Experimental Systems-Biology Approaches for Clostridia-Based Bioenergy Production

Papoutsakis, Elefterios¹ 

[1] Univ. of Delaware, Newark, DE (United States)

关键词: biofuel;    clostridium;    toxic metabolite;    stress response;    systems biology;    model;    genetic network;    RNAseq;    proteome;   

DOI  :  10.2172/1178803
RP-ID  :  DOE-DELAWARE--SC0007092
PID  :  OSTI ID: 1178803

学科分类：燃料技术

美国|英语

来源: SciTech Connect

【 摘 要 】

This is the final project report for project "Experimental Systems-Biology Approaches for Clostridia-Based Bioenergy Production" for the funding period of 9/1/12 to 2/28/2015 (three years with a 6-month no-cost extension) OVERVIEW AND PROJECT GOALS The bottleneck of achieving higher rates and titers of toxic metabolites (such as solvents and carboxylic acids that can used as biofuels or biofuel precursors) can be overcome by engineering the stress response system. Thus, understanding and modeling the response of cells to toxic metabolites is a problem of great fundamental and practical significance. In this project, our goal is to dissect at the molecular systems level and build models (conceptual and quantitative) for the stress response of C. acetobutylicum (Cac) to its two toxic metabolites: butanol (BuOH) and butyrate (BA). Transcriptional (RNAseq and microarray based), proteomic and fluxomic data and their analysis are key requirements for this goal. Transcriptional data from mid-exponential cultures of Cac under 4 different levels of BuOH and BA stress was obtained using both microarrays (Papoutsakis group) and deep sequencing (RNAseq; Meyers and Papoutsakis groups). These two sets of data do not only serve to validate each other, but are also used for identification of stress-induced changes in transcript levels, small regulatory RNAs, & in transcriptional start sites. Quantitative proteomic data (Lee group), collected using the iTRAQ technology, are essential for understanding of protein levels and turnover under stress and the various protein-protein interactions that orchestrate the stress response. Metabolic flux changes (Antoniewicz group) of core pathways, which provide important information on the re-allocation of energy and carbon resources under metabolite stress, were examined using 13C-labelled chemicals. Omics data are integrated at different levels and scales. At the metabolic-pathway level, omics data are integrated into a 2nd generation genome-scale model (GSM) (Maranas group). Omics data are also integrated using bioinformatics (Wu and Huang group), whereby regulatory details of gene and protein expression, protein-protein interactions and metabolic flux regulation are incorporated. The PI (Papoutsakis) facilitated project integration through monthly meeting and reports, conference calls, and collaborative manuscript preparation. The five groups collaborated extensively and made a large number of presentations in national and international meetings. It has also published several papers, with several more in the preparation stage. Several PhD, MS and postdoctoral students were trained as part of this collaborative and interdisciplinary project.