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United States Department of Agriculture

Agricultural Research Service

2008 Annual Report

1a.Objectives (from AD-416)
The overall objective of this project is to elucidate genomic mechanisms of detoxification and tolerance of ethanologenic yeast to biomass conversion inhibitors furfural and 5-hydroxymethylfurfural (HMF), and thereafter to genome-wise manipulate and engineer more robust strains for low-cost biomass conversion to ethanol. This study will identify and characterize genes involved in pathways relevant to detoxification, biotransformation, and tolerance to furfural and HMF involved in biomass conversion to ethanol; and elucidate regulatory mechanisms of major gene interactions in relevant pathways involved in furfural and HMF detoxification and tolerance using computational prediction and mathematical modeling.

1b.Approach (from AD-416)
We plan to study genomic regulatory mechanisms of inhibitor detoxification by yeast during ethanol production from dilute acid-hydrolyzed biomass. We propose to characterize the genomic transcriptional profiling of wild-type and several improved, more inhibitor-tolerant strains in response to furfural and 5-hydroxymethylfurfural (HMF) supplied in a defined culture medium. To accomplish this, yeast cells will be sampled in a time-course study to isolate total ribonucleic acid (RNA) and conduct microarray experiments using two-color microarray with spiking universal external RNA quality controls. Inhibitor and inhibitor-conversion products, glucose consumption, ethanol production, and other byproducts generated during the fermentation process will also be monitored during the time-course study to establish metabolic profiles for wild-type and more tolerant strains involved in detoxification of biomass conversion inhibitors. Based on data from culture time-course studies, we will propose computational models to predict the behavior of the gene function and expression of natural and genetically engineered networks under furfural and HMF stress. A dynamic mathematical model using difference equations and estimate parameters will be applied and tested for its ability to describe gene regulatory network behavior. Based on these approaches, we will form testable hypotheses to explain molecular and genomic mechanisms of yeast detoxification and tolerance to furfural and HMF.

3.Progress Report

We developed a discrete dynamic modeling system and created preliminary gene regulatory networks in response to 5-hydroxymethylfurfural (HMF) challenge in collaboration with scientists at New Mexico State University. Yeast tolerance to biomass conversion inhibitors such as furfural and HMF are important to a sustainable low-cost biomass-to-ethanol industry. However, development of tolerant strains is hampered due to a lack of understanding of genetic mechanisms underlying stress tolerance for ethanologenic yeast. We investigated global transcriptome profiling of yeast under challenge of HMF and identified more than 300 genes significantly involved in regulation of HMF tolerance and detoxification. We described a data-driven discrete dynamic system modeling methodology to detect gene regulatory interactions and to predict system dynamic behavior based on large-scale microarray data sets. Using our model system, we identified 12 potentially significant regulatory interactions, among which, two transcription factors were significant tolerant regulatory elements for HMF tolerance in yeast. A discrete dynamic system model has potential for broad applications in biology. Application of such a model has contributed to a better understanding of mechanisms of yeast stress tolerance, and has benefited research and development of more tolerant strains for biomass conversion to ethanol. Currently we are continuing to pursue our research objectives outlined in this project. The Authorized Departmental Officer’s Designated Representative has been keeping a close working relationship and monitoring activities by regular conference calls and frequent e-mail communications. This research addresses NP 307, Component 1, Problem Area Process Efficiencies; and NP 306, Component 2, Problem Area 2c.

Last Modified: 8/28/2016
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