Abstract
As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.
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Acknowledgements
We thank Jodie Nicotra for help with comments and edits on the manuscript as well as members of CMCI, IBEST and BCB at the University of Idaho for helpful discussions on the research topic. Research reported in this publication was partially supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Numbers P20GM104420 and P30GM103324 to University of Idaho. Funding was also provided in part by NSF DEB-1342615 to M.D. and Johnson and Johnson to B.R. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsoring agencies.
Author contributions
BR and CR conceived the modeling; AM and MD conceived the feeding experiment; AM performed the feeding experiment; BR conducted all analyses; SB, JW and JVL helped refine the modeling procedures and prepare the manuscript. All authors reviewed the manuscript.
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Ridenhour, B., Brooker, S., Williams, J. et al. Modeling time-series data from microbial communities. ISME J 11, 2526–2537 (2017). https://doi.org/10.1038/ismej.2017.107
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DOI: https://doi.org/10.1038/ismej.2017.107
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