Difference between revisions of "Metabolomics"

 
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We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.
 
We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.
  
A particular interest lies in small molecules underlying metabolism, whose concentration can be measured quantitatively in body fluids, like blood and urine. In our recent article "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" that was accepted for publication in PLoS Genetics on 10.2.2013 we studied such data derived from the ''Cohorte Lausannoise''. Below is more information on this publication and details on the our method can be found [[Metabomatching|here]]. For inquiries please contact '''[[User:Sven|Sven Bergmann]]'''.
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Clearly any genetic effect starts off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the ''Cohorte Lausannoise'' we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.
  
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In our first paper "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in [PLoS Genetics http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004132]) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.
  
'''Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links'''
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In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in [PLoS Comp Bio http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839]) we elaborated on our [[Metabomatching]] method. Our main point is that metabolome-wide genome-wide association studies  typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. We provide a method for  an untargeted approach, which follows an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. Our method relies on the fact that when the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations.
  
*'''Authors'''
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[[Category:Bulletins]]
Rico Rueedi1,2,♣, Mirko Ledda3,♣, Andrew W. Nicholls4, Reza M. Salek5,6, Pedro Marques-Vidal7, Edgard Morya8,9, Koichi Sameshima10, Ivan Montoliu11, Laeticia Da Silva11, Sebastiano Collino11, François-Pierre Martin11, Serge Rezzi11, Christoph Steinbeck5, Dawn M. Waterworth12, Gérard Waeber13, Peter Vollenweider13, Jacques S. Beckmann1,2,14, Johannes Le Coutre3,15, Vincent Mooser16, Sven Bergmann1,2,♠,*, Ulrich K. Genick3,♠, and Zoltán Kutalik1,2,7,♠
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<newstitle>Augmenting genomics through metabolomics</newstitle>   
1 Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland
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<teaser> In a metabolome-wide genome-wide association study (MWGWAS) on the <a href="http://www.colaus.ch">CoLaus</a> cohort, we found two novel gene-metabolite associations, with both gene-metabolite pairs additionally linked to clinical phenotypes. For this "untargeted"  MWGWAS, we used metabolic features -- rather than metbolite concentrations -- as phenotypes, and developed a metabolite identification method based on genetic association signals. Details, and future progress, on the method can be found on the <a href="http://www2.unil.ch/cbg/index.php?title=Metabomatching">metabomatching</a> page. The paper has been published in <a href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004132">PLOS Genetics</a><date>21 Feb 2014 — 09:00</date> </teaser>
2 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
 
3 Department of Food-Consumer Interaction, Nestlé Research Center, Lausanne, Switzerland
 
4 Investigative Preclinical Toxicology, GlaxoSmithKline R&D, Park Road, Ware, Herts SG12 0DP, UK
 
5 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
 
6 Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Cambridge, CB2 1GA, UK
 
7 Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois (CHUV), 1010 Lausanne, Switzerland, and University of Lausanne
 
8 Sensonomic Laboratory of Alberto Santos Dumont Research Support Association and IEP Sirio
 
Libanes Hospital, São Paulo, Brazil
 
9 Edmond and Lily Safra International Institute of Neuroscience of Natal, Natal, Brazil
 
10 Department of Radiology and Oncology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
 
11 Department of Bioanalytical Sciences, Nestlé Research Center, Lausanne, Switzerland.
 
12 Medical Genetics, GlaxoSmithKline, Philadelphia, PA, USA
 
13 Department of Medicine, Internal Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland
 
14 Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland
 
15 Organization for Interdisciplinary Research Projects, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan
 
16 Department of Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), 1011 Lausanne, Switzerland
 
♣,♠ These authors contributed equally to this work.
 
Correspondence to be addressed to: Sven Bergmann, email: sven.bergmann@unil.ch
 
 
 
*'''Abstract'''
 
Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10 8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. 56 of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to 11 gene-metabolite associations, 6 of which had been previously identified in the urine metabolome and 3 in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P=6.9×10-44) and lysine (rs8101881, P=1.2×10-33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn’s disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.
 
 
 
*'''Author summary'''
 
The concentrations of small molecules, known as metabolites, are subject to tight regulation in all organisms. Collectively, the metabolite concentrations make up the metabolome, which differs amongst individuals as a function of their environment and genetic makeup.  
 
In our study, we have further developed an untargeted approach to identify genetic factors affecting human metabolism. In this approach, we first identify all genetic variants that correlate with any of the measured metabolome features in a large set of individuals. For these variants, we then compute a profile of significance for association with all features, generating a signature that facilitates the expert or computational identification of the metabolite whose concentration is most likely affected by the genetic variant at hand.  
 
Our study replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.  
 
 

Latest revision as of 15:18, 18 December 2017

We are interested in how genotypic variability impacts molecular phenotypes and how, together with the environment, this affects human phenotypes, including disease susceptibility.

Clearly any genetic effect starts off at the molecular level, e.g. by impacting gene-expression which may then modulate the concentrations of other small molecules. In order to trace these effects we need molecular measurements. Indeed, for samples from the Cohorte Lausannoise we have generated RNAseq data from lymphoblastic cell lines and NMR profiles from urine and serum samples.

In our first paper "Genome-wide association study of metabolic traits reveals novel gene-metabolite-disease links" (published in [PLoS Genetics http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004132]) we reported results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. Our study was conducted within an untargeted approach, employing a novel method for compound identification. We replicated many of the previously reported genetically driven variations in human metabolism and revealed two new striking examples of genetic variations with a sizeable effect on the urine metabolome. Interestingly, in these two gene-metabolite pairs both the gene and the affected metabolite are related to human diseases – Crohn’s disease in the first case, and kidney disease in the second. This highlights the connection between genetic predispositions, affected metabolites, and human health.

In second paper "Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy" (published in [PLoS Comp Bio http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005839]) we elaborated on our Metabomatching method. Our main point is that metabolome-wide genome-wide association studies typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. We provide a method for an untargeted approach, which follows an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. Our method relies on the fact that when the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations.