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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Research

A cross-species analysis method to analyze animal models' similarity to human's disease state

Shuhao Yu1, Lulu Zheng45, Yun Li2, Chunyan Li3, Chenchen Ma2, Yixue Li125, Xuan Li2* and Pei Hao35*

Author Affiliations

1 College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, P.R.China

2 Key Lab of Systems Biology/Key Laboratory of Synthetic Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, P.R.China

3 Institut Pasteur of Shanghai, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, P.R.China

4 College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, P.R.China

5 Shanghai Center for Bioinformation Technology, Shanghai, P.R.China

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BMC Systems Biology 2012, 6(Suppl 3):S18  doi:10.1186/1752-0509-6-S3-S18

Published: 17 December 2012

Abstract

Background

Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism.

Results

We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery.

We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning.

Conclusions

We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.