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      Genetic Basis of Metabolome Variation in Yeast

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          Abstract

          Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across 100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosyl-homocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism.

          Author Summary

          Many traits, from human height to E. coli growth rate, quantitatively vary across members of a species. Among the most medically and agriculturally important traits are levels of cellular metabolites, such as cholesterol levels in humans or starch in food crops. Metabolic variation in yeast also holds practical importance with some Saccharomyces strains better suited to making ethanol for biofuel and others tailored to making flavorful wine. This metabolic heterogeneity can be used to gain insight into general principles of metabolic regulation which effect metabolite abundance in eukaryotes. To this end, we examined inter-strain differences in metabolism in over 100 closely related S. cerevisiae strains. We identified over 50 genetic loci that control the levels of specific metabolites, including not only loci that encode metabolic enzymes, but also those that encode global cellular regulators. For example, differences in the sequence of ira2, an inhibitor of Ras, lead to differences in central carbon metabolite levels, and polymorphisms in slt2, a poorly characterized MAP kinase, alter levels of sulfur-containing metabolites. These findings provide insights into the mechanisms cells use to control metabolite concentrations.

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          Most cited references53

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          A genome-wide association study identifies novel risk loci for type 2 diabetes.

          Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case-control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing beta-cells, and two linkage disequilibrium blocks that contain genes potentially involved in beta-cell development or function (IDE-KIF11-HHEX and EXT2-ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.
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            Metabolomics--the link between genotypes and phenotypes.

            Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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              Cancer's molecular sweet tooth and the Warburg effect.

              More than 80 years ago, the renowned biochemist Otto Warburg described how cancer cells avidly consume glucose and produce lactic acid under aerobic conditions. Recent studies arguing that cancer cells benefit from this phenomenon, termed the Warburg effect, have renewed discussions about its exact role as cause, correlate, or facilitator of cancer. Molecular advances in this area may reveal tactics to exploit the cancer cell's "sweet tooth" for cancer therapy.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                March 2014
                6 March 2014
                : 10
                : 3
                : e1004142
                Affiliations
                [1 ]Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
                [2 ]Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
                [3 ]Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, United States of America
                [4 ]Department of Chemistry, Princeton University, Princeton, New Jersey, United States of America
                [5 ]Departments of Human Genetics and Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, California, United States of America
                [6 ]Howard Hughes Medical Institute, UCLA, Los Angeles, California, United States of America
                The University of North Carolina at Chapel Hill, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JSB SRH JDR LK. Performed the experiments: JSB SRH. Analyzed the data: JSB SRH. Wrote the paper: JSB SRH JDR LK.

                Article
                PGENETICS-D-13-03062
                10.1371/journal.pgen.1004142
                3945093
                24603560
                0b9b89bf-bf85-4116-a7c7-15b82e70c162
                Copyright @ 2014

                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
                : 6 November 2013
                : 6 December 2013
                Page count
                Pages: 15
                Funding
                Funding was provided by NIH grants R37 MH059520 and R01 GM102308 to LK, and GM071508 to the Lewis-Sigler Institute, as well as a National Science Foundation CAREER Award MCB-0643859, Beckman Foundation and American Heart Association awards to JDR. LK is a James S. McDonnell Centennial Fellow and an Investigator of the Howard Hughes Medical Institute. This research is supported in part by the Department of Energy Office of Science Graduate Fellowship Program (DOE SCGF), made possible in part by the American Recovery and Reinvestment Act of 2009, administered by ORISE-ORAU under contract no. DE-AC05-06OR23100. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biochemistry
                Metabolism
                Genetics
                Genome-Wide Association Studies

                Genetics
                Genetics

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