Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping

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Abstract

Abstract A microbiome’s composition, stability, and response to perturbations is dictated by the community interaction matrix 1-10 that is commonly assayed by pair-wise species competition. In their natural environment however, microbes concurrently experience multiple species, face conditions that may be difficult to mimic in vitro, and have members that are impractical to isolate. Additionally, due to overlapping of evolutionary and ecological timescales, the community interaction matrix is also influenced by intra-species diversity, but how and to what extent remains poorly understood 11-14. Here, we develop a general approach called Dynamic Covariance Mapping (DCM) to estimate the interaction matrix of multispecies microbiome community in its natural environment from abundance time-series data. Together with intra-species high-resolution lineage tracking via chromosomal barcoding, we quantify the inter- and intra-species community interaction matrix during E. coli colonization of mice gut microbiome with increasing complexity: germ-free, antibiotic-perturbed, and innate microbiota. With DCM, we differentiate three temporal phases of invasion in the susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli clones coexisting with resident bacteria in a quasi-steady state. These phases are influenced by specific interactions between E. coli sub-lineages with other species in the community. These results highlight the transient nature and time-dependence of community interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. Our theoretical and experimental approach can be applied to characterize coupled ecological-evolutionary dynamics of bacterial communities in vitro and in situ.
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Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping Adrian Serohijos, Melis Gencel, Cang Hui, Zahra Sahaf, Louis Gauthier, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4355889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract A microbiome’s composition, stability, and response to perturbations is dictated by the community interaction matrix 1-10 that is commonly assayed by pair-wise species competition. In their natural environment however, microbes concurrently experience multiple species, face conditions that may be difficult to mimic in vitro, and have members that are impractical to isolate. Additionally, due to overlapping of evolutionary and ecological timescales, the community interaction matrix is also influenced by intra-species diversity, but how and to what extent remains poorly understood 11-14 . Here, we develop a general approach called Dynamic Covariance Mapping (DCM) to estimate the interaction matrix of multispecies microbiome community in its natural environment from abundance time-series data. Together with intra-species high-resolution lineage tracking via chromosomal barcoding, we quantify the inter- and intra-species community interaction matrix during E. coli colonization of mice gut microbiome with increasing complexity: germ-free, antibiotic-perturbed, and innate microbiota. With DCM, we differentiate three temporal phases of invasion in the susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli clones coexisting with resident bacteria in a quasi-steady state. These phases are influenced by specific interactions between E. coli sub-lineages with other species in the community. These results highlight the transient nature and time-dependence of community interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. Our theoretical and experimental approach can be applied to characterize coupled ecological-evolutionary dynamics of bacterial communities in vitro and in situ. Biological sciences/Microbiology/Microbial communities/Microbiome Biological sciences/Ecology/Microbial ecology Full Text Additional Declarations There is NO Competing Interest. All experimental protocols on mice were approved by Université de Sherbrooke Ethics Committee for Animal Care and in accordance with guidelines established by the Canadian Council on Animal Care. Supplementary Files NMIntraInterGutColonizationSupplementaryInformation.pdf Supplementary Notes and Fig. S1 to S6 SupplementaryMovie1a.mp4 Supporting Movie S1a SupplementaryMovie1b.mp4 Supporting Movie S1b SupplementaryMovie1c.mp4 Supporting Movie S1c SupplementaryMovie1d.mp4 Supporting Movie S1d SupplementaryMovie2a.mp4 Supporting Movie S2a SupplementaryMovie2b.mp4 Supporting Movie S2b SupplementaryMovie2c.mp4 Supporting Movie S2c SupplementaryMovie2d.mp4 Supporting Movie S2d SupplementaryMovie3a.mp4 Supporting Movie S3a SupplementaryMovie3b.mp4 Supporting Movie S3b SupplementaryMovie3c.mp4 Supporting Movie S3c SupplementaryMovie3d.mp4 Supporting Movie S3d SupplementaryMovie3e.mp4 Supporting Movie S3e SupplementaryMovie3f.mp4 Supporting Movie S3f SupplementaryMovie3g.mp4 Supporting Movie S3g SupplementaryMovie3h.mp4 Supporting Movie S3h SupplementaryMovie4a.mp4 Supporting Movie S4a SupplementaryMovie4b.mp4 Supporting Movie S4b SupplementaryMovie4c.mp4 Supporting Movie S4c SupplementaryMovie4d.mp4 Supporting Movie S4d SupplementaryTable1.xls Supplementary Table S1 SupplementaryTable2.xls Supplementary Table S2 Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In their natural environment however, microbes concurrently experience multiple species, face conditions that may be difficult to mimic in vitro, and have members that are impractical to isolate. Additionally, due to overlapping of evolutionary and ecological timescales, the community interaction matrix is also influenced by intra-species diversity, but how and to what extent remains poorly understood \u003csup\u003e11-14\u003c/sup\u003e. Here, we develop a general approach called Dynamic Covariance Mapping (DCM) to estimate the interaction matrix of multispecies microbiome community in its natural environment from abundance time-series data. Together with intra-species high-resolution lineage tracking via chromosomal barcoding, we quantify the inter- and intra-species community interaction matrix during E. coli colonization of mice gut microbiome with increasing complexity: germ-free, antibiotic-perturbed, and innate microbiota. With DCM, we differentiate three temporal phases of invasion in the susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli clones coexisting with resident bacteria in a quasi-steady state. These phases are influenced by specific interactions between E. coli sub-lineages with other species in the community. These results highlight the transient nature and time-dependence of community interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. Our theoretical and experimental approach can be applied to characterize coupled ecological-evolutionary dynamics of bacterial communities in vitro and in situ.\u003c/p\u003e","manuscriptTitle":"Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 05:56:16","doi":"10.21203/rs.3.rs-4355889/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c685e5b2-5943-4820-90d6-944b8596fddf","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":31760962,"name":"Biological sciences/Microbiology/Microbial communities/Microbiome"},{"id":31760963,"name":"Biological sciences/Ecology/Microbial ecology"}],"tags":[],"updatedAt":"2024-05-21T05:56:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-21 05:56:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4355889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4355889","identity":"rs-4355889","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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