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This article is part of the supplement: Proceedings of the 2011 International Conference on Bioinformatics and Computational Biology (BIOCOMP'11)

Open Access Research

Exploring the pathogenetic association between schizophrenia and type 2 diabetes mellitus diseases based on pathway analysis

Yanli Liu1, Zezhi Li2, Meixia Zhang3, Youping Deng4, Zhenghui Yi5* and Tieliu Shi1*

Author Affiliations

1 Center for Bioinformatics and Computational Biology, and The Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China

2 Department of Neurology, Shanghai Changhai Hospital, Secondary Military Medical University, 168 Changhai Road, Shanghai, China

3 Department of Ophthalmology, West China Hospital, Sichuan University 37 Guoxuexiang, Chengdu, Sichuan, 610041, China

4 Rush University Cancer Center, Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA

5 Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai 200030, China

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BMC Medical Genomics 2013, 6(Suppl 1):S17  doi:10.1186/1755-8794-6-S1-S17

Published: 23 January 2013

Abstract

Background

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2D) are both complex diseases. Accumulated studies indicate that schizophrenia patients are prone to present the type 2 diabetes symptoms, but the potential mechanisms behind their association remain unknown. Here we explored the pathogenetic association between SCZ and T2D based on pathway analysis and protein-protein interaction.

Results

With sets of prioritized susceptibility genes for SCZ and T2D, we identified significant pathways (with adjusted p-value < 0.05) specific for SCZ or T2D and for both diseases based on pathway enrichment analysis. We also constructed a network to explore the crosstalk among those significant pathways. Our results revealed that some pathways are shared by both SCZ and T2D diseases through a number of susceptibility genes. With 382 unique susceptibility proteins for SCZ and T2D, we further built a protein-protein interaction network by extracting their nearest interacting neighbours. Among 2,104 retrieved proteins, 364 of them were found simultaneously interacted with susceptibility proteins of both SCZ and T2D, and proposed as new candidate risk factors for both diseases. Literature mining supported the potential association of partial new candidate proteins with both SCZ and T2D. Moreover, some proteins were hub proteins with high connectivity and interacted with multiple proteins involved in both diseases, implying their pleiotropic effects for the pathogenic association. Some of these hub proteins are the components of our identified enriched pathways, including calcium signaling, g-secretase mediated ErbB4 signaling, adipocytokine signaling, insulin signaling, AKT signaling and type II diabetes mellitus pathways. Through the integration of multiple lines of information, we proposed that those signaling pathways, which contain susceptibility genes for both diseases, could be the key pathways to bridge SCZ and T2D. AKT could be one of the important shared components and may play a pivotal role to link both of the pathogenetic processes.

Conclusions

Our study is the first network and pathway-based systematic analysis for SCZ and T2D, and provides the general pathway-based view of pathogenetic association between two diseases. Moreover, we identified a set of candidate genes potentially contributing to the linkage between these two diseases. This research offers new insights into the potential mechanisms underlying the co-occurrence of SCZ and T2D, and thus, could facilitate the inference of novel hypotheses for the co-morbidity of the two diseases. Some etiological factors that exert pleiotropic effects shared by the significant pathways of two diseases may have important implications for the diseases and could be therapeutic intervention targets.