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

Abstract

BackgroundGene 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.MethodsIn 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.ResultsWe 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.ConclusionsThrough 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.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0431-1) contains supplementary material, which is available to authorized users.

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer’s Disease

“…In this study, we obtained 15 FGCN modules in AD samples from the five large transcriptomic datasets mentioned previously (Table 1), ranging from AD1 (largest, 1,247 genes) to AD15 (smallest, 10 genes) and 9 FGCN modules in control brains ranging from N1 (largest, 1,003 genes) to N9 (smallest, 10 genes, Supplementary Table 1). The majority of these modules are specific to each condition (AD or control) as determined by minimal gene overlaps ( Figure 1c, Supplementary Table 2), which is consistent with previous findings that AD is a complex disease with gene coexpression largely perturbed in AD brains compared with control brains (Xiang et al, 2018;Zhang et al, 2013). Only three pairs of modules contain genes largely overlapped between AD and non-AD conditions (Jaccard index > 0.3) and the highest overlapping is between AD1 and N1 (Jaccard index 0.47; Figure 1c, Supplementary Table 2).…”

Combinatorial analyses reveal that cellular composition changes have different impact on transcriptomic changes of cell type specific genes in Alzheimer’s Disease brains

“…(smallest, 10 genes)(Supplementary Table 1). The majority of these modules are specific to each condition (AD or normal) as determined by minimal gene overlaps (Supplementary Table 1), which is consistent with previous finding that AD is a complex disease with gene co-expression largely perturbed in AD brains compared with normal ones (Xiang et al, 2018;Zhang et al, 2013). Only three pairs of modules contain genes largely overlapped between AD and normal conditions (Jaccard index > 0.3) and the highest overlapping is between AD1 and N1 (Jaccard index 0.47; Supplementary Table 1).…”

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

“…With the high prevalence of neural networks and Deep Learning-based algorithms in the Computational Biology, it is clear that the advantages of optimization in a highly non-linear space are welcomed improvements in biomedicine [1][2][3][4][5][6][7]. In Bioinformatics, significant effort has been committed to harnessing transcriptomic data for multiple analyses [7][8][9][10][11][12][13] especially cancer survival prognosis [14,15]. Faraggi and Simon [16] was the first study to use clinical information to predict prostate cancer survival through an artificial neural network model.…”