Browsing by Author "Jiang, Tao"

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    MSOAR 2.0: Incorporating tandem duplications into ortholog assignment based on genome rearrangement
    Shi, Guanqun; Zhang, Liqing; Jiang, Tao (BioMed Central, 2010)
    Background: Ortholog assignment is a critical and fundamental problem in comparative genomics, since orthologs are considered to be functional counterparts in different species and can be used to infer molecular functions of one species from those of other species. MSOAR is a recently developed high-throughput system for assigning one-to-one orthologs between closely related species on a genome scale. It attempts to reconstruct the evolutionary history of input genomes in terms of genome rearrangement and gene duplication events. It assumes that a gene duplication event inserts a duplicated gene into the genome of interest at a random location (i.e., the random duplication model). However, in practice, biologists believe that genes are often duplicated by tandem duplications, where a duplicated gene is located next to the original copy (i.e., the tandem duplication model). Results: In this paper, we develop MSOAR 2.0, an improved system for one-to-one ortholog assignment. For a pair of input genomes, the system first focuses on the tandemly duplicated genes of each genome and tries to identify among them those that were duplicated after the speciation (i.e., the so-called inparalogs), using a simple phylogenetic tree reconciliation method. For each such set of tandemly duplicated inparalogs, all but one gene will be deleted from the concerned genome (because they cannot possibly appear in any one-to-one ortholog pairs), and MSOAR is invoked. Using both simulated and real data experiments, we show that MSOAR 2.0 is able to achieve a better sensitivity and specificity than MSOAR. In comparison with the well-known genome-scale ortholog assignment tool InParanoid, Ensembl ortholog database, and the orthology information extracted from the wellknown whole-genome multiple alignment program MultiZ, MSOAR 2.0 shows the highest sensitivity. Although the specificity of MSOAR 2.0 is slightly worse than that of InParanoid in the real data experiments, it is actually better than that of InParanoid in the simulation tests. Conclusions: Our preliminary experimental results demonstrate that MSOAR 2.0 is a highly accurate tool for oneto- one ortholog assignment between closely related genomes. The software is available to the public for free and included as online supplementary material.
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    Transcriptional profiling reveals molecular basis and novel genetic targets for improved resistance to multiple fermentation inhibitors in Saccharomyces cerevisiae
    Chen, Yingying; Sheng, Jiayuan; Jiang, Tao; Stevens, Joseph; Feng, Xueyang; Wei, Na (2016-01-13)
    Background Lignocellulosic biomass is a promising source of renewable biofuels. However, pretreatment of lignocellulosic biomass generates fermentation inhibitors that adversely affect the growth of industrial microorganisms such as Saccharomyces cerevisiae and prevent economic production of lignocellulosic biofuels. A critical challenge on developing S. cerevisiae with improved inhibitor resistance lies in incomplete understanding of molecular basis for inhibitor stress response and limited information on effective genetic targets for increasing yeast resistance to mixed fermentation inhibitors. In this study, we applied comparative transcriptomic analysis to determine the molecular basis for acetic acid and/or furfural resistance in S. cerevisiae. Results We recently developed a yeast strain YC1 with superior resistance to acetic acid, furfural, and their mixture through inverse metabolic engineering. In this study, we first determined transcriptional changes through RNA sequencing in YC1 versus the wild-type strain S-C1 under three different inhibitor conditions, including acetic acid alone, furfural alone, and mixture of acetic acid and furfural. The genes associated with stress responses of S. cerevisiae to single and mixed inhibitors were revealed. Specifically, we identified 184 consensus genes that were differentially regulated in response to the distinct inhibitor resistance between YC1 and S-C1. Bioinformatic analysis next revealed key transcription factors (TFs) that regulate these consensus genes. The top TFs identified, Sfp1p and Ace2p, were experimentally tested as overexpression targets for strain optimization. Overexpression of the SFP1 gene improved specific ethanol productivity by nearly four times, while overexpression of the ACE2 gene enhanced the rate by three times in the presence of acetic acid and furfural. Overexpression of SFP1 gene in the resistant strain YC1 further resulted in 42 % increase in ethanol productivity in the presence of acetic acid and furfural, suggesting the effect of Sfp1p in optimizing the yeast strain for improved tolerance to mixed fermentation inhibitor. Conclusions Transcriptional regulation underlying yeast resistance to acetic acid and furfural was determined. Two transcription factors, Sfp1p and Ace2p, were uncovered for the first time for their functions in improving yeast resistance to mixed fermentation inhibitors. The study demonstrated an omics-guided metabolic engineering framework, which could be developed as a promising strategy to improve complex microbial phenotypes.