Fluxomics-based elucidation of metabolic remodeling and optimization strategies for enhancing bioproduction efficiency in microbial factories under selection pressure
EMSL Project ID
49783
Abstract
Well characterized bacterial models such as Escherichia coli are ubiquitously used as microbial factories in the commercial production of biofuels and bioproducts. The increasingly optimized conversion of C6 and C5 sugars into high value C2 biofuel compounds using E. coli chassis -- including bioethanol, biodiesel, n-butanol, isobutanol, n-propanol and isopropanol -- illustrates the strengths of combined metabolic engineering and systems biology approaches. Strategies for bioproduct production often involve natural pathway improvements or heterologous expression of biosynthetic pathways, with genes of interest typically expressed from recombinant DNA plasmids, or less frequently, chromosomal integrations. Given the favored usage of selectable marker expression to maintain culture homogeneity and the frequent requirement for antibiotic supplementation to prevent contamination in fermentation tanks, this proposal seeks to address the understudied impact of burdensome selection strategies on metabolic flux and bioenergetics in E. coli chassis by coupling experiments with computational modeling. Here we seek to use antibiotic resistance as a maximally perturbed experimental model to determine the metabolic load of genetic modifications on microbial bioproduction systems. This represents a shift in the current paradigm, which utilizes metabolic engineering and systems biology to identify potential microbial chassis modifications that improve yield by focusing strictly on the production pathway. We propose to instead elucidate the metabolic remodeling and optimization strategies of recombinant bacterial factories, with a focus on identifying the most frequently utilized pathways that remain active under selection to maximize growth and biomass production. Our proposed research leverages targeted fluxomics and global transcriptomics measurements in the development of highly accurate and robust genome-scale mathematical models of bacterial metabolism. We further propose to computationally predict the minimal set of metabolic transformations that result in discretization of metabolic states associated with steady-state growth under stressful and non-stressful conditions; this approach will yield a hierarchical list of genes that, when perturbed, can optimize objective growth and biomass production in a manner similar to growth under selective pressure.
With expertise in systems biology and antimicrobial stress physiology, our research team is uniquely positioned to take full advantage of the advanced omics capabilities at EMSL and provide integrative answers to important questions related to bioenergy-relevant metabolic engineering. To accomplish our goals, we also seek to utilize the available microbial bioreactor resources, and will use the Department of Energy KnowledgeBase (KBase) platform for metabolic modeling of omics data; KBase co-PI Christopher Henry is co-PI of this proposal. More specifically, we seek instrument time on the: (1) Micro-24 bioreactor and BioFlo 310 benchtop fermentor-bioreactor systems offering real-time control and monitoring of diverse growth parameters, (2) 5500XL SOLiD and Ion Torrent Proton NGS platforms for RNA-Seq transcriptomics analysis, and (3) NMR instrumentation (e.g., Hood, Denali) for targeted 13C fluxomics measurements providing requisite high-resolution data for metabolic flux analysis. Instrument time is also requested on the Mass Spectrometry: GC MS (Agilent GC-MS 01 or 02) to fully utilize the advanced fluxomics analysis capabilities afforded by the MFAPipe computational tools developed at EMSL with proposal co-Investigator, Ronald Taylor.
Our integrative research goals specifically address Biosystem Dynamics and Design (BDD) interests, including carbon and nitrogen flux via isotope probing, molecular characterization of bioenergetics, post-transcriptional/translational processes influencing stress tolerance and biomass production, as well as metabolic pathway analyses supporting synthetic biology. We anticipate our results will yield critical new systems-level insights into the underappreciated impact of selection pressures (here, antibiotic resistant gene expression and antibiotic selection) on the metabolic behaviors of the most commonly used industrial chassis for bioproduction. We further anticipate that these insights can be leveraged in the development of novel metabolic engineering strategies for optimization of growth conditions and increased production efficiency of bioenergy-relevant deliverables.
Project Details
Project type
Large-Scale EMSL Research
Start Date
2017-10-01
End Date
2019-10-31
Status
Closed
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
Team Members