Species-specific transcriptomic network inference of interspecies interactions

The advent of high-throughput 'omics approaches coupled with computational analyses to reconstruct individual genomes from metagenomes provides a basis for species-resolved functional studies. Here, a mutual information approach was applied to build a gene association network of a commensal consortium, in which a unicellular cyanobacterium Thermosynechococcus elongatus BP1 supported the heterotrophic growth of Meiothermus ruber strain A. Specifically, we used the context likelihood of relatedness (CLR) algorithm to generate a gene association network from 25 transcriptomic datasets representing distinct growth conditions. The resulting interspecies network revealed a number of linkages between genes in each species. While many of the linkages were supported by the existing knowledge of phototroph-heterotroph interactions and the metabolism of these two species several new interactions were inferred as well. These include linkages between amino acid synthesis and uptake genes, as well as carbohydrate and vitamin metabolism, terpenoid metabolism and cell adhesion genes. Further topological examination and functional analysis of specific gene associations suggested that the interactions are likely to center around the exchange of energetically costly metabolites between T. elongatus and M. ruber. Both the approach and conclusions derived from this work are widely applicable to microbial communities for identification of the interactions between species and characterization of community functioning as a whole.