Evolution of genome fragility enables microbial division of labor

  1. E.S. Colizzi
  2. B. van Dijk
  3. R.M.H. Merks
  4. D.E. Rozen
  5. R.M.A. Vroomans

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Abstract

Division of labor can evolve when social groups benefit from the functional specialization of its members. Recently, a novel means of coordinating division of labor was found in the antibiotic-producing bacterium Streptomyces coelicolor , where functionally specialized cells are generated through large-scale genomic re-organization. Here, we investigate how the evolution of a genome architecture enables such mutation-driven division of labor, using a multi-scale mathematical model of bacterial evolution. We let bacteria compete on the basis of their antibiotic production and growth rate in a spatially structured environment. Bacterial behavior is determined by the structure and composition of their genome, which encodes antibiotics, growth-promoting genes and fragile genomic loci that can induce chromosomal deletions. We find that a genomic organization evolves that partitions growth-promoting genes and antibiotic-coding genes to distinct parts of the genome, separated by fragile genomic loci. Mutations caused by these fragile sites mostly delete growth-promoting genes, generating antibiotic-producing mutants from non-producing (and weakly-producing) progenitors, in agreement with experimental observations. Mutants protect their colony from competitors but are themselves unable to replicate. We further show that this division of labor enhances the local competition between colonies by promoting antibiotic diversity. These results show that genomic organization can co-evolve with genomic instabilities to enable reproductive division of labor.

Motivation of current work

Division of labor can evolve if trade-offs are present between different traits. To organize a division of labor, the genome architecture must evolve to enable differentiated cellular phenotypes. Cell differentiation may be coordinated through gene regulation, as occurs during embryonic development. Alternatively, when mutation rates are high, mutations themselves can guide cell and functional differentiation; however, how this evolves and is organized at the genome level remains unclear. Here, using a model of antibiotic-producing bacteria based on multicellular Streptomyces, we show that if antibiotic production trades off with replication, genome architecture can evolve to support a mutation-driven division of labor. These results are consistent with recent experimental observations and may underlie division of labor in many bacterial groups.

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    Reply to the reviewers

    This is a revision plan, the manuscript has not been modified yet as it is being transferred to a journal.

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

    This study proposes (and uses) an elegant model of bacteria evolution to study how division of labor can emerge through the interaction between non-random mutations (occurring at some specific ``fragile' genomic sites) and genome architecture. The study is very interesting and the results are convincing. My main concerns are about the presentation of the model and results. Although I am confident about the results, some elements should be clarified for a better understanding and for a correct interpretation of the results. Two points in particular (detailed below as major comments) require clarification.

    Major comments:

    • the notion of telomere/centromere is used all throughout the paper but I think it is used in a misleading way. First, it seems that here there is only one telomere (but this is actually a detail of the model). More importantly, as long as I know, it is well known that in S. coelicolor the sequence degenerates more rapidly when getting closer to the telomeres (but telomeres are defined independently from this property). But here, the notion of telomere is precisely directly determined by its mutational instability (respectively, the centromere is defined by its stability). Although this is reasonable given the objective of the model, it forbid the use of sentences like "we observed that the genome of the evolved colony founded had two distinct regions: a telomeric [...] and a centromeric [...]" (line 234) or "When bacteria divide, mutations induced at fragile sites lead to the deletion of the part of the genome distal to them, causing large telometic deletions" (line 239 - this is not a result but a hidden description of the model) as this distinction between the two regions is not an outcome of the simulation but rather given a priori as a coded property of the fragile sites that all lead to deletions on the same -- called telomeric -- side (of course, formally if the genome contains no fragile site, there is no distinction but still). Please clarify this in the main text and in the methods. *

    Authors response (AR, in the following): we agree with the reviewer that the directionality of the deletions determines centromere and telomere in our model (and the reviewer is correct that we only consider one arm of the chromosome). We will explicitly state both in the main text and in the methods that the model does not include any explicit centromeric and telomeric structure, and that the polarity of the genetic information (and thus centromere and telomere) depends on the choice of directionality of the deletions.

    - In most part of the paper (methods, results, figures, sup mat...) antibiotics are considered to have a concentration (or a high/low production) but at least twice in the text (lines 165 and 488) it is said that only the presence/absence of antibiotics is modelled. I was not able to understand how the continuous values are transformed into presence/absence (is there a threshold?) but more importantly, I strongly suspect that this choice has a strong influence on the outcome. For instance, with a diffusion radius equals to 10, it means that an antibiotics producing cell is able to protect 2*\pi*10=~60 replicating cells. Hence, one could conjecture that the fraction of antibiotic-producing mutants should a little more than 2%... which is what is observed by the authors. So (1) please clarify this point (2) discuss (or experiments) the consequences of this choice on the conclusion.

    AR: the reviewer is correct that antibiotics are modelled as presence/absence – this was done for computational efficiency. However, the probability that a bacterium deposits an antibiotic at a site within the deposition radius is a continuous number, as it depends on the number of antibiotic genes and growth genes. We will make this clear in the main text and in the methods.

    Secondly, we show the effect of varying the deposition radius for the evolutionary dynamics in Supplementary Section S17. We will make this clear in the main text. For the area covered by different radius of antibiotic deposition, please see below.

    Minor comments:

    • line 262: "We conclude that genome architecture is a key prerequisit for the maintenance of mutation-driven division of labor". Given the model hypotheses you cannot be so affirmative (it is a key prerequisit... in this model!) *

    AR: we will modify the statement as suggested. *

    - line 286: "cannot" is probably too strong. It has not been observed...

    AR: we will modify the statement as suggested.

    - line 288 and following: you seem to consider that there is "selection for diversity". Given the large number of possible antibiotics and given that cells are "automatically" resistant to the antibiotics they produce, could it be simply drift? There is a clear selection pressure to limit the number of growth-promoting genes but no such pressure exist for antibiotics. Hence their number could simply drift (note that figs 2 and SF1 both use a log scale; random variations due to drift could be hidden by the log. Fig. SF2 does use a log scale and shows a dynamics that---to my eyes---claims for drift rather than for selection of diversity).

    AR: we agree with the reviewer that drift might contribute to the overall antibiotic diversity. This might be especially true for the antibiotic genes residing downstream of the fragile sites, which have low probability of expression in the wild-type (because of the many growth genes) and are deleted in the mutants. Duplications, deletions and modifications of these genes are effectively neutral, and are therefore likley subject to drift. We will include this discussion in the main text. However, bacteria are highly susceptible to the diverse antibiotics produced by other colonies (i.e. those produced – largely – by the mutants). These antibiotics and their diversity drives colony invasion and is thus selective. The overall number and diversity of antibiotics is therefore, at least in part, under selection.

    *- line 340: "ends" should be "end" when discussing the model

    • line 345: "a telomeric region" should be "telomeric regions" when discussing the bacteria
    • line 359: "S. ambofaciens" should be italic
    • line 365: same for "Streptomyces"*

    AR: we will modify the statement as suggested (and thank the reviewer for carefully reading the text).

    - line 245 states that colonies begin clonally but methods (lines 434-438) don't support this. Colonies don't begin clonally but they begin without antibiotic-producing spores (see also line 618)

    AR: we agree with the reviewer that colonies are not specifically initialised as clonal. We will modify the sentence as: By this process colonies eventually evolve to become functionally differentiated throughout the growth cycle.

    *- line 442: "their" should be "its"

    • line 446: "hotspot for recombination" no, for "deletion"
    • line 449: please remove brackets around the reference.*

    AR: we will modify the statement as suggested.

    - line 458: if I understood it correctly, there is no explicit competition in the model. Competition simply comes from the asynchronous replication. Am I true? Could you clarify that point?

    AR: The reviewer is correct that through asynchronous updating only one focal lattice site is update at a time. However, if a site is empty, the bacteria surrounding it are competing based on their replication rate kreplication. Dividing by the neighbourhood size (eta) simply ensures that a bacterium surrounded by a completely empty neighborhood replicates on average alpha_g times (alpha_g being the max growth rate). We will mention this in the methods.

    - line 490: "the antibiotic deposited is chosen randomly and uniformly among them". This is not fully clear. I suppose the bacteria is still resistant to all the antibiotics it \it{can} produce?

    AR: Yes. This is mentioned in the methods section “Replication”.

    - figure SF1: please use the same scales as in figure 2 such that the two plots can be easily compared

    AR: we will modify the x-axis to include the number of growth cycles.

    - section S3 and figure SF4: What is to be understood from the figure is not clear to me. Seems that WTs win only if generalists produce less AB or replicate slower (?) Is it true?

    AR: The reviewer is correct. In other words: when the artificial generalist has the same replication rate and the same antibiotic production rate as the WT, then the competition experiment ends with a near draw (the generalist still wins, but slowly). This means that the fitness cost associated to division of labor, i.e. to having two cell types doing the same work as one generalist – is small.

    We will include this description in the section.

    The figure is unfortunately complicated by the fact that we do not know a-priori how high the effective antibiotic production rate is (because antibiotics are spatially distributed by the stochastically generated mutants) – and so we had to make a large parameter screen to figure out the parameter values for which the competition experiment made most sense.

    - I found it very difficult to draw conclusion from section S4, S5 and S6. These experiments should be analyzed with the help of mathematical analyses of the equations. Moreover, the understanding of these results are rendered difficult due to the lack of clarity regarding the discrete (or not) nature of the antibiotic production/action/diffusion

    AR: We hope that we have clarified the distinction between antibiotic production rate and antibiotic presence/absence in the lattice.

    The model is not amenable to analytical tractability, which makes it difficult to make exact statements based on the equations that govern it. However, we can check that the model is robust, and identify regions of parameter space where the model behaves in a qualitatively similar way to main text results.

    Sections S4, S5 and S6 are essentially parameter screens to verify that the model reproduces the results reported in the main text for a broad range of parameters. The primary conclusion that can be drawn is that the model is robust to parameter changes.

    Section S4 explores the model robustness to changes in two key parameters of the model: the antibiotic inhibition due to growth genes beta_g and the parameter h_g, which is the number of growth genes that produces half-maximum growth rate. Section S5 further analyses the relation between these parameters, and how they together determine the strength of the trade-off. Section S6, finally, shows that a strong trade-off is not a necessary requirement for evolution of division of labor as the division also depends (in a counterintuitive way) on the parameter alpha_g, the maximum antibiotic production rate.

    We will include and expand these summarizing statements in each section, to make clear what each section achieves.

    - S7 and fig SF9. It is unclear to me why the fraction of mutants decrease along time elapsed in the cycle. Please explain.

    AR: The reason is that not all mutants are born with the same number of antibiotic genes (Fig. 3A). A mutant with fewer antibiotic genes might be susceptible to some of the antibiotics produced by another mutant, and could be killed by these antibiotics. Once a mutant is killed in the inner colony, a wt will replicate to fill the spot, and likely a wt offspring will take that site rather than another mutant. Thus there is a decline in overall mutant population.

    We will include this discussion in Section S7.

    - Figure SF14: what are the tin lines? if they correspond to the five repeats, how can it be that the bold line be the median?

    AR: we realise that the caption should be clearer. Each of the five lines (both bold and thin) in each pane represents the median number of genetic elements over time. The bold line just highlights one randomly chosen simulation (the same for each genetic element), to better guide the eye.

    We will clarify the caption of the figure.

    - S13 and figure SF15: given that AB concentration is ON/OFF, is this result really surprising? This also questions about the accumulation of AB genes in the original model. Although the authors regularly claim that this is due to selection for diversity, drift could also be at play (see above)

    AR: As mentioned above, we agree with the reviewer and we will mention that drift may co-determine antibiotic gene accumulation.

    - S17: for radius 1, 2 and 3, the aliasing is likely to be strong. Hence, the results cannot be interpreted with this sole information. Please give e.g. how many cells are "protected" for each radius (e.g. for r_{alpha}=1, this value can vary between 1 and 9!)

    AR: for radius=1, 2, 3, 5 ,8, 10 the area covered by antibiotic production is respectively 5 ,13, 29, 81, 197, 317. We will include this information in the figure.

    - L742: "matching the antibiotic bitstring with the bitstring of the antibiotic". True and actually elegant but simpler formulation could ease the reading...

    AR: We will change the sentence as follows: “Both antibiotics and antibiotic genes are characterised by a bitstring, which determines their type. Antibiotic resistance in the model is determined by matching these two strings.”

    - lines 746-751 and figure SF21: There again, could it be a consequence of the AB ON/OFF diffusion model?

    AR: we agree with the reviewer that a continuous diffusion model could affect resistance to antibiotics. We expect that the main effect will come from some antibiotics antibiotics having different concentrations. For instance, we could have a situation in which many deleterious antibiotics are produced in small amount, but have a compounding effect on the susceptible bacterium. This finer model of antibiotic production, diffusion and killing was not included in the model to limit the computational load.

    - S18-S19-S20: what should the reader understand from these results? Please better comment the figures.

    AR: we agree that figures in Section S18,19 and 20 could have more descriptive captions. Sections S18, 19 and 20 are parameter screen to check that the model is robust to changes in the mutation rates affecting fragile sites activation and de-novo formation. The primary result of Section S18 is that that division of labor evolves over a broad range of fragile site activation rates and de-novo fragile site formation rates (and even when these parameters are decreased by one order of magnitude).

    Section S19 shows how these combination of parameters result in quantitative changes in genome composition.

    Section S20 shows that the de-novo fragile site formation rate can be zero: as long as the system is initialised genomes that can divide labor, the fragile sites will persist even though no new ones are generated.*

    • CROSS-CONSULTATION COMMENTS Sorry about the confusion about the computation of the number of cells protected by a single AB-producing cell. Of course it is of the order 10*\pi^2 !!! The global argument still holds but the number of cells protected is of course larger than 60 (note that, due to aliasing at the periphery the exact number of cells in the protected area is difficult to determine). *

    Author response: We hope the clarifications mentioned above answer the reviewer’s comment.

    Reviewer #1 (Significance (Required)):

    First, an very importantly, I must say that I am no familiar with the biological model (Streptomyces coelicolor). So I am not fully able to judge the biological significance of this research (i.e. whether the way division of labor is achieved here enlights---or not---the biology of this bacteria). However, on the computational side, the model and the results (as they are summarized in the conclusion) are very interesting on their own and deserve publication.

    Remark: a lots of supplementary results are added to the paper that are not not fully explained or analysed. Please, better discuss all these results and their significance.

    AR: we will extensively check and add detail to the supplementary material, ensuring that results are fully explained (see also response to reviewer 1).*

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

    The manuscript "Evolution of genome fragility enables microbial division of labor" presents a model of genetically-based division of labour in bacterial colonies. It is postulated that two essential processes, growth and the important for elimination of competitors production of antibiotics, are poorly compatible in a single cell. The beneficial for a colony cell specialization is assumed to be determined only by genetic differences that appear via deletions of growth- promoting loci. These deletions and production of various antibiotics are mediated by a rather elaborate genetic architecture, which includes position-sensitive "fragile" sites, mutable antibiotic and growth-promoting genes. The model produces rather predictable results that under sufficiently strong incompatibility between growth and antibiotic production, the long-term evolution results in formation of mosaic of colonies, each specialized in production of its specific set of antibiotics. Such production is facilitated by evolving rapidly mutable genomes that constantly generate non-reproducing antibiotic-pumping cells.

    The model appears very thoroughly developed and analyzed, and all major conclusion are intuitively appealing. Overall, the manuscript reads as a well-written quantitative proof of the principle of genetically-based division of labour between bacterial cells. The only part of the model that I'm a bit sceptical about is the unwarranted complexity of the genetic architecture. Unless the introduction of "fragile" sites and the directional ordering of genes is strongly justified by empirical data, a simpler and more clear assumption about mutational incapacitation of growth genes would suffice to reproduce the predicted phenomenology. So adding such empirical evidence would boost the relevance of the genetical part of the model. In the present form, all observed adaptations are inevitable simply because the expected division of labour will not evolve without each of them due to the design of the model.

    AR: We agree with the reviewer that a simpler model with a predetermined effect of mutations, such as to incapacitate the growth genes, would suffice to reproduce the phenomenology of the mutation-driven division of labor observed in Streptomyces. Adding the complexity of a genome architecture introduces one more hypothesis: that genome fragility can evolve to organize the division of labor. This hypothesis, supported by the results presented here, can be tested experimentally.

    However, there is already some empirical support for our modelling choices: 1) mutation rates along the genome of Streptomyces are highly heterogeneous, 2) the genetic content is partitioned along the chromosome so that some genes are preferentially located in the mutationally quiet centromere, and others are in the mutationally active (sub)telomeric regions, 3) some cis genetic elements in Steptomyces’ genomes readily recombine to produce large-scale duplications and deletions (which we heavily simplified in the model as deletion-inducing fragile sites).

    We will extend the introduction to include the references for the empirical support to our model.

    A couple of minor comments...

    217 This is achieved when fewer growth-promoting genes are required to inhibit antibiotic 218 production (i.e. lower βg). Shouldn't it be "larger \beta_g"?

    AR: yes. Thanks for catching this!

    Whether in the main text or Supplementary materials, it woud help to add a complete population dynamics equation with all gain and loss terms.

    AR: we agree with the reviewer that it would be interesting to obtain a comprehensive population dynamics equation that captures the spatial dynamics of replication, mutation, and antibiotic production, causing colony formation and between-colony competition. However, deriving such equation would be a very big effort in itself, and we suspect that it would not be analytically tractable. Because of this, we prefer the “procedural” model description we gave – which also mirrors the model implementation (see github repository at github.com/escolizzi/strepto2).

    Strikingly, we find the opposite: division of labor evolves when 224 bacteria produce fewer overall antibiotics (lower αa), under shallow trade-off conditions 225 (hgβg = 5; see Suppl. Section S6).

    I don't see why it is"striking". It seems perfectly explicable that a smaller \alpha requires more dedication to antibiotic production, thus favouring specialization.

    *AR: we agree that we have not conveyed why we found this result surprising. We have set the trade-off shallow enough (h_g beta_g =5) that the generalist wins when alpha_g =1. In addition, lowering alpha_a makes the benefit of creating a mutant smaller, because a highly specialised mutant with zero growth genes makes fewer antibiotics. A generalist is proportionally less affected. Intuitively, we have compunded two benefits for the generalist.

    But division of labor evolves, outcompeting the generalist – which surprised us.

    We will modify the paragraph to better explain what we expected, and we will tone down the wording, removing the word “strikingly”.

    *Reviewer #2 (Significance (Required)):

    Due to my relative lack of familiarity with the literature on evolution of genetically-based division of labour, I would rather not comment on the degree of innovation of the manuscript.

    The text is well written and is accessible to a wide readership, so it could be recommended to a general biological or evolutionary journal.

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

    Summary: In this manuscript the authors explore the co-evolution of genomic architecture and division of labour in antibiotic production, in a model inspired by the bacterium Streptomyces coelicolor. In the model a genetic trade-off is implemented where the having a large number of growths promoting genes (and thus fast growth) leads to a low production of antibiotics. On the other hand, having fewer growth promoting genes allows for a higher production of antibiotics. This trade-off selects for a division of labour, where one sub population specializes in antibiotic production and another sub population specializes in reproduction. This division of labour is achieved by evolving the genome structure, so that growth promoting genes are clustered together, separated from the rest of the genome by several fragile sites (sites that allow for large deletions). This allows a single mutational event to delete a large number of growth-promoting genes, which creates a cell, lacking growth genes and that thus has a high antibiotic production (cell specializing in antibiotic production). In other words, the genome structure evolves to shape evolvability, so as to allow cells with a high growth rate to rapidly and repeatably evolve/mutate into cells with a high antibiotic production. This creates a division of labour where a part of the population specializes in growth/reproduction and another part specializes in antibiotics production. This model provides a tangible mechanism to explain a similar division of labour observed in S. coelicolor. This mechanism also fits well with the large deletions observed in antibiotic-hyperproducing S. coelicolor cells, which are also repeatably generated during colony growth.

    Major comments: -Line 69, It would be good to give a bit more information here on the (number of) different types of antibiotics produced by S. coelicolor, to help the reader understand some of the modelling choices later on, such as allowing for the evolution of a large number (16 or higher if I understand correctly) of different antibiotics and a cell automatically being resistant to all antibiotics it produces (instead of having separate resistance genes). *

    AR: we agree with the reviewer that adding this information would put the model more in focus. The total number of antibiotics that can be produced by the genus Streptomyces has been estimated to be of the order of 100000 (ten to the fifth, [Watve et al., 2001]). Although we use S. Coelicolor as reference model organism for our computational model, we simulate long-term evolutionary dynamics that diversify the antibiotic repertoire. Each antibiotic is represented by a 16 bits string, meaning that there are 2^16 (= 65536) possible antibiotics in the system – consistent with the number of possible antibiotics in the genus.

    This being said, our model genomes evolve to have many more antibiotic genes than typical Streptomyces. Each species in the genus has up to 30 biosynthetic gene clusters [Genilloud, O. (2014)], a fraction of which make antibiotics. We discuss this discrepancy and propose solutions for this in the Discussion (also see below).

    Regarding the possibility of separating antibiotic resistance from antibiotic synthesis: we (and most literature on the eco-evolutionary dynamics of antibiotic-producing bacteria) simplified antibiotic production as depending on individual “antibiotic biosynthetic genes”. In reality several genes in a cluster must be expressed to synthesize an antibiotic. A typical biosynthetic gene cluster also encodes resistance genes for the cognate antibiotic, to prevent cell suicide [Mak et al., 2014] – hence antibiotic genes providing resistance in the model. This being said, Streptomyces genomes also host resistance genes to antibiotics for which they have no biosynthetic pathway themselves, including efflux pumps that give some nonspecific resistance [Nag et al 2021].

    Modelling antibiotic synthesis in more detail would allow to make a better model of antibiotic evolution, as well as to enrich the social dynamics of the model – because “cheaters” could evolve that are resistant but do not contribute to the antibiotics in the colony. These questions are certainly interesing, but would further complexify the model. They are exciting venues for future model expansions.

    We will include the literature mentioned above in the introduction, and use these references to better motivate the model.

    -Lines 127-129 It is mentioned here fragile sites in the genome might represent transposable elements or long inverted repeats. Would both of these types of fragile sites behave the same? Has it been shown that both transposable elements and long inverted repeats can lead to large deletions from a linear chromosome? It would be nice to have a bit more background on how fragile sites might work or what they might look like in an empirical context. I am a bit unsure on this, but depending on their exact empirical nature, should fragile sites not also lead to increased rates of gene duplication near themselves? *

    AR: we see that we have not made a clear connection between the introduction, where we introduce the mutational dynamics of Streptomyces, and the methods, where we introduce fragile sites.

    Briefly, both duplications and deletions occur in Streptomyces, as well as circularization of the linear chromosome, conjugation, etc. [Hoff et al 2018,Tidjani et al 2019]. However, the outcome of all these mutations is biased towards deletion [hoff et al 2018, Zhang et al, 2020, Zhang et al, 2022]. There are many mechanisms involved in producing these mutations, forming the mutational hotspots, handling DNA breaks, and in the horizontal transfer of genetic material [Tidjani et al 2019; Lorentzi et al, 2021]. As the reviewer suggests – they do not behave all in the same way. To construct the model, we simplified all these mutational mechanisms into one genetic element, the “fragile site”, and assumed that they are solely responsible for the chromosomal-scale mutations that produce deletions.

    We will add this information to the introduction (see also response to reviewer 2), and refer to it in the methods.

    -Line 160 As alluded to before, given the introduction provided, two assumptions come about here (lines 160-166) that lack a bit of justification/background/context. First, why does one allow the evolution of such a relatively large number of antibiotics? A bit more empirical in the introduction background would go a long way to making this assumption seem more justified. As far as I can see the genomic architecture leading to division of labour is only demonstrated for values of v that are 6 (i.e. 64 antibiotics) or above. Perhaps it is because I lack empirical background here, but this still seems to be a relatively large antibiotic space. Does the model also work with v=2? Perhaps it would be good to show a simulation with v=2 in supplementary material S16 as well. *

    AR: Hopefully the previous comment on the number of possible antibiotics also clarifies this point.

    We will carry out a simulation with v=2.

    -Line 166 The assumption is made that if a bacterium produces a certain antibiotic, it is automatically resistant to this antibiotic. Now it could be that this assumption is empirically rooted, in which case it would be good to allude to this empirical justification. I wonder how would the results be impacted if the resistance genes were separated from the antibiotic production genes? (I do not think additional simulations are in any way necessary on this point, but some more context/thoughts on this matter would be helpful, perhaps near lines 306-309) *

    AR: Please see response to major comment on the possibility of separating antibiotic resistance from antibiotic synthesis. We will add the discussion there in the Discussion session.

    -Figure 1 In the subscript it becomes evident that the probability of large deletions due to fragile sites is much higher (10 fold) than single gene duplications, it seems to me this should be the other way around, single gene duplications and deletions could be much more probable than fragile site induced large deletions. Would the model still produce the same results if the values for mu-d and mu-f were switched around? (Again, I do not think additional simulations are per se required, some justification for this assumption would already be plenty).

    AR: We chose these parameter values because, empirically, large scale chromosomal rearrangements (deletions) occur more frequently than single gene duplication/deletion in Streptomyces – as they are the primary mechanism for Streptomyces development and division of labor. We now mention this in the caption of Fig. 1.

    Still, would we expect results to be affected if mu_d > mu_f? We do not think so, for the following reason: mu_d and mu_f are per-gene probabilities, so the genomic probability of duplication/deletion and of fragile site activation will depend on the evolved number of genes.

    in Fig. 5 we show that mu_f can be decreased by more than one order of magnitude and results do not change qualitatively. To compensate for a smaller per-gene deletion rate (mu_f), the evolved number of fragile sites per genome becomes larger (Suppl. Section S19, Fig. SF23). A similar compensatory increase of fragile sites could happen if duplications and deletions rate per gene were larger.

    Minor comments: -Line 36, perhaps replace "must" with " can" as there are other ways to achieve a division of labour that do not hinge on genomic architecture such as those listed in the next sentence. This sentence seems at odds with the next one, which lists ways to achieve cell differentiation that do not per se completely rely on genomic architecture such as gene regulation. Maybe consider moving this sentence to be on line 40 (after "...organized at the genome level remains unclear") *

    AR: we will modify the text as suggested by the reviewer

    -Line 48, perhaps remove "disposable" as there is no particular reason the somatic tissue is disposable, furthermore it invokes the disposable soma theory of aging which is not relevant here *

    AR: we will remove “disposable”.

    -Line 147-148 Why these particular relationships, as a reader I do not understand how these functions were constructed and how they might influence the results, a bit more justification might be helpful. Perhaps later on (results/discussion) also address what might happen if you were to use different functions? *

    AR: we agree that these functions could use a little more explanation. The probability of replication is a function that increases with the number of growth genes. We assume that the function saturates, as growth cannot be arbitrarily large even if the genome hosts many growth genes. So we need at least two parameters: one for the maximum growth rate (alpha_g), and another that controls the curvature of the function (h_g). A simple choice is a Hill function, but other saturating functions would likely work just as well (e.g. an exponential function with a form alpha_g*(1-exp(-g/h_g)). Similarly, antibiotic synthesis inhibition from growth genes should tend to zero for larger numbers of growth genes, hence the exponential (but we expect that a hyperbolic form e.g 1/(1+g/beta_g) would work just the same).

    As this discussion is rather technical, we will include it in the methods section.

    -I am clearly biased on this matter, since I work on evolvability. So, the authors should feel free to ignore this comment. Regardless, I think the authors have shown a wonderful example of the evolution of evolvability. Perhaps it would be nice to add a little bit of an evolvability angle in the discussion. In particular thinking about how fragile sites shape evolvability. *

    AR: we agree with the reviewer that the work is a clear form of evolution of evolvability. We now explicitly mention this in the discussion.

    -Lines 404-411 It is great to see that the authors consider the wider applicability of their findings. It would be nice to add something here about the broader applicability in bacteria. As a large number of bacteria have circular chromosomes, how would these findings be impacted if circular chromosomes were at play? (I suspect they would largely still work in the same way, but keen to hear what the authors think). Referring to the work of Yona et al. 2012 on transient chromosomal duplications in yeast due to heat stress might also be good here, to show the more general applicability of the authors findings, this is another example where genomic architecture shapes evolvability. Yona AH, Manor YS, Herbst RH, Romano GH, Mitchell A, Kupiec M, Pilpel Y, Dahan O. Chromosomal duplication is a transient evolutionary solution to stress. Proc Natl Acad Sci U S A. 2012 Dec 18;109(51):21010-5. doi: 10.1073/pnas.1211150109. Epub 2012 Nov 29. PMID: 23197825; PMCID: PMC3529009. *

    AR: Bacteria show many forms of targeted mutational dynamics (we do already mention CRISPR and HGT). It recently came to our attention that many bacterial and archea genomes host so-called Diversity-Generating Retroelements (DGR) [Macadangdang et al, 2022]. DGRs accelerate microbial evolution at specific sites and generate functional diversity. We will include this reference in the discussion.

    We thank the reviewer for pointing us to the work on chromosomal duplication in yeast – we will also incorporate this “dramatic” form of duplication in the discussion.

    -Lines 412 -419 I agree with the authors that in practice the cells specializing in antibiotic production look somewhat like soma, however I would consider not using this term here as strictly speaking the antibiotics producing cells can still reproduce (be it at an extremely low rate, which leads to their loss). *

    AR: We tone down both mentions of soma, as follows: “This gives rise to a division of labor driven by mutation, reminiscent of the division between germ and soma in multicellular eukaryotes.”

    And, in the last sentence, we write: “...mutant cells *effectively* function as soma by enhancing...”

    - Lines 434-438 If I understand correctly authors did not explicitly model the sporulation process (instead selecting random cells from the end of a cycle). I think this is a very good modelling choice that should not be changed; however, I do wonder how the results would be affected if sporulation was more explicitly modelled (for example by adding genes for sporulation, creating a 3 way trade-off between growth, sporulation and antibiotic production). Perhaps something that could be mentioned in the discussion.

    AR: we agree with the reviewer that more complex evolutionary problem could be implemented in the system, e.g. through a gene type required for sporulation. They would likely have interesting outcomes. For instance, some bacteria may decide never to sporulate, while others could enhance their antibiotic resistance by turning into spores. Moreover, including additional functions together with an evolvable gene regulation could better capture the developmental dynamics observed through the life cycle of Streptomyces.

    I hope this review is of some use and helps the improvement of this manuscript.

    Yours sincerely,

    Timo van Eldijk

    Reviewer #3 (Significance (Required)):

    Significance: This study provides a clear conceptual advance by showing and studying how genome structure can evolve to create a division of labor. Thereby mechanistically explaining the division of labor in antibiotic production observed in S. coelicolor. It seems evident to me that whilst this study mainly focuses on S. coelicolor, the mechanism likely plays an important role in microbial evolution in general. Though others have previously theoretically explored such mechanisms, this study provides the first exploration modelled closely after an empirical system and hence provides a significant advance. In a more general sense, the evolution of genome architecture likely governs evolvability not just in microbes but in all life on earth. Therefore, I believe that this paper would be interesting for a general audience interested evolution. It would be of particular interest to those studying microbial evolution. My expertise lies in evolutionary biology, theoretical biology, microbial evolution and palaeontology. *

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    Referee #3

    Evidence, reproducibility and clarity

    Summary:

    In this manuscript the authors explore the co-evolution of genomic architecture and division of labour in antibiotic production, in a model inspired by the bacterium Streptomyces coelicolor. In the model a genetic trade-off is implemented where the having a large number of growths promoting genes (and thus fast growth) leads to a low production of antibiotics. On the other hand, having fewer growth promoting genes allows for a higher production of antibiotics. This trade-off selects for a division of labour, where one sub population specializes in antibiotic production and another sub population specializes in reproduction. This division of labour is achieved by evolving the genome structure, so that growth promoting genes are clustered together, separated from the rest of the genome by several fragile sites (sites that allow for large deletions). This allows a single mutational event to delete a large number of growth-promoting genes, which creates a cell, lacking growth genes and that thus has a high antibiotic production (cell specializing in antibiotic production). In other words, the genome structure evolves to shape evolvability, so as to allow cells with a high growth rate to rapidly and repeatably evolve/mutate into cells with a high antibiotic production. This creates a division of labour where a part of the population specializes in growth/reproduction and another part specializes in antibiotics production. This model provides a tangible mechanism to explain a similar division of labour observed in S. coelicolor. This mechanism also fits well with the large deletions observed in antibiotic-hyperproducing S. coelicolor cells, which are also repeatably generated during colony growth.

    Major comments:

    • Line 69, It would be good to give a bit more information here on the (number of) different types of antibiotics produced by S. coelicolor, to help the reader understand some of the modelling choices later on, such as allowing for the evolution of a large number (16 or higher if I understand correctly) of different antibiotics and a cell automatically being resistant to all antibiotics it produces (instead of having separate resistance genes).
    • Lines 127-129 It is mentioned here fragile sites in the genome might represent transposable elements or long inverted repeats. Would both of these types of fragile sites behave the same? Has it been shown that both transposable elements and long inverted repeats can lead to large deletions from a linear chromosome? It would be nice to have a bit more background on how fragile sites might work or what they might look like in an empirical context. I am a bit unsure on this, but depending on their exact empirical nature, should fragile sites not also lead to increased rates of gene duplication near themselves?
    • Line 160 As alluded to before, given the introduction provided, two assumptions come about here (lines 160-166) that lack a bit of justification/background/context. First, why does one allow the evolution of such a relatively large number of antibiotics? A bit more empirical in the introduction background would go a long way to making this assumption seem more justified. As far as I can see the genomic architecture leading to division of labour is only demonstrated for values of v that are 6 (i.e. 64 antibiotics) or above. Perhaps it is because I lack empirical background here, but this still seems to be a relatively large antibiotic space. Does the model also work with v=2? Perhaps it would be good to show a simulation with v=2 in supplementary material S16 as well.
    • Line 166 The assumption is made that if a bacterium produces a certain antibiotic, it is automatically resistant to this antibiotic. Now it could be that this assumption is empirically rooted, in which case it would be good to allude to this empirical justification. I wonder how would the results be impacted if the resistance genes were separated from the antibiotic production genes? (I do not think additional simulations are in any way necessary on this point, but some more context/thoughts on this matter would be helpful, perhaps near lines 306-309)
    • Figure 1 In the subscript it becomes evident that the probability of large deletions due to fragile sites is much higher (10 fold) than single gene duplications, it seems to me this should be the other way around, single gene duplications and deletions could be much more probable than fragile site induced large deletions. Would the model still produce the same results if the values for mu-d and mu-f were switched around? (Again, I do not think additional simulations are per se required, some justification for this assumption would already be plenty).

    Minor comments:

    • Line 36, perhaps replace "must" with " can" as there are other ways to achieve a division of labour that do not hinge on genomic architecture such as those listed in the next sentence. This sentence seems at odds with the next one, which lists ways to achieve cell differentiation that do not per se completely rely on genomic architecture such as gene regulation. Maybe consider moving this sentence to be on line 40 (after "...organized at the genome level remains unclear")
    • Line 48, perhaps remove "disposable" as there is no particular reason the somatic tissue is disposable, furthermore it invokes the disposable soma theory of aging which is not relevant here
    • Line 147-148 Why these particular relationships, as a reader I do not understand how these functions were constructed and how they might influence the results, a bit more justification might be helpful. Perhaps later on (results/discussion) also address what might happen if you were to use different functions?
    • I am clearly biased on this matter, since I work on evolvability. So, the authors should feel free to ignore this comment. Regardless, I think the authors have shown a wonderful example of the evolution of evolvability. Perhaps it would be nice to add a little bit of an evolvability angle in the discussion. In particularl thinking about how fragile sites shape evolvability.
    • Lines 404-411 It is great to see that the authors consider the wider applicability of their findings. It would be nice to add something here about the broader applicability in bacteria. As a large number of bacteria have circular chromosomes, how would these findings be impacted if circular chromosomes were at play? (I suspect they would largely still work in the same way, but keen to hear what the authors think). Referring to the work of Yona et al. 2012 on transient chromosomal duplications in yeast due to heat stress might also be good here, to show the more general applicability of the authors findings, this is another example where genomic architecture shapes evolvability. Yona AH, Manor YS, Herbst RH, Romano GH, Mitchell A, Kupiec M, Pilpel Y, Dahan O. Chromosomal duplication is a transient evolutionary solution to stress. Proc Natl Acad Sci U S A. 2012 Dec 18;109(51):21010-5. doi: 10.1073/pnas.1211150109. Epub 2012 Nov 29. PMID: 23197825; PMCID: PMC3529009.
    • Lines 412 -419 I agree with the authors that in practice the cells specializing in antibiotic production look somewhat like soma, however I would consider not using this term here as strictly speaking the antibiotics producing cells can still reproduce (be it at an extremely low rate, which leads to their loss).
    • Lines 434-438 If I understand correctly authors did not explicitly model the sporulation process (instead selecting random cells from the end of a cycle). I think this is a very good modelling choice that should not be changed; however, I do wonder how the results would be affected if sporulation was more explicitly modelled (for example by adding genes for sporulation, creating a 3 way trade-off between growth, sporulation and antibiotic production). Perhaps something that could be mentioned in the discussion.

    I hope this review is of some use and helps the improvement of this manuscript.

    Yours sincerely,

    Timo van Eldijk

    Significance

    This study provides a clear conceptual advance by showing and studying how genome structure can evolve to create a division of labor. Thereby mechanistically explaining the division of labor in antibiotic production observed in S. coelicolor. It seems evident to me that whilst this study mainly focuses on S. coelicolor, the mechanism likely plays an important role in microbial evolution in general. Though others have previously theoretically explored such mechanisms, this study provides the first exploration modelled closely after an empirical system and hence provides a significant advance. In a more general sense, the evolution of genome architecture likely governs evolvability not just in microbes but in all life on earth. Therefore, I believe that this paper would be interesting for a general audience interested evolution. It would be of particular interest to those studying microbial evolution. My expertise lies in evolutionary biology, theoretical biology, microbial evolution and palaeontology.

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    Referee #2

    Evidence, reproducibility and clarity

    The manuscript "Evolution of genome fragility enables microbial division of labor" presents a model of genetically-based division of labour in bacterial colonies. It is postulated that two essential processes, growth and the important for elimination of competitors production of antibiotics, are poorly compatible in a single cell. The beneficial for a colony cell specialization is assumed to be determined only by genetic differences that appear via deletions of growth- promoting loci. These deletions and production of various antibiotics are mediated by a rather elaborate genetic architecture, which includes position-sensitive "fragile" sites, mutable antibiotic and growth-promoting genes. The model produces rather predictable results that under sufficiently strong incompatibility between growth and antibiotic production, the long-term evolution results in formation of mosaic of colonies, each specialized in production of its specific set of antibiotics. Such production is facilitated by evolving rapidly mutable genomes that constantly generate non-reproducing antibiotic-pumping cells.

    The model appears very thoroughly developed and analyzed, and all major conclusion are intuitively appealing. Overall, the manuscript reads as a well-written quantitative proof of the principle of genetically-based division of labour between bacterial cells. The only part of the model that I'm a bit sceptical about is the unwarranted complexity of the genetic architecture. Unless the introduction of "fragile" sites and the directional ordering of genes is strongly justified by empirical data, a simpler and more clear assumption about mutational incapacitation of growth genes would suffice to reproduce the predicted phenomenology. So adding such empirical evidence would boost the relevance of the genetical part of the model. In the present form, all observed adaptations are inevitable simply because the expected division of labour will not evolve without each of them due to the design of the model.

    A couple of minor comments...

    217 This is achieved when fewer growth-promoting genes are required to inhibit antibiotic 218 production (i.e. lower βg). Shouldn't it be "larger \beta_g"?

    Whether in the main text or Supplementary materials, it woud help to add a complete population dynamics equation with all gain and loss terms.

    Strikingly, we find the opposite: division of labor evolves when 224 bacteria produce fewer overall antibiotics (lower αa), under shallow trade-off conditions 225 (hgβg = 5; see Suppl. Section S6).

    I don't see why it is"striking". It seems perfectly explicable that a smaller \alpha requires more dedication to antibiotic production, thus favouring specialization.

    Significance

    Due to my relative lack of familiarity with the literature on evolution of genetically-based division of labour, I would rather not comment on the degree of innovation of the manuscript.

    The text is well written and is accessible to a wide readership, so it could be recommended to a general biological or evolutionary journal.

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    Referee #1

    Evidence, reproducibility and clarity

    This study proposes (and uses) an elegant model of bacteria evolution to study how division of labor can emerge through the interaction between non-random mutations (occurring at some specific ``fragile' genomic sites) and genome architecture. The study is very interesting and the results are convincing. My main concerns are about the presentation of the model and results. Although I am confident about the results, some elements should be clarified for a better understanding and for a correct interpretation of the results. Two points in particular (detailed below as major comments) require clarification.

    Major comments:

    • the notion of telomere/centromere is used all throughout the paper but I think it is used in a misleading way. First, it seems that here there is only one telomere (but this is actually a detail of the model). More importantly, as long as I know, it is well known that in S. coelicolor the sequence degenerates more rapidly when getting closer to the telomeres (but telomeres are defined independently from this property). But here, the notion of telomere is precisely directly determined by its mutational instability (respectively, the centromere is defined by its stability). Although this is reasonable given the objective of the model, it forbid the use of sentences like "we observed that the genome of the evolved colony founded had two distinct regions: a telomeric [...] and a centromeric [...]" (line 234) or "When bacteria divide, mutations induced at fragile sites lead to the deletion of the part of the genome distal to them, causing large telometic deletions" (line 239 - this is not a result but a hidden description of the model) as this distinction between the two regions is not an outcome of the simulation but rather given a priori as a coded property of the fragile sites that all lead to deletions on the same -- called telomeric -- side (of course, formally if the genome contains no fragile site, there is no distinction but still). Please clarify this in the main text and in the methods.
    • In most part of the paper (methods, results, figures, sup mat...) antibiotics are considered to have a concentration (or a high/low production) but at least twice in the text (lines 165 and 488) it is said that only the presence/absence of antibiotics is modelled. I was not able to understand how the continuous values are transformed into presence/absence (is there a threshold?) but more importantly, I strongly suspect that this choice has a strong influence on the outcome. For instance, with a diffusion radius equals to 10, it means that an antibiotics producing cell is able to protect 2\pi10=~60 replicating cells. Hence, one could conjecture that the fraction of antibiotic-producing mutants should a little more than 2%... which is what is observed by the authors. So (1) please clarify this point (2) discuss (or experiments) the consequences of this choice on the conclusion.

    Minor comments:

    • line 262: "We conclude that genome architecture is a key prerequisit for the maintenance of mutation-driven division of labor". Given the model hypotheses you cannot be so affirmative (it is a key prerequisit... in this model!)
    • line 286: "cannot" is probably too strong. It has not been observed...
    • line 288 and following: you seem to consider that there is "selection for diversity". Given the large number of possible antibiotics and given that cells are "automatically" resistant to the antibiotics they produce, could it be simply drift? There is a clear selection pressure to limit the number of growth-promoting genes but no such pressure exist for antibiotics. Hence their number could simply drift (note that figs 2 and SF1 both use a log scale; random variations due to drift could be hidden by the log. Fig. SF2 does use a log scale and shows a dynamics that---to my eyes---claims for drift rather than for selection of diversity).
    • line 340: "ends" should be "end" when discussing the model
    • line 345: "a telomeric region" should be "telomeric regions" when discussing the bacteria
    • line 359: "S. ambofaciens" should be italic
    • line 365: same for "Streptomyces"
    • line 245 states that colonies begin clonally but methods (lines 434-438) don't support this. Colonies don't begin clonally but they begin without antibiotic-producing spores (see also line 618)
    • line 442: "their" should be "its"
    • line 446: "hotspot for recombination" no, for "deletion"
    • line 449: please remove brackets around the reference.
    • line 458: if I understood it correctly, there is no explicit competition in the model. Competition simply comes from the asynchronous replication. Am I true? Could you clarify that point?
    • line 490: "the antibiotic deposited is chosen randomly and uniformly among them". This is not fully clear. I suppose the bacteria is still resistant to all the antibiotics it \it{can} produce?
    • figure SF1: please use the same scales as in figure 2 such that the two plots can be easily compared
    • section S3 and figure SF4: What is to be understood from the figure is not clear to me. Seems that WTs win only if generalists produce less AB or replicate slower (?) Is it true?
    • I found it very difficult to draw conclusion from section S4, S5 and S6. These experiments should be analyzed with the help of mathematical analyses of the equations. Moreover, the understanding of these results are rendered difficult due to the lack of clarity regarding the discrete (or not) nature of the antibiotic production/action/diffusion
    • S7 and fig SF9. It is unclear to me why the fraction of mutants decrease along time elapsed in the cycle. Please explain.
    • Figure SF14: what are the tin lines? if they correspond to the five repeats, how can it be that the bold line be the median?
    • S13 and figure SF15: given that AB concentration is ON/OFF, is this result really surprising? This also questions about the accumulation of AB genes in the original model. Although the authors regularly claim that this is due to selection for diversity, drift could also be at play (see above)
    • S17: for radius 1, 2 and 3, the aliasing is likely to be strong. Hence, the results cannot be interpreted with this sole information. Please give e.g. how many cells are "protected" for each radius (e.g. for r_{alpha}=1, this value can vary between 1 and 9!)
    • L742: "matching the antibiotic bitstring with the bitstring of the antibiotic". True and actually elegant but simpler formulation could ease the reading...
    • lines 746-751 and figure SF21: There again, could it be a consequence of the AB ON/OFF diffusion model?
    • S18-S19-S20: what should the reader understand from these results? Please better comment the figures.

    Referees cross-commenting

    Sorry about the confusion about the computation of the number of cells protected by a single AB-producing cell. Of course it is of the order 10*\pi^2 !!! The global argument still holds but the number of cells protected is of course larger than 60 (note that, due to aliasing at the periphery the exact number of cells in the protected area is difficult to determine).

    Significance

    First, an very importantly, I must say that I am no familiar with the biological model (Streptomyces coelicolor). So I am not fully able to judge the biological significance of this research (i.e. whether the way division of labor is achieved here enlights---or not---the biology of this bacteria). However, on the computational side, the model and the results (as they are summarized in the conclusion) are very interesting on their own and deserve publication.

    Remark: a lots of supplementary results are added to the paper that are not not fully explained or analysed. Please, better discuss all these results and their significance.

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