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Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.

ABSTRACT: BACKGROUND:Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. RESULTS:We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4). CONCLUSION:Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

SUBMITTER: Panchy NL

PROVIDER: S-EPMC7020519 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.

Panchy Nicholas L NL Lloyd John P JP Shiu Shin-Han SH

BMC genomics 20200213 1

<h4>Background</h4>Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a chall ...[more]

PMID: 32054475

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Project description:BackgroundThe fermentation of lignocellulose hydrolysates to ethanol requires robust xylose-capable Saccharomyces cerevisiae strains able to operate in the presence of microbial inhibitory stresses. This study aimed at developing industrial S. cerevisiae strains with enhanced tolerance towards pretreatment-derived microbial inhibitors, by identifying novel gene combinations that confer resistance to multiple inhibitors (thus cumulative inhibitor resistance phenotype) with minimum impact on the xylose fermentation ability. The strategy consisted of multiple sequential delta-integrations of double-gene cassettes containing one gene conferring broad inhibitor tolerance (ARI1, PAD1 or TAL1) coupled with an inhibitor-specific gene (ADH6, FDH1 or ICT1). The performances of the transformants were compared with the parental strain in terms of biomass growth, ethanol yields and productivity, as well as detoxification capacities in a synthetic inhibitor cocktail, sugarcane bagasse hydrolysate as well as hardwood spent sulphite liquor.ResultsThe first and second round of delta-integrated transformants exhibited a trade-off between biomass and ethanol yield. Transformants showed increased inhibitor resistance phenotypes relative to parental controls specifically in fermentations with concentrated spent sulphite liquors at 40% and 80% v/v concentrations in 2% SC media. Unexpectedly, the xylose fermentation capacity of the transformants was reduced compared to the parental control, but certain combinations of genes had a minor impact (e.g. TAL1 + FDH1). The TAL1 + ICT1 combination negatively impacted on both biomass growth and ethanol yield, which could be linked to the ICT1 protein increasing transformant susceptibility to weak acids and temperature due to cell membrane changes.ConclusionsThe integration of the selected genes was proven to increase tolerance to pretreatment inhibitors in synthetic or industrial hydrolysates, but they were limited to the fermentation of glucose. However, some gene combination sequences had a reduced impact on xylose conversion.

Project description:Background:Reduction or elimination of by-product formation is of immediate economic relevance in fermentation processes for industrial bioethanol production with the yeast Saccharomyces cerevisiae. Anaerobic cultures of wild-type S. cerevisiae require formation of glycerol to maintain the intracellular NADH/NAD+ balance. Previously, functional expression of the Calvin-cycle enzymes ribulose-1,5-bisphosphate carboxylase (RuBisCO) and phosphoribulokinase (PRK) in S. cerevisiae was shown to enable reoxidation of NADH with CO2 as electron acceptor. In slow-growing cultures, this engineering strategy strongly decreased the glycerol yield, while increasing the ethanol yield on sugar. The present study explores engineering strategies to improve rates of growth and alcoholic fermentation in yeast strains that functionally express RuBisCO and PRK, while maximizing the positive impact on the ethanol yield. Results:Multi-copy integration of a bacterial-RuBisCO expression cassette was combined with expression of the Escherichia coli GroEL/GroES chaperones and expression of PRK from the anaerobically inducible DAN1 promoter. In anaerobic, glucose-grown bioreactor batch cultures, the resulting S. cerevisiae strain showed a 31% lower glycerol yield and a 31% lower specific growth rate than a non-engineered reference strain. Growth of the engineered strain in anaerobic, glucose-limited chemostat cultures revealed a negative correlation between its specific growth rate and the contribution of the Calvin-cycle enzymes to redox homeostasis. Additional deletion of GPD2, which encodes an isoenzyme of NAD+-dependent glycerol-3-phosphate dehydrogenase, combined with overexpression of the structural genes for enzymes of the non-oxidative pentose-phosphate pathway, yielded a CO2-reducing strain that grew at the same rate as a non-engineered reference strain in anaerobic bioreactor batch cultures, while exhibiting a 86% lower glycerol yield and a 15% higher ethanol yield. Conclusions:The metabolic engineering strategy presented here enables an almost complete elimination of glycerol production in anaerobic, glucose-grown batch cultures of S. cerevisiae, with an associated increase in ethanol yield, while retaining near wild-type growth rates and a capacity for glycerol formation under osmotic stress. Using current genome-editing techniques, the required genetic modifications can be introduced in one or a few transformations. Evaluation of this concept in industrial strains and conditions is therefore a realistic next step towards its implementation for improving the efficiency of first- and second-generation bioethanol production.

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Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.

Publications

Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.

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