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Mutant Alcohol Dehydrogenase Leads To Improved Ethanol Tolerance In Clostridium Thermocellum, Steven D. Brown, Adam M. Guss, Tatiana V. Karpinets, Jerry M. Parks
Mutant Alcohol Dehydrogenase Leads To Improved Ethanol Tolerance In Clostridium Thermocellum, Steven D. Brown, Adam M. Guss, Tatiana V. Karpinets, Jerry M. Parks
Dartmouth Scholarship
Clostridium thermocellum is a thermophilic, obligately anaerobic, Gram-positive bacterium that is a candidate microorganism for converting cellulosic biomass into ethanol through consolidated bioprocessing. Ethanol intolerance is an important metric in terms of process economics, and tolerance has often been described as a complex and likely multigenic trait for which complex gene interactions come into play. Here, we resequence the genome of an ethanol-tolerant mutant, show that the tolerant phenotype is primarily due to a mutated bifunctional acetaldehyde-CoA/alcohol dehydrogenase gene (adhE), hypothesize based on structural analysis that cofactor specificity may be affected, and confirm this hypothesis using enzyme assays. …
Ultrasensitive Detection Of Rare Mutations Using Next-Generation Targeted Resequencing, Patrick Flaherty, Georges Natsoulis, Omkar Muralidharan, Mark Winters, Jason Buenrostro, John Bell, Sheldon Brown, Mark Holodniy, Nancy Zhang, Hanlee P. Ji
Ultrasensitive Detection Of Rare Mutations Using Next-Generation Targeted Resequencing, Patrick Flaherty, Georges Natsoulis, Omkar Muralidharan, Mark Winters, Jason Buenrostro, John Bell, Sheldon Brown, Mark Holodniy, Nancy Zhang, Hanlee P. Ji
Mathematics and Statistics Department Faculty Publication Series
With next-generation DNA sequencing technologies, one can interrogate a specific genomic region of interest at very high depth of coverage and identify less prevalent, rare mutations in heterogeneous clinical samples. However, the mutation detection levels are limited by the error rate of the sequencing technology as well as by the availability of variant-calling algorithms with high statistical power and low false positive rates. We demonstrate that we can robustly detect mutations at 0.1% fractional representation. This represents accurate detection of one mutant per every 1000 wild-type alleles. To achieve this sensitive level of mutation detection, we integrate a high accuracy …