Literature DB >> 26283345

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health.

Lining Guo1, Michael V Milburn2, John A Ryals2, Shaun C Lonergan2, Matthew W Mitchell2, Jacob E Wulff2, Danny C Alexander2, Anne M Evans2, Brandi Bridgewater2, Luke Miller2, Manuel L Gonzalez-Garay3, C Thomas Caskey4.   

Abstract

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

Entities:  

Keywords:  disease assessment; functional phenotyping; gene penetrance; metabolomics; whole-exome sequencing

Mesh:

Year:  2015        PMID: 26283345      PMCID: PMC4568216          DOI: 10.1073/pnas.1508425112

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  62 in total

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9.  Gene variants in the novel type 2 diabetes loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B affect different aspects of pancreatic beta-cell function.

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  66 in total

1.  ORE identifies extreme expression effects enriched for rare variants.

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Journal:  Bioinformatics       Date:  2019-10-15       Impact factor: 6.937

2.  Early-Life Iron Deficiency and Its Natural Resolution Are Associated with Altered Serum Metabolomic Profiles in Infant Rhesus Monkeys.

Authors:  Brian J Sandri; Gabriele R Lubach; Eric F Lock; Michael K Georgieff; Pamela J Kling; Christopher L Coe; Raghavendra B Rao
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3.  Can Metabolic Profiles Be Used as a Phenotypic Readout of the Genome to Enhance Precision Medicine?

Authors:  Kévin Contrepois; Liang Liang; Michael Snyder
Journal:  Clin Chem       Date:  2016-03-09       Impact factor: 8.327

Review 4.  Investigating the aetiology of adverse events following HPV vaccination with systems vaccinology.

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5.  Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease.

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Journal:  Cell Metab       Date:  2017-05-02       Impact factor: 27.287

6.  Metabolomic biomarkers as strong correlates of Parkinson disease progression.

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Journal:  Neurology       Date:  2017-02-08       Impact factor: 9.910

7.  Fisetin Reduces the Impact of Aging on Behavior and Physiology in the Rapidly Aging SAMP8 Mouse.

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Journal:  J Gerontol A Biol Sci Med Sci       Date:  2018-03-02       Impact factor: 6.053

8.  Metabolic features of chronic fatigue syndrome.

Authors:  Robert K Naviaux; Jane C Naviaux; Kefeng Li; A Taylor Bright; William A Alaynick; Lin Wang; Asha Baxter; Neil Nathan; Wayne Anderson; Eric Gordon
Journal:  Proc Natl Acad Sci U S A       Date:  2016-08-29       Impact factor: 11.205

9.  Effects of dietary sodium on metabolites: the Dietary Approaches to Stop Hypertension (DASH)-Sodium Feeding Study.

Authors:  Andriy Derkach; Joshua Sampson; Justin Joseph; Mary C Playdon; Rachael Z Stolzenberg-Solomon
Journal:  Am J Clin Nutr       Date:  2017-08-30       Impact factor: 7.045

Review 10.  Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets.

Authors:  Dinesh Kumar Barupal; Sili Fan; Oliver Fiehn
Journal:  Curr Opin Biotechnol       Date:  2018-02-06       Impact factor: 9.740

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