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Atmospheric particulate matter with a diameter <2.5 µm (PM2.5) and pollution are worldwide environmental problems and may have negative effects on cardiovascular disease through the lung and gut. The dynamics of intestinal microflora in response to particulate pollutants is unclear. The present study investigated changes in the gut microbiota related to pollutant exposure using spontaneously hypertensive rats (SHR). DNA was extracted from fecal samples. Amplicon Generation and the quality control of PCR products were performed. PCR products was sequenced on an Illumina HiSeq 2500 platform. Data analysis included: operational taxonomic unit (OTU) clustering and species annotation, alpha diversity, beta diversity, principal coordinates analysis (PCoA), and the use of PICRUSt bioinformatics software. The microbial diversity of the SHR rats was inversely associated with exposure to pollutants. In terms of relative abundance, 24 bacterial genera and 2 genera in particular (Actinobacillus and Fusobacterium) significantly declined, and one genus (Treponema) increased. Moreover, pollutant exposure was associated with the accumulation of genes from the gut microbiota that are implicated in cardiovascular diseases. From the long-term exposure experiment, rats appeared to respond to pollutant injury. In conclusion, these results suggest that the effects of atmospheric pollutants on organisms are not limited to the respiratory tract, but also include the gastrointestinal tract. Pollutants are likely to influence the intestinal microbiota and promote the progression of cardiovascular disease.
Pubmed ID: 31602224
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 23,2023.Software package for comparison and analysis of microbial communities, primarily based on high-throughput amplicon sequencing data, but also supporting analysis of other types of data. QIMME analyzes and transforms raw sequencing data generated on Illumina or other platforms to publication quality graphics and statistics.
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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.
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