bioRxivMicrobiology
Genome mining, phylogenetic, and functional analysis of arsenic (As) resistance operons in Bacillus strains, isolated from As-rich hot spring microbial mats
A. Flores, M. F. Valencia-Marin, S. Chavez-Avila, M. I. Ramirez-Diaz, S. de los Santos Villalobos, V. Meza-Carmen, M. d. C. Orozco Mosqueda, G. Santoyo
Abstract
The geothermal zone of Araro, Mexico, is located within the trans-Mexican volcanic belt, an area with numerous arsenic (As)-rich hot springs. In this study, the draft genome sequence of two endemic Bacillus strains (ZAP17 and ZAP62) from Araro microbial mat hot springs was determined, which were able to grow on arsenate (up to 64 mM) and arsenite (up to 32 mM). Phylogenetic analysis based on 16S rRNA and gyrB sequences, as well as genome sequence analysis based on average nucleotide identity (>96%) and digital DNA-DNA hybridization (>70%), indicated that these strains belong to the Bacillus paralicheniformis ZAP17 and Bacillus altitudinis ZAP62. Furthermore, through genome mining, it was identified two arsenic resistance operons, arsRBC, and arsRBCDA in both strains as potential determinants of arsenic (As) resistance. Predicted ArsA (arsenial pump-driving ATPase), ArsB (Arsenical efflux pump protein), ArsC (Arsenate reductase), ArsD (Arsenical efflux pump protein) and ArsR (Metalloregulator/ars operon repressor) proteins, clearly grouped with their respective clades corresponding to other characterized bacterial species, mainly Firmicutes. To further evaluate the functionality of the ars operons in ZAP17 and ZAP62 strains, our results showed that arsRBC and arsRBCDA genes were expressed in the presence of arsenite (III). Finally, the presence of ars operons in the genome of Bacillus species residing in As-rich environments, such as the Araro hot springs, might be a potential mechanism to survive under such harsh conditions, as well as to design sustainable bioremediation strategies.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
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