Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome

View ORCID ProfileJohn L. Darcy, Alex D. Washburne, Michael S. Robeson, Tiffany Prest, Steven K. Schmidt, Catherine A. Lozupone
doi: https://doi.org/10.1101/685644
John L. Darcy
Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for John L. Darcy
  • For correspondence: darcyj@colorado.edu
Alex D. Washburne
Department of Microbiology and Immunology, Montana State University. Bozeman, Montana, 59717, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael S. Robeson
Department of Biomedical Informatics, University of Arkansas for Medical Sciences. Little Rock, Arkansas, 72205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tiffany Prest
Department of Ecology and Evolutionary Biology, University of Colorado. Boulder, Colorado, 80309, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Steven K. Schmidt
Department of Ecology and Evolutionary Biology, University of Colorado. Boulder, Colorado, 80309, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Catherine A. Lozupone
Division of Biomedical Informatics and Personalized Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Understanding when and why new species are recruited into microbial communities is a formidable problem. Much theory in microbial temporal dynamics is focused on how phylogenetic relationships between microbes impact the order in which those microbes are recruited; for example species that are closely related may exclude each other due to high niche overlap. However, several recent human microbiome studies have instead found that close phylogenetic relatives are often detected in microbial communities in short succession, suggesting factors such as shared adaptation to similar environments play a stronger role than competition. To address this, we developed a mathematical model that describes the probabilities of different species being detected in time-series microbiome data, within a phylogenetic framework. We use our model to test three hypothetical assembly modes: underdispersion (species are more likely to be detected if a close relative was previously detected), overdispersion (likelihood of detection is higher if a close relative has not been previously detected), and the neutral model (likelihood of detection is not related to phylogenetic relationships among species). We applied our model to longitudinal high-throughput sequencing data from the human microbiome, and found that for the individuals we analyzed, the human microbiome generally follows an assembly pattern characterized by phylogenetic underdispersion (i.e. nepotism). Exceptions were oral communities, which were not significantly different from the neutral model in either of two individuals analyzed, and the fecal communities of two infants that had undergone heavy antibiotic treatment. None of the datasets we analyzed showed statistically significant phylogenetic overdispersion.

Footnotes

  • Conflict of Interest Statement The authors declare that no conflict of interest exists.

  • Support Funding was provided by an NSF grant for studying microbial community assembly following disturbance (DEB-1258160) and by a NIH NLM Computational Biology training grant (5 T15 LM009451-12). The funding bodies had no role in study design, analysis, interpretation, or in the preparation of this manuscript.

  • Revision per reviewer comments. Language updated to better reflect that the model concerns empirical patterns in data. Hypotheses more explicitly described, and use of terms over- and underdispersion discussed. Added discussion of more recent ecological theory supporting underdispersion hypothesis.

  • https://github.com/darcyj/pd_model

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted November 15, 2019.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
Share
A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome
John L. Darcy, Alex D. Washburne, Michael S. Robeson, Tiffany Prest, Steven K. Schmidt, Catherine A. Lozupone
bioRxiv 685644; doi: https://doi.org/10.1101/685644
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome
John L. Darcy, Alex D. Washburne, Michael S. Robeson, Tiffany Prest, Steven K. Schmidt, Catherine A. Lozupone
bioRxiv 685644; doi: https://doi.org/10.1101/685644

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Microbiology
Subject Areas
All Articles
  • Animal Behavior and Cognition (1521)
  • Biochemistry (2474)
  • Bioengineering (1728)
  • Bioinformatics (9649)
  • Biophysics (3888)
  • Cancer Biology (2961)
  • Cell Biology (4178)
  • Clinical Trials (135)
  • Developmental Biology (2621)
  • Ecology (4088)
  • Epidemiology (2031)
  • Evolutionary Biology (6872)
  • Genetics (5196)
  • Genomics (6485)
  • Immunology (2180)
  • Microbiology (6913)
  • Molecular Biology (2748)
  • Neuroscience (17219)
  • Paleontology (125)
  • Pathology (425)
  • Pharmacology and Toxicology (705)
  • Physiology (1051)
  • Plant Biology (2480)
  • Scientific Communication and Education (642)
  • Synthetic Biology (828)
  • Systems Biology (2680)
  • Zoology (429)