Codon usage bias reveals genomic adaptations to environmental conditions in an acidophilic consortium

PLoS One. 2018 May 9;13(5):e0195869. doi: 10.1371/journal.pone.0195869. eCollection 2018.

Abstract

The analysis of codon usage bias has been widely used to characterize different communities of microorganisms. In this context, the aim of this work was to study the codon usage bias in a natural consortium of five acidophilic bacteria used for biomining. The codon usage bias of the consortium was contrasted with genes from an alternative collection of acidophilic reference strains and metagenome samples. Results indicate that acidophilic bacteria preferentially have low codon usage bias, consistent with both their capacity to live in a wide range of habitats and their slow growth rate, a characteristic probably acquired independently from their phylogenetic relationships. In addition, the analysis showed significant differences in the unique sets of genes from the autotrophic species of the consortium in relation to other acidophilic organisms, principally in genes which code for proteins involved in metal and oxidative stress resistance. The lower values of codon usage bias obtained in this unique set of genes suggest higher transcriptional adaptation to living in extreme conditions, which was probably acquired as a measure for resisting the elevated metal conditions present in the mine.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adaptation, Physiological / genetics*
  • Bacteria / genetics*
  • Bacterial Physiological Phenomena / genetics*
  • Codon / genetics*
  • Databases, Genetic
  • Genome, Bacterial / genetics*
  • Hydrogen-Ion Concentration

Substances

  • Codon

Grants and funding

This work was supported by FONDAP Grant 15090007 of the Center for Genome Regulation (CGR), Apoyo a la Formación de Redes Internacionales para Investigadores REDI170193 and FONDECYT Grant N° 11150679 to Mauricio Latorre. CONICYT Basal Grant PFB 03 of the Center for Mathematical Modeling (CMM) UMI2807 UCHILE-CNRS to Servet Martinez and Mauricio Latorre. All the authors also acknowledge the support of the National Laboratory of High Performance Computing (NLHPC) at the CMM (PIA ECM-02.-CONICYT).