: 31649032, PMID: 32633672) we also applied a similar method to display a distribution of FLAIL binding loci relative to distance from the nearest TSS in Fig. S7C.

In one sentence, the authors used the right model system and methodology, including advanced techniques, to characterize a new trans-acting lncRNA important for controlling the flowering time in Arabidopsis but lack evidence supporting a mechanism of action that goes beyond the interaction with several chromatin loci.

**minor points:**

line#63-64 the authors say the COLDAIR and ASL work on FLC in cis in my view the original papers suggested/showed they work in trans.

#1.4 We increased precision by changing this sentence to ‘Vernalization-induced flowering associates with several lncRNAs such as ____COOLAIR____, COLDAIR____, ANTISENSE LONG (ASL), and COLDWRAP____ that in cis or in trans locally repress gene expression of FLOWERING LOCUS C (FLC), a key flowering repressor at different stages of vernalization’____.

Fig 1B please add some more protein-coding RNAs for the bio-info analysis for comparison

#1.5 ____done.

Order of Supplementary Fig citation is mixed with S2 coming before S1B

#1.6 Thank you, we ordered all figures by appearance in the text. __

__

It would help the reader to have a schematic of the crisper deletions, T-DNA insertion, and position of primers used for the RT-qPCR.

#1.7 We enhanced our presentation of Fig. 1A. It shows a schematic of them as well as positions of primers.

In the supplementary PDF file, some of the text is missing on page 3 beginning and end of lines.

#1.8 we ensured all text in new submission.

Reviewer #1 (Significance (Required)):

The use of several different tools to validate the biological function of FLAIL locus is a major strength of this work.

The authors propose that flowering time and its gene regulation are controlled by sense FLAIL lncRNAs. However, the sense transcription of FLAIL locus is not detected in wild-type plants by TSS-Seq, TIF-Seq, or plaNET-Seq.

#1.9.1 There appears to be some confusions. Transcription of sense FLAIL can be observed in chr-DRS, TSS-seq, TIF-seq in wild type and even in plaNET-seq in NRPB2-FLAG nrpb2-1 plant. We enhanced presentation of Fig. 1 and provided a more clear description in Line 81-99.

If the authors would have explored further the expression of FLAIL transcripts in different stages of development (vegetative and non-vegetative) and in response to different conditions, it would make their claims on the function of FLAIL lncRNAs more convincing. Additionally, flail mutants could have been obtained in the hen-2 background, since it's there where we can observe FLAIL transcription.

#1.9.2 Thank you for the suggestion. We included additional analyses in ____Fig. S2 for FLAIL transcription level in different tissues and different abiotic stress conditions base on 20,000 publicly available RNA-seq libraries (PMID: 32768600). Although many libraries are non-stranded, this analysis determined that sense FLAIL or total FLAIL (including sense and antisense) is broadly expressed over many tissues and induced in response to many abiotic stresses (Fig. S2A-B), therefore suggesting that FLAIL may be needed broadly in Arabidopsis.

FLAIL locus lays on the proximal promoter region of PORCUPINE (PCP), an important regulator of plant development. As flail mutants, pcp mutants display an early flowering phenotype. The authors show no link between FLAIL and PCP from the overlap between re-analysis of published RNA-Seq data for pcp and RNA-Seq and ChIRP-Seq from the authors. This analysis is not enough to exclude the involvement of PCP from the FLAIL function. PCP expression using RT-qPCR should be performed in flail mutants to further support that FLAIL works independently from PCP.

#1.10 We strengthened this conclusion by adding the requested experiment. PCP transcription level in flail3 mutant was provided by RT-qPCR and RNA-seq in Fig. S11A-B.

This work does not hypothesize any molecular mechanism besides the interaction of FLAIL lncRNAs with several chromatin loci. It was recently proposed in Arabidopsis that a trans-acting lncRNA interacts with distant loci via the formation of R-loops. The authors do not comment on that. This work would benefit in correlating FLAIL binding sites with R-loop-forming regions mapped in Arabidopsis, regardless of the results from this analysis. Additionally, the authors could attempt to look for a motif responsible for FLAIL binding.

Check R-loop forming data R-loops (Santos-Pereira and Aguilera, 2015) in Arabidopsis, determined by DRIP-seq (Xu et al., 2017).

#1.11 Thanks very much for this excellent suggestions.

First, we searched for a consensus DNA motif on FLAIL binding regions by Homer. We determined four commonly enriched DNA sequence motifs among FLAIL target genes (Fig. 4G). Notably, the target genes CIR1 and LAC8 contained consensus sequences that matched to all FLAIL binding motifs (Fig. 4G). These data are consistent with a model where FLAIL binds DNA targets through a sequence complementary mechanism. Functionally important sequences are frequently conserved among evolutionarily distant species, we observed three motifs that appeared to cross-species conserved (Fig. S9), suggesting a potential evolutionarily constrained role.

Second, we indeed identified R-loops peaks on several of FLAIL binding sites by DRIP-seq (Xu et al., 2017). For example, we observed R-loop formation over three FLAIL binding motifs at CIR1 locus and one at LAC8 (Fig. R1), indicating that R-loop formation may also be a factor determining FLAIL binding. Even though R-loop peaks are present at several FLAIL targets, full elucidation if R-loop formation determines FLAIL targeting requires further experimental evidence is beyond the scope of the current manuscript.

Fig. R1 Representative tracks at LAC8 and CIR1 showing R-loop formation by DRIP-seq on Watson strand (w-R loops), Crick strand (c-R loops). Undetectable R-loops after RNAse-H treatment was shown as negative control. Four conserved sequence regions of FLAIL binding motifs were indicated by red arrows at LAC8 and CIR1 loci. Gene annotation was shown at the bottom.

Most of the key conclusions are convincing, except for the flowering time control directly through CIR1 and LAC8, which should be mentioned as speculative

____#1.12____ Thank you for finding most key conclusions convincing. We plan strengthen the manuscript with additional genetic evidence to as part of the full revision.

The words locus and loci are latin and they should be written in italic. The word Brassicaceae, referring to the family should be in italic, and should not be "Brassicaceaes". The word analysis has the wrong spelling.

#1.13 We follow conventions given in Scientific Style and Format: The CBE Manual for Authors, Editors and Publishers (1994) Cambridge University Press, Cambridge, UK, 6th edn. The words locus and loci are common Latin terms and should not be italicized. However, should the format of the final prefer these words in italics we will change it later. We improved consistency of using italics. “Brassicaceaes” was changed to “Brassicaceae”.

"How much time do you estimate the authors will need to complete the suggested revisions: this is difficult to answer as it depends to which level the author would like to take their work. In my view, if all new experiments would have to be started from scratch it is too far away to be estimated.

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

In this ms, the authors identified the FLAIL lncRNA that represses flowering in Arabidopsis from a locus producing sense and antisense transcripts. They use an allelic series involving T-DNA insertions, CRISPR/Cas9 and artificial miRNAs to study the role of FLAIL in flowering. A complementation series of constructs of the flail3 allele allowed them to show that the sense FLAIL lncRNA can act in trans. RNAseq revealed a small group of genes linked to the regulation of flowering whose expression is affected in the mutant and restored in the complementation line. To gain further insight into FLAIL function, the authors used a ChIRPeq approach to test whether the lncRNA can recognize potential target genes along the genome and they could show that FLAIL binds specific genomic regions. Clearly, this paper shows very nice evidence that the FLAIL lncRNA can act in trans to regulate gene expression. Nevertheless, there are certain points that need to be clarified to further support the action of the sense FLAIL transcript.

1.According to Fig. 1 A, the antisense FLAIL is "internal" to the DNA genomic area spanning the sense FLAIL. Hence, with direct RT-qPCR is very difficult to distinguish between these molecules as a minor "RT" activity of the Taq polymerase may lead to detection of low levels of antisense, idem if RDRs may generate low antisense levels. Although I think that the plaNET seq brings strong evidence about the start and ends of these molecules, to measure them by RT-qPCR is not trivial and requires the use of strand-specific RT-PCR using a 5' extension of the oligo and amplification with one oligo of the FLAIL sequence (sense or antisense) and the added oligo.

#2.1.1 Thanks for this good suggestion. We tested both sense and antisense FLAIL transcription using oligo linked gene specific reverse primers for RT and a pair of the linked oligo and gene specific forward primer for qPCR. Primer locations were shown in Fig. 1A and new data were in Fig. 1C-D, Fig. 2B-C, and Fig. S4B-C.

It is not clear how they could distinguish precisely sense and antisense particularly when both RNAs correlate as it is the case here in all alleles (Fig. 1 C and 1D). This should be more explicitly mentioned in the materials and methods section.

#2.1.2 We gave a description of strand specific RT-qPCR method in detail in Line 397-402.

2.In Fig. 2, what are the levels of antisense in the complementing lines with the sense transcript? And reciprocally sense levels in antisense constructs?

#2.2 We added this data in Fig. 2B-C and described in Line 136-143. We indeed observed that sense FLAIL transcripts in the transformed asFLAIL construct or asFLAIL transcripts in the transformed sense FLAIL construct was similar to the control 35S:GUS (Fig. 2B-C), validating that NOS terminator inhibits antisense transcripts. We also noted that the transformed 35S:GUS and sense FLAIL construct expressed higher asFLAIL compared to the flail3 mutant (Fig. 2C). This may be caused by a T-DNA insertion of the resulting transgenic plants.

This will definitively demonstrate the assumption that the T-NOS termination will not allow any expression on the other strand. At present, only one of the lncRNAs is measured in each experiment?

#2.3 We appreciate the next-level reflection of this reviewer, with so many regions initiating cryptic antisense transcription it is an interesting challenge to identify a 3´- terminator that initiates no or poor antisense transcription.

First, previous published data argue that the NOS terminator is largely abolishing initiation of antisense transcription (PMID: 33985972, PMID: 30385760, PMID: 27856735). All these studies address roles of antisense transcription by generating mutations abolishing antisense lncRNA transcription using the NOS terminator sequences.

Second, to satisfy the curiosity of this reviewer, we provide data below that from another manuscript of the lab in preparation. It’s a screenshot of plaNET-seq in fas2-4 NRPB2-FLAG nrpb2-1 mutant carrying a pROK2 construct. The pROK2 T-DNA coincidentally carries a NOS terminator. We mapped plaNET-seq reads to the pROK2 scaffold to display the reads. In pROK2, a NOS promoter activates NPTII expression (red) with NOS terminator as a terminator sequence. No antisense transcription (blue) is detectable by this sensitive method to detect nascent transcripts. Taken together, the selection of the NOS terminator as a region suppressing initiation of antisense transcription represents a valid choice.

Fig. R2 Genome browser screenshot of plaNET-seq at NPTII locus of pROK2 T-DNA vector in fas2-4 NRBP2-FLAG nrpb2-1 mutant. This mutant carries a pROK2 construct, in which a NOS promoter activates NPTII expression with NOS terminator a terminator sequence. Sense strand was shown in red and antisense strand in blue. pROK2 annotation was shown at the bottom.

3.In Fig. 3, it will be important to also show the FLAIL locus in the flail3 mutants (in comparison to the wt) as well as the transgene locus. Here the reads will be strand specific and furthermore this will allow to show that the transgene is not generating antisense transcripts (through RDRs for gene silencing?) and confirm that the sense FLAIL is required for the complementation.

#2.4 Thank you very much for this suggestion. NGS reads for endogenous FLAIL and transgenic FLAIL both map to the FLAIL locus, so we show the FLAIL locus in Fig 3B. This representation shows that sense FLAIL transcripts were significantly reduced in flail3 and rescued in complementation line comparing to wild type. These data argue against the idea of gene silencing and linked antisense production from the transgene. However, RNA-seq suggests that an isoform of asFLAIL appears to accumulate in flail3. Since we fail to identify this accumulation by strand specific RT-qPCR result in flail3 and in CRISPR-deletion lines, this may be an asFLAIL isoform resulting from the T-DNA insertion.

4.In Fig. S5, the expression of FLAIL is shown in the artificial miRNA lines. Is the antisense FLAIL affected "indirectly" by the cleavage of the amiRNA or remains constant? This is likely the case but should be shown.

#2.5 We added this result in Fig. S4C and expression level of asFLAIL remains constant compared to the transformed empty vector control.

5.The ChIRPseq data adds major novelty to the ms and brings new ideas about the way of action of FLAIL. However, are there any common epigenetic states between ChIRP targets (e.g. histone modifications, antisense RNA production, homologies "detected" in the conserved regions between Camelina and Arabidopsis and the target loci? Or others) that may highlight potential mechanisms leading to repression mediated by FLAIL of these loci? There are many databases that could be explored (even during flowering) to search for potential relationships. Although precise description of the mechanism is out of the scope of this ms, this can be discussed in more detail to further expand on the nice data obtained.

#2.6 We searched for a consensus DNA motif on FLAIL binding regions by Homer. We determined four commonly enriched DNA sequence motifs in target genes. Notably, the target genes CIR1 and LAC8 contained consensus sequences that matched to all FLAIL binding motifs (Fig. 4G). These data are consistent with a model where FLAIL binds DNA targets through a sequence complementarity mechanism. Functionally important sequences are frequently conserved among evolutionarily distant species, we observed three motifs that appeared to cross-species conserved (Fig. S9), suggesting a potential evolutionarily constrained role.

**Minor comments:**

6.In Fig. S3, a global alignment between FLAIL and two loci in Arabidopsis and Camelina is sown. What is the extent of homology? How conserved is this sequence at nucleotide level (small or very long?) to support the conservation of this lncRNA. Are there potential structures conserved among these lncRNAs?

#2.7 T____wo consensus regions of ____FLAIL____ sequences among eleven disparate Brassicaceae genomes were shown in Fig. S9. ____Camelina sativa_ shared 98-nucleotide_ conserved sequences with Arabidopsis thaliana. In the future, it will be interesting to explore evolutional conserved structures among Brassicaceae genomes. However, these analyses are beyond the scope of the current manuscript.

7.In Fig. S4B, arrows may help to understand which seeds were selected.

__#2.8 Thanks. Arrows were included.____

__

Reviewer #2 (Significance (Required)):

This paper is a very nice piece of work and demonstrate the action of a long non-coding RNA (lncRNA) in trans on specific targets involved in the regulation of a developmental process, flowering. There is growing evidences that the non-coding genome hides large number of lncRNAs and there is little detailed genetic support for the action of lncRNAs globally. In contrast to many descriptive papers in the field, this ms demonstrates genetically, through an allelic series and complementation experiments, that this lncRNA locus is involved in flowering regulation and that its sense lncRNA recognizes target loci genome-wide, bringing interesting perspectives on potential new mechanisms of transcriptional regulation mediated by non-coding RNAs.

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

In the manuscript by Jin et al authors characterize the FLAIL DNA locus in Arabidopsis (using a wide array of publicly available datasets), which produces a set of sense and anti-sense lncRNAs.

While our work on the FLAIL manuscript was ongoing we published the manuscripts where we presented these novel genomics methods and related data to capture nascent transcription and cryptic isoforms. We shared most data with TAIR, so we are happy to hear that these data are considered publically available.

Authors determined that the sense FLAIL lncRNA (or a set of sense lncRNAs, which isn't fully clear from the way the data are presented) is involved in flowering time in Arabidopsis based on the fact that the several flail mutants lead to the early flowering phenotype and this flowering defect is complemented by transgenic FLAIL DNA, meaning that FLAIL lncRNA acts in trans.

A series of experiments lead us to conclude that the sense isoform of FLAIL is responsible for the effect. We improved the data representation and writing of the manuscript to enhance accessibility.

The T DNA flail3 - mutant results in expression changes (up or down) of 1221 genes, including twenty genes linked to flowering in various ways. Expression of a group of these flowering-related genes could be either fully (for eight genes) or partially (for five genes) rescued by transgenic FLAIL. Authors also conducted the ChIRP-seq to determine which genes are physically bound by FLAIL lncRNA genome-wide. It was found that 210 genes in the genome are bound by FLAIL lncRNA. Comparison of the dataset of differentially expressed genes in the T-DNA flail3 mutant with the ChIRP-seq dataset of genes that are bound by FLAIL lncRNA revealed the 12 overlapping genes.

Among these twelve overlapping genes, four were found to be functionally connected to flowering with expression of these four genes being down in flail3 T-DNA mutant. Two out of these four genes were ruled out from being involved in the regulation of flowering by FLAIL. Authors conclude that the two other genes (Cir1 and Lac8) are responsible for the late flowering phenotype of flail mutants based on the three lines of evidence: (i) these genes expression is reduced in the flail mutant, (ii) FLAIL lncRNA directly interacts with these genes chromatin, (iii) the mutants of these genes were previously reported by others to display early flowering phenotypes too. While I find many of the findings reported in the manuscript very interesting, building a good foundation on which to expand the study and providing a very good leads for follow up experiments, I also have serious concerns about the manuscript in its current form.

Most importantly, this reviewer doesn't think that the mechanism of FLAIL lncRNA action was convincingly demonstrated. The main question would be how FLAIL lncRNA works and this question wasn't fully answered. It is great that FLAIL lncRNA binds directly to the two flowering-related genes, but what does it mean? Does it change any chromatin context of these genes quantitatively or qualitatively to affect the transcription? Or does it bind any components of transcriptional machinery and thus controls the transcriptional output?

#3.1 This manuscript addresses an important question in the field question: what is the evidence for functional elements in non-coding regions of genomes? Despite many efforts, convincing genetic support for these functions often remained limited. In addition to our strong genetic data, we provided new evidence that FLAIL recognizes targets with evolutionally conserved sequence motifs as part of the revision in Fig 4F and Fig. S9. Additionally, we plan to do ChIP-qPCR to identify histone modifications on FLAIL targets.

Additionally, flail3 T-DNA mutant affects the expression of 1221 genes and FLAIL lncRNA physically interact with 210 genes, so how can authors be fully sure that FLAIL lncRNA has only direct effect on these two genes and doesn't also contribute to the regulation of the upstream to Cir1 and Lac8 genes or even components of the transcriptional machinery that regulate these genes?

#3.2 We agree with this opinion. It is the reason why we felt stating this exact conclusion in our previous manuscript was justified. We improved accessibility of our manuscript in the revision, these clarify our model, that the trans-acting lncRNA sense FLAIL can interact with the chromatin regions of its target genes to directly or indirectly regulate gene expression changes involving flowering (Line 274).

Theoretically, doing RNA-seq in the amiR-FLAIL sense lncRNA mutant might have a chance of reducing the number of affected DEGs, making it easier to analyze the FLAIL targets, even if the allele can't be used for complementation experiments.

#3.3 Thanks for this suggestion. We plan to confirm key gene expression changes using amiRNA-FLAIL in full revision.

Also, auhors totally neglect putting the Cir1 and Lac8 genes into the context of flowering regulation, but it is something that needs to be done.

#3.4 ____We discussed roles of CIR1 and LAC8 in flowering regulation in Line 260-272. Flowering is fine-tuned to maximize reproductive success and seed production and by endogenous genetic cues and external environmental stimuli such as photoperiod. Nevertheless, many details of the flowering pathways and their integration remain to be investigated____. CIR1 is a circadian clock gene, induced by light and involved in a regulatory feedback loop that controls a subset of the circadian outputs and thus determines flowering time. Our GO analysis supports that a subset of DEGs are connected to the response to red or far red light that contains among other key flowering genes such as ____phytochrome interacting factor____ 4____ (PIF4) and CONSTANS (CO)____. FLAIL also binds the chromatin region of LAC8. LAC8 is a laccase family member that mainly modulates phenylpropanoid pathway for lignin biosynthesis____. Similar to flail, lac8 mutants flower early. While intermediates in this pathway or dysregulation of lignin-related genes could promote flowering in plants, the molecular connections of reduced LAC8 expression to effects on flowering time will require further investigation.

Lastly, the paper needs to be totally rewritten to be even properly evaluated. In its current state it reads like a very short draft.

#3.5 We reorganized the structure of manuscript, improved clarity and provided new mechanistic evidence in Fig. 4G and Fig. S9 to present a more complete manuscript.

The Abstract is weak, the Introduction is written in a such telegraphic style that it is barely readable, in many places there is no connections between sentences leading to the information appear to be presented as random, even if it isn't.

#3.6- We strengthened the Abstract by providing new evidence and improved for the Introduction.

The Results section is written rather rudimentary with information not being sufficiently provided to describe the results but rather scattered between the Results and Figure legends.

#3.7 Thanks for your suggestions, we described each FLAIL length and all constructs in detail in Results, put a schematic of T-DNA and CRISPR mutants in Fig. 1A, moved comparative genomics data to the end of Results and ensured all figures in order.

The Discussion is the best written part of the manuscript.

Thanks for your appreciation of the Discussion.

The Conclusion section carries no specific information and reads more like a little summary suitable for a review article rather than experimental paper.

#3.8 We agree this opinion, this paragraph fits Discussion better and Conclusion was removed.

Therefore, this reviewer thinks that regardless of how authors will choose to proceed with the current experimental version of the manuscript, it'd be in the authors' best interests to at least fully revise the paper before resubmitting anywhere. I'd also advise authors to seek professional editorial help specifically using an editor with the background in the plant sciences.

Authors might also want to consider moving Fig.3 into the Suppl. as it doesn't carry much weight or significance and perhaps make existing figures more meaningful and comprehensive and by including a better diagram of the locus (e.g., Fig. S1), etc.

#3.9 We thank this helpful suggestion. Fig.3 represents the RNA-seq data. In combination with supporting data in the supplementary material, it gives an easy visual readout of the reproducibility of the findings in replicates of stranded RNA-seq. In a new submission, we moved it to Fig. S5B and highlighted 13 differentially expressed flowering genes as well as sense FLAIL in flail3 that were rescued in complementation line in Fig. 3A. Moreover, we gave screenshots of FLAIL itself and four flowering related FLAIL targets in RNA-seq with a clear schematic representation of each locus. We believe these revisions improve Fig. 3.

It's not practical to list all issues with the writing as the paper requires total re-writing, so I can just make a few suggestions without any specific order to help authors improve the paper:

We are happy to improve our manuscript with the help of the reviewers. We addressed all comments including from reviewer #3 with a constructive spirit. However, since colleagues and reviewers #1 and #2 found the manuscript comprehensible to the point where they could make expert-level comments that illustrate understanding of the manuscript, a total re-writing did not feel like the most constructive suggestion to improve the manuscript.

--There is no statement anywhere that states the goal of the study.

#3.10____ We stated the goal of the study in line 50-69 and we think this is a misunderstanding. We summarized three issues currently exist in characterization of functional lncRNA in the last sentence of the first three paragraphs in Background: 1 in Line 50, the broad range of candidate hypotheses by which lncRNA loci may play functional roles call for multiple approaches to distinguish alternative molecular mechanisms. 2 in Line 59, functional characterization of trans-acting lncRNAs remains a key knowledge gap to understand the regulatory contributions of the non-coding genome. 3 in Line 69, the contribution of trans-acting lncRNAs to the regulation of distant flowering genes is currently unclear. So in the last paragraph of the background, we claimed that our goals are to address these questions through characterization of functional FLAIL lncRNA in flowering repression using multiple genetic approaches and various genomic data.

--No rational is provided on why authors decided to examine this specific genomic locus.

#3.11 For several years, our lab studies the rules and roles of non-coding transcription. We characterized and are characterizing several loci with evidence of non-coding transcription in a range of species. Early experiments suggested that FLAIL functioned in flowering, this manuscript clarifies that the function is executed as trans-acting lncRNA of the sense FLAIL isoform.

--Typically, the significance is in studying the function of lncRNA or a group of lncRNAs produced from a genomic locus, I don't think I ever encountered the instances when it was exciting to study just a specific genomic locus. If the locus does indeed have any significance for initiating the study, it needs to be explained.

#3.12 This study is remarkable in many aspects. We fully discuss key strengths in the discussion. First, we ____exhibit a trans-acting lncRNA FLAIL that represses flowering by promoting the expression of floral repressor genes as discussed in Line 247-281_; Second, in Line 284-306, we informed that this study provide a compelling model about how to apply _series of convincing genetic data____ to functionally characterize lncRNA loci. Third, in Line 307-312, evolutionary conserved FLAIL sequences across species is key to characterize the functional _microhomology in other _Brassicaceae.

--The locus can produce lncRNAs, but it can't harbor them.

#3.13 We clarified this confusion by enhancing ____presentation of Fig. 1 and providing a more clear description of each sequencing method and results in Line 81-99. Although we provided evidence that transcription of both sense and antisense FLAIL are more stable in hen2-2, they were clearly observed in chr-DRS in wild type and plaNET-seq in NRBP2-FLAG nrpb2-1 and sense FLAIL was even detected in TSS-seq and TIF-seq in wild type.

--No length of FLAIL lncRNAs or their range is provided in the first section of Results.

#3.14 We gave the length of sense FLAIL in Line 82 and antisense FLAIL in Line 86.

--On many occasions authors don't state rational for doing experiments, which leads to information often flowing as random.

#3.15 we enhanced clarity of the rational for each experiment and made some connections between sentences to make more fluent. For example, in sentences in Line 99, Line 113, Line 126, Line 159, Line 183, Line 214, and Line 219.

--What do authors mean by the subtitle "FLAIL characterizes a trans-acting lncRNA repressing flowering"? How can lncRNA FLAIL or FLAIL locus characterize lncRNA?

#3.16 We changed it to “FLAIL represses flowering as trans-acting lncRNA” in Line 112.

--Check all figures. E.g., Fig. 3B-E mentions only accession numbers for the genes.

#3.17 The systematic gene IDs are a valid way to represent data, in particular for genomics data since it facilitates cross-comparisons. To make it more accessible we also show systematic names of each gene in Fig. 3A-F, Fig. S6 and Table S3.

--It is not clear where exactly the T-DNA insertion is located relative to sense FLAIL in flail3 mutant (Fig. S4).

#3.18 We moved the schematic to clarify this to revised Fig. 1A and the exact T-DNA insertion site is mentioned in the legend.

--- What is the length of the complementing sense FLAIL lncRNA?

#3.19 We now include the length of the complementing sense and antisense FLAILs in Line 351-352.

--Check the description of each and every construct used and provide explanation for each in the Results. E.g., the pFLAIL:gFLAIL18/88 and pasFLAIL:gasFLAIL18/39 constructs aren't explained in Results, and can only be found in Fig. 2 legends.

#3.20 We described each construct including pFLAIL:gFLAIL18/88 and pasFLAIL:gasFLAIL18/39 constructs in Line 133, amiR-FLAIL-11 and amiR-FLAIL-11 in Line 149.

Reviewer #3 (Significance (Required)):

Tens of thousands of lncRNAs have been identified in various eukaryotes, but their biological roles have been shown only for a small fraction of them, and the mechanisms of their action are delineated for only a very few of them. Most of the advances on the field of lncRNAs are reported in metazoan, while the field of lncRNAs in plants is lagging far behind in terms of knowledge about lncRNAs with assigned biological functions or lncRNAs with delineated mechanisms of action. From this point of view, this reviewer is always excited to see any new functional plant lncRNAs for which either biological or mechanistic functions have been determined, and deems the information on this subject significant. The manuscript's findings are potentially very interesting and present a decent body of work that lays a very solid groundwork for future experiments. My main concern about the manuscript's significance in its current form is the fact that no real solid mechanism of action for the described lncRNA or a set of lncRNAs (?) has been demonstrated. The best mechanistically studied lncRNAs in Arabidopsis are involved in the regulation of flowering time, particularly those that function in the vernalization flowering pathway and to lesser extent in autonomous pathway. The new FLAIL lncRNA or lncRNAs (?) described in this manuscript also appear to regulate the flowering time in Arabidopsis, however more experiments would be needed to provide a definite conclusion about how direct FLAIL's effect is and how exactly it functions. That unfortunately obviously diminishes the significance of the manuscript and makes it potentially interesting only to researches studying flowering in Arabidopsis and even then the manuscript results would be incomplete to make solid conclusion.

Lots of functional phenotype have

Additionally, the manuscript requires complete re-writing.

We thank this reviewer for the appreciation of __a decent body of work and a very solid groundwork for future experiments. We are confident that our revisions make the manuscript more comprehensible to highlight the qualities of our manuscript more accessibly.____

__

Isn't this the way children are though ? The way things are born inside them, and it may because of something we may take for granted as ordinary, something small or something big can cause a tremble or ripple...and it can grow into something exciting. I like to think it is that way for us too.

Author Response

Reviewer #1 (Public Review):

Yang, Bhoo-Pathy, Brand et al detail their investigation of a large Swedish cohort compared with age matched controls to estimate the risk of short- and long-term cardiotoxicities of breast cancer therapies in a general breast cancer patient population. They find that breast cancer patients are at significantly increased risk of developing arrhythmia and heart failure both within the first year of cancer diagnosis as well as at least 10 years after. Interestingly, they find that there is an increased risk of ischemic heart disease within the first year after diagnosis, but no increased risk of ischemic heart disease in the long term.

The authors should be commended for this large cohort study that achieves its goal of identifying the incidence and hazard ratio of cardiotoxicity associated with breast cancer treatment within a general breast cancer population. Their findings of increased risk of heart failure in patients treated with anthracyclines and trastuzumab is consistent with multiple prior studies in the field of cardio-oncology and adds to the validity of the data.

The finding that there is only a slightly increased (and statistically insignificant) risk of ischemic heart disease after left sided radiotherapy is quite interesting, and as noted by the authors, differs from prior understandings about risk of ischemic heart disease associated with breast radiation therapy. Without data on mean heart dose or total radiation administered the results are hypothesis generating, but should not be utilized to guide medical decision making.

One of the major limitations of this study is that the authors' goal is to identify the incidence and risk of cardiotoxicity associated with the various breast cancer treatment regimens and determine these risks over time, and as noted by the authors, the registry utilized only includes planned treatment not whether patients did receive this therapy (and what dose of therapy). This is a key point that should be emphasized when interpreting the results.

As noted by the reviewer, the Stockholm-Gotland Breast Cancer Register only included the intended treatment without a detailed dosage of the therapy. However, the agreement between intended and administrated treatment was about 95% in Sweden (Löfgren,L et, al BMC Public Health. 2019). We have now further explained this in the discussion section.

In Discussion: “Overall, our results indicate only small risk of heart disease due to radiotherapy in women treated in Sweden after year 2000. Further studies with detailed information on the mean heart dose of radiation or total cumulative radiation dose administered are therefore needed to confirm and provide more context to this finding.”

In Discussion: “Besides, the Stockholm-Gotland Breast Cancer Register only records intended treatment, not whether patients actually received these therapies. However, the agreement between the intended and administered breast cancer treatment in Sweden has been previously reported to be about 95% (Löfgren et al., 2019).”

There are several conclusions included in the discussion section that are not supported by the data from the results section and the authors should be careful to suggest mechanisms of cardiotoxicity from an observational population-based study. Examples include suggesting anthracyclines cause cardiotoxicity of the myocardium but not the cardiac vessels; attributing early increased risk of ischemic heart disease to emotional distress alone; and that inhibition of HER2 receptors in myocytes may explain cardiotoxicity caused by trastuzumab. These are interesting hypotheses that would be better supported by references to lab/animal model studies.

We thank the reviewer for the suggestions and have now added the reference for the suggested mechanisms of cardiotoxicity with lab/animal model studies in the discussion section.

In Discussion: “As the long-term risk was observed for heart failure but not ischemic heart disease, the cardiotoxic effect of chemotherapy might be mainly on the myocardium mediated by the effect of DNA double-strand breaks through topoisomerase (Top) 2β, but not the cardiac vessels. (Lyu et al., 2007)”

In Discussion: “The finding that risk of ischemic heart disease in breast cancer patients was only transiently elevated after diagnosis is not unexpected, considering the emotional distress of dealing with a new cancer diagnosis in the patients, which may lead to higher short-term rates of ischemic heart disease (Fang et al., 2012; Schoormans, Pedersen, Dalton, Rottmann, & van de Poll-Franse, 2016). In addition, surgery after breast cancer diagnosis might increase the risk of arterial thromboembolism (Gervaso, Dave, & Khorana, 2021), which includes myocardial infarction, and the effect appears to attenuate one year after diagnosis. (Navi et al., 2017; Navi et al., 2019).”

In Discussion: “The cardiotoxic effect of trastuzumab meanwhile may be explained by inhibition of the HER2 receptors in myocytes, that activates the mitochondrial apoptosis pathway through modulation of Bcl-xL and -xS, which regulates cell development and growth (Grazette et al., 2004; Yeh & Bickford, 2009)”

The authors succeed in highlighting the increased risk of cardiotoxicity associated with breast cancer treatment in the observed patient population. Rather than exploring the mechanism of cardiotoxicity for the treatment regimens observed, the data presented may be more useful to propose a longitudinal cardiac monitoring schedule for patients who have been treated for breast cancer, and who the current data suggest, are at long term risk for heart failure and arrhythmia.

As we found increased long-term risk of heart failure in breast cancer patients, especially for those treated with Anthracyclines +Taxanes and Trastuzumab, we therefore suggest for a prolonged longitudinal cardiac monitoring schedule for ten or more years in these treated patients. We have added the suggestion in the discussion section.

In Discussion: “Analysis by time since diagnosis revealed long-term increased risks of arrhythmia and heart failure following breast cancer diagnosis, suggesting that a longitudinal cardiac monitoring schedule might be helpful to improve cardiac health in breast cancer patients.”

Reviewer #2 (Public Review):

This is a registry based study in which patients diagnosed with locoregional breast cancer ( stage 1-111) from 2001-2008, between the ages of 25-75 were compared to a randomly sampled cohort of 10 women matched by the year of birth and for three specific cardiac conditions as outlined in the key objective. Data was gathered by cross referencing Subject's unique identification numbers in Swedish Cancer Register, Patient Register, Cause of Death, and Migration Register. Prescribed Drug Register was reviewed to gather information about prescribed medication to perhaps infer the medical comorbid conditions for which medication was prescribed. Breast cancer treatment specific information was missing in cases and presumption of use of Anti Her2 therapy was made based on HER2 neu status in some cases. While the primary objective of the study to show increased evidence primarily Heart failure and arrythmias seem to have been met in this patient registry based study, there is some question of the specificity of the data since it was gathered from the various registers and is subject to operator dependent biases.

Strengths: Study is a long term follow up of patients treated with potential cardiotoxic drugs, confirming the previously known association of specific heart disease to the use of these drugs. Longest follow up seems to be for 16 yrs for the earliest cohort of 2001 and minimum approximately 10 yrs for the cohort of 2008. This study does confirm that long term risk that remains even after the treatment is completed and potentially suggests that more robust cardiac function monitoring guidelines for survivors may be warranted.

Weaknesses: This is a patient register based study. As outlined above, data was extracted by cross referencing various patient registers. Since the data was dependent on the ICD codes entered in the patient register, there seems to be potential for missed information.

The Swedish Patient Register has quite high validity for the heart diseases analyzed in this study, with a positive predictive value between 88%-98%, by using the main diagnosis in the register. However, it is still possible that we have missed some information for heart disease and we have emphasized this limitation in the discussion section.

In discussion: “The Swedish Patient Register has high validity for heart failure, arrhythmia and ischemic heart disease (with positive predictive value between 88%-98%) (Hammar et al., 2001; Ludvigsson et al., 2011), by analysing main diagnoses only. However, misclassification of heart diseases may still have occurred.”

Preexisting comorbidities were also extracted through Patient Registers hence may be subject to same potential for missed information.

The Swedish Patient Register has relatively high validity for the majority of comorbid diseases. However, patients without severe symptoms of the diseases might be treated in the primary health care centers, which were not included in the patient register. We have therefore pointed out this limitation in the discussion section.

In discussion: “In addition, preexisting comorbidities extracted from the patient registers may not include those patients with slight symptoms.”

In addition, information for use of Trastuzumab was extrapolated from the Her2neu status of the patient when such information may not have been accessible through Prescribed Drug Registers.

As the majority of HER-2 positive patients were treated in the clinics, the Swedish Prescribed Drug Register does not register their information. Because ~90% of HER-2 positive cancers were treated with trastuzumab between 2005 and 2008 in the Stockholm-Gotland region, we therefore used HER-2 positivity as a proxy for trastuzumab treatment. We have now further explained this in the methods section.

In Materials and Methods: “As ~90% of HER-2 positive cancers were treated with trastuzumab between 2005 and 2008 in the Stockholm-Gotland region and the Swedish Prescribed Drug Register does not cover data on treatment with trastuzumab, HER-2 positivity was used as a proxy when no registry data on trastuzumab was available during this time period (30% of the HER-2 positive patients had missing information on trastuzumab).

It is also unclear if there was any protocol in place for cardiac monitoring for patients receiving cardiotoxic chemotherapy or Anti Her2neu agents.

In Sweden, there is no cardiac monitoring for chemotherapy in routine clinical practice. For HER2-therapy, cardiac monitoring with a thorough cardiac assessment prior to treatment, including history, physical examination, and determination of left ventricular ejection fraction before, during and right after treatment has been mandatory since introduction in clinical routine. We have now added this information to the discussion.

In discussion: “As there is no cardiac monitoring for chemotherapy in routine clinical practice and cardiac assessment is only performed prior to and during the treatment period for HER-2 positive patients in Sweden, a longer-term cardiac monitoring program might be helpful for these patients.”

Reviewer #3 (Public Review):

This matched analysis uses data from patients newly diagnosed with breast cancer the Stockholm-Gotland Breast Cancer Register and data from patients in the general female population in Sweden to ask the question of whether breast cancer diagnosis (and subsequent treatments of breast cancer) is associated with an increased rate of heart disease after treatment. It is impossible to answer this question in a randomized controlled setting and would be unethical to randomize patients to not be treated for their cancer, thus a matched approach in theory would seem to make sense at face value. However, I have some concerns about the analysis that I believe impede their answering the research aims.

1. With regard to the matched analysis of time to heart disease diagnosis, I have several critiques/questions. First, for the breast cancer cohort, were patients with a diagnosis of heart disease prior to cancer diagnosis included in the analysis? If so, how was the event (which precedes time = 0) incorporated into the analysis? If not, please make sure to make note of this important restriction. I think the latter approach is the better / correct.

As suggested by Referee 3, we have now excluded those patients with a diagnosis of heart disease prior to cancer diagnosis. We have updated the results and the methods section accordingly.

In Materials and Methods:

“We included all patients diagnosed with non-metastatic breast cancer (stages I-III) and without prior diagnosis of heart disease at age 25 to 75 years (N = 8015).”

Second, for the matched cohort, what is time = 0 for these persons? i.e. how does one interpret "Time since diagnosis" on Figure 1 for a patient who has not been diagnosed with breast cancer?

We apologize for this misunderstanding and have revised it to “Time since index date (= date of diagnosis, which is the same date for corresponding matched individual from the general population) ” in Figure 1.

Third, how was the matching incorporated into the FPM? Presumably there should be a frailty term of some sort to indicate the matched groups, within which there is expected to be correlation.

In the flexible parametric survival model for matched cohort data, a shared frailty term was incorporated into the model to indicate the matched cluster. The maximum (penalized) marginal likelihood method is used to estimate the regression coefficients and the variance for the frailty. We have added this explanation in the methods part.

In Materials and Methods: “Considering the correlation within the matched clusters, a shared frailty term (as random effects) was incorporated into the model and the maximum (penalized) marginal likelihood method was used to estimate the regression coefficients and the variance for the frailty.”

1. It is noted that Kaplan Meier curves were used to estimate the cumulative incidence of heart disease. How was death of the patient prior to diagnosis of heart disease handled? I do not think that Kaplan Meier is the correct approach here but rather a Aaalen-Johansen-type estimator that treats death as a competing event. See e.g. https://pubmed.ncbi.nlm.nih.gov/10204198/ A Kaplan Meier will tend to overestimate the event rate when competing events are counted as censoring.

As suggested by the reviewer, we have now used the Aalen-Johansen method to estimate the cumulative incidence of heart disease and revised the text in the Methods, as well as the tables and figures in the supplement.

In Materials and Methods,: “Aalen-Johansen estimation was used to assess the cumulative incidences of heart diseases in breast cancer patients and matched reference individuals, while other causes of death were considered as competing events.”

1. The sentence "Missing indicators were included for the analysis of these covariates in the model" and the results in Table 3 suggest that some missing values were analyzed 'as is', meaning that missingness was used as a category itself. This of course is not desirable and there exists methodology+software for more appropriately handling these data, e.g. multiple imputation with chained equations. For example, how does one interpret that 'unknown chemotherapy' status is positively associated with heart failure but less so than anthracycline based chemo.

Missingness of the type of adjuvant treatment was considered as a category in the previous version of our manuscript. To address potential biases resulting from missing data, we have now used multiple imputation with chained equations and revised the methods and Table 3 accordingly.

In Materials and Methods: “Multiple imputation with chained equations was used to deal with the treatment categories with missing information. We replaced the missing data with 10 rounds of imputations and all the covariates were included in the imputation model.”

1. The reported HRs at the top of page 10 seem incongruous with the FPM model demonstrated in Figure 1, since there is clearly a non-linear relationship between the hazard and the outcome. In other words, there is little sense in which the hazards are proportional at all time points.

As shown in the FPM model in Fig. 1, HRs were not constant according to time since index date. Therefore, in the revised version, we only showed the HRs separately in <1, 1-2, 2-5, 5-10 and 10-17 years after diagnosis. We have revised the abstract, methods, and Table 2.

In Abstract: “Time-dependent analyses revealed long-term increased risks of arrhythmia and heart failure following breast cancer diagnosis. Hazard ratios (HRs) within the first year of diagnosis were 2.14 (95% CI = 1.63-2.81) for arrhythmia and 2.71 (95% CI = 1.70-4.33) for heart failure. HR more than 10 years following diagnosis was 1.42 (95% CI = 1.21-1.67) for arrhythmia and 1.28 (95% CI = 1.03-1.59) for heart failure. The risk for ischemic heart disease was significantly increased only during the first year after diagnosis (HR=1.45, 95% CI = 1.03-2.04).”

In Materials and Methods: “We compared the risk of heart diseases in breast cancer patients with that observed in the matched cohort, using flexible parametric model (FPM) with time since index date as underlying time scale.”

In Results: “A short-term increase in risks of arrhythmia and heart failure was found in breast cancer patients (Table 2, Figure 1, HR at first year for arrhythmia= 2.14; 95% CI = 1.63-2.81, for heart failure =2.71; 95% CI = 1.70-4.33, respectively).”

1. It seems unlikely that breast cancer diagnosis could ever be 'protective' for ischemic heart disease. A more constrained model that does not allow for the possibility of HR < 1 could provide a more sensible estimate of this time-dependent HR.

To the best of our knowledge, the inverse association between breast cancer and the long-term risk of ischemic heart disease is possible considering that some of the reproductive risk factors for breast cancer have protective effect on the risk of ischemic heart disease. We have now discussed about this in Discussion.

In Discussion: “The long term lower risk of ischemic heart disease in breast cancer patients compared to age-matched women might be explained by the opposite role of reproductive factors in breast cancer and ischemic heart disease. Women with younger age at menarche and older age at menopause were associated with increased risk of breast cancer, while decreased risk of ischemic heart disease were found among these women (Collaborative Group on Hormonal Factors in Breast, 2012; Okoth et al., 2020).”

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Reply to the reviewers

Manuscript number: RC-2021-01118

Corresponding author(s): Jun, Nakayama and Kentaro, Semba

1. General Statements

We are grateful to all of the reviewers for their critical comments and insightful suggestions that have helped us considerably improve our paper. As indicated in the responses that follow, we have taken all of these comments and suggestions into account in the revised version of our paper, including the supplementary information.

In the revised manuscript, we focus on the existence of two cancer stem cell-like populations in TNBC xenograft model and patients. The response to each reviewer is described below.

Sincerely,

Jun Nakayama

Kentaro Semba

Department of Life Science and Medical Bioscience

School of Advanced Science and Engineering

Waseda University

E-mail: junakaya@ncc.go.jp or jnakayama.re@gmail.com to JN

ksemba@waseda.jp to KS

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)): * **Summary:** Nakayama and colleagues use their previously developed automated tissue microdissection punching platform to perform spatial transcriptomics on a breast cancer xenograft model. Using transcriptomics on multiple clumps of 10-30 cells from different regions in a tumor and a lymph node metastasis they identified different cell-type clusters. Two of these clusters expressed different cancer stem cell markers. This led the authors to suggest that two distinct cancer stem cell(-like) populations may exist within one (breast) tumor, which could potentially make tumors more drug-resilient.

**Major comments:** While the quality of the presented sequencing data is good and the manuscript is mostly written in a clear and accessible style, there are some concerns that limit the impact of this story. Most importantly, the manuscript in its present form does not convince me that the MDA-MB-231 xenografts indeed contain two distinct populations of cancer stem(-like) cells.

1.The data obtained are not single cell data, which makes it difficult -if not impossible- to draw conclusions about presence of cancer stem cells. Each data point is the average of 10-30 cells, and the interpretation of the data is severely limited by this. How can the quantification of expression of CD44/MYC/HMGA1 in clumps of 10-30 cells teach us something about the stemness of tumor cells? *

Answer: We would thank the comment. The reviewer’s suggestion is an important point; however, this is technical limitation of spatial transcriptomics technology. Most advanced spatial transcriptomics technologies, e.g. Visium (10x Genomics), also have the same problem. It means that our technology and the advanced technologies are technics to analyze gene expression and characteristics of tissues from 10-30 cells in each spot. Although high resolution spatial transcriptomics has been developed in 2021 [1], it is not generally used yet as described in the comment (Significance) from reviewer1.

From our spatial analysis, we identified that CD44, MYC, and HMGA1 were expressed from human cancer cell. Their expression profiles were distinct among specific parts of the tumor section. To validate the existence of two types of cancer stem-like cells in TNBC tumors, we performed the additional analysis with the public scRNA-seq datasets of high-metastatic MDA-MB-23-LM2 xenograft model (GSE163210) [2]. This study performed scRNA-seq analysis of primary tumor and circulating tumor cells in MDA-MB-231-LM2 xenograft model. We analyzed it with Seurat/R (Figure A-1). As a result of reanalysis, HMGA1 and CD44 expression were confirmed at single-cell resolution (Figure A-2,3). These results verified the existence of two cancer stem cell-like populations (HMGA1-high, CD44-high) in MDA-MB-231 xenograft. Hence, the study of MDA-MB-231 xenograft supported our findings from spatial transcriptomics.

Additionally, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As a result, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B). We believe that our findings are solid results because the findings were also validated by other methods.

In the revised manuscript, Figure A are incorporated as Figure 3B-E. Figure B is incorporated as Figure 3A. Hope our new results will be now accepted by the learned Reviewer and Editor.

Figure A-1. Reanalysis of scRNA-seq of metastatic MDA-MB-231 xenograft

Flowchart of the public single-cell RNA-seq (scRNA-seq) reanalysis using GSE163210 datasets.

Figure A-2. UMAP plots of xenograft and CD44/HMGA1 expression

UMAP plot of MDA-MB-231-LM2 xenograft tumors and circulating tumor cells (Left). Expression of CD44 and HMGA1 in the UMAP plot (Right).

Figure A-3. Pie chart of CD44/HMGA1 positive cancer cells in MDA-MB-231 xenograft

Pie chart of cancer stem cell-like population ratio in MDA-MB-231-LM2 xenografts.

Figure B. Fluorescent immuno-staining of MDA-MB-231 primary tumor

Representative images immunostained with CD44 and HMGA1 in primary tumor sections of the MDA-MB-231 xenograft model. Red: HMGA1, Green: CD44, and Blue: Nucleus. Scale bars, 20 μm (left), 10 μm (right). White arrows represent cancer cells that independently expressed or co-expressed.

* 2.Furthermore, the authors should better explain their data analysis strategy with identification of gene expression profiles. It is unclear how they found CD44, MYC, and HMGA1 other than by cherry-picking from the list of cluster markers. *Answer: In this research, to identify the characteristics of clusters, we analyzed differentially expressed genes (DEGs) by ‘FindAllMarkers’ function of Seurat. As a result, ‘Cluster 0’ significantly expressed HMGA1 gene, and ‘cluster 1’ significantly expressed CD44. HMGA1 and CD44 are popular cancer stem cell markers in triple-negative breast cancer [3, 4]. In this study, we focus on metastasis-related genes and cancer stem cell markers (described in introduction section). Therefore, we focus on cancer-stem cell markers in the presented study. Cancer stemness is an important concept in cancer metastasis [5-7]. These results suggested that the existence of two cancer stem cell-like populations could potentially make tumors more drug-resilient in xenograft models and clinical patients.

To improve the manuscript, we revised the description in the revised manuscript (Pages 5-6, Lines 97-105).

* 3.Following up on the above point: I looked in the supplementary tables, but couldn't find MYC. How did the authors conclude that MYC is involved in cluster 1? In fact, when I ran a quick analysis in EnrichR, I saw that putative MYC target genes were strongly enriched among the markers in the HMGA1 cluster, but not the CD44/MYC. That's opposite to what I would expect. *__Answer: __We apologize for our confusing data and description. First, we found the expression of CD44 and HMGA1 in each cluster. Therefore, we performed the up-stream enrichment analysis using gene signatures of FindAllMakers by Metascape. From the result of enrichment analysis, we found the MYC activation in CD44 high-cluster; therefore, we named the cluster “CD44/MYC-high” cluster.

To improve the manuscript, we revised the Figure2, Supplementary Table S3, and manuscript (Pages 5-6, Lines 103-106).

* 4.All data were produced from 1 primary tumor and 1 metastasis. Thus, reproducibility and robustness of the methodology cannot be evaluated. The interpretation of the data could be strengthened when xenografts from at least 3 different mice are shown. *__Answer: __We would thank the suggestion. As the reviewer’s comment, we performed 1 primary tumor and 1 metastasis lesion from a transplanted mouse. Since this experiment take a long time, we tried to validate the findings by other methods (Figure A: scRNA-seq analysis of MDA-MB-231 xenografts, Figure B: Immuno-staining of MDA-MB-231 primary tumor, Figure C: scRNA-seq analysis of TNBC patients).

First, we reanalyzed the public dataset which performed single-cell RNA-seq analysis of MDA-MB-231 xenografted tumor and circulating tumor cells in immunodeficient mice as shown in the answer to comment 1 (Figure A). Next, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As results, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B). Next, we performed the reanalysis of 19 scRNA-seq samples from integrated 3 TNBC cohorts (Figure C-1). In a UMAP plot, differences between CD44-positive cancer cell and HMGA1-positive cancer cell were observed; however, these cells did not visually form the specific clusters (Figure C-2). CD44 and HMGA1 expressed globally in the UMAP plot, but CD44 makes some specific clusters (cluster at right side). Additionally, following the comment, we performed the population analysis in each patient (Figure C-3 and C-4). Detection of double-positive population in TNBC patients suggested that the population may be more undifferentiated cancer stem cells diving into both CD44-positive cells and HMGA1-positive cells.

In addition, we reanalyzed primary tumors and metastasis lesions from other mice as a test trial sample (Figure D-1). The microspots including test trial samples showed 3 human clusters which were classified into CD44/MYC, HMGA1, and Marker-low clusters. We believe that our findings are solid results because the findings were also validated by other methods.

In the revised manuscript, Figure A are incorporated as Figure 3B-E. Figure B is incorporated as Figure 3A. Figure C is incorporated as Figure 5. We only showed Figure D in the response to the reviewer’s comment. Hope our new results will be now accepted by the learned Reviewer and Editor.

Figure C-1. Reanalysis of integrated TNBC patients scRNA-seq

A flowchart of the reanalysis of a public scRNA-seq dataset. We downloaded GSE161529, GSE176078, and GSE180286 (scRNA-seq data of 19 TNBC patients). Integrated datasets were analyzed with Seurat. Log normalization, scaling, PCA and UMAP visualization were performed following the basic protocol in Seurat. To extract the cancer cells, cells expressing EPCAM/KRT8 (epithelial marker) were filtered. A UMAP plot of cancer cell from 19 TNBC patients (right).

Figure C-2. CD44/HMGA1 expression in TNBC patients

Expression analysis of CD44 (Expression level > 2) and HMGA1 (Expression level > 2) with UMAP plots.

Figure C-3. CD44/HMGA1-positive cancer cell with UMAP plot

UMAP plots of CD44-high, HMGA1-high, HMGA1/CD44-high, and Negative cancer cells.

Figure C-4. Ratio of CD44/HMGA1-positive cancer cell in each patient

The bar plot showed the ratio of cancer cells that expressed CD44 and HMGA1.

Figure D-1. Analysis of microspots of MDA-MB-231 xenografts including test trial samples

UMAP plots of CD44-high, HMGA1-high, and Marker-low clusters with test trial samples (2 primary tumors and 1 lung metastasis). ‘Primary tumor 1’ has 20 microspots, ‘Primary tumor 2’ has 24 microspots, and ‘lung metastasis’ has 7 microspots. Most microspots of lung metastasis failed extraction of RNA; therefore, these spots classified into Marker-low cluster.

Figure D-2. Expression analysis of CD44, HMGA1, and MYC

Feature plot of CD44-high, HMGA1-high, and Marker-low clusters with test trial samples.

* 5.The only methodology is single cell RNA-sequencing. Immuno-staining on relevant markers such as CD44, MYC, HMGA1 plus human epithelium and cell cycle markers would provide strong additional support for the claims made by the authors, because it's a complementary technique and it allows quantification at single cell resolution. *__Answer: __We would thank the comment. As described in the responses to the reviewer’s comment 1 and 4, we performed the immuno-staining of sections using anti-CD44 antibody and anti-HMGA1 antibody as described in reviewer’s comment 5. As a result, CD44 and HMGA1 were detected in primary tumor sections. There were cells that express either CD44 or HMGA1 and cells that co-express both CD44 and HMGA1 (Figure B).

In the revised manuscript, Figure B is incorporated as Figure 3A.

* 6.Line 173-175. The marker-low cluster look to me simply like spots containing a relatively high amount of dead/dying (tumor) cells. The identity/state of cells in the marker-low cluster should be characterized and discussed more extensively. *__Answer: __We would thank the comment. This suggestion is important. In fact, total count of RNA in the Marker-low cluster decreased as compared to HMGA1-high and CD44/MYC-high (Supplementary Figure S1B). Additionally, Ttr-high mouse cluster also has low total count of RNA (Supplementary Figure S1C).

Following the comment, we described that the Marker-low cluster and Ttr-high cluster have the possibility to include dead/dying cells (Page 13, Lines 268-279).

* 7.Figure 5 and accompanying text in line 182-194; the authors try to infer cell-to-cell interactions using a previously published tool. However, any biological interpretation is lacking. What can be concluded from this analysis? *__Answer: __Initially, algorithms of cell-to-cell interaction were reported with previously published tool [8, 9]; however, in this manuscript, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data) as previously described [10, 11]. We aimed to estimate the cell-to-cell interaction in each spot (including 10-30 cells). We think that this analysis will be helpful for discovering the cancer stem cell niche and metastatic niche [6].

However, in the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Therefore, CCI analysis in previous Figure 5 moved to Supplementary Figure S7. Previous Figure 6 is removed from revised manuscript.

* 8.Figure 6. Can the authors please explain more clearly what they mean by "PT" and "Mix" groups? I had a very hard time to understand what the data in figure mean. Again, an overall interpretation at the end (line 211) is lacking. *__Answer: __We apologize for the confusing result. We examined the combinations of human cancer cell cluster and mouse stromal cell cluster. To summarize, there are 10 combinations in the MDA-MB-231 xenograft. The combination groups in only primary tumor were named “PT”; on the other hand, the combination groups in both primary tumor and lymph-node metastasis were named “Mix”. These CCI analysis focused on cluster types of cancer cell and stromal cell. However, according to this revision, our presented study mainly focuses on the existence of two types of cancer stem cell-like population in TNBC xenograft and patients. Therefore, CCI analysis with cluster types was deleted from revised manuscript.

In the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Previous Figure 6 was removed from the revised manuscript.

* 9.Figure 7. I like the effort to align the results with public scRNA-seq data. But although the expression of the cluster-signatures is heterogeneous, there is no evidence for distinct (CSC-like) cell populations. Why don't these HMGA1 vs CD44 signature cells cluster away from each other in the UMAPs? Perhaps the patient-to-patient heterogeneity overwhelms differences within tumors, but in that case the authors could re-run their analysis for each patient separately, to make 6 patient-specific UMAPs. In its present form, this analysis does not convince me that two distinct CSC(-like) populations within one TNBC exist. *Answer: We would thank the comment. To improve the quality of reanalysis of clinical cohorts, we performed the reanalysis of 19 scRNA-seq samples from integrated 3 TNBC cohorts (Figure C-1). In a UMAP plot, there are differences between CD44-positive cancer cells and HMGA1-positive cancer cells; however, these cells did not visually form the specific clusters (Figure C-2). CD44 and HMGA1 were expressed globally in the UMAP plot, but CD44 made some specific clusters (cluster at right side). Additionally, following the comment, we performed the population analysis in each patient (Figure C-3 and C-4). There is double-positive population in TNBC patients suggesting that this population may be more undifferentiated cancer stem cells, dividing into both CD44-positive cells and HMGA1-positive cells.

In the revised manuscript, Figure C is incorporated as Figure 5.

* **Minor comments:** 10.In the Supplemental table 2 noticed that many of the marker genes have adjusted P values well above 0.05 (and even above 0.1). That makes the statistical analysis rather weak. This could especially be problematic since the authors entirely base their main claims on this marker analysis, and I recommend that the authors use more stringent P-value cut-offs in the cluster analysis. *Answer: We would thank the comment. We reshaped the list of differentially expressed genes (DEGs). Significantly expressed genes (adjusted p-value In mouse clusters, the enrichment analysis using significantly DEGs showed that only Tcell-like clusters had a lot of enriched terms. Citric acid (TCA) cycle, chemical stress response, and fatty acid oxidation were enriched in Tcell-like populations (Page 7, Lines 141-144).

In the revised manuscript, enrichment analyses are showed as Supplementary Figure S2 and S3B. We revised the sentence of enrichment analyses (Page 6, Lines 114-121), (Page 7, Lines 141-144). The network visualization of enrichment analysis was removed from the revised manuscript because this result did not support conclusions of the presented study.

* 11.Line 129/130. If I look at figure 3A, I don't see this tendency that the authors describe. Can the authors provide statistical support or visual aid to make their claim more apparent to the reader? *__Answer: __We would thank the suggestion. Following the comment, we performed the statistical analysis of spot position. The spots were categorized outer side (tumor edge) and Inner site (Center of tumor) in the primary tumor section (Figure E-1 upside). We counted the spot numbers of the clusters (Figure E-1 table) and performed statistical test by chi-test. As a result, CD44/MYC clusters significantly resided at outer side of primary tumor (Figure E-1 barplot). On the other hand, the spots in lymph-node metastasis are not readily defined the outer or inner. In addition, cell cycle analysis in the primary tumor and lymph node metastasis was performed with statistical test. As a result, HMGA1-high cluster and CD44/MYC-high cluster significantly proliferated in the lymph node metastasis section (Figure E-2).

Therefore, in the revised manuscript, we revised the sentence of spot position in lymph-node metastasis (Pages 8-9, Lines 159-172). Figure E-1 is incorporated as Figure 4D. Figure E-2 is incorporated as Figure 4F. Hope our new results will be now accepted by the Reviewer and Editor.

Figure E-1. Statistical analysis of spot position

Chi-test was performed by R. *p Figure E-2. Statistical analysis of cell cycle index

Fisher’s exact test was performed by R. *p * 12.Line 217; shouldn't this be 6 patients? I see six clusters and in the original paper six patients are mentioned. *Answer: We would thank the comment. ‘6 patients’ is correct, we revised it. However, in the revised manuscript, we added integrated analysis of TNBC as shown in the answer to comment 9.

Previous reanalysis of clinical scRNA-seq (previous Figure 7) was removed from the revised manuscript. The reanalysis using 3 integrated TNBC cohorts (Figure C) is incorporated as Figure 5.

Reviewer #1 (Significance (Required)): * Conceptual/biological impact: Showing the existence of distinct populations of CSCs within one (breast-)tumor potentially has a high impact on the field of fundamental and translational cancer research. As the authors state, it could be one key reason underlying drug resistance. However, the technology used by the authors does in my view not allow to make such a claim. First and foremost because the technology does not allow analysis at single cell resolution.

Technical impact: The platform used by the authors can be of interest for some applications, but they already published this in Scientic Reports a few years ago. I'm afraid that with the rapid recent developments in the field of spatial single cell transcriptomics (See for example Srivatsan et al Science 2021; 373: 111-117), the technical impact on the field is relatively low.

Audience: Researchers in the field of cancer biology with an interest to perform low-cost molecular analysis at low-resolution spatial-resolved tissue specimens (transcriptomics, but perhaps expanded with bisulfite sequencing, or ATAC sequencing) could be interested in the technology presented in this manuscript.

My expertise: single cell transcriptomics, (cancer) cell cycle, cancer drug resistance, cell plasticity, mouse models. *

**Referee Cross-commenting** I have read the comments and align mostly with reviewer #2. The authors need to improve this manuscript a lot before it's suitable for publication in any of the Review Commons journals. Answer: We are grateful to the reviewers. As indicated in the responses that follow, we have taken all of these comments and suggestions into account in the revised version of our paper, including the supplementary information.

*

*

Reviewer #2 (Evidence, reproducibility and clarity (Required)): * This manuscript uses spatial transcriptomics to perform single cell-like expression analysis between a breast cancer cell line and tumor microenvironment in mice xenografted with these cells. Unfortunately, from the title, abstract, and introduction, it is difficult to understand exactly what the authors are focusing and discussing. It is also unclear the advantage of their technique for evaluating the populations observed within this manuscript. Furthermore, there is very little explanation of the results, and it does not appear to be a scientific logical structure. Hence, this manuscript is not suitable for acceptance in the journal. In order to improve the scientific quality of this study, the following concerns are presented.

**Major concerns:** 1.Is cell-cell interaction (CCI) analysis novel method? If so, please specify detail in the manuscript. If the basic concept and the principle of CCI analysis have not been published, please mention in the discussion section as a limitation that a manuscript on CCI analysis is under submission to the preprint. In addition, please revise the abstract and related text. *__Answer: __Initially, algorithms of cell-to-cell interaction were reported with previously published tool [8, 9]; however, in this manuscript, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data) as previously described [10, 11]. We aimed to estimate the cell-to-cell interaction in each spot (including 10-30 cells). We think that this analysis will be helpful for discovering the cancer stem cell niche and metastatic niche [6].

However, in the revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients. Therefore, CCI analysis in previous Figure 5 is moved to Supplementary Figure S7. Previous Figure 6 are removed from the revised manuscript. We revised the description in the manuscript (Page 18, Lines 385-387).

* 2.The reviewer thinks that spatial transcriptomics plays an important role in your manuscript. Please describe the technique in the introduction. *__Answer: __We would thank the comments. Following the comments, we described the spatial technics in Introduction section. We revised the manuscript (Page 4, Lines 63-65) (Page 12, Lines 250-253).

* 3.The classification by expression profile (HMGA1, CD44/MYC and marker-low) lacks an explanation. Authors should mention in detail how these populations were extracted from breast cancer cell lines. *Answer: In this research, to identify the characteristics of clusters, we analyzed differentially expressed genes (DEGs) by FindAllmarkers function of Seurat. As a result, ‘Cluster 0’ significantly expressed HMGA1 gene, and ‘cluster 1’ significantly expressed CD44. Next, we performed the up-stream enrichment analysis using gene signatures of FindAllMakers by Metascape. From result of enrichment analysis, we found the MYC activation in CD44 high-cluster; therefore, we named the cluster “CD44/MYC-high” cluster.

HMGA1 and CD44 are popular cancer stem cell markers in triple-negative breast cancer [3, 4]; therefore, we focus on cancer-stem cell marker in presented study. Cancer stemness is an important concept in cancer metastasis [5-7].These results suggested that the existence of two cancer stem cell-like populations could potentially make tumors more drug-resilient in xenograft model and clinical patient.

To improve the manuscript, we revised the Figure2, Supplementary Table S2 and S4, and manuscript (Pages 5-6, Lines 97-106).

* 4.The description of the results is back and forth and confusing. Please reconsider the flow of the analysis. *__Answer: __We would thank the comment. We reconsidered the description and structure of manuscript. In revised manuscript, we focused on the existence of two cancer stem cell-like populations in TNBC xenograft and patients.

To improve the manuscript, we revised the Figure2 for examination of cluster characteristics by clustering and gene expression profiling. Figure 3 was revised for the validation of two cancer stem cell-like populations in TNBC xenograft model. Figure 4 was revised for the elucidation of spatial characteristics of each cluster. Figure 5 was revised for the validation of two cancer stem cell-like populations in TNBC patients.

* 5.How did you evaluate the outsides of the samples with very different spot positions in Figure 3A? Please mention your evaluation method in a scientific manner. In particular, authors should clearly indicate the outer evaluation for the metastatic case. *

Answer: We would thank the suggestion. Following the comment, we performed the statistical analysis of spot position. The spots were categorized outer side (tumor edge) and Inner site (Center of tumor) in primary tumor section (Figure E-1 upside). We counted the spot numbers of the clusters (Figure E-1 table) and performed statistical test by chi-test. As a result, CD44/MYC clusters significantly resided at outer side of primary tumor (Figure E-1 bar plot). On the other hand, the spots in lymph-node metastasis are not readily defined the outer or inner. In addition, cell cycle analysis in the primary tumor and lymph node metastasis was performed with statistical test. As a result, HMGA1-high cluster and CD44/MYC-high cluster significantly proliferated in the lymph node metastasis section (Figure E-2).

Therefore, in the revised manuscript, we revised the sentence of spot position in lymph-node metastasis (Pages 8-9, Lines 153-172). Figure E-1 are incorporated as Figure 4D. Figure E-2 are incorporated as Figure 4F. Hope our new results will be now accepted by the Reviewer and Editor.

Figure E-1. Statistical analysis of spot position

Chi-test was performed by R. *p Figure E-2. Statistical analysis of cell cycle index

Fisher’s exact test was performed by R. *p * 6.The spots in primary tumor have few counts derived from mouse stromal/immune cells, as shown in Figure S1A. Nevertheless, Figure 3C shows that mouse stromal/immune cells are evaluated in the same way in primary and metastatic sites. The reviewer thinks that the regions identified as Tcell-like in the metastatic site, where there are many mouse-derived counts, and in the primary, where there are few mouse-derived counts, do not have the same characteristics. If many mouse-derived counts were detected in a spot using the spatial transcriptomics, then there must be many mouse-derived cells in the spot. Please discuss how this expression is evaluated on this technique, which is not a single cell analysis. *__Answer: __We would thank the comment. The reviewer’s suggestion is an important point; however, this suggestion is technical limitation of spatial transcriptomics technology. Most advanced spatial transcriptomics technologies, e.g. Visium (10x Genomics), also have the same problem. It means that our technology and the advanced technologies are technics to analyze gene expression and characteristics of tissues from 10-30 cells in each spot.

In this spatial transcriptome analysis of mouse genes, we first performed the log normalization and scaling. Since Seurat used variable features among the samples for single-cell or spot clustering, we extracted the variable features for detection of clusters using the ‘FindVariableFeatures’ function. PCA and clustering using only mouse genes was performed for detecting the neighboring samples. After the clustering of mouse spots, we identified the character of clusters by finding the gene signatures. As the indication by the reviewer, the detected RNA counts and features are different, so it is difficult to define the exact character and cell type of stromal cells. Theoretically, spatial transcriptomics could only detect some kinds of stromal cells expressing the T-cell marker gene in the spot. Therefore, we named the cluster as “Tcell-like”. Not all of the Tcell-like cluster have the same characteristics or cell types, but they certainly express T-cell marker genes. This is also a technical limitation of spatial transcriptomics. Spatial transcriptomics with higher resolution probably is able to detect the stromal cells as a single-cell resolution, such as the one developed in previous research [1].

In the revised manuscript, we focused on the two types of cancer stem cell-like populations that were validated by other methods (scRNA-seq and Immuno-staining). As the method is not able to define the exact cluster characters, we moved CCI analyses to supplementary figures or removed partly.

We also revised the discussion in the revised manuscript (Pages 13-14, Lines 279-283).

* 7.Please explain how the gene symbols listed in Figure 4A were selected. Also, please indicate the characteristics of the gene groups that are not listed. *__Answer: __We selected the gene signature list from results of ‘FindAllMarker’ function in Seurat. ‘FindAllMarker’ function enables to extract the significantly expressed genes in each cluster. Heatmap in previous Figure 4A was drawn using these marker genes (Adjusted p-value 0.1). Highlighted genes in the heatmap have been reported as cancer-related genes or cell cycle-related genes.

The genes used for drawing heatmap are shown in Supplementary Table S2 and S4.

* 8.Please describe the details of the division and cycle index in lines 141-142. *__Answer: __Cell cycle index is a basic function of Seurat [12] (https://satijalab.org/seurat/archive/v3.1/cell_cycle_vignette.html). A list of cell cycle markers is loaded with Seurat. We can segregate this list into markers of G2/M phase and markers of S phase. We subjected this function into our spatial transcriptomics to estimate the cell cycle in each spot.

We revised the description manuscript (Page 16, Lines 331-332).

* 9.In Line 148-151, the expression and prognosis of TMSB10, CTSD, and LGALS1 is mentioned based on the previous reports. Aren't these findings the result of bulk? Is the HMGA1 cluster that the authors found involved in the prognosis of mice? Please clarify, as it is unclear what you want to discuss. *

Answer: We apologize for our confusing data and description. These highlighted genes (TMSB10, CTSD, LGALS1, CENPK, and CENPN) were extracted as DEGs of human cancer clusters (Supplementary Table S2). Previously, these genes have been reported as cancer-related genes or cell cycle-related genes, described in the manuscript (Page 6, Lines 107-110). To show the other expressed genes in each human cluster, we focused on these genes in the manuscript.

We extracted the gene signatures from DEGs and showed the gene signatures from HMGA1-high cluster correlated to poor prognosis in TNBC patients. Our data suggested that the HMGA1 signatures from the microspot resolution has the potential to be a novel biomarker for diagnosis, and HMGA1-high cancer stem cells may contribute to poor prognosis.

In this revision, since we reperformed DEGs analysis with significant threshold; therefore, survival analysis was reperformed with novel gene signatures with METABRIC TNBC cohorts (Figure F).

To improve the manuscript, we revised the description of DEGs extraction and heatmap (Page 6, Lines 106-112). Hope our Reviewer will approve this revised sentence.

Figure F. Survival analysis with gene signatures of HMGA1-high and CD44/MYC-high

Survival analysis of TNBC patients (claudin-low subtype and basal-like subtype) in METABRIC cohorts by the Kaplan-Meier method. (Left) Survival analysis with the expression of the HMGA1 signatures (High = 151, Low = 247). Shading along the curve indicates 95% confidential interval. Log-rank test, p = 0.012. (Right) Survival analysis with the expression of the CD44/MYC signatures (High = 333, Low = 65). Log-rank test, p = 0.079.

* 10.Please provide details of all statistical tests used in this manuscript and describe significance levels used in the p-values and FDR. *__Answer: __We performed the extraction of differentially expressed genes (DEGs) by ‘FindAllMarkers’ function with MAST method. MAST method identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data [13]. Adjusted p-value is calculated based on Bonferroni correction using all features in the dataset. In spatial spot analysis, statistical analyses were performed by Chi-test and Fisher’s exact test.

We revised materials and methods section in the manuscript (Page 19, Lines 391-394).

* 11.Please mention CCI score (line 198). *Answer: As described in answer to comment 1, the algorithms of CCI score calculation were performed using previously published tool [8, 9]; however, we originally conducted the code for cell-to-cell interaction with the interaction database of the Bader laboratory from Toronto University (https://baderlab.org/CellCellInteractions#Download_Data). We extracted the genes whose expression value was greater than 2. We selected the combinations representing ligand__-__receptor interactions, in which both ligand genes and receptor genes were expressed in the same spot.

We revised materials and methods section in the manuscript and Supplementary Legends (Page 18, Lines 385-387).

* 12.Lines 204-206 and Figure 6G show specific interaction of ITGB1 and CST3, but it is unclear why only these molecules were extracted. What about the other molecules? At least ITGB1 is not scored in mix5. *Answer: We selected genes that have been reported as cancer-related ones in breast cancer to discuss the interactions in primary tumor and lymph-node metastasis. However, according to this revision, our presented study mainly focused on the existence of two types of cancer stem cell-like population in TNBC xenografts and patients. Therefore, CCI analysis with cluster types moved to supplementary Figure or some were not shown now.

In the revised manuscript, previous Figure 6 is removed.

* 13.HMGA1 signature appears in Line 214, please explain in detail. *__Answer: __As described in answer to comment 7, we selected the gene signature list from results of ‘FindAllMarker’ function. ‘FindAllMarker’ function enables to extract the significantly expressed genes in each cluster. HMGA1 signature genes were selected from significantly differentially expressed genes of HMGA1-high clusters.

We revised the description in the revised manuscript (Pages 9-10, Lines 190-193).

* 14.Authors should discuss how the previously reported bulk expression data used in Figure 7E can be linked to the single-cell-like analysis in this study. *__Answer: __Previous research reported that gene signatures extracted from specific clusters in scRNA-seq study have the potential to be a prognosis marker [14]. We showed the gene signatures from HMGA1-high cluster correlated to poor prognosis in TNBC patients. Our results suggested that the gene signatures from the resolution of microspot (10-30 cells) could have the potential to be prognosis markers. This punching microdissection system enables to extract only the parts of a section that are necessary for diagnosis of cancer and to analyze at low-cost. It could be applied to diagnostics instead of the laser-capture microdissection methods.

We performed additional survival analysis with METABRIC cohorts. As described in this revision, since we reperformed DEGs analysis with significant threshold, survival analysis was reperformed with novel gene signatures with METABRIC TNBC cohorts (Figure F).

In revised manuscript, Figure F were incorporated as Figure 6. The usefulness of gene signatures from microspot resolution was additionally discussed (Page 12, Lines 242-245, 250-253).

* **Minor concerns:** 15.Please describe how the normalized centrality was calculated in UMAP algorithm and explain what this means in the results. __Answer: __The data showed that the expressional diversity in each cluster based on the network centrality of a correlational network with graph theory. The differences in the centrality among the clusters suggested expressional diversity in each (Supplementary Figure 4). Higher centrality represented lower expressional diversity and vice versa*. The detailed method for the calculation of centrality was previously shown to reveal the difference between smokers and never-smokers [10, 11].

We added the description in the Legend (Pages 7-8, Lines 145-150).

* 16.Please mention an explanation for the red X in Figure 1B to the legend. *__Answer: __The red X means failure spot for RNA extraction. We added the description in Figure 1B.

* 17.Please spell out the abbreviations in all figure legends. *__Answer: __We added the abbreviations in the legends of all figures.

* 18.Please explain what is meant by the color of the lines and the size of the circles in Figure 4D. *__Answer: __The network analysis was performed by Metascape (https://metascape.org/gp/index.html#/main/step1) [15]. The node size is proportional to the number of genes belonging to the term, and the node color represents the identity of the cluster. However, as described in the answer to reviewer’s comment 9, we reperformed enrichment analysis with significant DEGs. As a result, only CD44/MYC cluster had a lot of enrichment terms.

Therefore, network visualizations were removed from the revised manuscript.

* 19.Please mention an explanation for the color of the spots in Figure 5D and 5F to the legend. *__Answer: __The color showed the spots categorized into the selected group.

In the revised manuscript, previous Figure 5 was incorporated as Supplementary Figure S7. We added the description in Supplementary Figure S7 and S8 with the legends.

* 20.Is "S51" in Line 148 a typo for "S5A"? *Answer: Thank you. We revised “S5A”.

* 21.Please mention an explanation for the bars in Figure 6D and 6F to the legend. *__Answer: __The bars showed relative CCI scores. As described below, we removed the results of CCI analysis with cluster group (previous Figure 6) in the revised manuscript.

* 22.Please mention an explanation for the colors in Figure 7E to the legend. *__Answer: __The color showed patients’ group based on expression levels of gene signatures. We added the description in the Legend of Figure 6.

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*

Reviewer #2 (Significance (Required)): * The approach in Figure 5 is interesting, but the rest of the results do not take full advantage of the technology developed by the authors. The structure of the manuscript should be re-examined and new perspectives added. I look forward to the future of the authors' research.

*

Reviewer #3 (Evidence, reproducibility and clarity (Required)): Microtissue transcriptome analysis of triple-negative breast cancer cell line MDA-MB-231 xenograft model using automated tissue microdissection punching techonology revealed that the existence of three cell-type clusters in the primary tumor and axillary lymph node metastasis. The CD44/MYC-high cluster showed aggressive proliferation with MYC expression, the HMGA1-high cluster exhibited HIF1A activation and upregulation of ribosomal processes. The cell-cell-interaction analysis revealed the interaction dynamics generated by the combination of cancer cells and stromal cells in primary tumors and metastases. The gene signature of the HMGA1-high cancer stem cell-like cluster has the potential to serve as a novel biomarker for diagnosis. The key conclusions are convincing. The data and methods are presented in a reproducible way. The experiments are adequately replicated and statistical analysis is adequate. Prior studies are appropriately referenced. The text and figures are clear and accurate. __Answer: __We would thank the valuable comments. As the reviewer mentioned, our findings showed that the existence of two cancer stem cell-like populations has the potential to make tumors more drug-resilient. Our results suggested that the gene signatures from the resolution of microspot (10-30 cells) could have the potential to be prognosis markers. This punching microdissection system enables to extract only the parts of a section that are necessary for diagnosis of cancer and to analyze at low-cost. It could be applied to diagnostics instead of the laser-capture microdissection methods.

In this revision, we focused on the existence of two cancer stem cell-like populations in TNBC xenografts and patients. Following the other reviewer’s comments, we performed the extraction of DEGs with significant threshold; therefore, we revised the results of enrichment analysis but it did not influence our main findings.

To validate the existence of two types of cancer stem-like cells in TNBC tumors, we performed the additional analyses (reanalysis of public scRNA-seq datasets and immuno-staining of MDA-MB-231 primary tumor). These results verified two cancer stem cell-like populations (HMGA1-high, CD44-high) in MDA-MB-231 xenograft and TNBC patients. We believe that our findings are solid results because the findings were also validated by other methods.

Again, we would thank kind reviewing our manuscript.

Reviewer #3 (Significance (Required)): * In the past several studies showed the heterogeneity of cell-cell interactions between cancer cells and stromal cells in situ (Andersson et al, 2021; Wu et al, 2021) and tumor microheterogeneity (Jiang et al, 2016; Liu et al, 2016; Zhang et al, 2020). Spatial transcriptomics methods are important to reveal microheterogeneity of cancer. As a physician working in gynecology and obstetrics in my opinion the results of the study and spatial transcriptomic methods could be relevant to detect new biomarkers for diagnosis and prognosis of breast cancer in future and to find novel therapeutic targets to overcome drug resistance and facilitate curative treatment of breast cancer.

*

References in response letter

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Author Response

Reviewer #1 (Public Review):

The authors use ribosome profiling (RiboSeq) and RNA sequencing (RNASeq) to characterise the transcriptome and translatome of two PRRSV species as well as the host in response to infection. One particularly exciting feature of the study is that the analysis is carried out at different times of infection, which shows how both the virus and the host regulate their gene expression. The authors identify several new regulatory mechanisms of virus gene expression. Unexpectedly, they also find that the frameshifting efficiency at the ORF1ab frameshifting site changes with time. This contradicts the dogma in the field, which states that frameshifting is constant and has evolved to be constant to produce the a particular ratio of the two protein isoforms. The strength of the paper is in its comprehensible analysis. The paper is extremely rich in data, with 12 main and 23 Supplemental Figs and 11 Supplemental Tables, all of them rather complex. The main weakness is that it is written in a technical language that will be hardly readable by a non-specialist readership. Unfortunately, the authors do not make a good job in guiding the reader through their findings and hardly identify the the most important findings, while leaving the details to the specialists. This is particularly exemplified in Fig. 12, which should present the summary of the findings and would be extremely helpful, but hardly provides any text at all. This is potentially a very interesting paper, but the impact on the field could be increased considerably by better presentation of the work.

We would like to thank this reviewer for the positive comments about the scientific findings, and for their suggestions for improving the presentation of the work. This outside perspective was very useful in helping us see which parts of the paper required clearer explanation or less detail, which can be hard to discern when very close to the work. We have incorporated all of this reviewer’s suggestions and we think this has improved the manuscript and made it easier to follow.

Reviewer #2 (Public Review):

The authors used the ribosome profiling technique to study gene expression at transcriptional and translational levels in the cells infected with porcine reproductive and respiratory syndrome virus (PRRSV-1 and PRRSV-2) using ribosome profiling. The ribosome profiling was carried out on the cells at different time points within the first 12 hours of infection, thus providing information on gene expression changes during the time of infection.

The analysis of ribosome profiling data is exceptionally detailed and includes scrupulous characterization of footprint read lengths, de novo prediction of translated ORFs, characterisation of local pauses and differential gene expression of host and viral genes. The RNA-seq analysis is on par with that, the authors did a superb job at characterising the composition of the viral transcriptome that included identification of heteroclite RNAs and defective interfering RNAs. This provided the authors with reliable information for the interpretation of translational mechanisms responsible for the translation of ORFs discovered with ribosome profiling data.

A specific focus of the manuscript was placed on the characterisation of two instances of ribosomal frameshifting occurring in PRRSVs. In addition to "canonical" -1 frameshifting at a slippery sequence stimulated by downstream RNA secondary structure (common to many viruses), PRRSVs genome contains an additional frameshifting site whose efficiency is stimulated by a viral protein. The authors demonstrated that the efficiency of this frameshifting is increasing over time which is expected since the concentration of stimulating protein is increasing. Furthermore, the authors found that the efficiency of "canonical" frameshifting is also changed. The authors describe this as surprising since it directly contradicts the common description of its function as "setting the fixed ratio" between the synthesized products upstream and downstream of the frameshift site. Perhaps it is not so surprising in the hindsight, given that the frameshifting is dependent on so many different factors, folding states of RNA pseudoknots which are dynamic, ribosome density upstream, etc. it would be more surprising if the efficiency of frameshifting were indeed fixed. I think the "fixed ratio" was proposed mainly to draw a difference to ribosomal frameshifting occurring in cellular genes (like antizyme or bacterial release factor 2) where there seems to be only one functional product, but its synthesis level depends on the efficiency of frameshifting sensing certain conditions. It is great though that the authors observed such changes and I agree with the authors' speculations that this is unlikely to be unique to PRRSVs.

While I found the work to be largely descriptive, the authors did not shy away from speculating about potential mechanisms responsible for observed regulation. The manuscript is hard to get through simply due to its large length and a lot of data, but reading it is rewarding.

Again, we would like to thank this reviewer for their positive comments about the work, and to reiterate that hopefully the revised version of the manuscript will be easier to read.

Reviewer #3 (Public Review):

The manuscript by Cook et al. describes the first comprehensive gene expression analysis of two species of PRRSV, an important agricultural pathogen. Using ribosome profiling and RNA-sequencing, the authors systematically analyze the transcriptome of the virus and its translation, and their temporal kinetics. The analysis revealed non-canonical RNA species that are suggested to contribute to translation of parts of ORF1ab, changing the stoichiometry between the NSPs. In addition, the authors use the ribosome profiling data to identify novel overlapping ORFs, including a conserved uORF in the 5' leader, and to analyze the efficiency of frame-shift in two sites in the viral genome, one of which is trans-regulated by the viral nsp1β. The frame-shift efficiency in both sites is presented to be increasing late in infection. The authors also present conservation analysis from hundreds of available genomes. Finally, analysis of host gene expression uncovers a pattern suggesting translation inhibition of induced transcripts, and by comparing a WT virus to a mutant virus lacking the nsp2 site frame-shift, the authors identify a gene (TXNIP) whose expression is affected by nsp2TF.

In this rigorous work, the authors uncover new insights on an important pathogen, which can be of value to the wider field of virology. However, due to technical issues a few of the authors claims may require reconsideration.

We are grateful to this reviewer for their comments on the rigour and the impact of the work, as well as the suggestions for improvement which they included in their more detailed review. Within the detailed review, this reviewer expressed some concerns that ribosome run-off (seen in Figure 1—figure supplement 1 [formerly Supplementary Figure 1]) might confound the comparison of ribosome densities in different regions of the viral genome (particularly ORF1ab). However, this run-off only noticeably affects the first ~100 nt of host CDSs, which is very small compared to the ~12,000 nt total length of ORF1ab. The regions of ORF1ab in which we compare ribosome density in our study are almost all > 1,000 nt downstream of this ~100 nt run-off region and will therefore not be significantly affected by run-off. The exception to this is our assessment of heteroclite sgRNA translation, where the “heteroclite” region does include the first ~100 nt of ORF1a. As such, run-off may have a slight effect on this analysis, but we expect this to be minor, as the ~100 nt run-off region represents only a small proportion of the 1,550-nt “heteroclite” region. Further, any such effect would actually lead to under-estimation of heteroclite sgRNA translation, by artefactually reducing the relative RPF density in the heteroclite region. This would therefore strengthen our conclusion that our data provide evidence for heteroclite sgRNA translation.

Author Response

Reviewer #2 (Public Review):

Romand et al investigates the role of hyperphosphorylated guanosine nucleotides (ppGpp) in acclimation of plant chloroplasts to nitrogen limitation. The signaling role of ppGpp as alarmone is well established in the stringent response of bacteria. The stringent response allows bacteria to adapt to amino acid or carbon starvation and other acute abiotic stress conditions by downregulation of resource-consuming cell processes. A series of studies, including the current one, have demonstrated the retention of the bacterial-type ppGpp-mediated signaling response in plant and algal chloroplasts. The current study convincingly demonstrates the involvement of ppGpp in remodeling of photosynthetic machinery under nitrogen limitation. Using three Arabidopsis RSH lines (two underaccumulators and one overaccumulator of ppGpp), the authors show that the ppGpp is required for preventing excess ROS accumulation, oxidative stress and death of cotyledons under nitrogen limiting condition. The authors show a transient accumulation in ppGpp upon nitrogen limitation, which is followed by a sustained increase in the ratio of ppGpp to GTP. There is a prompt decline in maximum photochemical efficiency of photosystem II (PSII) and linear electron transport under nitrogen deficiency in wild type and ppGpp overaccumulator plants. However, mutants with low amount of ppGpp have a delayed decrease in these photosynthetic parameters. PpGpp is further shown to decrease (or degrade) photosynthetic proteins, and a remodeling of PSII that involves uncoupling of LHC II from the reaction center core has been suggested to occur under nitrogen starvation. The authors also show a ppGpp-mediated downregulation of chloroplast gene transcription and a coordinated plastid-nuclear gene expression under nitrogen deficiency.

Strengths 1. The conclusions of this paper are mostly well supported by data. With three different RSH lines, there is a convincing demonstration of the specific involvement of ppGpp in nutrient acclimation. The line carrying conditional overexpression of Drosophila ppGpp hydrolase (MESH) nicely complements the RSH lines and strengthens many of the conclusions. This is a detailed analysis of ppGpp function in a plant species. The data supplement accompanying each main figure is extensive and helpful. 2. The genomic analysis in nitrogen replete and deplete wild type uncovers an interesting regulation of RSH enzymes at the transcriptional level. This is likely to be part of a signaling response that works in conjunction with allosteric modulation of RSH activity under nitrogen limitation. 3. The large-scale analysis of plastid and nuclear gene transcripts supports the involvement of ppGpp in coordinated repression of plastid and nuclear gene transcription. 4. By the inclusion of mitochondrial genes and proteins in their analysis, the authors clearly show that the ppGpp action is limited to plastids and does not extend to mitochondria, which like chloroplasts, have a bacterial ancestry. 5. The thorough demonstration of the involvement of ppGpp in low nitrogen acclimation of photosynthetic metabolism adds greatly to the understanding of plant abiotic stress tolerance mechanisms and ppGpp function in both plants and bacteria.

We thank the reviewer for these observations on our work.

Weaknesses: 1. With two earlier reports from a different laboratory (Maekawa et al 2015 and Honoki et al 2018) showing the involvement of ppGpp in acclimation to nitrogen deficiency, the novelty of the current study is diminished. The authors mention that the double mutant (rsh2 rsh3) used by Honoki et al does not show a clear phenotype other than a delay in Rubisco degradation. It is not clear to me why the lack of two major RSH isoforms, involved in synthesis of ppGpp under light, would not produce any phenotype. This discrepancy should be discussed further in the manuscript.

The work of Maekawa et al., 2015 and Honoki et al., 2018 was indeed important for highlighting the potential involvement of ppGpp in the acclimation to nitrogen deficiency. However, these studies were based on the constitutive overaccumulation of ppGpp. Here, we demonstrate a physiological requirement for ppGpp signalling by the plant to allow acclimation to an abiotic stress- we consider this to be a major step forwards in understanding the role of ppGpp in plants, and one of the few examples of a physiological requirement for ppGpp in plants.

We mention the use of an RSH2 RSH3 mutant by Honoki et al. 2018 while putting our results into the context of previous findings in the discussion. We bring the attention of the reviewer to our analysis of an RSH2 RSH3 mutant in this study, and that in our hands the mutant phenotype was indistinguishable from the RSH quadruple mutant (rshQM) (Figure 2- figure supplement 1 panel B). Therefore, we do indeed consider that RSH2 and RSH3 are the main RSH isoforms involved in ppGpp-mediated acclimation to nitrogen deficiency, and we state this ( see p7 l161-164 in original manuscript). As we explain in the discussion there are probably technical reasons for the discrepancy with the results reported by Honoki et al. 2018. We also note here that the RSH2 RSH3 mutants used in our study and by Honoki et al. 2018 are not identical: the same SAIL insertion SAIL_305_B12 was used for rsh2, while the rsh3 allele used by Honoki was the GABIkat insertion GABI129D02 and here SAIL_99_G05). We now add this difference in the genetic identity of the mutants as an additional potential explanation for the different findings in the two studies.

1. The authors at times show a tendency to overinterpret their results. A ppGpp-mediated repression of chloroplast transcription and translation is sufficient to explain most of the observations in this study. However, the authors seem to go beyond this simple explanatory framework by invoking specific roles for ppGpp in remodeling of PSII antenna-core interaction and in blocking of PSII reaction center repair. There is no data in the manuscript in support of these two propositions. A coordinated decrease in synthesis of most chloroplast proteins, including the D1 reaction center protein of PSII, is sufficient to explain the decrease in Fv/Fm. There is no evidence in the manuscript for "photoinactivation gaining an upper hand via ppGpp-mediated signaling"

The circuit breaker analogy of PSII photoinhibition that the authors discuss in support is just an interpretation. The remodeling of PSII antenna-core interaction, likewise, could be a simple consequence of the ppGpp-mediated decrease in D1 protein synthesis. The high antenna-core ratio under nitrogen starvation likely reflects the lag in the decrease of LHCB1 (which eventually decreases significantly by day 16).

Since ppGpp-signaling primarily affects plastid transcription and translation, there is a rapid decrease in plastid psbA gene product (D1) relative to the nuclear-encoded LHCB1. The unconnected LHCII might simply be a result of the mismatch in antenna-core stoichiometry rather than an active regulation of PSII functional assembly by ppGpp.

We have re-worked the discussion to make these points more clearly, and also to tone down certain points where we may have over-stretched our interpretation.

We think that our interpretation is essentially the same as the reviewer’s- the ppGpp mediated inhibition of chloroplast translation and transcription is sufficient to explain the majority of our results. In the discussion we also discuss the possibility that ppGpp stimulates the active degradation of some chloroplast proteins, and put this in context of studies showing that N-starvation activates the specific proteolysis of certain photosynthetic proteins in Chlamydomonas and has an effect on the half lives of different chloroplast proteins in plants. We do not propose or present data suggesting that ppGpp has any other specific targets/effectors- for example within the PSII repair cycle or in remodelling PSII stoichiometry- although we also cannot exclude the possibility of targets in these processes.

We think that the ppGpp dependent change in PSII stoichiometry during N-starvation is not just a side effect of a general downregulation or a temporary mismatch as suggested- but due to its size, persistence and effect on photosynthesis is likely to be part of the acclimation process. For example, the ppGpp-dependent drop in Fv/Fm is maintained at day 16 and even beyond (Fig 2D). We also see that photosynthetic proteins are still degraded in low ppGpp mutants (Fig. 3A), but that the high Fv/Fm is maintained throughout. These points and the fact that the alteration of PSII stoichiometry is not caused by the direct action of ppGpp on PSII (but via transcription/translation) does not mean that it is not important or does not play a role in acclimation. Other studies report that PSII RC inactivation can protect PSI (e.g. Tikkanen et al. 2014) and ppGpp may be working in a similar fashion here by reducing the flow of energy into the photosynthetic electron transport chain. This interpretation is consistent with our results showing that wild-type plants and high ppGpp plants (rsh1-1) accumulate less ROS and ROS-related damage than plants defective in ppGpp biosynthesis (Fig. 1).

1. The work is mostly descriptive of the involvement of ppGpp in low nitrogen tolerance without any data on how the nitrogen deficiency is sensed by the RSH enzymes and how ppGpp orchestrates the multi-faceted acclimatory response. Perhaps, these aspects are beyond scope of the current manuscript, but they could be discussed more.

We agree that these are very important questions, and also that they are out of the scope of the current work. We think that our work goes beyond the descriptive by demonstrating the physiological functions of ppGpp-signalling during nitrogen deficiency and a framework for how it occurs (i.e downregulation of chloroplast function and avoidance of excess oxidative stress).

Reviewer #3 (Public Review):

The manuscript by Romand et al. explores the role of guanosine penta- and tetraphosphate, ppGpp, in the acclimation of plants to nitrogen limitation. It shows that an early and transient ppGpp accumulation - and a controlled ppGpp/GTP ratio - is necessary for a proper acclimation of plants to such stress. The pathway is shown to act on remodeling the photosynthetic machinery and downregulating photosynthesis during stress, thus limiting ROS damage to the plants. This regulation most likely takes place by affecting chloroplast transcription, maintaining the balance between nucleus- and chloroplast-encoded proteins.

The manuscript proposes a thorough analysis of the ppGpp-induced response including extensive wild type and mutant analyses at the gene and protein expression level as well as at the physiological level under nitrogen limitation together with heterologous expression of ppGpp hydrolase from Drosophila. The conclusions are carefully backed by the data (but for the lack of gene expression analysis in the high ppGpp line, rsh1-1), the figures and text clear, well-written and easy to follow. Altogether it represents a solid new step in improving the comprehension of plant response to nitrogen limitation, as well as on the role of ppGpp in plants and possibly throughout the green lineage. An alternative hypothesis to ppGpp photoprotective role could be discussed in that photoprotection may be an indirect effect due to photosynthetic protein degradation enabled by ppGpp, possibly through modulation of ppGpp/GTP ratio affecting chloroplast protease activity.

On this last point we agree with the reviewer- our data indicates that the photoprotective role of ppGpp is via the ppGpp-dependent control of the abundance of photosynthetic proteins. This is indirect in the sense that we have no evidence that ppGpp itself interacts with components of the photosynthetic machinery. However, as discussed below we do not think that photoprotection is just a side-effect of ppGpp’s action- we show that the capacity to synthetise ppGpp is required for avoiding the generation of ROS and tissue death.

Author Response

Reviewer #1 (Public Review):

In this paper, the authors examine the role of feedback from primary visual cortex (V1) to the dorsolateral geniculate nucleus of the thalamus (dLGN) under a variety of visual stimulus conditions. This is a well-defined circuit originating from a specific population of Layer 6 cells in the cortex, and the authors test the role of this projection by recording in dLGN during silencing of V1 via ChR2 expression in PV inhibitory cells. This is a well-established technique for strong silencing of cortex. However, because there are other disynaptic pathways from V1 to thalamus, they also perform a similar set of experiments using more targeted optogenetic inhibition of a genetically-defined class of Layer 6 (NTSR1) cells that make up most of the L6 corticothalamic projections. The fact that these experiments elicit similar results supports their interpretation that these direct projections are largely responsible for the observed results. While previous studies have manipulated corticothalamic projections pharmacologically, via V1 lesions, or via optogenetics, the authors rightly point out that most previous studies have focused on simple parametric stimuli and/or have been performed in anesthetized animals. The results of this study suggest feedback during natural visual stimuli and locomotion reveal effects that are distinct from these previous studies.

Overall, these are important and carefully-performed experiments that significantly advance our understanding of the role of corticothalamic feedback to the dLGN.

We thank the reviewer for the appreciation of our methods and results.

The authors suggestion that the different effects observed during simple and complex stimuli may be due to increased surround suppression during the full-field gratings seems reasonable, but I didn’t understand how the analysis of blank periods during these two conditions supported this argument. It wasn’t clear to me what mechanisms would be expected to support the alternative outcome, where suppressing feedback during the blank periods interleaved with the two different stimuli would have different effects - unless they are testing whether natural movies elicit some longer-lasting state change that would change the results observed during blank periods. This seems somewhat implausible, and unless the authors wish to expand the study to include different stimulus sizes, I think the interpretation regarding surround suppression is best left to the discussion, where it is already treated well.

We thank the reviewer for the recommendation. We fully agree that explaining the difference in CT feedback across blanks, gratings, and movies will require more experiments. We have followed the recommendation of the reviewer and removed the interpretation related to differences in surround suppression from the results section and treat it now in the discussion only.

The paper would benefit from more clearly highlighting results that agree or disagree with previous studies, with a brief mention of how the authors interpret these similarities or differences. For example the results of Olsen et al 2012 seem to be consistent with what the authors observe here with gratings but not with natural movies, and although Olsen et al performed some awake recordings, I think the LGN recordings were all under anesthesia. Specifically highlighting these differences (and suggesting an interpretation for them) would help emphasize the novelty of the study.

We thank the reviewer for the recommendation and now highlight throughout the results and discussion where our results agree or disagree with previous studies. As mentioned by the reviewer, we have similar results for gratings to the results obtained by Olsen et al. (2012), although in our study we have not explicitly centered the full field gratings on the RFs and we have not measured surround suppression. The results for the blank stimuli and the movies, however, are different, at least in terms of how CT feedback affects ring rate. A key insight of our study, at least in our view, is that CT feedback effects might well differ for different stimuli, and understanding the underlying mechanism (e.g., differential engagement of the excitatory and indirect inhibitory CT feedback pathway) will be an important avenue of research in the future.

The authors should comment more on the spatial extent of V1 silencing and potential effects of the variability observed across mice, especially given that they appear to have made only a single injection of ChR2 to label PV cells. While silencing with this method extends beyond the injection site, it probably doesn’t cover all of V1. Was any analysis done of variability across mice based on the size or location of the ChR2 expression measured post-hoc?

Unfortunately, we did not preserve enough slices to precisely quantify the extent of expression across animals. However, visual inspection of the slices revealed that even a single injection typically resulted in a widespread pattern of expression. In fact, we think that activation of PV neurons was determined in its spatial extent not so much by the virus expression but rather by the photoactivation light. With a distance of 0.5 0.1 mm of the optical fibre from the cortical surface, most of V1 was covered by light. A previous study performing a quantitative characterization of the lateral spread of optogenetic suppression by PV activation demonstrates that pyramidal neuron ring can be suppressed 2 3 mm from the laser center Li et al. (2019). Hence, we think that variability in opsin expression across mice is unlikely to have a substantial impact on our results.

The decrease in reliability and sparseness during running is attributed partially to increased eye movements. In cortex this has been studied in awake animals with natural movies in a variety of studies where the opposite effects are observed including Froudarakis et al 2014 where there was a small increase in both metrics during running, and Reimer et al 2014 where reliability strongly increased during pupil dilation. If there is enough data to condition on running periods where eye movements are stable or dilation outside of running to measure the effects of feedback suppression during these periods, this would be useful information.

We thank the reviewer for bringing up this interesting issue. We fully agree that our results recorded in dLGN are different from those measured by Froudarakis et al. (2014) and Reimer et al. (2014) in V1.

As suggested by the reviewer, we have repeated the analysis proposed by Reimer et al. (2014) to identify periods in the movie with the most rapid pupil dilation / constriction in face of continuous changes in overall luminance. Besides the effects of pupil dilation / constriction on ring rate, we have computed reliability both according to what we had used throughout our manuscript and in the way proposed in Reimer et al. (2014), which resembles our measure of SNR. We find that both measures of reliability are unaffected by pupil dilation.

Interestingly, in the meantime other studies have also reported that reliability might be differently affected by behavioral state in V1 compared to dLGN. For instance, Nestvogel and McCormick (2022) found that consistent with our results variability of membrane potential in visual thalamic neurons was not significantly altered by locomotion or whisker movement.

Reviewer #2 (Public Review):

Spacek et al. study the corticothalamic feedback of different visual stimuli on visual thalamus. With optogenetic suppression of visual cortex feedback and simultaneous multi-channel recordings in visual thalamus, the authors succeeded to acquire important data about this essential feedback loop in awake, behaving animals. The authors show in detail that the cortical feedback acts as a gain factor in thalamus for the transmission of signals from retina to cortex. They also show that naturalistic scenes result in robust feedback from cortex. As expected from anatomy, the authors find that modulatory feedback from cortex and modulatory input from brain stem act rather independently on thalamus. The paper is technically very impressive and the results are important for a wide range of readers.

We thank the reviewer for the positive feedback.

It is advisable to revise the Introduction and Discussion to better integrate the new findings into the existing literature.

We thank the reviewer for this advice, and have revised the title, abstract, introduction and discussion to better integrate our new findings into the existing literature, and highlight our advances in relation to previous findings.

The authors distinguish between awake, resting state and running state. However, the awake, resting state in mice comprises a wide range of alertness levels. This range of alertness will most likely affect the bursting probability of thalamocortical neurons.

We thank the reviewer for this comment. So far, our manuscript had only taken locomotion as a proxy for behavioral state, as locomotion typically goes along with increased pupil size (Erisken et al., 2014; McGinley et al., 2015) and increased levels of arousal (McGinley et al., 2015; Vinck et al., 2015). To also study the effects of locomotion-independent arousal, we have now applied the analysis mentioned by the reviewer: following methods originally suggested by Reimer et al. (2014), we identified periods of the movie presentation without locomotion that corresponded to the upper or the lower quartile of pupil size change. Similar to the results that Reimer et al. (2014) found for primary visual cortex, we observed that ring rate in dLGN is enhanced during times when the pupil was dilating faster than usual vs. when it was constricting faster than usual. Like the effects of running, the modulations by pupil-indexed arousal persisted even with V1 suppression. We present these new results in Figure 5 - Supplement 2.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors find CpGs within 500Kb of a gene that associate with transcript abundance (cis-eQTMs) in children from the HELIX study. There is much to admire about this work. With two notable exceptions, their work is solid and builds/improves on the work that came before it. Their catalogue of eQTMs could be useful to many other researchers that utilize methylation data from whole blood samples in children. Their annotation of eQTMs is well thought out and exhaustive. As this portion of the work is descriptive, most of their methods are appropriate.

Unfortunately, their use of results from a model that does not account for cell-type proportions across samples diminishes the utility and impact of their findings. I believe that their catalog of eQTMs contains a great deal of spurious results that primarily represent the differences in cell-type proportions across samples.

Lastly, the authors postulate that the eQTM gene associations found uniquely in their unadjusted model (in comparison to results from a model that does account for cell type proportion) represent cell-specific associations that are lost when a fully-adjusted model is assumed. To test this hypothesis, the authors appear to repurpose methods that were not intended for the purposes used in this manuscript. The manuscript lacks adequate statistical validation to support their repurposing of the method, as well as the methodological detail needed to peer review it. This section is a distraction from an otherwise worthy manuscript. But provide evidences that enriched for cell sp CpGs.

Major points

1. Line 414-475: In this section, the authors are suggesting that CpGs that are significant without adjusting for cell type are due to methylation-expression associations that are found only in one cell type, while association found in the fully adjusted model are associations that are shared across the cell types. I do not agree with this hypothesis, as I do not agree that the confounding that occurs when cell-type proportions are not accounted for would behave in this way. Although restricting their search for eQTMs to only those CpGs proximal to a gene will reduce the number of spurious associations, a great deal of the findings in the authors' unadjusted model likely reflect differences in cell-type proportions across samples alone. The Reinius manuscript, cited in this paper, indicates that geneproximal CpGs can have methylation patterns that vary across cell types.

Following reviewers’ recommendations, we have reconsidered our initial hypothesis about the role of cellular composition in the association between methylation and gene expression. Although we still think that some of the eQTMs only found in the model unadjusted for cellular composition could represent cell specific effects, we acknowledge that the majority might be confounded by the extensive gene expression and DNA methylation differences between cell types. Also, we recognize that more sophisticated statistical tests should be applied to prove our hypothesis. Because of this, we have decided to report the eQTMs of the model adjusted for cellular composition in the main manuscript and keep the results of the model unadjusted for cellular composition only in the online catalogue.

1. Line 476-488: Their evidence due to F-statistics is tenuous. The authors do not give enough methodological detail to explain how they're assessing their hypothesis in the results or methods (lines 932-946) sections. The methods they give are difficult to follow. The results in figure S19A are not compelling. The citation in the methods (by Reinius) do not make sense, because Reinius et al did not use F-statistics as a proxy for cell type specificity. The citation that the authors give for this method in the results does not appear to be appropriate for this analysis, either. Jaffe and Irizarry state that a CpG with a high Fstatistic indicates that the methylation at that CpG varies across cell type. They suggest removing these CpGs from significant results, or estimating and correcting for cell type proportions, as their presence would be evidence of statistical confounding. The authors of this manuscript indicate that they find higher F-statistics among the eQTMs uniquely found in the unadjusted model, which seems to only strengthen the idea that the unadjusted model is suffering from statistical confounding.

We recognize the miss-interpretation of the F-statistic in relation to cellular composition. We have deleted all this part from the updated version of the manuscript.

1. The methods used to generate adjusted p-values in this manuscript are not appropriate as they are written. Further, they are nothing like the methods used in the paper cited by the authors. The Bonder paper used permutations to estimate an empirical FDR and cites a publication by Westra et al for their method (below). The Westra paper is a better one to cite, because the methods are more clear. Neither the Bonder nor the Westra paper uses the BH procedure for FDR.

Westra, H.-J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238-1243 (2013).

We apologize for this misleading citation. Although Bonder et al applied a permutation approach to adjust for multiple testing, our approach was inspired by the method applied in the GTEx project (GTEx consortium, 2020), using CpGs instead of SNPs. The citation has been corrected in the manuscript. Moreover, we have explained in more detail the whole multiple-testing processes in the Material and Methods section (page 14, line 316):

“To ensure that CpGs paired to a higher number of Genes do not have higher chances of being part of an eQTM, multiple-testing was controlled at the CpG level, following a procedure previously applied in the Genotype-Tissue Expression (GTEx) project (Gamazon et al., 2018). Briefly, our statistic used to test the hypothesis that a pair CpGGene is significantly associated is based on considering the lowest p-value observed for a given CpG and all its pairs Gene (e.g. those in the 1 Mb window centered at the TSS). As we do not know the distribution of this statistic under the null, we used a permutation test. We generated 100 permuted gene expression datasets and ran our previous linear regression models obtaining 100 permuted p-values for each CpG-Gene pair. Then, for each CpG, we selected among all CpG-Gene pairs the minimum p-value in each permutation and fitted a beta distribution that is the distribution we obtain when dealing with extreme values (e.g. minimum) (Dudbridge and Gusnanto, 2008). Next, for each CpG, we took the minimum p-value observed in the real data and used the beta distribution to compute the probability of observing a lower p-value. We defined this probability as the empirical p-value of the CpG. Then, we considered as significant those CpGs with empirical p-values to be significant at 5% false discovery rate using BenjaminiHochberg method. Finally, we applied a last step to identify all significant CpG-Gene pairs for all eCpGs. To do so, we defined a genome-wide empirical p-value threshold as the empirical p-value of the eCpG closest to the 5% false discovery rate threshold. We used this empirical p-value to calculate a nominal p-value threshold for each eCpG, based on the beta distribution obtained from the minimum permuted p-values. This nominal p-value threshold was defined as the value for which the inverse cumulative distribution of the beta distribution was equal to the empirical p-value. Then, for each eCpG, we considered as significant all eCpG-Gene variants with a p-value smaller than nominal p-value.”

References:<br /> GTEx consortium, The GTEx Consortium atlas of genetic regulatory effects across human tissues, Science (2020) Sep 11;369(6509):1318-1330. doi: 10.1126/science.aaz1776.

Reviewer #2 (Public Review):

Strength:

Comprehensive analysis Considering genetic factors such as meQTL and comparing results with adult data are interesting.

We thank the reviewer for his/her positive feedback on the manuscript. We agree that the analysis of genetic data and the comparison with eQTMs described in adults are two important points of the study.

Weakness:

• Manuscript is not summarized well. Please send less important findings to supplementary materials. The manuscript is not well written, which includes every little detail in the text, resulting in 86 pages of the manuscript.

Following reviewers’ comments, we have simplified the manuscript. Now only the eQTMs identified in the model adjusted for cellular composition are reported. In addition, functional enrichment analyses have been simplified without reporting all odds ratios (OR) and p-values, which can be seen in the Figures.

• Any possible reason that the eQTM methylation probes are enriched in weak transcription regions? This is surprising.

Bonder et al also found that blood eQTMs were slightly enriched for weak transcription regions (TxWk). Weak transcription regions are highly constitutive and found across many different cell types (Roadmap Epigenetics Consortium, 2015). However, hematopoietic stem cells and immune cells have lower representation of TxWk and other active states, which may be related to their capacity to generate sub-lineages and enter quiescence.

Given that we analyzed whole blood and that ROADMAP chromatin states are only available for blood specific cell types, each CpG in the array was annotated to one or several chromatin states by taking a state as present in that locus if it was described in at least 1 of the 27 bloodrelated cell types. By applying this strategy we may be “over-representing” TxWk chromatin states, in the case TxWk are cell-type specific. As a result, even if each blood cell type might have few TxWk, many positions can be TxWk in at least one cell type, inflating the CpGs considered as TxWk. This might have affected some of the enrichments.

On the other hand, CpG probe reliability depends on methylation levels and variance. TxWk regions show high methylation levels, which tend to be measured with more error. This also might have impacted the results, however the analysis considering only reliable probes (ICC >0.4) showed similar enrichment for TxWk.

Besides these, we do not have a clear answer for the question raised by the reviewer.

References:

Bonder MJ, Luijk R, Zhernakova D V, Moed M, Deelen P, Vermaat M, et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet [Internet]. 2017 [cited 2017 Nov 2];49:131–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27918535

Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton ML, Wu YC, Pfenning AR, Wang X, Claussnitzer M, Liu Y, Coarfa C, Harris RA, Shoresh N, Epstein CB, Gjoneska E, Leung D, Xie W, Hawkins RD, Lister R, Hong C, Gascard P, Mungall AJ, Moore R, Chuah E, Tam A, Canfield TK, Hansen RS, Kaul R, Sabo PJ, Bansal MS, Carles A, Dixon JR, Farh KH, Feizi S, Karlic R, Kim AR, Kulkarni A, Li D, Lowdon R, Elliott G, Mercer TR, Neph SJ, Onuchic V, Polak P, Rajagopal N, Ray P, Sallari RC, Siebenthall KT, Sinnott-Armstrong NA, Stevens M, Thurman RE, Wu J, Zhang B, Zhou X, Beaudet AE, Boyer LA, De Jager PL, Farnham PJ, Fisher SJ, Haussler D, Jones SJ, Li W, Marra MA, McManus MT, Sunyaev S, Thomson JA, Tlsty TD, Tsai LH, Wang W, Waterland RA, Zhang MQ, Chadwick LH, Bernstein BE, Costello JF, Ecker JR, Hirst M, Meissner A, Milosavljevic A, Ren B, Stamatoyannopoulos JA, Wang T, Kellis M. Integrative analysis of 111 reference human epigenomes. Nature. 2015 Feb 19;518(7539):317-30. doi: 10.1038/nature14248. PMID: 25693563; PMCID: PMC4530010.

• The result that the magnitude of the effect was independent of the distance between the CpG and the TC TSS is surprising. Could you draw a figure where x-axis is the distance between the CpG site and TC TSS and y-axis is p-value?

As suggested by the reviewer, we have taken a more detailed look at the relationship between the effect size and the distance between the CpG and the TC’s TSS. First, we confirmed that the relative orientation (upstream or downstream) did not affect the strength of the association (p-value=0.68). Second, we applied a linear regression between the absolute log2 fold change and the log10 of the distance (in absolute value), finding that they were inversely related. We have updated the manuscript with this information (page 22, line 504):

“We observed an inverse linear association between the eCpG-eGene’s TSS distance and the effect size (p-value = 7.75e-9, Figure 2B); while we did not observe significant differences in effect size due to the relative orientation of the eCpG (upstream or downstream) with respect to the eGene’s TSS (p-value = 0.68).”

Results are shown in Figure 2B. Of note, we winsorized effect size values in order to improve the visualization. The winsorizing process is also explained in Figure 2 legend. Moreover, we have done the plot suggested by the reviewer (see below). It shows that associations with smallest p-values are found close to the TC’s TSS. Nonetheless, as this pattern is also observed for the effect sizes, we have decided to not include it in the manuscript.

• Concerned about too many significant eQTMs. Almost half of genes are associated with methylation. I wonder if false positives are well controlled using the empirical p-values. Using empirical p-value with permutation may mislead since especially you only use 100 permutations. I wonder the result would be similar if they compare their result with the traditional way, either adjusting p-values using p-values from entire TCs or adjusting pvalues using a gene-based method as commonly used in GWAS. Compare your previous result with my suggestion for the first analysis.

Despite the number of genes (TCs) whose expression is associated with DNA methylation is quite high, we do not think this is due to not correctly controlling false positives. Our approach is based on the method used by GTEx (GTEx consortium) and implemented in the FastQTL package (Ongen et al. 2016), to control for positives in the eQTLs discovery. As in GTEx, we run 100 permutations to estimate the parameters of a beta distribution, which we used to model the distribution of p-values for each CpG. Then, to correct for the number of TCs among significant CpGs, we applied False Discovery Rate (FDR) at a threshold < 0.05. Finally, we defined the final set of significant eQTMs using the beta distribution defined in a previous step.

For illustration, we compared the number of eQTMs with our approach to what we would obtain by uniquely applying the FDR method (adjusted p-value <0.05), getting fewer associations with our approach: eQTMs (45,203 with FDR vs 39,749 with our approach), eCpGs (24,611 vs 21,966) and eGenes (9,937 vs 8,886). Among the 8,886 significant eGenes, 6,288 of them are annotated to coding genes, thus representing 27% of the 23,054 eGenes coding for a gene included in the array.

References:

GTEx consortium, The GTEx Consortium atlas of genetic regulatory effects across human tissues, Science (2020) Sep 11;369(6509):1318-1330. doi: 10.1126/science.aaz1776.

Ongen et al. Fast and efficient QTL mapper for thousands of molecular phenotypes, Bioinformatics (2016) May 15;32(10):1479-85. doi: 10.1093/bioinformatics/btv722. Epub 2015 Dec 26.

• I recommend starting with cell type specific results. Without adjusting cell type, the result doesn't make sense.

As suggested by other reviewers, we have withdrawn the model unadjusted for cellular composition.

Reviewer #3 (Public Review):

Although several DNA methylation-gene expression studies have been carried out in adults, this is the first in children. The importance of this is underlined by the finding that surprisingly few associations are observed in both adults and children. This is a timely study and certain to be important for the interpretation of future omic studies in blood samples obtained from children.

We agree with the reviewer that eQTMs in children are important for interpreting EWAS findings conducted in child cohorts such as those of the Pregnancy And Childhood Epigenetics (PACE) consortium.

It is unfortunate that the authors chose to base their reporting on associations unadjusted for cell count heterogeneity. They incorrectly claim that associations linked to cell count variation are likely to be cell-type-specific. While possible, it is probably more likely that the association exists entirely due to cell type differences (which tend to be large) with little or no association within any of the cell types (which tend to be much smaller). In the interests of interpretability, it would be better to report only associations obtained after adjusting for cell count variation.

Following reviewers’ recommendations, we have reconsidered our initial hypothesis about the role of cellular composition in the association between methylation and gene expression. Although we still think that some of the eQTMs only found in the model unadjusted for cellular composition could represent cell specific effects, we acknowledge that the majority might be confounded by the extensive gene expression and DNA methylation differences between cell types. Also, we recognize that more sophisticated statistical tests should be applied to prove our hypothesis. Because of this we have decided to report the eQTMs of the model adjusted for cellular composition in the main manuscript and keep the results of the model unadjusted for cellular composition only in the online catalogue.

Several enrichments could be related to variation in probe quality across the DNA methylation arrays.

For example, enrichment for eQTM CpG sites among those that change with age could simply be due to the fact age and eQTM effects are more likely to be observed for CpG sites with high quality probes than low quality probes. It is more informative to instead ask if eQTM CpG sites are more likely to have increasing rather than decreasing methylation with age. This avoids the probe quality bias since probes with positive associations with age would be expected to have roughly the same quality as those with negative associations with age. There are several other analyses prone to the probe quality bias.

See answer to question 2, below.

Author Response

Reviewer #2 (Public Review):

The visual system must extract two basic features of visual stimuli: luminance, which we perceive as brightness, and contrast, the change in luminance over space or time (this paper focuses on changes over time). Contrast is separately processed by ON and OFF pathways, which encode luminance increments or decrements, respectively. Contrast must be robustly detected even if the overall luminance changes rapidly, as might occur if an animal is moving in and out of shadows. This paper addresses how such a luminance correction occurs in the fly.

In the fly, three types of first-order interneurons - L1, L2, and L3 - transmit information from photoreceptors to the medulla, where ON and OFF encoding emerges. Previous work suggested that all three interneurons primarily encode contrast signals and that they project to distinct pathways: L1 to the ON pathway and L2 and L3 to the OFF pathway. Ketkar et al. show that, contrary to this model, these interneurons encode both contrast and luminance in specific ways and are not cleanly segregated into ON versus OFF inputs.

This study reveals several new insights into early visual processing that are interesting and well-supported by the data:

1) The authors show that behavioral responses to ON stimuli can compensate for rapid changes in luminance. However, the purported sole input to the ON pathway, L1, shows activity that is highly dependent on luminance. This suggests that a luminance correction must arise downstream of L1. These results are analogous to findings previously made by the same group regarding the OFF pathway (Ketkar et al., 2020). The previous paper showed that L2 provides contrast information to the OFF pathway, and L3 provides luminance information to allow for a luminance correction in downstream contrast encoding. But unlike the multiple inputs to the OFF pathway, the ON pathway was thought to only receive input from L1, provoking the question of whether L1 is able to provide both contrast and luminance information.

2) Using well-designed calcium imaging studies, the authors surveyed the responses of the three interneurons and found that they encode different stimulus features: L1 encodes both contrast and luminance, L2 purely encodes contrast, and L3 purely encodes luminance (with a different dependence than L1). These are interesting and important findings revealing how both contrast and luminance encoding are distributed across the three interneurons.

3) Using neuronal manipulations, the authors dissected the contributions of the three interneurons to ON and OFF behavior under changing luminance. These experiments showed that L1 and L3 are required for the luminance correction in the behavior. Moreover, the finding that all three interneurons contribute to both ON and OFF behavior contrasts with the existing model of segregated pathways. Thus, this paper could change the way we think about early visual processing in the fly: rather than relaying similar information to distinct downstream pathways, first-order interneurons relay distinct information to common pathways.

Overall, the major claims of this paper are important and supported by the experiments. There are just a few concerns that I would note:

Thank you for the overall positive evaluation of our work, as well as for the constructive criticism, which we are going to address below.

1) The authors state that they have shown luminance invariance in ON behavior (e.g. line 376-377 of the Discussion), but this is not entirely accurate: the ON behavior decreases as luminance increases. This is still an interesting effect since it's the opposite of what L1 activity does, so it's clear that the circuit is implementing a luminance correction, but it is not "luminance invariance".

As pointed out in response to essential comment #2, we carefully edited the manuscript to talk about ‘near’ luminance invariance, or data approaching luminance invariance. More prominently, we rephrased the text to highlight the need for a luminance gain to scale behavioral responses to contrast, even if the resulting behavior is not entirely luminance invariant.

2) The visual stimuli presented for most imaging experiments (full-field) are not the same as those presented for behavior (moving edges). It is possible neuronal responses and their encoding of luminance and contrast may differ if tested with the moving edge stimuli (if so, this would be concerning). The authors did image L1 with both types of stimuli and could compare these responses. Also, testing behavior at 34º and imaging at 20º presents a possible discrepancy in comparing these data.

We use moving ON edges in Figure 1, and these data suggest that the transient response of L1 scales with step changes in luminance, consistent with data in Figure 2B. Although we did not point this out in the paper, the L1 responses in Figure 1 also decay to different response levels, consistent with the luminance-sensitive component that static stimuli reveal in Figure 2. Furthermore, for other ongoing projects in the lab, we have for example measured physiological responses in L2 with the same stimuli used in behavior, and there is no discrepancy with the data reported here. Overall, there is no reason to believe, following a vast amount of literature in Drosophila and other flies, that LMCs would respond any different to moving vs. static stimuli.

We can additionally point out that the behavioral data of L3 silencing (at 34ºC) nicely correlate with physiological contrast responses of L1 and L2 (at 20ºC, predicted from electrophysiological recordings for LMCs in Ketkar et al. 2020, measured for L1 here). Many previous studies, for example in motion detection, have linked data from physiological recordings at room temperature with behavioral experiments done at higher temperature (e.g., Ammer et al., 2015; Clark et al., 2011; Creamer et al., 2019; Fisher et al., 2015; Leonhardt et al., 2017; Salazar-Gatzimas et al., 2016; Serbe et al., 2016; Silies et al., 2013; Strother et al., 2017). We therefore do not think that these are major concerns.

3) I find it puzzling that silencing L1 has little effect on ON behavior at 100% contrast and varying luminance (Figure 3A), but severely affects ON behavior to 100% contrast (and lower values) when different contrasts are interleaved (Figure S1). The authors note this but do not provide a clear explanation of why this might be the case. Aside from mechanism, it is not clear whether the difference is due to varying luminance in the first experiment or varying contrast in the second one (e.g. they could test 100% contrast without varying luminance).

The two stimulus sets used here do not allow us to pinpoint why the L1 silencing phenotype differs between them, since they comprise more than one difference as discussed above (see point 4) in “Essential Revisions”). We now include two additional experiments that dissect the role of different stimulus parameters (Supp. Figure 2). To understand whether the difference is due to varying luminance, we tested responses to ON edges of fixed (100%) contrast and luminance at the same stimulus parameters (motion duration, speed) as used in Figure 3, and did not find reduced turning responses when silencing L1. Thus, varying luminance does not change the effect of L1 on ON behavior. However, when repeating this experiment with a bright inter-stimulus interval, L1 silencing lead to a strong response deficit. Therefore, differences in the interval luminance explain the differences in the L1 silencing phenotype observed not only in this study but also across studies. Although we hypothesize a role of contrast adaptation that may function differently with altered contrast statistics, a more detailed investigation would be necessary to understand the mechanism. Nevertheless, our experiments allow us to conclude that L1 is not the sole major input to the ON pathway, even though it is required under certain stimulus conditions.

4) I do not entirely agree with the authors' interpretation of the L1 ort rescue experiment for OFF behavior. They state that rescue flies "responded similarly to positive controls". However, the graph shows that the rescue flies generally fall in between the mutant and heterozygote control flies; they resemble the controls at low luminance but resemble the mutants at high luminance. One may conclude that L1 is sufficient to enhance OFF behavior at low luminance, but it is a stretch to say it's a complete rescue.

Sorry, we just meant to say that they “responded similarly to positive controls (...) at low luminance”, but the sentence was badly written. We corrected this to: “L1 ort rescue flies responded similarly to positive controls at low luminances, rescuing responses to OFF edges at dim backgrounds.”

5) The authors typically use t-tests to analyze experiments with 2 variables (genotype and luminance) and 3 or more conditions per variable. This is not the most appropriate statistical test; typically one would use a two-way ANOVA. At the least, it should be clear whether they are performing corrections for multiple comparisons if performing many t-tests on the same dataset.

Thank you for the suggestion, we now use a two-way ANOVA followed by corrected pairwise comparisons and state this clearly in the figure captions (also addressed above in essential comment #5).

Reviewer #3 (Public Review):

Ketkar et al combine calcium imaging and behavioral experiments to investigate the encoding of luminance and contrast in 3 first-order interneurons in the Drosophila lamina: L1, L2, and L3, as well as the role of these signals in moving ON edge behavior across luminance. The behavioral experiments are well performed. The rescue experiments are particularly interesting. Together with silencing they support and nicely extend previous work showing that L1/2/3 are not simply segregated between ON and OFF pathways. My main issue is the link that the authors make between the cellular responses and the behaviors performed and therefore the overall conclusions and claims of the paper about the roles of contrast vs luminance encoding of each neuron type (particularly L1) in the behaviors.

Major concerns:

1) The authors state that the main behavior they study, namely optomotor response to moving light edges at 100% contrast, is "luminance invariant". A strict definition of this would be that behavioral responses are constant with increasing luminance. However, there are very few plots in this paper where this is the case. In almost all examples, the response is decreasing with respect to increasing luminance. The authors do qualify a "nearly" invariant behavior, but this does not change the fact that interpretation of the data in the context of the framing of the paper is often problematic.

We thank the reviewer for this critical comment. The main point (that we apparently failed to make clear enough) is that there is a clear requirement for a luminance gain. Physiological LMC responses measured using calcium imaging to ON stimuli in Figure 1, or predicted from previous electrophysiological recordings to OFF stimuli in (Ketkar et al., 2020) cannot account for any of the (control) behavioral data. We now edited the text to tone down statements about luminance invariance, and instead highlighted the need for a luminance gain.

2) The manuscript would benefit from clear definitions of luminance and contrast, as well as an explanation of how contrast and luminance sensitivity can be inferred from experiments. In particular, the authors use transient vs. sustained response properties in L1, L2, and L3 as indicators of contrast and luminance sensitivity, but this is not stated clearly. It would be important to explain this to the reader early on.

We now added definitions of general terms to the introduction and added data and analysis to the manuscript (Figure S1, and Figure 2B-D) to more clearly test which component of the neurons’ responses encode contrast or luminance.

3) In the manuscript, it is often stated that "calcium imaging experiments reveal that each first order interneuron is unique in its contrast and luminance encoding properties" (line 110). This was shown clearly for L2 and L3 in their previous work in Ketkar et al. 2020, with a welldesigned two-step stimulus that was able to tease apart contrast vs. luminance invariance. Unfortunately it does not seem that this level of experimental detail and analysis is applied to L1 here. In particular, the authors state " L1 encodes both contrast and luminance in distinct response components." Line 112, in the summary of their findings. I would not agree that the authors have actually shown this properly in this manuscript.

Addressed above, in point 6 of “Essential Revisions”

4) The results as they are stated, are at times not well supported by the data. The manuscript would benefit from a careful assessment of the accuracy and precision of the language used to interpret the data. Sometime just moving some conclusions to the discussion and explaining the assumptions made to reach a particular conclusion would be enough. A few of examples:

We carefully edited the entire manuscript, in addition to addressing the specific points below.

o Figure 2: "Lamina neuron types L1-L3 are differently sensitive to contrast and luminance". It is overall true that from the raw traces, the response are different. However the quantification in C-E only pertains to luminance.

As stated above, we now did further analysis on the contrast encoding properties of L1 and L2 and pointed out the major differences between these neurons (Figure 2B-D).

o Figure 3: "L1 is not required but sufficient for ON behavior across luminance". The data convincingly shows this. I would however point out that the statement "this data [..] highlights its behavioral relevant role of its luminance component" line 231 is an overstatement.

We deleted this statement at the end of the paragraph.

o Figure 6: "L1 luminance signal is required and sufficient for OFF behavior" the data presented shows convincingly that when L1 is inactive the behavior becomes (more) intensity variant. However, it does not show that it is the "luminance signal" in L1 that is required for this effect. In general, because L1 has a sustained and a transient response, it is difficult to strictly implicate one or the other in supporting any behavior, short of manipulating L1 to make it fully transient or fully sustained.

We agree. The figure title now reads “L1 function is required and sufficient for OFF behavior”.

o It is often not clear which conclusions stem from this work and which from their previous work Ketkar et al. 2020, or even other previous work on contrast sensitivity in particular. Clarifying this might help with my concern about statements not well supported by the data in this paper, and also justify their overall novelty. In general the manuscript assumes familiarity with this previous work, which is not always helpful for the reader.

As stated above, we now more clearly separate previous findings from novel findings in the abstract, and throughout the text. We also expanded the introduction to better explain the core concepts that are needed to understand this work, without having read Ketkar et al. 2020.

Author Response

Reviewer #1 (Public Review):

In this paper, Fernandes et al. take advantage of synthetic constructs to test how Bicoid (Bcd) activates its downstream target Hunchback (Hb). They explore synthetic constructs containing only Bcd, Bcd and Hb, and Bcd and Zelda binding sites. They use these to develop theoretical models for how Bcd drives Hb in the early embryo. They show that Hb sites alone are insufficient to drive further Hb expression.

The paper's first half focuses on how well the synthetic constructs replicate the in vivo expression of hb. This approach is generally convincing, and the results are interesting. Consistent with previous work, they show that Bcd alone is sufficient to drive an expression profile that is similar to wild‐type, but the addition of Hb and Zelda are needed to generate precise and rapid formation of the boundaries. The experimental results are supported by modelling. The model does a nice job of encapsulating the key conclusions and clearly adds value to the analysis.

In the second part of the paper, the authors use their synthetic approach to look at how the Hb boundary alters depending on Bcd dosage. This part asks whether the observed Bcd gradient is the same as the activity gradient of Bcd (i.e. the "active" part of Bcd is not a priori the same as the protein gradient). This is a very interesting problem and good the authors have tried to tackle this. However, the strength of their conclusions needs to be substantially tempered as they rely on an overestimation of the Bcd gradient decay length.

Comments:

‐ My major concern regards the conclusions for the final section on the activity gradient. In the Introduction it is stated: "[the Bcd gradient has] an exponential AP gradient with a decay length of L ~ 20% egg‐length (EL)". While this was the initial estimate (Houchmandzadeh et al., Nature 2002), later measurements by the Gregor lab (see Supplementary Material of Liu et al., PNAS 2013) found that "The mean length constant was reduced to 16.5 ± 0.7%EL after corrections for EGFP maturation". The original measurements by Houchmandzadeh et al. had issues with background control, that also led to the longer measured decay length. In later work, Durrieu et al., Mol Sys Biol 2018, found a similar scale for the decay length to Liu et al. Looking at Figure 5, a value of 16.5%EL for the decay length is fully consistent with the activity and protein gradients for Bcd being similar. In short, the strength of the conclusions clearly does not match the known gradient and should be substantially toned down.

The reviewer is right: several studies aiming to quantitatively measure the Bicoid protein gradient ended‐up with quite different decay lengths.

A summary of the various decay lengths measured, and the method used for these measurements is given below:

As indicated, these measurements are quite variable among the different studies and the differences can potentially be attributed to different methods of detection (antibody staining on fixed samples vs fluorescent measurements on live sample) or to the type of protein detected (endogenous Bicoid vs fluorescently tagged).

We agree with the reviewer that given these discrepancies, the exact value of the Bcd protein gradient decay length is not known and that we only have measurements that put it in between 16 and 25 % EL (see the Table above). Therefore, we agree that we should tone down the difference between the protein vs activity gradient and focus on the measurements of the effective activity gradient decay length allowed by our synthetic reporters. This allows us to revisit the measurement of the Hill coefficient of the transcription step‐like response, which is based on the decay‐length for the Bcd protein gradient, and assumed in previous published work to be of 20% EL (Gregor et al., Cell, 2007a; Estrada et al., 2016; Tran et al., PLoS CB, 2018). Importantly, the new Hill coefficient allows us to set the Bcd system within the limits of an equilibrium model.

As mentioned by the reviewer, it is possible that the decay length of the protein gradient measured using antibody staining (Houchmandzadeh et al,, Nature, 2002) was not correct due to background controls. Such measurements were also performed in Xu et al. (2015) which agree with the original measurements (Houchmandzadeh et al., Nature 2002). As indicated in the table above, all the other measurements of the Bcd protein gradient decay length were done using fluorescently tagged Bcd proteins and we cannot exclude the possibility the wt vs tagged protein might have different decay lengths due to potentially different diffusion coefficients or half‐lives. Before drawing any conclusion on the exact value of the endogenous Bcd protein gradient decay length, it is essential to measure it again in conditions that correct for the background issues for immuno‐staining as it was done in Liu et al., PNAS, 2013 for the Bcd‐eGFP protein. In this study, the authors only measured the decay length of the Bcd fusion protein using immuno‐staining for the Bcd protein. Unfortunately, in this study, the authors did not measure again the decay length of the endogenous Bcd protein gradient using immuno‐staining and the same procedure for background control. Therefore, they do not firmly exclude the possibility that the endogenous vs tagged Bcd proteins might have different decay length.

We thank the reviewer for his comment which helped us to clarify the message. In addition, as there is clearly an issue for the measurements of the Bcd protein gradient, we added a section in the SI (Section E) and a Table (Table S4) describing the various decay length measured for the Bcd or the Bcd‐fluorescently tagged protein gradients from previous studies. In the discussion, together with the possibility that there might be a protein vs activity gradient (as we originally proposed and believe is still a valid possibility), we also discuss the alternative possibility proposed by the reviewer which is that the protein vs activity gradients have the same decay lengths but that the decay length of the Bcd protein gradient was potentially not correctly evaluated.

‐ All of the experiments are performed in a background with the hb gene present. Does this impact on the readout, as the synthetic lines are essentially competing with the wild‐type genes? What controls were done to account for this?

We agree with the reviewer that this concern might be particularly relevant at the hb boundary where a nucleus has been shown to only contain ~ 700 Bicoid molecules (Gregor et al., Cell, 2007b). However, ~1000 Bicoid binding regions have been identified by ChIP seq experiments in nc14 embryos (Hannon et al., Elife, 2017) and given that several Bcd binding sites are generally clustered together in a Bcd region, the number of Bcd binding sites in the fly genome is likely larger than 1000. It is much greater than the number of Bicoid binding sites in our synthetic reporters. Therefore, we think that it is unlikely that adding the synthetic reporters (which in the case of B12 only represents at most 1/100 of the Bcd binding sites in the genome) will severely alter the competition for Bcd binding between the other Bcd binding sites in the genome. Additionally, the insertion of a BAC spanning the endogenous hb locus with all its Bcd‐dependent enhancers did not affect (as far as we can tell) the regulation of the wildtype gene (Lucas, Tran et al., 2018).

We have added a sentence concerning this point in the main text (lines 108 to 111).

‐ Further, the activity of the synthetic reporters depends on the location of insertion. Erceg et al. PLoS Genetics 2014 showed that the same synthetic enhancer can have different readout depending on its genomic location. I'm aware that the authors use a landing site that appears to replicate similar hb kinetics, but did they try random insertion or other landing site? In short, how robust are their results to the specific local genome site? This should have been tested, especially given the boldly written conclusions from the work.

This concern of the reviewer has been tested and is addressed Fig S1 where we compare two random insertions of the hb‐P2 transgene (on chromosome II and III; Lucas, Tran et al., 2018) and the insertion at the VK33 landing site that was used for the whole study. As shown Fig. S1, the dynamics of transcription (kymographs) are very similar. In the main text, the reference Fig. S1 is found in the Materials and Methods section (bottom of the 1st paragraph concerning the Drosophila stocks, lines 518).

‐ Related to the above, it's also not obvious that readout is linear ‐ i.e. as more binding sites are added, there could be cooperativity between binding domains. This may have been accounted for in the model but it is not clear to me how.

The reviewer is totally correct. It is clear from our data that readout is not linear: comparing (increase of 1.5 X in the number of BS) B6 with B9 leads to a 4.5 X greater activation rate and this argues against independent activation of transcription by individual bound Bcd TF. There is almost no impact of adding 3 more sites when comparing B9 to B12 (even though it corresponds to an increase of 1.33 X in the number of BS). This issue has been rephrased in the main text (lines 200 to 203) and further developed for the modeling aspects in the SI section C and Figure S3. It is also discussed in the second paragraph of the discussion (lines 380 to 383).

‐ It would be good in the Introduction/Discussion to give a broader perspective on the advantages and disadvantages of the synthetic approach to study gene regulation. The intro only discusses Tran et al. Yet, there is a strong history of using this approach, which has also helped to reveal some of the approaches shortcoming. E.g. Gertz et al. Nature 2009 and Sharon et al. Nature Biotechnology 2012. Again, I may have missed, but from my reading I cannot see any critical analysis of the pros/cons of the synthetic approach in development. This is necessary to give readers a clearer context.

One sentence was added in the introduction concerning this point (lines 79 to 82).

A short review concerning the synthetic approach in development has also been added at the beginning of the discussion (lines 347 to 359).

Reviewer #2 (Public Review):

It is known that Bicoid increases in concentration across the syncytial division cycles, the gradient length scale for Bicoid does not change, and hunchback also increases in concentration during the syncytial cycles but the sharp boundary of the hunchback gradient is constantly seen despite the change in concentration of Bicoid. This manuscript shows that by increasing the Bicoid concentration or by adding Zelda binding sites, the expression of hunchback can be recapitulated to that of a previously studied promoter for hunchback.

I have the following comments to understand the implications of the study in the context of increasing concentrations of Bicoid during the syncytial division cycles:

‐ Bicoid itself is also increasing over the syncytial division cycles, how does this change in concentration of Bicoid affect the activation of the hunchback promoter given the cooperative binding of Bicoid and Bicoid and Zelda as documented by the study?

We thank the reviewer for this remark about the dynamics of the Bcd gradient, which we may have taken for granted. A seminal work on the dynamics of the Bcd gradient using fluorescent‐tagged Bcd (Gregor et al, Cell, 2007a) has shown that the gradient of Bcd nuclear concentration (this nuclear concentration is the one that matter for transcription) remains stable over nuclear cycles, despite a global increase of Bcd amount in the embryo. This can be explained by the fact that Bcd molecules are imported in the nuclei and that the number of nuclei double at every cycle, such that both processes compensate each other. Thus, we assumed that the gradient of Bcd nuclear concentration was stable over nc11 to nc13.

We have clarified this assumption in the model section in the manuscript (lines 165‐168).

Supporting our assumption, when looking at the transcription dynamics regulated by Bcd, in Lucas et al, PLoS Gen, 2018, we observed very reproducible expression pattern dynamics of the hb‐P2 reporter at each cycle nc11 to nc13. Such reproducibility in the pattern dynamics were also observed in this current work for hb‐P2, B6, B9, B12 and H6B6 reporters (Fig. S6A). Also, in Lucas et al, PLoS Gen, 2018, the shift in the established boundary positions of hb‐P2 reporter between nc11 to nc13 is ~2%EL (approximately a nucleus length ~10μm) and it is thus marginal.

In addition, as mentioned in the text (lines 105 to 107), we only focused our analysis on nc13 data which are statistically stronger given the higher number of nuclei analyzed. Thus, any change of Bcd nuclear concentration that would happen over nuclear cycles will not matter.

Concerning Zelda: Zelda’s transcriptional activity when measured on a reporter with only 6 Zld binding sites changes drastically over the nuclear cycles, with strong activity at nc11 and much weaker activity at nc13 (Fig S4A). This indicates that the changes in expression pattern dynamics of Z2B6 from nc11 to nc13 are caused predominantly by decreasing Zelda activity: the effect of Zld on the Z2B6 promoter is very strong during nc11 and nc12. It is also very strong at the beginning of nc13 (even though the Z6 reporter is almost silent) and became a bit weaker in the second part of nc13 (Fig S4B‐D).

‐ Does the change in concentration of Bicoid across the nuclear cycles shift the gradient similar to the change in numbers of Bicoid binding sites?

In both Lucas et al, PLoS Gen, 2018 and in this work (Fig. 1, Fig. 3 and Fig. S6A), we found that the positions of the expression boundary are very reproducible and stable in time for hb‐P2, B6, B9, B12, H6B6 during the interphase of nc12 to 13. For hb‐P2, the averaged shift of the established boundary position in nc11, 12 and 13 is within 2 %EL. This averaged shift between the cycles is of similar magnitude to the difference caused by embryo‐to‐embryo variability within nc13 (~2 %EL) (Gregor et al, Cell, 2007b, Lucas et al, PloS Gen, 2018). This shift is much smaller than the difference between the expression boundary positions of B6 and B9 (~ 8 % EL) and between B6 and Z2B6 (~17.5 %EL) in nc13.

For these reasons, we conclude that the difference between the expression patterns of B6, B9 and Z2B6 are caused predominantly by changing the TF binding site configurations of the reporters, rather than variability in the Bcd gradient.

The assumption of gradient stability has been clarified in the previous answer and in the manuscript (lines 165‐168).

‐ The intensity is a little higher for B9 and B12 at the anterior in 2B? Is this statistically different? is this likely to change the amount of Bicoid expression at the locus and lead to more robust activation?

We performed statistical tests to distinguish the spot intensities at the anterior pole for every pair of reporters in Fig. 2B (hb‐P2, B6, B9 and B12). All p‐values from pair‐wise KS tests are greater than 0.067, suggesting that the spot intensities at the anterior pole are not distinguishable between these reporters.

We have clarified this in the manuscript (line 157).

‐Are the fraction of active loci not changing across the syncytial cycles when the concentration of Bicoid also changes and consistent with the synthetic promoters?

To measure the reproducibility of the expression pattern dynamics in different nuclear cycles, we compared the boundary position of the fraction of active loci pattern as a function of time for all hbP2 and synthetic reporters (Fig. S6A). In this figure panel, for all reporters except Z2B6, the curves in nc12 and nc13 largely overlap, suggesting high reproducibility in the pattern dynamics between cycles and consequently low sensitivity to the subtle variation in the Bcd nuclear concentration gradient between the cycles.

For Z2B6, we attributed the difference in pattern dynamics between nc12 and nc13 to the changes in Zelda activity, as validated independently with a synthetic reporter with only 6 Zld binding sites (Fig. S4A).

‐How do the numbers of Hb BS change the expression of Hb? H6B6 has 6 Hb BS whereas the Hb‐P2 has 1? Are more controls needed to compare these 2 contexts?

As our goal was to determine to which mechanistic step of our model each TF (Bcd, Hb, Zld) contributed, we added BS numbers that are much higher than in the hb‐P2 promoter. The added number of Hb BS remains very low when compared to total number of Hb binding sites in the entire genome (Karplan et al, PLOS Gen, 2011), therefore, it is very unlikely to affect the endogenous expression of Hb protein.

We clarified this in the manuscript (lines 211 to 212).

Does Zelda concentration change across the syncytial division cycles? How does the change in concentration in the natural context affect the promoter activation of Hb?

Zelda concentration is stable over the nuclear cycles, as observed with the fluorescently‐tagged Zld protein (Dufourt et al., Nat Com, 2018). However, Zelda’s transcriptional activity when measured on a reporter with only 6 Zld binding sites changes drastically over the nuclear cycles, with strong activity at nc11 and much weaker activity at nc13 (Fig S4A, this work).

The impact of this change in Zld activity can be observed with the Z2B6 promoter, with the expression boundary moving from the posterior region toward the anterior region over the nuclear cycles (Fig. S4B‐D). However, we don’t detect any changes in the expression pattern dynamics of hb‐P2 over the nuclear cycles (Fig. S6A and in Lucas et al., PLoS Gen, 2018).

We have clarified this in lines 250‐251 of the main manuscript.

‐Changing the dose of Bicoid shifts the boundary of hunchback expression. It would be nice to model or test this in the context of varing doses of zelda or even reason this with respect to varying doses of zelda across the syncytial division cycles.

We thank the reviewer for this insight. Concerning Zelda, we did not perform any experiment reducing the amount of Zelda in the embryo. However, in a previous study (Lucas et al., PLoS Genetics, 2018), we observed that the boundary of hb was shifted towards the anterior when decreasing the amount of Zelda consistent to the fact that the dose of Zelda is critical to set the boundary position and the threshold of Bcd concentration required for activation. However, as Zelda is distributed homogeneously along the AP axis, it cannot bring per se positional information to the system.

Reviewer #3 (Public Review):

I think the framing could be improved to better reflect the contribution of the work. From the abstract, for example, it's unclear to me what the authors think is the most meaningful conclusion. Is it the observations about the finer details of TF regulation (bursting dynamics), the fact that Bcd is probably the sole source of "positional information" for hb‐p2, that Bcd exists in active/inactive form, or the fact that an equilibrium model probably suffices to explain what we observe? The first sentence itself seems to suggest this paper will discuss "dynamic positional information", in which case it's somewhat misleading to say this kind of work is "largely unexplored"; Johannes Jaeger in particular has been a strong proponent of this view since at least 2004. On that note some particularly relevant recent papers in the Drosophila early embryo include:

1) Jaeger and Verd (2020) Curr Topics Dev Biol

2) Verd et al. (2017) PLoS Comp Biol

3) Huang, Amourda, et al. and Saunders (2017) eLife

4) Yang, Zhu, et al. (2020) eLife [see also the second half of Perkins (2021) PLoS Comp Biol for further discussion of that model]

‐Some reviews from James Briscoe also discuss this perspective.

We agree with the reviewer that the phrasing of the abstract was not clear enough to emphasize the contribution of the work and we are also sorry if it suggested that the dynamic positional information is largely unexplored because this was not at all our intention.

We rephrased the abstract aiming to better highlight the most meaningful conclusions.

‐I would also recommend modifying the title to reflect the biology found in the new results.

We modified the title to better reflect the new results:<br /> “Synthetic reconstruction of the hunchback promoter specifies the role of Bicoid, Zelda and Hunchback in the dynamics of its transcription”

‐A major point that the authors should address is the design of the synthetic constructs. From table S1, the sites are often very closely linked (4‐7 base pairs). From the footprint of these proteins, we know they can cover DNA across this size (see, https://pubmed.ncbi.nlm.nih.gov/8620846/). As such, there may be direct competition/steric hindrance (see https://pubmed.ncbi.nlm.nih.gov/28052257/). What impact does this have on their interpretations? Note also that the native enhancer has spaced sites with variable identities.

We completely agree with the reviewer comment in the sense that we named our reporters according to the number (N) of Bcd binding sites sequences that they contain, even though we cannot prove definitively that they can effectively be bound simultaneously by N Bcd molecules. It is thus possible that B9 is not a B9 but an effective B6 (i.e. B9 can only be bound simultaneously by 6 molecules) if, for instance, the binding of a Bcd molecule to one site would prevent by the binding of another Bcd molecule to a nearby site (as proposed by the reviewer in the case of direct competition or steric hindrance).

Even though we cannot exclude this possibility, we think that our use of B6, B9, B12, in reference to the 6 Bcd BS of hb‐P2 promoter, is relevant for several reasons : i) some of the Bcd BS in the hb‐P2 promoter are also very close from each other (see Table S1); ii) the design of the synthetic construct was made by multimerizing a series of 3 strong Bcd binding sites with a similar spacing as found for the closest sites in the hb‐P2 promoter (as shown in Figure 1A and Table S1); iii) the binding of the Bicoid protein has been shown in foot printing experiments in vitro to be more efficient on sites of the hb‐P2 promoter that are close from each other, and this has even been interpreted as binding cooperativity (Ma et al., 1996); iv) even though these experiments were not performed with full‐length proteins, two molecules of the paired homeodomain (from the same family of DNA binding domain as Bcd) are able to simultaneously bind to two binding sites separated by only 2 base pairs. This binding to very close sites is even cooperative while when the two sites are distant by 5 base pairs or more, the simultaneous binding to the two sites occurs without cooperativity (Wilson et al., 1993).

Conversely, as it is very difficult to demonstrate that 9 Bcd molecules can effectively bind to our B9 promoter, it is very difficult to know exactly how many binding sites for Bcd the hb‐P2 contains, and a large debate concerning not only the number but also the identity of the Bcd sites in the hb promoter is still ongoing (Park et al., 2019; Ling et al., 2019).

As we cannot exclude the possibility that B9 is an effective B6, it remains possible that B9 and hb‐P2 (which is supposed to only contains 6 sites) have the same number of effective Bcd binding site and this could explain why the two reporters have very similar transcription dynamics and features.

Regarding other interpretations in the manuscript, we identified two other aspects that will be affected if our synthetic reporters have fewer effective sites than the number of sites they carry. The first one concerns the synergy, as the increase in the number of sites of 1.5 from B6 to B9 might be over‐estimated but this would even increase the synergistic effect given the 4.5 difference in activity of the two reporters (Fig. S3). The second one concerns the discussion on the Hill coefficient and the decay length where the effective number of binding sites (N) is required to determine the limit of concentration sensing (Fig. 5). This would particularly be important for the hb‐P2 promoter.

Except for these specific points, we don’t think that the possibility that reporters do not exactly contain as many as effective binding sites than proposed, has a huge impact on our interpretations and the general message conveyed in this manuscript. Most importantly, it is very clear that our B6 and B9 reporters differ only by three Bcd binding sites and have yet very distinct expression dynamics: while B9 recapitulates almost all transcription features of hb‐P2, B6 is far from achieving it. Similarly, H6B6 and Z2B6 have very different transcription features than B6 and these differences have been key for understanding the mechanistic functions of the three TF we studied.

This discussion has been added to the discussion (lines 400 to 414)

Reviewer #3 (Public Review):

I think the framing could be improved to better reflect the contribution of the work. From the abstract, for example, it's unclear to me what the authors think is the most meaningful conclusion. Is it the observations about the finer details of TF regulation (bursting dynamics), the fact that Bcd is probably the sole source of "positional information" for hb-p2, that Bcd exists in active/inactive form, or the fact that an equilibrium model probably suffices to explain what we observe? The first sentence itself seems to suggest this paper will discuss "dynamic positional information", in which case it's somewhat misleading to say this kind of work is "largely unexplored"; Johannes Jaeger in particular has been a strong proponent of this view since at least 2004. On that note some particularly relevant recent papers in the Drosophila early embryo include:<br /> 1) Jaeger and Verd (2020) Curr Topics Dev Biol<br /> 2) Verd et al. (2017) PLoS Comp Biol<br /> 3) Huang, Amourda, et al. and Saunders (2017) eLife<br /> 4) Yang, Zhu, et al. (2020) eLife [see also the second half of Perkins (2021) PLoS Comp Biol for further discussion of that model]<br /> Some reviews from James Briscoe also discuss this perspective.

I would also recommend modifying the title to reflect the biology found in the new results.

A major point that the authors should address is the design of the synthetic constructs. From table S1, the sites are often very closely linked (4-7 base pairs). From the footprint of these proteins, we know they can cover DNA across this size (see, https://pubmed.ncbi.nlm.nih.gov/8620846/). As such, there may be direct competition/steric hindrance (see https://pubmed.ncbi.nlm.nih.gov/28052257/). What impact does this have on their interpretations? Note also that the native enhancer has spaced sites with variable identities.

Author Response:

Evaluation Summary:

This paper will be of interest to researchers who perform single-molecule fluorescence imaging experiments as well as those who want to include machine learning in their data analyses. The authors have developed a machine learning algorithm that addresses some of the data analysis challenges in the field of single-molecule fluorescence imaging. The methods are rigorously benchmarked using simulated data and tested using real data. There are some concerns whether Tapqir is general enough for use by the broader community of single-molecule fluorescence researchers.

We thank the reviewers for their thorough review of the manuscript. In response to the reviewer comments, we posted to bioRxiv a revised manuscript with new data and edits to text. Concerns about generality are addressed in the revised manuscript and in the responses to specific reviewer comments below.

Reviewer #1 (Public Review):

"Bayesian machine learning analysis of single-molecule fluorescence colocalization images" by Ordabayev, et al. reports the development, benchmarking, and testing of a Bayesian machine learning-based method, which the authors name Tapqir, for analyzing single-molecule fluorescence colocalization data. Unlike currently available, more conventional analysis methods, Tapqir attempts to holistically model the microscopy images that are recorded during a colocalization experiment. Tapir uses a physics-based, global model with parameters describing all of the features of the experiment that are expected to contribute to the recorded microscopy images, including shot noise of the spots and background, camera noise, size and shape of the spots, and specific- and non-specific binders. Based on benchmarking on simulated data with widely varying properties (e.g., signal-to-noise; amounts, rates, and locations of specific and non-specific binders; etc.), Tapqir generally does as well and, in some cases, better than currently existing methods. The authors also test Tapqir on real microscopy images with similarly varying properties from studies that have been previously published by their research group and demonstrate that their Tapqir-based analysis is able to faithfully reproduce the previously published results, which were obtained using the more conventional analysis methods available at the time the data were originally published. This is a well-designed and executed study, Tapqir represents a conceptual and practical advance in the analysis of single-molecule fluorescence colocalization experiments, and its performance has been comprehensively and rigorously benchmarked on simulated data and tested on real data. The conclusions of this study are well supported by the data, but some of the limitations of the method need to be clarified and discussed in more depth, as outlined below.

1. Given that the AOI is centered at the target molecule and there is a strong prior for the binder also being located at the center of the AOI, the performance of Tapqir is dependent on several variables of the microscopy/optical system (e.g., the microscope point-spread function, magnification, accurate alignment of target and binder imaging channels, accurate drift correction, etc.). Although this caveat is mentioned and some of these factors are listed in the main text of the manuscript, the authors could have expanded this discussion in order to clarify the extent to which the performance of Tapqir depends on these factors.

We added relevant new data to the revised manuscript in Table 5. The question about alignment accuracy is now discussed in the Materials and Methods:

“Tests on data simulated with increasing proximity parameter values σxy (true) (i.e., with decreasing precision of spatial mapping between the binder and target image channels) confirm that the cosmos model accurately learns σxy (fit) from the data (Figure3–Figure Supplement 3D; Table 5). This was the case even if we substituted a less-informative σxy prior (Uniform vs. Exponential; Table 5).

The CoSMoS technique is premised on colocalization of the binder spots with the known location of the target molecule. Consequently, for any CoSMoS analysis method, classification accuracy will in general decline when the images in the target and binder channels are less accurately mapped. However, for the Tapqir cosmos model, low mapping precision has little effect on classification accuracy at typical non-specific binding densities (λ = 0.15; see MCC values in Table 5).”

The more general point about priors is now addressed in the Materials and Methods as follows:

“All simulated and experimental data sets in this work were analyzed using the prior distributions and hyperparameter values given above, which are compatible with a broad range of experimental conditions (Table 1). Many of the priors are uninformative and we anticipate that these will work well with images taken on variety of microscope hardware. However, it is possible that highly atypical microscope designs (e.g., those with effective magnifications that are sub-optimal for CoSMoS) might require adjustment of some fixed hyperparameters and distributions (those in Eqs. 6a, 6b, 11, 12, 13, 15, and 16). For example, if the microscope point spread function is more than 2 pixels wide, it may be necessary to increase the range of the w prior in Eq. 13. The Tapqir documentation (https://tapqir.readthedocs.io/en/stable/) gives instructions for changing the hyperparameters.”

1. The Tapqir model has many parameters, each with its own prior. The majority of these priors are designed to be uninformative and/or weak and the only very strong prior is the probability that a specific binder is located at or very near the center of the AOI. The authors could have tested and commented on how the strength of the prior on the location of a specific binder affects the performance of Tapqir.

The revised manuscript includes new data on and expanded discussion of this point. In our model, the position of a target-specific spot relative to the target position has a prior distribution illustrated as the green curve in Figure 2-Figure supplement 2. Importantly, the peak in this distribution does not have an a priori set width. Instead, the width of the peak is a model hyperparameter, σxy, that is learned from the image data set without user intervention. To make sure that this point is understood, we expanded and clarified the relevant Methods section and modified the legend of Figure 2-Figure supplement 2.

To address the reviewers’ specific question, we constructed simulated data sets with different mapping precision values and analyzed them; the results are presented in the (new) Table 5 and discussed:

“The CoSMoS technique is premised on colocalization of the binder spots with the known location of the target molecule. Consequently, for any analysis method, classification accuracy declines when the images in the target and binder channels are less accurately mapped. For the Tapqir cosmos model, low mapping precision has little effect on classification accuracy at typical non-specific binding densities (λ = 0.15; see MCC values in Table 5).”

1. Given the priors and variational parameters they report, the authors show that Tapqir performs robustly and seems to require no experiment-to-experiment optimization. This is expected to be the case for the simulated data, since they were simulated using the same model that Tapqir uses to perform the analysis. With regard to the real data, however, it is quite likely that this is due to the fact that the analyzed data all come from the same laboratory and, therefore, likely the same microscope(s). It would have therefore been very useful if the authors would have listed and discussed which microscope settings, experimental conditions, and/or other considerations, beyond those described in point 1 above, would result in a need for re-optimization of the priors and/or variational parameters.

As noted above, we now address this point in the Materials and Methods as follows:

“All simulated and experimental data sets in this work were analyzed using the prior distributions and hyperparameter values given above, which are compatible with a broad range of experimental conditions (Table 1). Many of the priors are uninformative and we anticipate that these will work well with images taken on variety of microscope hardware. However, it is possible that highly atypical microscope designs (e.g., those with effective magnifications that are sub-optimal for CoSMoS) might require adjustment of some fixed hyperparameters and distributions (those in Eqs. 6a, 6b, 11, 12, 13, 15, and 16). For example, if the microscope point spread function is more than 2 pixels wide, it may be necessary to increase the range of the w prior in Eq. 13. The Tapqir documentation (https://tapqir.readthedocs.io/en/stable/) gives instructions for changing the hyperparameters.”

1. Based on analysis of the simulated data shown in Figure 5, where the ground truth is known, the use of Tapqir to infer kinetics is less accurate that the use of Tapqir to infer equilibrium binding constants. The authors do a great job of discussing possible reasons for this. In the case of the real data analyzed in Figure 6 and in Figure 6 - Figure Supplements 1 and 2, the kinetic results obtained using Tapqir have different means and generally larger error bars than those obtained using Spot-Picker. To more comprehensively assess the performance of Tapqir versus Spot-Picker, the authors could have used the association and dissociation rates to calculate the corresponding equilibrium binding constants and then compared these kinetically calculated equilibrium binding constants to the population-calculated equilibrium binding constants that the authors calculate and report in the bottom plot in Panel D of Figure 6 and Figure 6 - Figure Supplements 1 and 2. This would provide some information on the accuracy of the kinetics in that the closer the kinetically and population-calculated equilibrium binding constants are to each other, the more accurately the kinetics have been estimated. Performing this type of analysis for the kinetics obtained using Tapqir and Spot-Picker would have allowed a more comprehensive comparison of the two methods.

This comment seems to reflect a misunderstanding. Fig. 6 and its figure supplements do not report any dissociation kinetics or binding equilibrium constants. Instead, they report ka (pseudo first-order target-specific association rate constant), kns (pseudo first-order target non-specific association rate constant), and Af (the active faction, i.e., the fraction of target molecules capable of association with binder). ka and Af values from the two methods agree within experimental uncertainty for all four data sets analyzed. kns values differ, but as we point out:

“We noted some differences between the two methods in the non-specific association rate constants kns. Differences are expected because these parameters are defined differently in the different non-specific binding models used in Tapqir and spot-picker (see Materials and Methods).”

(There is additional discussion of this point in Materials and Methods). The reviewer is correct that the estimated uncertainties (i.e., error bars in panels D) in ka and Af are generally larger for Tapqir than for spot-picker. This is expected, for the reasons that we explain:

“In general, previous approaches in essence assume that spot classifications are correct, and thus the uncertainties in the derived molecular properties (e.g., equilibrium constants) are systematically underestimated because the errors in spot classification, which can be large, are not accounted for. By performing a probabilistic spot classification, Tapqir enables reliable inference of molecular properties, such as thermodynamic and kinetic parameters, and allows statistically well-justified estimation of parameter uncertainties. This more inclusive error estimation likely accounts for the generally larger kinetic parameter error bars obtained from Tapqir compared to those from the existing spot-picker analysis method (Figure 6, Figure 6–Figure Supplement 1, Figure 6–Figure Supplement 2, and Figure 6–Figure Supplement 3). ”

Reviewer #2 (Public Review):

The work by Ordabayev et al. details a Bayesian inference-based data analysis method for colocalization single molecule spectroscopy (CoSMoS) experiments used to investigate biochemical and biophysical mechanisms. By using this probabilistic framework, their method is able to quantify the colocalization probabilities for individual molecules while accounting for the uncertainty in individual binding events, and accounting for camera and optical noise and even non-specific binding. The software implementation of this method, called Tapqir, uses a Python-based probabilistic programming language (PPL) called pyro to automate and speed-up the optimization of a variational Bayes approximation to the posterior probability distribution. Overall, Tapqir is a powerful new way to analyze CoSMoS data.

Tapqir works by analyzing small regions (14x14 pixels) of fluorescence microscopy images surrounding previously identified areas of interest (AOI). The collection of images of these AOIs through time are then analyzed collectively using a probabilistic model that accounts for each time frame of each AOI and is able to determine whether up to K "binders" (K=2 here) are present and which of them is specifically bound. This approach of directly modeling the contents of the image data is relatively novel, and few other examples exist. The details of the probabilistic model used incorporate an impressive amount of physical insight (e.g., camera gain) without overparameterization.

We thank the reviewer for these positive comments.

The gamma-distributed noise model used in Tapqir captures quite a lot of physics and, given the analyses in Figs. 3-6, clearly works, but might be limited to certain types of cameras used in the fluorescence microscopy (e.g., EMCCDs). For instance, sCMOS cameras have pixel-dependent amplification and noise profiles, rather than a single gain parameter, and are sometimes approximately modeled as normal distributions with both mean and variance having an intensity-dependent and independent contribution that is different for each pixel on the camera. It is unclear how Tapqir performs on different cameras.

In the revised manuscript, we expanded the discussion of the Image likelihood component of our model to emphasize that 1) all data sets we analyze are experimental or simulated EMCCD images, 2) sCMOS images have the different noise characteristics alluded to by the reviewer, and 3) optimal sCMOS image analysis might require a modified model, possibly including the ability to use per-pixel calibration data as a prior as was done in super-resolution work (now cited) that uses sCMOS data.

sCMOS cameras have in recent years become very popular for some kinds of single-molecule imaging (e.g., PALM/STORM or live-cell single-particle tracking). However, for the low-background/low-signal in vitro single-molecule TIRF that is our target application for the approach described in the manuscript, EMCCD is still preferable over sCMOS for many, but not all, imaging conditions (see https://andor.oxinst.com/learning/view/article/what-is-the-best-detector-for-single-molecule-studies). Thus, we think there will be plenty of interest in the approach we describe in the manuscript even if (which is not certain) the program functions better with EMCCD than with sCMOS images.

Going forward to develop and test an sCMOS-targeted version of the model, as we have done for EMCCD, will require revised model and code, but will also necessitate accurately simulating sCMOS CoSMoS images, obtaining experimental sCMOS CoSMoS images reflecting a broad range of realistic experimental conditions, and using the simulated and experimental images to test the new model. These may well be useful things to do in the future but would be a considerable step beyond the scope of the present manuscript.

The variational Bayes solution used by Tapqir provides many computational benefits, such as numerical tractability using pyro and speed. It is possible that the exact posterior, e.g., as obtained using a Markov chain Monte Carlo method, would be insignificantly different with the amount of data typical for CoSMoS experiments; however, this difference is not explored in the current work.

We agree. However, since we have not done any analyses using MCMC, there is nothing in particular that we can say about it in the context of CoSMoS data analysis. Implementation of an MCMC approach using our model will be easier in the future because the Pyro developers are currently working to optimize the implementations of MCMC methods in their software.

The intrinsic use of prior probability distributions in any Bayesian inference algorithm is extremely powerful, and in Tapqir offers the opportunity to "chain together" subsequent analyses by using the marginalized posteriors from one experiment as the basis for the priors for subsequent experiments (e.g., in \sigma^{xy}) for extremely high accuracy inference. While the manuscript discusses setting and leveraging the power of priors, it does not explore the power of such "chaining" and the positive effects upon accuracy.

Chaining is beneficial in principle. However, in practice it will help significantly only if the uncertainty in the posterior parameter values from the non-chained analysis is larger than the experiment-to-experiment variability in the “true” parameter values. For σxy we obtain very narrow credence intervals without chaining (Table 1). In our judgement, these are unlikely to be made more accurate by using prior information from another experiment where such factors as microscope focus adjustment may be slightly different.

A significant number of CoSMoS experiments use multiple, distinct color fluorophores to probe the colocalization of different species to the target. The current work focuses only upon analyzing data with a single color-channel. Extensions to multiple independent wavelengths are computationally trivial, given the automated variational inference ability of PPLs such as pyro, and would increase the impact of the work in the field.

Our current approach can be used to analyze multi-channel data simply by analyzing each channel independently. However, we agree that there would be advantages to joint analysis of multiple wavelength channels (especially if there is crosstalk between channels) and that implementing multi-channel analysis is a logical extension of our study. It is straightforward (though not trivial, in our experience) to implement such multi-wavelength models. However, testing the functioning of candidate models and validating them using simulation and experimental data would require extensive work that in our view goes beyond what is reasonable to include in the present manuscript.

Tapqir analysis provides time series of the probability of a specific binding event, p(specific), for each target analyzed (c.f., Fig. 5B), and kinetic parameters are extracted from these time series using secondary analyses that are distinct from Tapqir itself.

The method reported here is well designed, sound, and its utility is well supported by the analyses of simulated and experimental data sets reported here. Tapqir is a cutting-edge image analysis approach, and its proper treatment of the uncertainty inherent to CoSMoS experiments will certainly make an impact upon the analysis of CoSMoS data. However, many of the (necessary) assumptions about the data (e.g., fluorescence microscopy) and desired information (e.g., off-target vs on-target binding) are quite specific to CoSMoS experiments and therefore limit the direct applicability of Tapqir for the analysis of other single-molecule microscopy techniques. With that in mind, the direct Bayesian inference-based analysis of image data, as opposed to integrated time series, as demonstrated here is very powerful, and may encourage and inspire related methods to be developed.

Our approach is a powerful way to analyze CoSMoS data in part because it is specific to CoSMoS – it is premised on a physics-based model that incorporates known features of CoSMoS experiments. We agree that the general approach could be adapted to other image analysis applications.

Reviewer #3 (Public Review):

In this manuscript, the authors seek to improve the reproducibility and eliminate sources of bias in the analysis of single molecule colocalization fluorescence data. These types of data (i.e., CoSMoS data) have been obtained from a number of diverse biological systems and represent unique challenges for data analysis in comparison with smFRET. A key source of bias is what constitutes a binding event and if those events are colocalized or not with a surface-tethered molecule of interest. To solve these issues, the authors propose a Bayesian-based method in which each image is analyzed individually and locally around areas of interest (AOIs) identified from the surface tethered molecules. A strength of the research is that the approach eliminates many sources of bias (i.e., thresholding) in analysis, models realistic image features (noise), can be automated and carried out by novice users "hands-free", and returns a probability score for each event. The performance of the method is superb under a number of conditions and with varying levels of signal-to-noise. The analysis on a GPU is fairly quick-overnight-in comparison with by-hand analysis of the traces which can take days or longer. Tapqir has the potential to be the go-to software package for analysis of single molecule colocalization data.

The weaknesses of this work involve concerns about the approach and its usefulness to the single-molecule community at large as wells as a lack of information about how users implement and use the Tapqir software. For the first item, there are a number of common scenarios encountered in colocalization analysis that may exclude use of Tapqir including use of CMOS rather than EM-CCD cameras, significant numbers of tethered molecules on the surface that are dark/non-fluorescent, a high density/overlapping of AOIs, and cases where event intensity information is critical (i.e., FRET detection or sequential binding and simultaneous occupancy of multiple fluorescent molecules at the same AOI). In its current form, the use of Tapqir may be limited to only certain scenarios with data acquired by certain types of instruments.

In the following paragraphs, we address 1) concerns about application to CMOS, 2) dark target molecules, 3) overlapping AOIs, and 4) application to methods (e.g., smFRET) that require extraction of both colocalization and intensity data.

1) Application to CMOS images.

In the revised manuscript, we expanded the discussion of the Image likelihood component of our model to emphasize that 1) all data sets we analyze are experimental or simulated EMCCD images, 2) sCMOS images have the different noise characteristics alluded to by the reviewer, and 3) optimal sCMOS image analysis might require a modified model, possibly including the ability to use per-pixel calibration data as a prior as was done in super-resolution work (now cited) that uses sCMOS data.

sCMOS cameras have in recent years become very popular for some kinds of single-molecule imaging (e.g., PALM/STORM or live-cell single-particle tracking). However, for the low-background/low-signal in vitro single-molecule TIRF that is our target application for the approach described in the manuscript, EMCCD is still preferable over sCMOS for many, but not all, imaging conditions (see https://andor.oxinst.com/learning/view/article/what-is-the-best-detector-for-single-molecule-studies). Thus, we think there will be plenty of interest in the approach we describe in the manuscript even if (which is not certain) the program functions better with EMCCD than with sCMOS images.

Going forward to develop and test an sCMOS-targeted version of the model, as we have done for EMCCD, will require revised model and code, but will also necessitate accurately simulating sCMOS CoSMoS images, obtaining experimental sCMOS CoSMoS images reflecting a broad range of realistic experimental conditions, and using the simulated and experimental images to test the new model. These may well be useful things to do in the future but would be a considerable step beyond the scope of the present manuscript.

2) Dark target molecules.

In their detailed comments, the reviewers suggested a “no target molecules in sample” (NTIS) control instead of the “no fluorescent target molecules in control AOIs” (NFTICA) design that we illustrate in Fig. 1. Both types can be used as a Tapqir control dataset without any modification of the program or model. We have edited the Fig. 1 caption to explain that either type is acceptable. The reviewers are correct that, all else being equal, NTIS may be better if the target molecules are incompletely labeled. However, in practice experimenters usually know the fraction of molecules that are labeled and reduce the fluorescent target molecule surface density to hold the fraction of spots with two or more coincident target molecules (fluorescent or not) below a chosen threshold (typically 1 % or less), negating the possible advantage of NTIS (but at the expense of collecting less data per sample). On the other hand, NFTICA has the practical advantage that it is a control internal to the sample and is thus immune to problems caused by temporal or sample-to-sample variability (e.g., of surface properties).

3) Overlapping AOIs.

The method does not require non-overlapping AOIs – we used partially overlapping AOIs in the experimental data analyzed in the manuscript. Even though our analysis used larger AOI sizes (and hence, more overlap) than the spot-picker method, there was good agreement in the results, indicating that overlap does not cause any undue problems.

In the revised manuscript Results section we added the following discussion of the effect of AOI size:

“Since target-nonspecific spots are built into the cosmos model, there is no need to choose excessively small AOIs in an attempt to exclude non-specific spots from analysis. We found that reducing AOI size (from 14 x 14 to 6 x 6 pixels) did not appreciably affect analysis accuracy on simulated data (Table 2). In analysis of experimental data, smaller AOI sizes caused occasional changes in calculated p(specific) values reflecting apparent missed detection of a few spots (Figure 3–Figure supplement 4). Out of caution, we therefore used 14 x 14 pixel AOIs routinely, even though the larger AOIs somewhat reduced computation speed (Table 2 and Figure 3–Figure Supplement 4).”

4) Methods requiring extraction of intensity data.

The cosmos model we describe in the manuscript does not incorporate phenomena where the spot intensity at a single target changes, such as when there is FRET or multiple binders. As we point out in the final paragraph of the Discussion, more elaborate versions of the cosmos model that incorporate these phenomena could be developed. This would entail implementation, optimization, and validation with simulations and real data of the new model, which is beyond the scope of the present manuscript.

Second, for adoption by non-expert users information is missing in the main text about practical aspects of using the Tapqir software including a description of inputs/outputs, the GUI (I believe Taqpir runs at the command line but the output is in a GUI), and if Tapqir integrates the kinetic modeling or not.

This information is given in the online Tapqir documentation. The kinetic analysis (as in Fig. 6) is a simple Python script that is run after Tapqir; the instructions for using it are included in the documentation. Tapqir runs can be initiated using either a CLI or GUI. Output can be viewed in Tensorboard, in a Tapqir GUI, and/or passed to a Jupyter notebook or Python script for further analysis, plotting, etc.

Given that a competing approach has already been published by the Grunwald lab, it would be useful to compare these methods directly in both their accuracy, usefulness of the outputs, and calculation times.

The reviewer does not explain why comparing with the Grunwald method would be preferable to the comparison with spot-picker that is included in the manuscript. To be sure there is no misunderstanding, the following are the same for the two methods and therefore are not reasons to prefer one or the other of these methods for the comparison in Fig. 6 (see also Discussion):

1) Like Tapqir, both spot-picker and Grunwald methods analyze 2-D images, not integrated intensities.

2) Unlike Tapqir, neither spot-picker nor Grunwald is fully objective; both require subjective selection of classification thresholds by the analyst in order to tune the algorithm performance for analysis of a particular dataset.

3) Neither spot-picker nor Grunwald is a Bayesian method. “Bayesian” in the Grunwald paper title refers to their excellent work on a separate analytical method (described in the same paper) for evaluating the number of binder molecules colocalized with a target spot; this method is not relevant to a comparison with the model presented in our manuscript.

4) Unlike Tapqir, neither spot-picker nor Grunwald estimate classification probabilities. Instead, they simply assign binary spot/no-spot classifications that do not convey to downstream analyses the extent of uncertainty in each classification.

5) Neither spot-picker nor Grunwald has been validated previously using simulated image data. Consequently, the validity of image classification has not been established for either.

The comparison of Fig. 6 and supplements does not claim to and is not intended to show that Tapqir is better than spot-picker for real experimental data; we cannot make such a claim for these or any other methods because we do not know the true kinetic process and rate constants that generated the experimental data. Instead, our comparison uses experimental data sets with a broad range of characteristics (Table 1) to show that Tapqir yields similar association rate constants to those produced by spot-picker even though the former is objective and automatic while the latter requires subjective tuning by an analyst. Our choice to use spot-picker over Grunwald for this comparison was dictated by the fact that among the co-authors we have such an expert in the use of spot-picker, whereas we lack comparable expertise with Grunwald. We have little doubt that Grunwald would also produce results similar to the other methods in the hands of an expert user who is able to subjectively adjust classification parameters.

Along these lines, the utility of calculating event probability statistics (Fig. 6A) is not well fleshed-out. This is a key distinguishing feature between Tapqir and methods previously published by Grunwald et al. In the case of Tapqir, the probability outputs are not used to their fullest in the determination of kinetic parameters. Rather a subjective probability threshold is chosen for what events to include. This may introduce bias and degrade the objective Tapqir pipeline used to identify these same events.

This comment reflects a misunderstanding. No probability threshold is used in the kinetic analyses (Figs. 5 and 6). Instead, we make full use of the p(specific) probability output using the posterior sampling strategy that is illustrated in Fig. 5B and is described in the Results and in Materials and Methods. In the revised manuscript we modified the Results section to further emphasize this point.

Finally, the manuscript could be improved by clearly distinguishing between the fundamental approach of Bayesian image analysis from the Tapqir software that would be used to carry this out.

We have revised the manuscript to adopt this recommendation. We now call the mathematical model “the cosmos model” and use “Tapqir” to refer to the software.

A section devoted to describing the Tapqir interface and the inputs/outputs would be valuable. In the manuscript's current form, the lack of information on the interface along with the potential requirement for a GPU and need for the use of a relatively new programming language (Pyro) may hamper adoption and interest in colocalization methods by general audiences.

Description of the interface and inputs/outputs is given in the online Tapqir documentation.

Users do not need to own a GPU; they can instead run the program on a readily available cloud computing service. We have now added to Table 1 data showing that computation time on the Google Colab Pro cloud service is actually faster than that on our local GPU system. Colab Pro is inexpensive, readily accessible, and user friendly. We have added to the user manual a tutorial that shows how to run a sample data set using Tapqir on Colab.

Users do not need any knowledge of Pyro to use Tapqir; Pyro is merely used internally in the coding of Tapqir.

The main phrase “thinking out loud” is repeated throughout the entire song. It means to share one's thoughts so that other people can hear them. This can be seen throughout the poem as the speaker repeats the idea of displaying affection for his partner. “Thinking out loud” happens when people need help working though their thoughts. Likewise for the speaker, he has a lot of pent-up feelings and emotions that prevent him from thinking clearly thus this causes him to think out loud. And the one thought that always becomes apparent to him is that “Maybe we found love right where we are” which shows that the speaker thinks that he has found true meaning of love all because of his partner. This causes me to feel delight for the speaker as he is sure that no matter what doubts he may have about their relationship, he is convinced that he has found his true love. This also pushes me to reflect on the theme of love and that true love will find its way and will remain strong no matter the circumstances or the difficult situations they are put through.

Author Response:

Reviewer #2 (Public Review):

The authors have developed a new method that allows for two-color STED imaging. They have applied this method to measure spine head size and PSD95 changes following exposure to an enriched environment.

Strengths

-The new method is well-described and seems to have considerably less crosstalk than previous attempts at in vivo two-color STED imaging. The analyses and controls of the method are compelling. I think that this method could be valuable for examining how different components of the synapse are changing in response to sensory or environmental changes.

-The method is appropriate for measuring the size of PSD95 and spine head size in the enriched environment paradigm they use here. They find that in the short-term spine head size and PSD95 size are not always correlated.

-They also find that there is less variability in the spine head size in animals in an enriched environment.

Weaknesses<br /> -The authors use an enriched environment plasticity paradigm to showcase the method and measure spine head and PSD95 size and how they change over short periods of time. This particular biological study is not well-motivated and there is not a stated reason for studying the short-term (30-120 minutes) dynamics of PSD95 and spine head size, and their correlations. They also show that the variability in spine head size is decreased with the enriched environment, but do not show what the implications of that change would be from a biological point of view for synaptic dynamics or synaptic function.

-The authors show that there are differences in the morphology of PSD95 between mice reared in enriched environments and those in control environments. While this quantification is done blindly by three different analysts, it is not done in a quantitative way. Also the authors do not show or explain the biological relevance of differences in the morphologies of PSD95, thus it is not clear what this measure means for synaptic plasticity or function.

-The authors use a cranial window preparation, which is commonly used in the literature. However, it is not clear how long they wait to image the mice after the cranial window. Previous work from Xu et al. (PMID: 17417634) suggests that there is in an increase in glial activation for a period of up to a month after surgery. The authors have not shown the degree of glial activation that follows after their surgeries and if they have not waited a month, there may be upregulation of microglia, which may alter synaptic stability (also demonstrated in the same paper). The authors have not discussed this point or the implications for their findings.

We thank the reviewer for his/her valuable input.

The time-scale we study is similar to what is known from structural changes after LTP and thus we wanted to study the same time scale in vivo. We revised the motivation and explained better the biological relevance of the observed changes. We absolutely agree with the reviewer on his/her concern for chronic imaging. However, we performed acute experiments and imaged directly after implanting the window in the same session. After imaging the mice were sacrificed.

Reviewer #3 (Public Review):

Wegner et al. use two-color STED to follow spines and their PSDs in layer1 of mouse visual cortex over 2 hours under anesthesia. They compare mice that were kept in an enriched environment (EE) to control mice housed in standard laboratory cages. Spines in EE mice are larger and show larger fluctuations in size. PSDs in EE mice shrink during anesthesia and tend to change their nanostructure. Very importantly, changes in spine size were not driven by PSD size changes, or vice versa. Technologically, this is a landmark study, as tracking two different labeled structures in individual synapses at the nanoscale can obviously be applied to a large number of synaptic proteins and organelles, two at a time. Single-color superresolution microscopy is much less useful, as 'puncta in space', without cellular context, are difficult to interpret. This pioneering work is the first proof-of-concept of two-color in-vivo STED and of major importance for the community. Although stochastic processes seem to drive much of the synaptic dynamics under anesthesia, the environment shapes the spine size distribution and affects synaptic dynamics in a lasting fashion.

One major comment:

l.259: "These results suggest that Ctr housed mice undergo stronger morphological changes." This I find a bit misleading. What about: These results suggest that anesthesia induces stronger morphological changes in Ctr housed mice? Altogether, a discussion of the potential effects of anesthesia on spine/PSD dynamics is missing (see e.g. Yang et al., DOI: 10.1371/journal.pbio.3001146). The fact that there was weak correlation between spine head and PSD fluctuation could have something to do with the state of suppressed activity the system was in during imaging. Under conditions of intense processing of visual information, changes might have been more rapid and more tightly correlated. This could be mentioned as a perspective for the future - to visually stimulate the anesthetized animal.

We agree with the reviewer that it should be mentioned here that the morphological change was observed under anesthesia. However, the sentence suggested by the reviewer is also a bit misleading since it suggests that the anesthesia has triggered the change. We think that anesthesia might affect the amplitude and dynamic of the observed changes but does not induce the change. Thus we rephrased as follows: These results suggest that Ctr housed mice undergo stronger morphological changes under anesthesia.

We absolutely agree about the potential influence of the anesthesia on the spine and PSD95 nanoplasticity and added the following comment. Of course, we would like to perform the measurement in the future also in awake mice and after visual stimulation.

Added to discussion: However, it was shown that MMF anesthesia reduces spiking activity and mildly increases spine turnover in the hippocampus (Yang et al., 2021). Thus, the plasticity of spine heads and PSD95 assemblies might be different in the awake state and under intense processing of visual information.

Author Response:

Reviewer #2 (Public Review):

The reported study includes an overall well-conducted and well-presented set of experiments. Ample data are reported and a clear and conclusive picture of the findings is portrayed.

1. The Introduction falls short of providing the background needed for fully appreciating the current findings and their importance. The authors don't present the current understanding regarding the role of 4-vinylanisole in locusts (mostly their own work). Nor do they present the accepted knowledge of the control of sexual maturation in locusts (mostly several decades-old work). Moreover, the importance of reproductive synchrony in the life history of gregarious locusts, including its tentative roles in maintenance of the homogeneity and integrity of the swarm, in ensuring high density conditions for the next generation, and more, is also not adequately presented.

We appreciate the reviewer’s helpful comments. According to these comments, we have revised the introduction part by enriching the significance of reproductive synchrony in ecological adaption of gregarious locusts and the research progresses on sexual maturation control in locusts. Details were shown as: “Depending on population density, locusts display striking phenotypic plasticity, with a cryptic solitarious phase and an active gregarious phase (Wang and Kang, 2014). Gregarious locusts, compared to solitarious conspecifics, show much higher synchrony in physiological and behavioral events, such as egg hatching and sexual maturation, as well as synchronous feeding and marching behaviors (Norris, 1954, Uvarov, 1977). Reproductive synchrony in gregarious locusts provides benefits for individuals in several aspects, such as more favorable microenvironment, lower risk of predation, efficiently forging, as well we more encounters with mates, therefore ensures high density conditions for the next generation, and is essential for maintenance of locust swarm (Beekman et al., 2008, Maeno et al., 2021). Some sort of vibratory stimulus, maternal microRNAs, and SNARE protein play important roles in the egg-hatching synchrony of gregarious locusts (Chen et al., 2015b, He et al., 2016, Nishide and Tanaka, 2016). It has been revealed that the presence of mature male adults has effectively accelerating effects on synchrony of sexual maturation of immature male and female conspecifics in two locust species, Schistocerca gregaria and Locusta migratoria (Norris, 1952, Loher, 1961, Guo and Xia, 1964, Norris, 1964). The accelerating effects of several prominent volatiles released by gregarious mature males in male maturation have been exampled in the desert locust. Four volatile pheromones (benzaldehyde, veratrole, phenylacetonitrile, and 4-vinylveratrole) have significantly stimulatory effects on sexual maturation of male adults, with phenylacetonitrile having the most pronounced effect. (Mahamat et al., 1993, Assad et al., 1997). However, how conspecific interaction affects female sexual maturation remains unclear and the pheromones those contribute to maturation synchrony of females have not been determined so far”. In the current study, we identify 4-vinylanisole as a key pheromone promoting sexual maturation synchrony through validating the role of five gregarious male-abundant volatiles one by one, instead of following up our previous work on 4-VA. Thus, we have fully elaborated the multifunction of 4-VA as both aggregation pheromone and maturation accelerating pheromone in the formation and maintenance of locust swarm in the discussion part.

2. Research on pheromonal signaling in locusts have traditionally focused on compounds with a putative role in density-dependent phase-specific behaviors. Hence, it is common to compare the response of crowd-reared vs. solitary locusts to applied chemicals. The challenge, however, is maintaining the density context, while attempting to conduct controlled similar experiments with locusts of the two phases (i.e. keeping the solitary phase locusts isolated, while the gregarious locusts must always be crowded). This is even more challenging when studying reproductive physiology. By the basic nature of the two phases, there can be a multitude of interacting factors (behavioral and/or physiological) affecting the much-desired reproductive synchronization in gregarious locusts, while such synchronization is not expected at all in solitary ones (it may even be claimed to have no fitness-related advantage).

3. In general, the authors of the current report have dealt well with these challenges, taking extra care to conduct multiple controls and making an effort to specifically test all the possible factors. However, there are several points that raise some uncertainties. For example:

o If I am not mistaken, females of both phases were included in the study only if already mated by day A+7 (LL355-357). While this is reasonable for gregarious locusts, it may not be suitable for the solitary locusts, imposing an undesired and unequal selection criterion.

We thank the reviewer’s comments. We don’t think the criterion (mated at PAE 6-7 days) cause significant bias in either gregarious locusts or solitarious locusts. In fact, the limitation of mating before PAE 7 days is used to rule out the effects on oviposition synchrony caused by difference in mating age among individuals. This criterion is only limited during the analysis of the first oviposition date. On the premise of consistent mating time, oviposition consistency in gregarious female adults may largely present the sexual maturation synchrony among individuals (Figure 1A). For subsequent experiments, we mainly concentrate on regulation of sexual maturation using only virgin females in all experiments.

o In the test of the effects of conspecifics interactions, 10 gregarious locusts provided stimulation to the tested gregarious female, while only one insect was the stimulating factor for the solitary female.

Actually, we carried out two independent experiments to test the effects of conspecifics interactions. The population densities were kept in solitarious context for comparison of female sexual maturation synchrony between typical gregarious and solitarious phases (Figure 1D). For locust emissions treatments, ten solitarious locusts were used to ensure the stimulations at the same density level (Figure 1F). Both of two experiments suggested that solitarious male adults had no effects on female sexual maturation.

o It is not clear how were egg pods attributed to specific gregarious females (maintained in groups of 10)

Thanks for the reviewer’s comments. To monitor the oviposition activities of each individual of gregarious females in a group, locusts were individually marked, and their first oviposition times were determined by collecting egg pods every 4 hours per day after mating. Females those laid new eggs could be easily distinguished by much thinner abdomen with white foam around ovipositor. We have provided the method details in the revised manuscript.

Overall, since the focus of this study is actually not on the comparison between the phases, it might have been beneficial to the readers if the focus was on the gregarious locusts only, with maybe a couple of experiments conducted on solitary insects and presented separately.

We understand the reviewer’s concern. Actually, the aim of this study is to explore the mechanism underlying sexual maturation synchrony by comparing phase- and sex-dependent conspecific interactions in locusts. The reproductive synchrony in gregarious might be not highlighted without comparison with solitarious locusts, including both first oviposition time and sexual maturation, although the mechanism studies were mostly performed in gregarious locusts. Moreover, phase-dependent comparison of volatile contents is helpful for us to screen candidate volatiles responsible for the acceleration of sexual maturation synchrony in females.

4. Assuming that within a locust group there is overall agreement in the age of males and females, there seem to be a not-fully-explained mismatch between the age of max 4-VA release by males (linearly increasing with age) and the age of max effect in females (critical period at A+3-4)

We appreciate the reviewer’s query. We have provided additional discussions on the “mismatch” of between age-dependent release of 4-VA by males and the age of max effect in females (PAE 3-4 days). Details were shown as: “. We find that the release of 4-VA by gregarious males continuously increased after adult eclosion, with maximal 4-VA release at PAE 8 days. The age of maximal 4-VA production outwardly seems to be unmatched with the sensitive developmental stage to 4-VA of females (PAE 3-4 days). In insects, it is very common for males to mature earlier than females (Alonzo, 2013). In the locust, male adults also display earlier sexual maturation for several days, compared to females. In given locust population, individuals emerge to adults successively in a couple of days, not in completely synchronous period. Therefore, age-dependent increase in 4-VA release in gregarious male adults presents a persistent stimulus for less-developed young female adults, and thus maximizes synchronous maturation of female locusts, which could reduce male competitions for mate selection”.

5. Similar to the introduction, the discussion section also does not present comprehensive arguments regarding the importance of reproductive synchronization in female locusts. Points that could have been discussed include: females' oviposition disrupting migration, synchronization affecting sexual selection, accelerating intra-sex competition over mates as well as oviposition sites, and more.

We appreciate the reviewer’s nice suggestions. We have provided additional discussions on this point following these suggestions. Details were shown as: “Reproduction synchrony involves consistence in maturation, mating, and egg laying, among which sexual maturation synchrony serves as the most foundational step for oviposition uniformity (Hassanali et al., 2005). Extremely high energy cost for female reproduction could restrict migration to pre, post, or inter oviposition period in locusts, thus have crucial effects on collective movement of local populations (Min et al., 2004). Given this, a balance of sexual maturation timing among female members presents an essential subject for maintenance of locust swarms. We here demonstrated that young female adults reared with older gregarious male adults show faster and more synchronous sexual maturation in the migratory locust, supporting the accelerate role of crowding in sexual maturation of females (Guo and Xia, 1964, Norris and Richards, 1964,). Together with the accelerating effects on immature male sexual maturation induced by older gregarious male adults reported previously (Torto et al., 1994, Mahamat et al., 2000), young adults of both sexes lived in gregarious conditions prefers more synchronous maturation than individuals reared in solitary. The consistent maturation in both sexes will greatly reduce intra- and inter-sexes competitions for mate selection and thus ensures reproductive synchronous in whole locust populations. We demonstrated that a single minor component (4-VA) of the volatiles abundantly released by gregarious male adults is sufficient to induce the maturation synchrony of female adults. By comparison, four volatiles (benzaldehyde, veratrole, phenylacetonitrile, and 4-vinylveratrole) showed stimulatory effects on male maturation (Mahamat et al., 2000). Thus, there might exist a sex-dependent action modes of maturation-accelerating pheromones: multi-component pheromones for males and single active component for females, possibly due to different selective pressures between two sexes in response to social interaction. Further exploration will be performed to confirm this hypothesis by determining whether 4-VA has maturation-accelerating effects on male adults in the migratory locust in future”.

Reviewer #3 (Public Review):

Strengths: Grouping behavior for marching, sexual maturation, swarming, oviposition and egg hatching in gregarious locusts is complex and it's mediated by a combination of cues-olfactory, tactile, and visual cues to ensure synchronous behavior. The authors show that only olfactory cues released by gregarious adult males mediates maturation synchrony of females. This finding is a confirmatory result of a well-established phenomenon for maturation synchrony in both sexes of adult locusts, although in this study, the authors focused on only females. Further, the authors validated their findings using gene editing techniques to show that maturation synchrony was diffused in Or35-/- mutant adult females but not in wild type females exposed to adult male volatiles and the individual component identified as 4-vinylanisole among five male-abundant volatiles as promoting synchronous sexual maturation in only post adult eclosion females (PAE) 3-4 days old. Use of molecular and single sensillum recordings, followed by physiological experiments focused on the interaction between this specific adult pheromone and juvenile hormone to validate the behavioral results found for females add scientific value to the study.

Weaknesses: Firstly, synchronous and accelerated sexual maturation of young adults by older pheromone-producing ones, is a primer effect driven by males and this facilitates 'integration and cohesion' of both sexes of adults. In my view, the fact that this study focused on only females but not on both sexes, weakens the contribution of the study towards increased understanding of the biology/ecology of locusts.

We accepted the reviewer’s comment that synchronous and accelerated sexual maturation of young adults by older pheromone-producing ones occurs in both sexes. In fact, early studies have reported that mature males can accelerate sexual maturation of young males through several candidate compounds (Mahamat et al.,1993, Chemoecology; and Mahamat et al., 2000; International Journal of Tropical Insect Science). However, the effects of conspecific interaction on sexual maturation of females are rarely reported. Moreover, distinct volatiles that can accelerate female sexual maturation have not been characterized before this work. Therefore, we focus on female sexual maturation synchrony in the current study. A comparison of regulatory mechanisms underlying sexual maturation synchrony in males and females has been discussed in the revised manuscript.

There are also weaknesses in the methods, such as focusing on only the five-abundant male volatiles based on heat maps. Basically, the decision as to which components in adult male volatiles may be contributing to sexual maturation should be made by antennae of different ages of PAE females and males to avoid selecting only abundant compounds based on artificial intelligence (AI). Since most studies in this subject area have demonstrated that there is no direct correlation between volatile abundance and detection at the periphery or central nervous systems of an insect, I believe that the authors will agree with me that often some of the minor volatile components tend to contribute more to the chemical ecology of an insect than the more abundant components. Without testing minor components identified in male volatiles as a blend or individually, as additional controls to increase the robustness of the study, I am not convinced that the authors have fully achieved their aim in identifying a male-produced volatile that promotes sexual maturation in females.

We agree the reviewer’s comments that the activities of volatiles are not always determined by the absolute contents. In fact, in our work, the selection of candidate effective compounds for female sexual maturation did not rely on the absolute content of these volatiles, but mainly based on comparative analysis of their relative contents between gregarious and solitarious male adults, because only volatiles from gregarious male adults could accelerate sexual maturation of females (Figure 1C-F). In the revision process, given that the volatiles released by gregarious males, rather than gregarious females and solitarious males, have the accelerate effects on female sexual maturation, we further performed more comparative analysis of volatile contents among these three groups (G-males, G-females, and S-males). Compared to volatiles released by G-females, and S-males, only five kinds of volatiles display significantly higher emission in G-males (PAN, guaicol, 4-VA, vertrole, and anisole). The roles of five candidate volatiles in female sexual maturation were individually validated by removing the volatile from the stimulation blend one by one. The results showed that only the omission of 4-VA from the blends lost the accelerating effects on sexual maturation synchrony of gregarious females (Figure 2B). Based on these findings, we inferred that 4-VA played major roles in promoting female sexual maturation synchrony.

JH experiments- My main concern is the lack of proper controls to fully investigate the interactive effect of the male-produced pheromone promoting sexual maturation and juvenile hormone production. JH titers were not measured in females exposed to the other male-abundant compounds including PAN, guaiacol, veratrole and anisole or blend/individual minor components.

We understand the reviewer’s query. In fact, the potential role of JH pathway was inferred firstly by the RNA-seq analysis of CC-CA, which showed that the expression levels of JH metabolism-related genes were significantly affected by 4-VA treatment at PAE 3-4 days. The measurement of JH titer after 4-VA treatment was further performed to support the involvement of JH in 4-VA-accelerated sexual maturation in female adults. Since other male-abundant compounds have been excluded due to the omission of any of the four volatiles (Figure 2B), we don’t think it is necessary to detect their effects on JH titers in females including PAN, guaiacol, veratrole, or anisole.

Another notable weakness is the 'JH Rescue Experiment'. The authors did not inhibit JH synthesis in the corpora allata (allalectomized locusts) in treated locusts before injecting the JH-analog methoprene to accelerate maturation and reproduction in females.

Thanks for the reviewer’s comments. The JH rescue experiments in Figure 4D-F were performed in Or35 female mutants, which showed lower JH levels and sexual maturation rate. Thus, the JH analog was applied to Or35^-/- females to test whether activation of JH pathway could recover sexual maturation rate and Vg expression. To provide additional evidence, we performed addition rescue experiments in WT females by inhibiting JH synthesis using Precocene (PI) before JH treatment. The results showed that PI treatment significantly inhibited sexual maturation rate and Vg expression in 4-VA-exposed WT females, whereas JH treatment post PI application can obviously recovered the sexual maturation rate and Vg expression (Figure 4G-I).

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Referee #2

Evidence, reproducibility and clarity

Review of 'Mother centrioles generate a local pulse of Polo/PLK1 activity to initiate mitotic centrosome assembly' from Wong et al.

In this paper, Wong et al address the mechanisms of centrosome assembly in flies. They start with the interesting observation that Polo localized at centrosomes oscillates before cells enter mitosis, while Cnn (and with it centrosome maturation) either increases or reaches a plateau. The phenomenon is local, since Polo levels at in the cell are high during mitosis. They propose that the oscillation is driven by a negative feedback loop whereby Polo inhibits its own binding to the centrosome, Ana1 being the most likely relevant receptor. Finally, they discuss the possible meaning of this oscillatory behavior, in the light of the rapidity of the early embryonic cell cycles.

Major comments

1- One can imagine different reasons for the fact that the model displays different dynamics for Cnn and Spd-2/Polo. For example, a major difference may be due to the different dissociation rates of the clusters Cstar and Shat. These are governed by different laws and different parameters (kdis vs kidsCstar1/n). If I understand, both parameters and dependency on Cstar^2 are assumptions. Hence, it would be important to pinpoint which component of the model is more directly responsible for the observed behavior. The analysis should not be limited to the dissociation, but should be extended to the whole model. To this aim, one could test the robustness of the model's parameters. The results of this analysis will also be a prediction of the model.

2- The presence of a positive-feedback loop involving Cnn could offer an alternative and more robust explanation for the slower dynamics of Cnn. Such a loop between Cnn and Spd-2 was proposed by the authors (Conduit, eLife, 2014). I think some comment on this point would be interesting (eg, could the Cnn/Spd-2 loop proposed earlier work in this context? If not, why? If yes, should not this option be explored?).

3- The prediction presented in Figure 6 is very relevant. I wonder how robust this behavior is to changes in parameters values.

4- Additional testing of the model would be important to confirm that the negative feedback loop is actually in place, although I understand experiments may be difficult to be performed. Possible examples: constantly high levels of Polo are expected to decrease its centrosomal localization, is that correct and, if so, testable? Is it possible to delay one cycle, and then observe the decay in Cnn values? This latter experiment, for example, could help to distinguish positive feedback vs slow decay rates. If the experiments are not possible, it may be worth anyway to present some predictions worth testing.

5- The difference between Models 2 and 3 is not clear to me. In mathematical terms, they seem to be basically the same thing: reaction (50)=(33), (51)~(34) given (40) and (52)~(35) again given (40). This is precisely since the model comes with the assumption of a well-stirred system, and thus adding P in solution is not so different from assuming P=Rphat (40). I would have imagined that also Model 2 accounts for the fact that in Spd-2-S16T and Ana1-S347T Polo is recruited slower and for a longer period. Is it not true? If so, is model 3 really needed? More in general, assuming a role for an increase of local concentration of P* is quite a jump, especially given the small distances involved, and the fast diffusion occurring within cells.

Minor points

1-Could the authors use the FRAP data to estimate the different kdis? If so, a comparison with the 20-fold difference used in the model would be useful.

2- p. 6, The authors should state clearly for the worm-uneducated like me whether the fusions were done with the endogenous proteins or not.

3- p.7 Figure 1B, in the text it is referred to display 'levels of peaks' and in the figure and legend we find 'growth period'. Not clear how the two refer to the same quantity.

4- Spd2-mCherry is present in both Figure 1C and D, but with very different amplitudes. Why is that the case?

5- The fact that Polo peaks in mitosis is a key observation. Unfortunately, this is often reported as a personal communication. The authors never tried to produce this piece of data?

6- p.11 It is explained that NM and OM differ for their initial values because the OM starts with some PCM from the previous cycle. However in Figure 3A, for example, the values of Polo at the end of the cycle are identical in the two. Is not this in contrast with the explenation?

Still p11, there is reference to Figure 3C,D, but Figure 3D does not exist, I guess it should be 3A,C.

7- In the formulation of the model (page numbers in Suppl Mat are unfortunately missing..), one citation for the total amount of Polo being large is needed.

8- I do not understand this point: scaled c output is 1, and the initial condition for c=1 also?

9- It has been shown in different systems (from yeast -- haase winey reed, NCB, 2001-- to worms -- McCLeland O-Farrell CB 2008) that centrosome duplication can occur independently from the cell cycle oscillator. I was wondering whether the proposed negative feedback loop may play a role in this phenomenon. This is only a curiosity, which does not need to be addressed.

Significance

The new observation and hypotheses presented in the paper provide a sizeable advance. The presence of an oscillation in Polo, uncoupled from cellular levels, is new, and the model proposes a testable hypothesis to explain it. Some additional experiments to verify the model would strengthen the manuscript.

The work is probably more appropriate for experts in the centrosome field. My primary expertise for this review was in mathematical models.

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The lives of these Utahns are all being shaped by spending around 50% or more of their income on housing each month.

Their challenges are part of a larger statewide housing crisis, one that is being blamed on both a shortage of homes and sluggish income growth that isn’t keeping pace with soaring real estate prices.

While past spikes in housing costs have priced people out of home ownership in Utah, the current affordability crisis is more all-encompassing — so it’s also stretching renters to the breaking point, said James Wood of the University of Utah’s Kem C. Gardner Policy Institute.

“I speak from personal experience,” said Wood, a senior fellow at the Gardner Institute. “I have people in my basement, and I’ve tried to help them find places. It’s really tough.”

Nearly one in five renters in Utah is severely cost-burdened, meaning they spend at least half their income on housing and often struggle to pay for food, transportation and other bills, according to federal data for 2013 to 2017. And more than 63% of the state’s lowest-income residents fall into this category, this data shows.

[Read more: Do you spend more than half your income on rent? Here are resources that can help.]

The disparities are particularly acute for Utahns of color, with a recent Gardner analysis showing that Black and Hispanic renters are more likely to face severe housing cost burdens. The research found that 32% of Black renters in the state spend more than half of their income on housing, making them almost twice as likely to face severe cost burden as white renters.

For a minimum wage worker in Utah, a rental home would have to cost $377 per month or less in order to be affordable, according to an analysis by the National Low Income Housing Coalition. But the average rent for a one-bedroom Salt Lake City apartment is nearly triple that, at $1,099 a month, according to a June report from the popular rental website, Zumper.

Wood said some of the state’s lowest-income residents receive public housing assistance, but there’s not enough money to reach everyone who needs help. Without government support, this group of Utahns lives on the brink of homelessness, with any additional hardship potentially pushing them over the edge.

“Whether it’s domestic violence, or whether it’s the loss of job or a health incident or a traffic accident,” he said. “That’s a disaster.”

Tara Rollins, executive director of the Utah Housing Coalition, notes that it’s easier to prevent people from losing their housing than it is to get them off the streets. The coalition advocates for increased wages and additional units of deeply affordable housing, to help people in this position before they’re pushed into homelessness.

Even those who are moderately cost-burdened — meaning they spend more than a third of their income on housing — face challenges, Rollins noted. But she doesn’t think that many Utahns and policy makers are paying enough attention to this swath of Utahns who are barely keeping their heads above water.

“Unless they feel it, see it, they don’t get it,” she said. “You can’t see somebody’s wallet and how empty it is.”

(Rick Egan | The Salt Lake Tribune) Jan Aus, a 74-year-old apartment resident in Sandy, says her rent keeps rising.(Rick Egan | The Salt Lake Tribune) Jan Aus, a 74-year-old apartment resident in Sandy, says her rent keeps rising. (Rick Egan/)

‘Nobody has my back’

When Jan Aus, 74, moved into a senior living community in Sandy seven years ago, she was shelling out $720 for rent each month.

“And then they raised it $10 two or three years after that,” she recounted. “And then, bing, they hit me with $65 a year.”

Today, Aus is paying $925 to live in her one-bedroom apartment ― a figure that sucks up the bulk of her Social Security check. She knows the amount she has to budget each month down to the cent: $1,251.80.

Aus said there are people who are “worse off than I am,” noting that she’s receiving government assistance available to low-income Utahns to help pay for electricity and food. She owns her car and considers her health insurance “good,” as long as she makes sure to get generic prescriptions.

Still, she said the amount of money she’s putting toward rent has become stressful, especially as she waits to see whether the apartment complex where she lives will raise her rent again this fall.

“It scares me,” she said. “And like I said, they’re going to hit me in September ... and it scares me to think they’re going to raise it again. I just feel like nobody has my back.”

If not for the federal pandemic stimulus checks, Aus said, she wouldn’t have any kind of savings, money she’s socked away in hopes that she can put a security deposit down on a more affordable apartment soon.

The problem, she said, is that there’s very little available in her price range of $800 to $900 a month, other than a room in someone else’s house.

“I don’t see myself going that way,” she said. “That to me is kind of scary. I think we need more affordable housing, I really do. Because it’s not going to get any better. To me, it’s going to get worse.”

‘I want to take care of more of my health’

Jazmin May has cut back her therapy sessions from once a week to once a month. The 24-year-old Salt Lake City resident can’t go in for an eye exam as soon as she’d like. And she’s had to ask her parents to chip in some money when her car needed repairs.

That’s all because rent claims about 50% of her income, and she has to stretch the rest to pay her other bills.

“For right now, it works for me,” she said. “I do wish I had more money left over in my paycheck just to be able to afford other things. I want to take care of more of my health.”

May says many of her other friends from college are also struggling, as they strain their early-career salaries to cover the cost of housing. Some have found it impossible and have gone back to live with their parents, she said.

She and a friend signed a lease for their two-bedroom apartment near Liberty Park in 2019, but the pandemic that arrived just months later quickly jeopardized the living arrangement. Her friend lost a retail job and had to move back to her family home in Ogden.

May said she considered looking for another roommate and decided it would be better for her mental health if she lived alone for a while.

Taking on the entire rental payment meant accepting a job at an area museum rather than continuing to hop between political campaigns — work that she loves but is too unreliable for her right now.

“I feel like I have sacrificed, in a way, my passion, to be able to afford housing, because I love campaigns and politics and outreach,” she said. “But campaigns are also not a stable job, and often you don’t get benefits. So I decided to just take a break from politics for a little.”

Even with the more predictable salary, May said home ownership seems like an unattainable goal at this point, especially since she’s worried that housing costs will always be one step ahead of her income growth.

She peruses rental listings for fun sometimes, but she’s not convinced she could find something cheaper, especially considering the pet fees she’d have to pay for her cat, Lilith. Her only other option, she said, would probably be to move into her parents’ home, as some of her friends have done.

‘You just kind of want to be an adult’

Fresh out of college 10 years ago, Orem resident Eric Wilson set a long-term goal of saving enough for a down payment on a home.

He’s passed up concerts he wanted to attend, vacations he wanted to take and movies he wanted to see. He’d love to buy the latest tech gadgets and the newest iPhone, but he’s socking away every extra penny in his investment portfolio instead.

Still, the 31-year-old marketing specialist said he doesn’t feel much closer to buying a house than he did a decade ago — and perhaps even further away, as he watches home values grow at warp speed compared to his slow-and-steady savings. So he can’t help but wish Utah’s economy would hit the tiniest snag.

“Just a little bit. Not enough to hurt anybody,” he half-jokes. “Just to make house prices go down.”

Making it especially hard to save is his current rent, which eats up nearly half of his salary.

Wilson has lived in the two-bedroom unit since shortly after he graduated from Utah Valley University. He had a roommate initially but opted not to get another one after his friend married and moved out.

“You just kind of want to be an adult and go off and do your own thing and have your own space and not have to worry about marking your milk,” he said. “But at a certain point, if prices keep rising, it’s not really feasible.”

Wilson had gotten about a fifth of the way to his goal of saving $100,000 when COVID-19 struck and his marketing agency had to cut jobs, including his. His unemployment lasted six months, forcing him to deplete the nest egg he’d spent so long accumulating.

He keeps browsing online real estate listings, despite knowing how far away he is from becoming a homeowner. The hobby is becoming increasingly demoralizing, though, he said.

Three years ago, he toured a modest home in a nice neighborhood that was pretty affordable for him, priced at a bit less than $200,000. Recently, he saw the same place had sold again for $415,000.

Wilson said he likes his apartment and knows he’d have to pay much more if he relocated. But he’s also weary of renting. He’s tired of feeling like he has to put off his life — and delay buying a dog or becoming a foster parent.

“I’m just kind of not at that point where I can do that space-wise,” he said. “But I would love to do that. And having a home would help make that possible.”

(Christopher Cherrington | The Salt Lake Tribune)(Christopher Cherrington | The Salt Lake Tribune)

‘This is where you belong’

Anna, 50, is a single mother supported by disability payments from Social Security and living in an income-restricted apartment complex in Holladay — but with around $700 left over each month after she pays her rent, she said, she’s still struggling.

While her monthly housing costs have ballooned from $850 when she first moved into the two-bedroom apartment in 2016 to $1,077 now, her disability income hasn’t increased at the same rate.

“It’s a huge stress,” Anna said. She fears she might be pushed out of the unit for speaking out about her rent increase, and The Salt Lake Tribune is not publishing her surname.

Among her biggest challenges is making sure her 13-year-old daughter can access nutritious foods, a goal she said is easier thanks to assistance from The Church of Jesus Christ of Latter-day Saints.

“Otherwise, my daughter probably would just be eating rice,” Anna said.

She’s also struggled at times to supply her daughter with new clothes that fit as she outgrows old ones.

Anna said she’s trying to save money, but “life keeps happening,” such as a car problem earlier this year that claimed everything she had saved and more. Sometimes, she worries that one misstep could land her and her daughter on the streets.

Drowning in monthly housing costs, Anna said she’s been searching online for a more affordable apartment in the hopes that she wouldn’t have to stretch so much to make ends meet — but she’s growing increasingly discouraged.

“I do keep on looking,” she said. “I keep hoping maybe I’ll find somewhere that is rent manageable as well as safe that I can move my daughter and I to so we can be able to provide for ourselves without having to rely on governmental programs completely. And basically feel like we’re being pushed into that hole that, well, if you can’t work for yourself, then this is where you belong. That’s how it feels.”

Hours after speaking with The Tribune, Anna received a notice taped to her door that her rent was being increased once again: to $1,122 starting July 1.

Crédito: Taylor Stevens, Bethany Rodgers

Word count: 2156 Copyright The Salt Lake Tribune Jun 10, 2021

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Big data can provide a lot of information, and we will finally get the analysis results when we analyze it. But does huge data necessarily give us the right result, I don't think so. Excessively large data sometimes not only brings us a greater amount of calculation and analysis difficulty, but also provides me with some repetitive, complicated and useless information. This information may lead us to deviate from the correct results, or to obtain results that are too dependent on the same environment. If we want to get a general conclusion, we may not only rely on the analysis of these numbers, but also have some prior knowledge or more efficient data processing methods.

I really think that banning books that address the topics of racism, we are limiting our progress as a society. If we can't learn about the problem, it won't ever be addressed, and true change cannot occur. I was thinking about this in my Spanish class as this week we were reading about bullfighting and the running of the bulls. While it made me feel uncomfortable, I noted the importance of reading about both sides of the issue. If I remained in ignorance, how could I add my voice to changing customs? I may not live in Spain, and may not be able to change much in regards to bullfighting practices, but I can help make a difference in the racism embedded in our society by learning more about the issue through reading books on the subject, especially personal experiences of others.

Hall states his belief that psychology is much more than philosophy. Psychology is its own science, just like medicine. Despite the contributions of theology and philosophy, psychology is scientific and researchable.

This excerpt from the passage might seem a bit overwhelming as the phrasing is a bit odd to modern language but I think I arrived at a general basis for what Krishna is saying in the beginning of this chapter. The practice of Yoga to many have a direct correlation to harmony and control but Krishna considers another meaning, one that might surprise many. Yoga is learning to let go, it is to detach oneself from their desires and thus coming to the realization that the desire has a direct link to the pain we all face in life. By letting go of those desires you are breaking the tether that binds you to worldly aspects. Ones soul can turn into an enemy if hatred takes over.

Source: V, Jayaram. Descriptions of Soul or Atman In The Bhagavad Gita. HInduWebsite.com. https://www.hinduwebsite.com/soul.asp

It's clear that they are writing the book in the perspective of maximizing body recomposition in whatever way possible. I believe that this is a little misguided for most people as I personally don't feel that good if I eat a ton of protein and nothing else. It makes my stomach feel a little poopy.

I think it's extremely important that children in the education system have someone from their cultures who they can look towards and relate to. However, it is equally as important to provide children with exposure to different cultures that they may not interact with or know much about. It's so important that school districts start hiring a diverse range of teachers in their schools instead of it being primarily white teachers.

isolated nor learned in an isolated fashion, but hang together in a network of ideas, or “latticework of mental models” (Munger, 1994), it becomes easier to make sense of new information. That makes it easier not only to learn and remember, but also to retrieve the information later in the moment and context it is needed.

Our natural memories are limited in their capacities, but it becomes easier to remember facts when they've got an association to other things in our minds. The building of mental models makes it easier to acquire and remember new information. The down side is that it may make it harder to dramatically change those mental models and re-associate knowledge to them without additional amounts of work.

The mental work involved here may be one of the reasons for some cognitive biases and the reason why people are more apt to stay stuck in their mental ruts. An example would be not changing their minds about ideas of racism and inequality, both because it's easier to keep their pre-existing ideas and biases than to do the necessary work to change their minds. Similar things come into play with respect to tribalism and political party identifications as well.

This could be an interesting area to explore more deeply. Connect with George Lakoff.

Author Response:

Reviewer #2:

Weaknesses:

The competition assay used in this study may not truly reflect the competitiveness of SSIMS males. The mating assay used 20 virgin WT females and 4 males (including both WT and SSIMS), resulting 5:1 sex ratio so the males are not really competing for females. A more competitive ratio (such as WT females: WT males: SSIMA males at 1:1:1) should be designed to address this. Also, the sperm competition assay mixed the mated WT females with SSIMS males for 12 days, allowing plenty of time for the females to remate with these males. Therefore, it's more like a sperm replacement assay rather than competition assay. The authors should either repeat it with a strict time control, or soften their statements for sperm competitiveness.

We have repeated the experiment at a 1:1:1 ratio as suggested. The new results are reported in the revised Figure 3. It is not clear to us how the timing of the mating experiments differentiates sperm competition versus sperm displacement, but we agree that sperm displacement is a better term to describe what we did. We have repeated the sperm displacement experiment with strict time control based on several published literature precedents and describe the results in the revised manuscript.

Some necessary information or statistics are not shown or mis-presented. For example, the alternative splicing diagram in Figure 1c likely was taken from the original transformer gene, but here it's the tTA gene so the male intron should be removed since it's not in the construct;

We have revised text in the manuscript to clarify some of these points. First of all, the male intron is still in the construct, even though we fused the intron to the tTA gene. The alternative splicing between males and females is caused by use of alternative 5' splice sites, which means the intron that is spliced out in males is just a smaller section of the intron that is spliced out in females. Use of an alternative 5' splice site in males means that a protein-coding sequence with multiple stop codons is incorporated to the mature mRNA. We do not support the precise splicing mechanism with empirical data in this paper, but this has been done in a number of previous publications (https://doi.org/10.1016/j.ibmb.2014.06.001; https://doi.org/10.1371/journal.pone.0056303).

Because the construct works as predicted (100% female lethality in the absence of tetracycline), and we did not change the genetic design in a way that would impact the mechanism of female lethality, we think there is little reason to believe that the splicing is occurring in a different way.

the panels of Figure 2 were not consistent to the legend and confusing; the statistics for different tetracycline concentration tests were not shown in Figure 2 or text to answer their hypothesis "(to) optimize rearing of SSIMS stock, …..we titrated Tet in the food";

We re-wrote the text describing Figure 2 to make the results more clear. We clarified in the legend that the symbol signifies p<0.0001 (we were not trying to imply that all experiments had this level of significance, only the ones marked with the symbol in the figure). We removed the word ‘optimize’ from the main text. Optimization was not the true aim of the experiment, and as the review points out, we did not statistically determine an optimal concentration of Tet. Our main goal was to show a dose- dependent response in the number of females surviving on Tet-free medium, which the data supports and which does not require statistical support.

Figure 3b shows 5-8 day old females were used but in the text it's 5-6 day, and it didn't mention the duration of the first crossing and time lag until the second crossing which are critical in such experiments; the conclusion and statistics for Figure 3c among tests with mixed males should also be mentioned.

We have corrected the figure (now Figure 3c) to indicate that the females were 5-6 days old. The first mating was for 5-6 days and there was no lag time between being co-housed with different males. We have performed multiple new experiments in revision that have been added to Figure 3. We have revised the discussion of these new experiments (and how they relate to the originally performed experiments) in the revised submission.

The discussion is largely towards the merits of SSIMS but missing some key points that might decide how it can be translated into applications or transferred to other species. First, the actual basis for tTA lethality that employed in this study is still unknown which is subject to suppression by a pre-existing inherent variation in the targeted field population. The very phenomenon may also be true for any gene-overexpression-based lethality including EGI lines generated here. Second, the complete penetrance observed from the relatively small sample size here can be hardly used to predict field or mass-rearing condition. Previous study showed that mutations in such lethal construct could occur at a one out of 10,000 frequency, and typical SIT program release millions of sterile insects every week. Third, while the authors claimed SSIMS is "one of the most complex engineered systems in insects", they also proposed that "the genetic design is likely to be portable to other species" without mention any potential obstacles along the way. Therefore, efforts should be made to give full picture of SSIMS including rain and sunshine.

We have added discussion of possible failure modes for this genetic biocontrol approach to the discussion section. We have also added text to discuss how the complexity of SSIMS is a potential obstacle to its translation to non-model organisms.

Author Response:

Reviewer #1 (Public Review):

This manuscript is a follow-up of an earlier manuscript using the LRET technology, but extends the study by identifying a new "open" state and using experimental distance constraints to provide molecular models of the different states. All in all, the manuscript is well written, the experiments are described in sufficient details and experiments are done to high quality with the appropriate controls. The data corroborate the partially open state as published early, but extend the study to a second, open state. It is very good to see that the observed states are not only present in the catalytic head but the authors also use the full-length protein and find similar states. However, in the present manuscript, I find the conceptual advance with respect to the mechanism of MR somewhat limited. The authors curiously do not include any DNA in their structural studies, so the observed states are only relevant for the free MR complex, but not the complex "in action" bound to DNA where quite different conformations might occur. As one consequence, the structurally proposed states do not directly correlate with the functional nuclease states that are necessarily bound to DNA. Perhaps as a consequence, in the author's model, Rad50 is merely a gate-keeper for Mre11, but this is not the case as recent structural work shows that Rad50 forms a joint DNA binding surface with Mre11. Likewise, biochemical studies are done with physiologically unclear/less relevant 3' exonuclease activity only, but not with the physiological important 5' endonuclease activity. In my opinion, it is important for a publication in a journal with the scope of eLife and addressed to a broad audience to provide structural analysis in the presence of DNA and validate the structures using the endonuclease activity.

We thank the reviewer for these comments.

Specific recommendations:

1) Instead of using the physiological unclear exo activity, I suggest to use the more relevant endonuclease activity to validate the mutants.

We now include plate- and gel-based endonuclease activity assays, using a variety of DNA substrates, for all of the validation mutants. We have expanded Fig. 3 and included a new Supplemental Fig. S4 to show this data. We have expanded the Results section of the modified manuscript to present and discuss these findings.

2) Since the authors mutated one side of newly identified/proposed salt-bridges, I also suggest to test whether a charge reversal on both sides of the salt bridge rescues the phenoptype. I find this important because MR has quite many conformations, and mutating a single residue might not unambiguously validate the proposed conformation, a rescue by a charge reversed salt bridge is much stronger.

We thank the Reviewer for this suggested experiment, and we tried to do it. Although we were successful in generating each of the charge reversal mutations in full-length Rad50, all of the mutants unfortunately had issues with either expression or purification. For example, the 6x His-tag for several of the new Rad50 mutants was not accessible to the TEV protease for cleavage indicating that the mutated proteins were mis-folded (the His-tag of the WT full-length Rad50 is readily cleaved off by TEV). As such, we did not feel confident using these proteins in subsequent MR activity assays.

3) Since all LRET experiments are done without DNA, the authors do not capture relevant DNA processing states and comparison of structural (w/o DNA) and biochemical data (w/ DNA) is not really justified, in my opinion. Also, they might miss critical conformations. Is there a technical reason for not including DNA in the LRET studies?

We have collected LRET data on ATP-bound MRNBD in the presence of a hairpin DNA or a ssDNA as substrates. We still observe three states in the presence of both DNAs; however, the open conformation appears to be slightly more compact (i.e., closer distance between Rad50NBD protomers) in the presence of ssDNA. As described above, we have added to the Results section of the modified manuscript and included a new figure (Fig. 4) describing these data.

4) If the authors want to claim processive movement coupled to partially open/open state interchanges, they should provide experimental evidence. Where would the energy come from for such a movement, this is not clear from the model?

On the surface, ATP hydrolysis by Rad50 would seem to be the perfect source of energy for the conformational changes that drive the sequential and/or processive nuclease functions of the MR complex. However, the D313K mutant is not as good at ATP hydrolysis as the wild type enzyme (Fig. 3E), and the data in Fig. 3 and Supplemental Fig. S4 clearly demonstrate that D313K is by far the best nuclease. If the free energy for the movement does not come from ATP hydrolysis, where else could it come? Richardson and co-workers measured a release of -5.3 kcal mol-1 (-22.17 kJ mol-1) of free energy for the hydrolysis of a DNA phosphodiester bond (Dickson, K.S. et al. 2000 J. Biol. Chem. 275:15828–15831). Thus, the free energy released from the Mre11 nuclease activity could be the driving force for the conformational changes we propose. We have made this point in the Discussion of the revised manuscript.

5) The SAXS data for the "open" state do not validate the model, in my opinion. Experimental data and model are not inconsistent, but the curve looks to me as if the open state is perhaps much more flexible (i.e. an ensemble) or extended? Please comment.

We agree with the Reviewer on this point. We have updated Fig. 5A (original Fig. 4) to include the two-state fits to the experimental SAXS data. Although the multi-state fit to the apo MR SAXS data is better than any of the single model fits (2 = 1.05 vs. 1.26, respectively), the 2 is still larger than the multi-state fits to the ATP-bound MR SAXS data. Thus, an additional unobserved conformation (perhaps the so-called “extended”) might be present in solution for apo MRNBD. We have added a sentence to the revised manuscript with this point.

To explore the possibility that the previously described “extended” structure might be contributing to the SAXS data, we built a model of the extended conformation of Pf MRNBD based on the Tm MRNBD structure (PDB: 3QG5) and used Rosetta to connect the coiled-coils and add the linker to the Mre11 HLH. When this model was used in the FoXS calculations for the apo SAXS data, the 2 was 4.77 (versus 2 of 1.26 for the “open” model). The MultiFoXS two-state fit gave 90% open + 10% closed (2 of 1.04), whereas the three-state fit gave 65% open + 20% extended + 15% part open (2 of 0.84). Thus, there is some improvement when using the extended model, but since that model is not measurable in our LRET experiments and we are unsure of its validity as we have modeled it for Pf MR, we have chosen to omit it from the analysis.

6) Distance errors for the full complex are much smaller than those for the catalytic module only (Fig. 1d). Does that mean that the full complex is more rigid, please comment?

From looking at the data presented in Fig. 1D, it is logical to suggest that the full-length complex may be more rigid or better defined by the LRET data. However, we note that there are nearly as many distance errors which are similar between MRNBD and MR as there are MR errors less than MRNBD. And although many are not identical, most are of a similar magnitude. Because of this, we do not think the variations in LRET errors are systematic (i.e., related to a more rigid full-length complex).

propositional attitude SHOULD ONLY BE LEFT PARAGRAPH. Also, there's a bug in the code here.

This makes you think about privilege in another light. Although someone may have privilege they could also have some disadvantages. Which isn't a bad thing but we have to ask ourselves these things.

This really gets to the heart of the matter, it is justifiable that Employees are the lowest of the priorities for an executive.

Based on the article priorities are: 1. External Customers - They bring money into the company 2. Your boss - They being money into you 3. Internal Customers (stakeholders or peers) - They make things work for external customers and your boss 4. Employees - They are paid to work for the company and are the lowest of the four priorities if you have to stack rank

SciScore for 10.1101/2022.01.30.22270029: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations and future clinical considerations: The main limitation of this study was also what made it possible - the unexpected circumstance of supporting families during home-confinement orders. It was not possible to randomise groups or complete formal measures of child and parent outcome, and our satisfaction questionnaires needed to be created very quickly. This was a period of great uncertainty, and relative solidarity, where parents seemed open to try new modes of communication and were motivated to keep a sense of continuity for their child’s program, all of which may have impacted their level of participation and satisfaction. It should also be noted that the parents in our study had all met or worked with their therapists in-person prior to being asked to meet online, which may have increased their willingness to take part in the new approach.1,30 This study did not examine whether a family without previous experience in early intervention would have the same level of engagement with the remote delivery of services. In considering ideal sessions frequency, the current study did not compare parent experience between varying durations of sessions (30 vs 60 vs 90 minutes), which would be important to take into account in future research. The COVID-19 pandemic disrupted our intervention program for children on the autism spectrum, forcing us to re-think our service provision model and giving us the chance to experience very frequent interactions with the families. It r...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

The most interesting/helpful idea here is logos= logic. It's not about emotion or facts, it's using logic to inform the audience/ reader. The four parts help me understand logos the best. They are the claim, data to support it, a warrant connecting the data to the claim, and backing. This is a solid structure to follow when using logos.

Author Response:

Evaluation Summary:

This manuscript addresses a phenomenon of great interest to researchers in cell metabolism and cancer biology: namely, why do cancer cells often secrete high levels of lactate, despite the presence of abundant oxygen to power nutrient oxidation (Warburg effect). The authors propose that lactate export and subsequent extracellular acidification provides a selective advantage and the concomitant rise in intracellular pH is sufficient to drive flux through glycolysis, thereby sustaining the Warburg effect. This is an intriguing hypothesis that ties together many published observations, but it would require further support both from the technical and conceptual side.

The concept proposed in the evaluation summary is not quite correct, in this paper we have tried to show that it is not lactate export that drives extracellular acidification, but that cells which can increase proton export, via over-expression or increased activity of proton exporting proteins, can subsequently drive upregulation of glycolysis and increased lactate production, likely due to increased intracellular pH (pHi) and the ability of glycolytic enzymes to have enhanced activity under slightly higher pHi. As mentioned in the summary, although some of these observations are known, the novelty lies in that they have not been directly proven by inducing acid export prior to a glycolytic phenotype, we believe showing the casual nature of proton export on glycolysis is the novelty of this research.

Reviewer #1 (Public Review):

In this manuscript, the authors tackle an interesting puzzle: why do cancer cells secrete most of their glucose as lactate? The authors propose that acid export is sufficient to enhance glycolysis and provide a selective advantage to cancer cells growing in vivo. To this end, the authors show that clonal lines expressing CA-IX or PMA1, each of which will facilitate proton export, have elevated capacity to acidify extracellular medium and can drive increased migration/invasion and tumor growth or metastases. In support of the model that extracellular pH is a key driver of metastases, the effect of CA-IX expression on lung metastases is reversed following bicarbonate treatment. While many of the individual conclusions of the manuscript are not novel-for example, pH has been reported to control glycolysis and it is established that CA-IX expression modulates migration/metastases-providing a comprehensive assessment of the ability of proton export to drive the Warburg effect, and assessing the significance of metabolic rewiring driven by acid export on tumor growth, would represent an important resource for researchers intrigued by the pervasive observation that cancer cells secrete lactate despite potential bioenergetic disadvantages of discarding biomass.

The strength of the manuscript lies therefore in tying these disparate observations together in a coherent model and testing the role of acid export per se on glycolytic flux. The technical weaknesses of the paper prevent such coherent model building. A major concern is that all cell lines appear to be generated by transient transfection followed by clonal selection, giving rise to cells with notable variability and inconsistent phenotypes. More traditional approaches to manipulate enzyme expression will provide more robust model systems to test the proposed model. Similarly, direct measures of glycolytic flux are required to make conclusions about the role of acid export in promoting glycolysis. Another strength is the use of heterologous enzyme systems to alter proton export in cancer cells, but alternative explanations for these results are not fully considered. Ultimately, to what extent acid export per se, as opposed to altered metabolism driven by acid export, drives enhanced tumor metastases is not addressed.

We agree wholly with Reviewer 1 that although individual components of this manuscript have previously been implicated in cancer research, the novelty lies in directly assessing metabolic changes, specifically the Warburg effect, as a result of proton production to determine causality rather than correlation as previous studies have shown. The reviewer makes a valid point about our use of clones and this is something we considered at length. When originally designing these experiments, we had many conversations within our lab and with collaborators and colleagues, and the overall consensus was that bulk populations are more likely to have heterogeneous expression levels unrelated to transfection, which could result in the phenotype generated being noisy and not indicative of what occurs when proton exporters are over-expressed. We chose to isolate single clones, maintaining these in antibiotic selection media, to ensure stable over-expression. After confirming over-expression, cells were grown without antibiotics and screened regularly for maintenance of protein expression. This was also one of the reasons why we utilized over-expression of two different proton exporters in multiple different cell lines to be confident that proton export was changing the metabolic phenotype and not just due to changes in an individual isolated clonal line. We utilized bulk population for the MOCK clones, to ensure we weren’t selecting for a clone which had inherently different metabolic traits from the parental population. As described in the paper, while some of the behaviors of the different clones are indeed divergent, the impact of expression on increased glucose uptake and lactate production is wholly consistent and highly correlated to expression of PMA1 or CA-IX. Although we utilized metabolic profiling, we do not claim to infer flux from these data. Flux was assessed via lactate production and glucose consumption rates. The metabolomic analyses showed that glycolytic intermediates upstream of Pyruvate Kinase (PK) were uniformly increased in transfectants. This was an unequivocal finding and, given the increased flux, we have concluded that transfection results in activating glycolytic enzymes upstream of PK. The pleiotropic nature of these effects have led us to propose that intracellular pH was increasing and likely enhancing glycolytic enzyme activity throughout the glycolytic pathway. We measured the intracellular pH and showed that it was generally elevated in the transfectants. Finally, the reviewer was concerned that we did not address the mechanism by which pH increases metastases. Such a study would be beyond the scope of this paper and, indeed, was the subject of a two-volume special issue of Cancer Mets. Rev. in 2019 (PMC6625888). Hence, in this paper, we were not trying to address the mechanism by which pH affects metastasis, but simply wanted to show additional biological relevance.

Reviewer #2 (Public Review):

The work by Xu et al proposes that the Warburg effect - the increase of glycolytic metabolism usually displayed by tumor cells, is driven by increased proton excretion rather than by oncogenic dysregulation of glycolytic enzyme levels. As a proof-of-principle, they engineered tumor cells to increase proton excretion. They observed an increase in glycolytic rate, pH, and malignancy in their engineered cells.

1. My main issue with this work is that I do not agree with the authors when they say that the "canonical view" is that oncolytic mutations are thought to drive the Warburg effect. What I understand the consensus to be, is that it is fast proliferating cells - rather than malignant cells - the ones who display this form of metabolism. The rationale is that glycolytic metabolism allows keeping biomass by redirecting lactate and from the phosphate pentose pathway. In contrast, the end product of oxidative phosphorylation is CO2 that cannot be further utilized in cell metabolism.

They claim that they Vander Heiden et al., 2009 shows that "fermentation under aerobic conditions is energetically unfavorable and does not confer any clear evolutionary benefits." This is incorrect. While that review states that the Warburg effect has little effect on the ATP/ADP ratio, they do show this form of metabolism has significant benefits for fast proliferating cells. In fact, the whole review is about how the Warburg effect is a necessary metabolic adaptation for fast proliferation rather than a unique feature of malignant cells.

1. Their main observation is not surprising. From a biochemical standpoint, protons are final product of glycolysis (from the production of lactic acid). Thus, by mass action, any mechanism to remove protons from the cell will result in accelerated glycolytic rate. Similarly, reducing intracellular pH will necessarily slow down LDHA's activity, which in turn will slow down pyruvate kinase and so on.

2. Their experiments are conducted on transformed cells - that by definition - have oncogenic driver mutations. They should test the effect of proton exporter using primary non-transformed cells (fresh MEFs, immune cells, etc). I would expect that they will still see the increase in glycolysis in this case. And yet, I would still have my concerns I expressed in my previous point.

3. The fact that they can accelerate the Warburg effect by increasing proton export does not mean is the mechanism used by tumor cells in patients or "the driver" of this effect. As I mentioned, their observation is expected by mass action but tumors that do not overexpress proton transporter may still drive their Warburg effect via oncogenic mutations. The biochemical need here is to increase the sources of biomass and redox potential and evolution will select for more glycolytic phenotypes.

Comment 1: We disagree with the reviewer that the energetic demands of a faster proliferating cell drive glycolysis in order to produce the biomass needed for generation of new cells. Available evidence does not support this hypothesis. As the reviewer mentioned, there is a correlation between proliferation and aerobic glycolysis (i.e. if cells are stimulated to grow they will consume more glucose), and the same can be said for motility (i.e. more motile cells have higher aerobic glycolysis). This is also true for normal cells and tissues that exhibit high levels of aerobic glycolysis. We agree that glycolytic ATP generation is more rapid than oxidative phosphorylation and that this may confer some selective advantage for transporters, as we described in PMC4060846. Nonetheless, it is clear that under conditions of similar proliferation and motility, more aggressive cancer cells ferment glucose at much higher rates. However, correlations between neither proliferation nor motility are the “Warburg Effect” which is a higher rate of aerobic glycolysis in cancers, regardless of proliferation or migration. As we described in PMID 18523064, the prevailing view in the cancer literature is that the Warburg effect is driven by oncogenes (ras, myc), transcription factors (HIF) and tumor suppressors (p53/TIGAR) through increased expression of glycolytic enzymes. This assumes that expression levels drive flux which has not been proved empirically. In biochemical pathways, it is canon that flux is regulated by demand (e.g. ATP) or through some post-transcriptional control (e.g. pH). In Vander Heiden’s paper the steady state levels are reported of ATP/ADP ratios, not flux. The first paragraph of the intro has been modified to accommodate this concern.

Comment 2: The fact that our results are not surprising is our major argument: i.e. that glycolytic flux can be enhanced by increasing the rate of H+ export. We saw an increase in intracellular pH (pHi), but our metabolomics data do not support a direct effect on LDHA or PK. Instead, we show that clones with higher pHi have a crossover point at PK, due to reduced inhibition of upstream enzymes which is not there in clones at lower pHi.

Comment 3: We agree it would be interesting to study the effects of proton export on immune cells especially given the increase in immunotherapy use in cancer treatment. We did utilize HEK 293 cells shown in supplemental figure S6, to show this was not a cancer cell line specific phenomenon, and we saw increased aerobic glycolysis with over-expression of CA-IX.

Comment 4: We agree that oncogenic mutations can alter glycolytic rate, but we observed that increased expression and activity of proton exporters is sufficient to drive a Warburg effect. Although the reviewer indicates that glycolysis is responsible for generating the biomass needed for these faster proliferating cells, we have shown that proton exporter driven aerobic glycolysis does not increase proliferation rates. The literature, see Vander Heiden’s paper below, suggests that amino acids, mainly glutamine, can support the majority of biomass needs of a proliferating cell. Hence, reliance on aerobic glycolysis remains energetically inefficient and inefficient in that most of the carbons are removed, and thus will not be selected by evolution.

Hosios, A.M., Hecht, V.C., Danai, L.V., Johnson, M.O., Rathmell, J.C., Steinhauser, M.L., Manalis, S.R., & Vander Heiden, M.G. (2016). Amino Acids Rather than Glucose Account for the Majority of Cell Mass in Proliferating Mammalian Cells. Developmental cell, 36 5, 540-9 .

Reviewer #3 (Public Review):

The authors claim that "proton export drives the Warburg effect". For this, they expressed proton-exporting proteins in cells and measured the intracellular proton concentration and the Warburg effect. Based on their data, however, I do not see elevated Warburg effect in these cells and thus conclude that the claim is not supported.

The authors concluded that the CA-IX or PMA1 expressing cells had increased Warburg effect. I don't think this conclusion can be made based on the data presented. For the MCF-7 cells, the glucose consumption is ~18 pmol/cell/24hr (Fig. 5E) and lactate production is ~0.6 pmol/cell/24hr (Fig. 5F), indicating that 0.6/18/2 = 1.7% of the glucose is excreted as lactate. This low percentage remains true for the PMA1 expressing cells. For example, for the PMA1-C5 cells, the percentage of glucose going to lactate is about 1.8/38/2 = 2.4% (Fig. 5EF). While indeed there was an increase of both the glucose and lactate fluxes in the PMA1 expressing cells, the vast majority of the glucose flux ends up elsewhere likely the TCA cycle. This is a very different phenotype from cancer cells that have Warburg effect. The same calculation can be done for the CA-IX cells but the data on the glucose and lactate concentration there are inconsistent and expressed in confusing units (which I will elaborate in the next paragraph). Nevertheless, as there were at most a few folds of increase in lactate production flux in the M1 and M6 cells, the glucose flux going to lactate production is likely also a few percent of the total glucose uptake flux. Again, these cells do not really have Warburg effect.

The glucose and lactate concentration data are key to the study. The data however appear to lack consistency. The lactate concentration data in Fig. 1F shows a ~5-fold increase in the M1 and M6 cells than the controls but the same data in S. Fig. 2 shows a mere ~50% increase. The meaning of the units on these figures is not clear. While "1 ng/ug protein" means 1ng of lactate is produced by 1 ug protein of cells over a 24 hour period, I do not understand what "ng/ul/ug protein" means (Fig. 1F). Also, "g/L/cell" must be a typo (S. Fig. 2). Furthermore, regarding the important glucose consumption flux, it is not clear why the authors did not directly measure it as they did for the PMA1 cells (Fig. 5E). Instead, they showed two indirect measurements which are not consistent with each other (Fig. 1E and S. Fig. 1).

The reviewer pointed out discrepancies in our data and, upon reviewing, we have identified a dilution error leading to miscalculation of glucose consumption in Fig 5E. We have also repeated these experiments which agree with our re-calculation. Originally, it appeared from the data we presented that there was very little lactate flux, we have re-calculated the glucose excreted as lactate (average % using data from Fig. 5E and 5F) and present in a table below. We do believe we observed a Warburg effect in our proton exporting cells consistently. The reviewer points out that we utilized multiple methods to measure glycolysis in these cells leading to inconsistency, however we felt using multiple methods/instruments/kits to assess glucose consumption, lactate production, and glucose induced proton production rates was a strength of our findings as we consistently saw increased glycolysis in our proton exporting clones, irrespective of proton exporter, cell line, or method utilized. We are also not suggesting that glucose is solely being metabolized through glycolysis and do agree that it can metabolized through other metabolic pathways too such as TCA cycle, as the reviewer stated. The units used for these graphs are described in the methods and figure legends, in some assays such as Fig. 1F lactate was graphed as the ng of lactate per ul of cell culture media and then normalized per ug protein, which was determined by calculating the protein concentration of cells per well of the assay. Supplementary figure 2 has been re plotted per 10K cells to match other normalization values in the paper. Fig 1E and Fig. S1 are two different time points, M6 acidified media faster than M1 and this is likely why at 1 hour we are not yet seeing substantial increase in glucose uptake of M1.