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

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Abstract:

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.

Info:

Periodical:
International Letters of Natural Sciences (Volume 45)
Pages:
9-17
DOI:
https://doi.org/10.18052/www.scipress.com/ILNS.45.9
Citation:
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
Authors:
Ashok Kumar, Ruchika Puri, Kanika Gupta, Amit Pal
Keywords:
Biomarker, Comparative Genomics, Comparative Proteomics, Obesity
Export:
RIS, BibTeX, Text
Distribution:
Open Access

Creative Commons License
This work is licensed under a
Creative Commons Attribution 4.0 International License

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