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Despite the importance of microbial activity in mobilizing arsenic in groundwater aquifers, the phylogenetic distribution of contributing microbial metabolisms is understudied. Groundwater samples from Ohio aquifers were analyzed using metagenomic sequencing to identify functional potential that could drive arsenic cycling, and revealed mechanisms for direct (i.e., Ars system) and indirect (i.e., iron reduction) arsenic mobilization in all samples, despite differing geochemical conditions. Analyses of 194 metagenome-assembled genomes (MAGs) revealed widespread functionality related to arsenic mobilization throughout the bacterial tree of life. While arsB and arsC genes (components of an arsenic resistance system) were found in diverse lineages with no apparent phylogenetic bias, putative aioA genes (aerobic arsenite oxidase) were predominantly identified in Methylocystaceae MAGs. Both previously described and undescribed respiratory arsenate reduction potential via arrA was detected in Betaproteobacteria, Deltaproteobacteria, and Nitrospirae MAGs, whereas sulfate reduction potential was primarily limited to members of the Deltaproteobacteria and Nitrospirae. Lastly, iron reduction potential was detected in the Ignavibacteria, Deltaproteobacteria, and Nitrospirae. These results expand the phylogenetic distribution of taxa that may play roles in arsenic mobilization in subsurface systems. Specifically, the Nitrospirae are a much more functionally diverse group than previously assumed and may play key biogeochemical roles in arsenic-contaminated ecosystems.
Pubmed ID: 31490939
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsA database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).
View all literature mentionsSoftware package for functional analysis of sequences by classifying them into families and predicting presence of domains and sites. Scans sequences against InterPro's signatures. Characterizes nucleotide or protein function by matching it with models from several different databases. Used in large scale analysis of whole proteomes, genomes and metagenomes. Available as Web based version and standalone Perl version and SOAP Web Service.
View all literature mentionsA graphical viewer of phylogenetic trees and a program for producing publication-ready figures. It is designed to display summarized and annotated trees produced by BEAST.
View all literature mentionsSoftware package for sequence alignment, assembly and analysis. Integrated and extendable desktop software platform for organization and analysis of sequence data. Bioinformatics software platform packed with molecular biology and sequence analysis tools.
View all literature mentionsMultiple sequence alignment method with reduced time and space complexity.Multiple sequence alignment with high accuracy and high throughput. Data analysis service for multiple sequence comparison by log- expectation.
View all literature mentionsSoftware tool for protein coding gene prediction for prokaryotic genomes.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsWeb-based software used for the selection of best-fit models of protein evolution.
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