Subscribe to our Newsletter and get informed about new publication regulary and special discounts for subscribers!

ILNS > Volume 45 > To Predict Human Biomarker for the Obesity Using...
< Back to Volume

To Predict Human Biomarker for the Obesity Using Mouse Homologous Expression Data at Different Theiler Stages

Full Text PDF


There are numerous genetic factors like MC4R (Melanocortin-4 receptor), POMC (Pro-opiomelanocortin), SIM1 (Single Minded Gene) etc. important in obesity, which can be used as biomarker. But more reliable diagnostic markers are the need for today, along with new therapeutic strategies that target specific molecules in the disease pathways. As in mouse and human genes, where mutations in one or both species are associated with some phenotypic characteristics as observed in human disease. In molecular mechanisms of development, differentiation, and disease gene expression data provide crucial insights. Up-regulation and down-regulation of selective genes can have major effects on diet-induced obesity, but there is little or no effect when animals are fed a low-fat diet. In present study we have studied the gene expression data of mouse at different theiler stages using GXD BioMart. The interacting partners and pathway of the genes that are already used as biomarker in mouse as well as in humans have been studied. A gene NPY1R (Neuropeptide Y1 receptor) was taken as common after STRING and KEGG results on the basis of biochemical pathways and interactions similar to MC4R. Our present work focuses on comparative genomics and proteomics analysis of NPY1R, which has led to identification of biomarker by comparing it with already known MC4R human and mouse biomarker. It has been concluded that both the proteins are structurally and functionally similar.


International Letters of Natural Sciences (Volume 45)
A. Kumar et al., "To Predict Human Biomarker for the Obesity Using Mouse Homologous Expression Data at Different Theiler Stages", International Letters of Natural Sciences, Vol. 45, pp. 9-17, 2015
Online since:
Aug 2015

M. Zamanian-Azodi et al., Molecular approaches in obesity studies, Gastroenterol Hepatol Bed Bench, 6 (2013) S23-31.

X. Zhan et al., Identifying module biomarker in type 2 diabetes mellitus by discriminative area of functional activity, BMC Bioinformatics, 16 (2015) 92.

R.A. Koza et al., Changes in gene expression foreshadow diet-induced obesity in genetically identical mice, PLoS Genet, 1 (5) (2006) e81.

J.T. Eppig et al., The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease, Nucleic Acids Res, 43(Database issue) (2015) D726-36.

A. Franceschini et al., STRING v9. 1: protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Res, 41(Database issue) (2013) D808-15.

M. Kanehisa and S. Goto, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res, 28(1) (2000) 27-30.

D.A. Benson et al., GenBank, Nucleic Acids Res, 33(Database issue) (2005) D34-8.

J. Song et al., Alignment of multiple proteins with an ensemble of hidden Markov models, Int J Data Min Bioinform, 4(1) (2010) 60-71.

E. Gasteiger et al., ExPASy: The proteomics server for in-depth protein knowledge and analysis, Nucleic Acids Res, 31(13) (2003) 3784-8.

A. Garg, M. Bhasin, and G.P. Raghava, Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search, J Biol Chem, 280(15) (2005) 14427-32.

H.M. Berman, et al., The Protein Data Bank, Nucleic Acids Res, 28(1) (2000) 235-42.

[B. Rost, and C. Sander, Prediction of protein secondary structure at better than 70% accuracy, J Mol Biol, 232(2) (1993) 584-99.

I. Letunic, T. Doerks, and P. Bork, SMART 7: recent updates to the protein domain annotation resource, Nucleic Acids Res, 40(Database issue) (2012) D302-5.

E.F. Pettersen, et al., UCSF Chimera-a visualization system for exploratory research and analysis, J Comput Chem, 25(13) (2004) 1605-12.

A. Volkamer, et al., DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics, 28(15) (2012) 2074-5.

D. Schneidman-Duhovny, et al., PatchDock and SymmDock: servers for rigid and symmetric docking, Nucleic Acids Res, 33(Web Server issue) (2005) W363-7.

F.T. Yazdi, S.M. Clee, and D. Meyre, Obesity genetics in mouse and human: back and forth, and back again, PeerJ, 3 (2015) e856.

T.A. Lutz and S.C. Woods, Overview of animal models of obesity, Curr Protoc Pharmacol, Chapter 5 (2012) Unit5 61.

A. Wraith, et al., Evolution of the neuropeptide Y receptor family: gene and chromosome duplications deduced from the cloning and mapping of the five receptor subtype genes in pig, Genome Res, 10(3) (2000) 302-10.

C. Lubrano-Berthelier, et al., Intracellular retention is a common characteristic of childhood obesity associated MC4R mutations, Hum. Mol. Genet, 12 (2) (2003) 145-153.

R.M. Shawky, et al,. Genetics of obesity, Egyptian Journal of Medical Human Genetics, 13(1) (2012) 11-17.

Bertocchi I, et al., Regulatory functions of limbic Y1 receptors in body weight and anxiety uncovered by conditional knockout and maternal care, Proc Natl AcadSci U S A, 29; 108(48) (2011) 19395-19400.

R.R. Copley, et al., Protein domain analysis in the era of complete genomes, FEBS Letters, 20, 129-134.

V. Philip Peplow and James D. Adams Jr, The Relevance of Biomarkers, Risk Factors and Gene-Environment Interactions in Disease: Scientific Developments and Therapeutic Approaches, Cardiovascular and Metabolic Disease: Scientific Discoveries and NewTherapies, Chapter1, (2015).

Show More Hide