Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

Abstract

This study presents a machine learning-based analysis supporting the hypothesis that microbial adapta- tions to extreme temperatures and pH conditions can result in a pervasive environmental component within their genomic signatures. To this end, an alignment-free method was used in conjunction with both supervised and unsupervised machine learning, to analyze genomic signatures extracted from a curated dataset of approximately 700 extremophilic (temperature, pH) bacteria and archaea genomes. The dataset analyzed in this paper is, to the best of our knowledge, the largest and most comprehensive to date for the study of the genomic signatures of extremophilic organisms. The supervised learning identified specific sets of k-mers that are relevant for the identification of the environmental components in the genomic signatures of extremophiles. For k = 3, our findings are consistent with amino acid compositional biases and codon usage patterns in coding regions that were previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences, by multiple algorithms, identified some hyperthermophilic organisms with large genomic signature similarities in spite of belonging to different domains This computational finding was confirmed by supervised learning experiments under challenging training scenarios, and corroborated by the presence of shared phenotypic traits and characteristics of the isolating environments.