Literature DB >> 22868675

Metabolome 2.0: quantitative genetics and network biology of metabolic phenotypes.

Marc-Emmanuel Dumas1.   

Abstract

The characterization of the metabolome has rapidly evolved over two decades, from early developments in analytical chemistry to systems biology. Metabolites and small molecules are not independent; they are organized in biochemical pathways and in a wider metabolic network, which is itself dependent on various genetic and signaling networks for its regulation. Recent advances in genomics, transcriptomics, proteomics and metabolomics have been matched by the development of publicly available repositories, which have helped shaping a new generation of integrative studies using metabolite measurements in molecular epidemiology and genetic studies. Although the environment influences metabolism, the identification of the genetic determinants of metabolic phenotypes (metabotypes) was made possible by the development of metabotype quantitative trait locus (mQTL) mapping and metabolomic genome-wide association studies (mGWAS) in a rigorous statistical genetics framework, deriving associations between metabolite concentrations and genetic polymorphisms. However, given the complexity of the biomolecular events involved in the regulation of metabolic patterns, alternative network biology approaches have also been recently introduced, such as integrated metabolome and interactome mapping (iMIM). This unprecedented convergence of metabolic biochemistry, quantitative genetics and network biology already has had a strong impact on the role of the metabolome in biomedical sciences, and this review gives a foretaste of its anticipated successes in eventually delivering personalized medicine.

Mesh:

Year:  2012        PMID: 22868675     DOI: 10.1039/c2mb25167a

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  16 in total

1.  Identifying biochemical phenotypic differences between cryptic species.

Authors:  Manuel Liebeke; Michael W Bruford; Robert K Donnelly; Timothy M D Ebbels; Jie Hao; Peter Kille; Elma Lahive; Rachael M Madison; A John Morgan; Gabriela A Pinto-Juma; David J Spurgeon; Claus Svendsen; Jacob G Bundy
Journal:  Biol Lett       Date:  2014-09       Impact factor: 3.703

2.  Understanding mixed environmental exposures using metabolomics via a hierarchical community network model in a cohort of California women in 1960's.

Authors:  Shuzhao Li; Piera Cirillo; Xin Hu; ViLinh Tran; Nickilou Krigbaum; Shaojun Yu; Dean P Jones; Barbara Cohn
Journal:  Reprod Toxicol       Date:  2019-07-09       Impact factor: 3.143

Review 3.  [Application of metabolomics in neonatal clinical practice].

Authors:  Qiu-Tong Liu; Xiao-Yun Zhong
Journal:  Zhongguo Dang Dai Er Ke Za Zhi       Date:  2019-09

4.  Untargeted metabolomics reveals alterations in metabolites of lipid metabolism and immune pathways in the serum of rats after long-term oral administration of Amalaki rasayana.

Authors:  Vikas Kumar; A Aneesh Kumar; Vinod Joseph; Vipin Mohan Dan; Abdul Jaleel; T R Santhosh Kumar; Chandrasekharan C Kartha
Journal:  Mol Cell Biochem       Date:  2019-10-08       Impact factor: 3.396

Review 5.  Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions.

Authors:  Marc-Emmanuel Dumas; Laetitia Davidovic
Journal:  J Neuroimmune Pharmacol       Date:  2015-01-24       Impact factor: 4.147

6.  Invariance and plasticity in the Drosophila melanogaster metabolomic network in response to temperature.

Authors:  Ramkumar Hariharan; Jessica M Hoffman; Ariel S Thomas; Quinlyn A Soltow; Dean P Jones; Daniel E L Promislow
Journal:  BMC Syst Biol       Date:  2014-12-24

7.  Multi-Target Analysis and Design of Mitochondrial Metabolism.

Authors:  Claudio Angione; Jole Costanza; Giovanni Carapezza; Pietro Lió; Giuseppe Nicosia
Journal:  PLoS One       Date:  2015-09-16       Impact factor: 3.240

Review 8.  Functional metabolomics: from biomarker discovery to metabolome reprogramming.

Authors:  Bo Peng; Hui Li; Xuan-Xian Peng
Journal:  Protein Cell       Date:  2015-07-02       Impact factor: 14.870

9.  Metabolomics as a tool to investigate abiotic stress tolerance in plants.

Authors:  Vicent Arbona; Matías Manzi; Carlos de Ollas; Aurelio Gómez-Cadenas
Journal:  Int J Mol Sci       Date:  2013-03-01       Impact factor: 5.923

Review 10.  Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare.

Authors:  Prashanth Suravajhala; Lisette J A Kogelman; Haja N Kadarmideen
Journal:  Genet Sel Evol       Date:  2016-04-29       Impact factor: 4.297

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