Sweeny, A.R. orcid.org/0000-0003-4230-171X, Lemon, H., Ibrahim, A. et al. (6 more authors) (2023) A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance. mSystems, 8 (4). e0004023. ISSN 2379-5077
Abstract
Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 Sweeny et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. https://creativecommons.org/licenses/by/4.0/ |
Keywords: | microbiota; metabarcoding; 16S; amplicon sequence variants; generalized linear mixed-effects model; community composition; differential abundance; Bayesian estimation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) |
Funding Information: | Funder Grant number NATURAL ENVIRONMENT RESEARCH COUNCIL NE/R016801/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Dec 2023 12:31 |
Last Modified: | 01 Dec 2023 12:31 |
Status: | Published |
Publisher: | American Society for Microbiology |
Refereed: | Yes |
Identification Number: | 10.1128/msystems.00040-23 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205812 |