Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics.

environmental stochasticity multivariate autoregressive model of population dynamics persistence probability population viability monitoring stochastic community population dynamics stochastic population dynamics of the microbiome stochastic stability

Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
03 Mar 2024
Historique:
pubmed: 11 3 2024
medline: 11 3 2024
entrez: 11 3 2024
Statut: epublish

Résumé

The interplay of stochastic and ecological processes that govern the establishment and persistence of host-associated microbial communities is not well understood. Here we illustrate the conceptual and practical advantages of fitting stochastic population dynamics models to multi-species bacterial time series data. We show how the stability properties, fluctuation regimes and persistence probabilities of human vaginal microbial communities can be better understood by explicitly accommodating three sources of variability in ecological stochastic models of multi-species abundances: 1) stochastic biotic and abiotic forces, 2) ecological feedback and 3) sampling error. Rooting our modeling tool in stochastic population dynamics modeling theory was key to apply standardized measures of a community's reaction to environmental variation that ultimately depends on the nature and intensity of the intra-specific and inter-specific interaction strengths. Using estimates of model parameters, we developed a Risk Prediction Monitoring (RPM) tool that estimates temporal changes in persistence probabilities for any bacterial group of interest. This method mirrors approaches that are often used in conservation biology in which a measure of extinction risks is periodically updated with any change in a population or community. Additionally, we show how to use estimates of interaction strengths and persistence probabilities to formulate hypotheses regarding the molecular mechanisms and genetic composition that underpin different types of interactions. Instead of seeking a definition of "dysbiosis" we propose to translate concepts of theoretical ecology and conservation biology methods into practical approaches for the management of human-associated bacterial communities.

Identifiants

pubmed: 38464272
doi: 10.1101/2024.03.02.581600
pmc: PMC10925280
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM103604
Pays : United States

Déclaration de conflit d'intérêts

CONFLICT OF INTEREST STATEMENT JR is the cofounder of LUCA Biologics, a biotechnology company focusing on translating microbiome research into live biotherapeutics drugs for women’s health.

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Auteurs

José M Ponciano (JM)

Department of Biology, University of Florida, Gainesville, FL.

Juan P Gómez (JP)

Departamento de Química y Biología, Universidad del Norte, Barranquilla, Colombia.

Jacques Ravel (J)

Institute for Genome Sciences and Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD.

Larry J Forney (LJ)

Institute for Interdisciplinary Data Science and Department of Biological Sciences, University of Idaho, Moscow, ID.

Classifications MeSH