Molecular signatures for obesity and associated disorders identified through partial least square regression models

BMC Systems Biology

Samrat Chatterjee¹  Kanury VS Rao²  Dhiraj Kumar²  Kamiya Tikoo²  Simarjeet Kaur Negi²  Parul Tripathi²  Sachin Sharma²  Neeraj Sinha² 

[1] Present address: Drug Discovery Research Centre, Translational Health Science & Technology Institute, Gurgaon 122016, India;Immunology Group, International Centre for Genetic Engineering and Biotechnology, Aruna, Asaf Ali Marg, New Delhi 110067, India

关键词: Hub-proteins;    Biological classifications;    Partial least square model;    Gene signature;    Type-II diabetes;    Obesity;   

Others  :  1127084
DOI  :  10.1186/s12918-014-0104-4

 received in 2012-07-18, accepted in 2014-08-18,  发布年份 2014

【 摘 要 】

Background

Obesity is now a worldwide epidemic disease and poses a major risk for diet related diseases like type 2 diabetes, cardiovascular disease, stroke and fatty liver among others. In the present study we employed the murine model of diet-induced obesity to determine the early, tissue-specific, gene expression signatures that characterized progression to obesity and type 2 diabetes.

Results

We used the C57BL/6 J mouse which is known as a counterpart for diet-induced human diabetes and obesity model. Our initial experiments involved two groups of mice, one on normal diet (ND) and the other on high-fat and high-sucrose (HFHSD). The later were then further separated into subgroups that either received no additional treatment, or were treated with different doses of the Ayurvedic formulation KAL-1. At different time points (week3, week6, week9, week12, week15 and week18) eight different tissues were isolated from mice being fed on different diet compositions. These tissues were used to extract gene-expression data through microarray experiment. Simultaneously, we also measured different body parameters like body weight, blood Glucose level and cytokines profile (anti-inflammatory & pro-inflammatory) at each time point for all the groups.

Using partial least square discriminant analysis (PLS-DA) method we identified gene-expression signatures that predict physiological parameters like blood glucose levels, body weight and the balance of pro- versus anti-inflammatory cytokines. The resulting models successfully predicted diet-induced changes in body weight and blood glucose levels, although the predictive power for cytokines profiles was relatively poor. In the former two instances, however, we could exploit the models to further extract the early gene-expression signatures that accurately predict the onset of diabetes and obesity. These extracted genes allowed definition of the regulatory network involved in progression of disease.

Conclusion

We identified the early gene-expression signature for the onset of obesity and type 2 diabetes. Further analysis of this data suggests that some of these genes could be used as potential biomarkers for these two disease-states.

【 授权许可】

   
2014 Sinha et al.; licensee BioMed Central; licensee BioMed Central Ltd.

【 图 表 】

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