Characterizing the metabolism of Dehalococcoides with a constraint-based model
journal contributionposted on 07.11.2017, 10:22 by Ahsan IslamAhsan Islam, Elizabeth A. Edwards, Radhakrishnan Mahadevan
Dehalococcoides strains respire a wide variety of chloro-organic compounds and are important for the bioremediation of toxic, persistent, carcinogenic, and ubiquitous ground water pollutants. In order to better understand metabolism and optimize their application, we have developed a pan-genome-scale metabolic network and constraint-based metabolic model of Dehalococcoides. The pan-genome was constructed from publicly available complete genome sequences of Dehalococcoides sp. strain CBDB1, strain 195, strain BAV1, and strain VS. We found that Dehalococcoides pan-genome consisted of 1118 core genes (shared by all), 457 dispensable genes (shared by some), and 486 unique genes (found in only one genome). The model included 549 metabolic genes that encoded 356 proteins catalyzing 497 gene-associated model reactions. Of these 497 reactions, 477 were associated with core metabolic genes, 18 with dispensable genes, and 2 with unique genes. This study, in addition to analyzing the metabolism of an environmentally important phylogenetic group on a pan-genome scale, provides valuable insights into Dehalococcoides metabolic limitations, low growth yields, and energy conservation. The model also provides a framework to anchor and compare disparate experimental data, as well as to give insights on the physiological impact of "incomplete" pathways, such as the TCA-cycle, CO 2 fixation, and cobalamin biosynthesis pathways. The model, referred to as iAI549, highlights the specialized and highly conserved nature of Dehalococcoides metabolism, and suggests that evolution of Dehalococcoides species is driven by the electron acceptor availability.
This research was funded by the University of Toronto, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Government of Canada through Genome Canada and the Ontario Genomics Institute (2009-OGI-ABC-1405) and the United States Department of Defense Strategic Environmental Research and Development Program (SERDP). MAI was funded by the Ontario Graduate Scholarship (OGS), the SERDP and Genome Canada funds to EAE and the departmental faculty start-up funds to RM. We would also like to acknowledge Dr. Derek Lovley and funding from the US Department of Energy (Cooperative Agreement No. DE-FC02-02ER63446) for enabling access to the SimPheny program. After acceptance of the paper, the authors acknowledge Ontario Genomics Institute Genomics Publication Fund for defraying the open-access publication costs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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