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. 2023 Nov 7;13(11):1564.
doi: 10.3390/brainsci13111564.

Navigating the Gene Co-Expression Network and Drug Repurposing Opportunities for Brain Disorders Associated with Neurocognitive Impairment

Mathew Timothy Artuz Manuel  1   2 , Lemmuel L Tayo  1   2   3
Affiliations

Navigating the Gene Co-Expression Network and Drug Repurposing Opportunities for Brain Disorders Associated with Neurocognitive Impairment

Mathew Timothy Artuz Manuel et al. Brain Sci. .

Abstract

Neurocognitive impairment refers to a spectrum of disorders characterized by a decline in cognitive functions such as memory, attention, and problem-solving, which are often linked to structural or functional abnormalities in the brain. While its exact etiology remains elusive, genetic factors play a pivotal role in disease onset and progression. This study aimed to identify highly correlated gene clusters (modules) and key hub genes shared across neurocognition-impairing diseases, including Alzheimer's disease (AD), Parkinson's disease with dementia (PDD), HIV-associated neurocognitive disorders (HAND), and glioma. Herein, the microarray datasets AD (GSE5281), HAND (GSE35864), glioma (GSE15824), and PD (GSE7621) were used to perform Weighted Gene Co-expression Network Analysis (WGCNA) to identify highly preserved modules across the studied brain diseases. Through gene set enrichment analysis, the shared modules were found to point towards processes including neuronal transcriptional dysregulation, neuroinflammation, protein aggregation, and mitochondrial dysfunction, hallmarks of many neurocognitive disorders. These modules were used in constructing protein-protein interaction networks to identify hub genes shared across the diseases of interest. These hub genes were found to play pivotal roles in processes including protein homeostasis, cell cycle regulation, energy metabolism, and signaling, all associated with brain and CNS diseases, and were explored for their drug repurposing experiments. Drug repurposing based on gene signatures highlighted drugs including Dorzolamide and Oxybuprocaine, which were found to modulate the expression of the hub genes in play and may have therapeutic implications in neurocognitive disorders. While both drugs have traditionally been used for other medical purposes, our study underscores the potential of a combined WGCNA and drug repurposing strategy for searching for new avenues in the simultaneous treatment of different diseases that have similarities in gene co-expression networks.

Keywords: Alzheimer’s disease; WGCNA; drug repurposing; hub genes; microarray; neurocognitive disorder.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Boxplot of microarray datasets (A) HAND dataset, (B) AD dataset, (C) PD dataset, and (D) Glioma dataset after normalization and filtering steps. RMA normalization was performed, control samples were omitted, and genes with missing symbols and gene expression values were removed.
Figure A1
Figure A1
Boxplot of microarray datasets (A) HAND dataset, (B) AD dataset, (C) PD dataset, and (D) Glioma dataset after normalization and filtering steps. RMA normalization was performed, control samples were omitted, and genes with missing symbols and gene expression values were removed.
Figure A2
Figure A2
Preliminary sample clustering dendrogram of the glioma dataset based on Euclidean distance to remove sample outliers. The smaller clusters cut by the red line were treated as sample outliers.
Figure A3
Figure A3
Preliminary sample clustering dendrogram of the HIV-associated Neurocognitive Disorder (HAND) dataset based on Euclidean distance to remove sample outliers. The smaller clusters cut by the red line were treated as sample outliers.
Figure A4
Figure A4
Preliminary sample clustering dendrogram of the Alzheimer’s Disease dataset based on Euclidean distance to remove sample outliers. The smaller clusters cut by the red line were treated as sample outliers.
Figure A5
Figure A5
Preliminary sample clustering dendrogram of the Parkinson’s dataset based on Euclidean distance to remove sample outliers. The smaller clusters cut by the red line were treated as sample outliers.
Figure A6
Figure A6
Summary of ranked expression plots and ranked connectivity plots, respectively, for each dataset comparison for (A,B) AD vs. HAND, (C,D) PD vs. HAND, (E,F) PD vs. AD, (G,H) HAND vs. Glioma, (I,J) AD vs. Glioma, and (K,L) PD vs. Glioma.
Figure A6
Figure A6
Summary of ranked expression plots and ranked connectivity plots, respectively, for each dataset comparison for (A,B) AD vs. HAND, (C,D) PD vs. HAND, (E,F) PD vs. AD, (G,H) HAND vs. Glioma, (I,J) AD vs. Glioma, and (K,L) PD vs. Glioma.
Figure 1
Figure 1
A summary of the network indices to approximate scale-free topology (a) and a plot of the mean connectivity (b) are measures of the average number of connections per gene in the networks and indicate the overall interconnectedness.
Figure 2
Figure 2
(a) histogram of network connectivity, k, is used to calculate (b) the approximate straight-line relationship for the PD dataset with beta = 10 as the soft-thresholding power.
Figure 3
Figure 3
PD dendrogram of gene clustering using TOM-based dissimilarity and the visualization of the different module split sensitivities for hybrid tree cutting. The red box indicates the chosen deep split parameter, 1, and its resulting modules indicated by the different colors.
Figure 4
Figure 4
The dendrograms of the co-expressed genes clustered into their representative modules. Modules under the same branches have relatively similar co-expression patterns but are not as comparable to the co-expression patterns of genes within each module.
Figure 5
Figure 5
Module preservation analysis of the 18 identified gene co-expression modules from the PD network in the (a) glioblastoma dataset, (b) Alzheimer’s disease dataset, and (c) HIV-associated neurocognitive Disorders dataset. The dashed line at Z = 10 indicates the threshold for high module preservation. The PD modules midnight-blue, black, yellow, and blue all exhibit high preservation in GM, AD, and HAND.
Figure 6
Figure 6
Top enriched terms for the black, blue, midnight-blue, and yellow modules in terms of (a) biological processes, (b) cellular components, (c) molecular functions, and (d) KEGG pathways.
Figure 7
Figure 7
The identified top 10 hub gene networks based on the PPI networks of the (a) yellow module, (b) midnight-blue module, (c) blue module, and (d) black module are visualized based on degree. The color intensity of yellow to red indicates the rank of each node from lowest to highest, respectively.
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