In Computational and structural biotechnology journal
Arsenic is a ubiquitous toxic element, the global cycle of which is highly affected by microbial redox reactions and assimilation into organoarsenic compounds through sequential methylation reactions. While microbial biotransformation of arsenic has been studied for decades, the past years have seen the discovery of multiple new genes related to arsenic metabolism. Still, most studies focus on a small set of key genes or a small set of cultured microorganisms. Here, we leveraged the recently greatly expanded availability of microbial genomes of diverse organisms from lineages lacking cultivated representatives, including those reconstructed from metagenomes, to investigate genetic repertoires of taxonomic and environmental controls on arsenic metabolic capacities. Based on the collection of arsenic-related genes, we identified thirteen distinct metabolic guilds, four of which combine the aio and ars operons. We found that the best studied phyla have very different combinations of capacities than less well-studied phyla, including phyla lacking isolated representatives. We identified a distinct arsenic gene signature in the microbiomes of humans exposed or likely exposed to drinking water contaminated by arsenic and that arsenic methylation is important in soil and in human microbiomes. Thus, the microbiomes of humans exposed to arsenic have the potential to exacerbate arsenic toxicity. Finally, we show that machine learning can predict bacterial arsenic metabolism capacities based on their taxonomy and the environment from which they were sampled.
Keren Ray, Méheust Raphaël, Santini Joanne M, Thomas Alex, West-Roberts Jacob, Banfield Jillian F, Alvarez-Cohen Lisa
2022
Arsenic, Human microbiome, Machine Learning, Microbial genomics
