bioRxivMicrobiologyPreprint
Data-mining of Antibiotic Resistance Genes Provides Insight into the Community Structure of Ocean Microbiome
Shiguang Hao, Pengshuo YangiD, Maozhen HaniD, Junjie Xu, Shaojun YuiD, Chaoyun CheniD, Wei-Hua Chen, Houjin Zhang, Kang NingiD
Abstract
BackgroundAntibiotics have been spread widely in environments, asserting profound effects on environmental microbes as well as antibiotic resistance genes (ARGs) within these microbes. Therefore, investigating the associations between ARGs and bacterial communities become an important issue for environment protection. Ocean microbiomes are potentially large ARG reservoirs, but the marine ARG distribution and its associations with bacterial communities remain unclear.\n\nMethodswe have utilized the big-data mining techniques on ocean microbiome data to analysis the marine ARGs and bacterial distribution on a global scale, and applied comprehensive statistical analysis to unveil the associations between ARG contents, ocean microbial community structures, and environmental factors by reanalyzing 132 metagenomic samples from the Tara Oceans project.\n\nResultsWe identified in total 1,926 unique ARGs and found that: firstly, ARGs are more abundant and diverse in the mesopelagic zone than other water layers. Additionally, ARG-enriched genera are closely connected in co-occurrence network. We also found that ARG-enriched genera are often more abundant than their ARG-less neighbors. Furthermore, we found that samples from the Mediterranean that is surrounded by human activities often contain more ARGs.\n\nConclusionOur research for investigating the marine ARG distribution and revealing the association between ARG and bacterial communities provide a deeper insight into the marine bacterial communities. We found that ARG-enriched genera were often more abundant than their ARG-less neighbors in the same environment, indicating that genera enriched with ARGs might possess an advantage over others in the competition for survival in the oceanic microbial communities.
- assay: metagenomics
- research focus: clustering
- research focus: differential expression
- approach: statistical model
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