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The increase in antibiotic resistant bacteria has raised global concern regarding the future effectiveness of antibiotics. Human activities that influence microbial communities and environmental resistomes can generate additional risks to human health. In this work, we characterized aquatic microbial communities and their resistomes in samples collected at three sites along the Bogotá River and from wastewaters at three city hospitals, and investigated community profiles and antibiotic resistance genes (ARGs) as a function of anthropogenic contamination. The presence of antibiotics and other commonly used drugs increased in locations highly impacted by human activities, while the diverse microbial communities varied among sites and sampling times, separating upstream river samples from more contaminated hospital and river samples. Clinically relevant antibiotic resistant pathogens and ARGs were more abundant in contaminated water samples. Tracking of resistant determinants to upstream river waters and city sources suggested that human activities foster the spread of ARGs, some of which were co-localized with mobile genetic elements in assembled metagenomic contigs. Human contamination of this water ecosystem changed both community structure and environmental resistomes that can pose a risk to human health.
Pubmed ID: 31409850
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Tool for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs). Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.
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