Biofuels Production programs
Quantitative Engineering of Industrial Yeast
Bacteria are an attractive alternative to yeast for industrial-level fuel production, and they can be engineered to greatly increase output. This requires a thorough systems-level understanding of how the bacterial metabolism, gene regulation, and stress response influence the process. This facility includes an experimental and computational comparative microbial systems biology infrastructure that can identify, understand and engineer the central pathways in bacteria and fungi.
The long-term goal of our program is to uncover the rules for engineering complex traits in S. cerevisiae and for trait stacking (i.e. combining multiple complex traits into a single host) that will enable cost-effective cellulosic biofuel production. In 2014, we focused on QTL fine mapping to identify the causative SNP(s) and gene(s) that underlie hydrolysate tolerance in S. cerevisiae strain JAY291 (isolated from a Brazilian ethanol plant). We identified 17 QTL peaks for hydrolysate tolerance from a JAY291 x S288C mapping cross. We have made 36 out of 60 total RHA strains. Thus far, 34 out of 36 strains have been phenotyped. If successful, we could identify eight genes that underlie JAY291 hydrolysate tolerance that can be used to engineer S288C for improved tolerance. Finally, we hope to be able to improve JAY291 tolerance even further, by the introduction of S288C favorable alleles. In collaboration with the Jin lab, we have performed a X-QTL study of xylose utilization. Parental strains have been engineered for xylose utilization by the introduction of a three-gene cassette (X123) encoding three enzymes: xylose reductase, xylitol dehydrogenase, and xylulose kinase. In collaboration with the Pereira lab at USP, Brazil, we have identified a haploid strain (272-1a) that is derived from industrial parents, which is more hydrolysate-tolerant than JAY291. Haploid 272-1a can grow in 45% miscanthus hydrolysate; in contrast, SA-1 and JAY291 are unable to grow in this condition. We performed an X-QTL experiment in a bioreactor and samples have been submitted for Illumina library prep and sequencing.
The goal of our program is to understand the genetic basis of complex traits in Saccharomyces cerevisiae in order to engineer improved strains for cellulosic biofuel production. We are using high-resolution QTL mapping to identify the genes that underlie useful industrial traits, such as hydrolysate tolerance, ethanol productivity, and co-fermentation of xylose and glucose. In 2013, we completed our first QTL mapping experiment for the Brazilian industrial yeast strain JAY291, which is more hydrolysate-tolerant than the laboratory strain S288C. Fine mapping of the QTL intervals is currently underway and we expect to identify the causative gene(s) in 2014. A complementary approach to QTL mapping is laboratory evolution and genome resequencing. We have completed a computational pipeline for the analysis of evolved yeast strains and can use it to identify mutations that occurred during evolution. In collaboration with the Jin lab we have shown that a combination of rational and laboratory evolution can be used to improve xylose fermentation. We have a number of evolution and genome resequencing projects underway for improved cellobiose fermentation and sugar co-fermentation. A third approach for understanding complex traits is to domesticate wild and industrial yeast strains. We have isolated a haploid derivative from the polyploid molasses distillery yeast ATCC4124 and found that it has better xylose fermentation properties than laboratory strains. We are currently analyzing the genome sequence of this haploid derivative to understand the genetic basis of its improved xylose fermentation.
Our Microbial Characterization Facility has fully transitioned to the genetic analysis of industrial yeast strains, with a focus on ATCC 4124 (isolated from a molasses distillery) and JAY 291 (isolated from a Brazilian ethanol plant). Our primary goal is to understand why industrial yeast strains are better than laboratory strains for ethanol production and then to exploit this knowledge for improvement. Most industrial strains have a number of useful traits relevant to cellulosic ethanol production; they are more resistant to environmental stresses encountered in an industrial bioreactor and are able to outcompete other contaminating organisms. We are using a combination of high-throughput genome sequencing and quantitative trait mapping (QTL) to identify the genes that confer these useful traits. We have completed our first QTL cross between the laboratory strain S288C and the Brazilian strain JAY291, and mapped a number of QTLs that are important for growth in Miscanthus plant hydrolysate.
Another focus of our facility is the domestication of industrial yeast strains in order to develop a more robust host for biofuel pathway engineering. We have completed our Z. mobilis project and expect to publish three manuscripts based on this work. The first manuscript will describe the use of chemogenomic profiling to understand the genetic basis of tolerance to plant hydrolysate (in review); the second paper is a comparative analysis of fitness and gene expression datasets in three different bacteria (in revision); and the third will describe the use of large-scale fitness datasets to uncover phenotypes for nearly all genes in the Z. mobilis genome. Our Z. mobilis work led to a surprising but important conclusion that fitness in hydrolysate can be modeled as the linear combination of fitness in the individual components. This has significant implications for strain engineering, and we believe that it can be applied to our current S. cerevisiae work.
In 2011, Arkin’s group subjected its Z. mobilis library of mutants to about 60 stress conditions — including 37 different single inhibitors and 14 stress conditions, including redox stress, salt, and high temperature — which might be encountered in an industrial setting. The team consequently identified 49 genes that represent at least seven broad functional categories, indicating that tolerance of hydrolysates is a vastly complex response involving multiple pathways. Arkin’s group also identified four Z. mobilis genes that, when overexpressed, improved growth and/or ethanol production in the presence of Miscanthus hydrolysate. Researchers also identified 25 genes that may influence tolerance in yeast. The group also has developed a preliminary model of hydrolysate fitness, or tolerance, which so far accounts for 18 of 37 components.
Arkin’s group applied the high-throughput screening of the libraries approach to the industrial ethanol producing bacterium Zymomonas mobilis and produced a library of 6,304 insertions interrupting 85% of all protein coding genes. Researchers have identified ~100 genes that have specific fitness defects in hydrolysate. They have identified a number of uncharacterized genes and pathways that should lead to a deeper understanding of tolerance.
Published in 2014
Selection of Chromosomal DNA Libraries Using a Multiplex CRISPR System, Owen W. Ryan, Jeffrey M. Skerker, Matthew J. Maurer, Xin Li, Jordan C. Tsai, Snigdha Poddar, Michael E. Lee, Will DeLoache, John E. Dueber, Adam P. Arkin, Jamie H.D. Cate, eLIFE, 2014;3:e03703, doi: http://dx.doi.org/10.7554/eLife.03703.001, August 19, 2014.
Towards an Informative Mutant Phenotype for Every Bacterial Gene, Adam Deutschbauer, Morgan N. Price, Kelly M. Wetmore, Daniel R. Tarjan, Zhuchen Xu, Wenjun Shao, Dacia Leon, Adam P. Arkin, and Jeffrey M. Skerker, Journal of Bacteriology V. 196 (20): pp. 3643-55, August 11, 2014.
Fermentation of Hydrolysate Detoxified by Pervaporation through Block Copolymer Membranes, D. R. Greer, T. P. Basso, A. B. Ibanez, S. Bauer, J. Skerker, A. E. Ozcam, D. Leon, C. Shin, A. P. Arkin, N. P. Balsara, Green Chemistry, V. 16, pp. 4206, July 11, 2014.
Published in 2013
Rational and Evolutionary Engineering Approaches Uncover a Small Set of Genetic Changes Efficient for Rapid Xylose Fermentation in Saccharomyces cerevisae, Soo Rin Kim, Jeffrey M. Skerker, Wei Kang, Anastashia Lesmana, Na Wei, Adam P. Arkin, Yong-Su Jin, PLoS One 8 (2), doi: 10.1371/journal.pone.0057048, February 26, 2013.
Indirect and Suboptimal Control of Gene Expression is Widespread in Bacteria, Morgan N. Price, Adam M. Deutschbauer, Jeffrey M. Skerker, Kelly M. Wetmore, Troy Ruths, Jordan S. Mar, Jennifer V. Kuehl, Wenjun Shao, Adam P. Arkin, Molecular Systems Biology 9:60, doi:10.1038/msb.2013.16, April 16, 2013.
Dissecting a Complex Chemical Stress: Chemogenomic Profiling of Plant Hydrolysates, Jeffrey M. Skerker, Dacia Leon, Morgan N. Price, Jordan S. Mar, Daniel R. Tarjan, Kelly M. Wetmore, Adam M. Deutschbauer, Jason K. Baumohl, Stefan Bauer, Ana B. Ibáñez, Valerie D. Mitchell, Cindy H. Wu, Ping Hu, Terry Hazen, and Adam P. Arkin, Molecular Systems Biology 9: 674, doi: 10.1038/msb.2013.30, June 18, 2013.
Published in 2012
Engineering Robust Control of Two-Component System Phosphotransfer Using Modular Scaffolds, Weston R. Whitaker, Stephanie A. Davis, Adam P. Arkin, and John E. Dueber, Proceedings of the National Academy of Sciences, doi: : 10.1073/pnas.1209230109, October 15, 2012 online.
Indirect and Suboptimal Control of Gene Expression is Widespread in Bacteria, Morgan N. Price, Adam M. Deutschbauer, Jeffrey M. Skerker, Kelly M. Wetmore, Troy Ruths, Jordan S. Mar, Jennifer V. Kuehl, Wenjun Shao, Adam P. Arkin. Molecular Systems Biology (in revision).
Dissecting a Complex Chemical Stress: Chemogenomic Profiling of Plant Hydrolysates and 37 components, Jeffrey M. Skerker, Dacia Leon, Morgan Price, Jordan Mar, Dan R. Tarjan, Kelly M. Wetmore, Adam Deutschbauer, Jason Baumohl, Stefan Bauer, Ana Ibanez, Valerie Mitchell, Cindy H. Wu, Ping Hu, Terry Hazen, Adam P. Arkin, Molecular Systems Biology (in review).
Uncovering a Phenotype for All Genes in a Bacterium, Adam Deutschbauer, Morgan N. Price, Kelly Wetmore, Dan Tarjan, Zhuchen Xu, Wenjun Shao, Dacia Leon, Adam P. Arkin, Jeffrey Skerker (in preparation).