Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer's disease patients

Shunian Xiang; Zhi Huang; Tianfu Wang; Zhi Han; Christina Y Yu; Dong Ni; Kun Huang; Jie Zhang

doi:10.1186/s12920-018-0431-1

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer's disease patients

BMC Med Genomics. 2018 Dec 31;11(Suppl 6):115. doi: 10.1186/s12920-018-0431-1.

Authors

Shunian Xiang^{1

2}, Zhi Huang³, Tianfu Wang¹, Zhi Han⁴, Christina Y Yu^{4

5}, Dong Ni⁶, Kun Huang⁷, Jie Zhang⁸

Affiliations

¹ Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China.
² Department of Medical & Molecular Genetics, Indiana University, Indianapolis, IN, 46202, USA.
³ Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
⁴ Department of Medicine, Indiana University, Indianapolis, IN, 46202, USA.
⁵ Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
⁶ Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China. nidong@szu.edu.cn.
⁷ Department of Medicine, Indiana University, Indianapolis, IN, 46202, USA. kunhuang@iu.edu.
⁸ Department of Medical & Molecular Genetics, Indiana University, Indianapolis, IN, 46202, USA. jizhan@iu.edu.

Abstract

Background: Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer's disease (AD) patients and looked for their specific functions.

Methods: In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules.

Results: We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology.

Conclusions: Through our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.

Keywords: Alzheimer’s Disease; Bacterial and viral infectious pathway; Co-expression; Condition-specific module.

MeSH terms

Algorithms
Alzheimer Disease / genetics*
Alzheimer Disease / metabolism
Bacterial Infections / metabolism
Brain / metabolism*
Data Mining
Datasets as Topic
Gene Expression Profiling
Gene Regulatory Networks
Humans
Transcription Factors / metabolism
Virus Diseases / metabolism

Substances

Transcription Factors