Abstract
The genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise measures of microbe-microbe interaction. The first (genome content similarity index) quantifies similarity in genome composition between two microbes, while the second (microbe-microbe functional association index) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets, accounting for phylogenetic relatedness of the taxa. Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data, but genome composition was found to be a strong predictor as well, it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination, even after accounting for evolutionary relationships between the species. On their own, the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons. In summary, results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology, and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions.
Highlights
Due to the rise of polymicrobial infections [1], the potential of community replacement therapy in preventing infections after antibiotic treatment [2,3,4], and the developing interest in microbiome engineering [5,6], there is a pressing need to better understand the mechanisms behind microbial community assembly and function
Gene neighbor-based predictions incorporate a large fraction of genes across bacterial genomes than Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways
Genomics-based approaches to the problem might provide a handle on the mechanisms driving microbial community assembly and dynamics, complementing conclusions derived from ecological surveys
Summary
Due to the rise of polymicrobial infections [1], the potential of community replacement therapy in preventing infections after antibiotic treatment [2,3,4], and the developing interest in microbiome engineering [5,6], there is a pressing need to better understand the mechanisms behind microbial community assembly and function. Classical approaches for characterizing microbe-microbe interactions include environmental surveys where the presence or abundance of different species in the community is estimated from the presence or abundances of lineage specific 16S rRNA or other phylogenetic markers [7,8]. These types of data collected from a variety of different but related habitats [9,10,11] or from the same habitat across time or space [12,13] are used to understand microbe-microbe interactions. While 16S rRNA based approaches to the problem are informative, they do not provide a clear way to understand the molecular mechanisms of inferred dependencies between the species
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