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Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries using Sparse Linear Regression
Human associated microbial communities exert tremendous influence over human
health and disease. With modern metagenomic sequencing methods it is 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 interactions between species from sequence
data. Any algorithm for inferring species interactions must overcome three
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. 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
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