4.6 Article

Long-Term Oil Contamination Alters the Molecular Ecological Networks of Soil Microbial Functional Genes

期刊

FRONTIERS IN MICROBIOLOGY
卷 7, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2016.00060

关键词

microbial interaction; oil contamination; molecular ecological network; functional genes; hydrocarbon degradation

资金

  1. National Natural Scientific Foundation of China [41371256]
  2. Strategic Priority Research Program of the Chinese Academy of Sciences [XDB15010100]
  3. Foundation for Distinguished Young Talents in State Key Laboratory of Soil and Sustainable Agriculture [Y412010008]
  4. Youth Innovation Promotion Association of Chinese Academy of Sciences
  5. Foundation of Jiangsu Educational committee [13KJB610001]

向作者/读者索取更多资源

With knowledge on microbial composition and diversity, investigation of within community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P <- 0.001). Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors) were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential keystone genes, defined as either hubs or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

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