The human disease network in terms of dysfunctional regulatory mechanisms

Biology Direct

Jing Yang³  Su-Juan Wu¹  Wen-Tao Dai²  Yi-Xue Li²  Yuan-Yuan Li² 

[1] Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai 201203, P.R. China

[2] Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai 201203, P.R. China

[3] Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P.R. China

关键词: Differential regulation analysis;    Differential coexpression analysis;    Dysfunctional regulation mechanism;    Disease similarity;    Human disease network;   

Others  :  1229682
DOI  :  10.1186/s13062-015-0088-z

 received in 2015-06-02, accepted in 2015-09-25,  发布年份 2015

【 摘 要 】

Background

Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases.

Methods

We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways’ differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test.

Results

Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular mechanisms. Additionally, it was found that both the type of disease and the type of affected tissue influenced the degree of disease similarity. A sub-network including Allergic asthma, Type 2 diabetes and Chronic kidney disease was extracted to demonstrate the exploration of their common pathogenesis.

Conclusion

The present study produces a global view of human diseasome for the first time from the viewpoint of regulation mechanisms, which therefore could provide insightful clues to etiology and pathogenesis, and help to perform drug repositioning and design novel therapeutic interventions.

Reviewers

This article was reviewed by Limsoon Wong, Rui Wang-Sattler, and Andrey Rzhetsky.

【 授权许可】

   
2015 Yang et al.

【 图 表 】

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