Literature DB >> 28652377

Causal Genetic Variation Underlying Metabolome Differences.

Devjanee Swain-Lenz1,2, Igor Nikolskiy3, Jiye Cheng1,4, Priya Sudarsanam1,2, Darcy Nayler2, Max V Staller1,2, Barak A Cohen5,2.   

Abstract

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81, cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype.
Copyright © 2017 by the Genetics Society of America.

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Year:  2017        PMID: 28652377      PMCID: PMC5560816          DOI: 10.1534/genetics.117.203752

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  32 in total

1.  R/qtl: QTL mapping in experimental crosses.

Authors:  Karl W Broman; Hao Wu; Saunak Sen; Gary A Churchill
Journal:  Bioinformatics       Date:  2003-05-01       Impact factor: 6.937

2.  The landscape of genetic complexity across 5,700 gene expression traits in yeast.

Authors:  Rachel B Brem; Leonid Kruglyak
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-19       Impact factor: 11.205

3.  Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism.

Authors:  Wei Chen; Yanqiang Gao; Weibo Xie; Liang Gong; Kai Lu; Wensheng Wang; Yang Li; Xianqing Liu; Hongyan Zhang; Huaxia Dong; Wan Zhang; Lejing Zhang; Sibin Yu; Gongwei Wang; Xingming Lian; Jie Luo
Journal:  Nat Genet       Date:  2014-06-08       Impact factor: 38.330

4.  AUA1, a gene involved in ammonia regulation of amino acid transport in Saccharomyces cerevisiae.

Authors:  V Sophianopoulou; G Diallinas
Journal:  Mol Microbiol       Date:  1993-04       Impact factor: 3.501

5.  Genetic basis for systems of skeletal quantitative traits: principal component analysis of the canid skeleton.

Authors:  Kevin Chase; David R Carrier; Frederick R Adler; Tyler Jarvik; Elaine A Ostrander; Travis D Lorentzen; Karl G Lark
Journal:  Proc Natl Acad Sci U S A       Date:  2002-07-11       Impact factor: 11.205

6.  HMDB 3.0--The Human Metabolome Database in 2013.

Authors:  David S Wishart; Timothy Jewison; An Chi Guo; Michael Wilson; Craig Knox; Yifeng Liu; Yannick Djoumbou; Rupasri Mandal; Farid Aziat; Edison Dong; Souhaila Bouatra; Igor Sinelnikov; David Arndt; Jianguo Xia; Philip Liu; Faizath Yallou; Trent Bjorndahl; Rolando Perez-Pineiro; Roman Eisner; Felicity Allen; Vanessa Neveu; Russ Greiner; Augustin Scalbert
Journal:  Nucleic Acids Res       Date:  2012-11-17       Impact factor: 16.971

7.  YMDB: the Yeast Metabolome Database.

Authors:  Timothy Jewison; Craig Knox; Vanessa Neveu; Yannick Djoumbou; An Chi Guo; Jacqueline Lee; Philip Liu; Rupasri Mandal; Ram Krishnamurthy; Igor Sinelnikov; Michael Wilson; David S Wishart
Journal:  Nucleic Acids Res       Date:  2011-11-07       Impact factor: 16.971

8.  Metabolic QTL analysis links chloroquine resistance in Plasmodium falciparum to impaired hemoglobin catabolism.

Authors:  Ian A Lewis; Mark Wacker; Kellen L Olszewski; Simon A Cobbold; Katelynn S Baska; Asako Tan; Michael T Ferdig; Manuel Llinás
Journal:  PLoS Genet       Date:  2014-01-02       Impact factor: 5.917

9.  Metabolic variation between japonica and indica rice cultivars as revealed by non-targeted metabolomics.

Authors:  Chaoyang Hu; Jianxin Shi; Sheng Quan; Bo Cui; Sabrina Kleessen; Zoran Nikoloski; Takayuki Tohge; Danny Alexander; Lining Guo; Hong Lin; Jing Wang; Xiao Cui; Jun Rao; Qian Luo; Xiangxiang Zhao; Alisdair R Fernie; Dabing Zhang
Journal:  Sci Rep       Date:  2014-05-27       Impact factor: 4.379

10.  Estimation of the warfarin dose with clinical and pharmacogenetic data.

Authors:  T E Klein; R B Altman; N Eriksson; B F Gage; S E Kimmel; M-T M Lee; N A Limdi; D Page; D M Roden; M J Wagner; M D Caldwell; J A Johnson
Journal:  N Engl J Med       Date:  2009-02-19       Impact factor: 91.245

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  2 in total

1.  Systems genetics of the Drosophila metabolome.

Authors:  Shanshan Zhou; Fabio Morgante; Matthew S Geisz; Junwu Ma; Robert R H Anholt; Trudy F C Mackay
Journal:  Genome Res       Date:  2019-11-06       Impact factor: 9.043

2.  Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.

Authors:  Emrin Horgusluoglu; Ryan Neff; Won-Min Song; Minghui Wang; Qian Wang; Matthias Arnold; Jan Krumsiek; Beatriz Galindo-Prieto; Chen Ming; Kwangsik Nho; Gabi Kastenmüller; Xianlin Han; Rebecca Baillie; Qi Zeng; Shea Andrews; Haoxiang Cheng; Ke Hao; Alison Goate; David A Bennett; Andrew J Saykin; Rima Kaddurah-Daouk; Bin Zhang
Journal:  Alzheimers Dement       Date:  2021-11-10       Impact factor: 16.655

  2 in total

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