Characterization of Clostridium ljungdahlii OTA1: a non-autotrophic hyper ethanol-producing strain.
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2017-02
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A Clostridium ljungdahlii lab-isolated spontaneous-mutant strain, OTA1, has been shown to produce twice as much ethanol as the C. ljungdahlii ATCC 55383 strain when cultured in a mixotrophic medium containing fructose and syngas. Whole-genome sequencing identified four unique single nucleotide polymorphisms (SNPs) in the C. ljungdahlii OTA1 genome. Among these, two SNPs were found in the gene coding for AcsA and HemL, enzymes involved in acetyl-CoA formation from CO/CO2. Homology models of the respective mutated enzymes revealed alterations in the size and hydrogen bonding of the amino acids in their active sites. Failed attempts to grow OTA1 autotrophically suggested that one or both of these mutated genes prevented acetyl-CoA synthesis from CO/CO2, demonstrating that its activity was required for autotrophic growth by C. ljungdahlii. An inoperable Wood-Ljungdahl pathway resulted in higher CO2 and ethanol yields and lower biomass and acetate yields compared to WT for multiple growth conditions including heterotrophic and mixotrophic conditions. The two other SNPs identified in the C. ljungdahlii OTA1 genome were in genes coding for transcriptional regulators (CLJU_c09320 and CLJU_c18110) and were found to be responsible for deregulated expression of co-localized arginine catabolism and 2-deoxy-D-ribose catabolism genes. Growth medium supplementation experiments suggested that increased arginine metabolism and 2-deoxy-D-ribose were likely to have minor effects on biomass and fermentation product yields. In addition, in silico flux balance analysis simulating mixotrophic and heterotrophic conditions showed no change in flux to ethanol when flux through HemL was changed whereas limited flux through AcsA increased the ethanol flux for both simulations. In characterizing the effects of the SNPs identified in the C. ljungdahlii OTA1 genome, a non-autotrophic hyper ethanol-producing strain of C. ljungdahlii was identified that has utility for further physiology and strain performance studies and as a biocatalyst for industrial applications.
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Whitham, Jason M, Mark J Schulte, Benjamin G Bobay, Jose M Bruno-Barcena, Mari S Chinn, Michael C Flickinger, Joel J Pawlak, Amy M Grunden, et al. (2017). Characterization of Clostridium ljungdahlii OTA1: a non-autotrophic hyper ethanol-producing strain. Applied microbiology and biotechnology, 101(4). pp. 1615–1630. 10.1007/s00253-016-7978-6 Retrieved from https://hdl.handle.net/10161/28907.
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Benjamin Bobay
I am the Assistant Director of the Duke University NMR Center and an Assistant Professor in the Duke Radiology Department. I was originally trained as a structural biochemist with an emphasis on utilizing NMR and continue to use this technique daily helping collaborators characterize protein structures and small molecules through a diverse set of NMR experiments. Through the structural characterization of various proteins, from both planta and eukaryotes, I have developed a robust protocol of utilizing computational biology for describing binding events, mutations, post-translations modifications (PTMs), and/or general behavior within in silico solution scenarios. I have utilized these techniques in collaborations ranging from plant pathologists at the Swammerdam Institute for Life Sciences department at the University of Amsterdam to biomedical engineers at North Carolina State University to professors in the Pediatrics department at Duke University. These studies have centered around the structural and functional consequences of PTMs (such as phosphorylation), mutation events, truncation of multi-domain proteins, dimer pulling experiments, to screening of large databases of ligands for potential binding events. Through this combination of NMR and computational biology I have amassed 50 peer-reviewed published articles and countless roles on scientific projects, as well as the development of several tutorials concerning the creation of ligand databases and high-throughput screening of large databases utilizing several different molecular dynamic and computational docking programs.
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