OCDD: an obesity and co-morbid disease database

Indrani Ray,Rajat K De,Anindya Bhattacharya

doi:10.1186/s13040-017-0153-5

Indrani Ray, Rajat K De + Show 1 more

Open Access

https://doi.org/10.1186/s13040-017-0153-5

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Abstract

BackgroundObesity is a medical condition that is known for increased body mass index (BMI). It is also associated with chronic low level inflammation. Obesity disrupts the immune-metabolic homeostasis by changing the secretion of adipocytes. This affects the end-organs, and gives rise to several diseases including type 2 diabetes, asthma, non-alcoholic fatty liver diseases and cancers. These diseases are known as co-morbid diseases. Several studies have explored the underlying molecular mechanisms of developing obesity associated comorbid diseases. To understand the development and progression of diseases associated with obesity, we need a detailed scenario of gene interactions and the distribution of the responsible genes in human system.ResultsObesity and Co-morbid Disease Database (OCDD) is designed for relating obesity and its co-morbid diseases using literature mining, and computational and systems biology approaches. OCDD is aimed to investigate the genes associated with comorbidity. Several existing databases have been used to extract molecular interactions and functional annotations of each gene. The degree of co-morbid associations has been measured and made available to the users. The database is available at http://www.isical.ac.in/~systemsbiology/OCDD/home.phpConclusionsThe main objective of the database is to derive the relations among the genes that are involved in both obesity and its co-morbid diseases. Functional annotation of common genes, gene interaction networks and key driver analyses have made the database a valuable and comprehensive resource for investigating the causal links between obesity and co-morbid diseases.

Highlights

Obesity is the new epidemic of 21st century [1]
The functional enrichment and gene interaction networks have further been analyzed for elucidating the molecular mechanisms
Distribution of top 5% connected genes The top most connected genes of each disease, i.e., the list of 225 top 5% connected genes from 26 co-morbid diseases are further analyzed on the basis of gene ontology terms (GO terms)

Summary

Results

The functional enrichment and gene interaction networks have further been analyzed for elucidating the molecular mechanisms. Discussions The gene networks of type 2 diabetes, non-alcoholic fatty liver diseases and endometrial cancer show how the genes are densely connected. A densely connected gene interaction network has been built with 416 genes including highly connected genes, like insulin receptor gene (IRS), insulin growth factor gene (IGFBP5), intracellular adhesion molecule gene (ICAM), and many genes involved in the immune system (IL, NFKB, TLR, CASP). This proves that secretion of proinflammatory adipokines and adhesion molecules increase with the onset of obesity. Increase in androgens and estrogens, and decrease in SHBG and progesterone causes endometrial cancer [48]

Conclusions

Introduction

Conclusion and future work