Intraspecies characterization of bacteria via evolutionary modeling of protein domains

The ability to detect and characterize bacteria within a biological sample is crucial for the monitoring of infections and epidemics, as well as for the study of human health and its relationship with commensal microorganisms. To this aim, a commonly used technique is the 16S rRNA gene targeted sequencing. PCR-amplified 16S sequences derived from the sample of interest are usually clustered into the so-called Operational Taxonomic Units (OTUs) based on pairwise similarities. Then, representative OTU sequences are compared with reference (human-made) databases to derive their phylogeny and taxonomic classification. Here, we propose a new reference-free approach to define the phylogenetic distance between bacteria based on protein domains, which are the evolving units of proteins. We extract the protein domain profiles of 3368 bacterial genomes and we use an ecological approach to model their Relative Species Abundance distribution. Based on the model parameters, we then derive a new measurement of phylogenetic distance. Finally, we show that such model-based distance is capable of detecting differences between bacteria in cases in which the 16S rRNA-based method fails, providing a possibly complementary approach , which is particularly promising for the analysis of bacterial populations measured by shotgun sequencing.

Automated summary

The detection and characterization of bacteria in biological samples is important for monitoring infections and epidemics, as well as studying human health and its relationship with commensal microorganisms. In this study, a new reference-free approach based on protein domains was proposed to define the phylogenetic distance between bacteria. By extracting protein domain profiles from bacterial genomes and modeling their relative species abundance distribution, a new measurement of phylogenetic distance was derived. The model-based distance was shown to detect differences between bacteria in cases where the 16S rRNA-based method failed, providing a potentially complementary approach for analyzing bacterial populations measured by shotgun sequencing.