RNA-Seq for identifying mechanisms of evolved tolerance to biobased chemical products
EMSL Project ID
50144
Abstract
One of the largest barriers to achieving economical bio-based production of bulk chemicals such as biofuels and polymer precursors is poor tolerance of microbial production hosts toward high concentrations of excreted product. These concentrations are often in excess of 100 g/L in order to minimize capital and downstream purification costs. Virtually all chemicals at these levels result in stress and cessation of growth in the majority of microbial hosts, which can decrease product yields and productivities. To help address this issue, we utilized a robotic platform to evolve parallel populations of Escherichia coli K-12 MG1655 for enhanced growth in the presence of toxic concentrations of 11 chemicals representing diverse functional classes that are of interest as biofuels or their precursors, polymer precursors, and other bulk chemicals and intermediates. Resequencing of over 200 strains and subsequent reconstruction of sets of mutations has provided unparalleled insight on the genomic basis of tolerance. While knowledge of causative mutations is a preliminary necessary step toward developing and engineering tolerant host strains, further work is required to bridge the genotype-phenotype gap and work toward understanding the biochemical mechanisms of tolerance. To address this, we had a proposal approved in FY2017 to select evolved strains for several of the chemicals and to further characterize these strains using a multi-omics approach. An additional rapid access request is now proposed in FY2018 to complete differential transcriptional profiling via RNA sequencing to allow the collection of the complete multi-omics dataset for these strains (proteomics analysis on the same strains was completed in FY2017).
Integration of these data sets is expected to reveal probable biochemical mechanisms of tolerance that can be tested in more specific follow-on studies. To achieve this, transcriptomic and proteomic data will first be analyzed using existing genome-scale interaction networks and gene regulatory databases. Intracellular metabolite and lipid levels collected in FY2016 and at The NNF Center for Biosustainability and ETHZ can provide direct data to support hypotheses generated from transcriptomic and proteomic data sets. Finally, all data can be used to constrain genome-scale metabolic reconstructions in order to understand the potential impact of tolerance-imparting mutations on host cell metabolism, as well as to predict the effect on endogenous production of the chemical.
Project Details
Project type
Limited Scope
Start Date
2018-02-01
End Date
2018-04-03
Status
Closed
Released Data Link
Team
Principal Investigator