Visualizing microbial dechlorination processes in underground ecosystem by statistical correlation and network analysis approach

• Published in: Journal of bioscience and bioengineering Vol. 117; no. 3; pp. 305 - 309
• Main Authors: Yamazawa, Akira, Date, Yasuhiro, Ito, Keijiro, Kikuchi, Jun
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-03-2014; Elsevier
• Subjects: Anaerobiosis; Bacteria - metabolism; Biodegradation, Environmental; Biological and medical sciences; Bioremediation; Biotechnology; Chlorinated ethenes; Dechlorination; Denaturing Gradient Gel Electrophoresis; Ecosystem; Ethylenes - metabolism; Fundamental and applied biological sciences. Psychology; Halogenation - physiology; Hydrocarbons, Chlorinated - metabolism; Metabolic network; Metabolic Networks and Pathways; Microbial Interactions; Microflora; Models, Statistical; Statistical correlation; Visualization; Visualization; Dechlorination; Metabolic network; Microflora; Statistical correlation; Bioremediation; Chlorinated ethenes; Correlation; Ethylene; Chlorine compounds; Statistical method; Ecosystem; Microorganism; Operating process
• ISSN: 1389-1723; 1347-4421
• DOI: 10.1016/j.jbiosc.2013.08.010

Microbial ecosystems are typified by diverse microbial interactions and competition. Consequently, the microbial networks and metabolic dynamics of bioprocesses catalyzed by these ecosystems are highly complex, and their visualization is regarded as essential to bioengineering technology and innovation. Here we describe a means of visualizing the variants in a microbial community and their metabolic profiles. The approach enables previously unidentified bacterial functions in the ecosystems to be elucidated. We investigated the anaerobic bioremediation of chlorinated ethene in a soil column experiment as a case study. Microbial community and dechlorination profiles in the ecosystem were evaluated by denaturing gradient gel electrophoresis (DGGE) fingerprinting and gas chromatography, respectively. Dechlorination profiles were obtained from changes in dechlorination by microbial community (evaluated by data mining methods). Individual microbes were then associated with their dechlorination profiles by heterogenous correlation analysis. Our correlation-based visualization approach enables deduction of the roles and functions of bacteria in the dechlorination of chlorinated ethenes. Because it estimates functions and relationships between unidentified microbes and metabolites in microbial ecosystems, this approach is proposed as a control-logic tool by which to understand complex microbial processes.