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ARS Home » Midwest Area » Peoria, Illinois » National Center for Agricultural Utilization Research » Crop Bioprotection Research » Research » Publications at this Location » Publication #198172
Title: Transcriptional regulatory analysis reveals PDR3 and GCR1 as regulators of significantly induced genes by 5-hydroxymethylfurfural stress involved in bioethanol conversion for ethanologenic yeast Saccharomyces cerevisiae

Author
item Liu, Zonglin
item SINHA, SARAH - UNIV IL, CHAMPAIGN, IL

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/10/2006
Publication Date: 9/7/2007
Citation: Liu, Z., Sinha, S. 2007. Transcriptional regulatory analysis reveals PDR3 and GCR1 as regulators of significantly induced genes by 5-hydroxymethylfurfural stress involved in bioethanol conversion for ethanologenic yeast saccharomyces cerevisiae [abstract]. Microarray Gene Expression Data Society (MGED 9) Meeting.

Interpretive Summary:

Technical Abstract: 5-Hydroxymethylfurfural (HMF) is one of major inhibitory compounds derived from dehydration of hexoses during biomass degradation using dilute acid hydrolysis. It inhibits yeast growth, reduces enzymatic activities, breaks down DNA, and represses protein and RNA synthesis. We study stress tolerance of inhibitors involved in biomass conversion to ethanol, including HMF, for efficient and cost-competitive bioethanol conversion. In this study, we examined global transcriptome profiles of ethanologenic yeast Saccharomyces cerevisiae in response to the HMF stress. Cell samples were periodically taken and RNA extracted at 0, 10, 30, 60, and 120 minutes after the yeast was exposed to HMF at 30 mM on a defined medium. DNA oligo microarray was fabricated using GeneMachine OmniGrid 300 using an updated version of 70-mer oligo set representing 6,388 genes of Saccharomyces cerevisiae. Two biological replications and two technical replications were carried out for each of the microarray experiments. Analysis of variance was performed and significant differentially expressed genes were identified. Among 440 genes selected, eight subsets of genes were classified by co-expression patterns over time using self organizing map method. Each subset of genes was mined for statistically over-represented DNA binding motifs in the promoter regions. Moreover, association between a transcription factor and a gene cluster was statistically tested using previously catalogued targets of the transcription factor. Using these two different statistical schemes to search for potential regulators, we found numerous significant relationships among which PDR3 and GCR1 appeared to be convincing regulators of significantly induced genes under the HMF stress.