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      Rewired cellular signaling coordinates sugar and hypoxic responses for anaerobic xylose fermentation in yeast

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          Abstract

          Microbes can be metabolically engineered to produce biofuels and biochemicals, but rerouting metabolic flux toward products is a major hurdle without a systems-level understanding of how cellular flux is controlled. To understand flux rerouting, we investigated a panel of Saccharomyces cerevisiae strains with progressive improvements in anaerobic fermentation of xylose, a sugar abundant in sustainable plant biomass used for biofuel production. We combined comparative transcriptomics, proteomics, and phosphoproteomics with network analysis to understand the physiology of improved anaerobic xylose fermentation. Our results show that upstream regulatory changes produce a suite of physiological effects that collectively impact the phenotype. Evolved strains show an unusual co-activation of Protein Kinase A (PKA) and Snf1, thus combining responses seen during feast on glucose and famine on non-preferred sugars. Surprisingly, these regulatory changes were required to mount the hypoxic response when cells were grown on xylose, revealing a previously unknown connection between sugar source and anaerobic response. Network analysis identified several downstream transcription factors that play a significant, but on their own minor, role in anaerobic xylose fermentation, consistent with the combinatorial effects of small-impact changes. We also discovered that different routes of PKA activation produce distinct phenotypes: deletion of the RAS/PKA inhibitor IRA2 promotes xylose growth and metabolism, whereas deletion of PKA inhibitor BCY1 decouples growth from metabolism to enable robust fermentation without division. Comparing phosphoproteomic changes across ira2Δ and bcy1Δ strains implicated regulatory changes linked to xylose-dependent growth versus metabolism. Together, our results present a picture of the metabolic logic behind anaerobic xylose flux and suggest that widespread cellular remodeling, rather than individual metabolic changes, is an important goal for metabolic engineering.

          Author summary

          An important strategy for sustainable energy is microbial production of biofuels from non-food plant material. However, many microbes, including yeast, cannot use the xylose comprising ~30% of hemicellulosic sugars, especially under anaerobic conditions. Although cells can be engineered with required enzymes, they fail to recognize xylose as a consumable sugar for unknown reasons. We used comparative systems biology across strains with progressive improvements in xylose utilization to understand the metabolic and regulatory logic of anaerobic xylose fermentation. Mutations in evolved strains trigger signaling pathways that are normally antagonistic, producing a cascade of regulatory events coordinating metabolism and growth. Integrative modeling implicates causal events linked to growth versus metabolism and shows the hypoxic response is dependent on carbon sensing in yeast.

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          Most cited references124

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The genetic landscape of a cell.

            A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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              Combining evidence using p-values: application to sequence homology searches.

              To illustrate an intuitive and statistically valid method for combining independent sources of evidence that yields a p-value for the complete evidence, and to apply it to the problem of detecting simultaneous matches to multiple patterns in sequence homology searches. In sequence analysis, two or more (approximately) independent measures of the membership of a sequence (or sequence region) in some class are often available. We would like to estimate the likelihood of the sequence being a member of the class in view of all the available evidence. An example is estimating the significance of the observed match of a macromolecular sequence (DNA or protein) to a set of patterns (motifs) that characterize a biological sequence family. An intuitive way to do this is to express each piece of evidence as a p-value, and then use the product of these p-values as the measure of membership in the family. We derive a formula and algorithm (QFAST) for calculating the statistical distribution of the product of n independent p-values. We demonstrate that sorting sequences by this p-value effectively combines the information present in multiple motifs, leading to highly accurate and sensitive sequence homology searches.

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                11 March 2019
                March 2019
                : 15
                : 3
                : e1008037
                Affiliations
                [1 ] Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, United States of America
                [2 ] Department of Chemistry, University of Wisconsin-Madison, Madison, WI, United States of America
                [3 ] Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, United States of America
                [4 ] Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, United States of America
                [5 ] Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, United States of America
                [6 ] Morgridge Institute for Research, Madison, WI, United States of America
                Pacific Northwest Research Institute, UNITED STATES
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests: Two patents have been submitted regarding findings described in this work.

                Author information
                http://orcid.org/0000-0002-1536-2966
                http://orcid.org/0000-0001-6592-9337
                http://orcid.org/0000-0002-8182-257X
                Article
                PGENETICS-D-18-01990
                10.1371/journal.pgen.1008037
                6428351
                30856163
                d794efd6-7aa7-4b24-a072-3ae21cc526c8
                © 2019 Myers et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 October 2018
                : 20 February 2019
                Page count
                Figures: 5, Tables: 1, Pages: 33
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100006206, Biological and Environmental Research;
                Award ID: DE-SC0018409
                Funded by: funder-id http://dx.doi.org/10.13039/100006206, Biological and Environmental Research;
                Award ID: DE-FC02-07ER64494
                This material is based upon work supported by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Numbers DE-SC0018409 and DE-FC02-07ER64494. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Xylose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Xylose
                Biology and Life Sciences
                Biochemistry
                Proteins
                Post-Translational Modification
                Phosphorylation
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolic Processes
                Fermentation
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Alcohols
                Ethanol
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Alcohols
                Ethanol
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                DNA construction
                Plasmid Construction
                Research and analysis methods
                Molecular biology techniques
                DNA construction
                Plasmid Construction
                Biology and Life Sciences
                Cell Biology
                Hypoxia
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Biology and Life Sciences
                Organisms
                Eukaryota
                Fungi
                Yeast
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-03-21
                Raw and processed RNA-seq data were deposited in the NIH GEO database under project number GSE92908. All raw mass spectrometry files and associated information about identifications are available on Chorus under Project ID 999 and Experiment IDs 3007, 3016, and 3166.

                Genetics
                Genetics

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