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Downloaded by guest on September 27, 2021 cooperation of parameter key science. engineering a and as predictive microbial relatedness advance of to interest relevance central the Hamilton’s and and of life-history generality insights the other supports in This details. variation encompass tax- that microbial broad levels Hence, over onomic cooperation . of gut-microbe predictive in is coop- relatedness evolution the of content predictive is relatedness erative forms that six find and we gut cooperation, of the of sur- diversity full is the across microbes, analyses comparative in phylogenetic Using controversy. particularly by rounded relatedness, of power the However, predictive cooperation. relatedness should as so equal, increases, being individuals else among all Therefore, car- individual . an similar of rying reproduction the helping by genes its transmit indirectly of can that cooperator A is framework. explanation inclusive-fitness Hamilton’s management, leading A manipulation. microbial as such successful achieve therefore to is cooperate crucial of microbial dis- driver why human major Understanding in a ease. including 2020) as communities, 29, microbial known July of review is functioning for This the (received . 2020 their 17, December exploit coop- approved microbes eratively and molecules, goods” MO, “public Louis, of St. secretion the Louis, Through St. in University Kingdom Washington United Strassmann, 3BF, E. EH9 Joan Edinburgh by Edinburgh, Edited of University Sciences, Biological of School , Systems and a Simonet Camille in microbiota cooperation gut of the evolution the explains selection Kin PNAS relatedness genetic their by weighted benefits (rb recipient by the overcome are to helper the provided is to relative costs to Therefore, fitness close genes (20). when its favored indirectly a on albeit pass helping generation, still next by can the individual reproduction cooperative a own selfish Even (19). reproduce, its explanation: cost by sacrificing the an invasion of provides if any from theory paying kin-selection risk without Hamilton’s reward at the are reaping cheats, behavior such exhibiting communities. these up to making species evolution essential the the therefore on in influence cooperation is broad of it a having MCs, factors of the understand engineer evolution including and and 17), dynamics predict (16, the To communities (18). real communities of -associated demon- the function have in interactions and studies cooperative such evolution “omics” of Recent role important 15). the strated (14, habitat mod- and their exploit ify cooperatively microbes enzymes, (6–13). molecules, toxins, signaling as environment such or their goods,” “public modify of and secretion the with Through interact they how of as translation those the up facilitate tools. making actionable they into species hold theory secondly, ability. microbial consistently of predictive and, that diversity broad communities, predictions vast of the allow theories and across are theories science MCs this such predictive of to Firstly, more Crucial field into 5). the advance (4, requires to engi- research communities successful microbiome such The food (3). of sustainable regulation neering climate to and (1) (2) manipulation production microbiome from lenges M evolutionary nttt fEouinr ilg,Sho fBooia cecs nvriyo dnug,EibrhE93L ntdKndm and Kingdom; United 3FL, EH9 Edinburgh Edinburgh, of University Sciences, Biological of School Biology, Evolutionary of Institute o oprto vle spzln eas populations because puzzling is evolves cooperation How well as lives, microbes’ to central are interactions Cooperative > 01Vl 1 o e2016046118 6 No. 118 Vol. 2021 Hmlo’ ue) hsgvsacnrlrl ogenetic to role central a gives This rule”). “Hamilton’s c, ag fapiain ntemdto u oit’ chal- society’s our of midst the to in key applications is of range (MCs) a communities microbial complex anaging | oprtv analysis comparative a,1 n ueMcNally Luke and | microbiome a,b | doi:10.1073/pnas.2016046118/-/DCSupplemental at online information supporting contains article This 1 ulse eray1 2021. 1, February Published See under distributed BY).y is (CC article access open This Submission.y Direct PNAS a is article This C.S. interest.y research; competing no performed declare paper.y authors C.S. the The wrote research; L.M. and designed C.S. L.M. and data; and analyzed C.S. contributions: Author pce neatos(.. hnpbi od rvd interspecific provide inter- goods that public suggested when cooperative been (i.e., of interactions has species it types Secondly, different species. across and consistent predic- production behavior that be means public-good not effect may of of direction tion the level in variability relationship the Such negative (34). and a becomes privatized to relatedness partly good leading be between public therefore can trait, the has biol- competitive good receptors), it a the public -specific example, of a with For details when (e.g., behavior. the that cooperative on shown particular been depend a may the of effect cooperation, ogy drives its relatedness three of if 28–33), direction even 24, power 23, Firstly, predictive 19, microbes. and generality 13, in its (12, on doubt laboratory cast arguments the main in in and vertebrates field to the from cooperation of then evolution is microbes? question predictive in broadly cooperation The relatedness of evolution Is key. the practice: of in is true unifying is principle this hugely a are whether unifying identifies that MCs such it mastering of that diverse, of context sce- the in generality particular In (27). the useful [e.g., parameter 26)], is taxa (25, of prediction preadaptations amount Hamilton’s limited upon a calling 24)] to narios (23, apply genes specific that greenbeard on or or based (22) lead pleiotropy predictions should [e.g., to relatedness mechanisms Contrary increased cooperation. equal, more being to gen- great else of All (21) prediction a benefits erality: generates fitness theory indirect Hamilton’s 1A). those (Fig. limits it because relatedness, owo orsodnemyb drse.Eal [email protected] Email: addressed. be may correspondence whom To omnte,sc stegtmicrobiota. gut the as such microbial communities, engineering for to opportunity variable an offer unifying of may manipulation relatedness a The applications. to broad and critical science predictive is Explain- microbiota. patterns gut holds broad-scale human content the ing gene of diversity cooperative full predict the to show- across power by arguments. relatedness its those of that role ing for central ever the support without rehabilitate but we taxonomic Here, microbes, broad in the prediction testing this dismissed of have validity arguments the Various cooperation. general the species. of drive very should microbial evolution relatedness the genetic of increased pat- makes that number prediction the theory a explain inclusive-fitness across to Hamilton’s cooperation attempting study of comparative tern a is This Significance lhuhknslcinhsbe edn xlnto o the for explanation leading a been has selection kin Although online o eae otn uha Commentaries.y as such content related for https://doi.org/10.1073/pnas.2016046118 raieCmosAtiuinLcne4.0 License Attribution Commons Creative . y https://www.pnas.org/lookup/suppl/ b etefrSynthetic for Centre | f10 of 1

EVOLUTION Fig. 1. Genetic relatedness in the human gut microbiome. (A) Schematic illustration of indirect fitness benefits. The cooperative cell loses the opportunity to produce c daughter cells (cost c). The help provided to the recipient cells allows them to each produce an additional b daughter cells (benefit b). The cooperative genes of the altruist cell are “indirectly transmitted” if the benefits provided enhance, on average, the reproduction of cells that also carry those cooperative genes, i.e., are genetically related; r > 0. (B) Methods schematic summary. Detailed within- and across-samples core size and nucleotide diversity are given in Dataset S1. SNPs, single-nucleotide polymorphisms. (C) Relatedness measures obtained for 101 species of the human gut microbiome. Vertical ticks are single point estimates of relatedness. The number of point estimates (i.e., number of hosts within which each species was found) is indicated on the right. The black dots represent the mean. Blue ticks are values between 25% and 75% quantiles.

benefit) may render relatedness unimportant at driving cooper- in real-world, complex communities. Third, theoretical work ation within species. This has been observed in the production predicts that the population-genetics effects at work in the kin- of (a secreted iron-scavenging molecule acting as a selection framework may be unimportant in microbes owing to public good) in . In conditions such that strong selection (25, 36, 37). Together, these arguments suggest siderophores also provided cross-species benefits (environment that intraspecific relatedness may have minor or idiosyncratic detoxification), the addition of a compost community allowed effects on the evolution of cooperation in microbes. the growth of noncooperators, irrespective of the level of related- Although these studies highlight potential limitations in the ness (35). This challenges the effective importance of relatedness power of relatedness to predict the evolution of microbial

2 of 10 | PNAS Simonet and McNally https://doi.org/10.1073/pnas.2016046118 explains the evolution of cooperation in the Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 i eeto xlisteeouino oprto ntegtmicrobiota gut the in cooperation of evolution the explains selection Kin McNally and Simonet h ul-po eoe ihseishvn ihrrelated- 1A; (Fig. higher genes having social species more carry with to genome, expected a ness of build-up the genes captures secretion, it in themselves. that secretions involved in than components rather secretome cellular the structural from for coding differs cooperation it sixth a as as class, perceived cooperation. be can bacterial systems of secretion forms Finally, well-described four (public are create Second, enzymes -degrading (50). they it and microbes that siderophores, other from quorum-sensing, on biofilm, effects directly reduced antagonistic have the benefit they perspective, of if can cell’s because kin producing or the good) their First, from because cooperation. cooperative either can as bacterial “secretomes”) as seen of to be classes referred (henceforth broad reproducing products secreted six sexually in for siblings eration, as related gut-microbiota as most least for species. at their that within greater is with means interact relatedness host potentially This they average vast conspecific 1C). the an species, the species (Fig. had the that 0.5 analysis) of than found (>90% this and species in relatedness genetic gut-microbiome included pairs the of the calculate host–species estimates of to majority observed obtained hosts all We all for across 1B). and (Fig. similar- on host genomic relatedness capitalized the a compute we within to Therefore, ity (49) hosts. tools competition analysis across spread var- that strain-level in globally strains meaning hosts occur (48), bacterial colonize assemblages can and parallel, the dynamic 47) and In (46, at mod- ious areas host. other immune-system geographic whole each large host over the with (e.g., of interact 45) scale potentially (5, host their population- ulation), their Through average individuals). on the (competing to effect similarity relative genetic behavior) wide altruistic genetic the the of of beneficiaries (potential measure individuals statistical interacting between a similarity (see is hosts Relatedness details). 227 host to 0.004 2 from in ranged detected selected relative this mean In (Collinsella the analysis. species, coverage this of minimum for set meeting requirements We genera) prevalence microbiome. (37 gut and the species in 101 species of identified diversity relat- large computed a we for (44), Microbiota. edness metagenomes Gut stool Human human healthy the 239 in Cooperation and Relatedness Results of forms different six on 37 cooperation. relatedness encompassing microbial of microbiota, effect gut the human testing the genera, of diversity full the promiscuity as such proxy of (41). than relatedness level rather genomic data, actual using single from a 3) on and focus behavior), studies cooperative existing different most very (while have of that contexts com- behaviors ecological number 2) cooperative of large ecology, variety different a a paring with including species 1) distant set by broad phylogenetically a idiosyncrasies over gen- ecological power of predictive of claim relatedness the assesses test rule. It to Hamilton’s erality: system of excellent in test an additional analysis constitute mere Microbes comparative a than a more been is Conducting have microbes none (38), microbes. 43)], [shrimps (42, in species Hymenoptera performed and animal (41), of range 40), (39, a stud- the for such com- predict exist While a role can traits. ies use cooperative relatedness broad to of whether the distribution is assess phylogenetic of cooperation to test of analysis evolution ultimate parative the The across in life. importance relatedness of actual of tree their microbial assess not the do they cooperation, h i-eeto rmwr a eapidt understand to applied be can framework kin-selection The coop- for propensity species’ microbial each assessed then We across analysis comparative phylogenetic such conducted We sp o02 (Prevotella 0.26 to 62205) aae S1 Dataset copri o per-species-per- for n was and 61740) IAppendix, SI Using P (β relatedness Appendix (SI genes essential constant of process set Text). a gene-sampling SI with with a genes genome from cooperative a arises in of simply number which the size, genome of scaling nonlinear tial model, response (Poisson n models mixed using cooperation, phylogenetic of Bayesian form each for cooperation, in and relatedness Evolution Content Genomes. Gene Gut-Microbe Cooperative Predicts Theory Hamilton’s distinct two pro- be (e.g., would which one ). likely pyochelin, cooperative than still and more are of genes ducing siderophore production biosynthesis more mean pyoverdine but to (e.g., necessary genes), coop- be 14 of may the involves classes gene of other one expression For than for are more goods. measures), which public (GO proteins, different eration secreted be different to two genes likely secretome are in two there phenotypes secretome, cooperative that the of mean for number example, For the class. of that measure a is measures, ation other S1 (Dataset five annotations the (GO) S1–S5). Figs. ontology For Appendix, gene products. used secreted For we localization- 2). sequences for protein-coding sequence-motif-based (Fig. of number coding a cooperation the count of to used tool of classes prediction we number six secretome, their those quantifying the in by falling content, genome genes their of basis Text SI tv feto eaens ntenme fgnscdn for coding (respectively: genes classes of biofilm number and siderophores: the siderophores on for relatedness cooperation of effect itive coli; (Escherichia coding ( genes r with relatedness of species measured number gut-microbiome lowest the the genome the between in for products increase controlling secreted 60% for after a predict that we means size, coefficient this cally, 6 2.03; −0.26, P biofilm: frltdeso h ubro ee novdi cooperation in esti- effect involved genes global class-specific of significant each number a (β the in found on we certainty relatedness approach, of the with this of content, for With estimate gene mate. accounting cooperative global the CI a on a obtain relatedness the of to effect across cooperation the meta-analysis of random-effects forms a S1 different captured used (Dataset successfully we genes cooperation Therefore, of of sets differ- forms measure distinct have distinct our that Here, six cooperation constraints. across evolutionary of and forms diversity, ecological various microbial ent the across across also both but relatedness, of importance (respec- 6. degradation antibiotic quorum-sensing: and tively: quorum- systems, for effect secretion significant no sensing, was There S1). Table Appendix, lsial,teifiieiln oe rdcsta na nnt or infinite an in species. that of predicts dynamic model infinite-island ecological the the Classically, by shaped Microbiome. likely Gut is itself the edness in Relatedness and Ecology Organismal (SI estimates relatedness in Appendix, uncertainty the for accounting when P .2 n h pce ihtehgetmaue relatedness measured highest the with species the and 0.12) = MCMC MCMC MCMC 7 0 pce,3 eea.Ormdln consfrpoten- for accounts modeling Our genera). 37 species, 101 = u ups eei ognrlz ocuin bu the about conclusions generalize to is here purpose Our o ertm ie efudasgicn oiieefc of effect positive significant a found we size, secretome For cooper- of class given a for genes of number the case, each In 0 = × 10 .78, .Teeoe emaue irb oprtvt nthe on cooperativity microbe measured we Therefore, ). 7 = 6 = 1 = −1 β IText SI .9 . . erto systems: secretion ; Std 2 5 1 = P × × × MCMC .error β .06, 0 = 10 10 10 1 = −2 −1 −5 n alsS n S5). and S2 Tables and 5 rdbeitra [CI interval credible 95% .59, r 1 = .56, CI i.3; Fig. ; niitcdegradation: antibiotic ; ; .3.W lofudasgicn pos- significant a found also We 0.93). = 0 = .Teerslshold results These S6 ). Table Appendix, SI 95 etse o nascainbetween association an for tested We .4 . β CI -au 3.99, = z-value 194, 0 = × 0 = 95 10 6 2.10; .06, 0 = .Biologi- S1). Table Appendix, SI .39, −1 https://doi.org/10.1073/pnas.2016046118 ; 2 2.78; .42, β al S1). Table Appendix, SI rspltihca bacterium; Erysipelotrichaceae CI 0 = and 95 .48, P = MCMC i.S6). Fig. Appendix, SI 4 2.21; −1.34, P CI MCMC CI 4 = β 95 95 0 = 95 = .6 ] 9 = PNAS 0 = =0. 7 3.08; −2.27, .86, × .5 10 0 1.16; .40, P 8 1.07; 08, × | MCMC −2 CI and Relat- 10 f10 of 3 ; 95 −3 SI SI = = , ,

EVOLUTION Fig. 2. Genetic relatedness and cooperation across the gut-microbiome phylogeny. Relatedness is the mean genetic relatedness. The secretome is the number of protein-coding sequences coding for a secreted product. The five other forms of cooperation are measured as the number of protein-coding sequences annotated with a GO term falling in that cooperation category. n = 101.

very large number of demes all connected by migration, related- Relatedness Holds the Same Effect on Cooperation after Account- ness should decrease with both group size and migration (51, 52) ing for the Ecological Factors Shaping It. Given their significant (Fig. 4, Upper). We constructed a Bayesian phylogenetic mixed effects on relatedness, it is possible that group size and migra- model of within-host relatedness to test these predictions over tion rate could be the drivers of the apparent effect of relatedness the pattern of relatedness we measured across the human gut on cooperation via some alternative mechanism. We tested this microbiota. by including sporulation score and mean relative abundance Adaptations facilitating migration should correlate with gut- in our phylogenetic mixed models as predictors of coopera- species migration rate. The ability to form is an adap- tion. We found that relatedness remained significantly predictive tation that allows efficient dispersal of the through (with similar effect size) of microbe cooperative gene content the environment and among hosts (53). Hence, we computed after controlling for sporulation scores and relative abundance sporulation scores as a first-order proxy for migration rate. We (results for the models for each form of cooperation reported used within-host relative abundance to account for group size. in SI Appendix, Table S4; random-effect meta-analysis over the In agreement with the predictions of the infinite-island model, six models: βmean relatedness = 0.74, Std.error = 0.20, z − value = −4 we found a negative relationship between sporulation scores 3.75, CI95 = 0.35, 1.13; PMCMC = 1.78 × 10 ; SI Appendix, and relatedness (β = 0.69, −CI95 = −1.25, 0.10; PMCMC = 1.5 × Table S6). 10−2; Fig. 4, Lower Left; SI Appendix, Table S3). However, we found a positive relationship between relative abundance and A Mix of Direct and Indirect Effects. Hence, we find that these −2 relatedness (β = 0.09, CI95 = 0.008, 0.17; PMCMC = 2.5 × 10 ; two ecological factors shape relatedness, but that related- Fig. 4, Lower Right). ness retains the same predictive power after controlling for

4 of 10 | PNAS Simonet and McNally https://doi.org/10.1073/pnas.2016046118 Kin selection explains the evolution of cooperation in the gut microbiota Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 oiiesgicn feto urmsnigadamarginal both a a that and themselves quorum-sensing shows on have also effect score significant model sporulation positive and The abundance factors. relative shows panel ecological secretome The lines). trends. solid these and the circles illustrate (filled to -negative lines and trend lines) least-square dashed ordinary and are circles Lines (open cooperation. Gram-positive of for lines form trend each separate for relatedness mean of function eaieaudne(rx o ru ie ntegntcrltdesi h ua u irboe ersinlnsadpCCsonaefo the microbiota gut from the are in cooperation shown of pMCMC evolution the and explains selection lines Kin Regression (n microbiome. predictors McNally fixed and gut as Simonet human abundance relative the within-host in and relatedness score (Lower sporulation genetic with (52). the relatedness (2010) within-host on al. of size) et model Mouden group mixed El phylogenetic for from (proxy by abundance derived were relative equilibria Theoretical model. island 4. Fig. mixed phylogenetic each for (B distribution CI). posterior 95% the and of (mean interval meta-analysis credible random-effects 95% the the from and obtained mean estimate the the is represent effect” bar “overall horizontal The and model. dot The relatedness. on 3. Fig. oa ersincefiin siae fcooperation of estimates coefficient regression Total (A) cooperation. of forms several over microbes gut in cooperation predicts relatedness Genetic h clgcldieso eaens.(Upper relatedness. of drivers ecological The Siderophores degradation A

Relatedness Relatedness Secretome Antibiotic Secretion systems Overall −1.0 −0.5 0.0 0.5 1.0 0.00 0.25 0.50 0.75 1.00 effect .000 .200 .40.05 0.04 0.03 0.02 0.01 0.00 . . . . 0.5 0.4 0.3 0.2 0.1 − 2 Sporulation score Migration rate − Estimated effect 0123 2 1 10 hoeia rdcin o h feto irto aeadgopsz nrltdesi h infinite the in relatedness on size group and rate migration of effect the for predictions Theoretical ) (pMCMC =0.01) B eaaayi oe ugssa vrl agnlefc of effect sporulation of marginal effect no overall but The cooperation, an S4). on Table abundance suggests relative Appendix, model (SI degradation meta-analysis antibiotic on effect Proportion of cooperative genes (10 −03) Relatedness Relatedness 0230 240 36

−1.0 −0.5 0.0 0.5 1.0 0.00 0.25 0.50 0.75 1.00 1 0.5 0 1 0.5 0 1 0.5 0 Within hostrelativeabundance . . . . 0.8 0.6 0.4 0.2 0.0 10203040500 bevdefcso prlto poyfrmgain and migration) for (proxy sporulation of effects Observed ) Group size Mean relatedness

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EVOLUTION (βrelative abundance = 1.79, Std.error = 1.02, z − value = 1.75, Discussion −2 CI95 = −0.21, 3.78; PMCMC = 8.00 × 10 and βsporulation score = Relatedness in the Human Gut Microbiome. Defining the “refer- −0.30, Std.error = 0.69, z − value = −0.44, CI95 = −1.66, 1.05; ence” and the “target” populations, respectively, implies a choice −1 PMCMC = 6.62 × 10 ; SI Appendix, Table S6). about the scale of competition (who do beneficiaries and altru- Finally, we tested if the association between cooperation and ists compete against?) and the scale of interaction (who are the relatedness might actually be owing to reverse causation, i.e., if potential beneficiaries?). Precise quantification of those scales, cooperation drives relatedness. To do so, we included coopera- and the population genetic processes at work within the human tion along with the ecological predictors in our model predicting gut microbiome, is a current avenue of research enabled by within-host relatedness. We did not detect a significant effect for recent advances in strain-level resolution bioinformatics tools any of the six forms of cooperation (SI Appendix, Table S5) or a (54). Evidence of the worldwide spread of strains (46, 47) and joint effect (Wald test on the posterior joint distribution of the host-strain replacement (48) suggests that the across-host popu- 2 six cooperative traits: Chi = 1.66, df = 6, p-value = 0.95). lation is the relevant scale of competition. Regarding the scale To summarize (Fig. 5), this path analysis shows that within of interaction, using the host whole-gut population means that the microbiome, migration and group size shape patterns of our estimate is a lower-bound estimate of relatedness. Indeed, relatedness, which, in turn, drives the evolution of coopera- while bacteria can potentially interact at the host scale (espe- tion. Therefore, these ecological factors have an indirect effect cially via their effects on the host), in some cases, there will be on cooperation, via their effects on relatedness. Relatedness more within-host structure and localized interactions. In those retains a direct positive effect on cooperation that is not cases, interacting individuals will be, on average, more similar accounted for by these ecological factors. Finally, ecological fac- than what we estimate from the whole gut, and true related- tors also have, particularly for group size, a direct effect on ness will be higher than that estimated from the whole gut. cooperation for some specific forms of cooperative behavior This lower-bound estimate remains predictive of the evolution of (Fig. 5). cooperation within the gut microbiota. This suggests that whole

Fig. 5. Kin selection explains the evolution of cooperation in the human gut microbiota. Summary schematic of the scenario supported by the path analysis conducted in this study is shown. Ecological factors (migration and group size) shape patterns of relatedness (average population genetic similarity), which, in turn, drive the evolution of cooperation. For certain specific forms of cooperation, ecological factors also have a direct effect: a, biofilm; b, siderophores; c, secreted products; d, quorum-sensing; e, antibiotic degradation; and f, secretion systems.

6 of 10 | PNAS Simonet and McNally https://doi.org/10.1073/pnas.2016046118 Kin selection explains the evolution of cooperation in the gut microbiota Downloaded by guest on September 27, 2021 Downloaded by guest on September 27, 2021 i eeto xlisteeouino oprto ntegtmicrobiota gut the in cooperation of evolution the explains selection Kin McNally and Simonet ope n once omnte,sc stehuman the as such evo- communities, of long-term connected importance highly the the some and in in overcome even complex not context (relatedness), suggests does context community population-genetics cooperation This of gene of phylogeny. role related- cooperative lution gut-microbiota curtail of the evolution not the that the does across for 61) in show content power network (60, results metabolic predictive microbiota Our connected ness’ gut highly MCs. human a real-world of the public- complex existence prediction of the in Hamilton’s evolution that hold that the not suggesting on may (35), relatedness production of goods role the minimize for mechanism of favored. this that be from of to benefits. distinguished importance synergistic it be the cannot for evolution results, relatedness quorum-sensing high present require the not which In in does 59), (58, it recognition as kin case of and acts reciprocity privatization quorum-sensing of that the mechanism suggested a to been has Akin it dominant. siderophores, effect remains the cen- exist, relatedness may Hamilton’s of privatization disrupt such via Although not benefits prediction. does tral of (34) privatization receptors the Similarly, strain-specific that relatedness. suggests relatedness of siderophores between observed importance and we relation the positive limited significant lessen remains the not cooperation mecha- microbial does this of of and evolution importance the larger general a in have the nism cases, relatedness, some show than in results may, effect these benefits Together, synergistic while effect. that, significant overall over- relatedness marginal an while cooperation, a holds on has abundance only relative abundance, of it relative effect that all of shows effect gen- model overall More meta-analysis the abundance. our at relative an looking of find when not did erally, we but which relatedness, for are 57), of (56, effect benefits well synergistic as siderophores Yet, in for that known production. evo- claim public-goods the the driving of at and relatedness lution scenario over traits prevails a size two group supports these cases, This such for effect. benefits predictive synergistic a of of have number numbers related- not on traits, on does two ness effect effect these find for marginal predictive while We a enzymes, significant antibiotic-degrading and (55). a genes pay quorum-sensing has to of size cooperation must group There for relation that cooperation: cooperators a syn- of implies enough benefit be the curve be can and benefit benefits size group example, accelerating between For an question generally not. Such the behaviors or ergistic. prediction, cooperative features Hamilton’s these bacterial of have whether break being the then to lead can species of range wide details. a life-history over and ecology is, of microbiota—that diversity gut the full human of the the across power evidence content predictive gene empirical hold cooperative fea- the gut-microbe does account by Relatedness into contravened here. Such directly presented (36). take world is to microbial argument the fails an characterize microbial it generally for that that tures calculations arguing useful (25), provide evolution to general unable the altogether test evolution. actually microbial in to mechanisms us those of been allows importance approach have comparative mecha- only prediction However, a work. Various Hamilton’s theoretical and cooperation. experimental invalidate human by of highlighted the could forms of and that six diversity nisms relatedness for full genomic the microbiota over gut between content relation gene cooperative the Cooperation. tests Microbial analysis Predicts Relatedness species. bacterial of population gut average human scale the of of across-hosts structure depiction accurate global an are and competition interactions scale within-host ial,cosseisbnfiso ulcgoswr hw to shown were goods public of benefits cross-species Finally, that mechanisms specific on focused are arguments Other is theory inclusive-fitness the that that imply claims Some u comparative Our fe potnte osermcoilcoeaini asto ways in cooperation medicine microbial Hamiltonian health. steer human 67). enhance to 15, cooperate microbes—may opportunities (6, they our in offer host when relatedness of their health manipulation 68)—the harm on (62, or effects help largest impor- to of their microbes only as have not role medicine, is for can key also This but a (17). biology, evolutionary dynamics play for MC tance and the gut ubiquitous reinforce our be driving microbes in might gut cooperation human that in idea of observed measures high relatedness we generally microbiome of relatedness the role turn cooperation, the to microbial Given driving needed science. at predictive is a what into general is research is communities. ability real-world theory predictive complex Hamilton’s in Broad of it power insight predictive models, central holds kin-selection and the and that framework clear fitness assumptions is and inclusive parame- exactness genetic reveal the the population and over of key continues microbiota debate this gut While of ter. drivers human the the cooperation. into across of insights hold prediction evolution results central the Over- its drives Our Hamilton’s taxa. relatedness in support across increased is strongly evolution that theory results trait evolutionary of our patterns general all, explain any to of ability test ultimate The Conclusion promising a is so. do 66) to methods (48, which and data in understand tools metagenomic avenue to of in onset strains critical The tracking is it. for shape it factors (25), ecological cooperation how on influence the relatedness from away of focus our abundance shifting strains. than high competitor rather clears Therefore, reach which response immune that host a strains provoke positively if are example, abilities if correlated—for case competitive the within-species be microbiota and might the This between- in bottlenecks. taxa genetic (i.e., individual with blooms of associated are of size) that group context is in the possibility increase in One large least microbiome. at gut microbes, human reduced in the to hold leads not size does group relatedness increased that model general infinite-island make such, to As (27) level. variable taxonomic broad unifying predictions. a a at provides so relatedness shape less collectively factors even ecological poorly relatedness, of remains variety it a while how ecology, understood organismal of complexity It aspects the ability: various collapses of which predictive parameter why broad genetic of for population heart a quantity the is relevant to gets a This is size. relatedness group combi- and the migration by of captured not nation is ecological relatedness two that those means for This controlling factors. the after Yet, strong as con- cases. gene remained some cooperative tent microbe in on relatedness relatedness of effects of effect direct predictive that the overcome and abundance, even relative may for show directly, and results indirectly our both as act may factors coopera- ecological drives Some relatedness tion. and relatedness, coop- their drive on does effect via it no eration, has indirectly migration Although cooperation relatedness: 65). on drive eco- effects (25, various ultimately where drivers factors scenario, ultimate general logical a the depict results are our ecological traits Indeed, while life-history cooperation, and for on factors emphasis explanation the an of as criticism cre- relatedness motivates which This ecology, (64). organismal assortment of ate aspects many of consequence Relatedness. vs. Ecology bio- Emphasizing soil for cooperation manipulating dif- (63). remediation involve than may challenges successful (62) ferent community engineer communities of to fecal-transplant cooperation type human-gut manipulating engineering). the community example, on MC For vs. depend microbiome may mammalian (e.g., This microbiota. gut ee u eut hwta h lsi rdcino the of prediction classic the that show results our Here, https://doi.org/10.1073/pnas.2016046118 eaens tefi ieya likely is itself Relatedness PNAS | f10 of 7

EVOLUTION Materials and Methods reported in the original descriptions of the species. The final secretome size Metagenomic Samples. We used healthy individuals’ stool metagenomes obtained as well as the gram profiles are reported in Dataset S1. from the Human Metagenome Project (HMP) (44) (HMP portal, accessed April 2020, under: Project > HMP, Body Site > feces, Studies > WGS- Cooperation Quantification from GOs. We established a list of “social GO PP1, File Type > WGS raw sequences set, File format > FASTQ. The terms” corresponding to five well-described forms of bacterial social behav- resulting manifest file is available at https://github.com/CamilleAnna/ iors (biofilm formation, quorum-sensing, secretion systems, siderophores HamiltonRuleMicrobiome gitRepos.git). For each host, we kept the earliest production and usage, and antibiotic degradation). To do so, we first iden- time point available and quality-filtered reads using the MOCAT pipeline tified a broad list of 14,702 “bacterial GO terms” by annotating all of the with default settings (64). This resulted in 239 individual host metagenomic representative genomes cataloged in the MIDAS database (5,944 genomes) samples included in the analysis (list and access link provided in Dataset S1). with GO terms using PANNZER2 (73) with default settings and keeping all hits (this full list is reported in Dataset S1). From this bacterial GO set, we Relatedness Calculation. We used the strain-profiling pipeline MIDAS (49) to identified a list of “bacterial cooperation GO terms” for the five types of identify species in each metagenomic sample, estimate their relative abun- cooperative behaviors. We first established a list of keywords describing dance, and compute allele frequencies at each genomic site of the core those behaviors using the 10 most cited reviews on the topic (web of sci- genome. Specifically, we ran “run midas.py snps” on the 239 metagenomes ence search “TI=((microb* OR bacter* OR microorganis* OR micro-organis*) and identified 141 species meeting the default minimum coverage require- AND (coop* OR social*)”, selecting English, Reviews, All field, and manually ment for allele-frequency computation along the entire genome (per-host- filtering out reviews that were not specifically on . The per-species allele frequencies). We then used “merge midas.py snps” to selected reviews are reported in Dataset S1). From the full list of bacterial identify core genomic sites and compute diversity at those sites. GOs, we performed a keyword match to retrieve all GO terms containing After quality filtering of samples and genomic sites (which we left to these social keywords, as well as all corresponding child terms and direct MIDAS defaults), core genomic sites were identified as sites present in most parent term. This gave a list of 673 potentially social GO terms. Finally, samples. For this, we set options site prev 0.90, which means that a genomic we manually curated this list to ensure that the terms selected were spe- site is considered part of the core genome if it is present in >90% of the cific enough. For example, while “polysaccharide production” could refer samples. At this stage, we excluded xylanisolvens 57185, which to a general aspect of metabolism, “extracellular polysaccharide produc- had a substantially smaller number of genomic sites passing quality filtering, tion” can confidently be associated with biofilm formation. The detailed resulting in a very small core genome size. curation process is provided in Dataset S1. The final list of social GO terms Finally, we used these diversity estimates to run “snp diversity.py” to comprises 118 terms (biofilm, 48; quorum sensing, 5; secretion systems, 11; compute the within-sample (sample type per-sample) and across-samples siderophores, 29; and antibiotic degradation, 25; listed in Dataset S1). To (sample type pooled-samples) diversity. Computing relatedness requires at quantify cooperation for these five classes of cooperative behaviors in our least two hosts, so we filtered out species present in only a single host. We study species, we annotated their genome with GO using PANNZER2 with then proceeded to computing relatedness on the remaining 101 species. default settings and retaining only the top hit in each of the three ontolo- Following Lynch and Ritland (69), the genomic similarity averaged over gies (biological processes, cellular compartment, and molecular function). any random pair of haploid individuals in a population is given by: We then quantified cooperation for each of the five classes as the number of CDS for which at least one of its associated GO terms falls within the list of n n social GO terms for each given cooperation type. These counts are reported ¯ X X S = pipjSij [1] in Dataset S1. i=1 j=1 Phylogenetic Comparative Analyses. For all comparative analyses, we built where pi and pj denote the frequencies of haplotypes i and j and Sij the phylogenetic mixed models implemented in a Bayesian framework using proportion of identical genomic sites for this interacting pair. This can also the MCMCglmm package (74) in R (version 3.5.2) (75). To control for the phy- be derived from the allelic frequencies at each site n in a genome of length logenetic relationships among species, we used the phylogeny provided in k. The average genomic similarity is: the MIDAS database, which we trimmed to keep our focus species-only and ultrametricized using chronopl function in (76). We ran all models for 1 k 4 1 X X million iterations with a burn-in phase of 5,000 and a thinning interval of 50. ¯S = p2, [2] k a We used visual inspection of traces, as well as the Gelman–Rubin tests (77) n=1 a=1 on two independent chains to assess model convergence. Across all mod- els and all effects estimated, the maximum potential scale-reduction factor with pa the frequency of allele a (out of the four possible alleles A, T, C, or G) at site k. This quantity is simply the average probability of being identical observed was 1.03. The model summaries for the first chain of each model by state (or expected homozygosity in diploids) or 1− genetic diversity. are provided in SI Appendix, Tables S1–S5. We report the significance of our We used the MIDAS “diversity” function with default parameters to com- fixed effects in terms of PMCMC , which is twice the posterior probability that pute diversity within-hosts and diversity across-hosts to derive, respectively, the estimate is negative. ¯ ¯ the within-hosts (Ss,h) and across-hosts (Ss) average probability of being ¯ The Effect of Mean Relatedness on Cooperation. We ran six models, one identical by state. Ss,h is the genomic similarity of species s within the sub- ¯ population living in host h. Ss is the similarity that would be expected for each form of cooperation (secretome size and the five GO-based mea- between any two random bacteria of that species’ entire population. The sures).). Each model was a univariate mixed model with a Poisson error genetic relatedness for species s in host h is then: structure, with cooperation (i.e., a number of genes) as the response variable: ¯ ¯ Ss,h − Ss rs,h = ¯ . [3] Y Y Y Y Y 1 − Ss E[Yi] = β0 + βr Ri + βn log(Ni) + up,i + i [4]

Y ∼ Pois(E[Yi]), Similar as an FST , an rs,h > 0 denotes a higher genomic similarity between any pair of interacting bacterium within a host gut subpopulation than Y would be expected from the average similarity in the global population, with Ri denoting the mean relatedness of species i and βr the regres- i.e., the statistical association between two interacting individuals relative sion coefficient of cooperation Y on relatedness. We also include log(Ni) to the average population, as defined by Hamilton’s kin-selection coefficient as predictor with Ni the number of CDS not involved in the coopera- of relatedness (20). tive behavior Y (i.e., total number of CDS − Y), which we include in the model to account for potential nonlinear scaling of Y with genome size Y Secretome Size. For each species, we downloaded the coding DNA sequence (SI Appendix, SI Text). The term up,i is the phylogenetic species effect on (CDS) fasta sequences from the PATRIC database (ref. (70); accessed January Y—that is, the amount of interspecies variance in Y explained by a Brown- 29th, 2019). We used the same genomes as the reference genomes used ian motion model of evolution along the phylogeny. The residual variance Y for these species in the MIDAS database. We then ran PSORTb 3.0 (71) to i is equivalent to the nonphylogenetic interspecies variance in this case, determine protein localization. The secretome size is the number of CDS as there is a single measure of mean relatedness per species. In the model coding for a product predicted to have an extracellular final localization. for secretome size, we also included the gram profile in main effects to PSORTb requires information on the gram profile. We assigned these follow- estimate a different intercept for each profile, since the PSORTb algorithm ing Bergey’s manual of systematic bacteriology (72) and/or the gram profile differs between Gram-positive and Gram-negative bacteria. We used an

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West, in Reece, E. competition S. Brown, and P. A. S. S. Cooperation Leggett, C. Buckling, H. A. Campbell, 14. West, for A. S. S. theory G. Griffin, S. evolution A. Griffin, Social 13. Diggle, S. A. P. S. Gardner, Diggle, A. P. S. Griffin, 12. S. A. West, A. S. 11. omaueeterr(m error measurement to species of ness), trait, Gaussian The is: model the where (79) by 2.1-0.) setting (v frequentist metaphor a package in R the implemented using was analysis The posteriors. model cooperation. of form aspect each of technical uncer- specificity this the biological both the from for from arising accounts and estimate, meta-analysis trait-specific each the in The from tainty annotation-based). vs. obtained (algorithmic estimate aspects global technical by differ also genes; of and sets different (i.e., behaviors cooperative tinct oe a ela h sebe aa r vial nGtu (https://github. 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Biol. B. Lond. Soc. R. Trans. Phil. populations. bacteria. bacterial quorum-sensing (2007). in conflict microorganisms. > G-P,Fl Type File WGS-PP1, 354(2018). e38594 7, o eal) h oe umre r rvddin provided are summaries model The details). for ecnutdi oa he eaaaye:1 vrtesix the over 1) meta-analyses: three total in conducted We µ. Nature nu e.Eo.Eo.Syst. Evol. Ecol. Rev. Annu. rvtsto ece ucinfloigls fcooperation. of loss following function rescues Privatisation al., et i rnsGenet. Trends 0412 (2004). 1024–1027 430, a.Rv Microbiol. Rev. Nat. h eaeoi aaaepbil vial tteHMP the at available publicly are data metagenomic The lsrno fet due effects random plus (µ) mean grand the by given is y a.Commun. Nat. etmt ftesoeo oprto vrrelated- over cooperation of slope the of (estimate etakJro afil o icsin tvari- at discussions for Hadfield Jarrod thank We n eed for legends and , S1–S6 Tables S1–S6, Figs. Text, SI > i n eiul( residual and ) G a eune e,Fl format File set, sequences raw WGS ecmue prlto crsfor scores sporulation computed We S1. Dataset y 0–1 (2017). 408–419 33, i ∼ aae S1. Dataset 0335(2014). 20130365 369, µ 9–0 (2006). 597–607 4, 37 (2007). 53–77 38, 54(2014). 4594 5, aae S1. Dataset + IAppendix SI https://doi.org/10.1073/pnas.2016046118 m .Teabun- The Calculation). Relatedness i + i > .W ekt siaeteover- the estimate to seek We ).  edrcl uldseisrela- species pulled directly We i anal- S1, Dataset gitRepos.git). . M,Bd Site Body HMP, rc al cd c.U.S.A. Sci. Acad. Natl. Proc. spoie o this for provided is Nature i.S6) Fig. Appendix, SI PNAS > > IAppendix, SI 411–414 450, IAppendix, SI ee,Stud- feces, AT) All FASTQ). | f10 of 9 [7]

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