NutriGenomeDB: a nutrigenomics exploratory and analytical platform

Author:

Martín-Hernández Roberto1,Reglero Guillermo23,Ordovás José M45,Dávalos Alberto6

Affiliation:

1. Bioinformatics and Biostatistics Unit, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 28049, Spain

2. Sección Departamental de Ciencias de la Alimentación, Facultad de Ciencias, Universidad Autónoma de Madrid, CEI UAM+CSIC, C/ Nicolas Cabrera 9, Madrid 28049, Spain

3. Laboratory of Food Products for Precision Nutrition, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 28049, Spain

4. Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA 02111, USA

5. Laboratory of Nutritional Genomics, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 280149, Spain

6. Laboratory of Epigenetics of Lipid Metabolism, IMDEA Food Institute, CEI UAM+CSIC, Ctra. De Canto Blanco 8, Madrid 28049, Spain

Abstract

Abstract Habitual consumption of certain foods has shown beneficial and protective effects against multiple chronic diseases. However, it is not clear by which molecular mechanisms they may exert their beneficial effects. Multiple -omic experiments available in public databases have generated gene expression data following the treatment of human cells with different food nutrients and bioactive compounds. Exploration of such data in an integrative manner offers excellent possibilities for gaining insights into the molecular effects of food compounds and bioactive molecules at the cellular level. Here we present NutriGenomeDB, a web-based application that hosts manually curated gene sets defined from gene expression signatures, after differential expression analysis of nutrigenomics experiments performed on human cells available in the Gene Expression Omnibus (GEO) repository. Through its web interface, users can explore gene expression data with interactive visualizations. In addition, external gene signatures can be connected with nutrigenomics gene sets using a gene pattern-matching algorithm. We further demonstrate how the application can capture the primary molecular mechanisms of a drug used to treat hypertension and thus connect its mode of action with hosted food compounds.

Funder

Fundación Ramón Areces

Spanish Agencia Estatal de Investigación

Fondo Europeo de Desarrollo Regional

US Department of Agriculture

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Information Systems

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