Revealing Heavy Metal Resistances in the Yanomami Microbiome
Conteville, L. C.; Ferreira, J. d. O.; Vicente, A. C. P.
Show abstract
BACKGROUNDThe Amazon hosts invaluable and unique biodiversity as well as mineral resources. As a consequence, there are large illegal and artisanal gold mining areas in indigenous territories. Mercury has been used in gold mining, and some are released into the environment and atmosphere, primarily affecting indigenous as the Yanomami. In addition, other heavy metals have been associated with gold mining and other metal-dispersing activities in the region. OBJECTIVEInvestigation of the Yanomami gut microbiome focusing on metal resistance. METHODSMetagenomic data from the Yanomami gut microbiome were assembled into contigs, and their putative proteins were matched to a database of metal resistance proteins. FINDINGSMost identified proteins have the potential to confer resistance to multiple metals (two or more), followed by mercury, copper, zinc, chromium, arsenic, and others. Operons with potential resistance to mercury, arsenic, chromium, nickel, zinc, copper, copper/silver, and cobalt/nickel were identified. Mercury resistance operon was the most abundant, even though a diversity of operons in the Yanomami microbiome was observed to have the potential to confer resistance to various metals CONCLUSIONThe Yanomami gut microbiome gene composition shows that these people have been exposed directly or indirectly to mercury and other heavy metals. SponsorshipsThis study was partly financed by the Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ); and PAEF (IOC-023-FIO-18-2-47).
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comparative Genomic Analysis of Metal-Tolerant Bacteria Reveals Significant Differences in Metal Adaptation Strategies 95%
- Living to the high extreme: unraveling the composition, structure, and functional insights of bacterial communities thriving in the arsenic-rich Salar de Huasco - Altiplanic ecosystem. 95%
- Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes 94%
Similar papers in this journal
- Native plasmid-encoded mercury resistance genes are functional and demonstrate natural transformation in environmental bacterial isolates 94%
- Microbiota analysis of rural and urban surface waters and sediments in Bangladesh identifies human waste as driver of antibiotic resistance 94%
- Aerobic Adaptation and Metabolic Dynamics of Propionibacterium freudenreichii DSM 20271: Insights from Comparative Transcriptomics and Surfaceome Analysis 94%
Similar papers in this journal
- From the Andes to the desert: First characterization of bacterial communities in the Rimac river, the main source of water for Lima, Peru 93%
- Assessment of pathogens in flood waters in coastal rural regions: Case study after Hurricane Michael and Florence 93%
- Iron Chelator-Mediated Anoxic Biotransformation of Lignin by Novel sp., Tolumonas lignolytica BRL6-1 93%
Similar papers in this journal
- Genome-resolved metagenomics and detailed geochemical speciation analyses yield new insights into microbial mercury cycling in geothermal springs 94%
- Convergent community assembly among globally separated acidic cave biofilms 94%
- Genomic Evidence for Formate Metabolism by Chloroflexi as the Key to Unlocking Deep Carbon in Lost City Microbial Ecosystems 94%
Similar papers in this journal
- CANT-HYD: A curated database of phylogeny-derived Hidden Markov Models for annotation of marker genes involved in hydrocarbon degradation 93%
- Exploring Taxonomic and Functional Microbiome of Hawaiian Stream and Spring Irrigation Water Systems Using Illumina and Oxford Nanopore Sequencing Platforms 93%
- SARS-CoV-2 virus in Raw Wastewater from Student Residence Halls with concomitant 16S rRNA Bacterial Community Structure changes 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.