Abstract
Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome.
Highlights
Metagenomic sequencing technologies have revolutionized the study of the human-associated microbial consortia making up the human microbiome
They are revolutionizing our knowledge of microbial ecology and highlight the important roles played by the human microbiome in health and disease
We have highlighted three major obstacles that must be addressed by any study designed to use metagenomic data to analyze species interactions: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units (OTUs), bias inferrences of species interactions due to a statistical problem called ‘‘errors-invariables’’
Summary
Metagenomic sequencing technologies have revolutionized the study of the human-associated microbial consortia making up the human microbiome. Sequencing methods allow researchers to estimate the relative abundance of the species in a community without having to culture individual species [1,2,3]. These studies have shown that microbial cells vastly outnumber human cells in the body, and that symbiotic microbial communities are important contributors to human health [1]. Despite the recent revelations highlighting the importance of the microbiome to human health, relatively little is known about the ecological structure and dynamics of these microbial communities
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