Multi-omics approach to identifying mechanisms of evolved tolerance to biobased chemical products
EMSL Project ID
49341
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, it is proposed to select evolved strains for 5 of the chemicals, plus approximately two strains corresponding to each evolved strain that contain subsets of reconstructed causative mutations, and to further characterize these strains using a multi-omics approach. Using both EMSL resources and facilities at the Novo Nordisk Foundation Center for Biosustainability, it is proposed to perform differential transcriptional profiling via RNA sequencing, differential profiling of soluble and membrane protein levels via mass spectrometry, and lipidomics and metabolomics analyses by mass spectrometry on these selected strains.
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 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. These predicted effects will be tested using engineered production hosts for at least two chemicals.
Project Details
Project type
Large-Scale EMSL Research
Start Date
2016-10-01
End Date
2017-09-30
Status
Closed
Released Data Link
Team
Principal Investigator